NmFYQWlPaU))[a-zA-Z0-9\/\\_+\-\r\n]{40,}={0,2}"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/kraken_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/kraken_access_token.py
new file mode 100644
index 0000000000..cb7357cfd9
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/kraken_access_token.py
@@ -0,0 +1,24 @@
+"""
+This plugin searches for Kraken Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class KrakenAccessTokenDetector(RegexBasedDetector):
+ """Scans for Kraken Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Kraken Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Kraken Access Token
+ re.compile(
+ r"""(?i)(?:kraken)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9\/=_\+\-]{80,90})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/kucoin.py b/enterprise/enterprise_hooks/secrets_plugins/kucoin.py
new file mode 100644
index 0000000000..02e990bd8b
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/kucoin.py
@@ -0,0 +1,28 @@
+"""
+This plugin searches for Kucoin Access Tokens and Secret Keys.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class KucoinDetector(RegexBasedDetector):
+ """Scans for Kucoin Access Tokens and Secret Keys."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Kucoin Secret"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Kucoin Access Token
+ re.compile(
+ r"""(?i)(?:kucoin)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-f0-9]{24})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Kucoin Secret Key
+ re.compile(
+ r"""(?i)(?:kucoin)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/launchdarkly_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/launchdarkly_access_token.py
new file mode 100644
index 0000000000..9779909847
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/launchdarkly_access_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Launchdarkly Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class LaunchdarklyAccessTokenDetector(RegexBasedDetector):
+ """Scans for Launchdarkly Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Launchdarkly Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:launchdarkly)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9=_\-]{40})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/linear.py b/enterprise/enterprise_hooks/secrets_plugins/linear.py
new file mode 100644
index 0000000000..1224b5ec46
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/linear.py
@@ -0,0 +1,26 @@
+"""
+This plugin searches for Linear API Tokens and Linear Client Secrets.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class LinearDetector(RegexBasedDetector):
+ """Scans for Linear secrets."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Linear Secret"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Linear API Token
+ re.compile(r"""(?i)lin_api_[a-z0-9]{40}"""),
+ # Linear Client Secret
+ re.compile(
+ r"""(?i)(?:linear)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-f0-9]{32})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/linkedin.py b/enterprise/enterprise_hooks/secrets_plugins/linkedin.py
new file mode 100644
index 0000000000..53ff0c30aa
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/linkedin.py
@@ -0,0 +1,28 @@
+"""
+This plugin searches for LinkedIn Client IDs and LinkedIn Client secrets.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class LinkedInDetector(RegexBasedDetector):
+ """Scans for LinkedIn secrets."""
+
+ @property
+ def secret_type(self) -> str:
+ return "LinkedIn Secret"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # LinkedIn Client ID
+ re.compile(
+ r"""(?i)(?:linkedin|linked-in)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{14})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # LinkedIn Client secret
+ re.compile(
+ r"""(?i)(?:linkedin|linked-in)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{16})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/lob.py b/enterprise/enterprise_hooks/secrets_plugins/lob.py
new file mode 100644
index 0000000000..623ac4f1f9
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/lob.py
@@ -0,0 +1,28 @@
+"""
+This plugin searches for Lob API secrets and Lob Publishable API keys.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class LobDetector(RegexBasedDetector):
+ """Scans for Lob secrets."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Lob Secret"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Lob API Key
+ re.compile(
+ r"""(?i)(?:lob)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}((live|test)_[a-f0-9]{35})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Lob Publishable API Key
+ re.compile(
+ r"""(?i)(?:lob)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}((test|live)_pub_[a-f0-9]{31})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/mailgun.py b/enterprise/enterprise_hooks/secrets_plugins/mailgun.py
new file mode 100644
index 0000000000..c403d24546
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/mailgun.py
@@ -0,0 +1,32 @@
+"""
+This plugin searches for Mailgun API secrets, public validation keys, and webhook signing keys.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class MailgunDetector(RegexBasedDetector):
+ """Scans for Mailgun secrets."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Mailgun Secret"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Mailgun Private API Token
+ re.compile(
+ r"""(?i)(?:mailgun)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(key-[a-f0-9]{32})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Mailgun Public Validation Key
+ re.compile(
+ r"""(?i)(?:mailgun)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(pubkey-[a-f0-9]{32})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Mailgun Webhook Signing Key
+ re.compile(
+ r"""(?i)(?:mailgun)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-h0-9]{32}-[a-h0-9]{8}-[a-h0-9]{8})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/mapbox_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/mapbox_api_token.py
new file mode 100644
index 0000000000..0326b7102a
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/mapbox_api_token.py
@@ -0,0 +1,24 @@
+"""
+This plugin searches for MapBox API tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class MapBoxApiTokenDetector(RegexBasedDetector):
+ """Scans for MapBox API tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "MapBox API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # MapBox API Token
+ re.compile(
+ r"""(?i)(?:mapbox)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(pk\.[a-z0-9]{60}\.[a-z0-9]{22})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/mattermost_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/mattermost_access_token.py
new file mode 100644
index 0000000000..d65b0e7554
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/mattermost_access_token.py
@@ -0,0 +1,24 @@
+"""
+This plugin searches for Mattermost Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class MattermostAccessTokenDetector(RegexBasedDetector):
+ """Scans for Mattermost Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Mattermost Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Mattermost Access Token
+ re.compile(
+ r"""(?i)(?:mattermost)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{26})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/messagebird.py b/enterprise/enterprise_hooks/secrets_plugins/messagebird.py
new file mode 100644
index 0000000000..6adc8317a8
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/messagebird.py
@@ -0,0 +1,28 @@
+"""
+This plugin searches for MessageBird API tokens and client IDs.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class MessageBirdDetector(RegexBasedDetector):
+ """Scans for MessageBird secrets."""
+
+ @property
+ def secret_type(self) -> str:
+ return "MessageBird Secret"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # MessageBird API Token
+ re.compile(
+ r"""(?i)(?:messagebird|message-bird|message_bird)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{25})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # MessageBird Client ID
+ re.compile(
+ r"""(?i)(?:messagebird|message-bird|message_bird)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/microsoft_teams_webhook.py b/enterprise/enterprise_hooks/secrets_plugins/microsoft_teams_webhook.py
new file mode 100644
index 0000000000..298fd81b0a
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/microsoft_teams_webhook.py
@@ -0,0 +1,24 @@
+"""
+This plugin searches for Microsoft Teams Webhook URLs.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class MicrosoftTeamsWebhookDetector(RegexBasedDetector):
+ """Scans for Microsoft Teams Webhook URLs."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Microsoft Teams Webhook"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Microsoft Teams Webhook
+ re.compile(
+ r"""https:\/\/[a-z0-9]+\.webhook\.office\.com\/webhookb2\/[a-z0-9]{8}-([a-z0-9]{4}-){3}[a-z0-9]{12}@[a-z0-9]{8}-([a-z0-9]{4}-){3}[a-z0-9]{12}\/IncomingWebhook\/[a-z0-9]{32}\/[a-z0-9]{8}-([a-z0-9]{4}-){3}[a-z0-9]{12}"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/netlify_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/netlify_access_token.py
new file mode 100644
index 0000000000..cc7a575a42
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/netlify_access_token.py
@@ -0,0 +1,24 @@
+"""
+This plugin searches for Netlify Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class NetlifyAccessTokenDetector(RegexBasedDetector):
+ """Scans for Netlify Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Netlify Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Netlify Access Token
+ re.compile(
+ r"""(?i)(?:netlify)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9=_\-]{40,46})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/new_relic.py b/enterprise/enterprise_hooks/secrets_plugins/new_relic.py
new file mode 100644
index 0000000000..cef640155c
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/new_relic.py
@@ -0,0 +1,32 @@
+"""
+This plugin searches for New Relic API tokens and keys.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class NewRelicDetector(RegexBasedDetector):
+ """Scans for New Relic API tokens and keys."""
+
+ @property
+ def secret_type(self) -> str:
+ return "New Relic API Secrets"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # New Relic ingest browser API token
+ re.compile(
+ r"""(?i)(?:new-relic|newrelic|new_relic)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(NRJS-[a-f0-9]{19})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # New Relic user API ID
+ re.compile(
+ r"""(?i)(?:new-relic|newrelic|new_relic)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{64})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # New Relic user API Key
+ re.compile(
+ r"""(?i)(?:new-relic|newrelic|new_relic)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(NRAK-[a-z0-9]{27})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/nytimes_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/nytimes_access_token.py
new file mode 100644
index 0000000000..567b885e5a
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/nytimes_access_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for New York Times Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class NYTimesAccessTokenDetector(RegexBasedDetector):
+ """Scans for New York Times Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "New York Times Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:nytimes|new-york-times,|newyorktimes)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9=_\-]{32})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/okta_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/okta_access_token.py
new file mode 100644
index 0000000000..97109767b0
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/okta_access_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Okta Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class OktaAccessTokenDetector(RegexBasedDetector):
+ """Scans for Okta Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Okta Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:okta)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9=_\-]{42})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/openai_api_key.py b/enterprise/enterprise_hooks/secrets_plugins/openai_api_key.py
new file mode 100644
index 0000000000..c5d20f7590
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/openai_api_key.py
@@ -0,0 +1,19 @@
+"""
+This plugin searches for OpenAI API Keys.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class OpenAIApiKeyDetector(RegexBasedDetector):
+ """Scans for OpenAI API Keys."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Strict OpenAI API Key"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [re.compile(r"""(sk-[a-zA-Z0-9]{5,})""")]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/planetscale.py b/enterprise/enterprise_hooks/secrets_plugins/planetscale.py
new file mode 100644
index 0000000000..23a53667e3
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/planetscale.py
@@ -0,0 +1,32 @@
+"""
+This plugin searches for PlanetScale API tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class PlanetScaleDetector(RegexBasedDetector):
+ """Scans for PlanetScale API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "PlanetScale API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # the PlanetScale API token
+ re.compile(
+ r"""(?i)\b(pscale_tkn_[a-z0-9=\-_\.]{32,64})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # the PlanetScale OAuth token
+ re.compile(
+ r"""(?i)\b(pscale_oauth_[a-z0-9=\-_\.]{32,64})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # the PlanetScale password
+ re.compile(
+ r"""(?i)\b(pscale_pw_[a-z0-9=\-_\.]{32,64})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/postman_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/postman_api_token.py
new file mode 100644
index 0000000000..9469e8191c
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/postman_api_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Postman API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class PostmanApiTokenDetector(RegexBasedDetector):
+ """Scans for Postman API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Postman API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)\b(PMAK-[a-f0-9]{24}-[a-f0-9]{34})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/prefect_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/prefect_api_token.py
new file mode 100644
index 0000000000..35cdb71cae
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/prefect_api_token.py
@@ -0,0 +1,19 @@
+"""
+This plugin searches for Prefect API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class PrefectApiTokenDetector(RegexBasedDetector):
+ """Scans for Prefect API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Prefect API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [re.compile(r"""(?i)\b(pnu_[a-z0-9]{36})(?:['|\"|\n|\r|\s|\x60|;]|$)""")]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/pulumi_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/pulumi_api_token.py
new file mode 100644
index 0000000000..bae4ce211b
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/pulumi_api_token.py
@@ -0,0 +1,19 @@
+"""
+This plugin searches for Pulumi API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class PulumiApiTokenDetector(RegexBasedDetector):
+ """Scans for Pulumi API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Pulumi API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [re.compile(r"""(?i)\b(pul-[a-f0-9]{40})(?:['|\"|\n|\r|\s|\x60|;]|$)""")]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/pypi_upload_token.py b/enterprise/enterprise_hooks/secrets_plugins/pypi_upload_token.py
new file mode 100644
index 0000000000..d4cc913857
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/pypi_upload_token.py
@@ -0,0 +1,19 @@
+"""
+This plugin searches for PyPI Upload Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class PyPiUploadTokenDetector(RegexBasedDetector):
+ """Scans for PyPI Upload Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "PyPI Upload Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [re.compile(r"""pypi-AgEIcHlwaS5vcmc[A-Za-z0-9\-_]{50,1000}""")]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/rapidapi_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/rapidapi_access_token.py
new file mode 100644
index 0000000000..18b2346148
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/rapidapi_access_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for RapidAPI Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class RapidApiAccessTokenDetector(RegexBasedDetector):
+ """Scans for RapidAPI Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "RapidAPI Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:rapidapi)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9_-]{50})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/readme_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/readme_api_token.py
new file mode 100644
index 0000000000..47bdffb120
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/readme_api_token.py
@@ -0,0 +1,21 @@
+"""
+This plugin searches for Readme API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class ReadmeApiTokenDetector(RegexBasedDetector):
+ """Scans for Readme API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Readme API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(r"""(?i)\b(rdme_[a-z0-9]{70})(?:['|\"|\n|\r|\s|\x60|;]|$)""")
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/rubygems_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/rubygems_api_token.py
new file mode 100644
index 0000000000..d49c58e73e
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/rubygems_api_token.py
@@ -0,0 +1,21 @@
+"""
+This plugin searches for Rubygem API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class RubygemsApiTokenDetector(RegexBasedDetector):
+ """Scans for Rubygem API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Rubygem API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(r"""(?i)\b(rubygems_[a-f0-9]{48})(?:['|\"|\n|\r|\s|\x60|;]|$)""")
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/scalingo_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/scalingo_api_token.py
new file mode 100644
index 0000000000..3f8a59ee41
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/scalingo_api_token.py
@@ -0,0 +1,19 @@
+"""
+This plugin searches for Scalingo API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class ScalingoApiTokenDetector(RegexBasedDetector):
+ """Scans for Scalingo API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Scalingo API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [re.compile(r"""\btk-us-[a-zA-Z0-9-_]{48}\b""")]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/sendbird.py b/enterprise/enterprise_hooks/secrets_plugins/sendbird.py
new file mode 100644
index 0000000000..4b270d71e5
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/sendbird.py
@@ -0,0 +1,28 @@
+"""
+This plugin searches for Sendbird Access IDs and Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class SendbirdDetector(RegexBasedDetector):
+ """Scans for Sendbird Access IDs and Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Sendbird Credential"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Sendbird Access ID
+ re.compile(
+ r"""(?i)(?:sendbird)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Sendbird Access Token
+ re.compile(
+ r"""(?i)(?:sendbird)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-f0-9]{40})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/sendgrid_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/sendgrid_api_token.py
new file mode 100644
index 0000000000..bf974f4fd7
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/sendgrid_api_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for SendGrid API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class SendGridApiTokenDetector(RegexBasedDetector):
+ """Scans for SendGrid API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "SendGrid API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)\b(SG\.[a-z0-9=_\-\.]{66})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/sendinblue_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/sendinblue_api_token.py
new file mode 100644
index 0000000000..a6ed8c15ee
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/sendinblue_api_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for SendinBlue API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class SendinBlueApiTokenDetector(RegexBasedDetector):
+ """Scans for SendinBlue API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "SendinBlue API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)\b(xkeysib-[a-f0-9]{64}-[a-z0-9]{16})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/sentry_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/sentry_access_token.py
new file mode 100644
index 0000000000..181fad2c7f
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/sentry_access_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Sentry Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class SentryAccessTokenDetector(RegexBasedDetector):
+ """Scans for Sentry Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Sentry Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:sentry)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-f0-9]{64})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/shippo_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/shippo_api_token.py
new file mode 100644
index 0000000000..4314c68768
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/shippo_api_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Shippo API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class ShippoApiTokenDetector(RegexBasedDetector):
+ """Scans for Shippo API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Shippo API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)\b(shippo_(live|test)_[a-f0-9]{40})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/shopify.py b/enterprise/enterprise_hooks/secrets_plugins/shopify.py
new file mode 100644
index 0000000000..f5f97c4478
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/shopify.py
@@ -0,0 +1,31 @@
+"""
+This plugin searches for Shopify Access Tokens, Custom Access Tokens,
+Private App Access Tokens, and Shared Secrets.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class ShopifyDetector(RegexBasedDetector):
+ """Scans for Shopify Access Tokens, Custom Access Tokens, Private App Access Tokens,
+ and Shared Secrets.
+ """
+
+ @property
+ def secret_type(self) -> str:
+ return "Shopify Secret"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Shopify access token
+ re.compile(r"""shpat_[a-fA-F0-9]{32}"""),
+ # Shopify custom access token
+ re.compile(r"""shpca_[a-fA-F0-9]{32}"""),
+ # Shopify private app access token
+ re.compile(r"""shppa_[a-fA-F0-9]{32}"""),
+ # Shopify shared secret
+ re.compile(r"""shpss_[a-fA-F0-9]{32}"""),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/slack.py b/enterprise/enterprise_hooks/secrets_plugins/slack.py
new file mode 100644
index 0000000000..4896fd76b2
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/slack.py
@@ -0,0 +1,38 @@
+"""
+This plugin searches for Slack tokens and webhooks.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class SlackDetector(RegexBasedDetector):
+ """Scans for Slack tokens and webhooks."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Slack Secret"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Slack App-level token
+ re.compile(r"""(?i)(xapp-\d-[A-Z0-9]+-\d+-[a-z0-9]+)"""),
+ # Slack Bot token
+ re.compile(r"""(xoxb-[0-9]{10,13}\-[0-9]{10,13}[a-zA-Z0-9-]*)"""),
+ # Slack Configuration access token and refresh token
+ re.compile(r"""(?i)(xoxe.xox[bp]-\d-[A-Z0-9]{163,166})"""),
+ re.compile(r"""(?i)(xoxe-\d-[A-Z0-9]{146})"""),
+ # Slack Legacy bot token and token
+ re.compile(r"""(xoxb-[0-9]{8,14}\-[a-zA-Z0-9]{18,26})"""),
+ re.compile(r"""(xox[os]-\d+-\d+-\d+-[a-fA-F\d]+)"""),
+ # Slack Legacy Workspace token
+ re.compile(r"""(xox[ar]-(?:\d-)?[0-9a-zA-Z]{8,48})"""),
+ # Slack User token and enterprise token
+ re.compile(r"""(xox[pe](?:-[0-9]{10,13}){3}-[a-zA-Z0-9-]{28,34})"""),
+ # Slack Webhook URL
+ re.compile(
+ r"""(https?:\/\/)?hooks.slack.com\/(services|workflows)\/[A-Za-z0-9+\/]{43,46}"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/snyk_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/snyk_api_token.py
new file mode 100644
index 0000000000..839bb57317
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/snyk_api_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Snyk API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class SnykApiTokenDetector(RegexBasedDetector):
+ """Scans for Snyk API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Snyk API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:snyk)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/squarespace_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/squarespace_access_token.py
new file mode 100644
index 0000000000..0dc83ad91d
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/squarespace_access_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Squarespace Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class SquarespaceAccessTokenDetector(RegexBasedDetector):
+ """Scans for Squarespace Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Squarespace Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:squarespace)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/sumologic.py b/enterprise/enterprise_hooks/secrets_plugins/sumologic.py
new file mode 100644
index 0000000000..7117629acc
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/sumologic.py
@@ -0,0 +1,22 @@
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class SumoLogicDetector(RegexBasedDetector):
+ """Scans for SumoLogic Access ID and Access Token."""
+
+ @property
+ def secret_type(self) -> str:
+ return "SumoLogic"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i:(?:sumo)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3})(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(su[a-zA-Z0-9]{12})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ re.compile(
+ r"""(?i)(?:sumo)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{64})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/telegram_bot_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/telegram_bot_api_token.py
new file mode 100644
index 0000000000..30854fda1d
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/telegram_bot_api_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Telegram Bot API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class TelegramBotApiTokenDetector(RegexBasedDetector):
+ """Scans for Telegram Bot API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Telegram Bot API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:^|[^0-9])([0-9]{5,16}:A[a-zA-Z0-9_\-]{34})(?:$|[^a-zA-Z0-9_\-])"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/travisci_access_token.py b/enterprise/enterprise_hooks/secrets_plugins/travisci_access_token.py
new file mode 100644
index 0000000000..90f9b48f46
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/travisci_access_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Travis CI Access Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class TravisCiAccessTokenDetector(RegexBasedDetector):
+ """Scans for Travis CI Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Travis CI Access Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:travis)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{22})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/twitch_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/twitch_api_token.py
new file mode 100644
index 0000000000..1e0e3ccf8f
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/twitch_api_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Twitch API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class TwitchApiTokenDetector(RegexBasedDetector):
+ """Scans for Twitch API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Twitch API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:twitch)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{30})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/twitter.py b/enterprise/enterprise_hooks/secrets_plugins/twitter.py
new file mode 100644
index 0000000000..99ad170d1e
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/twitter.py
@@ -0,0 +1,36 @@
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class TwitterDetector(RegexBasedDetector):
+ """Scans for Twitter Access Secrets, Access Tokens, API Keys, API Secrets, and Bearer Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Twitter Secret"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Twitter Access Secret
+ re.compile(
+ r"""(?i)(?:twitter)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{45})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Twitter Access Token
+ re.compile(
+ r"""(?i)(?:twitter)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([0-9]{15,25}-[a-zA-Z0-9]{20,40})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Twitter API Key
+ re.compile(
+ r"""(?i)(?:twitter)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{25})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Twitter API Secret
+ re.compile(
+ r"""(?i)(?:twitter)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{50})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Twitter Bearer Token
+ re.compile(
+ r"""(?i)(?:twitter)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(A{22}[a-zA-Z0-9%]{80,100})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/typeform_api_token.py b/enterprise/enterprise_hooks/secrets_plugins/typeform_api_token.py
new file mode 100644
index 0000000000..8d9dc0e875
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/typeform_api_token.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Typeform API Tokens.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class TypeformApiTokenDetector(RegexBasedDetector):
+ """Scans for Typeform API Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Typeform API Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:typeform)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(tfp_[a-z0-9\-_\.=]{59})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/vault.py b/enterprise/enterprise_hooks/secrets_plugins/vault.py
new file mode 100644
index 0000000000..5ca552cd9e
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/vault.py
@@ -0,0 +1,24 @@
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class VaultDetector(RegexBasedDetector):
+ """Scans for Vault Batch Tokens and Vault Service Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Vault Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Vault Batch Token
+ re.compile(
+ r"""(?i)\b(hvb\.[a-z0-9_-]{138,212})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Vault Service Token
+ re.compile(
+ r"""(?i)\b(hvs\.[a-z0-9_-]{90,100})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/yandex.py b/enterprise/enterprise_hooks/secrets_plugins/yandex.py
new file mode 100644
index 0000000000..a58faec0d1
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/yandex.py
@@ -0,0 +1,28 @@
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class YandexDetector(RegexBasedDetector):
+ """Scans for Yandex Access Tokens, API Keys, and AWS Access Tokens."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Yandex Token"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ # Yandex Access Token
+ re.compile(
+ r"""(?i)(?:yandex)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(t1\.[A-Z0-9a-z_-]+[=]{0,2}\.[A-Z0-9a-z_-]{86}[=]{0,2})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Yandex API Key
+ re.compile(
+ r"""(?i)(?:yandex)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(AQVN[A-Za-z0-9_\-]{35,38})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ # Yandex AWS Access Token
+ re.compile(
+ r"""(?i)(?:yandex)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}(YC[a-zA-Z0-9_\-]{38})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ ),
+ ]
diff --git a/enterprise/enterprise_hooks/secrets_plugins/zendesk_secret_key.py b/enterprise/enterprise_hooks/secrets_plugins/zendesk_secret_key.py
new file mode 100644
index 0000000000..42c087c5b6
--- /dev/null
+++ b/enterprise/enterprise_hooks/secrets_plugins/zendesk_secret_key.py
@@ -0,0 +1,23 @@
+"""
+This plugin searches for Zendesk Secret Keys.
+"""
+
+import re
+
+from detect_secrets.plugins.base import RegexBasedDetector
+
+
+class ZendeskSecretKeyDetector(RegexBasedDetector):
+ """Scans for Zendesk Secret Keys."""
+
+ @property
+ def secret_type(self) -> str:
+ return "Zendesk Secret Key"
+
+ @property
+ def denylist(self) -> list[re.Pattern]:
+ return [
+ re.compile(
+ r"""(?i)(?:zendesk)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:{1,3}=|\|\|:|<=|=>|:|\?=)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{40})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
+ )
+ ]
diff --git a/enterprise/utils.py b/enterprise/utils.py
index 90b14314c2..baff3b1b02 100644
--- a/enterprise/utils.py
+++ b/enterprise/utils.py
@@ -1,5 +1,7 @@
# Enterprise Proxy Util Endpoints
+from typing import Optional, List
from litellm._logging import verbose_logger
+from litellm.proxy.proxy_server import PrismaClient, HTTPException
import collections
from datetime import datetime
@@ -19,27 +21,76 @@ async def get_spend_by_tags(start_date=None, end_date=None, prisma_client=None):
return response
-async def ui_get_spend_by_tags(start_date: str, end_date: str, prisma_client):
-
- sql_query = """
- SELECT
- jsonb_array_elements_text(request_tags) AS individual_request_tag,
- DATE(s."startTime") AS spend_date,
- COUNT(*) AS log_count,
- SUM(spend) AS total_spend
- FROM "LiteLLM_SpendLogs" s
- WHERE
- DATE(s."startTime") >= $1::date
- AND DATE(s."startTime") <= $2::date
- GROUP BY individual_request_tag, spend_date
- ORDER BY spend_date
- LIMIT 100;
+async def ui_get_spend_by_tags(
+ start_date: str,
+ end_date: str,
+ prisma_client: Optional[PrismaClient] = None,
+ tags_str: Optional[str] = None,
+):
"""
- response = await prisma_client.db.query_raw(
- sql_query,
- start_date,
- end_date,
- )
+ Should cover 2 cases:
+ 1. When user is getting spend for all_tags. "all_tags" in tags_list
+ 2. When user is getting spend for specific tags.
+ """
+
+ # tags_str is a list of strings csv of tags
+ # tags_str = tag1,tag2,tag3
+ # convert to list if it's not None
+ tags_list: Optional[List[str]] = None
+ if tags_str is not None and len(tags_str) > 0:
+ tags_list = tags_str.split(",")
+
+ if prisma_client is None:
+ raise HTTPException(status_code=500, detail={"error": "No db connected"})
+
+ response = None
+ if tags_list is None or (isinstance(tags_list, list) and "all-tags" in tags_list):
+ # Get spend for all tags
+ sql_query = """
+ SELECT
+ jsonb_array_elements_text(request_tags) AS individual_request_tag,
+ DATE(s."startTime") AS spend_date,
+ COUNT(*) AS log_count,
+ SUM(spend) AS total_spend
+ FROM "LiteLLM_SpendLogs" s
+ WHERE
+ DATE(s."startTime") >= $1::date
+ AND DATE(s."startTime") <= $2::date
+ GROUP BY individual_request_tag, spend_date
+ ORDER BY total_spend DESC;
+ """
+ response = await prisma_client.db.query_raw(
+ sql_query,
+ start_date,
+ end_date,
+ )
+ else:
+ # filter by tags list
+ sql_query = """
+ SELECT
+ individual_request_tag,
+ COUNT(*) AS log_count,
+ SUM(spend) AS total_spend
+ FROM (
+ SELECT
+ jsonb_array_elements_text(request_tags) AS individual_request_tag,
+ DATE(s."startTime") AS spend_date,
+ spend
+ FROM "LiteLLM_SpendLogs" s
+ WHERE
+ DATE(s."startTime") >= $1::date
+ AND DATE(s."startTime") <= $2::date
+ ) AS subquery
+ WHERE individual_request_tag = ANY($3::text[])
+ GROUP BY individual_request_tag
+ ORDER BY total_spend DESC;
+ """
+ response = await prisma_client.db.query_raw(
+ sql_query,
+ start_date,
+ end_date,
+ tags_list,
+ )
# print("tags - spend")
# print(response)
@@ -71,236 +122,6 @@ async def ui_get_spend_by_tags(start_date: str, end_date: str, prisma_client):
return {"spend_per_tag": ui_tags}
-async def view_spend_logs_from_clickhouse(
- api_key=None, user_id=None, request_id=None, start_date=None, end_date=None
-):
- verbose_logger.debug("Reading logs from Clickhouse")
- import os
-
- # if user has setup clickhouse
- # TODO: Move this to be a helper function
- # querying clickhouse for this data
- import clickhouse_connect
- from datetime import datetime
-
- port = os.getenv("CLICKHOUSE_PORT")
- if port is not None and isinstance(port, str):
- port = int(port)
-
- client = clickhouse_connect.get_client(
- host=os.getenv("CLICKHOUSE_HOST"),
- port=port,
- username=os.getenv("CLICKHOUSE_USERNAME", ""),
- password=os.getenv("CLICKHOUSE_PASSWORD", ""),
- )
- if (
- start_date is not None
- and isinstance(start_date, str)
- and end_date is not None
- and isinstance(end_date, str)
- ):
- # Convert the date strings to datetime objects
- start_date_obj = datetime.strptime(start_date, "%Y-%m-%d")
- end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
-
- # get top spend per day
- response = client.query(
- f"""
- SELECT
- toDate(startTime) AS day,
- sum(spend) AS total_spend
- FROM
- spend_logs
- WHERE
- toDate(startTime) BETWEEN toDate('2024-02-01') AND toDate('2024-02-29')
- GROUP BY
- day
- ORDER BY
- total_spend
- """
- )
-
- results = []
- result_rows = list(response.result_rows)
- for response in result_rows:
- current_row = {}
- current_row["users"] = {"example": 0.0}
- current_row["models"] = {}
-
- current_row["spend"] = float(response[1])
- current_row["startTime"] = str(response[0])
-
- # stubbed api_key
- current_row[""] = 0.0 # type: ignore
- results.append(current_row)
-
- return results
- else:
- # check if spend logs exist, if it does then return last 10 logs, sorted in descending order of startTime
- response = client.query(
- """
- SELECT
- *
- FROM
- default.spend_logs
- ORDER BY
- startTime DESC
- LIMIT
- 10
- """
- )
-
- # get size of spend logs
- num_rows = client.query("SELECT count(*) FROM default.spend_logs")
- num_rows = num_rows.result_rows[0][0]
-
- # safely access num_rows.result_rows[0][0]
- if num_rows is None:
- num_rows = 0
-
- raw_rows = list(response.result_rows)
- response_data = {
- "logs": raw_rows,
- "log_count": num_rows,
- }
- return response_data
-
-
-def _create_clickhouse_material_views(client=None, table_names=[]):
- # Create Materialized Views if they don't exist
- # Materialized Views send new inserted rows to the aggregate tables
-
- verbose_logger.debug("Clickhouse: Creating Materialized Views")
- if "daily_aggregated_spend_per_model_mv" not in table_names:
- verbose_logger.debug("Clickhouse: Creating daily_aggregated_spend_per_model_mv")
- client.command(
- """
- CREATE MATERIALIZED VIEW daily_aggregated_spend_per_model_mv
- TO daily_aggregated_spend_per_model
- AS
- SELECT
- toDate(startTime) as day,
- sumState(spend) AS DailySpend,
- model as model
- FROM spend_logs
- GROUP BY
- day, model
- """
- )
- if "daily_aggregated_spend_per_api_key_mv" not in table_names:
- verbose_logger.debug(
- "Clickhouse: Creating daily_aggregated_spend_per_api_key_mv"
- )
- client.command(
- """
- CREATE MATERIALIZED VIEW daily_aggregated_spend_per_api_key_mv
- TO daily_aggregated_spend_per_api_key
- AS
- SELECT
- toDate(startTime) as day,
- sumState(spend) AS DailySpend,
- api_key as api_key
- FROM spend_logs
- GROUP BY
- day, api_key
- """
- )
- if "daily_aggregated_spend_per_user_mv" not in table_names:
- verbose_logger.debug("Clickhouse: Creating daily_aggregated_spend_per_user_mv")
- client.command(
- """
- CREATE MATERIALIZED VIEW daily_aggregated_spend_per_user_mv
- TO daily_aggregated_spend_per_user
- AS
- SELECT
- toDate(startTime) as day,
- sumState(spend) AS DailySpend,
- user as user
- FROM spend_logs
- GROUP BY
- day, user
- """
- )
- if "daily_aggregated_spend_mv" not in table_names:
- verbose_logger.debug("Clickhouse: Creating daily_aggregated_spend_mv")
- client.command(
- """
- CREATE MATERIALIZED VIEW daily_aggregated_spend_mv
- TO daily_aggregated_spend
- AS
- SELECT
- toDate(startTime) as day,
- sumState(spend) AS DailySpend
- FROM spend_logs
- GROUP BY
- day
- """
- )
-
-
-def _create_clickhouse_aggregate_tables(client=None, table_names=[]):
- # Basic Logging works without this - this is only used for low latency reporting apis
- verbose_logger.debug("Clickhouse: Creating Aggregate Tables")
-
- # Create Aggregeate Tables if they don't exist
- if "daily_aggregated_spend_per_model" not in table_names:
- verbose_logger.debug("Clickhouse: Creating daily_aggregated_spend_per_model")
- client.command(
- """
- CREATE TABLE daily_aggregated_spend_per_model
- (
- `day` Date,
- `DailySpend` AggregateFunction(sum, Float64),
- `model` String
- )
- ENGINE = SummingMergeTree()
- ORDER BY (day, model);
- """
- )
- if "daily_aggregated_spend_per_api_key" not in table_names:
- verbose_logger.debug("Clickhouse: Creating daily_aggregated_spend_per_api_key")
- client.command(
- """
- CREATE TABLE daily_aggregated_spend_per_api_key
- (
- `day` Date,
- `DailySpend` AggregateFunction(sum, Float64),
- `api_key` String
- )
- ENGINE = SummingMergeTree()
- ORDER BY (day, api_key);
- """
- )
- if "daily_aggregated_spend_per_user" not in table_names:
- verbose_logger.debug("Clickhouse: Creating daily_aggregated_spend_per_user")
- client.command(
- """
- CREATE TABLE daily_aggregated_spend_per_user
- (
- `day` Date,
- `DailySpend` AggregateFunction(sum, Float64),
- `user` String
- )
- ENGINE = SummingMergeTree()
- ORDER BY (day, user);
- """
- )
- if "daily_aggregated_spend" not in table_names:
- verbose_logger.debug("Clickhouse: Creating daily_aggregated_spend")
- client.command(
- """
- CREATE TABLE daily_aggregated_spend
- (
- `day` Date,
- `DailySpend` AggregateFunction(sum, Float64),
- )
- ENGINE = SummingMergeTree()
- ORDER BY (day);
- """
- )
- return
-
-
def _forecast_daily_cost(data: list):
import requests # type: ignore
from datetime import datetime, timedelta
diff --git a/entrypoint.sh b/entrypoint.sh
index 80adf8d077..a028e54262 100755
--- a/entrypoint.sh
+++ b/entrypoint.sh
@@ -1,48 +1,13 @@
-#!/bin/sh
+#!/bin/bash
+echo $(pwd)
-# Check if DATABASE_URL is not set
-if [ -z "$DATABASE_URL" ]; then
- # Check if all required variables are provided
- if [ -n "$DATABASE_HOST" ] && [ -n "$DATABASE_USERNAME" ] && [ -n "$DATABASE_PASSWORD" ] && [ -n "$DATABASE_NAME" ]; then
- # Construct DATABASE_URL from the provided variables
- DATABASE_URL="postgresql://${DATABASE_USERNAME}:${DATABASE_PASSWORD}@${DATABASE_HOST}/${DATABASE_NAME}"
- export DATABASE_URL
- else
- echo "Error: Required database environment variables are not set. Provide a postgres url for DATABASE_URL."
- exit 1
- fi
-fi
+# Run the Python migration script
+python3 litellm/proxy/prisma_migration.py
-# Set DIRECT_URL to the value of DATABASE_URL if it is not set, required for migrations
-if [ -z "$DIRECT_URL" ]; then
- export DIRECT_URL=$DATABASE_URL
-fi
-
-# Apply migrations
-retry_count=0
-max_retries=3
-exit_code=1
-
-until [ $retry_count -ge $max_retries ] || [ $exit_code -eq 0 ]
-do
- retry_count=$((retry_count+1))
- echo "Attempt $retry_count..."
-
- # Run the Prisma db push command
- prisma db push --accept-data-loss
-
- exit_code=$?
-
- if [ $exit_code -ne 0 ] && [ $retry_count -lt $max_retries ]; then
- echo "Retrying in 10 seconds..."
- sleep 10
- fi
-done
-
-if [ $exit_code -ne 0 ]; then
- echo "Unable to push database changes after $max_retries retries."
+# Check if the Python script executed successfully
+if [ $? -eq 0 ]; then
+ echo "Migration script ran successfully!"
+else
+ echo "Migration script failed!"
exit 1
fi
-
-echo "Database push successful!"
-
diff --git a/index.yaml b/index.yaml
index 8faeeeae6b..8c53e745fe 100644
--- a/index.yaml
+++ b/index.yaml
@@ -1,6 +1,25 @@
apiVersion: v1
entries:
litellm-helm:
+ - apiVersion: v2
+ appVersion: v1.41.8
+ created: "2024-07-10T00:59:11.1889+08:00"
+ dependencies:
+ - condition: db.deployStandalone
+ name: postgresql
+ repository: oci://registry-1.docker.io/bitnamicharts
+ version: '>=13.3.0'
+ - condition: redis.enabled
+ name: redis
+ repository: oci://registry-1.docker.io/bitnamicharts
+ version: '>=18.0.0'
+ description: Call all LLM APIs using the OpenAI format
+ digest: eeff5e4e6cebb4c977cb7359c1ec6c773c66982f6aa39dbed94a674890144a43
+ name: litellm-helm
+ type: application
+ urls:
+ - https://berriai.github.io/litellm/litellm-helm-0.2.1.tgz
+ version: 0.2.1
- apiVersion: v2
appVersion: v1.35.38
created: "2024-05-06T10:22:24.384392-07:00"
@@ -33,7 +52,7 @@ entries:
licenses: Apache-2.0
apiVersion: v2
appVersion: 16.2.0
- created: "2024-05-06T10:22:24.387717-07:00"
+ created: "2024-07-10T00:59:11.191731+08:00"
dependencies:
- name: common
repository: oci://registry-1.docker.io/bitnamicharts
@@ -60,7 +79,7 @@ entries:
sources:
- https://github.com/bitnami/charts/tree/main/bitnami/postgresql
urls:
- - charts/postgresql-14.3.1.tgz
+ - https://berriai.github.io/litellm/charts/postgresql-14.3.1.tgz
version: 14.3.1
redis:
- annotations:
@@ -79,7 +98,7 @@ entries:
licenses: Apache-2.0
apiVersion: v2
appVersion: 7.2.4
- created: "2024-05-06T10:22:24.391903-07:00"
+ created: "2024-07-10T00:59:11.195667+08:00"
dependencies:
- name: common
repository: oci://registry-1.docker.io/bitnamicharts
@@ -103,6 +122,6 @@ entries:
sources:
- https://github.com/bitnami/charts/tree/main/bitnami/redis
urls:
- - charts/redis-18.19.1.tgz
+ - https://berriai.github.io/litellm/charts/redis-18.19.1.tgz
version: 18.19.1
-generated: "2024-05-06T10:22:24.375026-07:00"
+generated: "2024-07-10T00:59:11.179952+08:00"
diff --git a/litellm-helm-0.2.1.tgz b/litellm-helm-0.2.1.tgz
new file mode 100644
index 0000000000..acecd9f943
Binary files /dev/null and b/litellm-helm-0.2.1.tgz differ
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 3c78c9b270..7dcc934a68 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -5,8 +5,18 @@ warnings.filterwarnings("ignore", message=".*conflict with protected namespace.*
### INIT VARIABLES ###
import threading, requests, os
from typing import Callable, List, Optional, Dict, Union, Any, Literal
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.caching import Cache
-from litellm._logging import set_verbose, _turn_on_debug, verbose_logger, json_logs
+from litellm._logging import (
+ set_verbose,
+ _turn_on_debug,
+ verbose_logger,
+ json_logs,
+ _turn_on_json,
+ log_level,
+)
+
+from litellm.types.guardrails import GuardrailItem
from litellm.proxy._types import (
KeyManagementSystem,
KeyManagementSettings,
@@ -27,7 +37,9 @@ input_callback: List[Union[str, Callable]] = []
success_callback: List[Union[str, Callable]] = []
failure_callback: List[Union[str, Callable]] = []
service_callback: List[Union[str, Callable]] = []
-_custom_logger_compatible_callbacks_literal = Literal["lago", "openmeter"]
+_custom_logger_compatible_callbacks_literal = Literal[
+ "lago", "openmeter", "logfire", "dynamic_rate_limiter", "langsmith", "galileo"
+]
callbacks: List[Union[Callable, _custom_logger_compatible_callbacks_literal]] = []
_langfuse_default_tags: Optional[
List[
@@ -53,6 +65,9 @@ _async_failure_callback: List[Callable] = (
pre_call_rules: List[Callable] = []
post_call_rules: List[Callable] = []
turn_off_message_logging: Optional[bool] = False
+log_raw_request_response: bool = False
+redact_messages_in_exceptions: Optional[bool] = False
+store_audit_logs = False # Enterprise feature, allow users to see audit logs
## end of callbacks #############
email: Optional[str] = (
@@ -63,7 +78,7 @@ token: Optional[str] = (
)
telemetry = True
max_tokens = 256 # OpenAI Defaults
-drop_params = False
+drop_params = bool(os.getenv("LITELLM_DROP_PARAMS", False))
modify_params = False
retry = True
### AUTH ###
@@ -91,11 +106,16 @@ aleph_alpha_key: Optional[str] = None
nlp_cloud_key: Optional[str] = None
common_cloud_provider_auth_params: dict = {
"params": ["project", "region_name", "token"],
- "providers": ["vertex_ai", "bedrock", "watsonx", "azure"],
+ "providers": ["vertex_ai", "bedrock", "watsonx", "azure", "vertex_ai_beta"],
}
use_client: bool = False
ssl_verify: bool = True
+ssl_certificate: Optional[str] = None
disable_streaming_logging: bool = False
+in_memory_llm_clients_cache: dict = {}
+safe_memory_mode: bool = False
+### DEFAULT AZURE API VERSION ###
+AZURE_DEFAULT_API_VERSION = "2024-02-01" # this is updated to the latest
### GUARDRAILS ###
llamaguard_model_name: Optional[str] = None
openai_moderations_model_name: Optional[str] = None
@@ -105,9 +125,13 @@ llamaguard_unsafe_content_categories: Optional[str] = None
blocked_user_list: Optional[Union[str, List]] = None
banned_keywords_list: Optional[Union[str, List]] = None
llm_guard_mode: Literal["all", "key-specific", "request-specific"] = "all"
+guardrail_name_config_map: Dict[str, GuardrailItem] = {}
##################
### PREVIEW FEATURES ###
enable_preview_features: bool = False
+return_response_headers: bool = (
+ False # get response headers from LLM Api providers - example x-remaining-requests,
+)
##################
logging: bool = True
caching: bool = (
@@ -220,12 +244,17 @@ default_user_params: Optional[Dict] = None
default_team_settings: Optional[List] = None
max_user_budget: Optional[float] = None
max_end_user_budget: Optional[float] = None
+#### REQUEST PRIORITIZATION ####
+priority_reservation: Optional[Dict[str, float]] = None
#### RELIABILITY ####
-request_timeout: Optional[float] = 6000
+request_timeout: float = 6000
+module_level_aclient = AsyncHTTPHandler(timeout=request_timeout)
+module_level_client = HTTPHandler(timeout=request_timeout)
num_retries: Optional[int] = None # per model endpoint
default_fallbacks: Optional[List] = None
fallbacks: Optional[List] = None
context_window_fallbacks: Optional[List] = None
+content_policy_fallbacks: Optional[List] = None
allowed_fails: int = 0
num_retries_per_request: Optional[int] = (
None # for the request overall (incl. fallbacks + model retries)
@@ -298,6 +327,7 @@ api_base = None
headers = None
api_version = None
organization = None
+project = None
config_path = None
####### COMPLETION MODELS ###################
open_ai_chat_completion_models: List = []
@@ -306,6 +336,7 @@ cohere_models: List = []
cohere_chat_models: List = []
mistral_chat_models: List = []
anthropic_models: List = []
+empower_models: List = []
openrouter_models: List = []
vertex_language_models: List = []
vertex_vision_models: List = []
@@ -322,6 +353,7 @@ bedrock_models: List = []
deepinfra_models: List = []
perplexity_models: List = []
watsonx_models: List = []
+gemini_models: List = []
for key, value in model_cost.items():
if value.get("litellm_provider") == "openai":
open_ai_chat_completion_models.append(key)
@@ -335,6 +367,8 @@ for key, value in model_cost.items():
mistral_chat_models.append(key)
elif value.get("litellm_provider") == "anthropic":
anthropic_models.append(key)
+ elif value.get("litellm_provider") == "empower":
+ empower_models.append(key)
elif value.get("litellm_provider") == "openrouter":
openrouter_models.append(key)
elif value.get("litellm_provider") == "vertex_ai-text-models":
@@ -368,16 +402,22 @@ for key, value in model_cost.items():
perplexity_models.append(key)
elif value.get("litellm_provider") == "watsonx":
watsonx_models.append(key)
-
+ elif value.get("litellm_provider") == "gemini":
+ gemini_models.append(key)
# known openai compatible endpoints - we'll eventually move this list to the model_prices_and_context_window.json dictionary
openai_compatible_endpoints: List = [
"api.perplexity.ai",
"api.endpoints.anyscale.com/v1",
"api.deepinfra.com/v1/openai",
"api.mistral.ai/v1",
+ "codestral.mistral.ai/v1/chat/completions",
+ "codestral.mistral.ai/v1/fim/completions",
"api.groq.com/openai/v1",
+ "https://integrate.api.nvidia.com/v1",
"api.deepseek.com/v1",
"api.together.xyz/v1",
+ "app.empower.dev/api/v1",
+ "inference.friendli.ai/v1",
]
# this is maintained for Exception Mapping
@@ -385,12 +425,18 @@ openai_compatible_providers: List = [
"anyscale",
"mistral",
"groq",
+ "nvidia_nim",
+ "volcengine",
+ "codestral",
"deepseek",
"deepinfra",
"perplexity",
"xinference",
"together_ai",
"fireworks_ai",
+ "empower",
+ "friendliai",
+ "azure_ai",
]
@@ -491,6 +537,10 @@ huggingface_models: List = [
"meta-llama/Llama-2-70b",
"meta-llama/Llama-2-70b-chat",
] # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers
+empower_models = [
+ "empower/empower-functions",
+ "empower/empower-functions-small",
+]
together_ai_models: List = [
# llama llms - chat
@@ -574,6 +624,7 @@ model_list = (
+ maritalk_models
+ vertex_language_models
+ watsonx_models
+ + gemini_models
)
provider_list: List = [
@@ -589,12 +640,14 @@ provider_list: List = [
"together_ai",
"openrouter",
"vertex_ai",
+ "vertex_ai_beta",
"palm",
"gemini",
"ai21",
"baseten",
"azure",
"azure_text",
+ "azure_ai",
"sagemaker",
"bedrock",
"vllm",
@@ -608,16 +661,22 @@ provider_list: List = [
"anyscale",
"mistral",
"groq",
+ "nvidia_nim",
+ "volcengine",
+ "codestral",
+ "text-completion-codestral",
"deepseek",
"maritalk",
"voyage",
"cloudflare",
"xinference",
"fireworks_ai",
+ "friendliai",
"watsonx",
"triton",
"predibase",
"databricks",
+ "empower",
"custom", # custom apis
]
@@ -644,6 +703,7 @@ models_by_provider: dict = {
"perplexity": perplexity_models,
"maritalk": maritalk_models,
"watsonx": watsonx_models,
+ "gemini": gemini_models,
}
# mapping for those models which have larger equivalents
@@ -695,21 +755,25 @@ all_embedding_models = (
openai_image_generation_models = ["dall-e-2", "dall-e-3"]
from .timeout import timeout
+from .cost_calculator import completion_cost
+from litellm.litellm_core_utils.litellm_logging import Logging
+from litellm.litellm_core_utils.core_helpers import remove_index_from_tool_calls
+from litellm.litellm_core_utils.token_counter import get_modified_max_tokens
from .utils import (
client,
exception_type,
get_optional_params,
+ get_response_string,
modify_integration,
token_counter,
create_pretrained_tokenizer,
create_tokenizer,
- cost_per_token,
- completion_cost,
supports_function_calling,
+ supports_response_schema,
supports_parallel_function_calling,
supports_vision,
+ supports_system_messages,
get_litellm_params,
- Logging,
acreate,
get_model_list,
get_max_tokens,
@@ -728,10 +792,12 @@ from .utils import (
get_api_base,
get_first_chars_messages,
ModelResponse,
+ EmbeddingResponse,
ImageResponse,
- ImageObject,
get_provider_fields,
)
+
+from .types.utils import ImageObject
from .llms.huggingface_restapi import HuggingfaceConfig
from .llms.anthropic import AnthropicConfig
from .llms.databricks import DatabricksConfig, DatabricksEmbeddingConfig
@@ -748,13 +814,18 @@ from .llms.gemini import GeminiConfig
from .llms.nlp_cloud import NLPCloudConfig
from .llms.aleph_alpha import AlephAlphaConfig
from .llms.petals import PetalsConfig
-from .llms.vertex_ai import VertexAIConfig
+from .llms.vertex_httpx import VertexGeminiConfig, GoogleAIStudioGeminiConfig
+from .llms.vertex_ai import VertexAIConfig, VertexAITextEmbeddingConfig
from .llms.vertex_ai_anthropic import VertexAIAnthropicConfig
from .llms.sagemaker import SagemakerConfig
from .llms.ollama import OllamaConfig
from .llms.ollama_chat import OllamaChatConfig
from .llms.maritalk import MaritTalkConfig
-from .llms.bedrock_httpx import AmazonCohereChatConfig
+from .llms.bedrock_httpx import (
+ AmazonCohereChatConfig,
+ AmazonConverseConfig,
+ BEDROCK_CONVERSE_MODELS,
+)
from .llms.bedrock import (
AmazonTitanConfig,
AmazonAI21Config,
@@ -770,9 +841,20 @@ from .llms.openai import (
OpenAIConfig,
OpenAITextCompletionConfig,
MistralConfig,
+ MistralEmbeddingConfig,
DeepInfraConfig,
+ GroqConfig,
+ AzureAIStudioConfig,
+)
+from .llms.nvidia_nim import NvidiaNimConfig
+from .llms.fireworks_ai import FireworksAIConfig
+from .llms.volcengine import VolcEngineConfig
+from .llms.text_completion_codestral import MistralTextCompletionConfig
+from .llms.azure import (
+ AzureOpenAIConfig,
+ AzureOpenAIError,
+ AzureOpenAIAssistantsAPIConfig,
)
-from .llms.azure import AzureOpenAIConfig, AzureOpenAIError
from .llms.watsonx import IBMWatsonXAIConfig
from .main import * # type: ignore
from .integrations import *
@@ -792,8 +874,21 @@ from .exceptions import (
APIConnectionError,
APIResponseValidationError,
UnprocessableEntityError,
+ InternalServerError,
+ JSONSchemaValidationError,
+ LITELLM_EXCEPTION_TYPES,
+ MockException,
)
from .budget_manager import BudgetManager
from .proxy.proxy_cli import run_server
from .router import Router
from .assistants.main import *
+from .batches.main import *
+from .files.main import *
+from .scheduler import *
+from .cost_calculator import response_cost_calculator, cost_per_token
+
+### ADAPTERS ###
+from .types.adapter import AdapterItem
+
+adapters: List[AdapterItem] = []
diff --git a/litellm/_logging.py b/litellm/_logging.py
index 0759ad51e9..e3835458cf 100644
--- a/litellm/_logging.py
+++ b/litellm/_logging.py
@@ -1,20 +1,40 @@
-import logging, os, json
+import json
+import logging
+import os
+import traceback
+from datetime import datetime
from logging import Formatter
set_verbose = False
+
+if set_verbose is True:
+ logging.warning(
+ "`litellm.set_verbose` is deprecated. Please set `os.environ['LITELLM_LOG'] = 'DEBUG'` for debug logs."
+ )
json_logs = bool(os.getenv("JSON_LOGS", False))
# Create a handler for the logger (you may need to adapt this based on your needs)
+log_level = os.getenv("LITELLM_LOG", "DEBUG")
+numeric_level: str = getattr(logging, log_level.upper())
handler = logging.StreamHandler()
-handler.setLevel(logging.DEBUG)
+handler.setLevel(numeric_level)
class JsonFormatter(Formatter):
def __init__(self):
super(JsonFormatter, self).__init__()
+ def formatTime(self, record, datefmt=None):
+ # Use datetime to format the timestamp in ISO 8601 format
+ dt = datetime.fromtimestamp(record.created)
+ return dt.isoformat()
+
def format(self, record):
- json_record = {}
- json_record["message"] = record.getMessage()
+ json_record = {
+ "message": record.getMessage(),
+ "level": record.levelname,
+ "timestamp": self.formatTime(record),
+ }
+
return json.dumps(json_record)
@@ -39,6 +59,23 @@ verbose_proxy_logger.addHandler(handler)
verbose_logger.addHandler(handler)
+def _turn_on_json():
+ handler = logging.StreamHandler()
+ handler.setFormatter(JsonFormatter())
+
+ # Define a list of the loggers to update
+ loggers = [verbose_router_logger, verbose_proxy_logger, verbose_logger]
+
+ # Iterate through each logger and update its handlers
+ for logger in loggers:
+ # Remove all existing handlers
+ for h in logger.handlers[:]:
+ logger.removeHandler(h)
+
+ # Add the new handler
+ logger.addHandler(handler)
+
+
def _turn_on_debug():
verbose_logger.setLevel(level=logging.DEBUG) # set package log to debug
verbose_router_logger.setLevel(level=logging.DEBUG) # set router logs to debug
diff --git a/litellm/_service_logger.py b/litellm/_service_logger.py
index dc6f35642b..be8d7cf895 100644
--- a/litellm/_service_logger.py
+++ b/litellm/_service_logger.py
@@ -1,10 +1,19 @@
-import litellm, traceback
+from datetime import datetime, timedelta
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+import litellm
from litellm.proxy._types import UserAPIKeyAuth
-from .types.services import ServiceTypes, ServiceLoggerPayload
-from .integrations.prometheus_services import PrometheusServicesLogger
+
from .integrations.custom_logger import CustomLogger
-from datetime import timedelta
-from typing import Union
+from .integrations.prometheus_services import PrometheusServicesLogger
+from .types.services import ServiceLoggerPayload, ServiceTypes
+
+if TYPE_CHECKING:
+ from opentelemetry.trace import Span as _Span
+
+ Span = _Span
+else:
+ Span = Any
class ServiceLogging(CustomLogger):
@@ -40,7 +49,13 @@ class ServiceLogging(CustomLogger):
self.mock_testing_sync_failure_hook += 1
async def async_service_success_hook(
- self, service: ServiceTypes, duration: float, call_type: str
+ self,
+ service: ServiceTypes,
+ call_type: str,
+ duration: float,
+ parent_otel_span: Optional[Span] = None,
+ start_time: Optional[Union[datetime, float]] = None,
+ end_time: Optional[Union[datetime, float]] = None,
):
"""
- For counting if the redis, postgres call is successful
@@ -60,6 +75,16 @@ class ServiceLogging(CustomLogger):
await self.prometheusServicesLogger.async_service_success_hook(
payload=payload
)
+ elif callback == "otel":
+ from litellm.proxy.proxy_server import open_telemetry_logger
+
+ if parent_otel_span is not None and open_telemetry_logger is not None:
+ await open_telemetry_logger.async_service_success_hook(
+ payload=payload,
+ parent_otel_span=parent_otel_span,
+ start_time=start_time,
+ end_time=end_time,
+ )
async def async_service_failure_hook(
self,
@@ -67,6 +92,9 @@ class ServiceLogging(CustomLogger):
duration: float,
error: Union[str, Exception],
call_type: str,
+ parent_otel_span: Optional[Span] = None,
+ start_time: Optional[Union[datetime, float]] = None,
+ end_time: Optional[Union[float, datetime]] = None,
):
"""
- For counting if the redis, postgres call is unsuccessful
@@ -95,6 +123,16 @@ class ServiceLogging(CustomLogger):
payload=payload
)
+ from litellm.proxy.proxy_server import open_telemetry_logger
+
+ if parent_otel_span is not None and open_telemetry_logger is not None:
+ await open_telemetry_logger.async_service_failure_hook(
+ payload=payload,
+ parent_otel_span=parent_otel_span,
+ start_time=start_time,
+ end_time=end_time,
+ )
+
async def async_post_call_failure_hook(
self, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth
):
diff --git a/litellm/adapters/anthropic_adapter.py b/litellm/adapters/anthropic_adapter.py
new file mode 100644
index 0000000000..7d9d799b66
--- /dev/null
+++ b/litellm/adapters/anthropic_adapter.py
@@ -0,0 +1,50 @@
+# What is this?
+## Translates OpenAI call to Anthropic `/v1/messages` format
+import json
+import os
+import traceback
+import uuid
+from typing import Literal, Optional
+
+import dotenv
+import httpx
+from pydantic import BaseModel
+
+import litellm
+from litellm import ChatCompletionRequest, verbose_logger
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.types.llms.anthropic import AnthropicMessagesRequest, AnthropicResponse
+
+
+class AnthropicAdapter(CustomLogger):
+ def __init__(self) -> None:
+ super().__init__()
+
+ def translate_completion_input_params(
+ self, kwargs
+ ) -> Optional[ChatCompletionRequest]:
+ """
+ - translate params, where needed
+ - pass rest, as is
+ """
+ request_body = AnthropicMessagesRequest(**kwargs) # type: ignore
+
+ translated_body = litellm.AnthropicConfig().translate_anthropic_to_openai(
+ anthropic_message_request=request_body
+ )
+
+ return translated_body
+
+ def translate_completion_output_params(
+ self, response: litellm.ModelResponse
+ ) -> Optional[AnthropicResponse]:
+
+ return litellm.AnthropicConfig().translate_openai_response_to_anthropic(
+ response=response
+ )
+
+ def translate_completion_output_params_streaming(self) -> Optional[BaseModel]:
+ return super().translate_completion_output_params_streaming()
+
+
+anthropic_adapter = AnthropicAdapter()
diff --git a/litellm/assistants/main.py b/litellm/assistants/main.py
index 25d2433d7b..ba169f5e20 100644
--- a/litellm/assistants/main.py
+++ b/litellm/assistants/main.py
@@ -1,27 +1,92 @@
# What is this?
## Main file for assistants API logic
-from typing import Iterable
+import asyncio
+import contextvars
import os
+from functools import partial
+from typing import Any, Coroutine, Dict, Iterable, List, Literal, Optional, Union
+
+import httpx
+from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
+from openai.types.beta.assistant import Assistant
+from openai.types.beta.assistant_deleted import AssistantDeleted
+
import litellm
-from openai import OpenAI
from litellm import client
-from litellm.utils import supports_httpx_timeout
+from litellm.types.router import GenericLiteLLMParams
+from litellm.utils import (
+ exception_type,
+ get_llm_provider,
+ get_secret,
+ supports_httpx_timeout,
+)
+
+from ..llms.azure import AzureAssistantsAPI
from ..llms.openai import OpenAIAssistantsAPI
from ..types.llms.openai import *
from ..types.router import *
+from .utils import get_optional_params_add_message
####### ENVIRONMENT VARIABLES ###################
openai_assistants_api = OpenAIAssistantsAPI()
+azure_assistants_api = AzureAssistantsAPI()
### ASSISTANTS ###
+async def aget_assistants(
+ custom_llm_provider: Literal["openai", "azure"],
+ client: Optional[AsyncOpenAI] = None,
+ **kwargs,
+) -> AsyncCursorPage[Assistant]:
+ loop = asyncio.get_event_loop()
+ ### PASS ARGS TO GET ASSISTANTS ###
+ kwargs["aget_assistants"] = True
+ try:
+ # Use a partial function to pass your keyword arguments
+ func = partial(get_assistants, custom_llm_provider, client, **kwargs)
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider( # type: ignore
+ model="", custom_llm_provider=custom_llm_provider
+ ) # type: ignore
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response
+ return response # type: ignore
+ except Exception as e:
+ raise exception_type(
+ model="",
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs={},
+ extra_kwargs=kwargs,
+ )
+
+
def get_assistants(
- custom_llm_provider: Literal["openai"],
- client: Optional[OpenAI] = None,
+ custom_llm_provider: Literal["openai", "azure"],
+ client: Optional[Any] = None,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
**kwargs,
) -> SyncCursorPage[Assistant]:
- optional_params = GenericLiteLLMParams(**kwargs)
+ aget_assistants: Optional[bool] = kwargs.pop("aget_assistants", None)
+ if aget_assistants is not None and not isinstance(aget_assistants, bool):
+ raise Exception(
+ "Invalid value passed in for aget_assistants. Only bool or None allowed"
+ )
+ optional_params = GenericLiteLLMParams(
+ api_key=api_key, api_base=api_base, api_version=api_version, **kwargs
+ )
### TIMEOUT LOGIC ###
timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
@@ -60,6 +125,7 @@ def get_assistants(
or litellm.openai_key
or os.getenv("OPENAI_API_KEY")
)
+
response = openai_assistants_api.get_assistants(
api_base=api_base,
api_key=api_key,
@@ -67,6 +133,43 @@ def get_assistants(
max_retries=optional_params.max_retries,
organization=organization,
client=client,
+ aget_assistants=aget_assistants, # type: ignore
+ ) # type: ignore
+ elif custom_llm_provider == "azure":
+ api_base = (
+ optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE")
+ ) # type: ignore
+
+ api_version = (
+ optional_params.api_version
+ or litellm.api_version
+ or get_secret("AZURE_API_VERSION")
+ ) # type: ignore
+
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key
+ or litellm.azure_key
+ or get_secret("AZURE_OPENAI_API_KEY")
+ or get_secret("AZURE_API_KEY")
+ ) # type: ignore
+
+ extra_body = optional_params.get("extra_body", {})
+ azure_ad_token: Optional[str] = None
+ if extra_body is not None:
+ azure_ad_token = extra_body.pop("azure_ad_token", None)
+ else:
+ azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore
+
+ response = azure_assistants_api.get_assistants(
+ api_base=api_base,
+ api_key=api_key,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ client=client,
+ aget_assistants=aget_assistants, # type: ignore
)
else:
raise litellm.exceptions.BadRequestError(
@@ -84,11 +187,332 @@ def get_assistants(
return response
+async def acreate_assistants(
+ custom_llm_provider: Literal["openai", "azure"],
+ client: Optional[AsyncOpenAI] = None,
+ **kwargs,
+) -> Assistant:
+ loop = asyncio.get_event_loop()
+ ### PASS ARGS TO GET ASSISTANTS ###
+ kwargs["async_create_assistants"] = True
+ try:
+ model = kwargs.pop("model", None)
+ kwargs["client"] = client
+ # Use a partial function to pass your keyword arguments
+ func = partial(create_assistants, custom_llm_provider, model, **kwargs)
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider( # type: ignore
+ model=model, custom_llm_provider=custom_llm_provider
+ ) # type: ignore
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response
+ return response # type: ignore
+ except Exception as e:
+ raise exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs={},
+ extra_kwargs=kwargs,
+ )
+
+
+def create_assistants(
+ custom_llm_provider: Literal["openai", "azure"],
+ model: str,
+ name: Optional[str] = None,
+ description: Optional[str] = None,
+ instructions: Optional[str] = None,
+ tools: Optional[List[Dict[str, Any]]] = None,
+ tool_resources: Optional[Dict[str, Any]] = None,
+ metadata: Optional[Dict[str, str]] = None,
+ temperature: Optional[float] = None,
+ top_p: Optional[float] = None,
+ response_format: Optional[Union[str, Dict[str, str]]] = None,
+ client: Optional[Any] = None,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
+ **kwargs,
+) -> Assistant:
+ async_create_assistants: Optional[bool] = kwargs.pop(
+ "async_create_assistants", None
+ )
+ if async_create_assistants is not None and not isinstance(
+ async_create_assistants, bool
+ ):
+ raise ValueError(
+ "Invalid value passed in for async_create_assistants. Only bool or None allowed"
+ )
+ optional_params = GenericLiteLLMParams(
+ api_key=api_key, api_base=api_base, api_version=api_version, **kwargs
+ )
+
+ ### TIMEOUT LOGIC ###
+ timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ response: Optional[Assistant] = None
+ if custom_llm_provider == "openai":
+ api_base = (
+ optional_params.api_base # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+
+ create_assistant_data = {
+ "model": model,
+ "name": name,
+ "description": description,
+ "instructions": instructions,
+ "tools": tools,
+ "tool_resources": tool_resources,
+ "metadata": metadata,
+ "temperature": temperature,
+ "top_p": top_p,
+ "response_format": response_format,
+ }
+
+ response = openai_assistants_api.create_assistants(
+ api_base=api_base,
+ api_key=api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=organization,
+ create_assistant_data=create_assistant_data,
+ client=client,
+ async_create_assistants=async_create_assistants, # type: ignore
+ ) # type: ignore
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_assistants'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ if response is None:
+ raise litellm.exceptions.InternalServerError(
+ message="No response returned from 'create_assistants'",
+ model=model,
+ llm_provider=custom_llm_provider,
+ )
+ return response
+
+
+async def adelete_assistant(
+ custom_llm_provider: Literal["openai", "azure"],
+ client: Optional[AsyncOpenAI] = None,
+ **kwargs,
+) -> AssistantDeleted:
+ loop = asyncio.get_event_loop()
+ ### PASS ARGS TO GET ASSISTANTS ###
+ kwargs["async_delete_assistants"] = True
+ try:
+ kwargs["client"] = client
+ # Use a partial function to pass your keyword arguments
+ func = partial(delete_assistant, custom_llm_provider, **kwargs)
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider( # type: ignore
+ model="", custom_llm_provider=custom_llm_provider
+ ) # type: ignore
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response
+ return response # type: ignore
+ except Exception as e:
+ raise exception_type(
+ model="",
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs={},
+ extra_kwargs=kwargs,
+ )
+
+
+def delete_assistant(
+ custom_llm_provider: Literal["openai", "azure"],
+ assistant_id: str,
+ client: Optional[Any] = None,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
+ **kwargs,
+) -> AssistantDeleted:
+ optional_params = GenericLiteLLMParams(
+ api_key=api_key, api_base=api_base, api_version=api_version, **kwargs
+ )
+
+ async_delete_assistants: Optional[bool] = kwargs.pop(
+ "async_delete_assistants", None
+ )
+ if async_delete_assistants is not None and not isinstance(
+ async_delete_assistants, bool
+ ):
+ raise ValueError(
+ "Invalid value passed in for async_delete_assistants. Only bool or None allowed"
+ )
+
+ ### TIMEOUT LOGIC ###
+ timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ response: Optional[AssistantDeleted] = None
+ if custom_llm_provider == "openai":
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+
+ response = openai_assistants_api.delete_assistant(
+ api_base=api_base,
+ api_key=api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=organization,
+ assistant_id=assistant_id,
+ client=client,
+ async_delete_assistants=async_delete_assistants,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'delete_assistant'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(
+ method="delete_assistant", url="https://github.com/BerriAI/litellm"
+ ),
+ ),
+ )
+ if response is None:
+ raise litellm.exceptions.InternalServerError(
+ message="No response returned from 'delete_assistant'",
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ )
+ return response
+
+
### THREADS ###
+async def acreate_thread(
+ custom_llm_provider: Literal["openai", "azure"], **kwargs
+) -> Thread:
+ loop = asyncio.get_event_loop()
+ ### PASS ARGS TO GET ASSISTANTS ###
+ kwargs["acreate_thread"] = True
+ try:
+ # Use a partial function to pass your keyword arguments
+ func = partial(create_thread, custom_llm_provider, **kwargs)
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider( # type: ignore
+ model="", custom_llm_provider=custom_llm_provider
+ ) # type: ignore
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response
+ return response # type: ignore
+ except Exception as e:
+ raise exception_type(
+ model="",
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs={},
+ extra_kwargs=kwargs,
+ )
+
+
def create_thread(
- custom_llm_provider: Literal["openai"],
+ custom_llm_provider: Literal["openai", "azure"],
messages: Optional[Iterable[OpenAICreateThreadParamsMessage]] = None,
metadata: Optional[dict] = None,
tool_resources: Optional[OpenAICreateThreadParamsToolResources] = None,
@@ -117,6 +541,7 @@ def create_thread(
)
```
"""
+ acreate_thread = kwargs.get("acreate_thread", None)
optional_params = GenericLiteLLMParams(**kwargs)
### TIMEOUT LOGIC ###
@@ -165,7 +590,49 @@ def create_thread(
max_retries=optional_params.max_retries,
organization=organization,
client=client,
+ acreate_thread=acreate_thread,
)
+ elif custom_llm_provider == "azure":
+ api_base = (
+ optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE")
+ ) # type: ignore
+
+ api_version = (
+ optional_params.api_version
+ or litellm.api_version
+ or get_secret("AZURE_API_VERSION")
+ ) # type: ignore
+
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key
+ or litellm.azure_key
+ or get_secret("AZURE_OPENAI_API_KEY")
+ or get_secret("AZURE_API_KEY")
+ ) # type: ignore
+
+ extra_body = optional_params.get("extra_body", {})
+ azure_ad_token = None
+ if extra_body is not None:
+ azure_ad_token = extra_body.pop("azure_ad_token", None)
+ else:
+ azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore
+
+ if isinstance(client, OpenAI):
+ client = None # only pass client if it's AzureOpenAI
+
+ response = azure_assistants_api.create_thread(
+ messages=messages,
+ metadata=metadata,
+ api_base=api_base,
+ api_key=api_key,
+ azure_ad_token=azure_ad_token,
+ api_version=api_version,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ client=client,
+ acreate_thread=acreate_thread,
+ ) # type :ignore
else:
raise litellm.exceptions.BadRequestError(
message="LiteLLM doesn't support {} for 'create_thread'. Only 'openai' is supported.".format(
@@ -179,16 +646,55 @@ def create_thread(
request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
- return response
+ return response # type: ignore
+
+
+async def aget_thread(
+ custom_llm_provider: Literal["openai", "azure"],
+ thread_id: str,
+ client: Optional[AsyncOpenAI] = None,
+ **kwargs,
+) -> Thread:
+ loop = asyncio.get_event_loop()
+ ### PASS ARGS TO GET ASSISTANTS ###
+ kwargs["aget_thread"] = True
+ try:
+ # Use a partial function to pass your keyword arguments
+ func = partial(get_thread, custom_llm_provider, thread_id, client, **kwargs)
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider( # type: ignore
+ model="", custom_llm_provider=custom_llm_provider
+ ) # type: ignore
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response
+ return response # type: ignore
+ except Exception as e:
+ raise exception_type(
+ model="",
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs={},
+ extra_kwargs=kwargs,
+ )
def get_thread(
- custom_llm_provider: Literal["openai"],
+ custom_llm_provider: Literal["openai", "azure"],
thread_id: str,
- client: Optional[OpenAI] = None,
+ client=None,
**kwargs,
) -> Thread:
"""Get the thread object, given a thread_id"""
+ aget_thread = kwargs.pop("aget_thread", None)
optional_params = GenericLiteLLMParams(**kwargs)
### TIMEOUT LOGIC ###
@@ -228,6 +734,7 @@ def get_thread(
or litellm.openai_key
or os.getenv("OPENAI_API_KEY")
)
+
response = openai_assistants_api.get_thread(
thread_id=thread_id,
api_base=api_base,
@@ -236,6 +743,47 @@ def get_thread(
max_retries=optional_params.max_retries,
organization=organization,
client=client,
+ aget_thread=aget_thread,
+ )
+ elif custom_llm_provider == "azure":
+ api_base = (
+ optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE")
+ ) # type: ignore
+
+ api_version = (
+ optional_params.api_version
+ or litellm.api_version
+ or get_secret("AZURE_API_VERSION")
+ ) # type: ignore
+
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key
+ or litellm.azure_key
+ or get_secret("AZURE_OPENAI_API_KEY")
+ or get_secret("AZURE_API_KEY")
+ ) # type: ignore
+
+ extra_body = optional_params.get("extra_body", {})
+ azure_ad_token = None
+ if extra_body is not None:
+ azure_ad_token = extra_body.pop("azure_ad_token", None)
+ else:
+ azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore
+
+ if isinstance(client, OpenAI):
+ client = None # only pass client if it's AzureOpenAI
+
+ response = azure_assistants_api.get_thread(
+ thread_id=thread_id,
+ api_base=api_base,
+ api_key=api_key,
+ azure_ad_token=azure_ad_token,
+ api_version=api_version,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ client=client,
+ aget_thread=aget_thread,
)
else:
raise litellm.exceptions.BadRequestError(
@@ -250,28 +798,90 @@ def get_thread(
request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
- return response
+ return response # type: ignore
### MESSAGES ###
-def add_message(
- custom_llm_provider: Literal["openai"],
+async def a_add_message(
+ custom_llm_provider: Literal["openai", "azure"],
thread_id: str,
role: Literal["user", "assistant"],
content: str,
attachments: Optional[List[Attachment]] = None,
metadata: Optional[dict] = None,
- client: Optional[OpenAI] = None,
+ client=None,
+ **kwargs,
+) -> OpenAIMessage:
+ loop = asyncio.get_event_loop()
+ ### PASS ARGS TO GET ASSISTANTS ###
+ kwargs["a_add_message"] = True
+ try:
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ add_message,
+ custom_llm_provider,
+ thread_id,
+ role,
+ content,
+ attachments,
+ metadata,
+ client,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider( # type: ignore
+ model="", custom_llm_provider=custom_llm_provider
+ ) # type: ignore
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ # Call the synchronous function using run_in_executor
+ response = init_response
+ return response # type: ignore
+ except Exception as e:
+ raise exception_type(
+ model="",
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs={},
+ extra_kwargs=kwargs,
+ )
+
+
+def add_message(
+ custom_llm_provider: Literal["openai", "azure"],
+ thread_id: str,
+ role: Literal["user", "assistant"],
+ content: str,
+ attachments: Optional[List[Attachment]] = None,
+ metadata: Optional[dict] = None,
+ client=None,
**kwargs,
) -> OpenAIMessage:
### COMMON OBJECTS ###
- message_data = MessageData(
+ a_add_message = kwargs.pop("a_add_message", None)
+ _message_data = MessageData(
role=role, content=content, attachments=attachments, metadata=metadata
)
optional_params = GenericLiteLLMParams(**kwargs)
+ message_data = get_optional_params_add_message(
+ role=_message_data["role"],
+ content=_message_data["content"],
+ attachments=_message_data["attachments"],
+ metadata=_message_data["metadata"],
+ custom_llm_provider=custom_llm_provider,
+ )
+
### TIMEOUT LOGIC ###
timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
# set timeout for 10 minutes by default
@@ -318,6 +928,45 @@ def add_message(
max_retries=optional_params.max_retries,
organization=organization,
client=client,
+ a_add_message=a_add_message,
+ )
+ elif custom_llm_provider == "azure":
+ api_base = (
+ optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE")
+ ) # type: ignore
+
+ api_version = (
+ optional_params.api_version
+ or litellm.api_version
+ or get_secret("AZURE_API_VERSION")
+ ) # type: ignore
+
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key
+ or litellm.azure_key
+ or get_secret("AZURE_OPENAI_API_KEY")
+ or get_secret("AZURE_API_KEY")
+ ) # type: ignore
+
+ extra_body = optional_params.get("extra_body", {})
+ azure_ad_token = None
+ if extra_body is not None:
+ azure_ad_token = extra_body.pop("azure_ad_token", None)
+ else:
+ azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore
+
+ response = azure_assistants_api.add_message(
+ thread_id=thread_id,
+ message_data=message_data,
+ api_base=api_base,
+ api_key=api_key,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ client=client,
+ a_add_message=a_add_message,
)
else:
raise litellm.exceptions.BadRequestError(
@@ -333,15 +982,61 @@ def add_message(
),
)
- return response
+ return response # type: ignore
+
+
+async def aget_messages(
+ custom_llm_provider: Literal["openai", "azure"],
+ thread_id: str,
+ client: Optional[AsyncOpenAI] = None,
+ **kwargs,
+) -> AsyncCursorPage[OpenAIMessage]:
+ loop = asyncio.get_event_loop()
+ ### PASS ARGS TO GET ASSISTANTS ###
+ kwargs["aget_messages"] = True
+ try:
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ get_messages,
+ custom_llm_provider,
+ thread_id,
+ client,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider( # type: ignore
+ model="", custom_llm_provider=custom_llm_provider
+ ) # type: ignore
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ # Call the synchronous function using run_in_executor
+ response = init_response
+ return response # type: ignore
+ except Exception as e:
+ raise exception_type(
+ model="",
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs={},
+ extra_kwargs=kwargs,
+ )
def get_messages(
- custom_llm_provider: Literal["openai"],
+ custom_llm_provider: Literal["openai", "azure"],
thread_id: str,
- client: Optional[OpenAI] = None,
+ client: Optional[Any] = None,
**kwargs,
) -> SyncCursorPage[OpenAIMessage]:
+ aget_messages = kwargs.pop("aget_messages", None)
optional_params = GenericLiteLLMParams(**kwargs)
### TIMEOUT LOGIC ###
@@ -389,6 +1084,44 @@ def get_messages(
max_retries=optional_params.max_retries,
organization=organization,
client=client,
+ aget_messages=aget_messages,
+ )
+ elif custom_llm_provider == "azure":
+ api_base = (
+ optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE")
+ ) # type: ignore
+
+ api_version = (
+ optional_params.api_version
+ or litellm.api_version
+ or get_secret("AZURE_API_VERSION")
+ ) # type: ignore
+
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key
+ or litellm.azure_key
+ or get_secret("AZURE_OPENAI_API_KEY")
+ or get_secret("AZURE_API_KEY")
+ ) # type: ignore
+
+ extra_body = optional_params.get("extra_body", {})
+ azure_ad_token = None
+ if extra_body is not None:
+ azure_ad_token = extra_body.pop("azure_ad_token", None)
+ else:
+ azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore
+
+ response = azure_assistants_api.get_messages(
+ thread_id=thread_id,
+ api_base=api_base,
+ api_key=api_key,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ client=client,
+ aget_messages=aget_messages,
)
else:
raise litellm.exceptions.BadRequestError(
@@ -404,14 +1137,21 @@ def get_messages(
),
)
- return response
+ return response # type: ignore
### RUNS ###
+def arun_thread_stream(
+ *,
+ event_handler: Optional[AssistantEventHandler] = None,
+ **kwargs,
+) -> AsyncAssistantStreamManager[AsyncAssistantEventHandler]:
+ kwargs["arun_thread"] = True
+ return run_thread(stream=True, event_handler=event_handler, **kwargs) # type: ignore
-def run_thread(
- custom_llm_provider: Literal["openai"],
+async def arun_thread(
+ custom_llm_provider: Literal["openai", "azure"],
thread_id: str,
assistant_id: str,
additional_instructions: Optional[str] = None,
@@ -420,10 +1160,79 @@ def run_thread(
model: Optional[str] = None,
stream: Optional[bool] = None,
tools: Optional[Iterable[AssistantToolParam]] = None,
- client: Optional[OpenAI] = None,
+ client: Optional[Any] = None,
+ **kwargs,
+) -> Run:
+ loop = asyncio.get_event_loop()
+ ### PASS ARGS TO GET ASSISTANTS ###
+ kwargs["arun_thread"] = True
+ try:
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ run_thread,
+ custom_llm_provider,
+ thread_id,
+ assistant_id,
+ additional_instructions,
+ instructions,
+ metadata,
+ model,
+ stream,
+ tools,
+ client,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider( # type: ignore
+ model="", custom_llm_provider=custom_llm_provider
+ ) # type: ignore
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ # Call the synchronous function using run_in_executor
+ response = init_response
+ return response # type: ignore
+ except Exception as e:
+ raise exception_type(
+ model="",
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs={},
+ extra_kwargs=kwargs,
+ )
+
+
+def run_thread_stream(
+ *,
+ event_handler: Optional[AssistantEventHandler] = None,
+ **kwargs,
+) -> AssistantStreamManager[AssistantEventHandler]:
+ return run_thread(stream=True, event_handler=event_handler, **kwargs) # type: ignore
+
+
+def run_thread(
+ custom_llm_provider: Literal["openai", "azure"],
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str] = None,
+ instructions: Optional[str] = None,
+ metadata: Optional[dict] = None,
+ model: Optional[str] = None,
+ stream: Optional[bool] = None,
+ tools: Optional[Iterable[AssistantToolParam]] = None,
+ client: Optional[Any] = None,
+ event_handler: Optional[AssistantEventHandler] = None, # for stream=True calls
**kwargs,
) -> Run:
"""Run a given thread + assistant."""
+ arun_thread = kwargs.pop("arun_thread", None)
optional_params = GenericLiteLLMParams(**kwargs)
### TIMEOUT LOGIC ###
@@ -463,6 +1272,7 @@ def run_thread(
or litellm.openai_key
or os.getenv("OPENAI_API_KEY")
)
+
response = openai_assistants_api.run_thread(
thread_id=thread_id,
assistant_id=assistant_id,
@@ -478,7 +1288,53 @@ def run_thread(
max_retries=optional_params.max_retries,
organization=organization,
client=client,
+ arun_thread=arun_thread,
+ event_handler=event_handler,
)
+ elif custom_llm_provider == "azure":
+ api_base = (
+ optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE")
+ ) # type: ignore
+
+ api_version = (
+ optional_params.api_version
+ or litellm.api_version
+ or get_secret("AZURE_API_VERSION")
+ ) # type: ignore
+
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key
+ or litellm.azure_key
+ or get_secret("AZURE_OPENAI_API_KEY")
+ or get_secret("AZURE_API_KEY")
+ ) # type: ignore
+
+ extra_body = optional_params.get("extra_body", {})
+ azure_ad_token = None
+ if extra_body is not None:
+ azure_ad_token = extra_body.pop("azure_ad_token", None)
+ else:
+ azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore
+
+ response = azure_assistants_api.run_thread(
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ stream=stream,
+ tools=tools,
+ api_base=str(api_base) if api_base is not None else None,
+ api_key=str(api_key) if api_key is not None else None,
+ api_version=str(api_version) if api_version is not None else None,
+ azure_ad_token=str(azure_ad_token) if azure_ad_token is not None else None,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ client=client,
+ arun_thread=arun_thread,
+ ) # type: ignore
else:
raise litellm.exceptions.BadRequestError(
message="LiteLLM doesn't support {} for 'run_thread'. Only 'openai' is supported.".format(
@@ -492,4 +1348,4 @@ def run_thread(
request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
- return response
+ return response # type: ignore
diff --git a/litellm/assistants/utils.py b/litellm/assistants/utils.py
new file mode 100644
index 0000000000..ca5a1293dc
--- /dev/null
+++ b/litellm/assistants/utils.py
@@ -0,0 +1,158 @@
+import litellm
+from typing import Optional, Union
+from ..types.llms.openai import *
+
+
+def get_optional_params_add_message(
+ role: Optional[str],
+ content: Optional[
+ Union[
+ str,
+ List[
+ Union[
+ MessageContentTextObject,
+ MessageContentImageFileObject,
+ MessageContentImageURLObject,
+ ]
+ ],
+ ]
+ ],
+ attachments: Optional[List[Attachment]],
+ metadata: Optional[dict],
+ custom_llm_provider: str,
+ **kwargs,
+):
+ """
+ Azure doesn't support 'attachments' for creating a message
+
+ Reference - https://learn.microsoft.com/en-us/azure/ai-services/openai/assistants-reference-messages?tabs=python#create-message
+ """
+ passed_params = locals()
+ custom_llm_provider = passed_params.pop("custom_llm_provider")
+ special_params = passed_params.pop("kwargs")
+ for k, v in special_params.items():
+ passed_params[k] = v
+
+ default_params = {
+ "role": None,
+ "content": None,
+ "attachments": None,
+ "metadata": None,
+ }
+
+ non_default_params = {
+ k: v
+ for k, v in passed_params.items()
+ if (k in default_params and v != default_params[k])
+ }
+ optional_params = {}
+
+ ## raise exception if non-default value passed for non-openai/azure embedding calls
+ def _check_valid_arg(supported_params):
+ if len(non_default_params.keys()) > 0:
+ keys = list(non_default_params.keys())
+ for k in keys:
+ if (
+ litellm.drop_params is True and k not in supported_params
+ ): # drop the unsupported non-default values
+ non_default_params.pop(k, None)
+ elif k not in supported_params:
+ raise litellm.utils.UnsupportedParamsError(
+ status_code=500,
+ message="k={}, not supported by {}. Supported params={}. To drop it from the call, set `litellm.drop_params = True`.".format(
+ k, custom_llm_provider, supported_params
+ ),
+ )
+ return non_default_params
+
+ if custom_llm_provider == "openai":
+ optional_params = non_default_params
+ elif custom_llm_provider == "azure":
+ supported_params = (
+ litellm.AzureOpenAIAssistantsAPIConfig().get_supported_openai_create_message_params()
+ )
+ _check_valid_arg(supported_params=supported_params)
+ optional_params = litellm.AzureOpenAIAssistantsAPIConfig().map_openai_params_create_message_params(
+ non_default_params=non_default_params, optional_params=optional_params
+ )
+ for k in passed_params.keys():
+ if k not in default_params.keys():
+ optional_params[k] = passed_params[k]
+ return optional_params
+
+
+def get_optional_params_image_gen(
+ n: Optional[int] = None,
+ quality: Optional[str] = None,
+ response_format: Optional[str] = None,
+ size: Optional[str] = None,
+ style: Optional[str] = None,
+ user: Optional[str] = None,
+ custom_llm_provider: Optional[str] = None,
+ **kwargs,
+):
+ # retrieve all parameters passed to the function
+ passed_params = locals()
+ custom_llm_provider = passed_params.pop("custom_llm_provider")
+ special_params = passed_params.pop("kwargs")
+ for k, v in special_params.items():
+ passed_params[k] = v
+
+ default_params = {
+ "n": None,
+ "quality": None,
+ "response_format": None,
+ "size": None,
+ "style": None,
+ "user": None,
+ }
+
+ non_default_params = {
+ k: v
+ for k, v in passed_params.items()
+ if (k in default_params and v != default_params[k])
+ }
+ optional_params = {}
+
+ ## raise exception if non-default value passed for non-openai/azure embedding calls
+ def _check_valid_arg(supported_params):
+ if len(non_default_params.keys()) > 0:
+ keys = list(non_default_params.keys())
+ for k in keys:
+ if (
+ litellm.drop_params is True and k not in supported_params
+ ): # drop the unsupported non-default values
+ non_default_params.pop(k, None)
+ elif k not in supported_params:
+ raise UnsupportedParamsError(
+ status_code=500,
+ message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
+ )
+ return non_default_params
+
+ if (
+ custom_llm_provider == "openai"
+ or custom_llm_provider == "azure"
+ or custom_llm_provider in litellm.openai_compatible_providers
+ ):
+ optional_params = non_default_params
+ elif custom_llm_provider == "bedrock":
+ supported_params = ["size"]
+ _check_valid_arg(supported_params=supported_params)
+ if size is not None:
+ width, height = size.split("x")
+ optional_params["width"] = int(width)
+ optional_params["height"] = int(height)
+ elif custom_llm_provider == "vertex_ai":
+ supported_params = ["n"]
+ """
+ All params here: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/imagegeneration?project=adroit-crow-413218
+ """
+ _check_valid_arg(supported_params=supported_params)
+ if n is not None:
+ optional_params["sampleCount"] = int(n)
+
+ for k in passed_params.keys():
+ if k not in default_params.keys():
+ optional_params[k] = passed_params[k]
+ return optional_params
diff --git a/litellm/batches/main.py b/litellm/batches/main.py
new file mode 100644
index 0000000000..af2dc5059d
--- /dev/null
+++ b/litellm/batches/main.py
@@ -0,0 +1,330 @@
+"""
+Main File for Batches API implementation
+
+https://platform.openai.com/docs/api-reference/batch
+
+- create_batch()
+- retrieve_batch()
+- cancel_batch()
+- list_batch()
+
+"""
+
+import asyncio
+import contextvars
+import os
+from functools import partial
+from typing import Any, Coroutine, Dict, Literal, Optional, Union
+
+import httpx
+
+import litellm
+from litellm import client
+from litellm.utils import supports_httpx_timeout
+
+from ..llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI
+from ..types.llms.openai import (
+ Batch,
+ CancelBatchRequest,
+ CreateBatchRequest,
+ CreateFileRequest,
+ FileContentRequest,
+ FileObject,
+ FileTypes,
+ HttpxBinaryResponseContent,
+ RetrieveBatchRequest,
+)
+from ..types.router import *
+
+####### ENVIRONMENT VARIABLES ###################
+openai_batches_instance = OpenAIBatchesAPI()
+#################################################
+
+
+async def acreate_batch(
+ completion_window: Literal["24h"],
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"],
+ input_file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ metadata: Optional[Dict[str, str]] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, Batch]:
+ """
+ Async: Creates and executes a batch from an uploaded file of request
+
+ LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["acreate_batch"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ create_batch,
+ completion_window,
+ endpoint,
+ input_file_id,
+ custom_llm_provider,
+ metadata,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def create_batch(
+ completion_window: Literal["24h"],
+ endpoint: Literal["/v1/chat/completions", "/v1/embeddings"],
+ input_file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ metadata: Optional[Dict[str, str]] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ """
+ Creates and executes a batch from an uploaded file of request
+
+ LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _is_async = kwargs.pop("acreate_batch", False) is True
+
+ _create_batch_request = CreateBatchRequest(
+ completion_window=completion_window,
+ endpoint=endpoint,
+ input_file_id=input_file_id,
+ metadata=metadata,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ )
+
+ response = openai_batches_instance.create_batch(
+ api_base=api_base,
+ api_key=api_key,
+ organization=organization,
+ create_batch_data=_create_batch_request,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ _is_async=_is_async,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+async def aretrieve_batch(
+ batch_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ metadata: Optional[Dict[str, str]] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, Batch]:
+ """
+ Async: Retrieves a batch.
+
+ LiteLLM Equivalent of GET https://api.openai.com/v1/batches/{batch_id}
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["aretrieve_batch"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ retrieve_batch,
+ batch_id,
+ custom_llm_provider,
+ metadata,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def retrieve_batch(
+ batch_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ metadata: Optional[Dict[str, str]] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ """
+ Retrieves a batch.
+
+ LiteLLM Equivalent of GET https://api.openai.com/v1/batches/{batch_id}
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _retrieve_batch_request = RetrieveBatchRequest(
+ batch_id=batch_id,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ )
+
+ _is_async = kwargs.pop("aretrieve_batch", False) is True
+
+ response = openai_batches_instance.retrieve_batch(
+ _is_async=_is_async,
+ retrieve_batch_data=_retrieve_batch_request,
+ api_base=api_base,
+ api_key=api_key,
+ organization=organization,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+def cancel_batch():
+ pass
+
+
+def list_batch():
+ pass
+
+
+async def acancel_batch():
+ pass
+
+
+async def alist_batch():
+ pass
diff --git a/litellm/caching.py b/litellm/caching.py
index c8c1736d86..67c15253c1 100644
--- a/litellm/caching.py
+++ b/litellm/caching.py
@@ -7,14 +7,21 @@
#
# Thank you users! We ❤️ you! - Krrish & Ishaan
-import litellm
-import time, logging, asyncio
-import json, traceback, ast, hashlib
-from typing import Optional, Literal, List, Union, Any, BinaryIO
+import ast
+import asyncio
+import hashlib
+import json
+import logging
+import time
+import traceback
+from datetime import timedelta
+from typing import Any, BinaryIO, List, Literal, Optional, Union
+
from openai._models import BaseModel as OpenAIObject
+
+import litellm
from litellm._logging import verbose_logger
from litellm.types.services import ServiceLoggerPayload, ServiceTypes
-import traceback
def print_verbose(print_statement):
@@ -26,6 +33,16 @@ def print_verbose(print_statement):
pass
+def _get_parent_otel_span_from_kwargs(kwargs: Optional[dict] = None):
+ try:
+ if kwargs is None:
+ return None
+ _metadata = kwargs.get("metadata") or {}
+ return _metadata.get("litellm_parent_otel_span")
+ except:
+ return None
+
+
class BaseCache:
def set_cache(self, key, value, **kwargs):
raise NotImplementedError
@@ -47,16 +64,62 @@ class BaseCache:
class InMemoryCache(BaseCache):
- def __init__(self):
- # if users don't provider one, use the default litellm cache
- self.cache_dict = {}
- self.ttl_dict = {}
+ def __init__(
+ self,
+ max_size_in_memory: Optional[int] = 200,
+ default_ttl: Optional[
+ int
+ ] = 600, # default ttl is 10 minutes. At maximum litellm rate limiting logic requires objects to be in memory for 1 minute
+ ):
+ """
+ max_size_in_memory [int]: Maximum number of items in cache. done to prevent memory leaks. Use 200 items as a default
+ """
+ self.max_size_in_memory = (
+ max_size_in_memory or 200
+ ) # set an upper bound of 200 items in-memory
+ self.default_ttl = default_ttl or 600
+
+ # in-memory cache
+ self.cache_dict: dict = {}
+ self.ttl_dict: dict = {}
+
+ def evict_cache(self):
+ """
+ Eviction policy:
+ - check if any items in ttl_dict are expired -> remove them from ttl_dict and cache_dict
+
+
+ This guarantees the following:
+ - 1. When item ttl not set: At minimumm each item will remain in memory for 5 minutes
+ - 2. When ttl is set: the item will remain in memory for at least that amount of time
+ - 3. the size of in-memory cache is bounded
+
+ """
+ for key in list(self.ttl_dict.keys()):
+ if time.time() > self.ttl_dict[key]:
+ removed_item = self.cache_dict.pop(key, None)
+ removed_ttl_item = self.ttl_dict.pop(key, None)
+
+ # de-reference the removed item
+ # https://www.geeksforgeeks.org/diagnosing-and-fixing-memory-leaks-in-python/
+ # One of the most common causes of memory leaks in Python is the retention of objects that are no longer being used.
+ # This can occur when an object is referenced by another object, but the reference is never removed.
+ removed_item = None
+ removed_ttl_item = None
def set_cache(self, key, value, **kwargs):
- print_verbose("InMemoryCache: set_cache")
+ print_verbose(
+ "InMemoryCache: set_cache. current size= {}".format(len(self.cache_dict))
+ )
+ if len(self.cache_dict) >= self.max_size_in_memory:
+ # only evict when cache is full
+ self.evict_cache()
+
self.cache_dict[key] = value
if "ttl" in kwargs:
self.ttl_dict[key] = time.time() + kwargs["ttl"]
+ else:
+ self.ttl_dict[key] = time.time() + self.default_ttl
async def async_set_cache(self, key, value, **kwargs):
self.set_cache(key=key, value=value, **kwargs)
@@ -68,6 +131,17 @@ class InMemoryCache(BaseCache):
else:
self.set_cache(key=cache_key, value=cache_value)
+ async def async_set_cache_sadd(self, key, value: List, ttl: Optional[float]):
+ """
+ Add value to set
+ """
+ # get the value
+ init_value = self.get_cache(key=key) or set()
+ for val in value:
+ init_value.add(val)
+ self.set_cache(key, init_value, ttl=ttl)
+ return value
+
def get_cache(self, key, **kwargs):
if key in self.cache_dict:
if key in self.ttl_dict:
@@ -111,6 +185,7 @@ class InMemoryCache(BaseCache):
init_value = await self.async_get_cache(key=key) or 0
value = init_value + value
await self.async_set_cache(key, value, **kwargs)
+
return value
def flush_cache(self):
@@ -137,10 +212,12 @@ class RedisCache(BaseCache):
namespace: Optional[str] = None,
**kwargs,
):
- from ._redis import get_redis_client, get_redis_connection_pool
- from litellm._service_logger import ServiceLogging
import redis
+ from litellm._service_logger import ServiceLogging
+
+ from ._redis import get_redis_client, get_redis_connection_pool
+
redis_kwargs = {}
if host is not None:
redis_kwargs["host"] = host
@@ -178,9 +255,15 @@ class RedisCache(BaseCache):
# asyncio.get_running_loop().create_task(self.ping())
result = asyncio.get_running_loop().create_task(self.ping())
except Exception as e:
- verbose_logger.error(
- "Error connecting to Async Redis client", extra={"error": str(e)}
- )
+ if "no running event loop" in str(e):
+ verbose_logger.debug(
+ "Ignoring async redis ping. No running event loop."
+ )
+ else:
+ verbose_logger.error(
+ "Error connecting to Async Redis client - {}".format(str(e)),
+ extra={"error": str(e)},
+ )
### SYNC HEALTH PING ###
try:
@@ -233,6 +316,9 @@ class RedisCache(BaseCache):
service=ServiceTypes.REDIS,
duration=_duration,
call_type="increment_cache",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
return result
@@ -246,6 +332,9 @@ class RedisCache(BaseCache):
duration=_duration,
error=e,
call_type="increment_cache",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
verbose_logger.error(
@@ -253,7 +342,6 @@ class RedisCache(BaseCache):
str(e),
value,
)
- traceback.print_exc()
raise e
async def async_scan_iter(self, pattern: str, count: int = 100) -> list:
@@ -277,6 +365,8 @@ class RedisCache(BaseCache):
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_scan_iter",
+ start_time=start_time,
+ end_time=end_time,
)
) # DO NOT SLOW DOWN CALL B/C OF THIS
return keys
@@ -291,6 +381,8 @@ class RedisCache(BaseCache):
duration=_duration,
error=e,
call_type="async_scan_iter",
+ start_time=start_time,
+ end_time=end_time,
)
)
raise e
@@ -304,7 +396,13 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
- service=ServiceTypes.REDIS, duration=_duration, error=e
+ service=ServiceTypes.REDIS,
+ duration=_duration,
+ error=e,
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
+ call_type="async_set_cache",
)
)
# NON blocking - notify users Redis is throwing an exception
@@ -313,7 +411,6 @@ class RedisCache(BaseCache):
str(e),
value,
)
- traceback.print_exc()
key = self.check_and_fix_namespace(key=key)
async with _redis_client as redis_client:
@@ -333,6 +430,9 @@ class RedisCache(BaseCache):
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_set_cache",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
except Exception as e:
@@ -344,6 +444,9 @@ class RedisCache(BaseCache):
duration=_duration,
error=e,
call_type="async_set_cache",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
# NON blocking - notify users Redis is throwing an exception
@@ -352,9 +455,8 @@ class RedisCache(BaseCache):
str(e),
value,
)
- traceback.print_exc()
- async def async_set_cache_pipeline(self, cache_list, ttl=None):
+ async def async_set_cache_pipeline(self, cache_list, ttl=None, **kwargs):
"""
Use Redis Pipelines for bulk write operations
"""
@@ -392,6 +494,9 @@ class RedisCache(BaseCache):
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_set_cache_pipeline",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
return results
@@ -405,6 +510,9 @@ class RedisCache(BaseCache):
duration=_duration,
error=e,
call_type="async_set_cache_pipeline",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
@@ -413,7 +521,80 @@ class RedisCache(BaseCache):
str(e),
cache_value,
)
- traceback.print_exc()
+
+ async def async_set_cache_sadd(
+ self, key, value: List, ttl: Optional[float], **kwargs
+ ):
+ start_time = time.time()
+ try:
+ _redis_client = self.init_async_client()
+ except Exception as e:
+ end_time = time.time()
+ _duration = end_time - start_time
+ asyncio.create_task(
+ self.service_logger_obj.async_service_failure_hook(
+ service=ServiceTypes.REDIS,
+ duration=_duration,
+ error=e,
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
+ call_type="async_set_cache_sadd",
+ )
+ )
+ # NON blocking - notify users Redis is throwing an exception
+ verbose_logger.error(
+ "LiteLLM Redis Caching: async set() - Got exception from REDIS %s, Writing value=%s",
+ str(e),
+ value,
+ )
+ raise e
+
+ key = self.check_and_fix_namespace(key=key)
+ async with _redis_client as redis_client:
+ print_verbose(
+ f"Set ASYNC Redis Cache: key: {key}\nValue {value}\nttl={ttl}"
+ )
+ try:
+ await redis_client.sadd(key, *value)
+ if ttl is not None:
+ _td = timedelta(seconds=ttl)
+ await redis_client.expire(key, _td)
+ print_verbose(
+ f"Successfully Set ASYNC Redis Cache SADD: key: {key}\nValue {value}\nttl={ttl}"
+ )
+ end_time = time.time()
+ _duration = end_time - start_time
+ asyncio.create_task(
+ self.service_logger_obj.async_service_success_hook(
+ service=ServiceTypes.REDIS,
+ duration=_duration,
+ call_type="async_set_cache_sadd",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
+ )
+ )
+ except Exception as e:
+ end_time = time.time()
+ _duration = end_time - start_time
+ asyncio.create_task(
+ self.service_logger_obj.async_service_failure_hook(
+ service=ServiceTypes.REDIS,
+ duration=_duration,
+ error=e,
+ call_type="async_set_cache_sadd",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
+ )
+ )
+ # NON blocking - notify users Redis is throwing an exception
+ verbose_logger.error(
+ "LiteLLM Redis Caching: async set_cache_sadd() - Got exception from REDIS %s, Writing value=%s",
+ str(e),
+ value,
+ )
async def batch_cache_write(self, key, value, **kwargs):
print_verbose(
@@ -438,6 +619,9 @@ class RedisCache(BaseCache):
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_increment",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
return result
@@ -451,6 +635,9 @@ class RedisCache(BaseCache):
duration=_duration,
error=e,
call_type="async_increment",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
verbose_logger.error(
@@ -458,7 +645,6 @@ class RedisCache(BaseCache):
str(e),
value,
)
- traceback.print_exc()
raise e
async def flush_cache_buffer(self):
@@ -495,8 +681,9 @@ class RedisCache(BaseCache):
return self._get_cache_logic(cached_response=cached_response)
except Exception as e:
# NON blocking - notify users Redis is throwing an exception
- traceback.print_exc()
- logging.debug("LiteLLM Caching: get() - Got exception from REDIS: ", e)
+ verbose_logger.error(
+ "LiteLLM Caching: get() - Got exception from REDIS: ", e
+ )
def batch_get_cache(self, key_list) -> dict:
"""
@@ -544,6 +731,9 @@ class RedisCache(BaseCache):
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_get_cache",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
return response
@@ -557,6 +747,9 @@ class RedisCache(BaseCache):
duration=_duration,
error=e,
call_type="async_get_cache",
+ start_time=start_time,
+ end_time=end_time,
+ parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
# NON blocking - notify users Redis is throwing an exception
@@ -587,6 +780,8 @@ class RedisCache(BaseCache):
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_batch_get_cache",
+ start_time=start_time,
+ end_time=end_time,
)
)
@@ -612,6 +807,8 @@ class RedisCache(BaseCache):
duration=_duration,
error=e,
call_type="async_batch_get_cache",
+ start_time=start_time,
+ end_time=end_time,
)
)
print_verbose(f"Error occurred in pipeline read - {str(e)}")
@@ -646,10 +843,9 @@ class RedisCache(BaseCache):
error=e,
call_type="sync_ping",
)
- print_verbose(
+ verbose_logger.error(
f"LiteLLM Redis Cache PING: - Got exception from REDIS : {str(e)}"
)
- traceback.print_exc()
raise e
async def ping(self) -> bool:
@@ -683,10 +879,9 @@ class RedisCache(BaseCache):
call_type="async_ping",
)
)
- print_verbose(
+ verbose_logger.error(
f"LiteLLM Redis Cache PING: - Got exception from REDIS : {str(e)}"
)
- traceback.print_exc()
raise e
async def delete_cache_keys(self, keys):
@@ -839,11 +1034,10 @@ class RedisSemanticCache(BaseCache):
def get_cache(self, key, **kwargs):
print_verbose(f"sync redis semantic-cache get_cache, kwargs: {kwargs}")
- from redisvl.query import VectorQuery
import numpy as np
+ from redisvl.query import VectorQuery
# query
-
# get the messages
messages = kwargs["messages"]
prompt = "".join(message["content"] for message in messages)
@@ -896,7 +1090,8 @@ class RedisSemanticCache(BaseCache):
async def async_set_cache(self, key, value, **kwargs):
import numpy as np
- from litellm.proxy.proxy_server import llm_router, llm_model_list
+
+ from litellm.proxy.proxy_server import llm_model_list, llm_router
try:
await self.index.acreate(overwrite=False) # don't overwrite existing index
@@ -951,12 +1146,12 @@ class RedisSemanticCache(BaseCache):
async def async_get_cache(self, key, **kwargs):
print_verbose(f"async redis semantic-cache get_cache, kwargs: {kwargs}")
- from redisvl.query import VectorQuery
import numpy as np
- from litellm.proxy.proxy_server import llm_router, llm_model_list
+ from redisvl.query import VectorQuery
+
+ from litellm.proxy.proxy_server import llm_model_list, llm_router
# query
-
# get the messages
messages = kwargs["messages"]
prompt = "".join(message["content"] for message in messages)
@@ -1114,7 +1309,8 @@ class S3Cache(BaseCache):
self.set_cache(key=key, value=value, **kwargs)
def get_cache(self, key, **kwargs):
- import boto3, botocore
+ import boto3
+ import botocore
try:
key = self.key_prefix + key
@@ -1138,22 +1334,23 @@ class S3Cache(BaseCache):
cached_response = ast.literal_eval(cached_response)
if type(cached_response) is not dict:
cached_response = dict(cached_response)
- print_verbose(
+ verbose_logger.debug(
f"Got S3 Cache: key: {key}, cached_response {cached_response}. Type Response {type(cached_response)}"
)
return cached_response
except botocore.exceptions.ClientError as e:
if e.response["Error"]["Code"] == "NoSuchKey":
- print_verbose(
+ verbose_logger.debug(
f"S3 Cache: The specified key '{key}' does not exist in the S3 bucket."
)
return None
except Exception as e:
# NON blocking - notify users S3 is throwing an exception
- traceback.print_exc()
- print_verbose(f"S3 Caching: get_cache() - Got exception from S3: {e}")
+ verbose_logger.error(
+ f"S3 Caching: get_cache() - Got exception from S3: {e}"
+ )
async def async_get_cache(self, key, **kwargs):
return self.get_cache(key=key, **kwargs)
@@ -1234,8 +1431,7 @@ class DualCache(BaseCache):
return result
except Exception as e:
- print_verbose(f"LiteLLM Cache: Excepton async add_cache: {str(e)}")
- traceback.print_exc()
+ verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}")
raise e
def get_cache(self, key, local_only: bool = False, **kwargs):
@@ -1262,7 +1458,7 @@ class DualCache(BaseCache):
print_verbose(f"get cache: cache result: {result}")
return result
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error(traceback.format_exc())
def batch_get_cache(self, keys: list, local_only: bool = False, **kwargs):
try:
@@ -1295,7 +1491,7 @@ class DualCache(BaseCache):
print_verbose(f"async batch get cache: cache result: {result}")
return result
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error(traceback.format_exc())
async def async_get_cache(self, key, local_only: bool = False, **kwargs):
# Try to fetch from in-memory cache first
@@ -1328,7 +1524,7 @@ class DualCache(BaseCache):
print_verbose(f"get cache: cache result: {result}")
return result
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error(traceback.format_exc())
async def async_batch_get_cache(
self, keys: list, local_only: bool = False, **kwargs
@@ -1368,7 +1564,7 @@ class DualCache(BaseCache):
return result
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error(traceback.format_exc())
async def async_set_cache(self, key, value, local_only: bool = False, **kwargs):
print_verbose(
@@ -1381,8 +1577,8 @@ class DualCache(BaseCache):
if self.redis_cache is not None and local_only == False:
await self.redis_cache.async_set_cache(key, value, **kwargs)
except Exception as e:
- print_verbose(f"LiteLLM Cache: Excepton async add_cache: {str(e)}")
- traceback.print_exc()
+ verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}")
+ verbose_logger.debug(traceback.format_exc())
async def async_batch_set_cache(
self, cache_list: list, local_only: bool = False, **kwargs
@@ -1401,11 +1597,11 @@ class DualCache(BaseCache):
if self.redis_cache is not None and local_only == False:
await self.redis_cache.async_set_cache_pipeline(
- cache_list=cache_list, ttl=kwargs.get("ttl", None)
+ cache_list=cache_list, ttl=kwargs.get("ttl", None), **kwargs
)
except Exception as e:
- print_verbose(f"LiteLLM Cache: Excepton async add_cache: {str(e)}")
- traceback.print_exc()
+ verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}")
+ verbose_logger.debug(traceback.format_exc())
async def async_increment_cache(
self, key, value: float, local_only: bool = False, **kwargs
@@ -1424,13 +1620,45 @@ class DualCache(BaseCache):
key, value, **kwargs
)
- if self.redis_cache is not None and local_only == False:
+ if self.redis_cache is not None and local_only is False:
result = await self.redis_cache.async_increment(key, value, **kwargs)
return result
except Exception as e:
- print_verbose(f"LiteLLM Cache: Excepton async add_cache: {str(e)}")
- traceback.print_exc()
+ verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}")
+ verbose_logger.debug(traceback.format_exc())
+ raise e
+
+ async def async_set_cache_sadd(
+ self, key, value: List, local_only: bool = False, **kwargs
+ ) -> None:
+ """
+ Add value to a set
+
+ Key - the key in cache
+
+ Value - str - the value you want to add to the set
+
+ Returns - None
+ """
+ try:
+ if self.in_memory_cache is not None:
+ _ = await self.in_memory_cache.async_set_cache_sadd(
+ key, value, ttl=kwargs.get("ttl", None)
+ )
+
+ if self.redis_cache is not None and local_only is False:
+ _ = await self.redis_cache.async_set_cache_sadd(
+ key, value, ttl=kwargs.get("ttl", None) ** kwargs
+ )
+
+ return None
+ except Exception as e:
+ verbose_logger.error(
+ "LiteLLM Cache: Excepton async set_cache_sadd: {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
raise e
def flush_cache(self):
@@ -1440,6 +1668,9 @@ class DualCache(BaseCache):
self.redis_cache.flush_cache()
def delete_cache(self, key):
+ """
+ Delete a key from the cache
+ """
if self.in_memory_cache is not None:
self.in_memory_cache.delete_cache(key)
if self.redis_cache is not None:
@@ -1846,8 +2077,8 @@ class Cache:
)
self.cache.set_cache(cache_key, cached_data, **kwargs)
except Exception as e:
- print_verbose(f"LiteLLM Cache: Excepton add_cache: {str(e)}")
- traceback.print_exc()
+ verbose_logger.error(f"LiteLLM Cache: Excepton add_cache: {str(e)}")
+ verbose_logger.debug(traceback.format_exc())
pass
async def async_add_cache(self, result, *args, **kwargs):
@@ -1864,8 +2095,8 @@ class Cache:
)
await self.cache.async_set_cache(cache_key, cached_data, **kwargs)
except Exception as e:
- print_verbose(f"LiteLLM Cache: Excepton add_cache: {str(e)}")
- traceback.print_exc()
+ verbose_logger.error(f"LiteLLM Cache: Excepton add_cache: {str(e)}")
+ verbose_logger.debug(traceback.format_exc())
async def async_add_cache_pipeline(self, result, *args, **kwargs):
"""
@@ -1897,8 +2128,8 @@ class Cache:
)
await asyncio.gather(*tasks)
except Exception as e:
- print_verbose(f"LiteLLM Cache: Excepton add_cache: {str(e)}")
- traceback.print_exc()
+ verbose_logger.error(f"LiteLLM Cache: Excepton add_cache: {str(e)}")
+ verbose_logger.debug(traceback.format_exc())
async def batch_cache_write(self, result, *args, **kwargs):
cache_key, cached_data, kwargs = self._add_cache_logic(
diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
new file mode 100644
index 0000000000..9bd5f90be3
--- /dev/null
+++ b/litellm/cost_calculator.py
@@ -0,0 +1,745 @@
+# What is this?
+## File for 'response_cost' calculation in Logging
+import time
+import traceback
+from typing import List, Literal, Optional, Tuple, Union
+
+from pydantic import BaseModel
+
+import litellm
+import litellm._logging
+from litellm import verbose_logger
+from litellm.litellm_core_utils.llm_cost_calc.google import (
+ cost_per_character as google_cost_per_character,
+)
+from litellm.litellm_core_utils.llm_cost_calc.google import (
+ cost_per_token as google_cost_per_token,
+)
+from litellm.litellm_core_utils.llm_cost_calc.google import (
+ cost_router as google_cost_router,
+)
+from litellm.litellm_core_utils.llm_cost_calc.utils import _generic_cost_per_character
+from litellm.types.llms.openai import HttpxBinaryResponseContent
+from litellm.types.router import SPECIAL_MODEL_INFO_PARAMS
+from litellm.utils import (
+ CallTypes,
+ CostPerToken,
+ EmbeddingResponse,
+ ImageResponse,
+ ModelResponse,
+ TextCompletionResponse,
+ TranscriptionResponse,
+ print_verbose,
+ token_counter,
+)
+
+
+def _cost_per_token_custom_pricing_helper(
+ prompt_tokens: float = 0,
+ completion_tokens: float = 0,
+ response_time_ms=None,
+ ### CUSTOM PRICING ###
+ custom_cost_per_token: Optional[CostPerToken] = None,
+ custom_cost_per_second: Optional[float] = None,
+) -> Optional[Tuple[float, float]]:
+ """Internal helper function for calculating cost, if custom pricing given"""
+ if custom_cost_per_token is None and custom_cost_per_second is None:
+ return None
+
+ if custom_cost_per_token is not None:
+ input_cost = custom_cost_per_token["input_cost_per_token"] * prompt_tokens
+ output_cost = custom_cost_per_token["output_cost_per_token"] * completion_tokens
+ return input_cost, output_cost
+ elif custom_cost_per_second is not None:
+ output_cost = custom_cost_per_second * response_time_ms / 1000 # type: ignore
+ return 0, output_cost
+
+ return None
+
+
+def cost_per_token(
+ model: str = "",
+ prompt_tokens: float = 0,
+ completion_tokens: float = 0,
+ response_time_ms=None,
+ custom_llm_provider: Optional[str] = None,
+ region_name=None,
+ ### CHARACTER PRICING ###
+ prompt_characters: float = 0,
+ completion_characters: float = 0,
+ ### CUSTOM PRICING ###
+ custom_cost_per_token: Optional[CostPerToken] = None,
+ custom_cost_per_second: Optional[float] = None,
+ ### CALL TYPE ###
+ call_type: Literal[
+ "embedding",
+ "aembedding",
+ "completion",
+ "acompletion",
+ "atext_completion",
+ "text_completion",
+ "image_generation",
+ "aimage_generation",
+ "moderation",
+ "amoderation",
+ "atranscription",
+ "transcription",
+ "aspeech",
+ "speech",
+ ] = "completion",
+) -> Tuple[float, float]:
+ """
+ Calculates the cost per token for a given model, prompt tokens, and completion tokens.
+
+ Parameters:
+ model (str): The name of the model to use. Default is ""
+ prompt_tokens (int): The number of tokens in the prompt.
+ completion_tokens (int): The number of tokens in the completion.
+ response_time (float): The amount of time, in milliseconds, it took the call to complete.
+ prompt_characters (float): The number of characters in the prompt. Used for vertex ai cost calculation.
+ completion_characters (float): The number of characters in the completion response. Used for vertex ai cost calculation.
+ custom_llm_provider (str): The llm provider to whom the call was made (see init.py for full list)
+ custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call.
+ custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
+ call_type: Optional[str]: the call type
+
+ Returns:
+ tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively.
+ """
+ args = locals()
+ if model is None:
+ raise Exception("Invalid arg. Model cannot be none.")
+ ## CUSTOM PRICING ##
+ response_cost = _cost_per_token_custom_pricing_helper(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ response_time_ms=response_time_ms,
+ custom_cost_per_second=custom_cost_per_second,
+ custom_cost_per_token=custom_cost_per_token,
+ )
+ if response_cost is not None:
+ return response_cost[0], response_cost[1]
+
+ # given
+ prompt_tokens_cost_usd_dollar: float = 0
+ completion_tokens_cost_usd_dollar: float = 0
+ model_cost_ref = litellm.model_cost
+ model_with_provider = model
+ if custom_llm_provider is not None:
+ model_with_provider = custom_llm_provider + "/" + model
+ if region_name is not None:
+ model_with_provider_and_region = (
+ f"{custom_llm_provider}/{region_name}/{model}"
+ )
+ if (
+ model_with_provider_and_region in model_cost_ref
+ ): # use region based pricing, if it's available
+ model_with_provider = model_with_provider_and_region
+ else:
+ _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
+ model_without_prefix = model
+ model_parts = model.split("/")
+ if len(model_parts) > 1:
+ model_without_prefix = model_parts[1]
+ else:
+ model_without_prefix = model
+ """
+ Code block that formats model to lookup in litellm.model_cost
+ Option1. model = "bedrock/ap-northeast-1/anthropic.claude-instant-v1". This is the most accurate since it is region based. Should always be option 1
+ Option2. model = "openai/gpt-4" - model = provider/model
+ Option3. model = "anthropic.claude-3" - model = model
+ """
+ if (
+ model_with_provider in model_cost_ref
+ ): # Option 2. use model with provider, model = "openai/gpt-4"
+ model = model_with_provider
+ elif model in model_cost_ref: # Option 1. use model passed, model="gpt-4"
+ model = model
+ elif (
+ model_without_prefix in model_cost_ref
+ ): # Option 3. if user passed model="bedrock/anthropic.claude-3", use model="anthropic.claude-3"
+ model = model_without_prefix
+
+ # see this https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
+ print_verbose(f"Looking up model={model} in model_cost_map")
+ if custom_llm_provider == "vertex_ai":
+ cost_router = google_cost_router(
+ model=model_without_prefix,
+ custom_llm_provider=custom_llm_provider,
+ prompt_characters=prompt_characters,
+ completion_characters=completion_characters,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ call_type=call_type,
+ )
+ if cost_router == "cost_per_character":
+ return google_cost_per_character(
+ model=model_without_prefix,
+ custom_llm_provider=custom_llm_provider,
+ prompt_characters=prompt_characters,
+ completion_characters=completion_characters,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ )
+ elif cost_router == "cost_per_token":
+ return google_cost_per_token(
+ model=model_without_prefix,
+ custom_llm_provider=custom_llm_provider,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ )
+ elif custom_llm_provider == "gemini":
+ return google_cost_per_token(
+ model=model_without_prefix,
+ custom_llm_provider=custom_llm_provider,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ )
+ elif call_type == "speech" or call_type == "aspeech":
+ prompt_cost, completion_cost = _generic_cost_per_character(
+ model=model_without_prefix,
+ custom_llm_provider=custom_llm_provider,
+ prompt_characters=prompt_characters,
+ completion_characters=completion_characters,
+ custom_prompt_cost=None,
+ custom_completion_cost=0,
+ )
+ if prompt_cost is None or completion_cost is None:
+ raise ValueError(
+ "cost for tts call is None. prompt_cost={}, completion_cost={}, model={}, custom_llm_provider={}, prompt_characters={}, completion_characters={}".format(
+ prompt_cost,
+ completion_cost,
+ model_without_prefix,
+ custom_llm_provider,
+ prompt_characters,
+ completion_characters,
+ )
+ )
+ return prompt_cost, completion_cost
+ elif model in model_cost_ref:
+ print_verbose(f"Success: model={model} in model_cost_map")
+ print_verbose(
+ f"prompt_tokens={prompt_tokens}; completion_tokens={completion_tokens}"
+ )
+ if (
+ model_cost_ref[model].get("input_cost_per_token", None) is not None
+ and model_cost_ref[model].get("output_cost_per_token", None) is not None
+ ):
+ ## COST PER TOKEN ##
+ prompt_tokens_cost_usd_dollar = (
+ model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
+ )
+ completion_tokens_cost_usd_dollar = (
+ model_cost_ref[model]["output_cost_per_token"] * completion_tokens
+ )
+ elif (
+ model_cost_ref[model].get("output_cost_per_second", None) is not None
+ and response_time_ms is not None
+ ):
+ print_verbose(
+ f"For model={model} - output_cost_per_second: {model_cost_ref[model].get('output_cost_per_second')}; response time: {response_time_ms}"
+ )
+ ## COST PER SECOND ##
+ prompt_tokens_cost_usd_dollar = 0
+ completion_tokens_cost_usd_dollar = (
+ model_cost_ref[model]["output_cost_per_second"]
+ * response_time_ms
+ / 1000
+ )
+ elif (
+ model_cost_ref[model].get("input_cost_per_second", None) is not None
+ and response_time_ms is not None
+ ):
+ print_verbose(
+ f"For model={model} - input_cost_per_second: {model_cost_ref[model].get('input_cost_per_second')}; response time: {response_time_ms}"
+ )
+ ## COST PER SECOND ##
+ prompt_tokens_cost_usd_dollar = (
+ model_cost_ref[model]["input_cost_per_second"] * response_time_ms / 1000
+ )
+ completion_tokens_cost_usd_dollar = 0.0
+ print_verbose(
+ f"Returned custom cost for model={model} - prompt_tokens_cost_usd_dollar: {prompt_tokens_cost_usd_dollar}, completion_tokens_cost_usd_dollar: {completion_tokens_cost_usd_dollar}"
+ )
+ return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+ elif "ft:gpt-3.5-turbo" in model:
+ print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
+ # fuzzy match ft:gpt-3.5-turbo:abcd-id-cool-litellm
+ prompt_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:gpt-3.5-turbo"]["input_cost_per_token"] * prompt_tokens
+ )
+ completion_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:gpt-3.5-turbo"]["output_cost_per_token"]
+ * completion_tokens
+ )
+ return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+ elif "ft:gpt-4-0613" in model:
+ print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
+ # fuzzy match ft:gpt-4-0613:abcd-id-cool-litellm
+ prompt_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:gpt-4-0613"]["input_cost_per_token"] * prompt_tokens
+ )
+ completion_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:gpt-4-0613"]["output_cost_per_token"] * completion_tokens
+ )
+ return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+ elif "ft:gpt-4o-2024-05-13" in model:
+ print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
+ # fuzzy match ft:gpt-4o-2024-05-13:abcd-id-cool-litellm
+ prompt_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:gpt-4o-2024-05-13"]["input_cost_per_token"]
+ * prompt_tokens
+ )
+ completion_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:gpt-4o-2024-05-13"]["output_cost_per_token"]
+ * completion_tokens
+ )
+ return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+
+ elif "ft:davinci-002" in model:
+ print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
+ # fuzzy match ft:davinci-002:abcd-id-cool-litellm
+ prompt_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:davinci-002"]["input_cost_per_token"] * prompt_tokens
+ )
+ completion_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:davinci-002"]["output_cost_per_token"]
+ * completion_tokens
+ )
+ return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+ elif "ft:babbage-002" in model:
+ print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
+ # fuzzy match ft:babbage-002:abcd-id-cool-litellm
+ prompt_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:babbage-002"]["input_cost_per_token"] * prompt_tokens
+ )
+ completion_tokens_cost_usd_dollar = (
+ model_cost_ref["ft:babbage-002"]["output_cost_per_token"]
+ * completion_tokens
+ )
+ return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+ elif model in litellm.azure_llms:
+ verbose_logger.debug(f"Cost Tracking: {model} is an Azure LLM")
+ model = litellm.azure_llms[model]
+ verbose_logger.debug(
+ f"applying cost={model_cost_ref[model]['input_cost_per_token']} for prompt_tokens={prompt_tokens}"
+ )
+ prompt_tokens_cost_usd_dollar = (
+ model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
+ )
+ verbose_logger.debug(
+ f"applying cost={model_cost_ref[model]['output_cost_per_token']} for completion_tokens={completion_tokens}"
+ )
+ completion_tokens_cost_usd_dollar = (
+ model_cost_ref[model]["output_cost_per_token"] * completion_tokens
+ )
+ return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+ elif model in litellm.azure_embedding_models:
+ verbose_logger.debug(f"Cost Tracking: {model} is an Azure Embedding Model")
+ model = litellm.azure_embedding_models[model]
+ prompt_tokens_cost_usd_dollar = (
+ model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
+ )
+ completion_tokens_cost_usd_dollar = (
+ model_cost_ref[model]["output_cost_per_token"] * completion_tokens
+ )
+ return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+ else:
+ # if model is not in model_prices_and_context_window.json. Raise an exception-let users know
+ error_str = f"Model not in model_prices_and_context_window.json. You passed model={model}, custom_llm_provider={custom_llm_provider}. Register pricing for model - https://docs.litellm.ai/docs/proxy/custom_pricing\n"
+ raise litellm.exceptions.NotFoundError( # type: ignore
+ message=error_str,
+ model=model,
+ llm_provider="",
+ )
+
+
+# Extract the number of billion parameters from the model name
+# only used for together_computer LLMs
+def get_model_params_and_category(model_name) -> str:
+ """
+ Helper function for calculating together ai pricing.
+
+ Returns
+ - str - model pricing category if mapped else received model name
+ """
+ import re
+
+ model_name = model_name.lower()
+ re_params_match = re.search(
+ r"(\d+b)", model_name
+ ) # catch all decimals like 3b, 70b, etc
+ category = None
+ if re_params_match is not None:
+ params_match = str(re_params_match.group(1))
+ params_match = params_match.replace("b", "")
+ if params_match is not None:
+ params_billion = float(params_match)
+ else:
+ return model_name
+ # Determine the category based on the number of parameters
+ if params_billion <= 4.0:
+ category = "together-ai-up-to-4b"
+ elif params_billion <= 8.0:
+ category = "together-ai-4.1b-8b"
+ elif params_billion <= 21.0:
+ category = "together-ai-8.1b-21b"
+ elif params_billion <= 41.0:
+ category = "together-ai-21.1b-41b"
+ elif params_billion <= 80.0:
+ category = "together-ai-41.1b-80b"
+ elif params_billion <= 110.0:
+ category = "together-ai-81.1b-110b"
+ if category is not None:
+ return category
+
+ return model_name
+
+
+def get_replicate_completion_pricing(completion_response=None, total_time=0.0):
+ # see https://replicate.com/pricing
+ # for all litellm currently supported LLMs, almost all requests go to a100_80gb
+ a100_80gb_price_per_second_public = (
+ 0.001400 # assume all calls sent to A100 80GB for now
+ )
+ if total_time == 0.0: # total time is in ms
+ start_time = completion_response["created"]
+ end_time = getattr(completion_response, "ended", time.time())
+ total_time = end_time - start_time
+
+ return a100_80gb_price_per_second_public * total_time / 1000
+
+
+def completion_cost(
+ completion_response=None,
+ model: Optional[str] = None,
+ prompt="",
+ messages: List = [],
+ completion="",
+ total_time=0.0, # used for replicate, sagemaker
+ call_type: Literal[
+ "embedding",
+ "aembedding",
+ "completion",
+ "acompletion",
+ "atext_completion",
+ "text_completion",
+ "image_generation",
+ "aimage_generation",
+ "moderation",
+ "amoderation",
+ "atranscription",
+ "transcription",
+ "aspeech",
+ "speech",
+ ] = "completion",
+ ### REGION ###
+ custom_llm_provider=None,
+ region_name=None, # used for bedrock pricing
+ ### IMAGE GEN ###
+ size=None,
+ quality=None,
+ n=None, # number of images
+ ### CUSTOM PRICING ###
+ custom_cost_per_token: Optional[CostPerToken] = None,
+ custom_cost_per_second: Optional[float] = None,
+) -> float:
+ """
+ Calculate the cost of a given completion call fot GPT-3.5-turbo, llama2, any litellm supported llm.
+
+ Parameters:
+ completion_response (litellm.ModelResponses): [Required] The response received from a LiteLLM completion request.
+
+ [OPTIONAL PARAMS]
+ model (str): Optional. The name of the language model used in the completion calls
+ prompt (str): Optional. The input prompt passed to the llm
+ completion (str): Optional. The output completion text from the llm
+ total_time (float): Optional. (Only used for Replicate LLMs) The total time used for the request in seconds
+ custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call.
+ custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
+
+ Returns:
+ float: The cost in USD dollars for the completion based on the provided parameters.
+
+ Exceptions:
+ Raises exception if model not in the litellm model cost map. Register model, via custom pricing or PR - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json
+
+
+ Note:
+ - If completion_response is provided, the function extracts token information and the model name from it.
+ - If completion_response is not provided, the function calculates token counts based on the model and input text.
+ - The cost is calculated based on the model, prompt tokens, and completion tokens.
+ - For certain models containing "togethercomputer" in the name, prices are based on the model size.
+ - For un-mapped Replicate models, the cost is calculated based on the total time used for the request.
+ """
+ try:
+ if (
+ (call_type == "aimage_generation" or call_type == "image_generation")
+ and model is not None
+ and isinstance(model, str)
+ and len(model) == 0
+ and custom_llm_provider == "azure"
+ ):
+ model = "dall-e-2" # for dall-e-2, azure expects an empty model name
+ # Handle Inputs to completion_cost
+ prompt_tokens = 0
+ prompt_characters = 0
+ completion_tokens = 0
+ completion_characters = 0
+ if completion_response is not None and (
+ isinstance(completion_response, BaseModel)
+ or isinstance(completion_response, dict)
+ ): # tts returns a custom class
+ # get input/output tokens from completion_response
+ prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0)
+ completion_tokens = completion_response.get("usage", {}).get(
+ "completion_tokens", 0
+ )
+ total_time = completion_response.get("_response_ms", 0)
+ verbose_logger.debug(
+ f"completion_response response ms: {completion_response.get('_response_ms')} "
+ )
+ model = model or completion_response.get(
+ "model", None
+ ) # check if user passed an override for model, if it's none check completion_response['model']
+ if hasattr(completion_response, "_hidden_params"):
+ if (
+ completion_response._hidden_params.get("model", None) is not None
+ and len(completion_response._hidden_params["model"]) > 0
+ ):
+ model = completion_response._hidden_params.get("model", model)
+ custom_llm_provider = completion_response._hidden_params.get(
+ "custom_llm_provider", ""
+ )
+ region_name = completion_response._hidden_params.get(
+ "region_name", region_name
+ )
+ size = completion_response._hidden_params.get(
+ "optional_params", {}
+ ).get(
+ "size", "1024-x-1024"
+ ) # openai default
+ quality = completion_response._hidden_params.get(
+ "optional_params", {}
+ ).get(
+ "quality", "standard"
+ ) # openai default
+ n = completion_response._hidden_params.get("optional_params", {}).get(
+ "n", 1
+ ) # openai default
+ else:
+ if model is None:
+ raise ValueError(
+ f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
+ )
+ if len(messages) > 0:
+ prompt_tokens = token_counter(model=model, messages=messages)
+ elif len(prompt) > 0:
+ prompt_tokens = token_counter(model=model, text=prompt)
+ completion_tokens = token_counter(model=model, text=completion)
+ if model is None:
+ raise ValueError(
+ f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
+ )
+
+ if custom_llm_provider is None:
+ try:
+ _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
+ except Exception as e:
+ verbose_logger.error(
+ "litellm.cost_calculator.py::completion_cost() - Error inferring custom_llm_provider - {}".format(
+ str(e)
+ )
+ )
+ if (
+ call_type == CallTypes.image_generation.value
+ or call_type == CallTypes.aimage_generation.value
+ ):
+ ### IMAGE GENERATION COST CALCULATION ###
+ if custom_llm_provider == "vertex_ai":
+ # https://cloud.google.com/vertex-ai/generative-ai/pricing
+ # Vertex Charges Flat $0.20 per image
+ return 0.020
+
+ # fix size to match naming convention
+ if "x" in size and "-x-" not in size:
+ size = size.replace("x", "-x-")
+ image_gen_model_name = f"{size}/{model}"
+ image_gen_model_name_with_quality = image_gen_model_name
+ if quality is not None:
+ image_gen_model_name_with_quality = f"{quality}/{image_gen_model_name}"
+ size = size.split("-x-")
+ height = int(size[0]) # if it's 1024-x-1024 vs. 1024x1024
+ width = int(size[1])
+ verbose_logger.debug(f"image_gen_model_name: {image_gen_model_name}")
+ verbose_logger.debug(
+ f"image_gen_model_name_with_quality: {image_gen_model_name_with_quality}"
+ )
+ if image_gen_model_name in litellm.model_cost:
+ return (
+ litellm.model_cost[image_gen_model_name]["input_cost_per_pixel"]
+ * height
+ * width
+ * n
+ )
+ elif image_gen_model_name_with_quality in litellm.model_cost:
+ return (
+ litellm.model_cost[image_gen_model_name_with_quality][
+ "input_cost_per_pixel"
+ ]
+ * height
+ * width
+ * n
+ )
+ else:
+ raise Exception(
+ f"Model={image_gen_model_name} not found in completion cost model map"
+ )
+ elif (
+ call_type == CallTypes.speech.value or call_type == CallTypes.aspeech.value
+ ):
+ prompt_characters = litellm.utils._count_characters(text=prompt)
+
+ # Calculate cost based on prompt_tokens, completion_tokens
+ if (
+ "togethercomputer" in model
+ or "together_ai" in model
+ or custom_llm_provider == "together_ai"
+ ):
+ # together ai prices based on size of llm
+ # get_model_params_and_category takes a model name and returns the category of LLM size it is in model_prices_and_context_window.json
+ model = get_model_params_and_category(model)
+ # replicate llms are calculate based on time for request running
+ # see https://replicate.com/pricing
+ elif (
+ model in litellm.replicate_models or "replicate" in model
+ ) and model not in litellm.model_cost:
+ # for unmapped replicate model, default to replicate's time tracking logic
+ return get_replicate_completion_pricing(completion_response, total_time)
+
+ if model is None:
+ raise ValueError(
+ f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
+ )
+
+ if custom_llm_provider is not None and custom_llm_provider == "vertex_ai":
+ # Calculate the prompt characters + response characters
+ if len("messages") > 0:
+ prompt_string = litellm.utils.get_formatted_prompt(
+ data={"messages": messages}, call_type="completion"
+ )
+ else:
+ prompt_string = ""
+
+ prompt_characters = litellm.utils._count_characters(text=prompt_string)
+ if completion_response is not None and isinstance(
+ completion_response, ModelResponse
+ ):
+ completion_string = litellm.utils.get_response_string(
+ response_obj=completion_response
+ )
+ completion_characters = litellm.utils._count_characters(
+ text=completion_string
+ )
+
+ (
+ prompt_tokens_cost_usd_dollar,
+ completion_tokens_cost_usd_dollar,
+ ) = cost_per_token(
+ model=model,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ custom_llm_provider=custom_llm_provider,
+ response_time_ms=total_time,
+ region_name=region_name,
+ custom_cost_per_second=custom_cost_per_second,
+ custom_cost_per_token=custom_cost_per_token,
+ prompt_characters=prompt_characters,
+ completion_characters=completion_characters,
+ call_type=call_type,
+ )
+ _final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
+ print_verbose(
+ f"final cost: {_final_cost}; prompt_tokens_cost_usd_dollar: {prompt_tokens_cost_usd_dollar}; completion_tokens_cost_usd_dollar: {completion_tokens_cost_usd_dollar}"
+ )
+ return _final_cost
+ except Exception as e:
+ raise e
+
+
+def response_cost_calculator(
+ response_object: Union[
+ ModelResponse,
+ EmbeddingResponse,
+ ImageResponse,
+ TranscriptionResponse,
+ TextCompletionResponse,
+ HttpxBinaryResponseContent,
+ ],
+ model: str,
+ custom_llm_provider: Optional[str],
+ call_type: Literal[
+ "embedding",
+ "aembedding",
+ "completion",
+ "acompletion",
+ "atext_completion",
+ "text_completion",
+ "image_generation",
+ "aimage_generation",
+ "moderation",
+ "amoderation",
+ "atranscription",
+ "transcription",
+ "aspeech",
+ "speech",
+ ],
+ optional_params: dict,
+ cache_hit: Optional[bool] = None,
+ base_model: Optional[str] = None,
+ custom_pricing: Optional[bool] = None,
+) -> Optional[float]:
+ """
+ Returns
+ - float or None: cost of response OR none if error.
+ """
+ try:
+ response_cost: float = 0.0
+ if cache_hit is not None and cache_hit is True:
+ response_cost = 0.0
+ else:
+ if isinstance(response_object, BaseModel):
+ response_object._hidden_params["optional_params"] = optional_params
+ if isinstance(response_object, ImageResponse):
+ response_cost = completion_cost(
+ completion_response=response_object,
+ model=model,
+ call_type=call_type,
+ custom_llm_provider=custom_llm_provider,
+ )
+ else:
+ if (
+ model in litellm.model_cost or custom_pricing is True
+ ): # override defaults if custom pricing is set
+ base_model = model
+ # base_model defaults to None if not set on model_info
+
+ response_cost = completion_cost(
+ completion_response=response_object,
+ call_type=call_type,
+ model=base_model,
+ custom_llm_provider=custom_llm_provider,
+ )
+ return response_cost
+ except litellm.NotFoundError as e:
+ print_verbose(
+ f"Model={model} for LLM Provider={custom_llm_provider} not found in completion cost map."
+ )
+ return None
+ except Exception as e:
+ verbose_logger.warning(
+ "litellm.cost_calculator.py::response_cost_calculator - Returning None. Exception occurred - {}/n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ return None
diff --git a/litellm/exceptions.py b/litellm/exceptions.py
index d189b7ebe2..414b3e002a 100644
--- a/litellm/exceptions.py
+++ b/litellm/exceptions.py
@@ -9,10 +9,11 @@
## LiteLLM versions of the OpenAI Exception Types
-import openai
-import httpx
from typing import Optional
+import httpx
+import openai
+
class AuthenticationError(openai.AuthenticationError): # type: ignore
def __init__(
@@ -20,18 +21,44 @@ class AuthenticationError(openai.AuthenticationError): # type: ignore
message,
llm_provider,
model,
- response: httpx.Response,
+ response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
self.status_code = 401
- self.message = message
+ self.message = "litellm.AuthenticationError: {}".format(message)
self.llm_provider = llm_provider
self.model = model
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
+ self.response = response or httpx.Response(
+ status_code=self.status_code,
+ request=httpx.Request(
+ method="GET", url="https://litellm.ai"
+ ), # mock request object
+ )
super().__init__(
- self.message, response=response, body=None
+ self.message, response=self.response, body=None
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
# raise when invalid models passed, example gpt-8
class NotFoundError(openai.NotFoundError): # type: ignore
@@ -40,18 +67,44 @@ class NotFoundError(openai.NotFoundError): # type: ignore
message,
model,
llm_provider,
- response: httpx.Response,
+ response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
self.status_code = 404
- self.message = message
+ self.message = "litellm.NotFoundError: {}".format(message)
self.model = model
self.llm_provider = llm_provider
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
+ self.response = response or httpx.Response(
+ status_code=self.status_code,
+ request=httpx.Request(
+ method="GET", url="https://litellm.ai"
+ ), # mock request object
+ )
super().__init__(
- self.message, response=response, body=None
+ self.message, response=self.response, body=None
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
class BadRequestError(openai.BadRequestError): # type: ignore
def __init__(
@@ -61,9 +114,11 @@ class BadRequestError(openai.BadRequestError): # type: ignore
llm_provider,
response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
self.status_code = 400
- self.message = message
+ self.message = "litellm.BadRequestError: {}".format(message)
self.model = model
self.llm_provider = llm_provider
self.litellm_debug_info = litellm_debug_info
@@ -73,10 +128,28 @@ class BadRequestError(openai.BadRequestError): # type: ignore
method="GET", url="https://litellm.ai"
), # mock request object
)
+ self.max_retries = max_retries
+ self.num_retries = num_retries
super().__init__(
self.message, response=response, body=None
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
class UnprocessableEntityError(openai.UnprocessableEntityError): # type: ignore
def __init__(
@@ -86,34 +159,75 @@ class UnprocessableEntityError(openai.UnprocessableEntityError): # type: ignore
llm_provider,
response: httpx.Response,
litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
self.status_code = 422
- self.message = message
+ self.message = "litellm.UnprocessableEntityError: {}".format(message)
self.model = model
self.llm_provider = llm_provider
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
super().__init__(
self.message, response=response, body=None
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
class Timeout(openai.APITimeoutError): # type: ignore
def __init__(
- self, message, model, llm_provider, litellm_debug_info: Optional[str] = None
+ self,
+ message,
+ model,
+ llm_provider,
+ litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
request = httpx.Request(method="POST", url="https://api.openai.com/v1")
super().__init__(
request=request
) # Call the base class constructor with the parameters it needs
self.status_code = 408
- self.message = message
+ self.message = "litellm.Timeout: {}".format(message)
self.model = model
self.llm_provider = llm_provider
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
# custom function to convert to str
def __str__(self):
- return str(self.message)
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
class PermissionDeniedError(openai.PermissionDeniedError): # type:ignore
@@ -124,16 +238,36 @@ class PermissionDeniedError(openai.PermissionDeniedError): # type:ignore
model,
response: httpx.Response,
litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
self.status_code = 403
- self.message = message
+ self.message = "litellm.PermissionDeniedError: {}".format(message)
self.llm_provider = llm_provider
self.model = model
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
super().__init__(
self.message, response=response, body=None
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
class RateLimitError(openai.RateLimitError): # type: ignore
def __init__(
@@ -141,18 +275,48 @@ class RateLimitError(openai.RateLimitError): # type: ignore
message,
llm_provider,
model,
- response: httpx.Response,
+ response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
self.status_code = 429
- self.message = message
+ self.message = "litellm.RateLimitError: {}".format(message)
self.llm_provider = llm_provider
- self.modle = model
+ self.model = model
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
+ if response is None:
+ self.response = httpx.Response(
+ status_code=429,
+ request=httpx.Request(
+ method="POST",
+ url=" https://cloud.google.com/vertex-ai/",
+ ),
+ )
+ else:
+ self.response = response
super().__init__(
- self.message, response=response, body=None
+ self.message, response=self.response, body=None
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
# sub class of rate limit error - meant to give more granularity for error handling context window exceeded errors
class ContextWindowExceededError(BadRequestError): # type: ignore
@@ -161,21 +325,40 @@ class ContextWindowExceededError(BadRequestError): # type: ignore
message,
model,
llm_provider,
- response: httpx.Response,
+ response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
):
self.status_code = 400
- self.message = message
+ self.message = "litellm.ContextWindowExceededError: {}".format(message)
self.model = model
self.llm_provider = llm_provider
self.litellm_debug_info = litellm_debug_info
+ request = httpx.Request(method="POST", url="https://api.openai.com/v1")
+ self.response = response or httpx.Response(status_code=400, request=request)
super().__init__(
message=self.message,
model=self.model, # type: ignore
llm_provider=self.llm_provider, # type: ignore
- response=response,
+ response=self.response,
+ litellm_debug_info=self.litellm_debug_info,
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
# sub class of bad request error - meant to help us catch guardrails-related errors on proxy.
class RejectedRequestError(BadRequestError): # type: ignore
@@ -188,7 +371,7 @@ class RejectedRequestError(BadRequestError): # type: ignore
litellm_debug_info: Optional[str] = None,
):
self.status_code = 400
- self.message = message
+ self.message = "litellm.RejectedRequestError: {}".format(message)
self.model = model
self.llm_provider = llm_provider
self.litellm_debug_info = litellm_debug_info
@@ -200,8 +383,25 @@ class RejectedRequestError(BadRequestError): # type: ignore
model=self.model, # type: ignore
llm_provider=self.llm_provider, # type: ignore
response=response,
+ litellm_debug_info=self.litellm_debug_info,
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
class ContentPolicyViolationError(BadRequestError): # type: ignore
# Error code: 400 - {'error': {'code': 'content_policy_violation', 'message': 'Your request was rejected as a result of our safety system. Image descriptions generated from your prompt may contain text that is not allowed by our safety system. If you believe this was done in error, your request may succeed if retried, or by adjusting your prompt.', 'param': None, 'type': 'invalid_request_error'}}
@@ -210,21 +410,40 @@ class ContentPolicyViolationError(BadRequestError): # type: ignore
message,
model,
llm_provider,
- response: httpx.Response,
+ response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
):
self.status_code = 400
- self.message = message
+ self.message = "litellm.ContentPolicyViolationError: {}".format(message)
self.model = model
self.llm_provider = llm_provider
self.litellm_debug_info = litellm_debug_info
+ request = httpx.Request(method="POST", url="https://api.openai.com/v1")
+ self.response = response or httpx.Response(status_code=500, request=request)
super().__init__(
message=self.message,
model=self.model, # type: ignore
llm_provider=self.llm_provider, # type: ignore
- response=response,
+ response=self.response,
+ litellm_debug_info=self.litellm_debug_info,
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
class ServiceUnavailableError(openai.APIStatusError): # type: ignore
def __init__(
@@ -232,18 +451,97 @@ class ServiceUnavailableError(openai.APIStatusError): # type: ignore
message,
llm_provider,
model,
- response: httpx.Response,
+ response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
self.status_code = 503
- self.message = message
+ self.message = "litellm.ServiceUnavailableError: {}".format(message)
self.llm_provider = llm_provider
self.model = model
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
+ if response is None:
+ self.response = httpx.Response(
+ status_code=self.status_code,
+ request=httpx.Request(
+ method="POST",
+ url=" https://cloud.google.com/vertex-ai/",
+ ),
+ )
+ else:
+ self.response = response
super().__init__(
- self.message, response=response, body=None
+ self.message, response=self.response, body=None
) # Call the base class constructor with the parameters it needs
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+
+class InternalServerError(openai.InternalServerError): # type: ignore
+ def __init__(
+ self,
+ message,
+ llm_provider,
+ model,
+ response: Optional[httpx.Response] = None,
+ litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
+ ):
+ self.status_code = 500
+ self.message = "litellm.InternalServerError: {}".format(message)
+ self.llm_provider = llm_provider
+ self.model = model
+ self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
+ if response is None:
+ self.response = httpx.Response(
+ status_code=self.status_code,
+ request=httpx.Request(
+ method="POST",
+ url=" https://cloud.google.com/vertex-ai/",
+ ),
+ )
+ else:
+ self.response = response
+ super().__init__(
+ self.message, response=self.response, body=None
+ ) # Call the base class constructor with the parameters it needs
+
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
# raise this when the API returns an invalid response object - https://github.com/openai/openai-python/blob/1be14ee34a0f8e42d3f9aa5451aa4cb161f1781f/openai/api_requestor.py#L401
class APIError(openai.APIError): # type: ignore
@@ -253,16 +551,38 @@ class APIError(openai.APIError): # type: ignore
message,
llm_provider,
model,
- request: httpx.Request,
+ request: Optional[httpx.Request] = None,
litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
self.status_code = status_code
- self.message = message
+ self.message = "litellm.APIError: {}".format(message)
self.llm_provider = llm_provider
self.model = model
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
+ if request is None:
+ request = httpx.Request(method="POST", url="https://api.openai.com/v1")
super().__init__(self.message, request=request, body=None) # type: ignore
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
# raised if an invalid request (not get, delete, put, post) is made
class APIConnectionError(openai.APIConnectionError): # type: ignore
@@ -273,47 +593,123 @@ class APIConnectionError(openai.APIConnectionError): # type: ignore
model,
request: httpx.Request,
litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
- self.message = message
+ self.message = "litellm.APIConnectionError: {}".format(message)
self.llm_provider = llm_provider
self.model = model
self.status_code = 500
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
super().__init__(message=self.message, request=request)
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
# raised if an invalid request (not get, delete, put, post) is made
class APIResponseValidationError(openai.APIResponseValidationError): # type: ignore
def __init__(
- self, message, llm_provider, model, litellm_debug_info: Optional[str] = None
+ self,
+ message,
+ llm_provider,
+ model,
+ litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
):
- self.message = message
+ self.message = "litellm.APIResponseValidationError: {}".format(message)
self.llm_provider = llm_provider
self.model = model
request = httpx.Request(method="POST", url="https://api.openai.com/v1")
response = httpx.Response(status_code=500, request=request)
self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
super().__init__(response=response, body=None, message=message)
+ def __str__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
+ def __repr__(self):
+ _message = self.message
+ if self.num_retries:
+ _message += f" LiteLLM Retried: {self.num_retries} times"
+ if self.max_retries:
+ _message += f", LiteLLM Max Retries: {self.max_retries}"
+ return _message
+
class OpenAIError(openai.OpenAIError): # type: ignore
- def __init__(self, original_exception):
- self.status_code = original_exception.http_status
- super().__init__(
- http_body=original_exception.http_body,
- http_status=original_exception.http_status,
- json_body=original_exception.json_body,
- headers=original_exception.headers,
- code=original_exception.code,
- )
+ def __init__(self, original_exception=None):
+ super().__init__()
self.llm_provider = "openai"
+class JSONSchemaValidationError(APIError):
+ def __init__(
+ self, model: str, llm_provider: str, raw_response: str, schema: str
+ ) -> None:
+ self.raw_response = raw_response
+ self.schema = schema
+ self.model = model
+ message = "litellm.JSONSchemaValidationError: model={}, returned an invalid response={}, for schema={}.\nAccess raw response with `e.raw_response`".format(
+ model, raw_response, schema
+ )
+ self.message = message
+ super().__init__(
+ model=model, message=message, llm_provider=llm_provider, status_code=500
+ )
+
+
+LITELLM_EXCEPTION_TYPES = [
+ AuthenticationError,
+ NotFoundError,
+ BadRequestError,
+ UnprocessableEntityError,
+ Timeout,
+ PermissionDeniedError,
+ RateLimitError,
+ ContextWindowExceededError,
+ RejectedRequestError,
+ ContentPolicyViolationError,
+ InternalServerError,
+ ServiceUnavailableError,
+ APIError,
+ APIConnectionError,
+ APIResponseValidationError,
+ OpenAIError,
+ InternalServerError,
+ JSONSchemaValidationError,
+]
+
+
class BudgetExceededError(Exception):
def __init__(self, current_cost, max_budget):
self.current_cost = current_cost
self.max_budget = max_budget
message = f"Budget has been exceeded! Current cost: {current_cost}, Max budget: {max_budget}"
+ self.message = message
super().__init__(message)
@@ -327,3 +723,28 @@ class InvalidRequestError(openai.BadRequestError): # type: ignore
super().__init__(
self.message, f"{self.model}"
) # Call the base class constructor with the parameters it needs
+
+
+class MockException(openai.APIError):
+ # used for testing
+ def __init__(
+ self,
+ status_code,
+ message,
+ llm_provider,
+ model,
+ request: Optional[httpx.Request] = None,
+ litellm_debug_info: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ num_retries: Optional[int] = None,
+ ):
+ self.status_code = status_code
+ self.message = "litellm.MockException: {}".format(message)
+ self.llm_provider = llm_provider
+ self.model = model
+ self.litellm_debug_info = litellm_debug_info
+ self.max_retries = max_retries
+ self.num_retries = num_retries
+ if request is None:
+ request = httpx.Request(method="POST", url="https://api.openai.com/v1")
+ super().__init__(self.message, request=request, body=None) # type: ignore
diff --git a/litellm/files/main.py b/litellm/files/main.py
new file mode 100644
index 0000000000..598bc48787
--- /dev/null
+++ b/litellm/files/main.py
@@ -0,0 +1,659 @@
+"""
+Main File for Files API implementation
+
+https://platform.openai.com/docs/api-reference/files
+
+"""
+
+import asyncio
+import contextvars
+import os
+from functools import partial
+from typing import Any, Coroutine, Dict, Literal, Optional, Union
+
+import httpx
+
+import litellm
+from litellm import client
+from litellm.llms.openai import FileDeleted, FileObject, OpenAIFilesAPI
+from litellm.types.llms.openai import (
+ Batch,
+ CreateFileRequest,
+ FileContentRequest,
+ FileTypes,
+ HttpxBinaryResponseContent,
+)
+from litellm.types.router import *
+from litellm.utils import supports_httpx_timeout
+
+####### ENVIRONMENT VARIABLES ###################
+openai_files_instance = OpenAIFilesAPI()
+#################################################
+
+
+async def afile_retrieve(
+ file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, FileObject]:
+ """
+ Async: Get file contents
+
+ LiteLLM Equivalent of GET https://api.openai.com/v1/files
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["is_async"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ file_retrieve,
+ file_id,
+ custom_llm_provider,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def file_retrieve(
+ file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> FileObject:
+ """
+ Returns the contents of the specified file.
+
+ LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _is_async = kwargs.pop("is_async", False) is True
+
+ response = openai_files_instance.retrieve_file(
+ file_id=file_id,
+ _is_async=_is_async,
+ api_base=api_base,
+ api_key=api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=organization,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+# Delete file
+async def afile_delete(
+ file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, FileObject]:
+ """
+ Async: Delete file
+
+ LiteLLM Equivalent of DELETE https://api.openai.com/v1/files
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["is_async"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ file_delete,
+ file_id,
+ custom_llm_provider,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def file_delete(
+ file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> FileDeleted:
+ """
+ Delete file
+
+ LiteLLM Equivalent of DELETE https://api.openai.com/v1/files
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _is_async = kwargs.pop("is_async", False) is True
+
+ response = openai_files_instance.delete_file(
+ file_id=file_id,
+ _is_async=_is_async,
+ api_base=api_base,
+ api_key=api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=organization,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+# List files
+async def afile_list(
+ custom_llm_provider: Literal["openai"] = "openai",
+ purpose: Optional[str] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+):
+ """
+ Async: List files
+
+ LiteLLM Equivalent of GET https://api.openai.com/v1/files
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["is_async"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ file_list,
+ custom_llm_provider,
+ purpose,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def file_list(
+ custom_llm_provider: Literal["openai"] = "openai",
+ purpose: Optional[str] = None,
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+):
+ """
+ List files
+
+ LiteLLM Equivalent of GET https://api.openai.com/v1/files
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _is_async = kwargs.pop("is_async", False) is True
+
+ response = openai_files_instance.list_files(
+ purpose=purpose,
+ _is_async=_is_async,
+ api_base=api_base,
+ api_key=api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=organization,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'file_list'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="file_list", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+async def acreate_file(
+ file: FileTypes,
+ purpose: Literal["assistants", "batch", "fine-tune"],
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, FileObject]:
+ """
+ Async: Files are used to upload documents that can be used with features like Assistants, Fine-tuning, and Batch API.
+
+ LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["acreate_file"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ create_file,
+ file,
+ purpose,
+ custom_llm_provider,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def create_file(
+ file: FileTypes,
+ purpose: Literal["assistants", "batch", "fine-tune"],
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Union[FileObject, Coroutine[Any, Any, FileObject]]:
+ """
+ Files are used to upload documents that can be used with features like Assistants, Fine-tuning, and Batch API.
+
+ LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _create_file_request = CreateFileRequest(
+ file=file,
+ purpose=purpose,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ )
+
+ _is_async = kwargs.pop("acreate_file", False) is True
+
+ response = openai_files_instance.create_file(
+ _is_async=_is_async,
+ api_base=api_base,
+ api_key=api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=organization,
+ create_file_data=_create_file_request,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
+
+
+async def afile_content(
+ file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Coroutine[Any, Any, HttpxBinaryResponseContent]:
+ """
+ Async: Get file contents
+
+ LiteLLM Equivalent of GET https://api.openai.com/v1/files
+ """
+ try:
+ loop = asyncio.get_event_loop()
+ kwargs["afile_content"] = True
+
+ # Use a partial function to pass your keyword arguments
+ func = partial(
+ file_content,
+ file_id,
+ custom_llm_provider,
+ extra_headers,
+ extra_body,
+ **kwargs,
+ )
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ response = init_response # type: ignore
+
+ return response
+ except Exception as e:
+ raise e
+
+
+def file_content(
+ file_id: str,
+ custom_llm_provider: Literal["openai"] = "openai",
+ extra_headers: Optional[Dict[str, str]] = None,
+ extra_body: Optional[Dict[str, str]] = None,
+ **kwargs,
+) -> Union[HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]]:
+ """
+ Returns the contents of the specified file.
+
+ LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files
+ """
+ try:
+ optional_params = GenericLiteLLMParams(**kwargs)
+ if custom_llm_provider == "openai":
+ # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base = (
+ optional_params.api_base
+ or litellm.api_base
+ or os.getenv("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ organization = (
+ optional_params.organization
+ or litellm.organization
+ or os.getenv("OPENAI_ORGANIZATION", None)
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ optional_params.api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or os.getenv("OPENAI_API_KEY")
+ )
+ ### TIMEOUT LOGIC ###
+ timeout = (
+ optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+ )
+ # set timeout for 10 minutes by default
+
+ if (
+ timeout is not None
+ and isinstance(timeout, httpx.Timeout)
+ and supports_httpx_timeout(custom_llm_provider) == False
+ ):
+ read_timeout = timeout.read or 600
+ timeout = read_timeout # default 10 min timeout
+ elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+ timeout = float(timeout) # type: ignore
+ elif timeout is None:
+ timeout = 600.0
+
+ _file_content_request = FileContentRequest(
+ file_id=file_id,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ )
+
+ _is_async = kwargs.pop("afile_content", False) is True
+
+ response = openai_files_instance.file_content(
+ _is_async=_is_async,
+ file_content_request=_file_content_request,
+ api_base=api_base,
+ api_key=api_key,
+ timeout=timeout,
+ max_retries=optional_params.max_retries,
+ organization=organization,
+ )
+ else:
+ raise litellm.exceptions.BadRequestError(
+ message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+ custom_llm_provider
+ ),
+ model="n/a",
+ llm_provider=custom_llm_provider,
+ response=httpx.Response(
+ status_code=400,
+ content="Unsupported provider",
+ request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
+ ),
+ )
+ return response
+ except Exception as e:
+ raise e
diff --git a/litellm/integrations/aispend.py b/litellm/integrations/aispend.py
index 2fe8ea0dfa..ca284e62e5 100644
--- a/litellm/integrations/aispend.py
+++ b/litellm/integrations/aispend.py
@@ -169,6 +169,5 @@ class AISpendLogger:
print_verbose(f"AISpend Logging - final data object: {data}")
except:
- # traceback.print_exc()
print_verbose(f"AISpend Logging Error - {traceback.format_exc()}")
pass
diff --git a/litellm/integrations/berrispend.py b/litellm/integrations/berrispend.py
index 7d30b706c8..d428fb54d8 100644
--- a/litellm/integrations/berrispend.py
+++ b/litellm/integrations/berrispend.py
@@ -178,6 +178,5 @@ class BerriSpendLogger:
print_verbose(f"BerriSpend Logging - final data object: {data}")
response = requests.post(url, headers=headers, json=data)
except:
- # traceback.print_exc()
print_verbose(f"BerriSpend Logging Error - {traceback.format_exc()}")
pass
diff --git a/litellm/integrations/clickhouse.py b/litellm/integrations/clickhouse.py
index 0c38b86267..eff1836ef1 100644
--- a/litellm/integrations/clickhouse.py
+++ b/litellm/integrations/clickhouse.py
@@ -226,14 +226,6 @@ def _start_clickhouse():
response = client.query("DESCRIBE default.spend_logs")
verbose_logger.debug(f"spend logs schema ={response.result_rows}")
- # RUN Enterprise Clickhouse Setup
- # TLDR: For Enterprise - we create views / aggregate tables for low latency reporting APIs
- from litellm.proxy.enterprise.utils import _create_clickhouse_aggregate_tables
- from litellm.proxy.enterprise.utils import _create_clickhouse_material_views
-
- _create_clickhouse_aggregate_tables(client=client, table_names=table_names)
- _create_clickhouse_material_views(client=client, table_names=table_names)
-
class ClickhouseLogger:
# Class variables or attributes
@@ -297,6 +289,5 @@ class ClickhouseLogger:
# make request to endpoint with payload
verbose_logger.debug(f"Clickhouse Logger - final response = {response}")
except Exception as e:
- traceback.print_exc()
verbose_logger.debug(f"Clickhouse - {str(e)}\n{traceback.format_exc()}")
pass
diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py
index e192cdaea7..5139723ca1 100644
--- a/litellm/integrations/custom_logger.py
+++ b/litellm/integrations/custom_logger.py
@@ -1,16 +1,21 @@
#### What this does ####
# On success, logs events to Promptlayer
-import dotenv, os
-
-from litellm.proxy._types import UserAPIKeyAuth
-from litellm.caching import DualCache
-from typing import Literal, Union, Optional
+import os
import traceback
+from typing import Any, Literal, Optional, Tuple, Union
+
+import dotenv
+from pydantic import BaseModel
+
+from litellm.caching import DualCache
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.types.llms.openai import ChatCompletionRequest
+from litellm.types.utils import ModelResponse
class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback#callback-class
# Class variables or attributes
- def __init__(self):
+ def __init__(self) -> None:
pass
def log_pre_api_call(self, model, messages, kwargs):
@@ -53,6 +58,30 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
def pre_call_check(self, deployment: dict) -> Optional[dict]:
pass
+ #### ADAPTERS #### Allow calling 100+ LLMs in custom format - https://github.com/BerriAI/litellm/pulls
+
+ def translate_completion_input_params(
+ self, kwargs
+ ) -> Optional[ChatCompletionRequest]:
+ """
+ Translates the input params, from the provider's native format to the litellm.completion() format.
+ """
+ pass
+
+ def translate_completion_output_params(
+ self, response: ModelResponse
+ ) -> Optional[BaseModel]:
+ """
+ Translates the output params, from the OpenAI format to the custom format.
+ """
+ pass
+
+ def translate_completion_output_params_streaming(self) -> Optional[BaseModel]:
+ """
+ Translates the streaming chunk, from the OpenAI format to the custom format.
+ """
+ pass
+
#### CALL HOOKS - proxy only ####
"""
Control the modify incoming / outgoung data before calling the model
@@ -70,6 +99,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
"image_generation",
"moderation",
"audio_transcription",
+ "pass_through_endpoint",
],
) -> Optional[
Union[Exception, str, dict]
@@ -88,6 +118,18 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
):
pass
+ async def async_logging_hook(
+ self, kwargs: dict, result: Any, call_type: str
+ ) -> Tuple[dict, Any]:
+ """For masking logged request/response. Return a modified version of the request/result."""
+ return kwargs, result
+
+ def logging_hook(
+ self, kwargs: dict, result: Any, call_type: str
+ ) -> Tuple[dict, Any]:
+ """For masking logged request/response. Return a modified version of the request/result."""
+ return kwargs, result
+
async def async_moderation_hook(
self,
data: dict,
@@ -115,7 +157,6 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
)
print_verbose(f"Custom Logger - model call details: {kwargs}")
except:
- traceback.print_exc()
print_verbose(f"Custom Logger Error - {traceback.format_exc()}")
async def async_log_input_event(
@@ -130,7 +171,6 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
)
print_verbose(f"Custom Logger - model call details: {kwargs}")
except:
- traceback.print_exc()
print_verbose(f"Custom Logger Error - {traceback.format_exc()}")
def log_event(
@@ -146,7 +186,6 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
end_time,
)
except:
- # traceback.print_exc()
print_verbose(f"Custom Logger Error - {traceback.format_exc()}")
pass
@@ -163,6 +202,5 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
end_time,
)
except:
- # traceback.print_exc()
print_verbose(f"Custom Logger Error - {traceback.format_exc()}")
pass
diff --git a/litellm/integrations/datadog.py b/litellm/integrations/datadog.py
index 6d5e08faff..d835b3d670 100644
--- a/litellm/integrations/datadog.py
+++ b/litellm/integrations/datadog.py
@@ -134,7 +134,6 @@ class DataDogLogger:
f"Datadog Layer Logging - final response object: {response_obj}"
)
except Exception as e:
- traceback.print_exc()
verbose_logger.debug(
f"Datadog Layer Error - {str(e)}\n{traceback.format_exc()}"
)
diff --git a/litellm/integrations/dynamodb.py b/litellm/integrations/dynamodb.py
index 21ccabe4b7..847f930ece 100644
--- a/litellm/integrations/dynamodb.py
+++ b/litellm/integrations/dynamodb.py
@@ -85,6 +85,5 @@ class DyanmoDBLogger:
)
return response
except:
- traceback.print_exc()
print_verbose(f"DynamoDB Layer Error - {traceback.format_exc()}")
pass
diff --git a/litellm/integrations/email_alerting.py b/litellm/integrations/email_alerting.py
new file mode 100644
index 0000000000..434efb63b0
--- /dev/null
+++ b/litellm/integrations/email_alerting.py
@@ -0,0 +1,136 @@
+"""
+Functions for sending Email Alerts
+"""
+
+import os
+from typing import Optional, List
+from litellm.proxy._types import WebhookEvent
+import asyncio
+from litellm._logging import verbose_logger, verbose_proxy_logger
+
+# we use this for the email header, please send a test email if you change this. verify it looks good on email
+LITELLM_LOGO_URL = "https://litellm-listing.s3.amazonaws.com/litellm_logo.png"
+LITELLM_SUPPORT_CONTACT = "support@berri.ai"
+
+
+async def get_all_team_member_emails(team_id: Optional[str] = None) -> list:
+ verbose_logger.debug(
+ "Email Alerting: Getting all team members for team_id=%s", team_id
+ )
+ if team_id is None:
+ return []
+ from litellm.proxy.proxy_server import premium_user, prisma_client
+
+ if prisma_client is None:
+ raise Exception("Not connected to DB!")
+
+ team_row = await prisma_client.db.litellm_teamtable.find_unique(
+ where={
+ "team_id": team_id,
+ }
+ )
+
+ if team_row is None:
+ return []
+
+ _team_members = team_row.members_with_roles
+ verbose_logger.debug(
+ "Email Alerting: Got team members for team_id=%s Team Members: %s",
+ team_id,
+ _team_members,
+ )
+ _team_member_user_ids: List[str] = []
+ for member in _team_members:
+ if member and isinstance(member, dict) and member.get("user_id") is not None:
+ _team_member_user_ids.append(member.get("user_id"))
+
+ sql_query = """
+ SELECT user_email
+ FROM "LiteLLM_UserTable"
+ WHERE user_id = ANY($1::TEXT[]);
+ """
+
+ _result = await prisma_client.db.query_raw(sql_query, _team_member_user_ids)
+
+ verbose_logger.debug("Email Alerting: Got all Emails for team, emails=%s", _result)
+
+ if _result is None:
+ return []
+
+ emails = []
+ for user in _result:
+ if user and isinstance(user, dict) and user.get("user_email", None) is not None:
+ emails.append(user.get("user_email"))
+ return emails
+
+
+async def send_team_budget_alert(webhook_event: WebhookEvent) -> bool:
+ """
+ Send an Email Alert to All Team Members when the Team Budget is crossed
+ Returns -> True if sent, False if not.
+ """
+ from litellm.proxy.utils import send_email
+
+ from litellm.proxy.proxy_server import premium_user, prisma_client
+
+ _team_id = webhook_event.team_id
+ team_alias = webhook_event.team_alias
+ verbose_logger.debug(
+ "Email Alerting: Sending Team Budget Alert for team=%s", team_alias
+ )
+
+ email_logo_url = os.getenv("SMTP_SENDER_LOGO", os.getenv("EMAIL_LOGO_URL", None))
+ email_support_contact = os.getenv("EMAIL_SUPPORT_CONTACT", None)
+
+ # await self._check_if_using_premium_email_feature(
+ # premium_user, email_logo_url, email_support_contact
+ # )
+
+ if email_logo_url is None:
+ email_logo_url = LITELLM_LOGO_URL
+ if email_support_contact is None:
+ email_support_contact = LITELLM_SUPPORT_CONTACT
+ recipient_emails = await get_all_team_member_emails(_team_id)
+ recipient_emails_str: str = ",".join(recipient_emails)
+ verbose_logger.debug(
+ "Email Alerting: Sending team budget alert to %s", recipient_emails_str
+ )
+
+ event_name = webhook_event.event_message
+ max_budget = webhook_event.max_budget
+ email_html_content = "Alert from LiteLLM Server"
+
+ if recipient_emails_str is None:
+ verbose_proxy_logger.error(
+ "Email Alerting: Trying to send email alert to no recipient, got recipient_emails=%s",
+ recipient_emails_str,
+ )
+
+ email_html_content = f"""
+ 
+
+ Budget Crossed for Team {team_alias}
+
+ Your Teams LLM API usage has crossed it's budget of ${max_budget} , current spend is ${webhook_event.spend}
+
+ API requests will be rejected until either (a) you increase your budget or (b) your budget gets reset
+
+ If you have any questions, please send an email to {email_support_contact}
+
+ Best,
+ The LiteLLM team
+ """
+
+ email_event = {
+ "to": recipient_emails_str,
+ "subject": f"LiteLLM {event_name} for Team {team_alias}",
+ "html": email_html_content,
+ }
+
+ await send_email(
+ receiver_email=email_event["to"],
+ subject=email_event["subject"],
+ html=email_event["html"],
+ )
+
+ return False
diff --git a/litellm/integrations/email_templates/templates.py b/litellm/integrations/email_templates/templates.py
new file mode 100644
index 0000000000..7029e8ce12
--- /dev/null
+++ b/litellm/integrations/email_templates/templates.py
@@ -0,0 +1,62 @@
+"""
+Email Templates used by the LiteLLM Email Service in slack_alerting.py
+"""
+
+KEY_CREATED_EMAIL_TEMPLATE = """
+
+
+ Hi {recipient_email},
+
+ I'm happy to provide you with an OpenAI Proxy API Key, loaded with ${key_budget} per month.
+
+
+ Key: {key_token}
+
+
+
Usage Example
+
+ Detailed Documentation on Usage with OpenAI Python SDK, Langchain, LlamaIndex, Curl
+
+
+
+ import openai
+ client = openai.OpenAI(
+ api_key="{key_token}",
+ base_url={{base_url}}
+ )
+
+ response = client.chat.completions.create(
+ model="gpt-3.5-turbo", # model to send to the proxy
+ messages = [
+ {{
+ "role": "user",
+ "content": "this is a test request, write a short poem"
+ }}
+ ]
+ )
+
+
+
+
+ If you have any questions, please send an email to {email_support_contact}
+
+ Best,
+ The LiteLLM team
+"""
+
+
+USER_INVITED_EMAIL_TEMPLATE = """
+
+
+ Hi {recipient_email},
+
+ You were invited to use OpenAI Proxy API for team {team_name}
+
+ Get Started here
+
+
+ If you have any questions, please send an email to {email_support_contact}
+
+ Best,
+ The LiteLLM team
+"""
diff --git a/litellm/integrations/galileo.py b/litellm/integrations/galileo.py
new file mode 100644
index 0000000000..51d845fcb3
--- /dev/null
+++ b/litellm/integrations/galileo.py
@@ -0,0 +1,159 @@
+import os
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+import httpx
+from pydantic import BaseModel, Field
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+
+
+# from here: https://docs.rungalileo.io/galileo/gen-ai-studio-products/galileo-observe/how-to/logging-data-via-restful-apis#structuring-your-records
+class LLMResponse(BaseModel):
+ latency_ms: int
+ status_code: int
+ input_text: str
+ output_text: str
+ node_type: str
+ model: str
+ num_input_tokens: int
+ num_output_tokens: int
+ output_logprobs: Optional[Dict[str, Any]] = Field(
+ default=None,
+ description="Optional. When available, logprobs are used to compute Uncertainty.",
+ )
+ created_at: str = Field(
+ ..., description='timestamp constructed in "%Y-%m-%dT%H:%M:%S" format'
+ )
+ tags: Optional[List[str]] = None
+ user_metadata: Optional[Dict[str, Any]] = None
+
+
+class GalileoObserve(CustomLogger):
+ def __init__(self) -> None:
+ self.in_memory_records: List[dict] = []
+ self.batch_size = 1
+ self.base_url = os.getenv("GALILEO_BASE_URL", None)
+ self.project_id = os.getenv("GALILEO_PROJECT_ID", None)
+ self.headers = None
+ self.async_httpx_handler = AsyncHTTPHandler(
+ timeout=httpx.Timeout(timeout=600.0, connect=5.0)
+ )
+ pass
+
+ def set_galileo_headers(self):
+ # following https://docs.rungalileo.io/galileo/gen-ai-studio-products/galileo-observe/how-to/logging-data-via-restful-apis#logging-your-records
+
+ headers = {
+ "accept": "application/json",
+ "Content-Type": "application/x-www-form-urlencoded",
+ }
+ galileo_login_response = self.async_httpx_handler.post(
+ url=f"{self.base_url}/login",
+ headers=headers,
+ data={
+ "username": os.getenv("GALILEO_USERNAME"),
+ "password": os.getenv("GALILEO_PASSWORD"),
+ },
+ )
+
+ access_token = galileo_login_response.json()["access_token"]
+
+ self.headers = {
+ "accept": "application/json",
+ "Content-Type": "application/json",
+ "Authorization": f"Bearer {access_token}",
+ }
+
+ def get_output_str_from_response(self, response_obj, kwargs):
+ output = None
+ if response_obj is not None and (
+ kwargs.get("call_type", None) == "embedding"
+ or isinstance(response_obj, litellm.EmbeddingResponse)
+ ):
+ output = None
+ elif response_obj is not None and isinstance(
+ response_obj, litellm.ModelResponse
+ ):
+ output = response_obj["choices"][0]["message"].json()
+ elif response_obj is not None and isinstance(
+ response_obj, litellm.TextCompletionResponse
+ ):
+ output = response_obj.choices[0].text
+ elif response_obj is not None and isinstance(
+ response_obj, litellm.ImageResponse
+ ):
+ output = response_obj["data"]
+
+ return output
+
+ async def async_log_success_event(
+ self,
+ kwargs,
+ start_time,
+ end_time,
+ response_obj,
+ ):
+ verbose_logger.debug(f"On Async Success")
+
+ _latency_ms = int((end_time - start_time).total_seconds() * 1000)
+ _call_type = kwargs.get("call_type", "litellm")
+ input_text = litellm.utils.get_formatted_prompt(
+ data=kwargs, call_type=_call_type
+ )
+
+ _usage = response_obj.get("usage", {}) or {}
+ num_input_tokens = _usage.get("prompt_tokens", 0)
+ num_output_tokens = _usage.get("completion_tokens", 0)
+
+ output_text = self.get_output_str_from_response(
+ response_obj=response_obj, kwargs=kwargs
+ )
+
+ request_record = LLMResponse(
+ latency_ms=_latency_ms,
+ status_code=200,
+ input_text=input_text,
+ output_text=output_text,
+ node_type=_call_type,
+ model=kwargs.get("model", "-"),
+ num_input_tokens=num_input_tokens,
+ num_output_tokens=num_output_tokens,
+ created_at=start_time.strftime(
+ "%Y-%m-%dT%H:%M:%S"
+ ), # timestamp str constructed in "%Y-%m-%dT%H:%M:%S" format
+ )
+
+ # dump to dict
+ request_dict = request_record.model_dump()
+ self.in_memory_records.append(request_dict)
+
+ if len(self.in_memory_records) >= self.batch_size:
+ await self.flush_in_memory_records()
+
+ async def flush_in_memory_records(self):
+ verbose_logger.debug("flushing in memory records")
+ response = await self.async_httpx_handler.post(
+ url=f"{self.base_url}/projects/{self.project_id}/observe/ingest",
+ headers=self.headers,
+ json={"records": self.in_memory_records},
+ )
+
+ if response.status_code == 200:
+ verbose_logger.debug(
+ "Galileo Logger:successfully flushed in memory records"
+ )
+ self.in_memory_records = []
+ else:
+ verbose_logger.debug("Galileo Logger: failed to flush in memory records")
+ verbose_logger.debug(
+ "Galileo Logger error=%s, status code=%s",
+ response.text,
+ response.status_code,
+ )
+
+ async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
+ verbose_logger.debug(f"On Async Failure")
diff --git a/litellm/integrations/helicone.py b/litellm/integrations/helicone.py
index 85e73258ea..f0666431a6 100644
--- a/litellm/integrations/helicone.py
+++ b/litellm/integrations/helicone.py
@@ -4,11 +4,12 @@ import dotenv, os
import requests # type: ignore
import litellm
import traceback
+from litellm._logging import verbose_logger
class HeliconeLogger:
# Class variables or attributes
- helicone_model_list = ["gpt", "claude"]
+ helicone_model_list = ["gpt", "claude", "command-r", "command-r-plus", "command-light", "command-medium", "command-medium-beta", "command-xlarge-nightly", "command-nightly"]
def __init__(self):
# Instance variables
@@ -37,15 +38,49 @@ class HeliconeLogger:
}
return claude_provider_request, claude_response_obj
+
+ @staticmethod
+ def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict:
+ """
+ Adds metadata from proxy request headers to Helicone logging if keys start with "helicone_"
+ and overwrites litellm_params.metadata if already included.
+
+ For example if you want to add custom property to your request, send
+ `headers: { ..., helicone-property-something: 1234 }` via proxy request.
+ """
+ if litellm_params is None:
+ return metadata
+
+ if litellm_params.get("proxy_server_request") is None:
+ return metadata
+
+ if metadata is None:
+ metadata = {}
+
+ proxy_headers = (
+ litellm_params.get("proxy_server_request", {}).get("headers", {}) or {}
+ )
+
+ for header_key in proxy_headers:
+ if header_key.startswith("helicone_"):
+ metadata[header_key] = proxy_headers.get(header_key)
+
+ return metadata
def log_success(
- self, model, messages, response_obj, start_time, end_time, print_verbose
+ self, model, messages, response_obj, start_time, end_time, print_verbose, kwargs
):
# Method definition
try:
print_verbose(
f"Helicone Logging - Enters logging function for model {model}"
)
+ litellm_params = kwargs.get("litellm_params", {})
+ litellm_call_id = kwargs.get("litellm_call_id", None)
+ metadata = (
+ litellm_params.get("metadata", {}) or {}
+ )
+ metadata = self.add_metadata_from_header(litellm_params, metadata)
model = (
model
if any(
@@ -85,11 +120,13 @@ class HeliconeLogger:
end_time_milliseconds = int(
(end_time.timestamp() - end_time_seconds) * 1000
)
+ meta = {"Helicone-Auth": f"Bearer {self.key}"}
+ meta.update(metadata)
data = {
"providerRequest": {
"url": self.provider_url,
"json": provider_request,
- "meta": {"Helicone-Auth": f"Bearer {self.key}"},
+ "meta": meta,
},
"providerResponse": providerResponse,
"timing": {
@@ -112,6 +149,5 @@ class HeliconeLogger:
)
print_verbose(f"Helicone Logging - Error {response.text}")
except:
- # traceback.print_exc()
print_verbose(f"Helicone Logging Error - {traceback.format_exc()}")
pass
diff --git a/litellm/integrations/lago.py b/litellm/integrations/lago.py
index e6d38f530c..0ddeb01734 100644
--- a/litellm/integrations/lago.py
+++ b/litellm/integrations/lago.py
@@ -1,13 +1,19 @@
# What is this?
## On Success events log cost to Lago - https://github.com/BerriAI/litellm/issues/3639
-import dotenv, os, json
+import json
+import os
+import traceback
+import uuid
+from typing import Literal, Optional
+
+import dotenv
+import httpx
+
import litellm
-import traceback, httpx
+from litellm import verbose_logger
from litellm.integrations.custom_logger import CustomLogger
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
-import uuid
-from typing import Optional, Literal
def get_utc_datetime():
@@ -143,6 +149,7 @@ class LagoLogger(CustomLogger):
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
try:
+ verbose_logger.debug("ENTERS LAGO CALLBACK")
_url = os.getenv("LAGO_API_BASE")
assert _url is not None and isinstance(
_url, str
diff --git a/litellm/integrations/langfuse.py b/litellm/integrations/langfuse.py
index a9c67547d6..0647afabcd 100644
--- a/litellm/integrations/langfuse.py
+++ b/litellm/integrations/langfuse.py
@@ -1,21 +1,27 @@
#### What this does ####
# On success, logs events to Langfuse
-import os
import copy
+import os
import traceback
+
from packaging.version import Version
-from litellm._logging import verbose_logger
+
import litellm
+from litellm._logging import verbose_logger
class LangFuseLogger:
# Class variables or attributes
def __init__(
- self, langfuse_public_key=None, langfuse_secret=None, flush_interval=1
+ self,
+ langfuse_public_key=None,
+ langfuse_secret=None,
+ langfuse_host=None,
+ flush_interval=1,
):
try:
- from langfuse import Langfuse
import langfuse
+ from langfuse import Langfuse
except Exception as e:
raise Exception(
f"\033[91mLangfuse not installed, try running 'pip install langfuse' to fix this error: {e}\n{traceback.format_exc()}\033[0m"
@@ -23,7 +29,15 @@ class LangFuseLogger:
# Instance variables
self.secret_key = langfuse_secret or os.getenv("LANGFUSE_SECRET_KEY")
self.public_key = langfuse_public_key or os.getenv("LANGFUSE_PUBLIC_KEY")
- self.langfuse_host = os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com")
+ self.langfuse_host = langfuse_host or os.getenv(
+ "LANGFUSE_HOST", "https://cloud.langfuse.com"
+ )
+ if not (
+ self.langfuse_host.startswith("http://")
+ or self.langfuse_host.startswith("https://")
+ ):
+ # add http:// if unset, assume communicating over private network - e.g. render
+ self.langfuse_host = "http://" + self.langfuse_host
self.langfuse_release = os.getenv("LANGFUSE_RELEASE")
self.langfuse_debug = os.getenv("LANGFUSE_DEBUG")
@@ -69,6 +83,43 @@ class LangFuseLogger:
else:
self.upstream_langfuse = None
+ @staticmethod
+ def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict:
+ """
+ Adds metadata from proxy request headers to Langfuse logging if keys start with "langfuse_"
+ and overwrites litellm_params.metadata if already included.
+
+ For example if you want to append your trace to an existing `trace_id` via header, send
+ `headers: { ..., langfuse_existing_trace_id: your-existing-trace-id }` via proxy request.
+ """
+ if litellm_params is None:
+ return metadata
+
+ if litellm_params.get("proxy_server_request") is None:
+ return metadata
+
+ if metadata is None:
+ metadata = {}
+
+ proxy_headers = (
+ litellm_params.get("proxy_server_request", {}).get("headers", {}) or {}
+ )
+
+ for metadata_param_key in proxy_headers:
+ if metadata_param_key.startswith("langfuse_"):
+ trace_param_key = metadata_param_key.replace("langfuse_", "", 1)
+ if trace_param_key in metadata:
+ verbose_logger.warning(
+ f"Overwriting Langfuse `{trace_param_key}` from request header"
+ )
+ else:
+ verbose_logger.debug(
+ f"Found Langfuse `{trace_param_key}` in request header"
+ )
+ metadata[trace_param_key] = proxy_headers.get(metadata_param_key)
+
+ return metadata
+
# def log_error(kwargs, response_obj, start_time, end_time):
# generation = trace.generation(
# level ="ERROR" # can be any of DEBUG, DEFAULT, WARNING or ERROR
@@ -97,6 +148,7 @@ class LangFuseLogger:
metadata = (
litellm_params.get("metadata", {}) or {}
) # if litellm_params['metadata'] == None
+ metadata = self.add_metadata_from_header(litellm_params, metadata)
optional_params = copy.deepcopy(kwargs.get("optional_params", {}))
prompt = {"messages": kwargs.get("messages")}
@@ -129,7 +181,7 @@ class LangFuseLogger:
or isinstance(response_obj, litellm.EmbeddingResponse)
):
input = prompt
- output = response_obj["data"]
+ output = None
elif response_obj is not None and isinstance(
response_obj, litellm.ModelResponse
):
@@ -182,9 +234,11 @@ class LangFuseLogger:
verbose_logger.info(f"Langfuse Layer Logging - logging success")
return {"trace_id": trace_id, "generation_id": generation_id}
- except:
- traceback.print_exc()
- verbose_logger.debug(f"Langfuse Layer Error - {traceback.format_exc()}")
+ except Exception as e:
+ verbose_logger.error(
+ "Langfuse Layer Error(): Exception occured - {}".format(str(e))
+ )
+ verbose_logger.debug(traceback.format_exc())
return {"trace_id": None, "generation_id": None}
async def _async_log_event(
@@ -211,7 +265,7 @@ class LangFuseLogger:
input,
response_obj,
):
- from langfuse.model import CreateTrace, CreateGeneration
+ from langfuse.model import CreateGeneration, CreateTrace
verbose_logger.warning(
"Please upgrade langfuse to v2.0.0 or higher: https://github.com/langfuse/langfuse-python/releases/tag/v2.0.1"
@@ -396,6 +450,8 @@ class LangFuseLogger:
cost = kwargs.get("response_cost", None)
print_verbose(f"trace: {cost}")
+ clean_metadata["litellm_response_cost"] = cost
+
if (
litellm._langfuse_default_tags is not None
and isinstance(litellm._langfuse_default_tags, list)
@@ -455,8 +511,13 @@ class LangFuseLogger:
}
generation_name = clean_metadata.pop("generation_name", None)
if generation_name is None:
- # just log `litellm-{call_type}` as the generation name
+ # if `generation_name` is None, use sensible default values
+ # If using litellm proxy user `key_alias` if not None
+ # If `key_alias` is None, just log `litellm-{call_type}` as the generation name
+ _user_api_key_alias = clean_metadata.get("user_api_key_alias", None)
generation_name = f"litellm-{kwargs.get('call_type', 'completion')}"
+ if _user_api_key_alias is not None:
+ generation_name = f"litellm:{_user_api_key_alias}"
if response_obj is not None and "system_fingerprint" in response_obj:
system_fingerprint = response_obj.get("system_fingerprint", None)
@@ -481,31 +542,14 @@ class LangFuseLogger:
"version": clean_metadata.pop("version", None),
}
+ parent_observation_id = metadata.get("parent_observation_id", None)
+ if parent_observation_id is not None:
+ generation_params["parent_observation_id"] = parent_observation_id
+
if supports_prompt:
- user_prompt = clean_metadata.pop("prompt", None)
- if user_prompt is None:
- pass
- elif isinstance(user_prompt, dict):
- from langfuse.model import (
- TextPromptClient,
- ChatPromptClient,
- Prompt_Text,
- Prompt_Chat,
- )
-
- if user_prompt.get("type", "") == "chat":
- _prompt_chat = Prompt_Chat(**user_prompt)
- generation_params["prompt"] = ChatPromptClient(
- prompt=_prompt_chat
- )
- elif user_prompt.get("type", "") == "text":
- _prompt_text = Prompt_Text(**user_prompt)
- generation_params["prompt"] = TextPromptClient(
- prompt=_prompt_text
- )
- else:
- generation_params["prompt"] = user_prompt
-
+ generation_params = _add_prompt_to_generation_params(
+ generation_params=generation_params, clean_metadata=clean_metadata
+ )
if output is not None and isinstance(output, str) and level == "ERROR":
generation_params["status_message"] = output
@@ -518,5 +562,58 @@ class LangFuseLogger:
return generation_client.trace_id, generation_id
except Exception as e:
- verbose_logger.debug(f"Langfuse Layer Error - {traceback.format_exc()}")
+ verbose_logger.error(f"Langfuse Layer Error - {traceback.format_exc()}")
return None, None
+
+
+def _add_prompt_to_generation_params(
+ generation_params: dict, clean_metadata: dict
+) -> dict:
+ from langfuse.model import (
+ ChatPromptClient,
+ Prompt_Chat,
+ Prompt_Text,
+ TextPromptClient,
+ )
+
+ user_prompt = clean_metadata.pop("prompt", None)
+ if user_prompt is None:
+ pass
+ elif isinstance(user_prompt, dict):
+ if user_prompt.get("type", "") == "chat":
+ _prompt_chat = Prompt_Chat(**user_prompt)
+ generation_params["prompt"] = ChatPromptClient(prompt=_prompt_chat)
+ elif user_prompt.get("type", "") == "text":
+ _prompt_text = Prompt_Text(**user_prompt)
+ generation_params["prompt"] = TextPromptClient(prompt=_prompt_text)
+ elif "version" in user_prompt and "prompt" in user_prompt:
+ # prompts
+ if isinstance(user_prompt["prompt"], str):
+ _prompt_obj = Prompt_Text(
+ name=user_prompt["name"],
+ prompt=user_prompt["prompt"],
+ version=user_prompt["version"],
+ config=user_prompt.get("config", None),
+ )
+ generation_params["prompt"] = TextPromptClient(prompt=_prompt_obj)
+
+ elif isinstance(user_prompt["prompt"], list):
+ _prompt_obj = Prompt_Chat(
+ name=user_prompt["name"],
+ prompt=user_prompt["prompt"],
+ version=user_prompt["version"],
+ config=user_prompt.get("config", None),
+ )
+ generation_params["prompt"] = ChatPromptClient(prompt=_prompt_obj)
+ else:
+ verbose_logger.error(
+ "[Non-blocking] Langfuse Logger: Invalid prompt format"
+ )
+ else:
+ verbose_logger.error(
+ "[Non-blocking] Langfuse Logger: Invalid prompt format. No prompt logged to Langfuse"
+ )
+ else:
+ generation_params["prompt"] = user_prompt
+
+ return generation_params
diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py
index 3e25b4ee77..81db798ae8 100644
--- a/litellm/integrations/langsmith.py
+++ b/litellm/integrations/langsmith.py
@@ -1,13 +1,43 @@
#### What this does ####
# On success, logs events to Langsmith
-import dotenv, os # type: ignore
-import requests # type: ignore
-from datetime import datetime
-import traceback
import asyncio
+import os
+import traceback
import types
+from datetime import datetime
+from typing import Any, List, Optional, Union
+
+import dotenv # type: ignore
+import httpx
+import requests # type: ignore
from pydantic import BaseModel # type: ignore
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+
+
+class LangsmithInputs(BaseModel):
+ model: Optional[str] = None
+ messages: Optional[List[Any]] = None
+ stream: Optional[bool] = None
+ call_type: Optional[str] = None
+ litellm_call_id: Optional[str] = None
+ completion_start_time: Optional[datetime] = None
+ temperature: Optional[float] = None
+ max_tokens: Optional[int] = None
+ custom_llm_provider: Optional[str] = None
+ input: Optional[List[Any]] = None
+ log_event_type: Optional[str] = None
+ original_response: Optional[Any] = None
+ response_cost: Optional[float] = None
+
+ # LiteLLM Virtual Key specific fields
+ user_api_key: Optional[str] = None
+ user_api_key_user_id: Optional[str] = None
+ user_api_key_team_alias: Optional[str] = None
+
def is_serializable(value):
non_serializable_types = (
@@ -19,7 +49,7 @@ def is_serializable(value):
return not isinstance(value, non_serializable_types)
-class LangsmithLogger:
+class LangsmithLogger(CustomLogger):
# Class variables or attributes
def __init__(self):
self.langsmith_api_key = os.getenv("LANGSMITH_API_KEY")
@@ -27,71 +57,121 @@ class LangsmithLogger:
self.langsmith_default_run_name = os.getenv(
"LANGSMITH_DEFAULT_RUN_NAME", "LLMRun"
)
+ self.langsmith_base_url = os.getenv(
+ "LANGSMITH_BASE_URL", "https://api.smith.langchain.com"
+ )
+ self.async_httpx_client = AsyncHTTPHandler(
+ timeout=httpx.Timeout(timeout=600.0, connect=5.0)
+ )
- def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
- # Method definition
- # inspired by Langsmith http api here: https://github.com/langchain-ai/langsmith-cookbook/blob/main/tracing-examples/rest/rest.ipynb
- metadata = (
- kwargs.get("litellm_params", {}).get("metadata", {}) or {}
- ) # if metadata is None
+ def _prepare_log_data(self, kwargs, response_obj, start_time, end_time):
+ import datetime
+ from datetime import timezone
+
+ metadata = kwargs.get("litellm_params", {}).get("metadata", {}) or {}
+
+ kwargs["user_api_key"] = metadata.get("user_api_key", None)
+ kwargs["user_api_key_user_id"] = metadata.get("user_api_key_user_id", None)
+ kwargs["user_api_key_team_alias"] = metadata.get(
+ "user_api_key_team_alias", None
+ )
- # set project name and run_name for langsmith logging
- # users can pass project_name and run name to litellm.completion()
- # Example: litellm.completion(model, messages, metadata={"project_name": "my-litellm-project", "run_name": "my-langsmith-run"})
- # if not set litellm will fallback to the environment variable LANGSMITH_PROJECT, then to the default project_name = litellm-completion, run_name = LLMRun
project_name = metadata.get("project_name", self.langsmith_project)
run_name = metadata.get("run_name", self.langsmith_default_run_name)
- print_verbose(
+ run_id = metadata.get("id", None)
+ verbose_logger.debug(
f"Langsmith Logging - project_name: {project_name}, run_name {run_name}"
)
- langsmith_base_url = os.getenv("LANGSMITH_BASE_URL", "https://api.smith.langchain.com")
try:
- print_verbose(
- f"Langsmith Logging - Enters logging function for model {kwargs}"
- )
- import requests
- import datetime
- from datetime import timezone
+ start_time = kwargs["start_time"].astimezone(timezone.utc).isoformat()
+ end_time = kwargs["end_time"].astimezone(timezone.utc).isoformat()
+ except:
+ start_time = datetime.datetime.utcnow().isoformat()
+ end_time = datetime.datetime.utcnow().isoformat()
+ # filter out kwargs to not include any dicts, langsmith throws an erros when trying to log kwargs
+ logged_kwargs = LangsmithInputs(**kwargs)
+ kwargs = logged_kwargs.model_dump()
+
+ new_kwargs = {}
+ for key in kwargs:
+ value = kwargs[key]
+ if key == "start_time" or key == "end_time" or value is None:
+ pass
+ elif key == "original_response" and not isinstance(value, str):
+ new_kwargs[key] = str(value)
+ elif type(value) == datetime.datetime:
+ new_kwargs[key] = value.isoformat()
+ elif type(value) != dict and is_serializable(value=value):
+ new_kwargs[key] = value
+ elif not is_serializable(value=value):
+ continue
+
+ if isinstance(response_obj, BaseModel):
try:
- start_time = kwargs["start_time"].astimezone(timezone.utc).isoformat()
- end_time = kwargs["end_time"].astimezone(timezone.utc).isoformat()
+ response_obj = response_obj.model_dump()
except:
- start_time = datetime.datetime.utcnow().isoformat()
- end_time = datetime.datetime.utcnow().isoformat()
+ response_obj = response_obj.dict() # type: ignore
- # filter out kwargs to not include any dicts, langsmith throws an erros when trying to log kwargs
- new_kwargs = {}
- for key in kwargs:
- value = kwargs[key]
- if key == "start_time" or key == "end_time" or value is None:
- pass
- elif type(value) == datetime.datetime:
- new_kwargs[key] = value.isoformat()
- elif type(value) != dict and is_serializable(value=value):
- new_kwargs[key] = value
+ data = {
+ "name": run_name,
+ "run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain"
+ "inputs": new_kwargs,
+ "outputs": response_obj,
+ "session_name": project_name,
+ "start_time": start_time,
+ "end_time": end_time,
+ }
- if isinstance(response_obj, BaseModel):
- try:
- response_obj = response_obj.model_dump()
- except:
- response_obj = response_obj.dict() # type: ignore
+ if run_id:
+ data["id"] = run_id
- data = {
- "name": run_name,
- "run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain"
- "inputs": new_kwargs,
- "outputs": response_obj,
- "session_name": project_name,
- "start_time": start_time,
- "end_time": end_time,
- }
+ verbose_logger.debug("Langsmith Logging data on langsmith: %s", data)
- url = f"{langsmith_base_url}/runs"
- print_verbose(
- f"Langsmith Logging - About to send data to {url} ..."
+ return data
+
+ async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+ try:
+ verbose_logger.debug(
+ "Langsmith Async Layer Logging - kwargs: %s, response_obj: %s",
+ kwargs,
+ response_obj,
)
+ data = self._prepare_log_data(kwargs, response_obj, start_time, end_time)
+ url = f"{self.langsmith_base_url}/runs"
+ verbose_logger.debug(f"Langsmith Logging - About to send data to {url} ...")
+
+ headers = {"x-api-key": self.langsmith_api_key}
+ response = await self.async_httpx_client.post(
+ url=url, json=data, headers=headers
+ )
+
+ if response.status_code >= 300:
+ verbose_logger.error(
+ f"Langmsith Error: {response.status_code} - {response.text}"
+ )
+ else:
+ verbose_logger.debug(
+ "Run successfully created, response=%s", response.text
+ )
+ verbose_logger.debug(
+ f"Langsmith Layer Logging - final response object: {response_obj}. Response text from langsmith={response.text}"
+ )
+ except:
+ verbose_logger.error(f"Langsmith Layer Error - {traceback.format_exc()}")
+
+ def log_success_event(self, kwargs, response_obj, start_time, end_time):
+ try:
+ verbose_logger.debug(
+ "Langsmith Sync Layer Logging - kwargs: %s, response_obj: %s",
+ kwargs,
+ response_obj,
+ )
+ data = self._prepare_log_data(kwargs, response_obj, start_time, end_time)
+ url = f"{self.langsmith_base_url}/runs"
+ verbose_logger.debug(f"Langsmith Logging - About to send data to {url} ...")
+
response = requests.post(
url=url,
json=data,
@@ -99,13 +179,21 @@ class LangsmithLogger:
)
if response.status_code >= 300:
- print_verbose(f"Error: {response.status_code}")
+ verbose_logger.error(f"Error: {response.status_code} - {response.text}")
else:
- print_verbose("Run successfully created")
- print_verbose(
- f"Langsmith Layer Logging - final response object: {response_obj}"
+ verbose_logger.debug("Run successfully created")
+ verbose_logger.debug(
+ f"Langsmith Layer Logging - final response object: {response_obj}. Response text from langsmith={response.text}"
)
except:
- # traceback.print_exc()
- print_verbose(f"Langsmith Layer Error - {traceback.format_exc()}")
- pass
+ verbose_logger.error(f"Langsmith Layer Error - {traceback.format_exc()}")
+
+ def get_run_by_id(self, run_id):
+
+ url = f"{self.langsmith_base_url}/runs/{run_id}"
+ response = requests.get(
+ url=url,
+ headers={"x-api-key": self.langsmith_api_key},
+ )
+
+ return response.json()
diff --git a/litellm/integrations/logfire_logger.py b/litellm/integrations/logfire_logger.py
index e27d848fb4..b4ab00820e 100644
--- a/litellm/integrations/logfire_logger.py
+++ b/litellm/integrations/logfire_logger.py
@@ -171,7 +171,6 @@ class LogfireLogger:
f"Logfire Layer Logging - final response object: {response_obj}"
)
except Exception as e:
- traceback.print_exc()
verbose_logger.debug(
f"Logfire Layer Error - {str(e)}\n{traceback.format_exc()}"
)
diff --git a/litellm/integrations/lunary.py b/litellm/integrations/lunary.py
index 2e16e44a14..b0cc069c40 100644
--- a/litellm/integrations/lunary.py
+++ b/litellm/integrations/lunary.py
@@ -14,6 +14,7 @@ def parse_usage(usage):
"prompt": usage["prompt_tokens"] if "prompt_tokens" in usage else 0,
}
+
def parse_tool_calls(tool_calls):
if tool_calls is None:
return None
@@ -26,13 +27,13 @@ def parse_tool_calls(tool_calls):
"function": {
"name": tool_call.function.name,
"arguments": tool_call.function.arguments,
- }
+ },
}
return serialized
-
+
return [clean_tool_call(tool_call) for tool_call in tool_calls]
-
+
def parse_messages(input):
@@ -104,19 +105,18 @@ class LunaryLogger:
end_time=datetime.now(timezone.utc),
error=None,
):
- # Method definition
try:
print_verbose(f"Lunary Logging - Logging request for model {model}")
+ template_id = None
litellm_params = kwargs.get("litellm_params", {})
optional_params = kwargs.get("optional_params", {})
metadata = litellm_params.get("metadata", {}) or {}
if optional_params:
- # merge into extra
extra = {**extra, **optional_params}
- tags = litellm_params.pop("tags", None) or []
+ tags = metadata.get("tags", None)
if extra:
extra.pop("extra_body", None)
@@ -176,6 +176,5 @@ class LunaryLogger:
)
except:
- # traceback.print_exc()
print_verbose(f"Lunary Logging Error - {traceback.format_exc()}")
pass
diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py
new file mode 100644
index 0000000000..215a4f09f8
--- /dev/null
+++ b/litellm/integrations/opentelemetry.py
@@ -0,0 +1,693 @@
+import os
+from dataclasses import dataclass
+from datetime import datetime
+from functools import wraps
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.types.services import ServiceLoggerPayload
+
+if TYPE_CHECKING:
+ from opentelemetry.trace import Span as _Span
+
+ from litellm.proxy._types import (
+ ManagementEndpointLoggingPayload as _ManagementEndpointLoggingPayload,
+ )
+ from litellm.proxy.proxy_server import UserAPIKeyAuth as _UserAPIKeyAuth
+
+ Span = _Span
+ UserAPIKeyAuth = _UserAPIKeyAuth
+ ManagementEndpointLoggingPayload = _ManagementEndpointLoggingPayload
+else:
+ Span = Any
+ UserAPIKeyAuth = Any
+ ManagementEndpointLoggingPayload = Any
+
+
+LITELLM_TRACER_NAME = os.getenv("OTEL_TRACER_NAME", "litellm")
+LITELLM_RESOURCE = {
+ "service.name": os.getenv("OTEL_SERVICE_NAME", "litellm"),
+ "deployment.environment": os.getenv("OTEL_ENVIRONMENT_NAME", "production"),
+}
+RAW_REQUEST_SPAN_NAME = "raw_gen_ai_request"
+LITELLM_REQUEST_SPAN_NAME = "litellm_request"
+
+
+@dataclass
+class OpenTelemetryConfig:
+ from opentelemetry.sdk.trace.export import SpanExporter
+
+ exporter: str | SpanExporter = "console"
+ endpoint: Optional[str] = None
+ headers: Optional[str] = None
+
+ @classmethod
+ def from_env(cls):
+ """
+ OTEL_HEADERS=x-honeycomb-team=B85YgLm9****
+ OTEL_EXPORTER="otlp_http"
+ OTEL_ENDPOINT="https://api.honeycomb.io/v1/traces"
+
+ OTEL_HEADERS gets sent as headers = {"x-honeycomb-team": "B85YgLm96******"}
+ """
+ from opentelemetry.sdk.trace.export.in_memory_span_exporter import (
+ InMemorySpanExporter,
+ )
+
+ if os.getenv("OTEL_EXPORTER") == "in_memory":
+ return cls(exporter=InMemorySpanExporter())
+ return cls(
+ exporter=os.getenv("OTEL_EXPORTER", "console"),
+ endpoint=os.getenv("OTEL_ENDPOINT"),
+ headers=os.getenv(
+ "OTEL_HEADERS"
+ ), # example: OTEL_HEADERS=x-honeycomb-team=B85YgLm96VGdFisfJVme1H"
+ )
+
+
+class OpenTelemetry(CustomLogger):
+ def __init__(self, config=OpenTelemetryConfig.from_env()):
+ from opentelemetry import trace
+ from opentelemetry.sdk.resources import Resource
+ from opentelemetry.sdk.trace import TracerProvider
+
+ self.config = config
+ self.OTEL_EXPORTER = self.config.exporter
+ self.OTEL_ENDPOINT = self.config.endpoint
+ self.OTEL_HEADERS = self.config.headers
+ provider = TracerProvider(resource=Resource(attributes=LITELLM_RESOURCE))
+ provider.add_span_processor(self._get_span_processor())
+
+ trace.set_tracer_provider(provider)
+ self.tracer = trace.get_tracer(LITELLM_TRACER_NAME)
+
+ _debug_otel = str(os.getenv("DEBUG_OTEL", "False")).lower()
+
+ if _debug_otel == "true":
+ # Set up logging
+ import logging
+
+ logging.basicConfig(level=logging.DEBUG)
+ logger = logging.getLogger(__name__)
+
+ # Enable OpenTelemetry logging
+ otel_exporter_logger = logging.getLogger("opentelemetry.sdk.trace.export")
+ otel_exporter_logger.setLevel(logging.DEBUG)
+
+ def log_success_event(self, kwargs, response_obj, start_time, end_time):
+ self._handle_sucess(kwargs, response_obj, start_time, end_time)
+
+ def log_failure_event(self, kwargs, response_obj, start_time, end_time):
+ self._handle_failure(kwargs, response_obj, start_time, end_time)
+
+ async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+ self._handle_sucess(kwargs, response_obj, start_time, end_time)
+
+ async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
+ self._handle_failure(kwargs, response_obj, start_time, end_time)
+
+ async def async_service_success_hook(
+ self,
+ payload: ServiceLoggerPayload,
+ parent_otel_span: Optional[Span] = None,
+ start_time: Optional[Union[datetime, float]] = None,
+ end_time: Optional[Union[datetime, float]] = None,
+ ):
+ from datetime import datetime
+
+ from opentelemetry import trace
+ from opentelemetry.trace import Status, StatusCode
+
+ _start_time_ns = start_time
+ _end_time_ns = end_time
+
+ if isinstance(start_time, float):
+ _start_time_ns = int(int(start_time) * 1e9)
+ else:
+ _start_time_ns = self._to_ns(start_time)
+
+ if isinstance(end_time, float):
+ _end_time_ns = int(int(end_time) * 1e9)
+ else:
+ _end_time_ns = self._to_ns(end_time)
+
+ if parent_otel_span is not None:
+ _span_name = payload.service
+ service_logging_span = self.tracer.start_span(
+ name=_span_name,
+ context=trace.set_span_in_context(parent_otel_span),
+ start_time=_start_time_ns,
+ )
+ service_logging_span.set_attribute(key="call_type", value=payload.call_type)
+ service_logging_span.set_attribute(
+ key="service", value=payload.service.value
+ )
+ service_logging_span.set_status(Status(StatusCode.OK))
+ service_logging_span.end(end_time=_end_time_ns)
+
+ async def async_service_failure_hook(
+ self,
+ payload: ServiceLoggerPayload,
+ parent_otel_span: Optional[Span] = None,
+ start_time: Optional[Union[datetime, float]] = None,
+ end_time: Optional[Union[float, datetime]] = None,
+ ):
+ from datetime import datetime
+
+ from opentelemetry import trace
+ from opentelemetry.trace import Status, StatusCode
+
+ _start_time_ns = start_time
+ _end_time_ns = end_time
+
+ if isinstance(start_time, float):
+ _start_time_ns = int(int(start_time) * 1e9)
+ else:
+ _start_time_ns = self._to_ns(start_time)
+
+ if isinstance(end_time, float):
+ _end_time_ns = int(int(end_time) * 1e9)
+ else:
+ _end_time_ns = self._to_ns(end_time)
+
+ if parent_otel_span is not None:
+ _span_name = payload.service
+ service_logging_span = self.tracer.start_span(
+ name=_span_name,
+ context=trace.set_span_in_context(parent_otel_span),
+ start_time=_start_time_ns,
+ )
+ service_logging_span.set_attribute(key="call_type", value=payload.call_type)
+ service_logging_span.set_attribute(
+ key="service", value=payload.service.value
+ )
+ service_logging_span.set_status(Status(StatusCode.ERROR))
+ service_logging_span.end(end_time=_end_time_ns)
+
+ async def async_post_call_failure_hook(
+ self, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth
+ ):
+ from opentelemetry import trace
+ from opentelemetry.trace import Status, StatusCode
+
+ parent_otel_span = user_api_key_dict.parent_otel_span
+ if parent_otel_span is not None:
+ parent_otel_span.set_status(Status(StatusCode.ERROR))
+ _span_name = "Failed Proxy Server Request"
+
+ # Exception Logging Child Span
+ exception_logging_span = self.tracer.start_span(
+ name=_span_name,
+ context=trace.set_span_in_context(parent_otel_span),
+ )
+ exception_logging_span.set_attribute(
+ key="exception", value=str(original_exception)
+ )
+ exception_logging_span.set_status(Status(StatusCode.ERROR))
+ exception_logging_span.end(end_time=self._to_ns(datetime.now()))
+
+ # End Parent OTEL Sspan
+ parent_otel_span.end(end_time=self._to_ns(datetime.now()))
+
+ def _handle_sucess(self, kwargs, response_obj, start_time, end_time):
+ from opentelemetry import trace
+ from opentelemetry.trace import Status, StatusCode
+
+ verbose_logger.debug(
+ "OpenTelemetry Logger: Logging kwargs: %s, OTEL config settings=%s",
+ kwargs,
+ self.config,
+ )
+ _parent_context, parent_otel_span = self._get_span_context(kwargs)
+
+ # Span 1: Requst sent to litellm SDK
+ span = self.tracer.start_span(
+ name=self._get_span_name(kwargs),
+ start_time=self._to_ns(start_time),
+ context=_parent_context,
+ )
+ span.set_status(Status(StatusCode.OK))
+ self.set_attributes(span, kwargs, response_obj)
+
+ if litellm.turn_off_message_logging is True:
+ pass
+ else:
+ # Span 2: Raw Request / Response to LLM
+ raw_request_span = self.tracer.start_span(
+ name=RAW_REQUEST_SPAN_NAME,
+ start_time=self._to_ns(start_time),
+ context=trace.set_span_in_context(span),
+ )
+
+ raw_request_span.set_status(Status(StatusCode.OK))
+ self.set_raw_request_attributes(raw_request_span, kwargs, response_obj)
+ raw_request_span.end(end_time=self._to_ns(end_time))
+
+ span.end(end_time=self._to_ns(end_time))
+
+ if parent_otel_span is not None:
+ parent_otel_span.end(end_time=self._to_ns(datetime.now()))
+
+ def _handle_failure(self, kwargs, response_obj, start_time, end_time):
+ from opentelemetry.trace import Status, StatusCode
+
+ span = self.tracer.start_span(
+ name=self._get_span_name(kwargs),
+ start_time=self._to_ns(start_time),
+ context=self._get_span_context(kwargs),
+ )
+ span.set_status(Status(StatusCode.ERROR))
+ self.set_attributes(span, kwargs, response_obj)
+ span.end(end_time=self._to_ns(end_time))
+
+ def set_tools_attributes(self, span: Span, tools):
+ import json
+
+ from litellm.proxy._types import SpanAttributes
+
+ if not tools:
+ return
+
+ try:
+ for i, tool in enumerate(tools):
+ function = tool.get("function")
+ if not function:
+ continue
+
+ prefix = f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.{i}"
+ span.set_attribute(f"{prefix}.name", function.get("name"))
+ span.set_attribute(f"{prefix}.description", function.get("description"))
+ span.set_attribute(
+ f"{prefix}.parameters", json.dumps(function.get("parameters"))
+ )
+ except Exception as e:
+ verbose_logger.error(
+ "OpenTelemetry: Error setting tools attributes: %s", str(e)
+ )
+ pass
+
+ def is_primitive(self, value):
+ if value is None:
+ return False
+ return isinstance(value, (str, bool, int, float))
+
+ def set_attributes(self, span: Span, kwargs, response_obj):
+ from litellm.proxy._types import SpanAttributes
+
+ optional_params = kwargs.get("optional_params", {})
+ litellm_params = kwargs.get("litellm_params", {}) or {}
+
+ # https://github.com/open-telemetry/semantic-conventions/blob/main/model/registry/gen-ai.yaml
+ # Following Conventions here: https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/llm-spans.md
+ #############################################
+ ############ LLM CALL METADATA ##############
+ #############################################
+ metadata = litellm_params.get("metadata", {}) or {}
+
+ for key, value in metadata.items():
+ if self.is_primitive(value):
+ span.set_attribute("metadata.{}".format(key), value)
+
+ #############################################
+ ########## LLM Request Attributes ###########
+ #############################################
+
+ # The name of the LLM a request is being made to
+ if kwargs.get("model"):
+ span.set_attribute(SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model"))
+
+ # The Generative AI Provider: Azure, OpenAI, etc.
+ span.set_attribute(
+ SpanAttributes.LLM_SYSTEM,
+ litellm_params.get("custom_llm_provider", "Unknown"),
+ )
+
+ # The maximum number of tokens the LLM generates for a request.
+ if optional_params.get("max_tokens"):
+ span.set_attribute(
+ SpanAttributes.LLM_REQUEST_MAX_TOKENS, optional_params.get("max_tokens")
+ )
+
+ # The temperature setting for the LLM request.
+ if optional_params.get("temperature"):
+ span.set_attribute(
+ SpanAttributes.LLM_REQUEST_TEMPERATURE,
+ optional_params.get("temperature"),
+ )
+
+ # The top_p sampling setting for the LLM request.
+ if optional_params.get("top_p"):
+ span.set_attribute(
+ SpanAttributes.LLM_REQUEST_TOP_P, optional_params.get("top_p")
+ )
+
+ span.set_attribute(
+ SpanAttributes.LLM_IS_STREAMING, str(optional_params.get("stream", False))
+ )
+
+ if optional_params.get("tools"):
+ tools = optional_params["tools"]
+ self.set_tools_attributes(span, tools)
+
+ if optional_params.get("user"):
+ span.set_attribute(SpanAttributes.LLM_USER, optional_params.get("user"))
+
+ if kwargs.get("messages"):
+ for idx, prompt in enumerate(kwargs.get("messages")):
+ if prompt.get("role"):
+ span.set_attribute(
+ f"{SpanAttributes.LLM_PROMPTS}.{idx}.role",
+ prompt.get("role"),
+ )
+
+ if prompt.get("content"):
+ if not isinstance(prompt.get("content"), str):
+ prompt["content"] = str(prompt.get("content"))
+ span.set_attribute(
+ f"{SpanAttributes.LLM_PROMPTS}.{idx}.content",
+ prompt.get("content"),
+ )
+ #############################################
+ ########## LLM Response Attributes ##########
+ #############################################
+ if response_obj.get("choices"):
+ for idx, choice in enumerate(response_obj.get("choices")):
+ if choice.get("finish_reason"):
+ span.set_attribute(
+ f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason",
+ choice.get("finish_reason"),
+ )
+ if choice.get("message"):
+ if choice.get("message").get("role"):
+ span.set_attribute(
+ f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role",
+ choice.get("message").get("role"),
+ )
+ if choice.get("message").get("content"):
+ if not isinstance(choice.get("message").get("content"), str):
+ choice["message"]["content"] = str(
+ choice.get("message").get("content")
+ )
+ span.set_attribute(
+ f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content",
+ choice.get("message").get("content"),
+ )
+
+ message = choice.get("message")
+ tool_calls = message.get("tool_calls")
+ if tool_calls:
+ span.set_attribute(
+ f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.function_call.name",
+ tool_calls[0].get("function").get("name"),
+ )
+ span.set_attribute(
+ f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.function_call.arguments",
+ tool_calls[0].get("function").get("arguments"),
+ )
+
+ # The unique identifier for the completion.
+ if response_obj.get("id"):
+ span.set_attribute("gen_ai.response.id", response_obj.get("id"))
+
+ # The model used to generate the response.
+ if response_obj.get("model"):
+ span.set_attribute(
+ SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model")
+ )
+
+ usage = response_obj.get("usage")
+ if usage:
+ span.set_attribute(
+ SpanAttributes.LLM_USAGE_TOTAL_TOKENS,
+ usage.get("total_tokens"),
+ )
+
+ # The number of tokens used in the LLM response (completion).
+ span.set_attribute(
+ SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
+ usage.get("completion_tokens"),
+ )
+
+ # The number of tokens used in the LLM prompt.
+ span.set_attribute(
+ SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
+ usage.get("prompt_tokens"),
+ )
+
+ def set_raw_request_attributes(self, span: Span, kwargs, response_obj):
+ from litellm.proxy._types import SpanAttributes
+
+ optional_params = kwargs.get("optional_params", {})
+ litellm_params = kwargs.get("litellm_params", {}) or {}
+ custom_llm_provider = litellm_params.get("custom_llm_provider", "Unknown")
+
+ _raw_response = kwargs.get("original_response")
+ _additional_args = kwargs.get("additional_args", {}) or {}
+ complete_input_dict = _additional_args.get("complete_input_dict")
+ #############################################
+ ########## LLM Request Attributes ###########
+ #############################################
+
+ # OTEL Attributes for the RAW Request to https://docs.anthropic.com/en/api/messages
+ if complete_input_dict:
+ for param, val in complete_input_dict.items():
+ if not isinstance(val, str):
+ val = str(val)
+ span.set_attribute(
+ f"llm.{custom_llm_provider}.{param}",
+ val,
+ )
+
+ #############################################
+ ########## LLM Response Attributes ##########
+ #############################################
+ if _raw_response and isinstance(_raw_response, str):
+ # cast sr -> dict
+ import json
+
+ try:
+ _raw_response = json.loads(_raw_response)
+ for param, val in _raw_response.items():
+ if not isinstance(val, str):
+ val = str(val)
+ span.set_attribute(
+ f"llm.{custom_llm_provider}.{param}",
+ val,
+ )
+ except json.JSONDecodeError:
+ verbose_logger.debug(
+ "litellm.integrations.opentelemetry.py::set_raw_request_attributes() - raw_response not json string - {}".format(
+ _raw_response
+ )
+ )
+ span.set_attribute(
+ f"llm.{custom_llm_provider}.stringified_raw_response",
+ _raw_response,
+ )
+
+ def _to_ns(self, dt):
+ return int(dt.timestamp() * 1e9)
+
+ def _get_span_name(self, kwargs):
+ return LITELLM_REQUEST_SPAN_NAME
+
+ def get_traceparent_from_header(self, headers):
+ if headers is None:
+ return None
+ _traceparent = headers.get("traceparent", None)
+ if _traceparent is None:
+ return None
+
+ from opentelemetry.trace.propagation.tracecontext import (
+ TraceContextTextMapPropagator,
+ )
+
+ verbose_logger.debug("OpenTelemetry: GOT A TRACEPARENT {}".format(_traceparent))
+ propagator = TraceContextTextMapPropagator()
+ _parent_context = propagator.extract(carrier={"traceparent": _traceparent})
+ verbose_logger.debug("OpenTelemetry: PARENT CONTEXT {}".format(_parent_context))
+ return _parent_context
+
+ def _get_span_context(self, kwargs):
+ from opentelemetry import trace
+ from opentelemetry.trace.propagation.tracecontext import (
+ TraceContextTextMapPropagator,
+ )
+
+ litellm_params = kwargs.get("litellm_params", {}) or {}
+ proxy_server_request = litellm_params.get("proxy_server_request", {}) or {}
+ headers = proxy_server_request.get("headers", {}) or {}
+ traceparent = headers.get("traceparent", None)
+ _metadata = litellm_params.get("metadata", {}) or {}
+ parent_otel_span = _metadata.get("litellm_parent_otel_span", None)
+
+ """
+ Two way to use parents in opentelemetry
+ - using the traceparent header
+ - using the parent_otel_span in the [metadata][parent_otel_span]
+ """
+ if parent_otel_span is not None:
+ return trace.set_span_in_context(parent_otel_span), parent_otel_span
+
+ if traceparent is None:
+ return None, None
+ else:
+ carrier = {"traceparent": traceparent}
+ return TraceContextTextMapPropagator().extract(carrier=carrier), None
+
+ def _get_span_processor(self):
+ from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
+ OTLPSpanExporter as OTLPSpanExporterGRPC,
+ )
+ from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+ OTLPSpanExporter as OTLPSpanExporterHTTP,
+ )
+ from opentelemetry.sdk.trace.export import (
+ BatchSpanProcessor,
+ ConsoleSpanExporter,
+ SimpleSpanProcessor,
+ SpanExporter,
+ )
+
+ verbose_logger.debug(
+ "OpenTelemetry Logger, initializing span processor \nself.OTEL_EXPORTER: %s\nself.OTEL_ENDPOINT: %s\nself.OTEL_HEADERS: %s",
+ self.OTEL_EXPORTER,
+ self.OTEL_ENDPOINT,
+ self.OTEL_HEADERS,
+ )
+ _split_otel_headers = {}
+ if self.OTEL_HEADERS is not None and isinstance(self.OTEL_HEADERS, str):
+ _split_otel_headers = self.OTEL_HEADERS.split("=")
+ _split_otel_headers = {_split_otel_headers[0]: _split_otel_headers[1]}
+
+ if isinstance(self.OTEL_EXPORTER, SpanExporter):
+ verbose_logger.debug(
+ "OpenTelemetry: intiializing SpanExporter. Value of OTEL_EXPORTER: %s",
+ self.OTEL_EXPORTER,
+ )
+ return SimpleSpanProcessor(self.OTEL_EXPORTER)
+
+ if self.OTEL_EXPORTER == "console":
+ verbose_logger.debug(
+ "OpenTelemetry: intiializing console exporter. Value of OTEL_EXPORTER: %s",
+ self.OTEL_EXPORTER,
+ )
+ return BatchSpanProcessor(ConsoleSpanExporter())
+ elif self.OTEL_EXPORTER == "otlp_http":
+ verbose_logger.debug(
+ "OpenTelemetry: intiializing http exporter. Value of OTEL_EXPORTER: %s",
+ self.OTEL_EXPORTER,
+ )
+ return BatchSpanProcessor(
+ OTLPSpanExporterHTTP(
+ endpoint=self.OTEL_ENDPOINT, headers=_split_otel_headers
+ )
+ )
+ elif self.OTEL_EXPORTER == "otlp_grpc":
+ verbose_logger.debug(
+ "OpenTelemetry: intiializing grpc exporter. Value of OTEL_EXPORTER: %s",
+ self.OTEL_EXPORTER,
+ )
+ return BatchSpanProcessor(
+ OTLPSpanExporterGRPC(
+ endpoint=self.OTEL_ENDPOINT, headers=_split_otel_headers
+ )
+ )
+ else:
+ verbose_logger.debug(
+ "OpenTelemetry: intiializing console exporter. Value of OTEL_EXPORTER: %s",
+ self.OTEL_EXPORTER,
+ )
+ return BatchSpanProcessor(ConsoleSpanExporter())
+
+ async def async_management_endpoint_success_hook(
+ self,
+ logging_payload: ManagementEndpointLoggingPayload,
+ parent_otel_span: Optional[Span] = None,
+ ):
+ from datetime import datetime
+
+ from opentelemetry import trace
+ from opentelemetry.trace import Status, StatusCode
+
+ _start_time_ns = logging_payload.start_time
+ _end_time_ns = logging_payload.end_time
+
+ start_time = logging_payload.start_time
+ end_time = logging_payload.end_time
+
+ if isinstance(start_time, float):
+ _start_time_ns = int(int(start_time) * 1e9)
+ else:
+ _start_time_ns = self._to_ns(start_time)
+
+ if isinstance(end_time, float):
+ _end_time_ns = int(int(end_time) * 1e9)
+ else:
+ _end_time_ns = self._to_ns(end_time)
+
+ if parent_otel_span is not None:
+ _span_name = logging_payload.route
+ management_endpoint_span = self.tracer.start_span(
+ name=_span_name,
+ context=trace.set_span_in_context(parent_otel_span),
+ start_time=_start_time_ns,
+ )
+
+ _request_data = logging_payload.request_data
+ if _request_data is not None:
+ for key, value in _request_data.items():
+ management_endpoint_span.set_attribute(f"request.{key}", value)
+
+ _response = logging_payload.response
+ if _response is not None:
+ for key, value in _response.items():
+ management_endpoint_span.set_attribute(f"response.{key}", value)
+ management_endpoint_span.set_status(Status(StatusCode.OK))
+ management_endpoint_span.end(end_time=_end_time_ns)
+
+ async def async_management_endpoint_failure_hook(
+ self,
+ logging_payload: ManagementEndpointLoggingPayload,
+ parent_otel_span: Optional[Span] = None,
+ ):
+ from datetime import datetime
+
+ from opentelemetry import trace
+ from opentelemetry.trace import Status, StatusCode
+
+ _start_time_ns = logging_payload.start_time
+ _end_time_ns = logging_payload.end_time
+
+ start_time = logging_payload.start_time
+ end_time = logging_payload.end_time
+
+ if isinstance(start_time, float):
+ _start_time_ns = int(int(start_time) * 1e9)
+ else:
+ _start_time_ns = self._to_ns(start_time)
+
+ if isinstance(end_time, float):
+ _end_time_ns = int(int(end_time) * 1e9)
+ else:
+ _end_time_ns = self._to_ns(end_time)
+
+ if parent_otel_span is not None:
+ _span_name = logging_payload.route
+ management_endpoint_span = self.tracer.start_span(
+ name=_span_name,
+ context=trace.set_span_in_context(parent_otel_span),
+ start_time=_start_time_ns,
+ )
+
+ _request_data = logging_payload.request_data
+ if _request_data is not None:
+ for key, value in _request_data.items():
+ management_endpoint_span.set_attribute(f"request.{key}", value)
+
+ _exception = logging_payload.exception
+ management_endpoint_span.set_attribute(f"exception", str(_exception))
+ management_endpoint_span.set_status(Status(StatusCode.ERROR))
+ management_endpoint_span.end(end_time=_end_time_ns)
diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py
index 6fbc6ca4ce..4a271d6e00 100644
--- a/litellm/integrations/prometheus.py
+++ b/litellm/integrations/prometheus.py
@@ -2,11 +2,18 @@
#### What this does ####
# On success, log events to Prometheus
-import dotenv, os
-import requests # type: ignore
+import datetime
+import os
+import subprocess
+import sys
import traceback
-import datetime, subprocess, sys
-import litellm, uuid
+import uuid
+from typing import Optional, Union
+
+import dotenv
+import requests # type: ignore
+
+import litellm
from litellm._logging import print_verbose, verbose_logger
@@ -17,33 +24,107 @@ class PrometheusLogger:
**kwargs,
):
try:
- from prometheus_client import Counter
+ from prometheus_client import Counter, Gauge
+
+ from litellm.proxy.proxy_server import premium_user
self.litellm_llm_api_failed_requests_metric = Counter(
name="litellm_llm_api_failed_requests_metric",
documentation="Total number of failed LLM API calls via litellm",
- labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
+ labelnames=[
+ "end_user",
+ "hashed_api_key",
+ "api_key_alias",
+ "model",
+ "team",
+ "team_alias",
+ "user",
+ ],
)
self.litellm_requests_metric = Counter(
name="litellm_requests_metric",
documentation="Total number of LLM calls to litellm",
- labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
+ labelnames=[
+ "end_user",
+ "hashed_api_key",
+ "api_key_alias",
+ "model",
+ "team",
+ "team_alias",
+ "user",
+ ],
)
# Counter for spend
self.litellm_spend_metric = Counter(
"litellm_spend_metric",
"Total spend on LLM requests",
- labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
+ labelnames=[
+ "end_user",
+ "hashed_api_key",
+ "api_key_alias",
+ "model",
+ "team",
+ "team_alias",
+ "user",
+ ],
)
# Counter for total_output_tokens
self.litellm_tokens_metric = Counter(
"litellm_total_tokens",
"Total number of input + output tokens from LLM requests",
- labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
+ labelnames=[
+ "end_user",
+ "hashed_api_key",
+ "api_key_alias",
+ "model",
+ "team",
+ "team_alias",
+ "user",
+ ],
)
+
+ # Remaining Budget for Team
+ self.litellm_remaining_team_budget_metric = Gauge(
+ "litellm_remaining_team_budget_metric",
+ "Remaining budget for team",
+ labelnames=["team_id", "team_alias"],
+ )
+
+ # Remaining Budget for API Key
+ self.litellm_remaining_api_key_budget_metric = Gauge(
+ "litellm_remaining_api_key_budget_metric",
+ "Remaining budget for api key",
+ labelnames=["hashed_api_key", "api_key_alias"],
+ )
+
+ # Litellm-Enterprise Metrics
+ if premium_user is True:
+ # Remaining Rate Limit for model
+ self.litellm_remaining_requests_metric = Gauge(
+ "litellm_remaining_requests",
+ "remaining requests for model, returned from LLM API Provider",
+ labelnames=[
+ "model_group",
+ "api_provider",
+ "api_base",
+ "litellm_model_name",
+ ],
+ )
+
+ self.litellm_remaining_tokens_metric = Gauge(
+ "litellm_remaining_tokens",
+ "remaining tokens for model, returned from LLM API Provider",
+ labelnames=[
+ "model_group",
+ "api_provider",
+ "api_base",
+ "litellm_model_name",
+ ],
+ )
+
except Exception as e:
print_verbose(f"Got exception on init prometheus client {str(e)}")
raise e
@@ -51,13 +132,17 @@ class PrometheusLogger:
async def _async_log_event(
self, kwargs, response_obj, start_time, end_time, print_verbose, user_id
):
- self.log_event(kwargs, response_obj, start_time, end_time, print_verbose)
+ self.log_event(
+ kwargs, response_obj, start_time, end_time, user_id, print_verbose
+ )
def log_event(
self, kwargs, response_obj, start_time, end_time, user_id, print_verbose
):
try:
# Define prometheus client
+ from litellm.proxy.proxy_server import premium_user
+
verbose_logger.debug(
f"prometheus Logging - Enters logging function for model {kwargs}"
)
@@ -72,9 +157,36 @@ class PrometheusLogger:
"user_api_key_user_id", None
)
user_api_key = litellm_params.get("metadata", {}).get("user_api_key", None)
+ user_api_key_alias = litellm_params.get("metadata", {}).get(
+ "user_api_key_alias", None
+ )
user_api_team = litellm_params.get("metadata", {}).get(
"user_api_key_team_id", None
)
+ user_api_team_alias = litellm_params.get("metadata", {}).get(
+ "user_api_key_team_alias", None
+ )
+
+ _team_spend = litellm_params.get("metadata", {}).get(
+ "user_api_key_team_spend", None
+ )
+ _team_max_budget = litellm_params.get("metadata", {}).get(
+ "user_api_key_team_max_budget", None
+ )
+ _remaining_team_budget = safe_get_remaining_budget(
+ max_budget=_team_max_budget, spend=_team_spend
+ )
+
+ _api_key_spend = litellm_params.get("metadata", {}).get(
+ "user_api_key_spend", None
+ )
+ _api_key_max_budget = litellm_params.get("metadata", {}).get(
+ "user_api_key_max_budget", None
+ )
+ _remaining_api_key_budget = safe_get_remaining_budget(
+ max_budget=_api_key_max_budget, spend=_api_key_spend
+ )
+
if response_obj is not None:
tokens_used = response_obj.get("usage", {}).get("total_tokens", 0)
else:
@@ -94,23 +206,123 @@ class PrometheusLogger:
user_api_key = hash_token(user_api_key)
self.litellm_requests_metric.labels(
- end_user_id, user_api_key, model, user_api_team, user_id
+ end_user_id,
+ user_api_key,
+ user_api_key_alias,
+ model,
+ user_api_team,
+ user_api_team_alias,
+ user_id,
).inc()
self.litellm_spend_metric.labels(
- end_user_id, user_api_key, model, user_api_team, user_id
+ end_user_id,
+ user_api_key,
+ user_api_key_alias,
+ model,
+ user_api_team,
+ user_api_team_alias,
+ user_id,
).inc(response_cost)
self.litellm_tokens_metric.labels(
- end_user_id, user_api_key, model, user_api_team, user_id
+ end_user_id,
+ user_api_key,
+ user_api_key_alias,
+ model,
+ user_api_team,
+ user_api_team_alias,
+ user_id,
).inc(tokens_used)
+ self.litellm_remaining_team_budget_metric.labels(
+ user_api_team, user_api_team_alias
+ ).set(_remaining_team_budget)
+
+ self.litellm_remaining_api_key_budget_metric.labels(
+ user_api_key, user_api_key_alias
+ ).set(_remaining_api_key_budget)
+
+ # set x-ratelimit headers
+ if premium_user is True:
+ self.set_remaining_tokens_requests_metric(kwargs)
+
### FAILURE INCREMENT ###
if "exception" in kwargs:
self.litellm_llm_api_failed_requests_metric.labels(
- end_user_id, user_api_key, model, user_api_team, user_id
+ end_user_id,
+ user_api_key,
+ user_api_key_alias,
+ model,
+ user_api_team,
+ user_api_team_alias,
+ user_id,
).inc()
except Exception as e:
- traceback.print_exc()
- verbose_logger.debug(
- f"prometheus Layer Error - {str(e)}\n{traceback.format_exc()}"
+ verbose_logger.error(
+ "prometheus Layer Error(): Exception occured - {}".format(str(e))
)
+ verbose_logger.debug(traceback.format_exc())
pass
+
+ def set_remaining_tokens_requests_metric(self, request_kwargs: dict):
+ try:
+ verbose_logger.debug("setting remaining tokens requests metric")
+ _response_headers = request_kwargs.get("response_headers")
+ _litellm_params = request_kwargs.get("litellm_params", {}) or {}
+ _metadata = _litellm_params.get("metadata", {})
+ litellm_model_name = request_kwargs.get("model", None)
+ model_group = _metadata.get("model_group", None)
+ api_base = _metadata.get("api_base", None)
+ llm_provider = _litellm_params.get("custom_llm_provider", None)
+
+ remaining_requests = None
+ remaining_tokens = None
+ # OpenAI / OpenAI Compatible headers
+ if (
+ _response_headers
+ and "x-ratelimit-remaining-requests" in _response_headers
+ ):
+ remaining_requests = _response_headers["x-ratelimit-remaining-requests"]
+ if (
+ _response_headers
+ and "x-ratelimit-remaining-tokens" in _response_headers
+ ):
+ remaining_tokens = _response_headers["x-ratelimit-remaining-tokens"]
+ verbose_logger.debug(
+ f"remaining requests: {remaining_requests}, remaining tokens: {remaining_tokens}"
+ )
+
+ if remaining_requests:
+ """
+ "model_group",
+ "api_provider",
+ "api_base",
+ "litellm_model_name"
+ """
+ self.litellm_remaining_requests_metric.labels(
+ model_group, llm_provider, api_base, litellm_model_name
+ ).set(remaining_requests)
+
+ if remaining_tokens:
+ self.litellm_remaining_tokens_metric.labels(
+ model_group, llm_provider, api_base, litellm_model_name
+ ).set(remaining_tokens)
+
+ except Exception as e:
+ verbose_logger.error(
+ "Prometheus Error: set_remaining_tokens_requests_metric. Exception occured - {}".format(
+ str(e)
+ )
+ )
+ return
+
+
+def safe_get_remaining_budget(
+ max_budget: Optional[float], spend: Optional[float]
+) -> float:
+ if max_budget is None:
+ return float("inf")
+
+ if spend is None:
+ return max_budget
+
+ return max_budget - spend
diff --git a/litellm/integrations/s3.py b/litellm/integrations/s3.py
index d131e44f0e..6e8c4a4e43 100644
--- a/litellm/integrations/s3.py
+++ b/litellm/integrations/s3.py
@@ -1,10 +1,14 @@
#### What this does ####
# On success + failure, log events to Supabase
+import datetime
import os
+import subprocess
+import sys
import traceback
-import datetime, subprocess, sys
-import litellm, uuid
+import uuid
+
+import litellm
from litellm._logging import print_verbose, verbose_logger
@@ -54,6 +58,7 @@ class S3Logger:
"s3_aws_session_token"
)
s3_config = litellm.s3_callback_params.get("s3_config")
+ s3_path = litellm.s3_callback_params.get("s3_path")
# done reading litellm.s3_callback_params
self.bucket_name = s3_bucket_name
@@ -180,6 +185,5 @@ class S3Logger:
print_verbose(f"s3 Layer Logging - final response object: {response_obj}")
return response
except Exception as e:
- traceback.print_exc()
verbose_logger.debug(f"s3 Layer Error - {str(e)}\n{traceback.format_exc()}")
pass
diff --git a/litellm/integrations/slack_alerting.py b/litellm/integrations/slack_alerting.py
index c822958d93..8ae23c629f 100644
--- a/litellm/integrations/slack_alerting.py
+++ b/litellm/integrations/slack_alerting.py
@@ -1,27 +1,36 @@
#### What this does ####
# Class for sending Slack Alerts #
-import dotenv, os, traceback
-from litellm.proxy._types import UserAPIKeyAuth, CallInfo, AlertType
-from litellm._logging import verbose_logger, verbose_proxy_logger
-import litellm, threading
-from typing import List, Literal, Any, Union, Optional, Dict, Set
-from litellm.caching import DualCache
-import asyncio, time
-import aiohttp
-from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+import asyncio
import datetime
-from pydantic import BaseModel, Field
-from enum import Enum
-from datetime import datetime as dt, timedelta, timezone
-from litellm.integrations.custom_logger import CustomLogger
-from litellm.proxy._types import WebhookEvent
+import os
import random
-from typing import TypedDict
-from openai import APIError
+import threading
+import time
+import traceback
+from datetime import datetime as dt
+from datetime import timedelta, timezone
+from enum import Enum
+from typing import Any, Dict, List, Literal, Optional, Set, TypedDict, Union
+import aiohttp
+import dotenv
+from openai import APIError
+from pydantic import BaseModel, Field
+
+import litellm
+import litellm.litellm_core_utils
+import litellm.litellm_core_utils.litellm_logging
import litellm.types
+from litellm._logging import verbose_logger, verbose_proxy_logger
+from litellm.caching import DualCache
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.litellm_core_utils.litellm_logging import Logging
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from litellm.proxy._types import AlertType, CallInfo, UserAPIKeyAuth, WebhookEvent
from litellm.types.router import LiteLLM_Params
+from .email_templates.templates import *
+
class BaseOutageModel(TypedDict):
alerts: List[int]
@@ -41,6 +50,7 @@ class ProviderRegionOutageModel(BaseOutageModel):
# we use this for the email header, please send a test email if you change this. verify it looks good on email
LITELLM_LOGO_URL = "https://litellm-listing.s3.amazonaws.com/litellm_logo.png"
+LITELLM_SUPPORT_CONTACT = "support@berri.ai"
class LiteLLMBase(BaseModel):
@@ -220,7 +230,7 @@ class SlackAlerting(CustomLogger):
"db_exceptions",
]
- def _add_langfuse_trace_id_to_alert(
+ async def _add_langfuse_trace_id_to_alert(
self,
request_data: Optional[dict] = None,
) -> Optional[str]:
@@ -233,23 +243,23 @@ class SlackAlerting(CustomLogger):
-> litellm_call_id
"""
# do nothing for now
- if request_data is not None:
- trace_id = None
- if (
- request_data.get("metadata", {}).get("existing_trace_id", None)
- is not None
- ):
- trace_id = request_data["metadata"]["existing_trace_id"]
- elif request_data.get("metadata", {}).get("trace_id", None) is not None:
- trace_id = request_data["metadata"]["trace_id"]
- elif request_data.get("litellm_logging_obj", None) is not None and hasattr(
- request_data["litellm_logging_obj"], "model_call_details"
- ):
- trace_id = request_data["litellm_logging_obj"].model_call_details[
- "litellm_call_id"
- ]
- if litellm.utils.langFuseLogger is not None:
- base_url = litellm.utils.langFuseLogger.Langfuse.base_url
+ if (
+ request_data is not None
+ and request_data.get("litellm_logging_obj", None) is not None
+ ):
+ trace_id: Optional[str] = None
+ litellm_logging_obj: Logging = request_data["litellm_logging_obj"]
+
+ for _ in range(3):
+ trace_id = litellm_logging_obj._get_trace_id(service_name="langfuse")
+ if trace_id is not None:
+ break
+ await asyncio.sleep(3) # wait 3s before retrying for trace id
+
+ if litellm.litellm_core_utils.litellm_logging.langFuseLogger is not None:
+ base_url = (
+ litellm.litellm_core_utils.litellm_logging.langFuseLogger.Langfuse.base_url
+ )
return f"{base_url}/trace/{trace_id}"
return None
@@ -324,10 +334,11 @@ class SlackAlerting(CustomLogger):
end_time=end_time,
)
)
- if litellm.turn_off_message_logging:
- messages = "Message not logged. `litellm.turn_off_message_logging=True`."
+ if litellm.turn_off_message_logging or litellm.redact_messages_in_exceptions:
+ messages = "Message not logged. litellm.redact_messages_in_exceptions=True"
request_info = f"\nRequest Model: `{model}`\nAPI Base: `{api_base}`\nMessages: `{messages}`"
slow_message = f"`Responses are slow - {round(time_difference_float,2)}s response time > Alerting threshold: {self.alerting_threshold}s`"
+ alerting_metadata: dict = {}
if time_difference_float > self.alerting_threshold:
# add deployment latencies to alert
if (
@@ -335,7 +346,7 @@ class SlackAlerting(CustomLogger):
and "litellm_params" in kwargs
and "metadata" in kwargs["litellm_params"]
):
- _metadata = kwargs["litellm_params"]["metadata"]
+ _metadata: dict = kwargs["litellm_params"]["metadata"]
request_info = litellm.utils._add_key_name_and_team_to_alert(
request_info=request_info, metadata=_metadata
)
@@ -347,10 +358,14 @@ class SlackAlerting(CustomLogger):
request_info += (
f"\nAvailable Deployment Latencies\n{_deployment_latency_map}"
)
+
+ if "alerting_metadata" in _metadata:
+ alerting_metadata = _metadata["alerting_metadata"]
await self.send_alert(
message=slow_message + request_info,
level="Low",
alert_type="llm_too_slow",
+ alerting_metadata=alerting_metadata,
)
async def async_update_daily_reports(
@@ -535,10 +550,15 @@ class SlackAlerting(CustomLogger):
cache_list=combined_metrics_cache_keys
)
- message += f"\n\nNext Run is in: `{time.time() + self.alerting_args.daily_report_frequency}`s"
+ message += f"\n\nNext Run is at: `{time.time() + self.alerting_args.daily_report_frequency}`s"
# send alert
- await self.send_alert(message=message, level="Low", alert_type="daily_reports")
+ await self.send_alert(
+ message=message,
+ level="Low",
+ alert_type="daily_reports",
+ alerting_metadata={},
+ )
return True
@@ -565,9 +585,12 @@ class SlackAlerting(CustomLogger):
except:
messages = ""
- if litellm.turn_off_message_logging:
+ if (
+ litellm.turn_off_message_logging
+ or litellm.redact_messages_in_exceptions
+ ):
messages = (
- "Message not logged. `litellm.turn_off_message_logging=True`."
+ "Message not logged. litellm.redact_messages_in_exceptions=True"
)
request_info = f"\nRequest Model: `{model}`\nMessages: `{messages}`"
else:
@@ -577,11 +600,19 @@ class SlackAlerting(CustomLogger):
await asyncio.sleep(
self.alerting_threshold
) # Set it to 5 minutes - i'd imagine this might be different for streaming, non-streaming, non-completion (embedding + img) requests
+ alerting_metadata: dict = {}
if (
request_data is not None
and request_data.get("litellm_status", "") != "success"
and request_data.get("litellm_status", "") != "fail"
):
+ ## CHECK IF CACHE IS UPDATED
+ litellm_call_id = request_data.get("litellm_call_id", "")
+ status: Optional[str] = await self.internal_usage_cache.async_get_cache(
+ key="request_status:{}".format(litellm_call_id), local_only=True
+ )
+ if status is not None and (status == "success" or status == "fail"):
+ return
if request_data.get("deployment", None) is not None and isinstance(
request_data["deployment"], dict
):
@@ -601,7 +632,7 @@ class SlackAlerting(CustomLogger):
):
# In hanging requests sometime it has not made it to the point where the deployment is passed to the `request_data``
# in that case we fallback to the api base set in the request metadata
- _metadata = request_data["metadata"]
+ _metadata: dict = request_data["metadata"]
_api_base = _metadata.get("api_base", "")
request_info = litellm.utils._add_key_name_and_team_to_alert(
@@ -610,6 +641,9 @@ class SlackAlerting(CustomLogger):
if _api_base is None:
_api_base = ""
+
+ if "alerting_metadata" in _metadata:
+ alerting_metadata = _metadata["alerting_metadata"]
request_info += f"\nAPI Base: `{_api_base}`"
# only alert hanging responses if they have not been marked as success
alerting_message = (
@@ -617,7 +651,7 @@ class SlackAlerting(CustomLogger):
)
if "langfuse" in litellm.success_callback:
- langfuse_url = self._add_langfuse_trace_id_to_alert(
+ langfuse_url = await self._add_langfuse_trace_id_to_alert(
request_data=request_data,
)
@@ -635,17 +669,21 @@ class SlackAlerting(CustomLogger):
message=alerting_message + request_info,
level="Medium",
alert_type="llm_requests_hanging",
+ alerting_metadata=alerting_metadata,
)
async def failed_tracking_alert(self, error_message: str):
"""Raise alert when tracking failed for specific model"""
_cache: DualCache = self.internal_usage_cache
- message = "Failed Tracking Cost for" + error_message
+ message = "Failed Tracking Cost for " + error_message
_cache_key = "budget_alerts:failed_tracking:{}".format(message)
result = await _cache.async_get_cache(key=_cache_key)
if result is None:
await self.send_alert(
- message=message, level="High", alert_type="budget_alerts"
+ message=message,
+ level="High",
+ alert_type="budget_alerts",
+ alerting_metadata={},
)
await _cache.async_set_cache(
key=_cache_key,
@@ -675,7 +713,7 @@ class SlackAlerting(CustomLogger):
return
if "budget_alerts" not in self.alert_types:
return
- _id: str = "default_id" # used for caching
+ _id: Optional[str] = "default_id" # used for caching
user_info_json = user_info.model_dump(exclude_none=True)
for k, v in user_info_json.items():
user_info_str = "\n{}: {}\n".format(k, v)
@@ -683,14 +721,16 @@ class SlackAlerting(CustomLogger):
event: Optional[
Literal["budget_crossed", "threshold_crossed", "projected_limit_exceeded"]
] = None
- event_group: Optional[Literal["user", "team", "key", "proxy"]] = None
+ event_group: Optional[
+ Literal["internal_user", "team", "key", "proxy", "customer"]
+ ] = None
event_message: str = ""
webhook_event: Optional[WebhookEvent] = None
if type == "proxy_budget":
event_group = "proxy"
event_message += "Proxy Budget: "
elif type == "user_budget":
- event_group = "user"
+ event_group = "internal_user"
event_message += "User Budget: "
_id = user_info.user_id or _id
elif type == "team_budget":
@@ -744,6 +784,7 @@ class SlackAlerting(CustomLogger):
level="High",
alert_type="budget_alerts",
user_info=webhook_event,
+ alerting_metadata={},
)
await _cache.async_set_cache(
key=_cache_key,
@@ -754,6 +795,42 @@ class SlackAlerting(CustomLogger):
return
return
+ async def customer_spend_alert(
+ self,
+ token: Optional[str],
+ key_alias: Optional[str],
+ end_user_id: Optional[str],
+ response_cost: Optional[float],
+ max_budget: Optional[float],
+ ):
+ if (
+ self.alerting is not None
+ and "webhook" in self.alerting
+ and end_user_id is not None
+ and token is not None
+ and response_cost is not None
+ ):
+ # log customer spend
+ event = WebhookEvent(
+ spend=response_cost,
+ max_budget=max_budget,
+ token=token,
+ customer_id=end_user_id,
+ user_id=None,
+ team_id=None,
+ user_email=None,
+ key_alias=key_alias,
+ projected_exceeded_date=None,
+ projected_spend=None,
+ event="spend_tracked",
+ event_group="customer",
+ event_message="Customer spend tracked. Customer={}, spend={}".format(
+ end_user_id, response_cost
+ ),
+ )
+
+ await self.send_webhook_alert(webhook_event=event)
+
def _count_outage_alerts(self, alerts: List[int]) -> str:
"""
Parameters:
@@ -904,7 +981,10 @@ class SlackAlerting(CustomLogger):
)
# send minor alert
await self.send_alert(
- message=msg, level="Medium", alert_type="outage_alerts"
+ message=msg,
+ level="Medium",
+ alert_type="outage_alerts",
+ alerting_metadata={},
)
# set to true
outage_value["minor_alert_sent"] = True
@@ -926,7 +1006,12 @@ class SlackAlerting(CustomLogger):
)
# send minor alert
- await self.send_alert(message=msg, level="High", alert_type="outage_alerts")
+ await self.send_alert(
+ message=msg,
+ level="High",
+ alert_type="outage_alerts",
+ alerting_metadata={},
+ )
# set to true
outage_value["major_alert_sent"] = True
@@ -1025,7 +1110,10 @@ class SlackAlerting(CustomLogger):
)
# send minor alert
await self.send_alert(
- message=msg, level="Medium", alert_type="outage_alerts"
+ message=msg,
+ level="Medium",
+ alert_type="outage_alerts",
+ alerting_metadata={},
)
# set to true
outage_value["minor_alert_sent"] = True
@@ -1044,7 +1132,10 @@ class SlackAlerting(CustomLogger):
)
# send minor alert
await self.send_alert(
- message=msg, level="High", alert_type="outage_alerts"
+ message=msg,
+ level="High",
+ alert_type="outage_alerts",
+ alerting_metadata={},
)
# set to true
outage_value["major_alert_sent"] = True
@@ -1106,7 +1197,10 @@ Model Info:
"""
alert_val = self.send_alert(
- message=message, level="Low", alert_type="new_model_added"
+ message=message,
+ level="Low",
+ alert_type="new_model_added",
+ alerting_metadata={},
)
if alert_val is not None and asyncio.iscoroutine(alert_val):
@@ -1122,6 +1216,9 @@ Model Info:
Currently only implemented for budget alerts
Returns -> True if sent, False if not.
+
+ Raises Exception
+ - if WEBHOOK_URL is not set
"""
webhook_url = os.getenv("WEBHOOK_URL", None)
@@ -1149,111 +1246,119 @@ Model Info:
email_logo_url: Optional[str] = None,
email_support_contact: Optional[str] = None,
):
- from litellm.proxy.proxy_server import premium_user
- from litellm.proxy.proxy_server import CommonProxyErrors
+ from litellm.proxy.proxy_server import CommonProxyErrors, premium_user
if premium_user is not True:
if email_logo_url is not None or email_support_contact is not None:
raise ValueError(
f"Trying to Customize Email Alerting\n {CommonProxyErrors.not_premium_user.value}"
)
+ return
- async def send_key_created_email(self, webhook_event: WebhookEvent) -> bool:
- from litellm.proxy.utils import send_email
+ async def send_key_created_or_user_invited_email(
+ self, webhook_event: WebhookEvent
+ ) -> bool:
+ try:
+ from litellm.proxy.utils import send_email
- if self.alerting is None or "email" not in self.alerting:
- # do nothing if user does not want email alerts
+ if self.alerting is None or "email" not in self.alerting:
+ # do nothing if user does not want email alerts
+ return False
+ from litellm.proxy.proxy_server import premium_user, prisma_client
+
+ email_logo_url = os.getenv(
+ "SMTP_SENDER_LOGO", os.getenv("EMAIL_LOGO_URL", None)
+ )
+ email_support_contact = os.getenv("EMAIL_SUPPORT_CONTACT", None)
+ await self._check_if_using_premium_email_feature(
+ premium_user, email_logo_url, email_support_contact
+ )
+ if email_logo_url is None:
+ email_logo_url = LITELLM_LOGO_URL
+ if email_support_contact is None:
+ email_support_contact = LITELLM_SUPPORT_CONTACT
+
+ event_name = webhook_event.event_message
+ recipient_email = webhook_event.user_email
+ recipient_user_id = webhook_event.user_id
+ if (
+ recipient_email is None
+ and recipient_user_id is not None
+ and prisma_client is not None
+ ):
+ user_row = await prisma_client.db.litellm_usertable.find_unique(
+ where={"user_id": recipient_user_id}
+ )
+
+ if user_row is not None:
+ recipient_email = user_row.user_email
+
+ key_name = webhook_event.key_alias
+ key_token = webhook_event.token
+ key_budget = webhook_event.max_budget
+ base_url = os.getenv("PROXY_BASE_URL", "http://0.0.0.0:4000")
+
+ email_html_content = "Alert from LiteLLM Server"
+ if recipient_email is None:
+ verbose_proxy_logger.error(
+ "Trying to send email alert to no recipient",
+ extra=webhook_event.dict(),
+ )
+
+ if webhook_event.event == "key_created":
+ email_html_content = KEY_CREATED_EMAIL_TEMPLATE.format(
+ email_logo_url=email_logo_url,
+ recipient_email=recipient_email,
+ key_budget=key_budget,
+ key_token=key_token,
+ base_url=base_url,
+ email_support_contact=email_support_contact,
+ )
+ elif webhook_event.event == "internal_user_created":
+ # GET TEAM NAME
+ team_id = webhook_event.team_id
+ team_name = "Default Team"
+ if team_id is not None and prisma_client is not None:
+ team_row = await prisma_client.db.litellm_teamtable.find_unique(
+ where={"team_id": team_id}
+ )
+ if team_row is not None:
+ team_name = team_row.team_alias or "-"
+ email_html_content = USER_INVITED_EMAIL_TEMPLATE.format(
+ email_logo_url=email_logo_url,
+ recipient_email=recipient_email,
+ team_name=team_name,
+ base_url=base_url,
+ email_support_contact=email_support_contact,
+ )
+ else:
+ verbose_proxy_logger.error(
+ "Trying to send email alert on unknown webhook event",
+ extra=webhook_event.model_dump(),
+ )
+
+ payload = webhook_event.model_dump_json()
+ email_event = {
+ "to": recipient_email,
+ "subject": f"LiteLLM: {event_name}",
+ "html": email_html_content,
+ }
+
+ response = await send_email(
+ receiver_email=email_event["to"],
+ subject=email_event["subject"],
+ html=email_event["html"],
+ )
+
+ return True
+
+ except Exception as e:
+ verbose_proxy_logger.error("Error sending email alert %s", str(e))
return False
- from litellm.proxy.proxy_server import premium_user, prisma_client
- email_logo_url = os.getenv("SMTP_SENDER_LOGO", None)
- email_support_contact = os.getenv("EMAIL_SUPPORT_CONTACT", None)
- await self._check_if_using_premium_email_feature(
- premium_user, email_logo_url, email_support_contact
- )
-
- event_name = webhook_event.event_message
- recipient_email = webhook_event.user_email
- recipient_user_id = webhook_event.user_id
- if (
- recipient_email is None
- and recipient_user_id is not None
- and prisma_client is not None
- ):
- user_row = await prisma_client.db.litellm_usertable.find_unique(
- where={"user_id": recipient_user_id}
- )
-
- if user_row is not None:
- recipient_email = user_row.user_email
-
- key_name = webhook_event.key_alias
- key_token = webhook_event.token
- key_budget = webhook_event.max_budget
-
- email_html_content = "Alert from LiteLLM Server"
- if recipient_email is None:
- verbose_proxy_logger.error(
- "Trying to send email alert to no recipient", extra=webhook_event.dict()
- )
- email_html_content = f"""
-
-
-
Hi {recipient_email},
-
- I'm happy to provide you with an OpenAI Proxy API Key, loaded with ${key_budget} per month.
-
-
- Key: {key_token}
-
-
-
Usage Example
-
- Detailed Documentation on Usage with OpenAI Python SDK, Langchain, LlamaIndex, Curl
-
-
-
- import openai
- client = openai.OpenAI(
- api_key="{key_token}",
- base_url={os.getenv("PROXY_BASE_URL", "http://0.0.0.0:4000")}
- )
-
- response = client.chat.completions.create(
- model="gpt-3.5-turbo", # model to send to the proxy
- messages = [
- {{
- "role": "user",
- "content": "this is a test request, write a short poem"
- }}
- ]
- )
-
-
-
-
- If you have any questions, please send an email to {email_support_contact}
-
- Best,
- The LiteLLM team
- """
-
- payload = webhook_event.model_dump_json()
- email_event = {
- "to": recipient_email,
- "subject": f"LiteLLM: {event_name}",
- "html": email_html_content,
- }
-
- response = await send_email(
- receiver_email=email_event["to"],
- subject=email_event["subject"],
- html=email_event["html"],
- )
-
- return False
-
- async def send_email_alert_using_smtp(self, webhook_event: WebhookEvent) -> bool:
+ async def send_email_alert_using_smtp(
+ self, webhook_event: WebhookEvent, alert_type: str
+ ) -> bool:
"""
Sends structured Email alert to an SMTP server
@@ -1261,16 +1366,22 @@ Model Info:
Returns -> True if sent, False if not.
"""
+ from litellm.proxy.proxy_server import premium_user, prisma_client
from litellm.proxy.utils import send_email
- from litellm.proxy.proxy_server import premium_user, prisma_client
-
- email_logo_url = os.getenv("SMTP_SENDER_LOGO", None)
+ email_logo_url = os.getenv(
+ "SMTP_SENDER_LOGO", os.getenv("EMAIL_LOGO_URL", None)
+ )
email_support_contact = os.getenv("EMAIL_SUPPORT_CONTACT", None)
await self._check_if_using_premium_email_feature(
premium_user, email_logo_url, email_support_contact
)
+ if email_logo_url is None:
+ email_logo_url = LITELLM_LOGO_URL
+ if email_support_contact is None:
+ email_support_contact = LITELLM_SUPPORT_CONTACT
+
event_name = webhook_event.event_message
recipient_email = webhook_event.user_email
user_name = webhook_event.user_id
@@ -1309,6 +1420,10 @@ Model Info:
subject=email_event["subject"],
html=email_event["html"],
)
+ if webhook_event.event_group == "team":
+ from litellm.integrations.email_alerting import send_team_budget_alert
+
+ await send_team_budget_alert(webhook_event=webhook_event)
return False
@@ -1317,6 +1432,7 @@ Model Info:
message: str,
level: Literal["Low", "Medium", "High"],
alert_type: Literal[AlertType],
+ alerting_metadata: dict,
user_info: Optional[WebhookEvent] = None,
**kwargs,
):
@@ -1350,7 +1466,9 @@ Model Info:
and user_info is not None
):
# only send budget alerts over Email
- await self.send_email_alert_using_smtp(webhook_event=user_info)
+ await self.send_email_alert_using_smtp(
+ webhook_event=user_info, alert_type=alert_type
+ )
if "slack" not in self.alerting:
return
@@ -1358,8 +1476,8 @@ Model Info:
if alert_type not in self.alert_types:
return
- from datetime import datetime
import json
+ from datetime import datetime
# Get the current timestamp
current_time = datetime.now().strftime("%H:%M:%S")
@@ -1374,6 +1492,9 @@ Model Info:
if kwargs:
for key, value in kwargs.items():
formatted_message += f"\n\n{key}: `{value}`\n\n"
+ if alerting_metadata:
+ for key, value in alerting_metadata.items():
+ formatted_message += f"\n\n*Alerting Metadata*: \n{key}: `{value}`\n\n"
if _proxy_base_url is not None:
formatted_message += f"\n\nProxy URL: `{_proxy_base_url}`"
@@ -1389,7 +1510,7 @@ Model Info:
slack_webhook_url = os.getenv("SLACK_WEBHOOK_URL", None)
if slack_webhook_url is None:
- raise Exception("Missing SLACK_WEBHOOK_URL from environment")
+ raise ValueError("Missing SLACK_WEBHOOK_URL from environment")
payload = {"text": formatted_message}
headers = {"Content-type": "application/json"}
@@ -1401,26 +1522,34 @@ Model Info:
if response.status_code == 200:
pass
else:
- print("Error sending slack alert. Error=", response.text) # noqa
+ verbose_proxy_logger.debug(
+ "Error sending slack alert. Error={}".format(response.text)
+ )
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
"""Log deployment latency"""
try:
if "daily_reports" in self.alert_types:
- model_id = (
- kwargs.get("litellm_params", {}).get("model_info", {}).get("id", "")
- )
+ litellm_params = kwargs.get("litellm_params", {}) or {}
+ model_info = litellm_params.get("model_info", {}) or {}
+ model_id = model_info.get("id", "") or ""
response_s: timedelta = end_time - start_time
final_value = response_s
total_tokens = 0
- if isinstance(response_obj, litellm.ModelResponse):
+ if isinstance(response_obj, litellm.ModelResponse) and (
+ hasattr(response_obj, "usage")
+ and response_obj.usage is not None
+ and hasattr(response_obj.usage, "completion_tokens")
+ ):
completion_tokens = response_obj.usage.completion_tokens
if completion_tokens is not None and completion_tokens > 0:
final_value = float(
response_s.total_seconds() / completion_tokens
)
+ if isinstance(final_value, timedelta):
+ final_value = final_value.total_seconds()
await self.async_update_daily_reports(
DeploymentMetrics(
@@ -1432,8 +1561,7 @@ Model Info:
)
except Exception as e:
verbose_proxy_logger.error(
- "[Non-Blocking Error] Slack Alerting: Got error in logging LLM deployment latency: ",
- e,
+ f"[Non-Blocking Error] Slack Alerting: Got error in logging LLM deployment latency: {str(e)}"
)
pass
@@ -1567,6 +1695,7 @@ Model Info:
message=_weekly_spend_message,
level="Low",
alert_type="spend_reports",
+ alerting_metadata={},
)
except Exception as e:
verbose_proxy_logger.error("Error sending weekly spend report", e)
@@ -1618,6 +1747,7 @@ Model Info:
message=_spend_message,
level="Low",
alert_type="spend_reports",
+ alerting_metadata={},
)
except Exception as e:
verbose_proxy_logger.error("Error sending weekly spend report", e)
diff --git a/litellm/integrations/supabase.py b/litellm/integrations/supabase.py
index 4e6bf517f3..7309342e4c 100644
--- a/litellm/integrations/supabase.py
+++ b/litellm/integrations/supabase.py
@@ -110,6 +110,5 @@ class Supabase:
)
except:
- # traceback.print_exc()
print_verbose(f"Supabase Logging Error - {traceback.format_exc()}")
pass
diff --git a/litellm/integrations/test_httpx.py b/litellm/integrations/test_httpx.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/litellm/integrations/traceloop.py b/litellm/integrations/traceloop.py
index bbdb9a1b0a..e1c419c6f7 100644
--- a/litellm/integrations/traceloop.py
+++ b/litellm/integrations/traceloop.py
@@ -1,114 +1,149 @@
+import traceback
+from litellm._logging import verbose_logger
+import litellm
+
+
class TraceloopLogger:
def __init__(self):
- from traceloop.sdk.tracing.tracing import TracerWrapper
- from traceloop.sdk import Traceloop
+ try:
+ from traceloop.sdk.tracing.tracing import TracerWrapper
+ from traceloop.sdk import Traceloop
+ from traceloop.sdk.instruments import Instruments
+ from opentelemetry.sdk.trace.export import ConsoleSpanExporter
+ except ModuleNotFoundError as e:
+ verbose_logger.error(
+ f"Traceloop not installed, try running 'pip install traceloop-sdk' to fix this error: {e}\n{traceback.format_exc()}"
+ )
- Traceloop.init(app_name="Litellm-Server", disable_batch=True)
+ Traceloop.init(
+ app_name="Litellm-Server",
+ disable_batch=True,
+ )
self.tracer_wrapper = TracerWrapper()
- def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
- from opentelemetry.trace import SpanKind
+ def log_event(
+ self,
+ kwargs,
+ response_obj,
+ start_time,
+ end_time,
+ user_id,
+ print_verbose,
+ level="DEFAULT",
+ status_message=None,
+ ):
+ from opentelemetry import trace
+ from opentelemetry.trace import SpanKind, Status, StatusCode
from opentelemetry.semconv.ai import SpanAttributes
try:
+ print_verbose(
+ f"Traceloop Logging - Enters logging function for model {kwargs}"
+ )
+
tracer = self.tracer_wrapper.get_tracer()
- model = kwargs.get("model")
-
- # LiteLLM uses the standard OpenAI library, so it's already handled by Traceloop SDK
- if kwargs.get("litellm_params").get("custom_llm_provider") == "openai":
- return
-
optional_params = kwargs.get("optional_params", {})
- with tracer.start_as_current_span(
- "litellm.completion",
- kind=SpanKind.CLIENT,
- ) as span:
- if span.is_recording():
+ start_time = int(start_time.timestamp())
+ end_time = int(end_time.timestamp())
+ span = tracer.start_span(
+ "litellm.completion", kind=SpanKind.CLIENT, start_time=start_time
+ )
+
+ if span.is_recording():
+ span.set_attribute(
+ SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model")
+ )
+ if "stop" in optional_params:
span.set_attribute(
- SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model")
+ SpanAttributes.LLM_CHAT_STOP_SEQUENCES,
+ optional_params.get("stop"),
)
- if "stop" in optional_params:
- span.set_attribute(
- SpanAttributes.LLM_CHAT_STOP_SEQUENCES,
- optional_params.get("stop"),
- )
- if "frequency_penalty" in optional_params:
- span.set_attribute(
- SpanAttributes.LLM_FREQUENCY_PENALTY,
- optional_params.get("frequency_penalty"),
- )
- if "presence_penalty" in optional_params:
- span.set_attribute(
- SpanAttributes.LLM_PRESENCE_PENALTY,
- optional_params.get("presence_penalty"),
- )
- if "top_p" in optional_params:
- span.set_attribute(
- SpanAttributes.LLM_TOP_P, optional_params.get("top_p")
- )
- if "tools" in optional_params or "functions" in optional_params:
- span.set_attribute(
- SpanAttributes.LLM_REQUEST_FUNCTIONS,
- optional_params.get(
- "tools", optional_params.get("functions")
- ),
- )
- if "user" in optional_params:
- span.set_attribute(
- SpanAttributes.LLM_USER, optional_params.get("user")
- )
- if "max_tokens" in optional_params:
- span.set_attribute(
- SpanAttributes.LLM_REQUEST_MAX_TOKENS,
- kwargs.get("max_tokens"),
- )
- if "temperature" in optional_params:
- span.set_attribute(
- SpanAttributes.LLM_TEMPERATURE, kwargs.get("temperature")
- )
-
- for idx, prompt in enumerate(kwargs.get("messages")):
- span.set_attribute(
- f"{SpanAttributes.LLM_PROMPTS}.{idx}.role",
- prompt.get("role"),
- )
- span.set_attribute(
- f"{SpanAttributes.LLM_PROMPTS}.{idx}.content",
- prompt.get("content"),
- )
-
+ if "frequency_penalty" in optional_params:
span.set_attribute(
- SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model")
+ SpanAttributes.LLM_FREQUENCY_PENALTY,
+ optional_params.get("frequency_penalty"),
+ )
+ if "presence_penalty" in optional_params:
+ span.set_attribute(
+ SpanAttributes.LLM_PRESENCE_PENALTY,
+ optional_params.get("presence_penalty"),
+ )
+ if "top_p" in optional_params:
+ span.set_attribute(
+ SpanAttributes.LLM_TOP_P, optional_params.get("top_p")
+ )
+ if "tools" in optional_params or "functions" in optional_params:
+ span.set_attribute(
+ SpanAttributes.LLM_REQUEST_FUNCTIONS,
+ optional_params.get("tools", optional_params.get("functions")),
+ )
+ if "user" in optional_params:
+ span.set_attribute(
+ SpanAttributes.LLM_USER, optional_params.get("user")
+ )
+ if "max_tokens" in optional_params:
+ span.set_attribute(
+ SpanAttributes.LLM_REQUEST_MAX_TOKENS,
+ kwargs.get("max_tokens"),
+ )
+ if "temperature" in optional_params:
+ span.set_attribute(
+ SpanAttributes.LLM_REQUEST_TEMPERATURE,
+ kwargs.get("temperature"),
)
- usage = response_obj.get("usage")
- if usage:
- span.set_attribute(
- SpanAttributes.LLM_USAGE_TOTAL_TOKENS,
- usage.get("total_tokens"),
- )
- span.set_attribute(
- SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
- usage.get("completion_tokens"),
- )
- span.set_attribute(
- SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
- usage.get("prompt_tokens"),
- )
- for idx, choice in enumerate(response_obj.get("choices")):
- span.set_attribute(
- f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason",
- choice.get("finish_reason"),
- )
- span.set_attribute(
- f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role",
- choice.get("message").get("role"),
- )
- span.set_attribute(
- f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content",
- choice.get("message").get("content"),
- )
+ for idx, prompt in enumerate(kwargs.get("messages")):
+ span.set_attribute(
+ f"{SpanAttributes.LLM_PROMPTS}.{idx}.role",
+ prompt.get("role"),
+ )
+ span.set_attribute(
+ f"{SpanAttributes.LLM_PROMPTS}.{idx}.content",
+ prompt.get("content"),
+ )
+
+ span.set_attribute(
+ SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model")
+ )
+ usage = response_obj.get("usage")
+ if usage:
+ span.set_attribute(
+ SpanAttributes.LLM_USAGE_TOTAL_TOKENS,
+ usage.get("total_tokens"),
+ )
+ span.set_attribute(
+ SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
+ usage.get("completion_tokens"),
+ )
+ span.set_attribute(
+ SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
+ usage.get("prompt_tokens"),
+ )
+
+ for idx, choice in enumerate(response_obj.get("choices")):
+ span.set_attribute(
+ f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason",
+ choice.get("finish_reason"),
+ )
+ span.set_attribute(
+ f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role",
+ choice.get("message").get("role"),
+ )
+ span.set_attribute(
+ f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content",
+ choice.get("message").get("content"),
+ )
+
+ if (
+ level == "ERROR"
+ and status_message is not None
+ and isinstance(status_message, str)
+ ):
+ span.record_exception(Exception(status_message))
+ span.set_status(Status(StatusCode.ERROR, status_message))
+
+ span.end(end_time)
except Exception as e:
print_verbose(f"Traceloop Layer Error - {e}")
diff --git a/litellm/integrations/weights_biases.py b/litellm/integrations/weights_biases.py
index a56233b22f..1ac535c4f2 100644
--- a/litellm/integrations/weights_biases.py
+++ b/litellm/integrations/weights_biases.py
@@ -217,6 +217,5 @@ class WeightsBiasesLogger:
f"W&B Logging Logging - final response object: {response_obj}"
)
except:
- # traceback.print_exc()
print_verbose(f"W&B Logging Layer Error - {traceback.format_exc()}")
pass
diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py
new file mode 100644
index 0000000000..d6d6495d00
--- /dev/null
+++ b/litellm/litellm_core_utils/core_helpers.py
@@ -0,0 +1,56 @@
+# What is this?
+## Helper utilities
+
+
+def map_finish_reason(
+ finish_reason: str,
+): # openai supports 5 stop sequences - 'stop', 'length', 'function_call', 'content_filter', 'null'
+ # anthropic mapping
+ if finish_reason == "stop_sequence":
+ return "stop"
+ # cohere mapping - https://docs.cohere.com/reference/generate
+ elif finish_reason == "COMPLETE":
+ return "stop"
+ elif finish_reason == "MAX_TOKENS": # cohere + vertex ai
+ return "length"
+ elif finish_reason == "ERROR_TOXIC":
+ return "content_filter"
+ elif (
+ finish_reason == "ERROR"
+ ): # openai currently doesn't support an 'error' finish reason
+ return "stop"
+ # huggingface mapping https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/generate_stream
+ elif finish_reason == "eos_token" or finish_reason == "stop_sequence":
+ return "stop"
+ elif (
+ finish_reason == "FINISH_REASON_UNSPECIFIED" or finish_reason == "STOP"
+ ): # vertex ai - got from running `print(dir(response_obj.candidates[0].finish_reason))`: ['FINISH_REASON_UNSPECIFIED', 'MAX_TOKENS', 'OTHER', 'RECITATION', 'SAFETY', 'STOP',]
+ return "stop"
+ elif finish_reason == "SAFETY" or finish_reason == "RECITATION": # vertex ai
+ return "content_filter"
+ elif finish_reason == "STOP": # vertex ai
+ return "stop"
+ elif finish_reason == "end_turn" or finish_reason == "stop_sequence": # anthropic
+ return "stop"
+ elif finish_reason == "max_tokens": # anthropic
+ return "length"
+ elif finish_reason == "tool_use": # anthropic
+ return "tool_calls"
+ elif finish_reason == "content_filtered":
+ return "content_filter"
+ return finish_reason
+
+
+def remove_index_from_tool_calls(messages, tool_calls):
+ for tool_call in tool_calls:
+ if "index" in tool_call:
+ tool_call.pop("index")
+
+ for message in messages:
+ if "tool_calls" in message:
+ tool_calls = message["tool_calls"]
+ for tool_call in tool_calls:
+ if "index" in tool_call:
+ tool_call.pop("index")
+
+ return
diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py
new file mode 100644
index 0000000000..5ac26c7ae5
--- /dev/null
+++ b/litellm/litellm_core_utils/exception_mapping_utils.py
@@ -0,0 +1,40 @@
+import json
+from typing import Optional
+
+
+def get_error_message(error_obj) -> Optional[str]:
+ """
+ OpenAI Returns Error message that is nested, this extract the message
+
+ Example:
+ {
+ 'request': "",
+ 'message': "Error code: 400 - {\'error\': {\'message\': \"Invalid 'temperature': decimal above maximum value. Expected a value <= 2, but got 200 instead.\", 'type': 'invalid_request_error', 'param': 'temperature', 'code': 'decimal_above_max_value'}}",
+ 'body': {
+ 'message': "Invalid 'temperature': decimal above maximum value. Expected a value <= 2, but got 200 instead.",
+ 'type': 'invalid_request_error',
+ 'param': 'temperature',
+ 'code': 'decimal_above_max_value'
+ },
+ 'code': 'decimal_above_max_value',
+ 'param': 'temperature',
+ 'type': 'invalid_request_error',
+ 'response': "",
+ 'status_code': 400,
+ 'request_id': 'req_f287898caa6364cd42bc01355f74dd2a'
+ }
+ """
+ try:
+ # First, try to access the message directly from the 'body' key
+ if error_obj is None:
+ return None
+
+ if hasattr(error_obj, "body"):
+ _error_obj_body = getattr(error_obj, "body")
+ if isinstance(_error_obj_body, dict):
+ return _error_obj_body.get("message")
+
+ # If all else fails, return None
+ return None
+ except Exception as e:
+ return None
diff --git a/litellm/litellm_core_utils/json_validation_rule.py b/litellm/litellm_core_utils/json_validation_rule.py
new file mode 100644
index 0000000000..0f37e67372
--- /dev/null
+++ b/litellm/litellm_core_utils/json_validation_rule.py
@@ -0,0 +1,28 @@
+import json
+
+
+def validate_schema(schema: dict, response: str):
+ """
+ Validate if the returned json response follows the schema.
+
+ Params:
+ - schema - dict: JSON schema
+ - response - str: Received json response as string.
+ """
+ from jsonschema import ValidationError, validate
+
+ from litellm import JSONSchemaValidationError
+
+ try:
+ response_dict = json.loads(response)
+ except json.JSONDecodeError:
+ raise JSONSchemaValidationError(
+ model="", llm_provider="", raw_response=response, schema=response
+ )
+
+ try:
+ validate(response_dict, schema=schema)
+ except ValidationError:
+ raise JSONSchemaValidationError(
+ model="", llm_provider="", raw_response=response, schema=json.dumps(schema)
+ )
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
new file mode 100644
index 0000000000..32633960f0
--- /dev/null
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -0,0 +1,2061 @@
+# What is this?
+## Common Utility file for Logging handler
+# Logging function -> log the exact model details + what's being sent | Non-Blocking
+import copy
+import datetime
+import json
+import os
+import subprocess
+import sys
+import time
+import traceback
+import uuid
+from typing import Any, Callable, Dict, List, Literal, Optional
+
+import litellm
+from litellm import (
+ json_logs,
+ log_raw_request_response,
+ turn_off_message_logging,
+ verbose_logger,
+)
+from litellm.caching import DualCache, InMemoryCache, S3Cache
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.litellm_core_utils.redact_messages import (
+ redact_message_input_output_from_logging,
+)
+from litellm.types.llms.openai import HttpxBinaryResponseContent
+from litellm.types.router import SPECIAL_MODEL_INFO_PARAMS
+from litellm.types.utils import (
+ CallTypes,
+ EmbeddingResponse,
+ ImageResponse,
+ ModelResponse,
+ TextCompletionResponse,
+ TranscriptionResponse,
+)
+from litellm.utils import (
+ _get_base_model_from_metadata,
+ add_breadcrumb,
+ capture_exception,
+ customLogger,
+ liteDebuggerClient,
+ logfireLogger,
+ lunaryLogger,
+ print_verbose,
+ prometheusLogger,
+ prompt_token_calculator,
+ promptLayerLogger,
+ supabaseClient,
+ weightsBiasesLogger,
+)
+
+from ..integrations.aispend import AISpendLogger
+from ..integrations.athina import AthinaLogger
+from ..integrations.berrispend import BerriSpendLogger
+from ..integrations.clickhouse import ClickhouseLogger
+from ..integrations.custom_logger import CustomLogger
+from ..integrations.datadog import DataDogLogger
+from ..integrations.dynamodb import DyanmoDBLogger
+from ..integrations.galileo import GalileoObserve
+from ..integrations.greenscale import GreenscaleLogger
+from ..integrations.helicone import HeliconeLogger
+from ..integrations.lago import LagoLogger
+from ..integrations.langfuse import LangFuseLogger
+from ..integrations.langsmith import LangsmithLogger
+from ..integrations.litedebugger import LiteDebugger
+from ..integrations.logfire_logger import LogfireLevel, LogfireLogger
+from ..integrations.lunary import LunaryLogger
+from ..integrations.openmeter import OpenMeterLogger
+from ..integrations.prometheus import PrometheusLogger
+from ..integrations.prometheus_services import PrometheusServicesLogger
+from ..integrations.prompt_layer import PromptLayerLogger
+from ..integrations.s3 import S3Logger
+from ..integrations.supabase import Supabase
+from ..integrations.traceloop import TraceloopLogger
+from ..integrations.weights_biases import WeightsBiasesLogger
+
+_in_memory_loggers: List[Any] = []
+
+### GLOBAL VARIABLES ###
+
+sentry_sdk_instance = None
+capture_exception = None
+add_breadcrumb = None
+posthog = None
+slack_app = None
+alerts_channel = None
+heliconeLogger = None
+athinaLogger = None
+promptLayerLogger = None
+logfireLogger = None
+weightsBiasesLogger = None
+customLogger = None
+langFuseLogger = None
+openMeterLogger = None
+lagoLogger = None
+dataDogLogger = None
+prometheusLogger = None
+dynamoLogger = None
+s3Logger = None
+genericAPILogger = None
+clickHouseLogger = None
+greenscaleLogger = None
+lunaryLogger = None
+aispendLogger = None
+berrispendLogger = None
+supabaseClient = None
+liteDebuggerClient = None
+callback_list: Optional[List[str]] = []
+user_logger_fn = None
+additional_details: Optional[Dict[str, str]] = {}
+local_cache: Optional[Dict[str, str]] = {}
+last_fetched_at = None
+last_fetched_at_keys = None
+
+
+####
+class ServiceTraceIDCache:
+ def __init__(self) -> None:
+ self.cache = InMemoryCache()
+
+ def get_cache(self, litellm_call_id: str, service_name: str) -> Optional[str]:
+ key_name = "{}:{}".format(service_name, litellm_call_id)
+ response = self.cache.get_cache(key=key_name)
+ return response
+
+ def set_cache(self, litellm_call_id: str, service_name: str, trace_id: str) -> None:
+ key_name = "{}:{}".format(service_name, litellm_call_id)
+ self.cache.set_cache(key=key_name, value=trace_id)
+ return None
+
+
+in_memory_trace_id_cache = ServiceTraceIDCache()
+
+
+class Logging:
+ global supabaseClient, liteDebuggerClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app
+ custom_pricing: bool = False
+ stream_options = None
+
+ def __init__(
+ self,
+ model,
+ messages,
+ stream,
+ call_type,
+ start_time,
+ litellm_call_id,
+ function_id,
+ dynamic_success_callbacks=None,
+ dynamic_failure_callbacks=None,
+ dynamic_async_success_callbacks=None,
+ langfuse_public_key=None,
+ langfuse_secret=None,
+ langfuse_host=None,
+ ):
+ if messages is not None:
+ if isinstance(messages, str):
+ messages = [
+ {"role": "user", "content": messages}
+ ] # convert text completion input to the chat completion format
+ elif (
+ isinstance(messages, list)
+ and len(messages) > 0
+ and isinstance(messages[0], str)
+ ):
+ new_messages = []
+ for m in messages:
+ new_messages.append({"role": "user", "content": m})
+ messages = new_messages
+ self.model = model
+ self.messages = copy.deepcopy(messages)
+ self.stream = stream
+ self.start_time = start_time # log the call start time
+ self.call_type = call_type
+ self.litellm_call_id = litellm_call_id
+ self.function_id = function_id
+ self.streaming_chunks = [] # for generating complete stream response
+ self.sync_streaming_chunks = [] # for generating complete stream response
+ self.model_call_details = {}
+ self.dynamic_input_callbacks = [] # [TODO] callbacks set for just that call
+ self.dynamic_failure_callbacks = dynamic_failure_callbacks
+ self.dynamic_success_callbacks = (
+ dynamic_success_callbacks # callbacks set for just that call
+ )
+ self.dynamic_async_success_callbacks = (
+ dynamic_async_success_callbacks # callbacks set for just that call
+ )
+ ## DYNAMIC LANGFUSE KEYS ##
+ self.langfuse_public_key = langfuse_public_key
+ self.langfuse_secret = langfuse_secret
+ self.langfuse_host = langfuse_host
+ ## TIME TO FIRST TOKEN LOGGING ##
+ self.completion_start_time: Optional[datetime.datetime] = None
+
+ def update_environment_variables(
+ self, model, user, optional_params, litellm_params, **additional_params
+ ):
+ self.optional_params = optional_params
+ self.model = model
+ self.user = user
+ self.litellm_params = litellm_params
+ self.logger_fn = litellm_params.get("logger_fn", None)
+ verbose_logger.debug(f"self.optional_params: {self.optional_params}")
+
+ self.model_call_details = {
+ "model": self.model,
+ "messages": self.messages,
+ "optional_params": self.optional_params,
+ "litellm_params": self.litellm_params,
+ "start_time": self.start_time,
+ "stream": self.stream,
+ "user": user,
+ "call_type": str(self.call_type),
+ "litellm_call_id": self.litellm_call_id,
+ "completion_start_time": self.completion_start_time,
+ **self.optional_params,
+ **additional_params,
+ }
+
+ ## check if stream options is set ## - used by CustomStreamWrapper for easy instrumentation
+ if "stream_options" in additional_params:
+ self.stream_options = additional_params["stream_options"]
+ ## check if custom pricing set ##
+ if (
+ litellm_params.get("input_cost_per_token") is not None
+ or litellm_params.get("input_cost_per_second") is not None
+ or litellm_params.get("output_cost_per_token") is not None
+ or litellm_params.get("output_cost_per_second") is not None
+ ):
+ self.custom_pricing = True
+
+ def _pre_call(self, input, api_key, model=None, additional_args={}):
+ """
+ Common helper function across the sync + async pre-call function
+ """
+ self.model_call_details["input"] = input
+ self.model_call_details["api_key"] = api_key
+ self.model_call_details["additional_args"] = additional_args
+ self.model_call_details["log_event_type"] = "pre_api_call"
+ if (
+ model
+ ): # if model name was changes pre-call, overwrite the initial model call name with the new one
+ self.model_call_details["model"] = model
+
+ def pre_call(self, input, api_key, model=None, additional_args={}):
+ # Log the exact input to the LLM API
+ litellm.error_logs["PRE_CALL"] = locals()
+ try:
+ self._pre_call(
+ input=input,
+ api_key=api_key,
+ model=model,
+ additional_args=additional_args,
+ )
+
+ # User Logging -> if you pass in a custom logging function
+ headers = additional_args.get("headers", {})
+ if headers is None:
+ headers = {}
+ data = additional_args.get("complete_input_dict", {})
+ api_base = str(additional_args.get("api_base", ""))
+ if "key=" in api_base:
+ # Find the position of "key=" in the string
+ key_index = api_base.find("key=") + 4
+ # Mask the last 5 characters after "key="
+ masked_api_base = (
+ api_base[:key_index] + "*" * 5 + api_base[key_index + 5 :]
+ )
+ else:
+ masked_api_base = api_base
+ self.model_call_details["litellm_params"]["api_base"] = masked_api_base
+ masked_headers = {
+ k: (
+ (v[:-44] + "*" * 44)
+ if (isinstance(v, str) and len(v) > 44)
+ else "*****"
+ )
+ for k, v in headers.items()
+ }
+ formatted_headers = " ".join(
+ [f"-H '{k}: {v}'" for k, v in masked_headers.items()]
+ )
+
+ verbose_logger.debug(f"PRE-API-CALL ADDITIONAL ARGS: {additional_args}")
+
+ curl_command = "\n\nPOST Request Sent from LiteLLM:\n"
+ curl_command += "curl -X POST \\\n"
+ curl_command += f"{api_base} \\\n"
+ curl_command += (
+ f"{formatted_headers} \\\n" if formatted_headers.strip() != "" else ""
+ )
+ curl_command += f"-d '{str(data)}'\n"
+ if additional_args.get("request_str", None) is not None:
+ # print the sagemaker / bedrock client request
+ curl_command = "\nRequest Sent from LiteLLM:\n"
+ curl_command += additional_args.get("request_str", None)
+ elif api_base == "":
+ curl_command = self.model_call_details
+
+ if json_logs:
+ verbose_logger.debug(
+ "POST Request Sent from LiteLLM",
+ extra={"api_base": {api_base}, **masked_headers},
+ )
+ else:
+ print_verbose(f"\033[92m{curl_command}\033[0m\n", log_level="DEBUG")
+ # log raw request to provider (like LangFuse) -- if opted in.
+ if log_raw_request_response is True:
+ try:
+ # [Non-blocking Extra Debug Information in metadata]
+ _litellm_params = self.model_call_details.get("litellm_params", {})
+ _metadata = _litellm_params.get("metadata", {}) or {}
+ if (
+ turn_off_message_logging is not None
+ and turn_off_message_logging is True
+ ):
+ _metadata["raw_request"] = (
+ "redacted by litellm. \
+ 'litellm.turn_off_message_logging=True'"
+ )
+ else:
+ _metadata["raw_request"] = str(curl_command)
+ except Exception as e:
+ _metadata["raw_request"] = (
+ "Unable to Log \
+ raw request: {}".format(
+ str(e)
+ )
+ )
+ if self.logger_fn and callable(self.logger_fn):
+ try:
+ self.logger_fn(
+ self.model_call_details
+ ) # Expectation: any logger function passed in by the user should accept a dict object
+ except Exception as e:
+ verbose_logger.error(
+ "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made
+ callbacks = litellm.input_callback + self.dynamic_input_callbacks
+ for callback in callbacks:
+ try:
+ if callback == "supabase":
+ verbose_logger.debug("reaches supabase for logging!")
+ model = self.model_call_details["model"]
+ messages = self.model_call_details["input"]
+ verbose_logger.debug(f"supabaseClient: {supabaseClient}")
+ supabaseClient.input_log_event(
+ model=model,
+ messages=messages,
+ end_user=self.model_call_details.get("user", "default"),
+ litellm_call_id=self.litellm_params["litellm_call_id"],
+ print_verbose=print_verbose,
+ )
+ elif callback == "sentry" and add_breadcrumb:
+ try:
+ details_to_log = copy.deepcopy(self.model_call_details)
+ except:
+ details_to_log = self.model_call_details
+ if litellm.turn_off_message_logging:
+ # make a copy of the _model_Call_details and log it
+ details_to_log.pop("messages", None)
+ details_to_log.pop("input", None)
+ details_to_log.pop("prompt", None)
+
+ add_breadcrumb(
+ category="litellm.llm_call",
+ message=f"Model Call Details pre-call: {details_to_log}",
+ level="info",
+ )
+ elif isinstance(callback, CustomLogger): # custom logger class
+ callback.log_pre_api_call(
+ model=self.model,
+ messages=self.messages,
+ kwargs=self.model_call_details,
+ )
+ elif callable(callback): # custom logger functions
+ customLogger.log_input_event(
+ model=self.model,
+ messages=self.messages,
+ kwargs=self.model_call_details,
+ print_verbose=print_verbose,
+ callback_func=callback,
+ )
+ except Exception as e:
+ verbose_logger.error(
+ "litellm.Logging.pre_call(): Exception occured - {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ verbose_logger.debug(
+ f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}"
+ )
+ if capture_exception: # log this error to sentry for debugging
+ capture_exception(e)
+ except Exception:
+ verbose_logger.error(
+ "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ verbose_logger.error(
+ f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}"
+ )
+ if capture_exception: # log this error to sentry for debugging
+ capture_exception(e)
+
+ def post_call(
+ self, original_response, input=None, api_key=None, additional_args={}
+ ):
+ # Log the exact result from the LLM API, for streaming - log the type of response received
+ litellm.error_logs["POST_CALL"] = locals()
+ if isinstance(original_response, dict):
+ original_response = json.dumps(original_response)
+ try:
+ self.model_call_details["input"] = input
+ self.model_call_details["api_key"] = api_key
+ self.model_call_details["original_response"] = original_response
+ self.model_call_details["additional_args"] = additional_args
+ self.model_call_details["log_event_type"] = "post_api_call"
+
+ if json_logs:
+ verbose_logger.debug(
+ "RAW RESPONSE:\n{}\n\n".format(
+ self.model_call_details.get(
+ "original_response", self.model_call_details
+ )
+ ),
+ )
+ else:
+ print_verbose(
+ "RAW RESPONSE:\n{}\n\n".format(
+ self.model_call_details.get(
+ "original_response", self.model_call_details
+ )
+ )
+ )
+ if self.logger_fn and callable(self.logger_fn):
+ try:
+ self.logger_fn(
+ self.model_call_details
+ ) # Expectation: any logger function passed in by the user should accept a dict object
+ except Exception as e:
+ verbose_logger.debug(
+ "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ original_response = redact_message_input_output_from_logging(
+ litellm_logging_obj=self, result=original_response
+ )
+ # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made
+
+ callbacks = litellm.input_callback + self.dynamic_input_callbacks
+ for callback in callbacks:
+ try:
+ if callback == "sentry" and add_breadcrumb:
+ verbose_logger.debug("reaches sentry breadcrumbing")
+ try:
+ details_to_log = copy.deepcopy(self.model_call_details)
+ except:
+ details_to_log = self.model_call_details
+ if litellm.turn_off_message_logging:
+ # make a copy of the _model_Call_details and log it
+ details_to_log.pop("messages", None)
+ details_to_log.pop("input", None)
+ details_to_log.pop("prompt", None)
+
+ add_breadcrumb(
+ category="litellm.llm_call",
+ message=f"Model Call Details post-call: {details_to_log}",
+ level="info",
+ )
+ elif isinstance(callback, CustomLogger): # custom logger class
+ callback.log_post_api_call(
+ kwargs=self.model_call_details,
+ response_obj=None,
+ start_time=self.start_time,
+ end_time=None,
+ )
+ except Exception as e:
+ verbose_logger.error(
+ "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while post-call logging with integrations {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ verbose_logger.debug(
+ f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}"
+ )
+ if capture_exception: # log this error to sentry for debugging
+ capture_exception(e)
+ except Exception as e:
+ verbose_logger.error(
+ "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+
+ def _success_handler_helper_fn(
+ self, result=None, start_time=None, end_time=None, cache_hit=None
+ ):
+ try:
+ if start_time is None:
+ start_time = self.start_time
+ if end_time is None:
+ end_time = datetime.datetime.now()
+ if self.completion_start_time is None:
+ self.completion_start_time = end_time
+ self.model_call_details["completion_start_time"] = (
+ self.completion_start_time
+ )
+ self.model_call_details["log_event_type"] = "successful_api_call"
+ self.model_call_details["end_time"] = end_time
+ self.model_call_details["cache_hit"] = cache_hit
+ ## if model in model cost map - log the response cost
+ ## else set cost to None
+ if (
+ result is not None and self.stream is not True
+ ): # handle streaming separately
+ if (
+ isinstance(result, ModelResponse)
+ or isinstance(result, EmbeddingResponse)
+ or isinstance(result, ImageResponse)
+ or isinstance(result, TranscriptionResponse)
+ or isinstance(result, TextCompletionResponse)
+ or isinstance(result, HttpxBinaryResponseContent) # tts
+ ):
+ custom_pricing = use_custom_pricing_for_model(
+ litellm_params=self.litellm_params
+ )
+ self.model_call_details["response_cost"] = (
+ litellm.response_cost_calculator(
+ response_object=result,
+ model=self.model,
+ cache_hit=self.model_call_details.get("cache_hit", False),
+ custom_llm_provider=self.model_call_details.get(
+ "custom_llm_provider", None
+ ),
+ base_model=_get_base_model_from_metadata(
+ model_call_details=self.model_call_details
+ ),
+ call_type=self.call_type,
+ optional_params=self.optional_params,
+ custom_pricing=custom_pricing,
+ )
+ )
+ else: # streaming chunks + image gen.
+ self.model_call_details["response_cost"] = None
+
+ if (
+ litellm.max_budget
+ and self.stream == False
+ and result is not None
+ and "content" in result
+ ):
+ time_diff = (end_time - start_time).total_seconds()
+ float_diff = float(time_diff)
+ litellm._current_cost += litellm.completion_cost(
+ model=self.model,
+ prompt="",
+ completion=result["content"],
+ total_time=float_diff,
+ )
+
+ return start_time, end_time, result
+ except Exception as e:
+ raise Exception(f"[Non-Blocking] LiteLLM.Success_Call Error: {str(e)}")
+
+ def success_handler(
+ self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs
+ ):
+ verbose_logger.debug(
+ f"Logging Details LiteLLM-Success Call: Cache_hit={cache_hit}"
+ )
+ start_time, end_time, result = self._success_handler_helper_fn(
+ start_time=start_time,
+ end_time=end_time,
+ result=result,
+ cache_hit=cache_hit,
+ )
+ # print(f"original response in success handler: {self.model_call_details['original_response']}")
+ try:
+ verbose_logger.debug(f"success callbacks: {litellm.success_callback}")
+ ## BUILD COMPLETE STREAMED RESPONSE
+ complete_streaming_response = None
+ if self.stream and isinstance(result, ModelResponse):
+ if (
+ result.choices[0].finish_reason is not None
+ ): # if it's the last chunk
+ self.sync_streaming_chunks.append(result)
+ # print_verbose(f"final set of received chunks: {self.sync_streaming_chunks}")
+ try:
+ complete_streaming_response = litellm.stream_chunk_builder(
+ self.sync_streaming_chunks,
+ messages=self.model_call_details.get("messages", None),
+ start_time=start_time,
+ end_time=end_time,
+ )
+ except Exception as e:
+ verbose_logger.error(
+ "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while building complete streaming response in success logging {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ complete_streaming_response = None
+ else:
+ self.sync_streaming_chunks.append(result)
+
+ if complete_streaming_response is not None:
+ verbose_logger.debug(
+ "Logging Details LiteLLM-Success Call streaming complete"
+ )
+ self.model_call_details["complete_streaming_response"] = (
+ complete_streaming_response
+ )
+ self.model_call_details["response_cost"] = (
+ litellm.response_cost_calculator(
+ response_object=complete_streaming_response,
+ model=self.model,
+ cache_hit=self.model_call_details.get("cache_hit", False),
+ custom_llm_provider=self.model_call_details.get(
+ "custom_llm_provider", None
+ ),
+ base_model=_get_base_model_from_metadata(
+ model_call_details=self.model_call_details
+ ),
+ call_type=self.call_type,
+ optional_params=(
+ self.optional_params
+ if hasattr(self, "optional_params")
+ else {}
+ ),
+ )
+ )
+ if self.dynamic_success_callbacks is not None and isinstance(
+ self.dynamic_success_callbacks, list
+ ):
+ callbacks = self.dynamic_success_callbacks
+ ## keep the internal functions ##
+ for callback in litellm.success_callback:
+ if (
+ isinstance(callback, CustomLogger)
+ and "_PROXY_" in callback.__class__.__name__
+ ):
+ callbacks.append(callback)
+ else:
+ callbacks = litellm.success_callback
+
+ result = redact_message_input_output_from_logging(
+ result=result, litellm_logging_obj=self
+ )
+
+ ## LOGGING HOOK ##
+
+ for callback in callbacks:
+ if isinstance(callback, CustomLogger):
+ self.model_call_details, result = callback.logging_hook(
+ kwargs=self.model_call_details,
+ result=result,
+ call_type=self.call_type,
+ )
+
+ for callback in callbacks:
+ try:
+ litellm_params = self.model_call_details.get("litellm_params", {})
+ if litellm_params.get("no-log", False) == True:
+ # proxy cost tracking cal backs should run
+ if not (
+ isinstance(callback, CustomLogger)
+ and "_PROXY_" in callback.__class__.__name__
+ ):
+ print_verbose("no-log request, skipping logging")
+ continue
+ if callback == "lite_debugger":
+ print_verbose("reaches lite_debugger for logging!")
+ print_verbose(f"liteDebuggerClient: {liteDebuggerClient}")
+ print_verbose(
+ f"liteDebuggerClient details function {self.call_type} and stream set to {self.stream}"
+ )
+ liteDebuggerClient.log_event(
+ end_user=kwargs.get("user", "default"),
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ litellm_call_id=self.litellm_call_id,
+ print_verbose=print_verbose,
+ call_type=self.call_type,
+ stream=self.stream,
+ )
+ if callback == "promptlayer":
+ print_verbose("reaches promptlayer for logging!")
+ promptLayerLogger.log_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ )
+ if callback == "supabase":
+ print_verbose("reaches supabase for logging!")
+ kwargs = self.model_call_details
+
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if self.stream:
+ if "complete_streaming_response" not in kwargs:
+ continue
+ else:
+ print_verbose("reaches supabase for streaming logging!")
+ result = kwargs["complete_streaming_response"]
+
+ model = kwargs["model"]
+ messages = kwargs["messages"]
+ optional_params = kwargs.get("optional_params", {})
+ litellm_params = kwargs.get("litellm_params", {})
+ supabaseClient.log_event(
+ model=model,
+ messages=messages,
+ end_user=optional_params.get("user", "default"),
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ litellm_call_id=litellm_params.get(
+ "litellm_call_id", str(uuid.uuid4())
+ ),
+ print_verbose=print_verbose,
+ )
+ if callback == "wandb":
+ print_verbose("reaches wandb for logging!")
+ weightsBiasesLogger.log_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ )
+ if callback == "logfire":
+ global logfireLogger
+ verbose_logger.debug("reaches logfire for success logging!")
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if self.stream:
+ if "complete_streaming_response" not in kwargs:
+ continue
+ else:
+ print_verbose("reaches logfire for streaming logging!")
+ result = kwargs["complete_streaming_response"]
+
+ logfireLogger.log_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ level=LogfireLevel.INFO.value,
+ )
+
+ if callback == "lunary":
+ print_verbose("reaches lunary for logging!")
+ model = self.model
+ kwargs = self.model_call_details
+
+ input = kwargs.get("messages", kwargs.get("input", None))
+
+ type = (
+ "embed"
+ if self.call_type == CallTypes.embedding.value
+ else "llm"
+ )
+
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if self.stream:
+ if "complete_streaming_response" not in kwargs:
+ continue
+ else:
+ result = kwargs["complete_streaming_response"]
+
+ lunaryLogger.log_event(
+ type=type,
+ kwargs=kwargs,
+ event="end",
+ model=model,
+ input=input,
+ user_id=kwargs.get("user", None),
+ # user_props=self.model_call_details.get("user_props", None),
+ extra=kwargs.get("optional_params", {}),
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ run_id=self.litellm_call_id,
+ print_verbose=print_verbose,
+ )
+ if callback == "helicone":
+ print_verbose("reaches helicone for logging!")
+ model = self.model
+ messages = self.model_call_details["input"]
+ kwargs = self.model_call_details
+ heliconeLogger.log_success(
+ model=model,
+ messages=messages,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ kwargs=kwargs,
+ )
+ if callback == "langfuse":
+ global langFuseLogger
+ print_verbose("reaches langfuse for success logging!")
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if self.stream:
+ verbose_logger.debug(
+ f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}"
+ )
+ if complete_streaming_response is None:
+ continue
+ else:
+ print_verbose("reaches langfuse for streaming logging!")
+ result = kwargs["complete_streaming_response"]
+ if langFuseLogger is None or (
+ (
+ self.langfuse_public_key is not None
+ and self.langfuse_public_key
+ != langFuseLogger.public_key
+ )
+ or (
+ self.langfuse_public_key is not None
+ and self.langfuse_public_key
+ != langFuseLogger.public_key
+ )
+ or (
+ self.langfuse_host is not None
+ and self.langfuse_host != langFuseLogger.langfuse_host
+ )
+ ):
+ langFuseLogger = LangFuseLogger(
+ langfuse_public_key=self.langfuse_public_key,
+ langfuse_secret=self.langfuse_secret,
+ langfuse_host=self.langfuse_host,
+ )
+ _response = langFuseLogger.log_event(
+ kwargs=kwargs,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ user_id=kwargs.get("user", None),
+ print_verbose=print_verbose,
+ )
+ if _response is not None and isinstance(_response, dict):
+ _trace_id = _response.get("trace_id", None)
+ if _trace_id is not None:
+ in_memory_trace_id_cache.set_cache(
+ litellm_call_id=self.litellm_call_id,
+ service_name="langfuse",
+ trace_id=_trace_id,
+ )
+ if callback == "datadog":
+ global dataDogLogger
+ verbose_logger.debug("reaches datadog for success logging!")
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if self.stream:
+ verbose_logger.debug(
+ f"datadog: is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}"
+ )
+ if complete_streaming_response is None:
+ continue
+ else:
+ print_verbose("reaches datadog for streaming logging!")
+ result = kwargs["complete_streaming_response"]
+ dataDogLogger.log_event(
+ kwargs=kwargs,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ user_id=kwargs.get("user", None),
+ print_verbose=print_verbose,
+ )
+ if callback == "prometheus":
+ verbose_logger.debug("reaches prometheus for success logging!")
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if self.stream:
+ verbose_logger.debug(
+ f"prometheus: is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}"
+ )
+ if complete_streaming_response is None:
+ continue
+ else:
+ print_verbose(
+ "reaches prometheus for streaming logging!"
+ )
+ result = kwargs["complete_streaming_response"]
+ prometheusLogger.log_event(
+ kwargs=kwargs,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ user_id=kwargs.get("user", None),
+ print_verbose=print_verbose,
+ )
+ if callback == "generic":
+ global genericAPILogger
+ verbose_logger.debug("reaches langfuse for success logging!")
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if self.stream:
+ verbose_logger.debug(
+ f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}"
+ )
+ if complete_streaming_response is None:
+ continue
+ else:
+ print_verbose("reaches langfuse for streaming logging!")
+ result = kwargs["complete_streaming_response"]
+ if genericAPILogger is None:
+ genericAPILogger = GenericAPILogger()
+ genericAPILogger.log_event(
+ kwargs=kwargs,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ user_id=kwargs.get("user", None),
+ print_verbose=print_verbose,
+ )
+ if callback == "clickhouse":
+ global clickHouseLogger
+ verbose_logger.debug("reaches clickhouse for success logging!")
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if self.stream:
+ verbose_logger.debug(
+ f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}"
+ )
+ if complete_streaming_response is None:
+ continue
+ else:
+ print_verbose(
+ "reaches clickhouse for streaming logging!"
+ )
+ result = kwargs["complete_streaming_response"]
+ if clickHouseLogger is None:
+ clickHouseLogger = ClickhouseLogger()
+ clickHouseLogger.log_event(
+ kwargs=kwargs,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ user_id=kwargs.get("user", None),
+ print_verbose=print_verbose,
+ )
+ if callback == "greenscale":
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if self.stream:
+ verbose_logger.debug(
+ f"is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}"
+ )
+ if complete_streaming_response is None:
+ continue
+ else:
+ print_verbose(
+ "reaches greenscale for streaming logging!"
+ )
+ result = kwargs["complete_streaming_response"]
+
+ greenscaleLogger.log_event(
+ kwargs=kwargs,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ )
+ if callback == "cache" and litellm.cache is not None:
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ print_verbose("success_callback: reaches cache for logging!")
+ kwargs = self.model_call_details
+ if self.stream:
+ if "complete_streaming_response" not in kwargs:
+ print_verbose(
+ f"success_callback: reaches cache for logging, there is no complete_streaming_response. Kwargs={kwargs}\n\n"
+ )
+ pass
+ else:
+ print_verbose(
+ "success_callback: reaches cache for logging, there is a complete_streaming_response. Adding to cache"
+ )
+ result = kwargs["complete_streaming_response"]
+ # only add to cache once we have a complete streaming response
+ litellm.cache.add_cache(result, **kwargs)
+ if callback == "athina":
+ deep_copy = {}
+ for k, v in self.model_call_details.items():
+ deep_copy[k] = v
+ athinaLogger.log_event(
+ kwargs=deep_copy,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ )
+ if callback == "traceloop":
+ deep_copy = {}
+ for k, v in self.model_call_details.items():
+ if k != "original_response":
+ deep_copy[k] = v
+ traceloopLogger.log_event(
+ kwargs=deep_copy,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ user_id=kwargs.get("user", None),
+ print_verbose=print_verbose,
+ )
+ if callback == "s3":
+ global s3Logger
+ if s3Logger is None:
+ s3Logger = S3Logger()
+ if self.stream:
+ if "complete_streaming_response" in self.model_call_details:
+ print_verbose(
+ "S3Logger Logger: Got Stream Event - Completed Stream Response"
+ )
+ s3Logger.log_event(
+ kwargs=self.model_call_details,
+ response_obj=self.model_call_details[
+ "complete_streaming_response"
+ ],
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ )
+ else:
+ print_verbose(
+ "S3Logger Logger: Got Stream Event - No complete stream response as yet"
+ )
+ else:
+ s3Logger.log_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ )
+ if (
+ callback == "openmeter"
+ and self.model_call_details.get("litellm_params", {}).get(
+ "acompletion", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "aembedding", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "aimage_generation", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "atranscription", False
+ )
+ is not True
+ ):
+ global openMeterLogger
+ if openMeterLogger is None:
+ print_verbose("Instantiates openmeter client")
+ openMeterLogger = OpenMeterLogger()
+ if self.stream and complete_streaming_response is None:
+ openMeterLogger.log_stream_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ )
+ else:
+ if self.stream and complete_streaming_response:
+ self.model_call_details["complete_response"] = (
+ self.model_call_details.get(
+ "complete_streaming_response", {}
+ )
+ )
+ result = self.model_call_details["complete_response"]
+ openMeterLogger.log_success_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ )
+
+ if (
+ isinstance(callback, CustomLogger)
+ and self.model_call_details.get("litellm_params", {}).get(
+ "acompletion", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "aembedding", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "aimage_generation", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "atranscription", False
+ )
+ is not True
+ ): # custom logger class
+ if self.stream and complete_streaming_response is None:
+ callback.log_stream_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ )
+ else:
+ if self.stream and complete_streaming_response:
+ self.model_call_details["complete_response"] = (
+ self.model_call_details.get(
+ "complete_streaming_response", {}
+ )
+ )
+ result = self.model_call_details["complete_response"]
+ callback.log_success_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ )
+ if (
+ callable(callback) == True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "acompletion", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "aembedding", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "aimage_generation", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "atranscription", False
+ )
+ is not True
+ ): # custom logger functions
+ print_verbose(
+ f"success callbacks: Running Custom Callback Function"
+ )
+ customLogger.log_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ callback_func=callback,
+ )
+
+ except Exception as e:
+ print_verbose(
+ f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging with integrations {traceback.format_exc()}"
+ )
+ print_verbose(
+ f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}"
+ )
+ if capture_exception: # log this error to sentry for debugging
+ capture_exception(e)
+ except Exception as e:
+ verbose_logger.error(
+ "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {}\n{}".format(
+ str(e), traceback.format_exc()
+ ),
+ )
+
+ async def async_success_handler(
+ self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs
+ ):
+ """
+ Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions.
+ """
+ print_verbose("Logging Details LiteLLM-Async Success Call")
+ start_time, end_time, result = self._success_handler_helper_fn(
+ start_time=start_time, end_time=end_time, result=result, cache_hit=cache_hit
+ )
+ ## BUILD COMPLETE STREAMED RESPONSE
+ complete_streaming_response = None
+ if self.stream:
+ if result.choices[0].finish_reason is not None: # if it's the last chunk
+ self.streaming_chunks.append(result)
+ # verbose_logger.debug(f"final set of received chunks: {self.streaming_chunks}")
+ try:
+ complete_streaming_response = litellm.stream_chunk_builder(
+ self.streaming_chunks,
+ messages=self.model_call_details.get("messages", None),
+ start_time=start_time,
+ end_time=end_time,
+ )
+ except Exception as e:
+ print_verbose(
+ "Error occurred building stream chunk in success logging: {}\n{}".format(
+ str(e), traceback.format_exc()
+ ),
+ log_level="ERROR",
+ )
+ complete_streaming_response = None
+ else:
+ self.streaming_chunks.append(result)
+ if complete_streaming_response is not None:
+ print_verbose("Async success callbacks: Got a complete streaming response")
+ self.model_call_details["async_complete_streaming_response"] = (
+ complete_streaming_response
+ )
+ try:
+ if self.model_call_details.get("cache_hit", False) is True:
+ self.model_call_details["response_cost"] = 0.0
+ else:
+ # check if base_model set on azure
+ base_model = _get_base_model_from_metadata(
+ model_call_details=self.model_call_details
+ )
+ # base_model defaults to None if not set on model_info
+ self.model_call_details["response_cost"] = litellm.completion_cost(
+ completion_response=complete_streaming_response,
+ model=base_model,
+ )
+ verbose_logger.debug(
+ f"Model={self.model}; cost={self.model_call_details['response_cost']}"
+ )
+ except litellm.NotFoundError as e:
+ verbose_logger.warning(
+ f"Model={self.model} not found in completion cost map. Setting 'response_cost' to None"
+ )
+ self.model_call_details["response_cost"] = None
+
+ if self.dynamic_async_success_callbacks is not None and isinstance(
+ self.dynamic_async_success_callbacks, list
+ ):
+ callbacks = self.dynamic_async_success_callbacks
+ ## keep the internal functions ##
+ for callback in litellm._async_success_callback:
+ callback_name = ""
+ if isinstance(callback, CustomLogger):
+ callback_name = callback.__class__.__name__
+ if callable(callback):
+ callback_name = callback.__name__
+ if "_PROXY_" in callback_name:
+ callbacks.append(callback)
+ else:
+ callbacks = litellm._async_success_callback
+
+ result = redact_message_input_output_from_logging(
+ result=result, litellm_logging_obj=self
+ )
+
+ ## LOGGING HOOK ##
+
+ for callback in callbacks:
+ if isinstance(callback, CustomLogger):
+ self.model_call_details, result = await callback.async_logging_hook(
+ kwargs=self.model_call_details,
+ result=result,
+ call_type=self.call_type,
+ )
+
+ for callback in callbacks:
+ # check if callback can run for this request
+ litellm_params = self.model_call_details.get("litellm_params", {})
+ if litellm_params.get("no-log", False) == True:
+ # proxy cost tracking cal backs should run
+ if not (
+ isinstance(callback, CustomLogger)
+ and "_PROXY_" in callback.__class__.__name__
+ ):
+ print_verbose("no-log request, skipping logging")
+ continue
+ try:
+ if kwargs.get("no-log", False) == True:
+ print_verbose("no-log request, skipping logging")
+ continue
+ if (
+ callback == "cache"
+ and litellm.cache is not None
+ and self.model_call_details.get("litellm_params", {}).get(
+ "acompletion", False
+ )
+ is True
+ ):
+ # set_cache once complete streaming response is built
+ print_verbose("async success_callback: reaches cache for logging!")
+ kwargs = self.model_call_details
+ if self.stream:
+ if "async_complete_streaming_response" not in kwargs:
+ print_verbose(
+ f"async success_callback: reaches cache for logging, there is no async_complete_streaming_response. Kwargs={kwargs}\n\n"
+ )
+ pass
+ else:
+ print_verbose(
+ "async success_callback: reaches cache for logging, there is a async_complete_streaming_response. Adding to cache"
+ )
+ result = kwargs["async_complete_streaming_response"]
+ # only add to cache once we have a complete streaming response
+ if litellm.cache is not None and not isinstance(
+ litellm.cache.cache, S3Cache
+ ):
+ await litellm.cache.async_add_cache(result, **kwargs)
+ else:
+ litellm.cache.add_cache(result, **kwargs)
+ if callback == "openmeter":
+ global openMeterLogger
+ if self.stream == True:
+ if (
+ "async_complete_streaming_response"
+ in self.model_call_details
+ ):
+ await openMeterLogger.async_log_success_event(
+ kwargs=self.model_call_details,
+ response_obj=self.model_call_details[
+ "async_complete_streaming_response"
+ ],
+ start_time=start_time,
+ end_time=end_time,
+ )
+ else:
+ await openMeterLogger.async_log_stream_event( # [TODO]: move this to being an async log stream event function
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ )
+ else:
+ await openMeterLogger.async_log_success_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ )
+ if isinstance(callback, CustomLogger): # custom logger class
+ if self.stream == True:
+ if (
+ "async_complete_streaming_response"
+ in self.model_call_details
+ ):
+ await callback.async_log_success_event(
+ kwargs=self.model_call_details,
+ response_obj=self.model_call_details[
+ "async_complete_streaming_response"
+ ],
+ start_time=start_time,
+ end_time=end_time,
+ )
+ else:
+ await callback.async_log_stream_event( # [TODO]: move this to being an async log stream event function
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ )
+ else:
+ await callback.async_log_success_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ )
+ if callable(callback): # custom logger functions
+ global customLogger
+ if customLogger is None:
+ customLogger = CustomLogger()
+ if self.stream:
+ if (
+ "async_complete_streaming_response"
+ in self.model_call_details
+ ):
+ await customLogger.async_log_event(
+ kwargs=self.model_call_details,
+ response_obj=self.model_call_details[
+ "async_complete_streaming_response"
+ ],
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ callback_func=callback,
+ )
+ else:
+ await customLogger.async_log_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ callback_func=callback,
+ )
+ if callback == "dynamodb":
+ global dynamoLogger
+ if dynamoLogger is None:
+ dynamoLogger = DyanmoDBLogger()
+ if self.stream:
+ if (
+ "async_complete_streaming_response"
+ in self.model_call_details
+ ):
+ print_verbose(
+ "DynamoDB Logger: Got Stream Event - Completed Stream Response"
+ )
+ await dynamoLogger._async_log_event(
+ kwargs=self.model_call_details,
+ response_obj=self.model_call_details[
+ "async_complete_streaming_response"
+ ],
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ )
+ else:
+ print_verbose(
+ "DynamoDB Logger: Got Stream Event - No complete stream response as yet"
+ )
+ else:
+ await dynamoLogger._async_log_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ )
+ except Exception as e:
+ verbose_logger.error(
+ f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}"
+ )
+ pass
+
+ def _failure_handler_helper_fn(
+ self, exception, traceback_exception, start_time=None, end_time=None
+ ):
+ if start_time is None:
+ start_time = self.start_time
+ if end_time is None:
+ end_time = datetime.datetime.now()
+
+ # on some exceptions, model_call_details is not always initialized, this ensures that we still log those exceptions
+ if not hasattr(self, "model_call_details"):
+ self.model_call_details = {}
+
+ self.model_call_details["log_event_type"] = "failed_api_call"
+ self.model_call_details["exception"] = exception
+ self.model_call_details["traceback_exception"] = traceback_exception
+ self.model_call_details["end_time"] = end_time
+ self.model_call_details.setdefault("original_response", None)
+ self.model_call_details["response_cost"] = 0
+ return start_time, end_time
+
+ def failure_handler(
+ self, exception, traceback_exception, start_time=None, end_time=None
+ ):
+ verbose_logger.debug(
+ f"Logging Details LiteLLM-Failure Call: {litellm.failure_callback}"
+ )
+ try:
+ start_time, end_time = self._failure_handler_helper_fn(
+ exception=exception,
+ traceback_exception=traceback_exception,
+ start_time=start_time,
+ end_time=end_time,
+ )
+ callbacks = [] # init this to empty incase it's not created
+
+ if self.dynamic_failure_callbacks is not None and isinstance(
+ self.dynamic_failure_callbacks, list
+ ):
+ callbacks = self.dynamic_failure_callbacks
+ ## keep the internal functions ##
+ for callback in litellm.failure_callback:
+ if (
+ isinstance(callback, CustomLogger)
+ and "_PROXY_" in callback.__class__.__name__
+ ):
+ callbacks.append(callback)
+ else:
+ callbacks = litellm.failure_callback
+
+ result = None # result sent to all loggers, init this to None incase it's not created
+
+ result = redact_message_input_output_from_logging(
+ result=result, litellm_logging_obj=self
+ )
+ for callback in callbacks:
+ try:
+ if callback == "lite_debugger":
+ print_verbose("reaches lite_debugger for logging!")
+ print_verbose(f"liteDebuggerClient: {liteDebuggerClient}")
+ result = {
+ "model": self.model,
+ "created": time.time(),
+ "error": traceback_exception,
+ "usage": {
+ "prompt_tokens": prompt_token_calculator(
+ self.model, messages=self.messages
+ ),
+ "completion_tokens": 0,
+ },
+ }
+ liteDebuggerClient.log_event(
+ model=self.model,
+ messages=self.messages,
+ end_user=self.model_call_details.get("user", "default"),
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ litellm_call_id=self.litellm_call_id,
+ print_verbose=print_verbose,
+ call_type=self.call_type,
+ stream=self.stream,
+ )
+ if callback == "lunary":
+ print_verbose("reaches lunary for logging error!")
+
+ model = self.model
+
+ input = self.model_call_details["input"]
+
+ _type = (
+ "embed"
+ if self.call_type == CallTypes.embedding.value
+ else "llm"
+ )
+
+ lunaryLogger.log_event(
+ type=_type,
+ event="error",
+ user_id=self.model_call_details.get("user", "default"),
+ model=model,
+ input=input,
+ error=traceback_exception,
+ run_id=self.litellm_call_id,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ )
+ if callback == "sentry":
+ print_verbose("sending exception to sentry")
+ if capture_exception:
+ capture_exception(exception)
+ else:
+ print_verbose(
+ f"capture exception not initialized: {capture_exception}"
+ )
+ elif callback == "supabase":
+ print_verbose("reaches supabase for logging!")
+ print_verbose(f"supabaseClient: {supabaseClient}")
+ result = {
+ "model": model,
+ "created": time.time(),
+ "error": traceback_exception,
+ "usage": {
+ "prompt_tokens": prompt_token_calculator(
+ model, messages=self.messages
+ ),
+ "completion_tokens": 0,
+ },
+ }
+ supabaseClient.log_event(
+ model=self.model,
+ messages=self.messages,
+ end_user=self.model_call_details.get("user", "default"),
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ litellm_call_id=self.model_call_details["litellm_call_id"],
+ print_verbose=print_verbose,
+ )
+ if callable(callback): # custom logger functions
+ customLogger.log_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ callback_func=callback,
+ )
+ if (
+ isinstance(callback, CustomLogger)
+ and self.model_call_details.get("litellm_params", {}).get(
+ "acompletion", False
+ )
+ is not True
+ and self.model_call_details.get("litellm_params", {}).get(
+ "aembedding", False
+ )
+ is not True
+ ): # custom logger class
+
+ callback.log_failure_event(
+ start_time=start_time,
+ end_time=end_time,
+ response_obj=result,
+ kwargs=self.model_call_details,
+ )
+ if callback == "langfuse":
+ global langFuseLogger
+ verbose_logger.debug("reaches langfuse for logging failure")
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+ # this only logs streaming once, complete_streaming_response exists i.e when stream ends
+ if langFuseLogger is None or (
+ (
+ self.langfuse_public_key is not None
+ and self.langfuse_public_key
+ != langFuseLogger.public_key
+ )
+ or (
+ self.langfuse_public_key is not None
+ and self.langfuse_public_key
+ != langFuseLogger.public_key
+ )
+ or (
+ self.langfuse_host is not None
+ and self.langfuse_host != langFuseLogger.langfuse_host
+ )
+ ):
+ langFuseLogger = LangFuseLogger(
+ langfuse_public_key=self.langfuse_public_key,
+ langfuse_secret=self.langfuse_secret,
+ langfuse_host=self.langfuse_host,
+ )
+ _response = langFuseLogger.log_event(
+ start_time=start_time,
+ end_time=end_time,
+ response_obj=None,
+ user_id=kwargs.get("user", None),
+ print_verbose=print_verbose,
+ status_message=str(exception),
+ level="ERROR",
+ kwargs=self.model_call_details,
+ )
+ if _response is not None and isinstance(_response, dict):
+ _trace_id = _response.get("trace_id", None)
+ if _trace_id is not None:
+ in_memory_trace_id_cache.set_cache(
+ litellm_call_id=self.litellm_call_id,
+ service_name="langfuse",
+ trace_id=_trace_id,
+ )
+ if callback == "traceloop":
+ traceloopLogger.log_event(
+ start_time=start_time,
+ end_time=end_time,
+ response_obj=None,
+ user_id=kwargs.get("user", None),
+ print_verbose=print_verbose,
+ status_message=str(exception),
+ level="ERROR",
+ kwargs=self.model_call_details,
+ )
+ if callback == "prometheus":
+ global prometheusLogger
+ verbose_logger.debug("reaches prometheus for success logging!")
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+ kwargs["exception"] = str(exception)
+ prometheusLogger.log_event(
+ kwargs=kwargs,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ user_id=kwargs.get("user", None),
+ print_verbose=print_verbose,
+ )
+
+ if callback == "logfire":
+ verbose_logger.debug("reaches logfire for failure logging!")
+ kwargs = {}
+ for k, v in self.model_call_details.items():
+ if (
+ k != "original_response"
+ ): # copy.deepcopy raises errors as this could be a coroutine
+ kwargs[k] = v
+ kwargs["exception"] = exception
+
+ logfireLogger.log_event(
+ kwargs=kwargs,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ level=LogfireLevel.ERROR.value,
+ print_verbose=print_verbose,
+ )
+
+ except Exception as e:
+ print_verbose(
+ f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging with integrations {str(e)}"
+ )
+ print_verbose(
+ f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}"
+ )
+ if capture_exception: # log this error to sentry for debugging
+ capture_exception(e)
+ except Exception as e:
+ verbose_logger.error(
+ "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+
+ async def async_failure_handler(
+ self, exception, traceback_exception, start_time=None, end_time=None
+ ):
+ """
+ Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions.
+ """
+ start_time, end_time = self._failure_handler_helper_fn(
+ exception=exception,
+ traceback_exception=traceback_exception,
+ start_time=start_time,
+ end_time=end_time,
+ )
+ result = None # result sent to all loggers, init this to None incase it's not created
+ for callback in litellm._async_failure_callback:
+ try:
+ if isinstance(callback, CustomLogger): # custom logger class
+ await callback.async_log_failure_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ ) # type: ignore
+ if callable(callback): # custom logger functions
+ await customLogger.async_log_event(
+ kwargs=self.model_call_details,
+ response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ print_verbose=print_verbose,
+ callback_func=callback,
+ )
+ except Exception as e:
+ verbose_logger.error(
+ "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success \
+ logging {}\n{}\nCallback={}".format(
+ str(e), traceback.format_exc(), callback
+ )
+ )
+
+ def _get_trace_id(self, service_name: Literal["langfuse"]) -> Optional[str]:
+ """
+ For the given service (e.g. langfuse), return the trace_id actually logged.
+
+ Used for constructing the url in slack alerting.
+
+ Returns:
+ - str: The logged trace id
+ - None: If trace id not yet emitted.
+ """
+ trace_id: Optional[str] = None
+ if service_name == "langfuse":
+ trace_id = in_memory_trace_id_cache.get_cache(
+ litellm_call_id=self.litellm_call_id, service_name=service_name
+ )
+
+ return trace_id
+
+
+def set_callbacks(callback_list, function_id=None):
+ """
+ Globally sets the callback client
+ """
+ global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger
+
+ try:
+ for callback in callback_list:
+ if callback == "sentry":
+ try:
+ import sentry_sdk
+ except ImportError:
+ print_verbose("Package 'sentry_sdk' is missing. Installing it...")
+ subprocess.check_call(
+ [sys.executable, "-m", "pip", "install", "sentry_sdk"]
+ )
+ import sentry_sdk
+ sentry_sdk_instance = sentry_sdk
+ sentry_trace_rate = (
+ os.environ.get("SENTRY_API_TRACE_RATE")
+ if "SENTRY_API_TRACE_RATE" in os.environ
+ else "1.0"
+ )
+ sentry_sdk_instance.init(
+ dsn=os.environ.get("SENTRY_DSN"),
+ traces_sample_rate=float(sentry_trace_rate),
+ )
+ capture_exception = sentry_sdk_instance.capture_exception
+ add_breadcrumb = sentry_sdk_instance.add_breadcrumb
+ elif callback == "posthog":
+ try:
+ from posthog import Posthog
+ except ImportError:
+ print_verbose("Package 'posthog' is missing. Installing it...")
+ subprocess.check_call(
+ [sys.executable, "-m", "pip", "install", "posthog"]
+ )
+ from posthog import Posthog
+ posthog = Posthog(
+ project_api_key=os.environ.get("POSTHOG_API_KEY"),
+ host=os.environ.get("POSTHOG_API_URL"),
+ )
+ elif callback == "slack":
+ try:
+ from slack_bolt import App
+ except ImportError:
+ print_verbose("Package 'slack_bolt' is missing. Installing it...")
+ subprocess.check_call(
+ [sys.executable, "-m", "pip", "install", "slack_bolt"]
+ )
+ from slack_bolt import App
+ slack_app = App(
+ token=os.environ.get("SLACK_API_TOKEN"),
+ signing_secret=os.environ.get("SLACK_API_SECRET"),
+ )
+ alerts_channel = os.environ["SLACK_API_CHANNEL"]
+ print_verbose(f"Initialized Slack App: {slack_app}")
+ elif callback == "traceloop":
+ traceloopLogger = TraceloopLogger()
+ elif callback == "athina":
+ athinaLogger = AthinaLogger()
+ print_verbose("Initialized Athina Logger")
+ elif callback == "helicone":
+ heliconeLogger = HeliconeLogger()
+ elif callback == "lunary":
+ lunaryLogger = LunaryLogger()
+ elif callback == "promptlayer":
+ promptLayerLogger = PromptLayerLogger()
+ elif callback == "langfuse":
+ langFuseLogger = LangFuseLogger(
+ langfuse_public_key=None, langfuse_secret=None, langfuse_host=None
+ )
+ elif callback == "openmeter":
+ openMeterLogger = OpenMeterLogger()
+ elif callback == "datadog":
+ dataDogLogger = DataDogLogger()
+ elif callback == "prometheus":
+ if prometheusLogger is None:
+ prometheusLogger = PrometheusLogger()
+ elif callback == "dynamodb":
+ dynamoLogger = DyanmoDBLogger()
+ elif callback == "s3":
+ s3Logger = S3Logger()
+ elif callback == "wandb":
+ weightsBiasesLogger = WeightsBiasesLogger()
+ elif callback == "logfire":
+ logfireLogger = LogfireLogger()
+ elif callback == "aispend":
+ aispendLogger = AISpendLogger()
+ elif callback == "berrispend":
+ berrispendLogger = BerriSpendLogger()
+ elif callback == "supabase":
+ print_verbose("instantiating supabase")
+ supabaseClient = Supabase()
+ elif callback == "greenscale":
+ greenscaleLogger = GreenscaleLogger()
+ print_verbose("Initialized Greenscale Logger")
+ elif callback == "lite_debugger":
+ print_verbose("instantiating lite_debugger")
+ if function_id:
+ liteDebuggerClient = LiteDebugger(email=function_id)
+ elif litellm.token:
+ liteDebuggerClient = LiteDebugger(email=litellm.token)
+ elif litellm.email:
+ liteDebuggerClient = LiteDebugger(email=litellm.email)
+ else:
+ liteDebuggerClient = LiteDebugger(email=str(uuid.uuid4()))
+ elif callable(callback):
+ customLogger = CustomLogger()
+ except Exception as e:
+ raise e
+
+
+def _init_custom_logger_compatible_class(
+ logging_integration: litellm._custom_logger_compatible_callbacks_literal,
+ internal_usage_cache: Optional[DualCache],
+ llm_router: Optional[
+ Any
+ ], # expect litellm.Router, but typing errors due to circular import
+) -> CustomLogger:
+ if logging_integration == "lago":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, LagoLogger):
+ return callback # type: ignore
+
+ lago_logger = LagoLogger()
+ _in_memory_loggers.append(lago_logger)
+ return lago_logger # type: ignore
+ elif logging_integration == "openmeter":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, OpenMeterLogger):
+ return callback # type: ignore
+
+ _openmeter_logger = OpenMeterLogger()
+ _in_memory_loggers.append(_openmeter_logger)
+ return _openmeter_logger # type: ignore
+
+ elif logging_integration == "langsmith":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, LangsmithLogger):
+ return callback # type: ignore
+
+ _langsmith_logger = LangsmithLogger()
+ _in_memory_loggers.append(_langsmith_logger)
+ return _langsmith_logger # type: ignore
+
+ elif logging_integration == "galileo":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, GalileoObserve):
+ return callback # type: ignore
+
+ galileo_logger = GalileoObserve()
+ _in_memory_loggers.append(galileo_logger)
+ return galileo_logger # type: ignore
+ elif logging_integration == "logfire":
+ if "LOGFIRE_TOKEN" not in os.environ:
+ raise ValueError("LOGFIRE_TOKEN not found in environment variables")
+ from litellm.integrations.opentelemetry import (
+ OpenTelemetry,
+ OpenTelemetryConfig,
+ )
+
+ otel_config = OpenTelemetryConfig(
+ exporter="otlp_http",
+ endpoint="https://logfire-api.pydantic.dev/v1/traces",
+ headers=f"Authorization={os.getenv('LOGFIRE_TOKEN')}",
+ )
+ for callback in _in_memory_loggers:
+ if isinstance(callback, OpenTelemetry):
+ return callback # type: ignore
+ _otel_logger = OpenTelemetry(config=otel_config)
+ _in_memory_loggers.append(_otel_logger)
+ return _otel_logger # type: ignore
+ elif logging_integration == "dynamic_rate_limiter":
+ from litellm.proxy.hooks.dynamic_rate_limiter import (
+ _PROXY_DynamicRateLimitHandler,
+ )
+
+ for callback in _in_memory_loggers:
+ if isinstance(callback, _PROXY_DynamicRateLimitHandler):
+ return callback # type: ignore
+
+ if internal_usage_cache is None:
+ raise Exception(
+ "Internal Error: Cache cannot be empty - internal_usage_cache={}".format(
+ internal_usage_cache
+ )
+ )
+
+ dynamic_rate_limiter_obj = _PROXY_DynamicRateLimitHandler(
+ internal_usage_cache=internal_usage_cache
+ )
+
+ if llm_router is not None and isinstance(llm_router, litellm.Router):
+ dynamic_rate_limiter_obj.update_variables(llm_router=llm_router)
+ _in_memory_loggers.append(dynamic_rate_limiter_obj)
+ return dynamic_rate_limiter_obj # type: ignore
+
+
+def get_custom_logger_compatible_class(
+ logging_integration: litellm._custom_logger_compatible_callbacks_literal,
+) -> Optional[CustomLogger]:
+ if logging_integration == "lago":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, LagoLogger):
+ return callback
+ elif logging_integration == "openmeter":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, OpenMeterLogger):
+ return callback
+ elif logging_integration == "galileo":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, GalileoObserve):
+ return callback
+ elif logging_integration == "langsmith":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, LangsmithLogger):
+ return callback
+ elif logging_integration == "logfire":
+ if "LOGFIRE_TOKEN" not in os.environ:
+ raise ValueError("LOGFIRE_TOKEN not found in environment variables")
+ from litellm.integrations.opentelemetry import OpenTelemetry
+
+ for callback in _in_memory_loggers:
+ if isinstance(callback, OpenTelemetry):
+ return callback # type: ignore
+
+ elif logging_integration == "dynamic_rate_limiter":
+ from litellm.proxy.hooks.dynamic_rate_limiter import (
+ _PROXY_DynamicRateLimitHandler,
+ )
+
+ for callback in _in_memory_loggers:
+ if isinstance(callback, _PROXY_DynamicRateLimitHandler):
+ return callback # type: ignore
+ return None
+
+
+def use_custom_pricing_for_model(litellm_params: Optional[dict]) -> bool:
+ if litellm_params is None:
+ return False
+ metadata: Optional[dict] = litellm_params.get("metadata", {})
+ if metadata is None:
+ return False
+ model_info: Optional[dict] = metadata.get("model_info", {})
+ if model_info is not None:
+ for k, v in model_info.items():
+ if k in SPECIAL_MODEL_INFO_PARAMS:
+ return True
+ return False
diff --git a/litellm/litellm_core_utils/llm_cost_calc/google.py b/litellm/litellm_core_utils/llm_cost_calc/google.py
new file mode 100644
index 0000000000..76da0da51e
--- /dev/null
+++ b/litellm/litellm_core_utils/llm_cost_calc/google.py
@@ -0,0 +1,236 @@
+# What is this?
+## Cost calculation for Google AI Studio / Vertex AI models
+import traceback
+from typing import List, Literal, Optional, Tuple, Union
+
+import litellm
+from litellm import verbose_logger
+
+"""
+Gemini pricing covers:
+- token
+- image
+- audio
+- video
+"""
+
+"""
+Vertex AI -> character based pricing
+
+Google AI Studio -> token based pricing
+"""
+
+models_without_dynamic_pricing = ["gemini-1.0-pro", "gemini-pro"]
+
+
+def _is_above_128k(tokens: float) -> bool:
+ if tokens > 128000:
+ return True
+ return False
+
+
+def cost_router(
+ model: str,
+ custom_llm_provider: str,
+ prompt_tokens: float,
+ completion_tokens: float,
+ prompt_characters: float,
+ completion_characters: float,
+ call_type: Union[Literal["embedding", "aembedding"], str],
+) -> Literal["cost_per_character", "cost_per_token"]:
+ """
+ Route the cost calc to the right place, based on model/call_type/etc.
+
+ Returns
+ - str, the specific google cost calc function it should route to.
+ """
+ if custom_llm_provider == "vertex_ai" and "claude" in model:
+ return "cost_per_token"
+ elif custom_llm_provider == "gemini":
+ return "cost_per_token"
+ elif custom_llm_provider == "vertex_ai" and (
+ call_type == "embedding" or call_type == "aembedding"
+ ):
+ return "cost_per_token"
+ return "cost_per_character"
+
+
+def cost_per_character(
+ model: str,
+ custom_llm_provider: str,
+ prompt_tokens: float,
+ completion_tokens: float,
+ prompt_characters: float,
+ completion_characters: float,
+) -> Tuple[float, float]:
+ """
+ Calculates the cost per character for a given VertexAI model, input messages, and response object.
+
+ Input:
+ - model: str, the model name without provider prefix
+ - custom_llm_provider: str, "vertex_ai-*"
+ - prompt_characters: float, the number of input characters
+ - completion_characters: float, the number of output characters
+
+ Returns:
+ Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
+
+ Raises:
+ Exception if model requires >128k pricing, but model cost not mapped
+ """
+ model_info = litellm.get_model_info(
+ model=model, custom_llm_provider=custom_llm_provider
+ )
+
+ ## GET MODEL INFO
+ model_info = litellm.get_model_info(
+ model=model, custom_llm_provider=custom_llm_provider
+ )
+
+ ## CALCULATE INPUT COST
+ try:
+ if (
+ _is_above_128k(tokens=prompt_characters * 4) # 1 token = 4 char
+ and model not in models_without_dynamic_pricing
+ ):
+ ## check if character pricing, else default to token pricing
+ assert (
+ "input_cost_per_character_above_128k_tokens" in model_info
+ and model_info["input_cost_per_character_above_128k_tokens"] is not None
+ ), "model info for model={} does not have 'input_cost_per_character_above_128k_tokens'-pricing for > 128k tokens\nmodel_info={}".format(
+ model, model_info
+ )
+ prompt_cost = (
+ prompt_characters
+ * model_info["input_cost_per_character_above_128k_tokens"]
+ )
+ else:
+ assert (
+ "input_cost_per_character" in model_info
+ and model_info["input_cost_per_character"] is not None
+ ), "model info for model={} does not have 'input_cost_per_character'-pricing\nmodel_info={}".format(
+ model, model_info
+ )
+ prompt_cost = prompt_characters * model_info["input_cost_per_character"]
+ except Exception as e:
+ verbose_logger.error(
+ "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Exception occured - {}\n{}\n\
+ Defaulting to (cost_per_token * 4) calculation for prompt_cost".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ initial_prompt_cost, _ = cost_per_token(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ )
+
+ prompt_cost = initial_prompt_cost * 4
+
+ ## CALCULATE OUTPUT COST
+ try:
+ if (
+ _is_above_128k(tokens=completion_characters * 4) # 1 token = 4 char
+ and model not in models_without_dynamic_pricing
+ ):
+ assert (
+ "output_cost_per_character_above_128k_tokens" in model_info
+ and model_info["output_cost_per_character_above_128k_tokens"]
+ is not None
+ ), "model info for model={} does not have 'output_cost_per_character_above_128k_tokens' pricing\nmodel_info={}".format(
+ model, model_info
+ )
+ completion_cost = (
+ completion_tokens
+ * model_info["output_cost_per_character_above_128k_tokens"]
+ )
+ else:
+ assert (
+ "output_cost_per_character" in model_info
+ and model_info["output_cost_per_character"] is not None
+ ), "model info for model={} does not have 'output_cost_per_character'-pricing\nmodel_info={}".format(
+ model, model_info
+ )
+ completion_cost = (
+ completion_tokens * model_info["output_cost_per_character"]
+ )
+ except Exception as e:
+ verbose_logger.error(
+ "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Exception occured - {}\n{}\n\
+ Defaulting to (cost_per_token * 4) calculation for completion_cost".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ _, initial_completion_cost = cost_per_token(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ )
+
+ completion_cost = initial_completion_cost * 4
+ return prompt_cost, completion_cost
+
+
+def cost_per_token(
+ model: str,
+ custom_llm_provider: str,
+ prompt_tokens: float,
+ completion_tokens: float,
+) -> Tuple[float, float]:
+ """
+ Calculates the cost per token for a given model, prompt tokens, and completion tokens.
+
+ Input:
+ - model: str, the model name without provider prefix
+ - custom_llm_provider: str, either "vertex_ai-*" or "gemini"
+ - prompt_tokens: float, the number of input tokens
+ - completion_tokens: float, the number of output tokens
+
+ Returns:
+ Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
+
+ Raises:
+ Exception if model requires >128k pricing, but model cost not mapped
+ """
+ ## GET MODEL INFO
+ model_info = litellm.get_model_info(
+ model=model, custom_llm_provider=custom_llm_provider
+ )
+
+ ## CALCULATE INPUT COST
+ if (
+ _is_above_128k(tokens=prompt_tokens)
+ and model not in models_without_dynamic_pricing
+ ):
+ assert (
+ "input_cost_per_token_above_128k_tokens" in model_info
+ and model_info["input_cost_per_token_above_128k_tokens"] is not None
+ ), "model info for model={} does not have pricing for > 128k tokens\nmodel_info={}".format(
+ model, model_info
+ )
+ prompt_cost = (
+ prompt_tokens * model_info["input_cost_per_token_above_128k_tokens"]
+ )
+ else:
+ prompt_cost = prompt_tokens * model_info["input_cost_per_token"]
+
+ ## CALCULATE OUTPUT COST
+ if (
+ _is_above_128k(tokens=completion_tokens)
+ and model not in models_without_dynamic_pricing
+ ):
+ assert (
+ "output_cost_per_token_above_128k_tokens" in model_info
+ and model_info["output_cost_per_token_above_128k_tokens"] is not None
+ ), "model info for model={} does not have pricing for > 128k tokens\nmodel_info={}".format(
+ model, model_info
+ )
+ completion_cost = (
+ completion_tokens * model_info["output_cost_per_token_above_128k_tokens"]
+ )
+ else:
+ completion_cost = completion_tokens * model_info["output_cost_per_token"]
+
+ return prompt_cost, completion_cost
diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
new file mode 100644
index 0000000000..e986a22a6c
--- /dev/null
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -0,0 +1,85 @@
+# What is this?
+## Helper utilities for cost_per_token()
+
+import traceback
+from typing import List, Literal, Optional, Tuple
+
+import litellm
+from litellm import verbose_logger
+
+
+def _generic_cost_per_character(
+ model: str,
+ custom_llm_provider: str,
+ prompt_characters: float,
+ completion_characters: float,
+ custom_prompt_cost: Optional[float],
+ custom_completion_cost: Optional[float],
+) -> Tuple[Optional[float], Optional[float]]:
+ """
+ Generic function to help calculate cost per character.
+ """
+ """
+ Calculates the cost per character for a given model, input messages, and response object.
+
+ Input:
+ - model: str, the model name without provider prefix
+ - custom_llm_provider: str, "vertex_ai-*"
+ - prompt_characters: float, the number of input characters
+ - completion_characters: float, the number of output characters
+
+ Returns:
+ Tuple[Optional[float], Optional[float]] - prompt_cost_in_usd, completion_cost_in_usd.
+ - returns None if not able to calculate cost.
+
+ Raises:
+ Exception if 'input_cost_per_character' or 'output_cost_per_character' is missing from model_info
+ """
+ args = locals()
+ ## GET MODEL INFO
+ model_info = litellm.get_model_info(
+ model=model, custom_llm_provider=custom_llm_provider
+ )
+
+ ## CALCULATE INPUT COST
+ try:
+ if custom_prompt_cost is None:
+ assert (
+ "input_cost_per_character" in model_info
+ and model_info["input_cost_per_character"] is not None
+ ), "model info for model={} does not have 'input_cost_per_character'-pricing\nmodel_info={}".format(
+ model, model_info
+ )
+ custom_prompt_cost = model_info["input_cost_per_character"]
+
+ prompt_cost = prompt_characters * custom_prompt_cost
+ except Exception as e:
+ verbose_logger.error(
+ "litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format(
+ str(e), traceback.format_exc()
+ )
+ )
+
+ prompt_cost = None
+
+ ## CALCULATE OUTPUT COST
+ try:
+ if custom_completion_cost is None:
+ assert (
+ "output_cost_per_character" in model_info
+ and model_info["output_cost_per_character"] is not None
+ ), "model info for model={} does not have 'output_cost_per_character'-pricing\nmodel_info={}".format(
+ model, model_info
+ )
+ custom_completion_cost = model_info["output_cost_per_character"]
+ completion_cost = completion_characters * custom_completion_cost
+ except Exception as e:
+ verbose_logger.error(
+ "litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format(
+ str(e), traceback.format_exc()
+ )
+ )
+
+ completion_cost = None
+
+ return prompt_cost, completion_cost
diff --git a/litellm/litellm_core_utils/llm_request_utils.py b/litellm/litellm_core_utils/llm_request_utils.py
new file mode 100644
index 0000000000..557d73b0ab
--- /dev/null
+++ b/litellm/litellm_core_utils/llm_request_utils.py
@@ -0,0 +1,28 @@
+from typing import Dict, Optional
+
+
+def _ensure_extra_body_is_safe(extra_body: Optional[Dict]) -> Optional[Dict]:
+ """
+ Ensure that the extra_body sent in the request is safe, otherwise users will see this error
+
+ "Object of type TextPromptClient is not JSON serializable
+
+
+ Relevant Issue: https://github.com/BerriAI/litellm/issues/4140
+ """
+ if extra_body is None:
+ return None
+
+ if not isinstance(extra_body, dict):
+ return extra_body
+
+ if "metadata" in extra_body and isinstance(extra_body["metadata"], dict):
+ if "prompt" in extra_body["metadata"]:
+ _prompt = extra_body["metadata"].get("prompt")
+
+ # users can send Langfuse TextPromptClient objects, so we need to convert them to dicts
+ # Langfuse TextPromptClients have .__dict__ attribute
+ if _prompt is not None and hasattr(_prompt, "__dict__"):
+ extra_body["metadata"]["prompt"] = _prompt.__dict__
+
+ return extra_body
diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py
new file mode 100644
index 0000000000..378c46ba0b
--- /dev/null
+++ b/litellm/litellm_core_utils/redact_messages.py
@@ -0,0 +1,89 @@
+# +-----------------------------------------------+
+# | |
+# | Give Feedback / Get Help |
+# | https://github.com/BerriAI/litellm/issues/new |
+# | |
+# +-----------------------------------------------+
+#
+# Thank you users! We ❤️ you! - Krrish & Ishaan
+
+import copy
+from typing import TYPE_CHECKING, Any
+
+import litellm
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import (
+ Logging as _LiteLLMLoggingObject,
+ )
+
+ LiteLLMLoggingObject = _LiteLLMLoggingObject
+else:
+ LiteLLMLoggingObject = Any
+
+
+def redact_message_input_output_from_logging(
+ litellm_logging_obj: LiteLLMLoggingObject, result
+):
+ """
+ Removes messages, prompts, input, response from logging. This modifies the data in-place
+ only redacts when litellm.turn_off_message_logging == True
+ """
+ _request_headers = (
+ litellm_logging_obj.model_call_details.get("litellm_params", {}).get(
+ "metadata", {}
+ )
+ or {}
+ )
+
+ request_headers = _request_headers.get("headers", {})
+
+ # check if user opted out of logging message/response to callbacks
+ if (
+ litellm.turn_off_message_logging is not True
+ and request_headers.get("litellm-enable-message-redaction", False) is not True
+ ):
+ return result
+
+ if request_headers and request_headers.get(
+ "litellm-disable-message-redaction", False
+ ):
+ return result
+
+ # remove messages, prompts, input, response from logging
+ litellm_logging_obj.model_call_details["messages"] = [
+ {"role": "user", "content": "redacted-by-litellm"}
+ ]
+ litellm_logging_obj.model_call_details["prompt"] = ""
+ litellm_logging_obj.model_call_details["input"] = ""
+
+ # response cleaning
+ # ChatCompletion Responses
+ if (
+ litellm_logging_obj.stream is True
+ and "complete_streaming_response" in litellm_logging_obj.model_call_details
+ ):
+ _streaming_response = litellm_logging_obj.model_call_details[
+ "complete_streaming_response"
+ ]
+ for choice in _streaming_response.choices:
+ if isinstance(choice, litellm.Choices):
+ choice.message.content = "redacted-by-litellm"
+ elif isinstance(choice, litellm.utils.StreamingChoices):
+ choice.delta.content = "redacted-by-litellm"
+ else:
+ if result is not None:
+ if isinstance(result, litellm.ModelResponse):
+ # only deep copy litellm.ModelResponse
+ _result = copy.deepcopy(result)
+ if hasattr(_result, "choices") and _result.choices is not None:
+ for choice in _result.choices:
+ if isinstance(choice, litellm.Choices):
+ choice.message.content = "redacted-by-litellm"
+ elif isinstance(choice, litellm.utils.StreamingChoices):
+ choice.delta.content = "redacted-by-litellm"
+
+ return _result
+
+ # by default return result
+ return result
diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py
new file mode 100644
index 0000000000..ebc0765c05
--- /dev/null
+++ b/litellm/litellm_core_utils/token_counter.py
@@ -0,0 +1,83 @@
+# What is this?
+## Helper utilities for token counting
+from typing import Optional
+
+import litellm
+from litellm import verbose_logger
+
+
+def get_modified_max_tokens(
+ model: str,
+ base_model: str,
+ messages: Optional[list],
+ user_max_tokens: Optional[int],
+ buffer_perc: Optional[float],
+ buffer_num: Optional[float],
+) -> Optional[int]:
+ """
+ Params:
+
+ Returns the user's max output tokens, adjusted for:
+ - the size of input - for models where input + output can't exceed X
+ - model max output tokens - for models where there is a separate output token limit
+ """
+ try:
+ if user_max_tokens is None:
+ return None
+
+ ## MODEL INFO
+ _model_info = litellm.get_model_info(model=model)
+
+ max_output_tokens = litellm.get_max_tokens(
+ model=base_model
+ ) # assume min context window is 4k tokens
+
+ ## UNKNOWN MAX OUTPUT TOKENS - return user defined amount
+ if max_output_tokens is None:
+ return user_max_tokens
+
+ input_tokens = litellm.token_counter(model=base_model, messages=messages)
+
+ # token buffer
+ if buffer_perc is None:
+ buffer_perc = 0.1
+ if buffer_num is None:
+ buffer_num = 10
+ token_buffer = max(
+ buffer_perc * input_tokens, buffer_num
+ ) # give at least a 10 token buffer. token counting can be imprecise.
+
+ input_tokens += int(token_buffer)
+ verbose_logger.debug(
+ f"max_output_tokens: {max_output_tokens}, user_max_tokens: {user_max_tokens}"
+ )
+ ## CASE 1: model input + output can't exceed X - happens when max input = max output, e.g. gpt-3.5-turbo
+ if _model_info["max_input_tokens"] == max_output_tokens:
+ verbose_logger.debug(
+ f"input_tokens: {input_tokens}, max_output_tokens: {max_output_tokens}"
+ )
+ if input_tokens > max_output_tokens:
+ pass # allow call to fail normally - don't set max_tokens to negative.
+ elif (
+ user_max_tokens + input_tokens > max_output_tokens
+ ): # we can still modify to keep it positive but below the limit
+ verbose_logger.debug(
+ f"MODIFYING MAX TOKENS - user_max_tokens={user_max_tokens}, input_tokens={input_tokens}, max_output_tokens={max_output_tokens}"
+ )
+ user_max_tokens = int(max_output_tokens - input_tokens)
+ ## CASE 2: user_max_tokens> model max output tokens
+ elif user_max_tokens > max_output_tokens:
+ user_max_tokens = max_output_tokens
+
+ verbose_logger.debug(
+ f"litellm.litellm_core_utils.token_counter.py::get_modified_max_tokens() - user_max_tokens: {user_max_tokens}"
+ )
+
+ return user_max_tokens
+ except Exception as e:
+ verbose_logger.error(
+ "litellm.litellm_core_utils.token_counter.py::get_modified_max_tokens() - Error while checking max token limit: {}\nmodel={}, base_model={}".format(
+ str(e), model, base_model
+ )
+ )
+ return user_max_tokens
diff --git a/litellm/llms/ai21.py b/litellm/llms/ai21.py
index a39a83f157..e65a810993 100644
--- a/litellm/llms/ai21.py
+++ b/litellm/llms/ai21.py
@@ -1,11 +1,16 @@
-import os, types, traceback
import json
+import os
+import time # type: ignore
+import traceback
+import types
from enum import Enum
-import requests # type: ignore
-import time, httpx # type: ignore
from typing import Callable, Optional
-from litellm.utils import ModelResponse, Choices, Message
+
+import httpx
+import requests # type: ignore
+
import litellm
+from litellm.utils import Choices, Message, ModelResponse
class AI21Error(Exception):
@@ -185,7 +190,7 @@ def completion(
message=message_obj,
)
choices_list.append(choice_obj)
- model_response["choices"] = choices_list
+ model_response.choices = choices_list # type: ignore
except Exception as e:
raise AI21Error(
message=traceback.format_exc(), status_code=response.status_code
@@ -197,13 +202,17 @@ def completion(
encoding.encode(model_response["choices"][0]["message"].get("content"))
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
- model_response["usage"] = {
- "prompt_tokens": prompt_tokens,
- "completion_tokens": completion_tokens,
- "total_tokens": prompt_tokens + completion_tokens,
- }
+ model_response.created = int(time.time())
+ model_response.model = model
+ setattr(
+ model_response,
+ "usage",
+ litellm.Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ ),
+ )
return model_response
diff --git a/litellm/llms/aleph_alpha.py b/litellm/llms/aleph_alpha.py
index 7edd11964b..163e96fde0 100644
--- a/litellm/llms/aleph_alpha.py
+++ b/litellm/llms/aleph_alpha.py
@@ -1,12 +1,15 @@
-import os, types
import json
-from enum import Enum
-import requests # type: ignore
+import os
import time
+import types
+from enum import Enum
from typing import Callable, Optional
-import litellm
-from litellm.utils import ModelResponse, Choices, Message, Usage
+
import httpx # type: ignore
+import requests # type: ignore
+
+import litellm
+from litellm.utils import Choices, Message, ModelResponse, Usage
class AlephAlphaError(Exception):
@@ -275,7 +278,7 @@ def completion(
message=message_obj,
)
choices_list.append(choice_obj)
- model_response["choices"] = choices_list
+ model_response.choices = choices_list # type: ignore
except:
raise AlephAlphaError(
message=json.dumps(completion_response),
@@ -291,8 +294,8 @@ def completion(
)
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/anthropic.py b/litellm/llms/anthropic.py
index 1ca048523c..da51e887d7 100644
--- a/litellm/llms/anthropic.py
+++ b/litellm/llms/anthropic.py
@@ -1,16 +1,67 @@
-import os, types
+import copy
import json
-from enum import Enum
-import requests, copy # type: ignore
+import os
import time
-from typing import Callable, Optional, List, Union
-from litellm.utils import ModelResponse, Usage, map_finish_reason, CustomStreamWrapper
-import litellm
-from .prompt_templates.factory import prompt_factory, custom_prompt
-from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
-from .base import BaseLLM
+import types
+from enum import Enum
+from functools import partial
+from typing import Callable, List, Optional, Union
+
import httpx # type: ignore
-from litellm.types.llms.anthropic import AnthropicMessagesToolChoice
+import requests # type: ignore
+
+import litellm
+import litellm.litellm_core_utils
+from litellm import verbose_logger
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.llms.custom_httpx.http_handler import (
+ AsyncHTTPHandler,
+ HTTPHandler,
+ _get_async_httpx_client,
+ _get_httpx_client,
+)
+from litellm.types.llms.anthropic import (
+ AnthopicMessagesAssistantMessageParam,
+ AnthropicFinishReason,
+ AnthropicMessagesRequest,
+ AnthropicMessagesTool,
+ AnthropicMessagesToolChoice,
+ AnthropicMessagesUserMessageParam,
+ AnthropicResponse,
+ AnthropicResponseContentBlockText,
+ AnthropicResponseContentBlockToolUse,
+ AnthropicResponseUsageBlock,
+ ContentBlockDelta,
+ ContentBlockStart,
+ MessageBlockDelta,
+ MessageStartBlock,
+)
+from litellm.types.llms.openai import (
+ AllMessageValues,
+ ChatCompletionAssistantMessage,
+ ChatCompletionAssistantToolCall,
+ ChatCompletionImageObject,
+ ChatCompletionImageUrlObject,
+ ChatCompletionRequest,
+ ChatCompletionResponseMessage,
+ ChatCompletionSystemMessage,
+ ChatCompletionTextObject,
+ ChatCompletionToolCallChunk,
+ ChatCompletionToolCallFunctionChunk,
+ ChatCompletionToolChoiceFunctionParam,
+ ChatCompletionToolChoiceObjectParam,
+ ChatCompletionToolChoiceValues,
+ ChatCompletionToolMessage,
+ ChatCompletionToolParam,
+ ChatCompletionToolParamFunctionChunk,
+ ChatCompletionUsageBlock,
+ ChatCompletionUserMessage,
+)
+from litellm.types.utils import Choices, GenericStreamingChunk
+from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
+
+from .base import BaseLLM
+from .prompt_templates.factory import custom_prompt, prompt_factory
class AnthropicConstants(Enum):
@@ -23,7 +74,7 @@ class AnthropicConstants(Enum):
class AnthropicError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
- self.message = message
+ self.message: str = message
self.request = httpx.Request(
method="POST", url="https://api.anthropic.com/v1/messages"
)
@@ -142,12 +193,295 @@ class AnthropicConfig:
optional_params["top_p"] = value
return optional_params
+ ### FOR [BETA] `/v1/messages` endpoint support
+
+ def translatable_anthropic_params(self) -> List:
+ """
+ Which anthropic params, we need to translate to the openai format.
+ """
+ return ["messages", "metadata", "system", "tool_choice", "tools"]
+
+ def translate_anthropic_messages_to_openai(
+ self,
+ messages: List[
+ Union[
+ AnthropicMessagesUserMessageParam,
+ AnthopicMessagesAssistantMessageParam,
+ ]
+ ],
+ ) -> List:
+ new_messages: List[AllMessageValues] = []
+ for m in messages:
+ user_message: Optional[ChatCompletionUserMessage] = None
+ tool_message_list: List[ChatCompletionToolMessage] = []
+ ## USER MESSAGE ##
+ if m["role"] == "user":
+ ## translate user message
+ if isinstance(m["content"], str):
+ user_message = ChatCompletionUserMessage(
+ role="user", content=m["content"]
+ )
+ elif isinstance(m["content"], list):
+ new_user_content_list: List[
+ Union[ChatCompletionTextObject, ChatCompletionImageObject]
+ ] = []
+ for content in m["content"]:
+ if content["type"] == "text":
+ text_obj = ChatCompletionTextObject(
+ type="text", text=content["text"]
+ )
+ new_user_content_list.append(text_obj)
+ elif content["type"] == "image":
+ image_url = ChatCompletionImageUrlObject(
+ url=f"data:{content['type']};base64,{content['source']}"
+ )
+ image_obj = ChatCompletionImageObject(
+ type="image_url", image_url=image_url
+ )
+
+ new_user_content_list.append(image_obj)
+ elif content["type"] == "tool_result":
+ if "content" not in content:
+ tool_result = ChatCompletionToolMessage(
+ role="tool",
+ tool_call_id=content["tool_use_id"],
+ content="",
+ )
+ tool_message_list.append(tool_result)
+ elif isinstance(content["content"], str):
+ tool_result = ChatCompletionToolMessage(
+ role="tool",
+ tool_call_id=content["tool_use_id"],
+ content=content["content"],
+ )
+ tool_message_list.append(tool_result)
+ elif isinstance(content["content"], list):
+ for c in content["content"]:
+ if c["type"] == "text":
+ tool_result = ChatCompletionToolMessage(
+ role="tool",
+ tool_call_id=content["tool_use_id"],
+ content=c["text"],
+ )
+ tool_message_list.append(tool_result)
+ elif c["type"] == "image":
+ image_str = (
+ f"data:{c['type']};base64,{c['source']}"
+ )
+ tool_result = ChatCompletionToolMessage(
+ role="tool",
+ tool_call_id=content["tool_use_id"],
+ content=image_str,
+ )
+ tool_message_list.append(tool_result)
+
+ if user_message is not None:
+ new_messages.append(user_message)
+
+ if len(tool_message_list) > 0:
+ new_messages.extend(tool_message_list)
+
+ ## ASSISTANT MESSAGE ##
+ assistant_message_str: Optional[str] = None
+ tool_calls: List[ChatCompletionAssistantToolCall] = []
+ if m["role"] == "assistant":
+ if isinstance(m["content"], str):
+ assistant_message_str = m["content"]
+ elif isinstance(m["content"], list):
+ for content in m["content"]:
+ if content["type"] == "text":
+ if assistant_message_str is None:
+ assistant_message_str = content["text"]
+ else:
+ assistant_message_str += content["text"]
+ elif content["type"] == "tool_use":
+ function_chunk = ChatCompletionToolCallFunctionChunk(
+ name=content["name"],
+ arguments=json.dumps(content["input"]),
+ )
+
+ tool_calls.append(
+ ChatCompletionAssistantToolCall(
+ id=content["id"],
+ type="function",
+ function=function_chunk,
+ )
+ )
+
+ if assistant_message_str is not None or len(tool_calls) > 0:
+ assistant_message = ChatCompletionAssistantMessage(
+ role="assistant",
+ content=assistant_message_str,
+ )
+ if len(tool_calls) > 0:
+ assistant_message["tool_calls"] = tool_calls
+ new_messages.append(assistant_message)
+
+ return new_messages
+
+ def translate_anthropic_tool_choice_to_openai(
+ self, tool_choice: AnthropicMessagesToolChoice
+ ) -> ChatCompletionToolChoiceValues:
+ if tool_choice["type"] == "any":
+ return "required"
+ elif tool_choice["type"] == "auto":
+ return "auto"
+ elif tool_choice["type"] == "tool":
+ tc_function_param = ChatCompletionToolChoiceFunctionParam(
+ name=tool_choice.get("name", "")
+ )
+ return ChatCompletionToolChoiceObjectParam(
+ type="function", function=tc_function_param
+ )
+ else:
+ raise ValueError(
+ "Incompatible tool choice param submitted - {}".format(tool_choice)
+ )
+
+ def translate_anthropic_tools_to_openai(
+ self, tools: List[AnthropicMessagesTool]
+ ) -> List[ChatCompletionToolParam]:
+ new_tools: List[ChatCompletionToolParam] = []
+ for tool in tools:
+ function_chunk = ChatCompletionToolParamFunctionChunk(
+ name=tool["name"],
+ parameters=tool["input_schema"],
+ )
+ if "description" in tool:
+ function_chunk["description"] = tool["description"]
+ new_tools.append(
+ ChatCompletionToolParam(type="function", function=function_chunk)
+ )
+
+ return new_tools
+
+ def translate_anthropic_to_openai(
+ self, anthropic_message_request: AnthropicMessagesRequest
+ ) -> ChatCompletionRequest:
+ """
+ This is used by the beta Anthropic Adapter, for translating anthropic `/v1/messages` requests to the openai format.
+ """
+ new_messages: List[AllMessageValues] = []
+
+ ## CONVERT ANTHROPIC MESSAGES TO OPENAI
+ new_messages = self.translate_anthropic_messages_to_openai(
+ messages=anthropic_message_request["messages"]
+ )
+ ## ADD SYSTEM MESSAGE TO MESSAGES
+ if "system" in anthropic_message_request:
+ new_messages.insert(
+ 0,
+ ChatCompletionSystemMessage(
+ role="system", content=anthropic_message_request["system"]
+ ),
+ )
+
+ new_kwargs: ChatCompletionRequest = {
+ "model": anthropic_message_request["model"],
+ "messages": new_messages,
+ }
+ ## CONVERT METADATA (user_id)
+ if "metadata" in anthropic_message_request:
+ if "user_id" in anthropic_message_request["metadata"]:
+ new_kwargs["user"] = anthropic_message_request["metadata"]["user_id"]
+
+ ## CONVERT TOOL CHOICE
+ if "tool_choice" in anthropic_message_request:
+ new_kwargs["tool_choice"] = self.translate_anthropic_tool_choice_to_openai(
+ tool_choice=anthropic_message_request["tool_choice"]
+ )
+ ## CONVERT TOOLS
+ if "tools" in anthropic_message_request:
+ new_kwargs["tools"] = self.translate_anthropic_tools_to_openai(
+ tools=anthropic_message_request["tools"]
+ )
+
+ translatable_params = self.translatable_anthropic_params()
+ for k, v in anthropic_message_request.items():
+ if k not in translatable_params: # pass remaining params as is
+ new_kwargs[k] = v # type: ignore
+
+ return new_kwargs
+
+ def _translate_openai_content_to_anthropic(
+ self, choices: List[Choices]
+ ) -> List[
+ Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse]
+ ]:
+ new_content: List[
+ Union[
+ AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse
+ ]
+ ] = []
+ for choice in choices:
+ if (
+ choice.message.tool_calls is not None
+ and len(choice.message.tool_calls) > 0
+ ):
+ for tool_call in choice.message.tool_calls:
+ new_content.append(
+ AnthropicResponseContentBlockToolUse(
+ type="tool_use",
+ id=tool_call.id,
+ name=tool_call.function.name or "",
+ input=json.loads(tool_call.function.arguments),
+ )
+ )
+ elif choice.message.content is not None:
+ new_content.append(
+ AnthropicResponseContentBlockText(
+ type="text", text=choice.message.content
+ )
+ )
+
+ return new_content
+
+ def _translate_openai_finish_reason_to_anthropic(
+ self, openai_finish_reason: str
+ ) -> AnthropicFinishReason:
+ if openai_finish_reason == "stop":
+ return "end_turn"
+ elif openai_finish_reason == "length":
+ return "max_tokens"
+ elif openai_finish_reason == "tool_calls":
+ return "tool_use"
+ return "end_turn"
+
+ def translate_openai_response_to_anthropic(
+ self, response: litellm.ModelResponse
+ ) -> AnthropicResponse:
+ ## translate content block
+ anthropic_content = self._translate_openai_content_to_anthropic(choices=response.choices) # type: ignore
+ ## extract finish reason
+ anthropic_finish_reason = self._translate_openai_finish_reason_to_anthropic(
+ openai_finish_reason=response.choices[0].finish_reason # type: ignore
+ )
+ # extract usage
+ usage: litellm.Usage = getattr(response, "usage")
+ anthropic_usage = AnthropicResponseUsageBlock(
+ input_tokens=usage.prompt_tokens, output_tokens=usage.completion_tokens
+ )
+ translated_obj = AnthropicResponse(
+ id=response.id,
+ type="message",
+ role="assistant",
+ model=response.model or "unknown-model",
+ stop_sequence=None,
+ usage=anthropic_usage,
+ content=anthropic_content,
+ stop_reason=anthropic_finish_reason,
+ )
+
+ return translated_obj
+
# makes headers for API call
-def validate_environment(api_key, user_headers):
+def validate_environment(api_key, user_headers, model):
if api_key is None:
- raise ValueError(
- "Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params"
+ raise litellm.AuthenticationError(
+ message="Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params. Please set `ANTHROPIC_API_KEY` in your environment vars",
+ llm_provider="anthropic",
+ model=model,
)
headers = {
"accept": "application/json",
@@ -160,138 +494,65 @@ def validate_environment(api_key, user_headers):
return headers
+async def make_call(
+ client: Optional[AsyncHTTPHandler],
+ api_base: str,
+ headers: dict,
+ data: str,
+ model: str,
+ messages: list,
+ logging_obj,
+):
+ if client is None:
+ client = _get_async_httpx_client() # Create a new client if none provided
+
+ try:
+ response = await client.post(api_base, headers=headers, data=data, stream=True)
+ except httpx.HTTPStatusError as e:
+ raise AnthropicError(
+ status_code=e.response.status_code, message=await e.response.aread()
+ )
+ except Exception as e:
+ raise AnthropicError(status_code=500, message=str(e))
+
+ if response.status_code != 200:
+ raise AnthropicError(
+ status_code=response.status_code, message=await response.aread()
+ )
+
+ completion_stream = ModelResponseIterator(
+ streaming_response=response.aiter_lines(), sync_stream=False
+ )
+
+ # LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response=completion_stream, # Pass the completion stream for logging
+ additional_args={"complete_input_dict": data},
+ )
+
+ return completion_stream
+
+
class AnthropicChatCompletion(BaseLLM):
def __init__(self) -> None:
super().__init__()
- def process_streaming_response(
+ def _process_response(
self,
model: str,
response: Union[requests.Response, httpx.Response],
model_response: ModelResponse,
stream: bool,
- logging_obj: litellm.utils.Logging,
- optional_params: dict,
- api_key: str,
- data: Union[dict, str],
- messages: List,
- print_verbose,
- encoding,
- ) -> CustomStreamWrapper:
- """
- Return stream object for tool-calling + streaming
- """
- ## LOGGING
- logging_obj.post_call(
- input=messages,
- api_key=api_key,
- original_response=response.text,
- additional_args={"complete_input_dict": data},
- )
- print_verbose(f"raw model_response: {response.text}")
- ## RESPONSE OBJECT
- try:
- completion_response = response.json()
- except:
- raise AnthropicError(
- message=response.text, status_code=response.status_code
- )
- text_content = ""
- tool_calls = []
- for content in completion_response["content"]:
- if content["type"] == "text":
- text_content += content["text"]
- ## TOOL CALLING
- elif content["type"] == "tool_use":
- tool_calls.append(
- {
- "id": content["id"],
- "type": "function",
- "function": {
- "name": content["name"],
- "arguments": json.dumps(content["input"]),
- },
- }
- )
- if "error" in completion_response:
- raise AnthropicError(
- message=str(completion_response["error"]),
- status_code=response.status_code,
- )
- _message = litellm.Message(
- tool_calls=tool_calls,
- content=text_content or None,
- )
- model_response.choices[0].message = _message # type: ignore
- model_response._hidden_params["original_response"] = completion_response[
- "content"
- ] # allow user to access raw anthropic tool calling response
-
- model_response.choices[0].finish_reason = map_finish_reason(
- completion_response["stop_reason"]
- )
-
- print_verbose("INSIDE ANTHROPIC STREAMING TOOL CALLING CONDITION BLOCK")
- # return an iterator
- streaming_model_response = ModelResponse(stream=True)
- streaming_model_response.choices[0].finish_reason = model_response.choices[ # type: ignore
- 0
- ].finish_reason
- # streaming_model_response.choices = [litellm.utils.StreamingChoices()]
- streaming_choice = litellm.utils.StreamingChoices()
- streaming_choice.index = model_response.choices[0].index
- _tool_calls = []
- print_verbose(
- f"type of model_response.choices[0]: {type(model_response.choices[0])}"
- )
- print_verbose(f"type of streaming_choice: {type(streaming_choice)}")
- if isinstance(model_response.choices[0], litellm.Choices):
- if getattr(
- model_response.choices[0].message, "tool_calls", None
- ) is not None and isinstance(
- model_response.choices[0].message.tool_calls, list
- ):
- for tool_call in model_response.choices[0].message.tool_calls:
- _tool_call = {**tool_call.dict(), "index": 0}
- _tool_calls.append(_tool_call)
- delta_obj = litellm.utils.Delta(
- content=getattr(model_response.choices[0].message, "content", None),
- role=model_response.choices[0].message.role,
- tool_calls=_tool_calls,
- )
- streaming_choice.delta = delta_obj
- streaming_model_response.choices = [streaming_choice]
- completion_stream = ModelResponseIterator(
- model_response=streaming_model_response
- )
- print_verbose(
- "Returns anthropic CustomStreamWrapper with 'cached_response' streaming object"
- )
- return CustomStreamWrapper(
- completion_stream=completion_stream,
- model=model,
- custom_llm_provider="cached_response",
- logging_obj=logging_obj,
- )
- else:
- raise AnthropicError(
- status_code=422,
- message="Unprocessable response object - {}".format(response.text),
- )
-
- def process_response(
- self,
- model: str,
- response: Union[requests.Response, httpx.Response],
- model_response: ModelResponse,
- stream: bool,
- logging_obj: litellm.utils.Logging,
+ logging_obj: litellm.litellm_core_utils.litellm_logging.Logging,
optional_params: dict,
api_key: str,
data: Union[dict, str],
messages: List,
print_verbose,
encoding,
+ json_mode: bool,
) -> ModelResponse:
## LOGGING
logging_obj.post_call(
@@ -315,27 +576,40 @@ class AnthropicChatCompletion(BaseLLM):
)
else:
text_content = ""
- tool_calls = []
- for content in completion_response["content"]:
+ tool_calls: List[ChatCompletionToolCallChunk] = []
+ for idx, content in enumerate(completion_response["content"]):
if content["type"] == "text":
text_content += content["text"]
## TOOL CALLING
elif content["type"] == "tool_use":
tool_calls.append(
- {
- "id": content["id"],
- "type": "function",
- "function": {
- "name": content["name"],
- "arguments": json.dumps(content["input"]),
- },
- }
+ ChatCompletionToolCallChunk(
+ id=content["id"],
+ type="function",
+ function=ChatCompletionToolCallFunctionChunk(
+ name=content["name"],
+ arguments=json.dumps(content["input"]),
+ ),
+ index=idx,
+ )
)
_message = litellm.Message(
tool_calls=tool_calls,
content=text_content or None,
)
+
+ ## HANDLE JSON MODE - anthropic returns single function call
+ if json_mode and len(tool_calls) == 1:
+ json_mode_content_str: Optional[str] = tool_calls[0]["function"].get(
+ "arguments"
+ )
+ if json_mode_content_str is not None:
+ args = json.loads(json_mode_content_str)
+ values: Optional[dict] = args.get("values")
+ if values is not None:
+ _message = litellm.Message(content=json.dumps(values))
+ completion_response["stop_reason"] = "stop"
model_response.choices[0].message = _message # type: ignore
model_response._hidden_params["original_response"] = completion_response[
"content"
@@ -350,8 +624,8 @@ class AnthropicChatCompletion(BaseLLM):
completion_tokens = completion_response["usage"]["output_tokens"]
total_tokens = prompt_tokens + completion_tokens
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@@ -379,23 +653,34 @@ class AnthropicChatCompletion(BaseLLM):
logger_fn=None,
headers={},
):
- self.async_handler = AsyncHTTPHandler(
- timeout=httpx.Timeout(timeout=600.0, connect=5.0)
- )
data["stream"] = True
- response = await self.async_handler.post(
- api_base, headers=headers, data=json.dumps(data), stream=True
- )
+ # async_handler = AsyncHTTPHandler(
+ # timeout=httpx.Timeout(timeout=600.0, connect=20.0)
+ # )
- if response.status_code != 200:
- raise AnthropicError(
- status_code=response.status_code, message=response.text
- )
+ # response = await async_handler.post(
+ # api_base, headers=headers, json=data, stream=True
+ # )
- completion_stream = response.aiter_lines()
+ # if response.status_code != 200:
+ # raise AnthropicError(
+ # status_code=response.status_code, message=response.text
+ # )
+
+ # completion_stream = response.aiter_lines()
streamwrapper = CustomStreamWrapper(
- completion_stream=completion_stream,
+ completion_stream=None,
+ make_call=partial(
+ make_call,
+ client=None,
+ api_base=api_base,
+ headers=headers,
+ data=json.dumps(data),
+ model=model,
+ messages=messages,
+ logging_obj=logging_obj,
+ ),
model=model,
custom_llm_provider="anthropic",
logging_obj=logging_obj,
@@ -417,31 +702,27 @@ class AnthropicChatCompletion(BaseLLM):
_is_function_call,
data: dict,
optional_params: dict,
+ json_mode: bool,
litellm_params=None,
logger_fn=None,
headers={},
+ client=None,
) -> Union[ModelResponse, CustomStreamWrapper]:
- self.async_handler = AsyncHTTPHandler(
- timeout=httpx.Timeout(timeout=600.0, connect=5.0)
- )
- response = await self.async_handler.post(
- api_base, headers=headers, data=json.dumps(data)
- )
- if stream and _is_function_call:
- return self.process_streaming_response(
- model=model,
- response=response,
- model_response=model_response,
- stream=stream,
- logging_obj=logging_obj,
+ async_handler = _get_async_httpx_client()
+
+ try:
+ response = await async_handler.post(api_base, headers=headers, json=data)
+ except Exception as e:
+ ## LOGGING
+ logging_obj.post_call(
+ input=messages,
api_key=api_key,
- data=data,
- messages=messages,
- print_verbose=print_verbose,
- optional_params=optional_params,
- encoding=encoding,
+ original_response=str(e),
+ additional_args={"complete_input_dict": data},
)
- return self.process_response(
+ raise e
+
+ return self._process_response(
model=model,
response=response,
model_response=model_response,
@@ -453,6 +734,7 @@ class AnthropicChatCompletion(BaseLLM):
print_verbose=print_verbose,
optional_params=optional_params,
encoding=encoding,
+ json_mode=json_mode,
)
def completion(
@@ -467,12 +749,14 @@ class AnthropicChatCompletion(BaseLLM):
api_key,
logging_obj,
optional_params: dict,
+ timeout: Union[float, httpx.Timeout],
acompletion=None,
litellm_params=None,
logger_fn=None,
headers={},
+ client=None,
):
- headers = validate_environment(api_key, headers)
+ headers = validate_environment(api_key, headers, model)
_is_function_call = False
messages = copy.deepcopy(messages)
optional_params = copy.deepcopy(optional_params)
@@ -523,20 +807,27 @@ class AnthropicChatCompletion(BaseLLM):
anthropic_tools = []
for tool in optional_params["tools"]:
- new_tool = tool["function"]
- new_tool["input_schema"] = new_tool.pop("parameters") # rename key
- anthropic_tools.append(new_tool)
+ if "input_schema" in tool: # assume in anthropic format
+ anthropic_tools.append(tool)
+ else: # assume openai tool call
+ new_tool = tool["function"]
+ new_tool["input_schema"] = new_tool.pop("parameters") # rename key
+ anthropic_tools.append(new_tool)
optional_params["tools"] = anthropic_tools
stream = optional_params.pop("stream", None)
+ is_vertex_request: bool = optional_params.pop("is_vertex_request", False)
+ json_mode: bool = optional_params.pop("json_mode", False)
data = {
- "model": model,
"messages": messages,
**optional_params,
}
+ if is_vertex_request is False:
+ data["model"] = model
+
## LOGGING
logging_obj.pre_call(
input=messages,
@@ -548,9 +839,9 @@ class AnthropicChatCompletion(BaseLLM):
},
)
print_verbose(f"_is_function_call: {_is_function_call}")
- if acompletion == True:
+ if acompletion is True:
if (
- stream and not _is_function_call
+ stream is True
): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
print_verbose("makes async anthropic streaming POST request")
data["stream"] = stream
@@ -590,11 +881,17 @@ class AnthropicChatCompletion(BaseLLM):
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=headers,
+ client=client,
+ json_mode=json_mode,
)
else:
## COMPLETION CALL
+ if client is None or isinstance(client, AsyncHTTPHandler):
+ client = HTTPHandler(timeout=timeout) # type: ignore
+ else:
+ client = client
if (
- stream and not _is_function_call
+ stream is True
): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
print_verbose("makes anthropic streaming POST request")
data["stream"] = stream
@@ -610,7 +907,9 @@ class AnthropicChatCompletion(BaseLLM):
status_code=response.status_code, message=response.text
)
- completion_stream = response.iter_lines()
+ completion_stream = ModelResponseIterator(
+ streaming_response=response.iter_lines(), sync_stream=True
+ )
streaming_response = CustomStreamWrapper(
completion_stream=completion_stream,
model=model,
@@ -620,29 +919,13 @@ class AnthropicChatCompletion(BaseLLM):
return streaming_response
else:
- response = requests.post(
- api_base, headers=headers, data=json.dumps(data)
- )
+ response = client.post(api_base, headers=headers, data=json.dumps(data))
if response.status_code != 200:
raise AnthropicError(
status_code=response.status_code, message=response.text
)
- if stream and _is_function_call:
- return self.process_streaming_response(
- model=model,
- response=response,
- model_response=model_response,
- stream=stream,
- logging_obj=logging_obj,
- api_key=api_key,
- data=data,
- messages=messages,
- print_verbose=print_verbose,
- optional_params=optional_params,
- encoding=encoding,
- )
- return self.process_response(
+ return self._process_response(
model=model,
response=response,
model_response=model_response,
@@ -654,6 +937,7 @@ class AnthropicChatCompletion(BaseLLM):
print_verbose=print_verbose,
optional_params=optional_params,
encoding=encoding,
+ json_mode=json_mode,
)
def embedding(self):
@@ -662,26 +946,206 @@ class AnthropicChatCompletion(BaseLLM):
class ModelResponseIterator:
- def __init__(self, model_response):
- self.model_response = model_response
- self.is_done = False
+ def __init__(self, streaming_response, sync_stream: bool):
+ self.streaming_response = streaming_response
+ self.response_iterator = self.streaming_response
+
+ def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
+ try:
+ verbose_logger.debug(f"\n\nRaw chunk:\n{chunk}\n")
+ type_chunk = chunk.get("type", "") or ""
+
+ text = ""
+ tool_use: Optional[ChatCompletionToolCallChunk] = None
+ is_finished = False
+ finish_reason = ""
+ usage: Optional[ChatCompletionUsageBlock] = None
+
+ index = int(chunk.get("index", 0))
+ if type_chunk == "content_block_delta":
+ """
+ Anthropic content chunk
+ chunk = {'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': 'Hello'}}
+ """
+ content_block = ContentBlockDelta(**chunk) # type: ignore
+ if "text" in content_block["delta"]:
+ text = content_block["delta"]["text"]
+ elif "partial_json" in content_block["delta"]:
+ tool_use = {
+ "id": None,
+ "type": "function",
+ "function": {
+ "name": None,
+ "arguments": content_block["delta"]["partial_json"],
+ },
+ "index": content_block["index"],
+ }
+ elif type_chunk == "content_block_start":
+ """
+ event: content_block_start
+ data: {"type":"content_block_start","index":1,"content_block":{"type":"tool_use","id":"toolu_01T1x1fJ34qAmk2tNTrN7Up6","name":"get_weather","input":{}}}
+ """
+ content_block_start = ContentBlockStart(**chunk) # type: ignore
+ if content_block_start["content_block"]["type"] == "text":
+ text = content_block_start["content_block"]["text"]
+ elif content_block_start["content_block"]["type"] == "tool_use":
+ tool_use = {
+ "id": content_block_start["content_block"]["id"],
+ "type": "function",
+ "function": {
+ "name": content_block_start["content_block"]["name"],
+ "arguments": "",
+ },
+ "index": content_block_start["index"],
+ }
+ elif type_chunk == "message_delta":
+ """
+ Anthropic
+ chunk = {'type': 'message_delta', 'delta': {'stop_reason': 'max_tokens', 'stop_sequence': None}, 'usage': {'output_tokens': 10}}
+ """
+ # TODO - get usage from this chunk, set in response
+ message_delta = MessageBlockDelta(**chunk) # type: ignore
+ finish_reason = map_finish_reason(
+ finish_reason=message_delta["delta"].get("stop_reason", "stop")
+ or "stop"
+ )
+ usage = ChatCompletionUsageBlock(
+ prompt_tokens=message_delta["usage"].get("input_tokens", 0),
+ completion_tokens=message_delta["usage"].get("output_tokens", 0),
+ total_tokens=message_delta["usage"].get("input_tokens", 0)
+ + message_delta["usage"].get("output_tokens", 0),
+ )
+ is_finished = True
+ elif type_chunk == "message_start":
+ """
+ Anthropic
+ chunk = {
+ "type": "message_start",
+ "message": {
+ "id": "msg_vrtx_011PqREFEMzd3REdCoUFAmdG",
+ "type": "message",
+ "role": "assistant",
+ "model": "claude-3-sonnet-20240229",
+ "content": [],
+ "stop_reason": null,
+ "stop_sequence": null,
+ "usage": {
+ "input_tokens": 270,
+ "output_tokens": 1
+ }
+ }
+ }
+ """
+ message_start_block = MessageStartBlock(**chunk) # type: ignore
+ usage = ChatCompletionUsageBlock(
+ prompt_tokens=message_start_block["message"]
+ .get("usage", {})
+ .get("input_tokens", 0),
+ completion_tokens=message_start_block["message"]
+ .get("usage", {})
+ .get("output_tokens", 0),
+ total_tokens=message_start_block["message"]
+ .get("usage", {})
+ .get("input_tokens", 0)
+ + message_start_block["message"]
+ .get("usage", {})
+ .get("output_tokens", 0),
+ )
+ elif type_chunk == "error":
+ """
+ {"type":"error","error":{"details":null,"type":"api_error","message":"Internal server error"} }
+ """
+ _error_dict = chunk.get("error", {}) or {}
+ message = _error_dict.get("message", None) or str(chunk)
+ raise AnthropicError(
+ message=message,
+ status_code=500, # it looks like Anthropic API does not return a status code in the chunk error - default to 500
+ )
+ returned_chunk = GenericStreamingChunk(
+ text=text,
+ tool_use=tool_use,
+ is_finished=is_finished,
+ finish_reason=finish_reason,
+ usage=usage,
+ index=index,
+ )
+
+ return returned_chunk
+
+ except json.JSONDecodeError:
+ raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
# Sync iterator
def __iter__(self):
return self
def __next__(self):
- if self.is_done:
+ try:
+ chunk = self.response_iterator.__next__()
+ except StopIteration:
raise StopIteration
- self.is_done = True
- return self.model_response
+ except ValueError as e:
+ raise RuntimeError(f"Error receiving chunk from stream: {e}")
+
+ try:
+ str_line = chunk
+ if isinstance(chunk, bytes): # Handle binary data
+ str_line = chunk.decode("utf-8") # Convert bytes to string
+ index = str_line.find("data:")
+ if index != -1:
+ str_line = str_line[index:]
+
+ if str_line.startswith("data:"):
+ data_json = json.loads(str_line[5:])
+ return self.chunk_parser(chunk=data_json)
+ else:
+ return GenericStreamingChunk(
+ text="",
+ is_finished=False,
+ finish_reason="",
+ usage=None,
+ index=0,
+ tool_use=None,
+ )
+ except StopIteration:
+ raise StopIteration
+ except ValueError as e:
+ raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
# Async iterator
def __aiter__(self):
+ self.async_response_iterator = self.streaming_response.__aiter__()
return self
async def __anext__(self):
- if self.is_done:
+ try:
+ chunk = await self.async_response_iterator.__anext__()
+ except StopAsyncIteration:
raise StopAsyncIteration
- self.is_done = True
- return self.model_response
+ except ValueError as e:
+ raise RuntimeError(f"Error receiving chunk from stream: {e}")
+
+ try:
+ str_line = chunk
+ if isinstance(chunk, bytes): # Handle binary data
+ str_line = chunk.decode("utf-8") # Convert bytes to string
+ index = str_line.find("data:")
+ if index != -1:
+ str_line = str_line[index:]
+
+ if str_line.startswith("data:"):
+ data_json = json.loads(str_line[5:])
+ return self.chunk_parser(chunk=data_json)
+ else:
+ return GenericStreamingChunk(
+ text="",
+ is_finished=False,
+ finish_reason="",
+ usage=None,
+ index=0,
+ tool_use=None,
+ )
+ except StopAsyncIteration:
+ raise StopAsyncIteration
+ except ValueError as e:
+ raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
diff --git a/litellm/llms/anthropic_text.py b/litellm/llms/anthropic_text.py
index 0093d9f353..d20e49daf1 100644
--- a/litellm/llms/anthropic_text.py
+++ b/litellm/llms/anthropic_text.py
@@ -1,15 +1,19 @@
-import os, types
import json
-from enum import Enum
-import requests
+import os
import time
+import types
+from enum import Enum
from typing import Callable, Optional
-from litellm.utils import ModelResponse, Usage, CustomStreamWrapper
-import litellm
-from .prompt_templates.factory import prompt_factory, custom_prompt
+
import httpx
-from .base import BaseLLM
+import requests
+
+import litellm
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
+
+from .base import BaseLLM
+from .prompt_templates.factory import custom_prompt, prompt_factory
class AnthropicConstants(Enum):
@@ -117,9 +121,9 @@ class AnthropicTextCompletion(BaseLLM):
)
else:
if len(completion_response["completion"]) > 0:
- model_response["choices"][0]["message"]["content"] = (
- completion_response["completion"]
- )
+ model_response.choices[0].message.content = completion_response[ # type: ignore
+ "completion"
+ ]
model_response.choices[0].finish_reason = completion_response["stop_reason"]
## CALCULATING USAGE
@@ -130,8 +134,8 @@ class AnthropicTextCompletion(BaseLLM):
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
) ##[TODO] use the anthropic tokenizer here
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/azure.py b/litellm/llms/azure.py
index 02fe4a08f2..a2928cf208 100644
--- a/litellm/llms/azure.py
+++ b/litellm/llms/azure.py
@@ -1,23 +1,63 @@
-from typing import Optional, Union, Any, Literal
-import types, requests
-from .base import BaseLLM
-from litellm.utils import (
- ModelResponse,
- Choices,
- Message,
- CustomStreamWrapper,
- convert_to_model_response_object,
- TranscriptionResponse,
- get_secret,
-)
-from typing import Callable, Optional, BinaryIO, List
-from litellm import OpenAIConfig
-import litellm, json
-import httpx # type: ignore
-from .custom_httpx.azure_dall_e_2 import CustomHTTPTransport, AsyncCustomHTTPTransport
-from openai import AzureOpenAI, AsyncAzureOpenAI
-import uuid
+import asyncio
+import json
import os
+import time
+import types
+import uuid
+from typing import (
+ Any,
+ BinaryIO,
+ Callable,
+ Coroutine,
+ Iterable,
+ List,
+ Literal,
+ Optional,
+ Union,
+)
+
+import httpx # type: ignore
+import requests
+from openai import AsyncAzureOpenAI, AzureOpenAI
+from pydantic import BaseModel
+from typing_extensions import overload
+
+import litellm
+from litellm import ImageResponse, OpenAIConfig
+from litellm.caching import DualCache
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.utils import (
+ Choices,
+ CustomStreamWrapper,
+ Message,
+ ModelResponse,
+ TranscriptionResponse,
+ UnsupportedParamsError,
+ convert_to_model_response_object,
+ get_secret,
+ modify_url,
+)
+
+from ..types.llms.openai import (
+ Assistant,
+ AssistantEventHandler,
+ AssistantStreamManager,
+ AssistantToolParam,
+ AsyncAssistantEventHandler,
+ AsyncAssistantStreamManager,
+ AsyncCursorPage,
+ HttpxBinaryResponseContent,
+ MessageData,
+ OpenAICreateThreadParamsMessage,
+ OpenAIMessage,
+ Run,
+ SyncCursorPage,
+ Thread,
+)
+from .base import BaseLLM
+
+azure_ad_cache = DualCache()
class AzureOpenAIError(Exception):
@@ -45,9 +85,9 @@ class AzureOpenAIError(Exception):
) # Call the base class constructor with the parameters it needs
-class AzureOpenAIConfig(OpenAIConfig):
+class AzureOpenAIConfig:
"""
- Reference: https://platform.openai.com/docs/api-reference/chat/create
+ Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions
The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. It inherits from `OpenAIConfig`. Below are the parameters::
@@ -85,18 +125,111 @@ class AzureOpenAIConfig(OpenAIConfig):
temperature: Optional[int] = None,
top_p: Optional[int] = None,
) -> None:
- super().__init__(
- frequency_penalty,
- function_call,
- functions,
- logit_bias,
- max_tokens,
- n,
- presence_penalty,
- stop,
- temperature,
- top_p,
- )
+ locals_ = locals().copy()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self):
+ return [
+ "temperature",
+ "n",
+ "stream",
+ "stop",
+ "max_tokens",
+ "tools",
+ "tool_choice",
+ "presence_penalty",
+ "frequency_penalty",
+ "logit_bias",
+ "user",
+ "function_call",
+ "functions",
+ "tools",
+ "tool_choice",
+ "top_p",
+ "logprobs",
+ "top_logprobs",
+ "response_format",
+ "seed",
+ "extra_headers",
+ ]
+
+ def map_openai_params(
+ self,
+ non_default_params: dict,
+ optional_params: dict,
+ model: str,
+ api_version: str, # Y-M-D-{optional}
+ drop_params,
+ ) -> dict:
+ supported_openai_params = self.get_supported_openai_params()
+
+ api_version_times = api_version.split("-")
+ api_version_year = api_version_times[0]
+ api_version_month = api_version_times[1]
+ api_version_day = api_version_times[2]
+ for param, value in non_default_params.items():
+ if param == "tool_choice":
+ """
+ This parameter requires API version 2023-12-01-preview or later
+
+ tool_choice='required' is not supported as of 2024-05-01-preview
+ """
+ ## check if api version supports this param ##
+ if (
+ api_version_year < "2023"
+ or (api_version_year == "2023" and api_version_month < "12")
+ or (
+ api_version_year == "2023"
+ and api_version_month == "12"
+ and api_version_day < "01"
+ )
+ ):
+ if litellm.drop_params == True or (
+ drop_params is not None and drop_params == True
+ ):
+ pass
+ else:
+ raise UnsupportedParamsError(
+ status_code=400,
+ message=f"""Azure does not support 'tool_choice', for api_version={api_version}. Bump your API version to '2023-12-01-preview' or later. This parameter requires 'api_version="2023-12-01-preview"' or later. Azure API Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions""",
+ )
+ elif value == "required" and (
+ api_version_year == "2024" and api_version_month <= "05"
+ ): ## check if tool_choice value is supported ##
+ if litellm.drop_params == True or (
+ drop_params is not None and drop_params == True
+ ):
+ pass
+ else:
+ raise UnsupportedParamsError(
+ status_code=400,
+ message=f"Azure does not support '{value}' as a {param} param, for api_version={api_version}. To drop 'tool_choice=required' for calls with this Azure API version, set `litellm.drop_params=True` or for proxy:\n\n`litellm_settings:\n drop_params: true`\nAzure API Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions",
+ )
+ else:
+ optional_params["tool_choice"] = value
+ elif param in supported_openai_params:
+ optional_params[param] = value
+ return optional_params
def get_mapped_special_auth_params(self) -> dict:
return {"token": "azure_ad_token"}
@@ -114,6 +247,68 @@ class AzureOpenAIConfig(OpenAIConfig):
return ["europe", "sweden", "switzerland", "france", "uk"]
+class AzureOpenAIAssistantsAPIConfig:
+ """
+ Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/assistants-reference-messages?tabs=python#create-message
+ """
+
+ def __init__(
+ self,
+ ) -> None:
+ pass
+
+ def get_supported_openai_create_message_params(self):
+ return [
+ "role",
+ "content",
+ "attachments",
+ "metadata",
+ ]
+
+ def map_openai_params_create_message_params(
+ self, non_default_params: dict, optional_params: dict
+ ):
+ for param, value in non_default_params.items():
+ if param == "role":
+ optional_params["role"] = value
+ if param == "metadata":
+ optional_params["metadata"] = value
+ elif param == "content": # only string accepted
+ if isinstance(value, str):
+ optional_params["content"] = value
+ else:
+ raise litellm.utils.UnsupportedParamsError(
+ message="Azure only accepts content as a string.",
+ status_code=400,
+ )
+ elif (
+ param == "attachments"
+ ): # this is a v2 param. Azure currently supports the old 'file_id's param
+ file_ids: List[str] = []
+ if isinstance(value, list):
+ for item in value:
+ if "file_id" in item:
+ file_ids.append(item["file_id"])
+ else:
+ if litellm.drop_params == True:
+ pass
+ else:
+ raise litellm.utils.UnsupportedParamsError(
+ message="Azure doesn't support {}. To drop it from the call, set `litellm.drop_params = True.".format(
+ value
+ ),
+ status_code=400,
+ )
+ else:
+ raise litellm.utils.UnsupportedParamsError(
+ message="Invalid param. attachments should always be a list. Got={}, Expected=List. Raw value={}".format(
+ type(value), value
+ ),
+ status_code=400,
+ )
+ return optional_params
+
+
def select_azure_base_url_or_endpoint(azure_client_params: dict):
# azure_client_params = {
# "api_version": api_version,
@@ -136,9 +331,12 @@ def select_azure_base_url_or_endpoint(azure_client_params: dict):
def get_azure_ad_token_from_oidc(azure_ad_token: str):
azure_client_id = os.getenv("AZURE_CLIENT_ID", None)
- azure_tenant = os.getenv("AZURE_TENANT_ID", None)
+ azure_tenant_id = os.getenv("AZURE_TENANT_ID", None)
+ azure_authority_host = os.getenv(
+ "AZURE_AUTHORITY_HOST", "https://login.microsoftonline.com"
+ )
- if azure_client_id is None or azure_tenant is None:
+ if azure_client_id is None or azure_tenant_id is None:
raise AzureOpenAIError(
status_code=422,
message="AZURE_CLIENT_ID and AZURE_TENANT_ID must be set",
@@ -152,8 +350,21 @@ def get_azure_ad_token_from_oidc(azure_ad_token: str):
message="OIDC token could not be retrieved from secret manager.",
)
+ azure_ad_token_cache_key = json.dumps(
+ {
+ "azure_client_id": azure_client_id,
+ "azure_tenant_id": azure_tenant_id,
+ "azure_authority_host": azure_authority_host,
+ "oidc_token": oidc_token,
+ }
+ )
+
+ azure_ad_token_access_token = azure_ad_cache.get_cache(azure_ad_token_cache_key)
+ if azure_ad_token_access_token is not None:
+ return azure_ad_token_access_token
+
req_token = httpx.post(
- f"https://login.microsoftonline.com/{azure_tenant}/oauth2/v2.0/token",
+ f"{azure_authority_host}/{azure_tenant_id}/oauth2/v2.0/token",
data={
"client_id": azure_client_id,
"grant_type": "client_credentials",
@@ -169,14 +380,27 @@ def get_azure_ad_token_from_oidc(azure_ad_token: str):
message=req_token.text,
)
- possible_azure_ad_token = req_token.json().get("access_token", None)
+ azure_ad_token_json = req_token.json()
+ azure_ad_token_access_token = azure_ad_token_json.get("access_token", None)
+ azure_ad_token_expires_in = azure_ad_token_json.get("expires_in", None)
- if possible_azure_ad_token is None:
+ if azure_ad_token_access_token is None:
raise AzureOpenAIError(
- status_code=422, message="Azure AD Token not returned"
+ status_code=422, message="Azure AD Token access_token not returned"
)
- return possible_azure_ad_token
+ if azure_ad_token_expires_in is None:
+ raise AzureOpenAIError(
+ status_code=422, message="Azure AD Token expires_in not returned"
+ )
+
+ azure_ad_cache.set_cache(
+ key=azure_ad_token_cache_key,
+ value=azure_ad_token_access_token,
+ ttl=azure_ad_token_expires_in,
+ )
+
+ return azure_ad_token_access_token
class AzureChatCompletion(BaseLLM):
@@ -195,6 +419,79 @@ class AzureChatCompletion(BaseLLM):
headers["Authorization"] = f"Bearer {azure_ad_token}"
return headers
+ def _get_sync_azure_client(
+ self,
+ api_version: Optional[str],
+ api_base: Optional[str],
+ api_key: Optional[str],
+ azure_ad_token: Optional[str],
+ model: str,
+ max_retries: int,
+ timeout: Union[float, httpx.Timeout],
+ client: Optional[Any],
+ client_type: Literal["sync", "async"],
+ ):
+ # init AzureOpenAI Client
+ azure_client_params = {
+ "api_version": api_version,
+ "azure_endpoint": api_base,
+ "azure_deployment": model,
+ "http_client": litellm.client_session,
+ "max_retries": max_retries,
+ "timeout": timeout,
+ }
+ azure_client_params = select_azure_base_url_or_endpoint(
+ azure_client_params=azure_client_params
+ )
+ if api_key is not None:
+ azure_client_params["api_key"] = api_key
+ elif azure_ad_token is not None:
+ if azure_ad_token.startswith("oidc/"):
+ azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
+ azure_client_params["azure_ad_token"] = azure_ad_token
+ if client is None:
+ if client_type == "sync":
+ azure_client = AzureOpenAI(**azure_client_params) # type: ignore
+ elif client_type == "async":
+ azure_client = AsyncAzureOpenAI(**azure_client_params) # type: ignore
+ else:
+ azure_client = client
+ if api_version is not None and isinstance(azure_client._custom_query, dict):
+ # set api_version to version passed by user
+ azure_client._custom_query.setdefault("api-version", api_version)
+
+ return azure_client
+
+ async def make_azure_openai_chat_completion_request(
+ self,
+ azure_client: AsyncAzureOpenAI,
+ data: dict,
+ timeout: Union[float, httpx.Timeout],
+ ):
+ """
+ Helper to:
+ - call chat.completions.create.with_raw_response when litellm.return_response_headers is True
+ - call chat.completions.create by default
+ """
+ try:
+ if litellm.return_response_headers is True:
+ raw_response = (
+ await azure_client.chat.completions.with_raw_response.create(
+ **data, timeout=timeout
+ )
+ )
+
+ headers = dict(raw_response.headers)
+ response = raw_response.parse()
+ return headers, response
+ else:
+ response = await azure_client.chat.completions.create(
+ **data, timeout=timeout
+ )
+ return None, response
+ except Exception as e:
+ raise e
+
def completion(
self,
model: str,
@@ -207,7 +504,7 @@ class AzureChatCompletion(BaseLLM):
azure_ad_token: str,
print_verbose: Callable,
timeout: Union[float, httpx.Timeout],
- logging_obj,
+ logging_obj: LiteLLMLoggingObj,
optional_params,
litellm_params,
logger_fn,
@@ -245,7 +542,9 @@ class AzureChatCompletion(BaseLLM):
azure_client_params["api_key"] = api_key
elif azure_ad_token is not None:
if azure_ad_token.startswith("oidc/"):
- azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
+ azure_ad_token = get_azure_ad_token_from_oidc(
+ azure_ad_token
+ )
azure_client_params["azure_ad_token"] = azure_ad_token
@@ -384,9 +683,9 @@ class AzureChatCompletion(BaseLLM):
data: dict,
timeout: Any,
model_response: ModelResponse,
+ logging_obj: LiteLLMLoggingObj,
azure_ad_token: Optional[str] = None,
client=None, # this is the AsyncAzureOpenAI
- logging_obj=None,
):
response = None
try:
@@ -436,17 +735,52 @@ class AzureChatCompletion(BaseLLM):
"complete_input_dict": data,
},
)
- response = await azure_client.chat.completions.create(
- **data, timeout=timeout
+
+ headers, response = await self.make_azure_openai_chat_completion_request(
+ azure_client=azure_client,
+ data=data,
+ timeout=timeout,
+ )
+ logging_obj.model_call_details["response_headers"] = headers
+
+ stringified_response = response.model_dump()
+ logging_obj.post_call(
+ input=data["messages"],
+ api_key=api_key,
+ original_response=stringified_response,
+ additional_args={"complete_input_dict": data},
)
return convert_to_model_response_object(
- response_object=response.model_dump(),
+ response_object=stringified_response,
model_response_object=model_response,
)
except AzureOpenAIError as e:
+ ## LOGGING
+ logging_obj.post_call(
+ input=data["messages"],
+ api_key=api_key,
+ additional_args={"complete_input_dict": data},
+ original_response=str(e),
+ )
exception_mapping_worked = True
raise e
+ except asyncio.CancelledError as e:
+ ## LOGGING
+ logging_obj.post_call(
+ input=data["messages"],
+ api_key=api_key,
+ additional_args={"complete_input_dict": data},
+ original_response=str(e),
+ )
+ raise AzureOpenAIError(status_code=500, message=str(e))
except Exception as e:
+ ## LOGGING
+ logging_obj.post_call(
+ input=data["messages"],
+ api_key=api_key,
+ additional_args={"complete_input_dict": data},
+ original_response=str(e),
+ )
if hasattr(e, "status_code"):
raise e
else:
@@ -516,7 +850,7 @@ class AzureChatCompletion(BaseLLM):
async def async_streaming(
self,
- logging_obj,
+ logging_obj: LiteLLMLoggingObj,
api_base: str,
api_key: str,
api_version: str,
@@ -565,9 +899,14 @@ class AzureChatCompletion(BaseLLM):
"complete_input_dict": data,
},
)
- response = await azure_client.chat.completions.create(
- **data, timeout=timeout
+
+ headers, response = await self.make_azure_openai_chat_completion_request(
+ azure_client=azure_client,
+ data=data,
+ timeout=timeout,
)
+ logging_obj.model_call_details["response_headers"] = headers
+
# return response
streamwrapper = CustomStreamWrapper(
completion_stream=response,
@@ -589,7 +928,7 @@ class AzureChatCompletion(BaseLLM):
azure_client_params: dict,
api_key: str,
input: list,
- client=None,
+ client: Optional[AsyncAzureOpenAI] = None,
logging_obj=None,
timeout=None,
):
@@ -679,7 +1018,7 @@ class AzureChatCompletion(BaseLLM):
},
)
- if aembedding == True:
+ if aembedding is True:
response = self.aembedding(
data=data,
input=input,
@@ -688,6 +1027,7 @@ class AzureChatCompletion(BaseLLM):
model_response=model_response,
azure_client_params=azure_client_params,
timeout=timeout,
+ client=client,
)
return response
if client is None:
@@ -714,6 +1054,252 @@ class AzureChatCompletion(BaseLLM):
else:
raise AzureOpenAIError(status_code=500, message=str(e))
+ async def make_async_azure_httpx_request(
+ self,
+ client: Optional[AsyncHTTPHandler],
+ timeout: Optional[Union[float, httpx.Timeout]],
+ api_base: str,
+ api_version: str,
+ api_key: str,
+ data: dict,
+ ) -> httpx.Response:
+ """
+ Implemented for azure dall-e-2 image gen calls
+
+ Alternative to needing a custom transport implementation
+ """
+ if client is None:
+ _params = {}
+ if timeout is not None:
+ if isinstance(timeout, float) or isinstance(timeout, int):
+ _httpx_timeout = httpx.Timeout(timeout)
+ _params["timeout"] = _httpx_timeout
+ else:
+ _params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
+
+ async_handler = AsyncHTTPHandler(**_params) # type: ignore
+ else:
+ async_handler = client # type: ignore
+
+ if (
+ "images/generations" in api_base
+ and api_version
+ in [ # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict
+ "2023-06-01-preview",
+ "2023-07-01-preview",
+ "2023-08-01-preview",
+ "2023-09-01-preview",
+ "2023-10-01-preview",
+ ]
+ ): # CREATE + POLL for azure dall-e-2 calls
+
+ api_base = modify_url(
+ original_url=api_base, new_path="/openai/images/generations:submit"
+ )
+
+ data.pop(
+ "model", None
+ ) # REMOVE 'model' from dall-e-2 arg https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#request-a-generated-image-dall-e-2-preview
+ response = await async_handler.post(
+ url=api_base,
+ data=json.dumps(data),
+ headers={
+ "Content-Type": "application/json",
+ "api-key": api_key,
+ },
+ )
+ if "operation-location" in response.headers:
+ operation_location_url = response.headers["operation-location"]
+ else:
+ raise AzureOpenAIError(status_code=500, message=response.text)
+ response = await async_handler.get(
+ url=operation_location_url,
+ headers={
+ "api-key": api_key,
+ },
+ )
+
+ await response.aread()
+
+ timeout_secs: int = 120
+ start_time = time.time()
+ if "status" not in response.json():
+ raise Exception(
+ "Expected 'status' in response. Got={}".format(response.json())
+ )
+ while response.json()["status"] not in ["succeeded", "failed"]:
+ if time.time() - start_time > timeout_secs:
+ timeout_msg = {
+ "error": {
+ "code": "Timeout",
+ "message": "Operation polling timed out.",
+ }
+ }
+
+ raise AzureOpenAIError(
+ status_code=408, message="Operation polling timed out."
+ )
+
+ await asyncio.sleep(int(response.headers.get("retry-after") or 10))
+ response = await async_handler.get(
+ url=operation_location_url,
+ headers={
+ "api-key": api_key,
+ },
+ )
+ await response.aread()
+
+ if response.json()["status"] == "failed":
+ error_data = response.json()
+ raise AzureOpenAIError(status_code=400, message=json.dumps(error_data))
+
+ result = response.json()["result"]
+ return httpx.Response(
+ status_code=200,
+ headers=response.headers,
+ content=json.dumps(result).encode("utf-8"),
+ request=httpx.Request(method="POST", url="https://api.openai.com/v1"),
+ )
+ return await async_handler.post(
+ url=api_base,
+ json=data,
+ headers={
+ "Content-Type": "application/json;",
+ "api-key": api_key,
+ },
+ )
+
+ def make_sync_azure_httpx_request(
+ self,
+ client: Optional[HTTPHandler],
+ timeout: Optional[Union[float, httpx.Timeout]],
+ api_base: str,
+ api_version: str,
+ api_key: str,
+ data: dict,
+ ) -> httpx.Response:
+ """
+ Implemented for azure dall-e-2 image gen calls
+
+ Alternative to needing a custom transport implementation
+ """
+ if client is None:
+ _params = {}
+ if timeout is not None:
+ if isinstance(timeout, float) or isinstance(timeout, int):
+ _httpx_timeout = httpx.Timeout(timeout)
+ _params["timeout"] = _httpx_timeout
+ else:
+ _params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
+
+ sync_handler = HTTPHandler(**_params) # type: ignore
+ else:
+ sync_handler = client # type: ignore
+
+ if (
+ "images/generations" in api_base
+ and api_version
+ in [ # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict
+ "2023-06-01-preview",
+ "2023-07-01-preview",
+ "2023-08-01-preview",
+ "2023-09-01-preview",
+ "2023-10-01-preview",
+ ]
+ ): # CREATE + POLL for azure dall-e-2 calls
+
+ api_base = modify_url(
+ original_url=api_base, new_path="/openai/images/generations:submit"
+ )
+
+ data.pop(
+ "model", None
+ ) # REMOVE 'model' from dall-e-2 arg https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#request-a-generated-image-dall-e-2-preview
+ response = sync_handler.post(
+ url=api_base,
+ data=json.dumps(data),
+ headers={
+ "Content-Type": "application/json",
+ "api-key": api_key,
+ },
+ )
+ if "operation-location" in response.headers:
+ operation_location_url = response.headers["operation-location"]
+ else:
+ raise AzureOpenAIError(status_code=500, message=response.text)
+ response = sync_handler.get(
+ url=operation_location_url,
+ headers={
+ "api-key": api_key,
+ },
+ )
+
+ response.read()
+
+ timeout_secs: int = 120
+ start_time = time.time()
+ if "status" not in response.json():
+ raise Exception(
+ "Expected 'status' in response. Got={}".format(response.json())
+ )
+ while response.json()["status"] not in ["succeeded", "failed"]:
+ if time.time() - start_time > timeout_secs:
+ raise AzureOpenAIError(
+ status_code=408, message="Operation polling timed out."
+ )
+
+ time.sleep(int(response.headers.get("retry-after") or 10))
+ response = sync_handler.get(
+ url=operation_location_url,
+ headers={
+ "api-key": api_key,
+ },
+ )
+ response.read()
+
+ if response.json()["status"] == "failed":
+ error_data = response.json()
+ raise AzureOpenAIError(status_code=400, message=json.dumps(error_data))
+
+ result = response.json()["result"]
+ return httpx.Response(
+ status_code=200,
+ headers=response.headers,
+ content=json.dumps(result).encode("utf-8"),
+ request=httpx.Request(method="POST", url="https://api.openai.com/v1"),
+ )
+ return sync_handler.post(
+ url=api_base,
+ json=data,
+ headers={
+ "Content-Type": "application/json;",
+ "api-key": api_key,
+ },
+ )
+
+ def create_azure_base_url(
+ self, azure_client_params: dict, model: Optional[str]
+ ) -> str:
+
+ api_base: str = azure_client_params.get(
+ "azure_endpoint", ""
+ ) # "https://example-endpoint.openai.azure.com"
+ if api_base.endswith("/"):
+ api_base = api_base.rstrip("/")
+ api_version: str = azure_client_params.get("api_version", "")
+ if model is None:
+ model = ""
+ new_api_base = (
+ api_base
+ + "/openai/deployments/"
+ + model
+ + "/images/generations"
+ + "?api-version="
+ + api_version
+ )
+
+ return new_api_base
+
async def aimage_generation(
self,
data: dict,
@@ -725,30 +1311,40 @@ class AzureChatCompletion(BaseLLM):
logging_obj=None,
timeout=None,
):
- response = None
+ response: Optional[dict] = None
try:
- if client is None:
- client_session = litellm.aclient_session or httpx.AsyncClient(
- transport=AsyncCustomHTTPTransport(),
- )
- azure_client = AsyncAzureOpenAI(
- http_client=client_session, **azure_client_params
- )
- else:
- azure_client = client
+ # response = await azure_client.images.generate(**data, timeout=timeout)
+ api_base: str = azure_client_params.get(
+ "api_base", ""
+ ) # "https://example-endpoint.openai.azure.com"
+ if api_base.endswith("/"):
+ api_base = api_base.rstrip("/")
+ api_version: str = azure_client_params.get("api_version", "")
+ img_gen_api_base = self.create_azure_base_url(
+ azure_client_params=azure_client_params, model=data.get("model", "")
+ )
+
## LOGGING
logging_obj.pre_call(
input=data["prompt"],
- api_key=azure_client.api_key,
+ api_key=api_key,
additional_args={
- "headers": {"api_key": azure_client.api_key},
- "api_base": azure_client._base_url._uri_reference,
- "acompletion": True,
"complete_input_dict": data,
+ "api_base": img_gen_api_base,
+ "headers": {"api_key": api_key},
},
)
- response = await azure_client.images.generate(**data, timeout=timeout)
- stringified_response = response.model_dump()
+ httpx_response: httpx.Response = await self.make_async_azure_httpx_request(
+ client=None,
+ timeout=timeout,
+ api_base=img_gen_api_base,
+ api_version=api_version,
+ api_key=api_key,
+ data=data,
+ )
+ response = httpx_response.json()
+
+ stringified_response = response
## LOGGING
logging_obj.post_call(
input=input,
@@ -831,28 +1427,30 @@ class AzureChatCompletion(BaseLLM):
response = self.aimage_generation(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_key=api_key, client=client, azure_client_params=azure_client_params, timeout=timeout) # type: ignore
return response
- if client is None:
- client_session = litellm.client_session or httpx.Client(
- transport=CustomHTTPTransport(),
- )
- azure_client = AzureOpenAI(http_client=client_session, **azure_client_params) # type: ignore
- else:
- azure_client = client
+ img_gen_api_base = self.create_azure_base_url(
+ azure_client_params=azure_client_params, model=data.get("model", "")
+ )
## LOGGING
logging_obj.pre_call(
- input=prompt,
- api_key=azure_client.api_key,
+ input=data["prompt"],
+ api_key=api_key,
additional_args={
- "headers": {"api_key": azure_client.api_key},
- "api_base": azure_client._base_url._uri_reference,
- "acompletion": False,
"complete_input_dict": data,
+ "api_base": img_gen_api_base,
+ "headers": {"api_key": api_key},
},
)
+ httpx_response: httpx.Response = self.make_sync_azure_httpx_request(
+ client=None,
+ timeout=timeout,
+ api_base=img_gen_api_base,
+ api_version=api_version or "",
+ api_key=api_key or "",
+ data=data,
+ )
+ response = httpx_response.json()
- ## COMPLETION CALL
- response = azure_client.images.generate(**data, timeout=timeout) # type: ignore
## LOGGING
logging_obj.post_call(
input=prompt,
@@ -861,7 +1459,7 @@ class AzureChatCompletion(BaseLLM):
original_response=response,
)
# return response
- return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="image_generation") # type: ignore
+ return convert_to_model_response_object(response_object=response, model_response_object=model_response, response_type="image_generation") # type: ignore
except AzureOpenAIError as e:
exception_mapping_worked = True
raise e
@@ -943,7 +1541,12 @@ class AzureChatCompletion(BaseLLM):
response = azure_client.audio.transcriptions.create(
**data, timeout=timeout # type: ignore
)
- stringified_response = response.model_dump()
+
+ if isinstance(response, BaseModel):
+ stringified_response = response.model_dump()
+ else:
+ stringified_response = TranscriptionResponse(text=response).model_dump()
+
## LOGGING
logging_obj.post_call(
input=audio_file.name,
@@ -996,7 +1599,10 @@ class AzureChatCompletion(BaseLLM):
**data, timeout=timeout
) # type: ignore
- stringified_response = response.model_dump()
+ if isinstance(response, BaseModel):
+ stringified_response = response.model_dump()
+ else:
+ stringified_response = TranscriptionResponse(text=response).model_dump()
## LOGGING
logging_obj.post_call(
@@ -1024,6 +1630,96 @@ class AzureChatCompletion(BaseLLM):
)
raise e
+ def audio_speech(
+ self,
+ model: str,
+ input: str,
+ voice: str,
+ optional_params: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ organization: Optional[str],
+ max_retries: int,
+ timeout: Union[float, httpx.Timeout],
+ azure_ad_token: Optional[str] = None,
+ aspeech: Optional[bool] = None,
+ client=None,
+ ) -> HttpxBinaryResponseContent:
+
+ max_retries = optional_params.pop("max_retries", 2)
+
+ if aspeech is not None and aspeech is True:
+ return self.async_audio_speech(
+ model=model,
+ input=input,
+ voice=voice,
+ optional_params=optional_params,
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ max_retries=max_retries,
+ timeout=timeout,
+ client=client,
+ ) # type: ignore
+
+ azure_client: AzureOpenAI = self._get_sync_azure_client(
+ api_base=api_base,
+ api_version=api_version,
+ api_key=api_key,
+ azure_ad_token=azure_ad_token,
+ model=model,
+ max_retries=max_retries,
+ timeout=timeout,
+ client=client,
+ client_type="sync",
+ ) # type: ignore
+
+ response = azure_client.audio.speech.create(
+ model=model,
+ voice=voice, # type: ignore
+ input=input,
+ **optional_params,
+ )
+ return response
+
+ async def async_audio_speech(
+ self,
+ model: str,
+ input: str,
+ voice: str,
+ optional_params: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ max_retries: int,
+ timeout: Union[float, httpx.Timeout],
+ client=None,
+ ) -> HttpxBinaryResponseContent:
+
+ azure_client: AsyncAzureOpenAI = self._get_sync_azure_client(
+ api_base=api_base,
+ api_version=api_version,
+ api_key=api_key,
+ azure_ad_token=azure_ad_token,
+ model=model,
+ max_retries=max_retries,
+ timeout=timeout,
+ client=client,
+ client_type="async",
+ ) # type: ignore
+
+ response = await azure_client.audio.speech.create(
+ model=model,
+ voice=voice, # type: ignore
+ input=input,
+ **optional_params,
+ )
+
+ return response
+
def get_headers(
self,
model: Optional[str],
@@ -1036,9 +1732,7 @@ class AzureChatCompletion(BaseLLM):
input: Optional[list] = None,
prompt: Optional[str] = None,
) -> dict:
- client_session = litellm.client_session or httpx.Client(
- transport=CustomHTTPTransport(), # handle dall-e-2 calls
- )
+ client_session = litellm.client_session or httpx.Client()
if "gateway.ai.cloudflare.com" in api_base:
## build base url - assume api base includes resource name
if not api_base.endswith("/"):
@@ -1111,9 +1805,10 @@ class AzureChatCompletion(BaseLLM):
input: Optional[list] = None,
prompt: Optional[str] = None,
) -> dict:
- client_session = litellm.aclient_session or httpx.AsyncClient(
- transport=AsyncCustomHTTPTransport(), # handle dall-e-2 calls
- )
+ client_session = (
+ litellm.aclient_session or httpx.AsyncClient()
+ ) # handle dall-e-2 calls
+
if "gateway.ai.cloudflare.com" in api_base:
## build base url - assume api base includes resource name
if not api_base.endswith("/"):
@@ -1192,3 +1887,828 @@ class AzureChatCompletion(BaseLLM):
response["x-ms-region"] = completion.headers["x-ms-region"]
return response
+
+
+class AzureAssistantsAPI(BaseLLM):
+ def __init__(self) -> None:
+ super().__init__()
+
+ def get_azure_client(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AzureOpenAI] = None,
+ ) -> AzureOpenAI:
+ received_args = locals()
+ if client is None:
+ data = {}
+ for k, v in received_args.items():
+ if k == "self" or k == "client":
+ pass
+ elif k == "api_base" and v is not None:
+ data["azure_endpoint"] = v
+ elif v is not None:
+ data[k] = v
+ azure_openai_client = AzureOpenAI(**data) # type: ignore
+ else:
+ azure_openai_client = client
+
+ return azure_openai_client
+
+ def async_get_azure_client(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI] = None,
+ ) -> AsyncAzureOpenAI:
+ received_args = locals()
+ if client is None:
+ data = {}
+ for k, v in received_args.items():
+ if k == "self" or k == "client":
+ pass
+ elif k == "api_base" and v is not None:
+ data["azure_endpoint"] = v
+ elif v is not None:
+ data[k] = v
+
+ azure_openai_client = AsyncAzureOpenAI(**data)
+ # azure_openai_client = AsyncAzureOpenAI(**data) # type: ignore
+ else:
+ azure_openai_client = client
+
+ return azure_openai_client
+
+ ### ASSISTANTS ###
+
+ async def async_get_assistants(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI],
+ ) -> AsyncCursorPage[Assistant]:
+ azure_openai_client = self.async_get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ response = await azure_openai_client.beta.assistants.list()
+
+ return response
+
+ # fmt: off
+
+ @overload
+ def get_assistants(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI],
+ aget_assistants: Literal[True],
+ ) -> Coroutine[None, None, AsyncCursorPage[Assistant]]:
+ ...
+
+ @overload
+ def get_assistants(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AzureOpenAI],
+ aget_assistants: Optional[Literal[False]],
+ ) -> SyncCursorPage[Assistant]:
+ ...
+
+ # fmt: on
+
+ def get_assistants(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client=None,
+ aget_assistants=None,
+ ):
+ if aget_assistants is not None and aget_assistants == True:
+ return self.async_get_assistants(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+ azure_openai_client = self.get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ api_version=api_version,
+ )
+
+ response = azure_openai_client.beta.assistants.list()
+
+ return response
+
+ ### MESSAGES ###
+
+ async def a_add_message(
+ self,
+ thread_id: str,
+ message_data: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI] = None,
+ ) -> OpenAIMessage:
+ openai_client = self.async_get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ thread_message: OpenAIMessage = await openai_client.beta.threads.messages.create( # type: ignore
+ thread_id, **message_data # type: ignore
+ )
+
+ response_obj: Optional[OpenAIMessage] = None
+ if getattr(thread_message, "status", None) is None:
+ thread_message.status = "completed"
+ response_obj = OpenAIMessage(**thread_message.dict())
+ else:
+ response_obj = OpenAIMessage(**thread_message.dict())
+ return response_obj
+
+ # fmt: off
+
+ @overload
+ def add_message(
+ self,
+ thread_id: str,
+ message_data: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI],
+ a_add_message: Literal[True],
+ ) -> Coroutine[None, None, OpenAIMessage]:
+ ...
+
+ @overload
+ def add_message(
+ self,
+ thread_id: str,
+ message_data: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AzureOpenAI],
+ a_add_message: Optional[Literal[False]],
+ ) -> OpenAIMessage:
+ ...
+
+ # fmt: on
+
+ def add_message(
+ self,
+ thread_id: str,
+ message_data: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client=None,
+ a_add_message: Optional[bool] = None,
+ ):
+ if a_add_message is not None and a_add_message == True:
+ return self.a_add_message(
+ thread_id=thread_id,
+ message_data=message_data,
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+ openai_client = self.get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ thread_message: OpenAIMessage = openai_client.beta.threads.messages.create( # type: ignore
+ thread_id, **message_data # type: ignore
+ )
+
+ response_obj: Optional[OpenAIMessage] = None
+ if getattr(thread_message, "status", None) is None:
+ thread_message.status = "completed"
+ response_obj = OpenAIMessage(**thread_message.dict())
+ else:
+ response_obj = OpenAIMessage(**thread_message.dict())
+ return response_obj
+
+ async def async_get_messages(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI] = None,
+ ) -> AsyncCursorPage[OpenAIMessage]:
+ openai_client = self.async_get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ response = await openai_client.beta.threads.messages.list(thread_id=thread_id)
+
+ return response
+
+ # fmt: off
+
+ @overload
+ def get_messages(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI],
+ aget_messages: Literal[True],
+ ) -> Coroutine[None, None, AsyncCursorPage[OpenAIMessage]]:
+ ...
+
+ @overload
+ def get_messages(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AzureOpenAI],
+ aget_messages: Optional[Literal[False]],
+ ) -> SyncCursorPage[OpenAIMessage]:
+ ...
+
+ # fmt: on
+
+ def get_messages(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client=None,
+ aget_messages=None,
+ ):
+ if aget_messages is not None and aget_messages == True:
+ return self.async_get_messages(
+ thread_id=thread_id,
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+ openai_client = self.get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ response = openai_client.beta.threads.messages.list(thread_id=thread_id)
+
+ return response
+
+ ### THREADS ###
+
+ async def async_create_thread(
+ self,
+ metadata: Optional[dict],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI],
+ messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+ ) -> Thread:
+ openai_client = self.async_get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ data = {}
+ if messages is not None:
+ data["messages"] = messages # type: ignore
+ if metadata is not None:
+ data["metadata"] = metadata # type: ignore
+
+ message_thread = await openai_client.beta.threads.create(**data) # type: ignore
+
+ return Thread(**message_thread.dict())
+
+ # fmt: off
+
+ @overload
+ def create_thread(
+ self,
+ metadata: Optional[dict],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+ client: Optional[AsyncAzureOpenAI],
+ acreate_thread: Literal[True],
+ ) -> Coroutine[None, None, Thread]:
+ ...
+
+ @overload
+ def create_thread(
+ self,
+ metadata: Optional[dict],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+ client: Optional[AzureOpenAI],
+ acreate_thread: Optional[Literal[False]],
+ ) -> Thread:
+ ...
+
+ # fmt: on
+
+ def create_thread(
+ self,
+ metadata: Optional[dict],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+ client=None,
+ acreate_thread=None,
+ ):
+ """
+ Here's an example:
+ ```
+ from litellm.llms.openai import OpenAIAssistantsAPI, MessageData
+
+ # create thread
+ message: MessageData = {"role": "user", "content": "Hey, how's it going?"}
+ openai_api.create_thread(messages=[message])
+ ```
+ """
+ if acreate_thread is not None and acreate_thread == True:
+ return self.async_create_thread(
+ metadata=metadata,
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ messages=messages,
+ )
+ azure_openai_client = self.get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ data = {}
+ if messages is not None:
+ data["messages"] = messages # type: ignore
+ if metadata is not None:
+ data["metadata"] = metadata # type: ignore
+
+ message_thread = azure_openai_client.beta.threads.create(**data) # type: ignore
+
+ return Thread(**message_thread.dict())
+
+ async def async_get_thread(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI],
+ ) -> Thread:
+ openai_client = self.async_get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ response = await openai_client.beta.threads.retrieve(thread_id=thread_id)
+
+ return Thread(**response.dict())
+
+ # fmt: off
+
+ @overload
+ def get_thread(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI],
+ aget_thread: Literal[True],
+ ) -> Coroutine[None, None, Thread]:
+ ...
+
+ @overload
+ def get_thread(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AzureOpenAI],
+ aget_thread: Optional[Literal[False]],
+ ) -> Thread:
+ ...
+
+ # fmt: on
+
+ def get_thread(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client=None,
+ aget_thread=None,
+ ):
+ if aget_thread is not None and aget_thread == True:
+ return self.async_get_thread(
+ thread_id=thread_id,
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+ openai_client = self.get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ response = openai_client.beta.threads.retrieve(thread_id=thread_id)
+
+ return Thread(**response.dict())
+
+ # def delete_thread(self):
+ # pass
+
+ ### RUNS ###
+
+ async def arun_thread(
+ self,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ stream: Optional[bool],
+ tools: Optional[Iterable[AssistantToolParam]],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI],
+ ) -> Run:
+ openai_client = self.async_get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ client=client,
+ )
+
+ response = await openai_client.beta.threads.runs.create_and_poll( # type: ignore
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ tools=tools,
+ )
+
+ return response
+
+ def async_run_thread_stream(
+ self,
+ client: AsyncAzureOpenAI,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ tools: Optional[Iterable[AssistantToolParam]],
+ event_handler: Optional[AssistantEventHandler],
+ ) -> AsyncAssistantStreamManager[AsyncAssistantEventHandler]:
+ data = {
+ "thread_id": thread_id,
+ "assistant_id": assistant_id,
+ "additional_instructions": additional_instructions,
+ "instructions": instructions,
+ "metadata": metadata,
+ "model": model,
+ "tools": tools,
+ }
+ if event_handler is not None:
+ data["event_handler"] = event_handler
+ return client.beta.threads.runs.stream(**data) # type: ignore
+
+ def run_thread_stream(
+ self,
+ client: AzureOpenAI,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ tools: Optional[Iterable[AssistantToolParam]],
+ event_handler: Optional[AssistantEventHandler],
+ ) -> AssistantStreamManager[AssistantEventHandler]:
+ data = {
+ "thread_id": thread_id,
+ "assistant_id": assistant_id,
+ "additional_instructions": additional_instructions,
+ "instructions": instructions,
+ "metadata": metadata,
+ "model": model,
+ "tools": tools,
+ }
+ if event_handler is not None:
+ data["event_handler"] = event_handler
+ return client.beta.threads.runs.stream(**data) # type: ignore
+
+ # fmt: off
+
+ @overload
+ def run_thread(
+ self,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ stream: Optional[bool],
+ tools: Optional[Iterable[AssistantToolParam]],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AsyncAzureOpenAI],
+ arun_thread: Literal[True],
+ ) -> Coroutine[None, None, Run]:
+ ...
+
+ @overload
+ def run_thread(
+ self,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ stream: Optional[bool],
+ tools: Optional[Iterable[AssistantToolParam]],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client: Optional[AzureOpenAI],
+ arun_thread: Optional[Literal[False]],
+ ) -> Run:
+ ...
+
+ # fmt: on
+
+ def run_thread(
+ self,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ stream: Optional[bool],
+ tools: Optional[Iterable[AssistantToolParam]],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ api_version: Optional[str],
+ azure_ad_token: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ client=None,
+ arun_thread=None,
+ event_handler: Optional[AssistantEventHandler] = None,
+ ):
+ if arun_thread is not None and arun_thread == True:
+ if stream is not None and stream == True:
+ azure_client = self.async_get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+ return self.async_run_thread_stream(
+ client=azure_client,
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ tools=tools,
+ event_handler=event_handler,
+ )
+ return self.arun_thread(
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ stream=stream,
+ tools=tools,
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+ openai_client = self.get_azure_client(
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ if stream is not None and stream == True:
+ return self.run_thread_stream(
+ client=openai_client,
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ tools=tools,
+ event_handler=event_handler,
+ )
+
+ response = openai_client.beta.threads.runs.create_and_poll( # type: ignore
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ tools=tools,
+ )
+
+ return response
diff --git a/litellm/llms/azure_text.py b/litellm/llms/azure_text.py
index 640ab82223..72d6f134b2 100644
--- a/litellm/llms/azure_text.py
+++ b/litellm/llms/azure_text.py
@@ -1,24 +1,27 @@
-from typing import Optional, Union, Any
-import types, requests # type: ignore
-from .base import BaseLLM
-from litellm.utils import (
- ModelResponse,
- Choices,
- Message,
- CustomStreamWrapper,
- convert_to_model_response_object,
- TranscriptionResponse,
- TextCompletionResponse,
-)
-from typing import Callable, Optional, BinaryIO
-from litellm import OpenAIConfig
-import litellm, json
-import httpx
-from .custom_httpx.azure_dall_e_2 import CustomHTTPTransport, AsyncCustomHTTPTransport
-from openai import AzureOpenAI, AsyncAzureOpenAI
-from ..llms.openai import OpenAITextCompletion, OpenAITextCompletionConfig
+import json
+import types # type: ignore
import uuid
-from .prompt_templates.factory import prompt_factory, custom_prompt
+from typing import Any, BinaryIO, Callable, Optional, Union
+
+import httpx
+import requests
+from openai import AsyncAzureOpenAI, AzureOpenAI
+
+import litellm
+from litellm import OpenAIConfig
+from litellm.utils import (
+ Choices,
+ CustomStreamWrapper,
+ Message,
+ ModelResponse,
+ TextCompletionResponse,
+ TranscriptionResponse,
+ convert_to_model_response_object,
+)
+
+from ..llms.openai import OpenAITextCompletion, OpenAITextCompletionConfig
+from .base import BaseLLM
+from .prompt_templates.factory import custom_prompt, prompt_factory
openai_text_completion_config = OpenAITextCompletionConfig()
diff --git a/litellm/llms/base.py b/litellm/llms/base.py
index 8c2f5101e1..7e80de9ab1 100644
--- a/litellm/llms/base.py
+++ b/litellm/llms/base.py
@@ -2,7 +2,7 @@
import litellm
import httpx, requests
from typing import Optional, Union
-from litellm.utils import Logging
+from litellm.litellm_core_utils.litellm_logging import Logging
class BaseLLM:
@@ -27,6 +27,25 @@ class BaseLLM:
"""
return model_response
+ def process_text_completion_response(
+ self,
+ model: str,
+ response: Union[requests.Response, httpx.Response],
+ model_response: litellm.utils.TextCompletionResponse,
+ stream: bool,
+ logging_obj: Logging,
+ optional_params: dict,
+ api_key: str,
+ data: Union[dict, str],
+ messages: list,
+ print_verbose,
+ encoding,
+ ) -> Union[litellm.utils.TextCompletionResponse, litellm.utils.CustomStreamWrapper]:
+ """
+ Helper function to process the response across sync + async completion calls
+ """
+ return model_response
+
def create_client_session(self):
if litellm.client_session:
_client_session = litellm.client_session
diff --git a/litellm/llms/baseten.py b/litellm/llms/baseten.py
index 643dae5304..d856efc9ab 100644
--- a/litellm/llms/baseten.py
+++ b/litellm/llms/baseten.py
@@ -1,9 +1,11 @@
-import os
import json
-from enum import Enum
-import requests # type: ignore
+import os
import time
+from enum import Enum
from typing import Callable
+
+import requests # type: ignore
+
from litellm.utils import ModelResponse, Usage
@@ -106,28 +108,32 @@ def completion(
and "data" in completion_response["model_output"]
and isinstance(completion_response["model_output"]["data"], list)
):
- model_response["choices"][0]["message"]["content"] = (
- completion_response["model_output"]["data"][0]
- )
+ model_response.choices[0].message.content = completion_response[ # type: ignore
+ "model_output"
+ ][
+ "data"
+ ][
+ 0
+ ]
elif isinstance(completion_response["model_output"], str):
- model_response["choices"][0]["message"]["content"] = (
- completion_response["model_output"]
- )
+ model_response.choices[0].message.content = completion_response[ # type: ignore
+ "model_output"
+ ]
elif "completion" in completion_response and isinstance(
completion_response["completion"], str
):
- model_response["choices"][0]["message"]["content"] = (
- completion_response["completion"]
- )
+ model_response.choices[0].message.content = completion_response[ # type: ignore
+ "completion"
+ ]
elif isinstance(completion_response, list) and len(completion_response) > 0:
if "generated_text" not in completion_response:
raise BasetenError(
message=f"Unable to parse response. Original response: {response.text}",
status_code=response.status_code,
)
- model_response["choices"][0]["message"]["content"] = (
- completion_response[0]["generated_text"]
- )
+ model_response.choices[0].message.content = completion_response[0][ # type: ignore
+ "generated_text"
+ ]
## GETTING LOGPROBS
if (
"details" in completion_response[0]
@@ -139,7 +145,7 @@ def completion(
sum_logprob = 0
for token in completion_response[0]["details"]["tokens"]:
sum_logprob += token["logprob"]
- model_response["choices"][0]["message"]._logprobs = sum_logprob
+ model_response.choices[0].logprobs = sum_logprob
else:
raise BasetenError(
message=f"Unable to parse response. Original response: {response.text}",
@@ -152,8 +158,8 @@ def completion(
encoding.encode(model_response["choices"][0]["message"]["content"])
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/bedrock.py b/litellm/llms/bedrock.py
index 4314032e76..e12b656ed1 100644
--- a/litellm/llms/bedrock.py
+++ b/litellm/llms/bedrock.py
@@ -1,25 +1,32 @@
-import json, copy, types
+####################################
+######### DEPRECATED FILE ##########
+####################################
+# logic moved to `bedrock_httpx.py` #
+
+import copy
+import json
import os
+import time
+import types
+import uuid
from enum import Enum
-import time, uuid
-from typing import Callable, Optional, Any, Union, List
+from typing import Any, Callable, List, Optional, Union
+
+import httpx
+
import litellm
-from litellm.utils import (
- ModelResponse,
- get_secret,
- Usage,
- ImageResponse,
- map_finish_reason,
-)
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.types.utils import ImageResponse, ModelResponse, Usage
+from litellm.utils import get_secret
+
from .prompt_templates.factory import (
- prompt_factory,
- custom_prompt,
construct_tool_use_system_prompt,
+ contains_tag,
+ custom_prompt,
extract_between_tags,
parse_xml_params,
- contains_tag,
+ prompt_factory,
)
-import httpx
class BedrockError(Exception):
@@ -633,7 +640,11 @@ def init_bedrock_client(
config = boto3.session.Config()
### CHECK STS ###
- if aws_web_identity_token is not None and aws_role_name is not None and aws_session_name is not None:
+ if (
+ aws_web_identity_token is not None
+ and aws_role_name is not None
+ and aws_session_name is not None
+ ):
oidc_token = get_secret(aws_web_identity_token)
if oidc_token is None:
@@ -642,9 +653,7 @@ def init_bedrock_client(
status_code=401,
)
- sts_client = boto3.client(
- "sts"
- )
+ sts_client = boto3.client("sts")
# https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
# https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
@@ -726,38 +735,31 @@ def init_bedrock_client(
def convert_messages_to_prompt(model, messages, provider, custom_prompt_dict):
# handle anthropic prompts and amazon titan prompts
- if provider == "anthropic" or provider == "amazon":
- if model in custom_prompt_dict:
- # check if the model has a registered custom prompt
- model_prompt_details = custom_prompt_dict[model]
- prompt = custom_prompt(
- role_dict=model_prompt_details["roles"],
- initial_prompt_value=model_prompt_details["initial_prompt_value"],
- final_prompt_value=model_prompt_details["final_prompt_value"],
- messages=messages,
- )
- else:
+ chat_template_provider = ["anthropic", "amazon", "mistral", "meta"]
+ if model in custom_prompt_dict:
+ # check if the model has a registered custom prompt
+ model_prompt_details = custom_prompt_dict[model]
+ prompt = custom_prompt(
+ role_dict=model_prompt_details["roles"],
+ initial_prompt_value=model_prompt_details["initial_prompt_value"],
+ final_prompt_value=model_prompt_details["final_prompt_value"],
+ messages=messages,
+ )
+ else:
+ if provider in chat_template_provider:
prompt = prompt_factory(
model=model, messages=messages, custom_llm_provider="bedrock"
)
- elif provider == "mistral":
- prompt = prompt_factory(
- model=model, messages=messages, custom_llm_provider="bedrock"
- )
- elif provider == "meta":
- prompt = prompt_factory(
- model=model, messages=messages, custom_llm_provider="bedrock"
- )
- else:
- prompt = ""
- for message in messages:
- if "role" in message:
- if message["role"] == "user":
- prompt += f"{message['content']}"
+ else:
+ prompt = ""
+ for message in messages:
+ if "role" in message:
+ if message["role"] == "user":
+ prompt += f"{message['content']}"
+ else:
+ prompt += f"{message['content']}"
else:
prompt += f"{message['content']}"
- else:
- prompt += f"{message['content']}"
return prompt
@@ -1120,7 +1122,7 @@ def completion(
logging_obj=logging_obj,
)
- model_response["finish_reason"] = map_finish_reason(
+ model_response.choices[0].finish_reason = map_finish_reason(
response_body["stop_reason"]
)
_usage = litellm.Usage(
@@ -1132,14 +1134,16 @@ def completion(
setattr(model_response, "usage", _usage)
else:
outputText = response_body["completion"]
- model_response["finish_reason"] = response_body["stop_reason"]
+ model_response.choices[0].finish_reason = response_body["stop_reason"]
elif provider == "cohere":
outputText = response_body["generations"][0]["text"]
elif provider == "meta":
outputText = response_body["generation"]
elif provider == "mistral":
outputText = response_body["outputs"][0]["text"]
- model_response["finish_reason"] = response_body["outputs"][0]["stop_reason"]
+ model_response.choices[0].finish_reason = response_body["outputs"][0][
+ "stop_reason"
+ ]
else: # amazon titan
outputText = response_body.get("results")[0].get("outputText")
@@ -1158,7 +1162,7 @@ def completion(
and getattr(model_response.choices[0].message, "tool_calls", None)
is None
):
- model_response["choices"][0]["message"]["content"] = outputText
+ model_response.choices[0].message.content = outputText
elif (
hasattr(model_response.choices[0], "message")
and getattr(model_response.choices[0].message, "tool_calls", None)
@@ -1197,8 +1201,8 @@ def completion(
)
setattr(model_response, "usage", usage)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
model_response._hidden_params["region_name"] = client.meta.region_name
print_verbose(f"model_response._hidden_params: {model_response._hidden_params}")
@@ -1321,9 +1325,9 @@ def _embedding_func_single(
def embedding(
model: str,
input: Union[list, str],
+ model_response: litellm.EmbeddingResponse,
api_key: Optional[str] = None,
logging_obj=None,
- model_response=None,
optional_params=None,
encoding=None,
):
@@ -1389,9 +1393,9 @@ def embedding(
"embedding": embedding,
}
)
- model_response["object"] = "list"
- model_response["data"] = embedding_response
- model_response["model"] = model
+ model_response.object = "list"
+ model_response.data = embedding_response
+ model_response.model = model
input_tokens = 0
input_str = "".join(input)
diff --git a/litellm/llms/bedrock_httpx.py b/litellm/llms/bedrock_httpx.py
index 337055dc2e..b41dd542b9 100644
--- a/litellm/llms/bedrock_httpx.py
+++ b/litellm/llms/bedrock_httpx.py
@@ -1,50 +1,85 @@
# What is this?
## Initial implementation of calling bedrock via httpx client (allows for async calls).
## V1 - covers cohere + anthropic claude-3 support
-
-import os, types
+import copy
import json
-from enum import Enum
-import requests, copy # type: ignore
+import os
import time
+import types
+import urllib.parse
+import uuid
+from enum import Enum
+from functools import partial
from typing import (
+ Any,
+ AsyncIterator,
Callable,
- Optional,
+ Iterator,
List,
Literal,
- Union,
- Any,
- TypedDict,
+ Optional,
Tuple,
- Iterator,
- AsyncIterator,
+ TypedDict,
+ Union,
)
+
+import httpx # type: ignore
+import requests # type: ignore
+
+import litellm
+from litellm.caching import DualCache
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.litellm_core_utils.litellm_logging import Logging
+from litellm.llms.custom_httpx.http_handler import (
+ AsyncHTTPHandler,
+ HTTPHandler,
+ _get_async_httpx_client,
+ _get_httpx_client,
+)
+from litellm.types.llms.bedrock import *
+from litellm.types.llms.openai import (
+ ChatCompletionDeltaChunk,
+ ChatCompletionResponseMessage,
+ ChatCompletionToolCallChunk,
+ ChatCompletionToolCallFunctionChunk,
+)
+from litellm.types.utils import Choices, Message
from litellm.utils import (
+ CustomStreamWrapper,
ModelResponse,
Usage,
- map_finish_reason,
- CustomStreamWrapper,
- Message,
- Choices,
get_secret,
- Logging,
+ print_verbose,
)
-import litellm, uuid
+
+from .base import BaseLLM
+from .bedrock import BedrockError, ModelResponseIterator, convert_messages_to_prompt
from .prompt_templates.factory import (
- prompt_factory,
- custom_prompt,
+ _bedrock_converse_messages_pt,
+ _bedrock_tools_pt,
cohere_message_pt,
construct_tool_use_system_prompt,
+ contains_tag,
+ custom_prompt,
extract_between_tags,
parse_xml_params,
- contains_tag,
+ prompt_factory,
)
-from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
-from .base import BaseLLM
-import httpx # type: ignore
-from .bedrock import BedrockError, convert_messages_to_prompt, ModelResponseIterator
-from litellm.types.llms.bedrock import *
-import urllib.parse
+
+BEDROCK_CONVERSE_MODELS = [
+ "anthropic.claude-3-5-sonnet-20240620-v1:0",
+ "anthropic.claude-3-opus-20240229-v1:0",
+ "anthropic.claude-3-sonnet-20240229-v1:0",
+ "anthropic.claude-3-haiku-20240307-v1:0",
+ "anthropic.claude-v2",
+ "anthropic.claude-v2:1",
+ "anthropic.claude-v1",
+ "anthropic.claude-instant-v1",
+]
+
+
+iam_cache = DualCache()
+_response_stream_shape_cache = None
class AmazonCohereChatConfig:
@@ -118,6 +153,8 @@ class AmazonCohereChatConfig:
"presence_penalty",
"seed",
"stop",
+ "tools",
+ "tool_choice",
]
def map_openai_params(
@@ -145,6 +182,76 @@ class AmazonCohereChatConfig:
return optional_params
+async def make_call(
+ client: Optional[AsyncHTTPHandler],
+ api_base: str,
+ headers: dict,
+ data: str,
+ model: str,
+ messages: list,
+ logging_obj,
+):
+ try:
+ if client is None:
+ client = _get_async_httpx_client() # Create a new client if none provided
+
+ response = await client.post(api_base, headers=headers, data=data, stream=True)
+
+ if response.status_code != 200:
+ raise BedrockError(status_code=response.status_code, message=response.text)
+
+ decoder = AWSEventStreamDecoder(model=model)
+ completion_stream = decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024))
+
+ # LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response="first stream response received",
+ additional_args={"complete_input_dict": data},
+ )
+
+ return completion_stream
+ except httpx.HTTPStatusError as err:
+ error_code = err.response.status_code
+ raise BedrockError(status_code=error_code, message=str(err))
+ except httpx.TimeoutException as e:
+ raise BedrockError(status_code=408, message="Timeout error occurred.")
+ except Exception as e:
+ raise BedrockError(status_code=500, message=str(e))
+
+
+def make_sync_call(
+ client: Optional[HTTPHandler],
+ api_base: str,
+ headers: dict,
+ data: str,
+ model: str,
+ messages: list,
+ logging_obj,
+):
+ if client is None:
+ client = _get_httpx_client() # Create a new client if none provided
+
+ response = client.post(api_base, headers=headers, data=data, stream=True)
+
+ if response.status_code != 200:
+ raise BedrockError(status_code=response.status_code, message=response.read())
+
+ decoder = AWSEventStreamDecoder(model=model)
+ completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024))
+
+ # LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response="first stream response received",
+ additional_args={"complete_input_dict": data},
+ )
+
+ return completion_stream
+
+
class BedrockLLM(BaseLLM):
"""
Example call
@@ -173,20 +280,24 @@ class BedrockLLM(BaseLLM):
# handle anthropic prompts and amazon titan prompts
prompt = ""
chat_history: Optional[list] = None
+ ## CUSTOM PROMPT
+ if model in custom_prompt_dict:
+ # check if the model has a registered custom prompt
+ model_prompt_details = custom_prompt_dict[model]
+ prompt = custom_prompt(
+ role_dict=model_prompt_details["roles"],
+ initial_prompt_value=model_prompt_details.get(
+ "initial_prompt_value", ""
+ ),
+ final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
+ messages=messages,
+ )
+ return prompt, None
+ ## ELSE
if provider == "anthropic" or provider == "amazon":
- if model in custom_prompt_dict:
- # check if the model has a registered custom prompt
- model_prompt_details = custom_prompt_dict[model]
- prompt = custom_prompt(
- role_dict=model_prompt_details["roles"],
- initial_prompt_value=model_prompt_details["initial_prompt_value"],
- final_prompt_value=model_prompt_details["final_prompt_value"],
- messages=messages,
- )
- else:
- prompt = prompt_factory(
- model=model, messages=messages, custom_llm_provider="bedrock"
- )
+ prompt = prompt_factory(
+ model=model, messages=messages, custom_llm_provider="bedrock"
+ )
elif provider == "mistral":
prompt = prompt_factory(
model=model, messages=messages, custom_llm_provider="bedrock"
@@ -213,24 +324,32 @@ class BedrockLLM(BaseLLM):
self,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
+ aws_session_token: Optional[str] = None,
aws_region_name: Optional[str] = None,
aws_session_name: Optional[str] = None,
aws_profile_name: Optional[str] = None,
aws_role_name: Optional[str] = None,
+ aws_web_identity_token: Optional[str] = None,
):
"""
Return a boto3.Credentials object
"""
import boto3
+ print_verbose(
+ f"Boto3 get_credentials called variables passed to function {locals()}"
+ )
+
## CHECK IS 'os.environ/' passed in
params_to_check: List[Optional[str]] = [
aws_access_key_id,
aws_secret_access_key,
+ aws_session_token,
aws_region_name,
aws_session_name,
aws_profile_name,
aws_role_name,
+ aws_web_identity_token,
]
# Iterate over parameters and update if needed
@@ -243,14 +362,81 @@ class BedrockLLM(BaseLLM):
(
aws_access_key_id,
aws_secret_access_key,
+ aws_session_token,
aws_region_name,
aws_session_name,
aws_profile_name,
aws_role_name,
+ aws_web_identity_token,
) = params_to_check
### CHECK STS ###
- if aws_role_name is not None and aws_session_name is not None:
+ if (
+ aws_web_identity_token is not None
+ and aws_role_name is not None
+ and aws_session_name is not None
+ ):
+ print_verbose(
+ f"IN Web Identity Token: {aws_web_identity_token} | Role Name: {aws_role_name} | Session Name: {aws_session_name}"
+ )
+ iam_creds_cache_key = json.dumps(
+ {
+ "aws_web_identity_token": aws_web_identity_token,
+ "aws_role_name": aws_role_name,
+ "aws_session_name": aws_session_name,
+ "aws_region_name": aws_region_name,
+ }
+ )
+
+ iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key)
+ if iam_creds_dict is None:
+ oidc_token = get_secret(aws_web_identity_token)
+
+ if oidc_token is None:
+ raise BedrockError(
+ message="OIDC token could not be retrieved from secret manager.",
+ status_code=401,
+ )
+
+ sts_client = boto3.client(
+ "sts",
+ region_name=aws_region_name,
+ endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com",
+ )
+
+ # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
+ # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
+ sts_response = sts_client.assume_role_with_web_identity(
+ RoleArn=aws_role_name,
+ RoleSessionName=aws_session_name,
+ WebIdentityToken=oidc_token,
+ DurationSeconds=3600,
+ )
+
+ iam_creds_dict = {
+ "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"],
+ "aws_secret_access_key": sts_response["Credentials"][
+ "SecretAccessKey"
+ ],
+ "aws_session_token": sts_response["Credentials"]["SessionToken"],
+ "region_name": aws_region_name,
+ }
+
+ iam_cache.set_cache(
+ key=iam_creds_cache_key,
+ value=json.dumps(iam_creds_dict),
+ ttl=3600 - 60,
+ )
+
+ session = boto3.Session(**iam_creds_dict)
+
+ iam_creds = session.get_credentials()
+
+ return iam_creds
+ elif aws_role_name is not None and aws_session_name is not None:
+ print_verbose(
+ f"Using STS Client AWS aws_role_name: {aws_role_name} aws_session_name: {aws_session_name}"
+ )
sts_client = boto3.client(
"sts",
aws_access_key_id=aws_access_key_id, # [OPTIONAL]
@@ -261,13 +447,38 @@ class BedrockLLM(BaseLLM):
RoleArn=aws_role_name, RoleSessionName=aws_session_name
)
- return sts_response["Credentials"]
+ # Extract the credentials from the response and convert to Session Credentials
+ sts_credentials = sts_response["Credentials"]
+ from botocore.credentials import Credentials
+
+ credentials = Credentials(
+ access_key=sts_credentials["AccessKeyId"],
+ secret_key=sts_credentials["SecretAccessKey"],
+ token=sts_credentials["SessionToken"],
+ )
+ return credentials
elif aws_profile_name is not None: ### CHECK SESSION ###
# uses auth values from AWS profile usually stored in ~/.aws/credentials
+ print_verbose(f"Using AWS profile: {aws_profile_name}")
client = boto3.Session(profile_name=aws_profile_name)
return client.get_credentials()
+ elif (
+ aws_access_key_id is not None
+ and aws_secret_access_key is not None
+ and aws_session_token is not None
+ ): ### CHECK FOR AWS SESSION TOKEN ###
+ print_verbose(f"Using AWS Session Token: {aws_session_token}")
+ from botocore.credentials import Credentials
+
+ credentials = Credentials(
+ access_key=aws_access_key_id,
+ secret_key=aws_secret_access_key,
+ token=aws_session_token,
+ )
+ return credentials
else:
+ print_verbose("Using Default AWS Session")
session = boto3.Session(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
@@ -312,7 +523,7 @@ class BedrockLLM(BaseLLM):
outputText = completion_response["text"] # type: ignore
elif "generations" in completion_response:
outputText = completion_response["generations"][0]["text"]
- model_response["finish_reason"] = map_finish_reason(
+ model_response.choices[0].finish_reason = map_finish_reason(
completion_response["generations"][0]["finish_reason"]
)
elif provider == "anthropic":
@@ -416,7 +627,7 @@ class BedrockLLM(BaseLLM):
logging_obj=logging_obj,
)
- model_response["finish_reason"] = map_finish_reason(
+ model_response.choices[0].finish_reason = map_finish_reason(
completion_response.get("stop_reason", "")
)
_usage = litellm.Usage(
@@ -429,7 +640,9 @@ class BedrockLLM(BaseLLM):
else:
outputText = completion_response["completion"]
- model_response["finish_reason"] = completion_response["stop_reason"]
+ model_response.choices[0].finish_reason = completion_response[
+ "stop_reason"
+ ]
elif provider == "ai21":
outputText = (
completion_response.get("completions")[0].get("data").get("text")
@@ -438,9 +651,9 @@ class BedrockLLM(BaseLLM):
outputText = completion_response["generation"]
elif provider == "mistral":
outputText = completion_response["outputs"][0]["text"]
- model_response["finish_reason"] = completion_response["outputs"][0][
- "stop_reason"
- ]
+ model_response.choices[0].finish_reason = completion_response[
+ "outputs"
+ ][0]["stop_reason"]
else: # amazon titan
outputText = completion_response.get("results")[0].get("outputText")
except Exception as e:
@@ -458,7 +671,7 @@ class BedrockLLM(BaseLLM):
and getattr(model_response.choices[0].message, "tool_calls", None)
is None
):
- model_response["choices"][0]["message"]["content"] = outputText
+ model_response.choices[0].message.content = outputText
elif (
hasattr(model_response.choices[0], "message")
and getattr(model_response.choices[0].message, "tool_calls", None)
@@ -514,8 +727,8 @@ class BedrockLLM(BaseLLM):
)
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@@ -554,7 +767,6 @@ class BedrockLLM(BaseLLM):
) -> Union[ModelResponse, CustomStreamWrapper]:
try:
import boto3
-
from botocore.auth import SigV4Auth
from botocore.awsrequest import AWSRequest
from botocore.credentials import Credentials
@@ -572,9 +784,10 @@ class BedrockLLM(BaseLLM):
provider = model.split(".")[0]
## CREDENTIALS ##
- # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
+ # pop aws_secret_access_key, aws_access_key_id, aws_session_token, aws_region_name from kwargs, since completion calls fail with them
aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
aws_access_key_id = optional_params.pop("aws_access_key_id", None)
+ aws_session_token = optional_params.pop("aws_session_token", None)
aws_region_name = optional_params.pop("aws_region_name", None)
aws_role_name = optional_params.pop("aws_role_name", None)
aws_session_name = optional_params.pop("aws_session_name", None)
@@ -582,6 +795,7 @@ class BedrockLLM(BaseLLM):
aws_bedrock_runtime_endpoint = optional_params.pop(
"aws_bedrock_runtime_endpoint", None
) # https://bedrock-runtime.{region_name}.amazonaws.com
+ aws_web_identity_token = optional_params.pop("aws_web_identity_token", None)
### SET REGION NAME ###
if aws_region_name is None:
@@ -605,10 +819,12 @@ class BedrockLLM(BaseLLM):
credentials: Credentials = self.get_credentials(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
+ aws_session_token=aws_session_token,
aws_region_name=aws_region_name,
aws_session_name=aws_session_name,
aws_profile_name=aws_profile_name,
aws_role_name=aws_role_name,
+ aws_web_identity_token=aws_web_identity_token,
)
### SET RUNTIME ENDPOINT ###
@@ -841,7 +1057,7 @@ class BedrockLLM(BaseLLM):
if isinstance(timeout, float) or isinstance(timeout, int):
timeout = httpx.Timeout(timeout)
_params["timeout"] = timeout
- self.client = HTTPHandler(**_params) # type: ignore
+ self.client = _get_httpx_client(_params) # type: ignore
else:
self.client = client
if (stream is not None and stream == True) and provider != "ai21":
@@ -854,7 +1070,7 @@ class BedrockLLM(BaseLLM):
if response.status_code != 200:
raise BedrockError(
- status_code=response.status_code, message=response.text
+ status_code=response.status_code, message=response.read()
)
decoder = AWSEventStreamDecoder(model=model)
@@ -923,16 +1139,16 @@ class BedrockLLM(BaseLLM):
if isinstance(timeout, float) or isinstance(timeout, int):
timeout = httpx.Timeout(timeout)
_params["timeout"] = timeout
- self.client = AsyncHTTPHandler(**_params) # type: ignore
+ client = _get_async_httpx_client(_params) # type: ignore
else:
- self.client = client # type: ignore
+ client = client # type: ignore
try:
- response = await self.client.post(api_base, headers=headers, data=data) # type: ignore
+ response = await client.post(api_base, headers=headers, data=data) # type: ignore
response.raise_for_status()
except httpx.HTTPStatusError as err:
error_code = err.response.status_code
- raise BedrockError(status_code=error_code, message=response.text)
+ raise BedrockError(status_code=error_code, message=err.response.text)
except httpx.TimeoutException as e:
raise BedrockError(status_code=408, message="Timeout error occurred.")
@@ -968,53 +1184,827 @@ class BedrockLLM(BaseLLM):
headers={},
client: Optional[AsyncHTTPHandler] = None,
) -> CustomStreamWrapper:
- if client is None:
- _params = {}
- if timeout is not None:
- if isinstance(timeout, float) or isinstance(timeout, int):
- timeout = httpx.Timeout(timeout)
- _params["timeout"] = timeout
- self.client = AsyncHTTPHandler(**_params) # type: ignore
- else:
- self.client = client # type: ignore
+ # The call is not made here; instead, we prepare the necessary objects for the stream.
- response = await self.client.post(api_base, headers=headers, data=data, stream=True) # type: ignore
-
- if response.status_code != 200:
- raise BedrockError(status_code=response.status_code, message=response.text)
-
- decoder = AWSEventStreamDecoder(model=model)
-
- completion_stream = decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024))
streaming_response = CustomStreamWrapper(
- completion_stream=completion_stream,
+ completion_stream=None,
+ make_call=partial(
+ make_call,
+ client=client,
+ api_base=api_base,
+ headers=headers,
+ data=data,
+ model=model,
+ messages=messages,
+ logging_obj=logging_obj,
+ ),
model=model,
custom_llm_provider="bedrock",
logging_obj=logging_obj,
)
-
- ## LOGGING
- logging_obj.post_call(
- input=messages,
- api_key="",
- original_response=streaming_response,
- additional_args={"complete_input_dict": data},
- )
-
return streaming_response
def embedding(self, *args, **kwargs):
return super().embedding(*args, **kwargs)
-def get_response_stream_shape():
- from botocore.model import ServiceModel
- from botocore.loaders import Loader
+class AmazonConverseConfig:
+ """
+ Reference - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html
+ #2 - https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html#conversation-inference-supported-models-features
+ """
- loader = Loader()
- bedrock_service_dict = loader.load_service_model("bedrock-runtime", "service-2")
- bedrock_service_model = ServiceModel(bedrock_service_dict)
- return bedrock_service_model.shape_for("ResponseStream")
+ maxTokens: Optional[int]
+ stopSequences: Optional[List[str]]
+ temperature: Optional[int]
+ topP: Optional[int]
+
+ def __init__(
+ self,
+ maxTokens: Optional[int] = None,
+ stopSequences: Optional[List[str]] = None,
+ temperature: Optional[int] = None,
+ topP: Optional[int] = None,
+ ) -> None:
+ locals_ = locals()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self, model: str) -> List[str]:
+ supported_params = [
+ "max_tokens",
+ "stream",
+ "stream_options",
+ "stop",
+ "temperature",
+ "top_p",
+ "extra_headers",
+ ]
+
+ if (
+ model.startswith("anthropic")
+ or model.startswith("mistral")
+ or model.startswith("cohere")
+ ):
+ supported_params.append("tools")
+
+ if model.startswith("anthropic") or model.startswith("mistral"):
+ # only anthropic and mistral support tool choice config. otherwise (E.g. cohere) will fail the call - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
+ supported_params.append("tool_choice")
+
+ return supported_params
+
+ def map_tool_choice_values(
+ self, model: str, tool_choice: Union[str, dict], drop_params: bool
+ ) -> Optional[ToolChoiceValuesBlock]:
+ if tool_choice == "none":
+ if litellm.drop_params is True or drop_params is True:
+ return None
+ else:
+ raise litellm.utils.UnsupportedParamsError(
+ message="Bedrock doesn't support tool_choice={}. To drop it from the call, set `litellm.drop_params = True.".format(
+ tool_choice
+ ),
+ status_code=400,
+ )
+ elif tool_choice == "required":
+ return ToolChoiceValuesBlock(any={})
+ elif tool_choice == "auto":
+ return ToolChoiceValuesBlock(auto={})
+ elif isinstance(tool_choice, dict):
+ # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
+ specific_tool = SpecificToolChoiceBlock(
+ name=tool_choice.get("function", {}).get("name", "")
+ )
+ return ToolChoiceValuesBlock(tool=specific_tool)
+ else:
+ raise litellm.utils.UnsupportedParamsError(
+ message="Bedrock doesn't support tool_choice={}. Supported tool_choice values=['auto', 'required', json object]. To drop it from the call, set `litellm.drop_params = True.".format(
+ tool_choice
+ ),
+ status_code=400,
+ )
+
+ def get_supported_image_types(self) -> List[str]:
+ return ["png", "jpeg", "gif", "webp"]
+
+ def map_openai_params(
+ self,
+ model: str,
+ non_default_params: dict,
+ optional_params: dict,
+ drop_params: bool,
+ ) -> dict:
+ for param, value in non_default_params.items():
+ if param == "max_tokens":
+ optional_params["maxTokens"] = value
+ if param == "stream":
+ optional_params["stream"] = value
+ if param == "stop":
+ if isinstance(value, str):
+ value = [value]
+ optional_params["stop_sequences"] = value
+ if param == "temperature":
+ optional_params["temperature"] = value
+ if param == "top_p":
+ optional_params["topP"] = value
+ if param == "tools":
+ optional_params["tools"] = value
+ if param == "tool_choice":
+ _tool_choice_value = self.map_tool_choice_values(
+ model=model, tool_choice=value, drop_params=drop_params # type: ignore
+ )
+ if _tool_choice_value is not None:
+ optional_params["tool_choice"] = _tool_choice_value
+ return optional_params
+
+
+class BedrockConverseLLM(BaseLLM):
+ def __init__(self) -> None:
+ super().__init__()
+
+ def process_response(
+ self,
+ model: str,
+ response: Union[requests.Response, httpx.Response],
+ model_response: ModelResponse,
+ stream: bool,
+ logging_obj: Logging,
+ optional_params: dict,
+ api_key: str,
+ data: Union[dict, str],
+ messages: List,
+ print_verbose,
+ encoding,
+ ) -> Union[ModelResponse, CustomStreamWrapper]:
+
+ ## LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key=api_key,
+ original_response=response.text,
+ additional_args={"complete_input_dict": data},
+ )
+ print_verbose(f"raw model_response: {response.text}")
+
+ ## RESPONSE OBJECT
+ try:
+ completion_response = ConverseResponseBlock(**response.json()) # type: ignore
+ except Exception as e:
+ raise BedrockError(
+ message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format(
+ response.text, str(e)
+ ),
+ status_code=422,
+ )
+
+ """
+ Bedrock Response Object has optional message block
+
+ completion_response["output"].get("message", None)
+
+ A message block looks like this (Example 1):
+ "output": {
+ "message": {
+ "role": "assistant",
+ "content": [
+ {
+ "text": "Is there anything else you'd like to talk about? Perhaps I can help with some economic questions or provide some information about economic concepts?"
+ }
+ ]
+ }
+ },
+ (Example 2):
+ "output": {
+ "message": {
+ "role": "assistant",
+ "content": [
+ {
+ "toolUse": {
+ "toolUseId": "tooluse_hbTgdi0CSLq_hM4P8csZJA",
+ "name": "top_song",
+ "input": {
+ "sign": "WZPZ"
+ }
+ }
+ }
+ ]
+ }
+ }
+
+ """
+ message: Optional[MessageBlock] = completion_response["output"]["message"]
+ chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
+ content_str = ""
+ tools: List[ChatCompletionToolCallChunk] = []
+ if message is not None:
+ for idx, content in enumerate(message["content"]):
+ """
+ - Content is either a tool response or text
+ """
+ if "text" in content:
+ content_str += content["text"]
+ if "toolUse" in content:
+ _function_chunk = ChatCompletionToolCallFunctionChunk(
+ name=content["toolUse"]["name"],
+ arguments=json.dumps(content["toolUse"]["input"]),
+ )
+ _tool_response_chunk = ChatCompletionToolCallChunk(
+ id=content["toolUse"]["toolUseId"],
+ type="function",
+ function=_function_chunk,
+ index=idx,
+ )
+ tools.append(_tool_response_chunk)
+ chat_completion_message["content"] = content_str
+ chat_completion_message["tool_calls"] = tools
+
+ ## CALCULATING USAGE - bedrock returns usage in the headers
+ input_tokens = completion_response["usage"]["inputTokens"]
+ output_tokens = completion_response["usage"]["outputTokens"]
+ total_tokens = completion_response["usage"]["totalTokens"]
+
+ model_response.choices = [
+ litellm.Choices(
+ finish_reason=map_finish_reason(completion_response["stopReason"]),
+ index=0,
+ message=litellm.Message(**chat_completion_message),
+ )
+ ]
+ model_response.created = int(time.time())
+ model_response.model = model
+ usage = Usage(
+ prompt_tokens=input_tokens,
+ completion_tokens=output_tokens,
+ total_tokens=total_tokens,
+ )
+ setattr(model_response, "usage", usage)
+
+ return model_response
+
+ def encode_model_id(self, model_id: str) -> str:
+ """
+ Double encode the model ID to ensure it matches the expected double-encoded format.
+ Args:
+ model_id (str): The model ID to encode.
+ Returns:
+ str: The double-encoded model ID.
+ """
+ return urllib.parse.quote(model_id, safe="")
+
+ def get_credentials(
+ self,
+ aws_access_key_id: Optional[str] = None,
+ aws_secret_access_key: Optional[str] = None,
+ aws_session_token: Optional[str] = None,
+ aws_region_name: Optional[str] = None,
+ aws_session_name: Optional[str] = None,
+ aws_profile_name: Optional[str] = None,
+ aws_role_name: Optional[str] = None,
+ aws_web_identity_token: Optional[str] = None,
+ ):
+ """
+ Return a boto3.Credentials object
+ """
+ import boto3
+
+ ## CHECK IS 'os.environ/' passed in
+ params_to_check: List[Optional[str]] = [
+ aws_access_key_id,
+ aws_secret_access_key,
+ aws_session_token,
+ aws_region_name,
+ aws_session_name,
+ aws_profile_name,
+ aws_role_name,
+ aws_web_identity_token,
+ ]
+
+ # Iterate over parameters and update if needed
+ for i, param in enumerate(params_to_check):
+ if param and param.startswith("os.environ/"):
+ _v = get_secret(param)
+ if _v is not None and isinstance(_v, str):
+ params_to_check[i] = _v
+ # Assign updated values back to parameters
+ (
+ aws_access_key_id,
+ aws_secret_access_key,
+ aws_session_token,
+ aws_region_name,
+ aws_session_name,
+ aws_profile_name,
+ aws_role_name,
+ aws_web_identity_token,
+ ) = params_to_check
+
+ ### CHECK STS ###
+ if (
+ aws_web_identity_token is not None
+ and aws_role_name is not None
+ and aws_session_name is not None
+ ):
+ iam_creds_cache_key = json.dumps(
+ {
+ "aws_web_identity_token": aws_web_identity_token,
+ "aws_role_name": aws_role_name,
+ "aws_session_name": aws_session_name,
+ "aws_region_name": aws_region_name,
+ }
+ )
+
+ iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key)
+ if iam_creds_dict is None:
+ oidc_token = get_secret(aws_web_identity_token)
+
+ if oidc_token is None:
+ raise BedrockError(
+ message="OIDC token could not be retrieved from secret manager.",
+ status_code=401,
+ )
+
+ sts_client = boto3.client(
+ "sts",
+ region_name=aws_region_name,
+ endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com",
+ )
+
+ # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
+ # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
+ sts_response = sts_client.assume_role_with_web_identity(
+ RoleArn=aws_role_name,
+ RoleSessionName=aws_session_name,
+ WebIdentityToken=oidc_token,
+ DurationSeconds=3600,
+ )
+
+ iam_creds_dict = {
+ "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"],
+ "aws_secret_access_key": sts_response["Credentials"][
+ "SecretAccessKey"
+ ],
+ "aws_session_token": sts_response["Credentials"]["SessionToken"],
+ "region_name": aws_region_name,
+ }
+
+ iam_cache.set_cache(
+ key=iam_creds_cache_key,
+ value=json.dumps(iam_creds_dict),
+ ttl=3600 - 60,
+ )
+
+ session = boto3.Session(**iam_creds_dict)
+
+ iam_creds = session.get_credentials()
+
+ return iam_creds
+ elif aws_role_name is not None and aws_session_name is not None:
+ sts_client = boto3.client(
+ "sts",
+ aws_access_key_id=aws_access_key_id, # [OPTIONAL]
+ aws_secret_access_key=aws_secret_access_key, # [OPTIONAL]
+ )
+
+ sts_response = sts_client.assume_role(
+ RoleArn=aws_role_name, RoleSessionName=aws_session_name
+ )
+
+ # Extract the credentials from the response and convert to Session Credentials
+ sts_credentials = sts_response["Credentials"]
+ from botocore.credentials import Credentials
+
+ credentials = Credentials(
+ access_key=sts_credentials["AccessKeyId"],
+ secret_key=sts_credentials["SecretAccessKey"],
+ token=sts_credentials["SessionToken"],
+ )
+ return credentials
+ elif aws_profile_name is not None: ### CHECK SESSION ###
+ # uses auth values from AWS profile usually stored in ~/.aws/credentials
+ client = boto3.Session(profile_name=aws_profile_name)
+
+ return client.get_credentials()
+ elif (
+ aws_access_key_id is not None
+ and aws_secret_access_key is not None
+ and aws_session_token is not None
+ ): ### CHECK FOR AWS SESSION TOKEN ###
+ from botocore.credentials import Credentials
+
+ credentials = Credentials(
+ access_key=aws_access_key_id,
+ secret_key=aws_secret_access_key,
+ token=aws_session_token,
+ )
+ return credentials
+ else:
+ session = boto3.Session(
+ aws_access_key_id=aws_access_key_id,
+ aws_secret_access_key=aws_secret_access_key,
+ region_name=aws_region_name,
+ )
+
+ return session.get_credentials()
+
+ async def async_streaming(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ data: str,
+ timeout: Optional[Union[float, httpx.Timeout]],
+ encoding,
+ logging_obj,
+ stream,
+ optional_params: dict,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+ client: Optional[AsyncHTTPHandler] = None,
+ ) -> CustomStreamWrapper:
+ streaming_response = CustomStreamWrapper(
+ completion_stream=None,
+ make_call=partial(
+ make_call,
+ client=client,
+ api_base=api_base,
+ headers=headers,
+ data=data,
+ model=model,
+ messages=messages,
+ logging_obj=logging_obj,
+ ),
+ model=model,
+ custom_llm_provider="bedrock",
+ logging_obj=logging_obj,
+ )
+ return streaming_response
+
+ async def async_completion(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ data: str,
+ timeout: Optional[Union[float, httpx.Timeout]],
+ encoding,
+ logging_obj,
+ stream,
+ optional_params: dict,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+ client: Optional[AsyncHTTPHandler] = None,
+ ) -> Union[ModelResponse, CustomStreamWrapper]:
+ if client is None:
+ _params = {}
+ if timeout is not None:
+ if isinstance(timeout, float) or isinstance(timeout, int):
+ timeout = httpx.Timeout(timeout)
+ _params["timeout"] = timeout
+ client = _get_async_httpx_client(_params) # type: ignore
+ else:
+ client = client # type: ignore
+
+ try:
+ response = await client.post(api_base, headers=headers, data=data) # type: ignore
+ response.raise_for_status()
+ except httpx.HTTPStatusError as err:
+ error_code = err.response.status_code
+ raise BedrockError(status_code=error_code, message=err.response.text)
+ except httpx.TimeoutException as e:
+ raise BedrockError(status_code=408, message="Timeout error occurred.")
+
+ return self.process_response(
+ model=model,
+ response=response,
+ model_response=model_response,
+ stream=stream if isinstance(stream, bool) else False,
+ logging_obj=logging_obj,
+ api_key="",
+ data=data,
+ messages=messages,
+ print_verbose=print_verbose,
+ optional_params=optional_params,
+ encoding=encoding,
+ )
+
+ def completion(
+ self,
+ model: str,
+ messages: list,
+ custom_prompt_dict: dict,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ encoding,
+ logging_obj,
+ optional_params: dict,
+ acompletion: bool,
+ timeout: Optional[Union[float, httpx.Timeout]],
+ litellm_params=None,
+ logger_fn=None,
+ extra_headers: Optional[dict] = None,
+ client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
+ ):
+ try:
+ import boto3
+ from botocore.auth import SigV4Auth
+ from botocore.awsrequest import AWSRequest
+ from botocore.credentials import Credentials
+ except ImportError:
+ raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
+
+ ## SETUP ##
+ stream = optional_params.pop("stream", None)
+ modelId = optional_params.pop("model_id", None)
+ if modelId is not None:
+ modelId = self.encode_model_id(model_id=modelId)
+ else:
+ modelId = model
+
+ provider = model.split(".")[0]
+
+ ## CREDENTIALS ##
+ # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
+ aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
+ aws_access_key_id = optional_params.pop("aws_access_key_id", None)
+ aws_session_token = optional_params.pop("aws_session_token", None)
+ aws_region_name = optional_params.pop("aws_region_name", None)
+ aws_role_name = optional_params.pop("aws_role_name", None)
+ aws_session_name = optional_params.pop("aws_session_name", None)
+ aws_profile_name = optional_params.pop("aws_profile_name", None)
+ aws_bedrock_runtime_endpoint = optional_params.pop(
+ "aws_bedrock_runtime_endpoint", None
+ ) # https://bedrock-runtime.{region_name}.amazonaws.com
+ aws_web_identity_token = optional_params.pop("aws_web_identity_token", None)
+
+ ### SET REGION NAME ###
+ if aws_region_name is None:
+ # check env #
+ litellm_aws_region_name = get_secret("AWS_REGION_NAME", None)
+
+ if litellm_aws_region_name is not None and isinstance(
+ litellm_aws_region_name, str
+ ):
+ aws_region_name = litellm_aws_region_name
+
+ standard_aws_region_name = get_secret("AWS_REGION", None)
+ if standard_aws_region_name is not None and isinstance(
+ standard_aws_region_name, str
+ ):
+ aws_region_name = standard_aws_region_name
+
+ if aws_region_name is None:
+ aws_region_name = "us-west-2"
+
+ credentials: Credentials = self.get_credentials(
+ aws_access_key_id=aws_access_key_id,
+ aws_secret_access_key=aws_secret_access_key,
+ aws_session_token=aws_session_token,
+ aws_region_name=aws_region_name,
+ aws_session_name=aws_session_name,
+ aws_profile_name=aws_profile_name,
+ aws_role_name=aws_role_name,
+ aws_web_identity_token=aws_web_identity_token,
+ )
+
+ ### SET RUNTIME ENDPOINT ###
+ endpoint_url = ""
+ env_aws_bedrock_runtime_endpoint = get_secret("AWS_BEDROCK_RUNTIME_ENDPOINT")
+ if aws_bedrock_runtime_endpoint is not None and isinstance(
+ aws_bedrock_runtime_endpoint, str
+ ):
+ endpoint_url = aws_bedrock_runtime_endpoint
+ elif env_aws_bedrock_runtime_endpoint and isinstance(
+ env_aws_bedrock_runtime_endpoint, str
+ ):
+ endpoint_url = env_aws_bedrock_runtime_endpoint
+ else:
+ endpoint_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com"
+
+ if (stream is not None and stream is True) and provider != "ai21":
+ endpoint_url = f"{endpoint_url}/model/{modelId}/converse-stream"
+ else:
+ endpoint_url = f"{endpoint_url}/model/{modelId}/converse"
+
+ sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
+
+ # Separate system prompt from rest of message
+ system_prompt_indices = []
+ system_content_blocks: List[SystemContentBlock] = []
+ for idx, message in enumerate(messages):
+ if message["role"] == "system":
+ if isinstance(message["content"], str):
+ _system_content_block = SystemContentBlock(text=message["content"])
+ elif isinstance(message["content"], list):
+ for m in message["content"]:
+ if m.get("type", "") == "text":
+ _system_content_block = SystemContentBlock(text=m["text"])
+ system_content_blocks.append(_system_content_block)
+ system_prompt_indices.append(idx)
+ if len(system_prompt_indices) > 0:
+ for idx in reversed(system_prompt_indices):
+ messages.pop(idx)
+
+ inference_params = copy.deepcopy(optional_params)
+ additional_request_keys = []
+ additional_request_params = {}
+ supported_converse_params = AmazonConverseConfig.__annotations__.keys()
+ supported_tool_call_params = ["tools", "tool_choice"]
+ ## TRANSFORMATION ##
+ # send all model-specific params in 'additional_request_params'
+ for k, v in inference_params.items():
+ if (
+ k not in supported_converse_params
+ and k not in supported_tool_call_params
+ ):
+ additional_request_params[k] = v
+ additional_request_keys.append(k)
+ for key in additional_request_keys:
+ inference_params.pop(key, None)
+
+ bedrock_messages: List[MessageBlock] = _bedrock_converse_messages_pt(
+ messages=messages,
+ model=model,
+ llm_provider="bedrock_converse",
+ )
+ bedrock_tools: List[ToolBlock] = _bedrock_tools_pt(
+ inference_params.pop("tools", [])
+ )
+ bedrock_tool_config: Optional[ToolConfigBlock] = None
+ if len(bedrock_tools) > 0:
+ tool_choice_values: ToolChoiceValuesBlock = inference_params.pop(
+ "tool_choice", None
+ )
+ bedrock_tool_config = ToolConfigBlock(
+ tools=bedrock_tools,
+ )
+ if tool_choice_values is not None:
+ bedrock_tool_config["toolChoice"] = tool_choice_values
+
+ _data: RequestObject = {
+ "messages": bedrock_messages,
+ "additionalModelRequestFields": additional_request_params,
+ "system": system_content_blocks,
+ "inferenceConfig": InferenceConfig(**inference_params),
+ }
+ if bedrock_tool_config is not None:
+ _data["toolConfig"] = bedrock_tool_config
+ data = json.dumps(_data)
+ ## COMPLETION CALL
+
+ headers = {"Content-Type": "application/json"}
+ if extra_headers is not None:
+ headers = {"Content-Type": "application/json", **extra_headers}
+ request = AWSRequest(
+ method="POST", url=endpoint_url, data=data, headers=headers
+ )
+ sigv4.add_auth(request)
+ prepped = request.prepare()
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=messages,
+ api_key="",
+ additional_args={
+ "complete_input_dict": data,
+ "api_base": prepped.url,
+ "headers": prepped.headers,
+ },
+ )
+
+ ### ROUTING (ASYNC, STREAMING, SYNC)
+ if acompletion:
+ if isinstance(client, HTTPHandler):
+ client = None
+ if stream is True and provider != "ai21":
+ return self.async_streaming(
+ model=model,
+ messages=messages,
+ data=data,
+ api_base=prepped.url,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ stream=True,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ headers=prepped.headers,
+ timeout=timeout,
+ client=client,
+ ) # type: ignore
+ ### ASYNC COMPLETION
+ return self.async_completion(
+ model=model,
+ messages=messages,
+ data=data,
+ api_base=prepped.url,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ stream=stream, # type: ignore
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ headers=prepped.headers,
+ timeout=timeout,
+ client=client,
+ ) # type: ignore
+
+ if (stream is not None and stream is True) and provider != "ai21":
+
+ streaming_response = CustomStreamWrapper(
+ completion_stream=None,
+ make_call=partial(
+ make_sync_call,
+ client=None,
+ api_base=prepped.url,
+ headers=prepped.headers, # type: ignore
+ data=data,
+ model=model,
+ messages=messages,
+ logging_obj=logging_obj,
+ ),
+ model=model,
+ custom_llm_provider="bedrock",
+ logging_obj=logging_obj,
+ )
+
+ return streaming_response
+ ### COMPLETION
+
+ if client is None or isinstance(client, AsyncHTTPHandler):
+ _params = {}
+ if timeout is not None:
+ if isinstance(timeout, float) or isinstance(timeout, int):
+ timeout = httpx.Timeout(timeout)
+ _params["timeout"] = timeout
+ client = _get_httpx_client(_params) # type: ignore
+ else:
+ client = client
+ try:
+ response = client.post(url=prepped.url, headers=prepped.headers, data=data) # type: ignore
+ response.raise_for_status()
+ except httpx.HTTPStatusError as err:
+ error_code = err.response.status_code
+ raise BedrockError(status_code=error_code, message=response.text)
+ except httpx.TimeoutException:
+ raise BedrockError(status_code=408, message="Timeout error occurred.")
+
+ return self.process_response(
+ model=model,
+ response=response,
+ model_response=model_response,
+ stream=stream,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ api_key="",
+ data=data,
+ messages=messages,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ )
+
+
+def get_response_stream_shape():
+ global _response_stream_shape_cache
+ if _response_stream_shape_cache is None:
+
+ from botocore.loaders import Loader
+ from botocore.model import ServiceModel
+
+ loader = Loader()
+ bedrock_service_dict = loader.load_service_model("bedrock-runtime", "service-2")
+ bedrock_service_model = ServiceModel(bedrock_service_dict)
+ _response_stream_shape_cache = bedrock_service_model.shape_for("ResponseStream")
+
+ return _response_stream_shape_cache
class AWSEventStreamDecoder:
@@ -1024,6 +2014,63 @@ class AWSEventStreamDecoder:
self.model = model
self.parser = EventStreamJSONParser()
+ def converse_chunk_parser(self, chunk_data: dict) -> GenericStreamingChunk:
+ try:
+ text = ""
+ tool_use: Optional[ChatCompletionToolCallChunk] = None
+ is_finished = False
+ finish_reason = ""
+ usage: Optional[ConverseTokenUsageBlock] = None
+
+ index = int(chunk_data.get("contentBlockIndex", 0))
+ if "start" in chunk_data:
+ start_obj = ContentBlockStartEvent(**chunk_data["start"])
+ if (
+ start_obj is not None
+ and "toolUse" in start_obj
+ and start_obj["toolUse"] is not None
+ ):
+ tool_use = {
+ "id": start_obj["toolUse"]["toolUseId"],
+ "type": "function",
+ "function": {
+ "name": start_obj["toolUse"]["name"],
+ "arguments": "",
+ },
+ "index": index,
+ }
+ elif "delta" in chunk_data:
+ delta_obj = ContentBlockDeltaEvent(**chunk_data["delta"])
+ if "text" in delta_obj:
+ text = delta_obj["text"]
+ elif "toolUse" in delta_obj:
+ tool_use = {
+ "id": None,
+ "type": "function",
+ "function": {
+ "name": None,
+ "arguments": delta_obj["toolUse"]["input"],
+ },
+ "index": index,
+ }
+ elif "stopReason" in chunk_data:
+ finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop"))
+ is_finished = True
+ elif "usage" in chunk_data:
+ usage = ConverseTokenUsageBlock(**chunk_data["usage"]) # type: ignore
+
+ response = GenericStreamingChunk(
+ text=text,
+ tool_use=tool_use,
+ is_finished=is_finished,
+ finish_reason=finish_reason,
+ usage=usage,
+ index=index,
+ )
+ return response
+ except Exception as e:
+ raise Exception("Received streaming error - {}".format(str(e)))
+
def _chunk_parser(self, chunk_data: dict) -> GenericStreamingChunk:
text = ""
is_finished = False
@@ -1031,24 +2078,17 @@ class AWSEventStreamDecoder:
if "outputText" in chunk_data:
text = chunk_data["outputText"]
# ai21 mapping
- if "ai21" in self.model: # fake ai21 streaming
+ elif "ai21" in self.model: # fake ai21 streaming
text = chunk_data.get("completions")[0].get("data").get("text") # type: ignore
is_finished = True
finish_reason = "stop"
######## bedrock.anthropic mappings ###############
- elif "completion" in chunk_data: # not claude-3
- text = chunk_data["completion"] # bedrock.anthropic
- stop_reason = chunk_data.get("stop_reason", None)
- if stop_reason != None:
- is_finished = True
- finish_reason = stop_reason
- elif "delta" in chunk_data:
- if chunk_data["delta"].get("text", None) is not None:
- text = chunk_data["delta"]["text"]
- stop_reason = chunk_data["delta"].get("stop_reason", None)
- if stop_reason != None:
- is_finished = True
- finish_reason = stop_reason
+ elif (
+ "contentBlockIndex" in chunk_data
+ or "stopReason" in chunk_data
+ or "metrics" in chunk_data
+ ):
+ return self.converse_chunk_parser(chunk_data=chunk_data)
######## bedrock.mistral mappings ###############
elif "outputs" in chunk_data:
if (
@@ -1057,7 +2097,7 @@ class AWSEventStreamDecoder:
):
text = chunk_data["outputs"][0]["text"]
stop_reason = chunk_data.get("stop_reason", None)
- if stop_reason != None:
+ if stop_reason is not None:
is_finished = True
finish_reason = stop_reason
######## bedrock.cohere mappings ###############
@@ -1075,11 +2115,12 @@ class AWSEventStreamDecoder:
is_finished = True
finish_reason = chunk_data["completionReason"]
return GenericStreamingChunk(
- **{
- "text": text,
- "is_finished": is_finished,
- "finish_reason": finish_reason,
- }
+ text=text,
+ is_finished=is_finished,
+ finish_reason=finish_reason,
+ usage=None,
+ index=0,
+ tool_use=None,
)
def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[GenericStreamingChunk]:
@@ -1116,9 +2157,14 @@ class AWSEventStreamDecoder:
parsed_response = self.parser.parse(response_dict, get_response_stream_shape())
if response_dict["status_code"] != 200:
raise ValueError(f"Bad response code, expected 200: {response_dict}")
+ if "chunk" in parsed_response:
+ chunk = parsed_response.get("chunk")
+ if not chunk:
+ return None
+ return chunk.get("bytes").decode() # type: ignore[no-any-return]
+ else:
+ chunk = response_dict.get("body")
+ if not chunk:
+ return None
- chunk = parsed_response.get("chunk")
- if not chunk:
- return None
-
- return chunk.get("bytes").decode() # type: ignore[no-any-return]
+ return chunk.decode() # type: ignore[no-any-return]
diff --git a/litellm/llms/clarifai.py b/litellm/llms/clarifai.py
index 4610911e14..613ee5ced1 100644
--- a/litellm/llms/clarifai.py
+++ b/litellm/llms/clarifai.py
@@ -1,13 +1,18 @@
-import os, types, traceback
import json
-import requests
+import os
import time
+import traceback
+import types
from typing import Callable, Optional
-from litellm.utils import ModelResponse, Usage, Choices, Message, CustomStreamWrapper
-import litellm
+
import httpx
+import requests
+
+import litellm
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
-from .prompt_templates.factory import prompt_factory, custom_prompt
+from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage
+
+from .prompt_templates.factory import custom_prompt, prompt_factory
class ClarifaiError(Exception):
@@ -87,7 +92,14 @@ def completions_to_model(payload):
def process_response(
- model, prompt, response, model_response, api_key, data, encoding, logging_obj
+ model,
+ prompt,
+ response,
+ model_response: litellm.ModelResponse,
+ api_key,
+ data,
+ encoding,
+ logging_obj,
):
logging_obj.post_call(
input=prompt,
@@ -116,7 +128,7 @@ def process_response(
message=message_obj,
)
choices_list.append(choice_obj)
- model_response["choices"] = choices_list
+ model_response.choices = choices_list # type: ignore
except Exception as e:
raise ClarifaiError(
@@ -128,17 +140,22 @@ def process_response(
completion_tokens = len(
encoding.encode(model_response["choices"][0]["message"].get("content"))
)
- model_response["model"] = model
- model_response["usage"] = Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
+ model_response.model = model
+ setattr(
+ model_response,
+ "usage",
+ Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ ),
)
return model_response
def convert_model_to_url(model: str, api_base: str):
user_id, app_id, model_id = model.split(".")
+ model_id = model_id.lower()
return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
@@ -171,19 +188,59 @@ async def async_completion(
async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
response = await async_handler.post(
- api_base, headers=headers, data=json.dumps(data)
+ url=model, headers=headers, data=json.dumps(data)
)
- return process_response(
- model=model,
- prompt=prompt,
- response=response,
- model_response=model_response,
+ logging_obj.post_call(
+ input=prompt,
api_key=api_key,
- data=data,
- encoding=encoding,
- logging_obj=logging_obj,
+ original_response=response.text,
+ additional_args={"complete_input_dict": data},
)
+ ## RESPONSE OBJECT
+ try:
+ completion_response = response.json()
+ except Exception:
+ raise ClarifaiError(
+ message=response.text, status_code=response.status_code, url=model
+ )
+ # print(completion_response)
+ try:
+ choices_list = []
+ for idx, item in enumerate(completion_response["outputs"]):
+ if len(item["data"]["text"]["raw"]) > 0:
+ message_obj = Message(content=item["data"]["text"]["raw"])
+ else:
+ message_obj = Message(content=None)
+ choice_obj = Choices(
+ finish_reason="stop",
+ index=idx + 1, # check
+ message=message_obj,
+ )
+ choices_list.append(choice_obj)
+ model_response.choices = choices_list # type: ignore
+
+ except Exception as e:
+ raise ClarifaiError(
+ message=traceback.format_exc(), status_code=response.status_code, url=model
+ )
+
+ # Calculate Usage
+ prompt_tokens = len(encoding.encode(prompt))
+ completion_tokens = len(
+ encoding.encode(model_response["choices"][0]["message"].get("content"))
+ )
+ model_response.model = model
+ setattr(
+ model_response,
+ "usage",
+ Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ ),
+ )
+ return model_response
def completion(
@@ -241,7 +298,7 @@ def completion(
additional_args={
"complete_input_dict": data,
"headers": headers,
- "api_base": api_base,
+ "api_base": model,
},
)
if acompletion == True:
diff --git a/litellm/llms/cloudflare.py b/litellm/llms/cloudflare.py
index 5a24b3b443..516b490f44 100644
--- a/litellm/llms/cloudflare.py
+++ b/litellm/llms/cloudflare.py
@@ -1,13 +1,17 @@
-import os, types
import json
-from enum import Enum
-import requests # type: ignore
+import os
import time
+import types
+from enum import Enum
from typing import Callable, Optional
-import litellm
+
import httpx # type: ignore
+import requests # type: ignore
+
+import litellm
from litellm.utils import ModelResponse, Usage
-from .prompt_templates.factory import prompt_factory, custom_prompt
+
+from .prompt_templates.factory import custom_prompt, prompt_factory
class CloudflareError(Exception):
@@ -147,9 +151,9 @@ def completion(
)
completion_response = response.json()
- model_response["choices"][0]["message"]["content"] = completion_response[
- "result"
- ]["response"]
+ model_response.choices[0].message.content = completion_response["result"][ # type: ignore
+ "response"
+ ]
## CALCULATING USAGE
print_verbose(
@@ -160,8 +164,8 @@ def completion(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
)
- model_response["created"] = int(time.time())
- model_response["model"] = "cloudflare/" + model
+ model_response.created = int(time.time())
+ model_response.model = "cloudflare/" + model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/cohere.py b/litellm/llms/cohere.py
index 14a66b54ac..d946a8ddee 100644
--- a/litellm/llms/cohere.py
+++ b/litellm/llms/cohere.py
@@ -1,12 +1,16 @@
-import os, types
import json
+import os
+import time
+import traceback
+import types
from enum import Enum
-import requests # type: ignore
-import time, traceback
from typing import Callable, Optional
-from litellm.utils import ModelResponse, Choices, Message, Usage
-import litellm
+
import httpx # type: ignore
+import requests # type: ignore
+
+import litellm
+from litellm.utils import Choices, Message, ModelResponse, Usage
class CohereError(Exception):
@@ -117,7 +121,7 @@ class CohereConfig:
def validate_environment(api_key):
headers = {
- "Request-Source":"unspecified:litellm",
+ "Request-Source": "unspecified:litellm",
"accept": "application/json",
"content-type": "application/json",
}
@@ -219,7 +223,7 @@ def completion(
message=message_obj,
)
choices_list.append(choice_obj)
- model_response["choices"] = choices_list
+ model_response.choices = choices_list # type: ignore
except Exception as e:
raise CohereError(
message=response.text, status_code=response.status_code
@@ -231,8 +235,8 @@ def completion(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@@ -245,9 +249,9 @@ def completion(
def embedding(
model: str,
input: list,
+ model_response: litellm.EmbeddingResponse,
api_key: Optional[str] = None,
logging_obj=None,
- model_response=None,
encoding=None,
optional_params=None,
):
@@ -294,14 +298,18 @@ def embedding(
output_data.append(
{"object": "embedding", "index": idx, "embedding": embedding}
)
- model_response["object"] = "list"
- model_response["data"] = output_data
- model_response["model"] = model
+ model_response.object = "list"
+ model_response.data = output_data
+ model_response.model = model
input_tokens = 0
for text in input:
input_tokens += len(encoding.encode(text))
- model_response["usage"] = Usage(
- prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
+ setattr(
+ model_response,
+ "usage",
+ Usage(
+ prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
+ ),
)
return model_response
diff --git a/litellm/llms/cohere_chat.py b/litellm/llms/cohere_chat.py
index 8ae8392438..830c924bd4 100644
--- a/litellm/llms/cohere_chat.py
+++ b/litellm/llms/cohere_chat.py
@@ -1,13 +1,19 @@
-import os, types
import json
+import os
+import time
+import traceback
+import types
from enum import Enum
-import requests # type: ignore
-import time, traceback
from typing import Callable, Optional
-from litellm.utils import ModelResponse, Choices, Message, Usage
-import litellm
+
import httpx # type: ignore
-from .prompt_templates.factory import cohere_message_pt
+import requests # type: ignore
+
+import litellm
+from litellm.types.llms.cohere import ToolResultObject
+from litellm.utils import Choices, Message, ModelResponse, Usage
+
+from .prompt_templates.factory import cohere_message_pt, cohere_messages_pt_v2
class CohereError(Exception):
@@ -112,7 +118,7 @@ class CohereChatConfig:
def validate_environment(api_key):
headers = {
- "Request-Source":"unspecified:litellm",
+ "Request-Source": "unspecified:litellm",
"accept": "application/json",
"content-type": "application/json",
}
@@ -196,17 +202,19 @@ def completion(
api_base: str,
model_response: ModelResponse,
print_verbose: Callable,
+ optional_params: dict,
encoding,
api_key,
logging_obj,
- optional_params=None,
litellm_params=None,
logger_fn=None,
):
headers = validate_environment(api_key)
completion_url = api_base
model = model
- prompt, tool_results = cohere_message_pt(messages=messages)
+ most_recent_message, chat_history = cohere_messages_pt_v2(
+ messages=messages, model=model, llm_provider="cohere_chat"
+ )
## Load Config
config = litellm.CohereConfig.get_config()
@@ -221,18 +229,18 @@ def completion(
_is_function_call = True
cohere_tools = construct_cohere_tool(tools=optional_params["tools"])
optional_params["tools"] = cohere_tools
- if len(tool_results) > 0:
- optional_params["tool_results"] = tool_results
-
+ if isinstance(most_recent_message, dict):
+ optional_params["tool_results"] = [most_recent_message]
+ elif isinstance(most_recent_message, str):
+ optional_params["message"] = most_recent_message
data = {
"model": model,
- "message": prompt,
**optional_params,
}
## LOGGING
logging_obj.pre_call(
- input=prompt,
+ input=most_recent_message,
api_key=api_key,
additional_args={
"complete_input_dict": data,
@@ -256,7 +264,7 @@ def completion(
else:
## LOGGING
logging_obj.post_call(
- input=prompt,
+ input=most_recent_message,
api_key=api_key,
original_response=response.text,
additional_args={"complete_input_dict": data},
@@ -299,8 +307,8 @@ def completion(
prompt_tokens = billed_units.get("input_tokens", 0)
completion_tokens = billed_units.get("output_tokens", 0)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/custom_httpx/azure_dall_e_2.py b/litellm/llms/custom_httpx/azure_dall_e_2.py
deleted file mode 100644
index f361ede5bf..0000000000
--- a/litellm/llms/custom_httpx/azure_dall_e_2.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import time, json, httpx, asyncio
-
-
-class AsyncCustomHTTPTransport(httpx.AsyncHTTPTransport):
- """
- Async implementation of custom http transport
- """
-
- async def handle_async_request(self, request: httpx.Request) -> httpx.Response:
- if "images/generations" in request.url.path and request.url.params[
- "api-version"
- ] in [ # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict
- "2023-06-01-preview",
- "2023-07-01-preview",
- "2023-08-01-preview",
- "2023-09-01-preview",
- "2023-10-01-preview",
- ]:
- request.url = request.url.copy_with(
- path="/openai/images/generations:submit"
- )
- response = await super().handle_async_request(request)
- operation_location_url = response.headers["operation-location"]
- request.url = httpx.URL(operation_location_url)
- request.method = "GET"
- response = await super().handle_async_request(request)
- await response.aread()
-
- timeout_secs: int = 120
- start_time = time.time()
- while response.json()["status"] not in ["succeeded", "failed"]:
- if time.time() - start_time > timeout_secs:
- timeout = {
- "error": {
- "code": "Timeout",
- "message": "Operation polling timed out.",
- }
- }
- return httpx.Response(
- status_code=400,
- headers=response.headers,
- content=json.dumps(timeout).encode("utf-8"),
- request=request,
- )
-
- await asyncio.sleep(int(response.headers.get("retry-after") or 10))
- response = await super().handle_async_request(request)
- await response.aread()
-
- if response.json()["status"] == "failed":
- error_data = response.json()
- return httpx.Response(
- status_code=400,
- headers=response.headers,
- content=json.dumps(error_data).encode("utf-8"),
- request=request,
- )
-
- result = response.json()["result"]
- return httpx.Response(
- status_code=200,
- headers=response.headers,
- content=json.dumps(result).encode("utf-8"),
- request=request,
- )
- return await super().handle_async_request(request)
-
-
-class CustomHTTPTransport(httpx.HTTPTransport):
- """
- This class was written as a workaround to support dall-e-2 on openai > v1.x
-
- Refer to this issue for more: https://github.com/openai/openai-python/issues/692
- """
-
- def handle_request(
- self,
- request: httpx.Request,
- ) -> httpx.Response:
- if "images/generations" in request.url.path and request.url.params[
- "api-version"
- ] in [ # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict
- "2023-06-01-preview",
- "2023-07-01-preview",
- "2023-08-01-preview",
- "2023-09-01-preview",
- "2023-10-01-preview",
- ]:
- request.url = request.url.copy_with(
- path="/openai/images/generations:submit"
- )
- response = super().handle_request(request)
- operation_location_url = response.headers["operation-location"]
- request.url = httpx.URL(operation_location_url)
- request.method = "GET"
- response = super().handle_request(request)
- response.read()
- timeout_secs: int = 120
- start_time = time.time()
- while response.json()["status"] not in ["succeeded", "failed"]:
- if time.time() - start_time > timeout_secs:
- timeout = {
- "error": {
- "code": "Timeout",
- "message": "Operation polling timed out.",
- }
- }
- return httpx.Response(
- status_code=400,
- headers=response.headers,
- content=json.dumps(timeout).encode("utf-8"),
- request=request,
- )
- time.sleep(int(response.headers.get("retry-after", None) or 10))
- response = super().handle_request(request)
- response.read()
- if response.json()["status"] == "failed":
- error_data = response.json()
- return httpx.Response(
- status_code=400,
- headers=response.headers,
- content=json.dumps(error_data).encode("utf-8"),
- request=request,
- )
-
- result = response.json()["result"]
- return httpx.Response(
- status_code=200,
- headers=response.headers,
- content=json.dumps(result).encode("utf-8"),
- request=request,
- )
- return super().handle_request(request)
diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py
index 4df25944b8..ddffe9ad81 100644
--- a/litellm/llms/custom_httpx/http_handler.py
+++ b/litellm/llms/custom_httpx/http_handler.py
@@ -1,5 +1,20 @@
-import httpx, asyncio
-from typing import Optional, Union, Mapping, Any
+import asyncio
+import os
+import traceback
+from typing import Any, Mapping, Optional, Union
+
+import httpx
+
+import litellm
+
+try:
+ from litellm._version import version
+except:
+ version = "0.0.0"
+
+headers = {
+ "User-Agent": f"litellm/{version}",
+}
# https://www.python-httpx.org/advanced/timeouts
_DEFAULT_TIMEOUT = httpx.Timeout(timeout=5.0, connect=5.0)
@@ -11,15 +26,34 @@ class AsyncHTTPHandler:
timeout: Optional[Union[float, httpx.Timeout]] = None,
concurrent_limit=1000,
):
+ self.timeout = timeout
+ self.client = self.create_client(
+ timeout=timeout, concurrent_limit=concurrent_limit
+ )
+
+ def create_client(
+ self, timeout: Optional[Union[float, httpx.Timeout]], concurrent_limit: int
+ ) -> httpx.AsyncClient:
+
+ # Check if the HTTP_PROXY and HTTPS_PROXY environment variables are set and use them accordingly.
+ ssl_verify = bool(os.getenv("SSL_VERIFY", litellm.ssl_verify))
+ cert = os.getenv(
+ "SSL_CERTIFICATE", litellm.ssl_certificate
+ ) # /path/to/client.pem
+
if timeout is None:
timeout = _DEFAULT_TIMEOUT
# Create a client with a connection pool
- self.client = httpx.AsyncClient(
+
+ return httpx.AsyncClient(
timeout=timeout,
limits=httpx.Limits(
max_connections=concurrent_limit,
max_keepalive_connections=concurrent_limit,
),
+ verify=ssl_verify,
+ cert=cert,
+ headers=headers,
)
async def close(self):
@@ -43,14 +77,63 @@ class AsyncHTTPHandler:
self,
url: str,
data: Optional[Union[dict, str]] = None, # type: ignore
+ json: Optional[dict] = None,
params: Optional[dict] = None,
headers: Optional[dict] = None,
stream: bool = False,
):
- req = self.client.build_request(
- "POST", url, data=data, params=params, headers=headers # type: ignore
+ try:
+ req = self.client.build_request(
+ "POST", url, data=data, json=json, params=params, headers=headers # type: ignore
+ )
+ response = await self.client.send(req, stream=stream)
+ response.raise_for_status()
+ return response
+ except (httpx.RemoteProtocolError, httpx.ConnectError):
+ # Retry the request with a new session if there is a connection error
+ new_client = self.create_client(timeout=self.timeout, concurrent_limit=1)
+ try:
+ return await self.single_connection_post_request(
+ url=url,
+ client=new_client,
+ data=data,
+ json=json,
+ params=params,
+ headers=headers,
+ stream=stream,
+ )
+ finally:
+ await new_client.aclose()
+ except httpx.HTTPStatusError as e:
+ setattr(e, "status_code", e.response.status_code)
+ if stream is True:
+ setattr(e, "message", await e.response.aread())
+ else:
+ setattr(e, "message", e.response.text)
+ raise e
+ except Exception as e:
+ raise e
+
+ async def single_connection_post_request(
+ self,
+ url: str,
+ client: httpx.AsyncClient,
+ data: Optional[Union[dict, str]] = None, # type: ignore
+ json: Optional[dict] = None,
+ params: Optional[dict] = None,
+ headers: Optional[dict] = None,
+ stream: bool = False,
+ ):
+ """
+ Making POST request for a single connection client.
+
+ Used for retrying connection client errors.
+ """
+ req = client.build_request(
+ "POST", url, data=data, json=json, params=params, headers=headers # type: ignore
)
- response = await self.client.send(req, stream=stream)
+ response = await client.send(req, stream=stream)
+ response.raise_for_status()
return response
def __del__(self) -> None:
@@ -70,6 +153,12 @@ class HTTPHandler:
if timeout is None:
timeout = _DEFAULT_TIMEOUT
+ # Check if the HTTP_PROXY and HTTPS_PROXY environment variables are set and use them accordingly.
+ ssl_verify = bool(os.getenv("SSL_VERIFY", litellm.ssl_verify))
+ cert = os.getenv(
+ "SSL_CERTIFICATE", litellm.ssl_certificate
+ ) # /path/to/client.pem
+
if client is None:
# Create a client with a connection pool
self.client = httpx.Client(
@@ -78,6 +167,9 @@ class HTTPHandler:
max_connections=concurrent_limit,
max_keepalive_connections=concurrent_limit,
),
+ verify=ssl_verify,
+ cert=cert,
+ headers=headers,
)
else:
self.client = client
@@ -96,12 +188,14 @@ class HTTPHandler:
self,
url: str,
data: Optional[Union[dict, str]] = None,
+ json: Optional[Union[dict, str]] = None,
params: Optional[dict] = None,
headers: Optional[dict] = None,
stream: bool = False,
):
+
req = self.client.build_request(
- "POST", url, data=data, params=params, headers=headers # type: ignore
+ "POST", url, data=data, json=json, params=params, headers=headers # type: ignore
)
response = self.client.send(req, stream=stream)
return response
@@ -111,3 +205,60 @@ class HTTPHandler:
self.close()
except Exception:
pass
+
+
+def _get_async_httpx_client(params: Optional[dict] = None) -> AsyncHTTPHandler:
+ """
+ Retrieves the async HTTP client from the cache
+ If not present, creates a new client
+
+ Caches the new client and returns it.
+ """
+ _params_key_name = ""
+ if params is not None:
+ for key, value in params.items():
+ try:
+ _params_key_name += f"{key}_{value}"
+ except Exception:
+ pass
+
+ _cache_key_name = "async_httpx_client" + _params_key_name
+ if _cache_key_name in litellm.in_memory_llm_clients_cache:
+ return litellm.in_memory_llm_clients_cache[_cache_key_name]
+
+ if params is not None:
+ _new_client = AsyncHTTPHandler(**params)
+ else:
+ _new_client = AsyncHTTPHandler(
+ timeout=httpx.Timeout(timeout=600.0, connect=5.0)
+ )
+ litellm.in_memory_llm_clients_cache[_cache_key_name] = _new_client
+ return _new_client
+
+
+def _get_httpx_client(params: Optional[dict] = None) -> HTTPHandler:
+ """
+ Retrieves the HTTP client from the cache
+ If not present, creates a new client
+
+ Caches the new client and returns it.
+ """
+ _params_key_name = ""
+ if params is not None:
+ for key, value in params.items():
+ try:
+ _params_key_name += f"{key}_{value}"
+ except Exception:
+ pass
+
+ _cache_key_name = "httpx_client" + _params_key_name
+ if _cache_key_name in litellm.in_memory_llm_clients_cache:
+ return litellm.in_memory_llm_clients_cache[_cache_key_name]
+
+ if params is not None:
+ _new_client = HTTPHandler(**params)
+ else:
+ _new_client = HTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
+
+ litellm.in_memory_llm_clients_cache[_cache_key_name] = _new_client
+ return _new_client
diff --git a/litellm/llms/custom_httpx/httpx_handler.py b/litellm/llms/custom_httpx/httpx_handler.py
index 3f3bd09bac..513b0adadc 100644
--- a/litellm/llms/custom_httpx/httpx_handler.py
+++ b/litellm/llms/custom_httpx/httpx_handler.py
@@ -1,6 +1,14 @@
from typing import Optional
import httpx
+try:
+ from litellm._version import version
+except:
+ version = "0.0.0"
+
+headers = {
+ "User-Agent": f"litellm/{version}",
+}
class HTTPHandler:
def __init__(self, concurrent_limit=1000):
@@ -9,7 +17,8 @@ class HTTPHandler:
limits=httpx.Limits(
max_connections=concurrent_limit,
max_keepalive_connections=concurrent_limit,
- )
+ ),
+ headers=headers,
)
async def close(self):
diff --git a/litellm/llms/databricks.py b/litellm/llms/databricks.py
index 7b2013710e..88fa58abec 100644
--- a/litellm/llms/databricks.py
+++ b/litellm/llms/databricks.py
@@ -1,25 +1,26 @@
# What is this?
## Handler file for databricks API https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html#chat-request
-import os, types
+import copy
import json
-from enum import Enum
-import requests, copy # type: ignore
+import os
import time
-from typing import Callable, Optional, List, Union, Tuple, Literal
-from litellm.utils import (
- ModelResponse,
- Usage,
- map_finish_reason,
- CustomStreamWrapper,
- EmbeddingResponse,
-)
-import litellm
-from .prompt_templates.factory import prompt_factory, custom_prompt
-from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
-from .base import BaseLLM
+import types
+from enum import Enum
+from functools import partial
+from typing import Callable, List, Literal, Optional, Tuple, Union
+
import httpx # type: ignore
+import requests # type: ignore
+
+import litellm
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.types.llms.databricks import GenericStreamingChunk
from litellm.types.utils import ProviderField
+from litellm.utils import CustomStreamWrapper, EmbeddingResponse, ModelResponse, Usage
+
+from .base import BaseLLM
+from .prompt_templates.factory import custom_prompt, prompt_factory
class DatabricksError(Exception):
@@ -123,7 +124,7 @@ class DatabricksConfig:
original_chunk = None # this is used for function/tool calling
chunk_data = chunk_data.replace("data:", "")
chunk_data = chunk_data.strip()
- if len(chunk_data) == 0:
+ if len(chunk_data) == 0 or chunk_data == "[DONE]":
return {
"text": "",
"is_finished": is_finished,
@@ -221,6 +222,32 @@ class DatabricksEmbeddingConfig:
return optional_params
+async def make_call(
+ client: AsyncHTTPHandler,
+ api_base: str,
+ headers: dict,
+ data: str,
+ model: str,
+ messages: list,
+ logging_obj,
+):
+ response = await client.post(api_base, headers=headers, data=data, stream=True)
+
+ if response.status_code != 200:
+ raise DatabricksError(status_code=response.status_code, message=response.text)
+
+ completion_stream = response.aiter_lines()
+ # LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response=completion_stream, # Pass the completion stream for logging
+ additional_args={"complete_input_dict": data},
+ )
+
+ return completion_stream
+
+
class DatabricksChatCompletion(BaseLLM):
def __init__(self) -> None:
super().__init__()
@@ -262,7 +289,7 @@ class DatabricksChatCompletion(BaseLLM):
response: Union[requests.Response, httpx.Response],
model_response: ModelResponse,
stream: bool,
- logging_obj: litellm.utils.Logging,
+ logging_obj: litellm.litellm_core_utils.litellm_logging.Logging,
optional_params: dict,
api_key: str,
data: Union[dict, str],
@@ -327,8 +354,8 @@ class DatabricksChatCompletion(BaseLLM):
completion_tokens = completion_response["usage"]["output_tokens"]
total_tokens = prompt_tokens + completion_tokens
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@@ -354,29 +381,21 @@ class DatabricksChatCompletion(BaseLLM):
litellm_params=None,
logger_fn=None,
headers={},
- ):
- self.async_handler = AsyncHTTPHandler(
- timeout=httpx.Timeout(timeout=600.0, connect=5.0)
- )
+ client: Optional[AsyncHTTPHandler] = None,
+ ) -> CustomStreamWrapper:
+
data["stream"] = True
- try:
- response = await self.async_handler.post(
- api_base, headers=headers, data=json.dumps(data), stream=True
- )
- response.raise_for_status()
-
- completion_stream = response.aiter_lines()
- except httpx.HTTPStatusError as e:
- raise DatabricksError(
- status_code=e.response.status_code, message=response.text
- )
- except httpx.TimeoutException as e:
- raise DatabricksError(status_code=408, message="Timeout error occurred.")
- except Exception as e:
- raise DatabricksError(status_code=500, message=str(e))
-
streamwrapper = CustomStreamWrapper(
- completion_stream=completion_stream,
+ completion_stream=None,
+ make_call=partial(
+ make_call,
+ api_base=api_base,
+ headers=headers,
+ data=json.dumps(data),
+ model=model,
+ messages=messages,
+ logging_obj=logging_obj,
+ ),
model=model,
custom_llm_provider="databricks",
logging_obj=logging_obj,
@@ -475,6 +494,8 @@ class DatabricksChatCompletion(BaseLLM):
},
)
if acompletion == True:
+ if client is not None and isinstance(client, HTTPHandler):
+ client = None
if (
stream is not None and stream == True
): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
@@ -496,6 +517,7 @@ class DatabricksChatCompletion(BaseLLM):
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=headers,
+ client=client,
)
else:
return self.acompletion_function(
diff --git a/litellm/llms/fireworks_ai.py b/litellm/llms/fireworks_ai.py
new file mode 100644
index 0000000000..e9caf887ad
--- /dev/null
+++ b/litellm/llms/fireworks_ai.py
@@ -0,0 +1,108 @@
+import types
+from typing import Literal, Optional, Union
+
+import litellm
+
+
+class FireworksAIConfig:
+ """
+ Reference: https://docs.fireworks.ai/api-reference/post-chatcompletions
+
+ The class `FireworksAIConfig` provides configuration for the Fireworks's Chat Completions API interface. Below are the parameters:
+ """
+
+ tools: Optional[list] = None
+ tool_choice: Optional[Union[str, dict]] = None
+ max_tokens: Optional[int] = None
+ temperature: Optional[int] = None
+ top_p: Optional[int] = None
+ top_k: Optional[int] = None
+ frequency_penalty: Optional[int] = None
+ presence_penalty: Optional[int] = None
+ n: Optional[int] = None
+ stop: Optional[Union[str, list]] = None
+ response_format: Optional[dict] = None
+ user: Optional[str] = None
+
+ # Non OpenAI parameters - Fireworks AI only params
+ prompt_truncate_length: Optional[int] = None
+ context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None
+
+ def __init__(
+ self,
+ tools: Optional[list] = None,
+ tool_choice: Optional[Union[str, dict]] = None,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[int] = None,
+ top_p: Optional[int] = None,
+ top_k: Optional[int] = None,
+ frequency_penalty: Optional[int] = None,
+ presence_penalty: Optional[int] = None,
+ n: Optional[int] = None,
+ stop: Optional[Union[str, list]] = None,
+ response_format: Optional[dict] = None,
+ user: Optional[str] = None,
+ prompt_truncate_length: Optional[int] = None,
+ context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None,
+ ) -> None:
+ locals_ = locals().copy()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self):
+ return [
+ "stream",
+ "tools",
+ "tool_choice",
+ "max_tokens",
+ "temperature",
+ "top_p",
+ "top_k",
+ "frequency_penalty",
+ "presence_penalty",
+ "n",
+ "stop",
+ "response_format",
+ "user",
+ "prompt_truncate_length",
+ "context_length_exceeded_behavior",
+ ]
+
+ def map_openai_params(
+ self,
+ non_default_params: dict,
+ optional_params: dict,
+ model: str,
+ ) -> dict:
+ supported_openai_params = self.get_supported_openai_params()
+ for param, value in non_default_params.items():
+ if param == "tool_choice":
+ if value == "required":
+ # relevant issue: https://github.com/BerriAI/litellm/issues/4416
+ optional_params["tool_choice"] = "any"
+ else:
+ # pass through the value of tool choice
+ optional_params["tool_choice"] = value
+ elif param in supported_openai_params:
+ if value is not None:
+ optional_params[param] = value
+ return optional_params
diff --git a/litellm/llms/gemini.py b/litellm/llms/gemini.py
index a55b39aef9..3ce63e93fe 100644
--- a/litellm/llms/gemini.py
+++ b/litellm/llms/gemini.py
@@ -1,14 +1,23 @@
-import os, types, traceback, copy, asyncio
-import json
-from enum import Enum
+# ####################################
+# ######### DEPRECATED FILE ##########
+# ####################################
+# # logic moved to `vertex_httpx.py` #
+
+import copy
import time
+import traceback
+import types
from typing import Callable, Optional
-from litellm.utils import ModelResponse, get_secret, Choices, Message, Usage
-import litellm
-import sys, httpx
-from .prompt_templates.factory import prompt_factory, custom_prompt, get_system_prompt
+
+import httpx
from packaging.version import Version
+import litellm
+from litellm import verbose_logger
+from litellm.utils import Choices, Message, ModelResponse, Usage
+
+from .prompt_templates.factory import custom_prompt, get_system_prompt, prompt_factory
+
class GeminiError(Exception):
def __init__(self, status_code, message):
@@ -83,330 +92,332 @@ class GeminiConfig:
}
-class TextStreamer:
- """
- A class designed to return an async stream from AsyncGenerateContentResponse object.
- """
+# class TextStreamer:
+# """
+# A class designed to return an async stream from AsyncGenerateContentResponse object.
+# """
- def __init__(self, response):
- self.response = response
- self._aiter = self.response.__aiter__()
+# def __init__(self, response):
+# self.response = response
+# self._aiter = self.response.__aiter__()
- async def __aiter__(self):
- while True:
- try:
- # This will manually advance the async iterator.
- # In the case the next object doesn't exists, __anext__() will simply raise a StopAsyncIteration exception
- next_object = await self._aiter.__anext__()
- yield next_object
- except StopAsyncIteration:
- # After getting all items from the async iterator, stop iterating
- break
+# async def __aiter__(self):
+# while True:
+# try:
+# # This will manually advance the async iterator.
+# # In the case the next object doesn't exists, __anext__() will simply raise a StopAsyncIteration exception
+# next_object = await self._aiter.__anext__()
+# yield next_object
+# except StopAsyncIteration:
+# # After getting all items from the async iterator, stop iterating
+# break
-def supports_system_instruction():
- import google.generativeai as genai
+# def supports_system_instruction():
+# import google.generativeai as genai
- gemini_pkg_version = Version(genai.__version__)
- return gemini_pkg_version >= Version("0.5.0")
+# gemini_pkg_version = Version(genai.__version__)
+# return gemini_pkg_version >= Version("0.5.0")
-def completion(
- model: str,
- messages: list,
- model_response: ModelResponse,
- print_verbose: Callable,
- api_key,
- encoding,
- logging_obj,
- custom_prompt_dict: dict,
- acompletion: bool = False,
- optional_params=None,
- litellm_params=None,
- logger_fn=None,
-):
- try:
- import google.generativeai as genai # type: ignore
- except:
- raise Exception(
- "Importing google.generativeai failed, please run 'pip install -q google-generativeai"
- )
- genai.configure(api_key=api_key)
- system_prompt = ""
- if model in custom_prompt_dict:
- # check if the model has a registered custom prompt
- model_prompt_details = custom_prompt_dict[model]
- prompt = custom_prompt(
- role_dict=model_prompt_details["roles"],
- initial_prompt_value=model_prompt_details["initial_prompt_value"],
- final_prompt_value=model_prompt_details["final_prompt_value"],
- messages=messages,
- )
- else:
- system_prompt, messages = get_system_prompt(messages=messages)
- prompt = prompt_factory(
- model=model, messages=messages, custom_llm_provider="gemini"
- )
+# def completion(
+# model: str,
+# messages: list,
+# model_response: ModelResponse,
+# print_verbose: Callable,
+# api_key,
+# encoding,
+# logging_obj,
+# custom_prompt_dict: dict,
+# acompletion: bool = False,
+# optional_params=None,
+# litellm_params=None,
+# logger_fn=None,
+# ):
+# try:
+# import google.generativeai as genai # type: ignore
+# except:
+# raise Exception(
+# "Importing google.generativeai failed, please run 'pip install -q google-generativeai"
+# )
+# genai.configure(api_key=api_key)
+# system_prompt = ""
+# if model in custom_prompt_dict:
+# # check if the model has a registered custom prompt
+# model_prompt_details = custom_prompt_dict[model]
+# prompt = custom_prompt(
+# role_dict=model_prompt_details["roles"],
+# initial_prompt_value=model_prompt_details["initial_prompt_value"],
+# final_prompt_value=model_prompt_details["final_prompt_value"],
+# messages=messages,
+# )
+# else:
+# system_prompt, messages = get_system_prompt(messages=messages)
+# prompt = prompt_factory(
+# model=model, messages=messages, custom_llm_provider="gemini"
+# )
- ## Load Config
- inference_params = copy.deepcopy(optional_params)
- stream = inference_params.pop("stream", None)
+# ## Load Config
+# inference_params = copy.deepcopy(optional_params)
+# stream = inference_params.pop("stream", None)
- # Handle safety settings
- safety_settings_param = inference_params.pop("safety_settings", None)
- safety_settings = None
- if safety_settings_param:
- safety_settings = [
- genai.types.SafetySettingDict(x) for x in safety_settings_param
- ]
+# # Handle safety settings
+# safety_settings_param = inference_params.pop("safety_settings", None)
+# safety_settings = None
+# if safety_settings_param:
+# safety_settings = [
+# genai.types.SafetySettingDict(x) for x in safety_settings_param
+# ]
- config = litellm.GeminiConfig.get_config()
- for k, v in config.items():
- if (
- k not in inference_params
- ): # completion(top_k=3) > gemini_config(top_k=3) <- allows for dynamic variables to be passed in
- inference_params[k] = v
+# config = litellm.GeminiConfig.get_config()
+# for k, v in config.items():
+# if (
+# k not in inference_params
+# ): # completion(top_k=3) > gemini_config(top_k=3) <- allows for dynamic variables to be passed in
+# inference_params[k] = v
- ## LOGGING
- logging_obj.pre_call(
- input=prompt,
- api_key="",
- additional_args={
- "complete_input_dict": {
- "inference_params": inference_params,
- "system_prompt": system_prompt,
- }
- },
- )
- ## COMPLETION CALL
- try:
- _params = {"model_name": "models/{}".format(model)}
- _system_instruction = supports_system_instruction()
- if _system_instruction and len(system_prompt) > 0:
- _params["system_instruction"] = system_prompt
- _model = genai.GenerativeModel(**_params)
- if stream == True:
- if acompletion == True:
+# ## LOGGING
+# logging_obj.pre_call(
+# input=prompt,
+# api_key="",
+# additional_args={
+# "complete_input_dict": {
+# "inference_params": inference_params,
+# "system_prompt": system_prompt,
+# }
+# },
+# )
+# ## COMPLETION CALL
+# try:
+# _params = {"model_name": "models/{}".format(model)}
+# _system_instruction = supports_system_instruction()
+# if _system_instruction and len(system_prompt) > 0:
+# _params["system_instruction"] = system_prompt
+# _model = genai.GenerativeModel(**_params)
+# if stream is True:
+# if acompletion is True:
- async def async_streaming():
- try:
- response = await _model.generate_content_async(
- contents=prompt,
- generation_config=genai.types.GenerationConfig(
- **inference_params
- ),
- safety_settings=safety_settings,
- stream=True,
- )
+# async def async_streaming():
+# try:
+# response = await _model.generate_content_async(
+# contents=prompt,
+# generation_config=genai.types.GenerationConfig(
+# **inference_params
+# ),
+# safety_settings=safety_settings,
+# stream=True,
+# )
- response = litellm.CustomStreamWrapper(
- TextStreamer(response),
- model,
- custom_llm_provider="gemini",
- logging_obj=logging_obj,
- )
- return response
- except Exception as e:
- raise GeminiError(status_code=500, message=str(e))
+# response = litellm.CustomStreamWrapper(
+# TextStreamer(response),
+# model,
+# custom_llm_provider="gemini",
+# logging_obj=logging_obj,
+# )
+# return response
+# except Exception as e:
+# raise GeminiError(status_code=500, message=str(e))
- return async_streaming()
- response = _model.generate_content(
- contents=prompt,
- generation_config=genai.types.GenerationConfig(**inference_params),
- safety_settings=safety_settings,
- stream=True,
- )
- return response
- elif acompletion == True:
- return async_completion(
- _model=_model,
- model=model,
- prompt=prompt,
- inference_params=inference_params,
- safety_settings=safety_settings,
- logging_obj=logging_obj,
- print_verbose=print_verbose,
- model_response=model_response,
- messages=messages,
- encoding=encoding,
- )
- else:
- params = {
- "contents": prompt,
- "generation_config": genai.types.GenerationConfig(**inference_params),
- "safety_settings": safety_settings,
- }
- response = _model.generate_content(**params)
- except Exception as e:
- raise GeminiError(
- message=str(e),
- status_code=500,
- )
+# return async_streaming()
+# response = _model.generate_content(
+# contents=prompt,
+# generation_config=genai.types.GenerationConfig(**inference_params),
+# safety_settings=safety_settings,
+# stream=True,
+# )
+# return response
+# elif acompletion == True:
+# return async_completion(
+# _model=_model,
+# model=model,
+# prompt=prompt,
+# inference_params=inference_params,
+# safety_settings=safety_settings,
+# logging_obj=logging_obj,
+# print_verbose=print_verbose,
+# model_response=model_response,
+# messages=messages,
+# encoding=encoding,
+# )
+# else:
+# params = {
+# "contents": prompt,
+# "generation_config": genai.types.GenerationConfig(**inference_params),
+# "safety_settings": safety_settings,
+# }
+# response = _model.generate_content(**params)
+# except Exception as e:
+# raise GeminiError(
+# message=str(e),
+# status_code=500,
+# )
- ## LOGGING
- logging_obj.post_call(
- input=prompt,
- api_key="",
- original_response=response,
- additional_args={"complete_input_dict": {}},
- )
- print_verbose(f"raw model_response: {response}")
- ## RESPONSE OBJECT
- completion_response = response
- try:
- choices_list = []
- for idx, item in enumerate(completion_response.candidates):
- if len(item.content.parts) > 0:
- message_obj = Message(content=item.content.parts[0].text)
- else:
- message_obj = Message(content=None)
- choice_obj = Choices(index=idx, message=message_obj)
- choices_list.append(choice_obj)
- model_response["choices"] = choices_list
- except Exception as e:
- traceback.print_exc()
- raise GeminiError(
- message=traceback.format_exc(), status_code=response.status_code
- )
+# ## LOGGING
+# logging_obj.post_call(
+# input=prompt,
+# api_key="",
+# original_response=response,
+# additional_args={"complete_input_dict": {}},
+# )
+# print_verbose(f"raw model_response: {response}")
+# ## RESPONSE OBJECT
+# completion_response = response
+# try:
+# choices_list = []
+# for idx, item in enumerate(completion_response.candidates):
+# if len(item.content.parts) > 0:
+# message_obj = Message(content=item.content.parts[0].text)
+# else:
+# message_obj = Message(content=None)
+# choice_obj = Choices(index=idx, message=message_obj)
+# choices_list.append(choice_obj)
+# model_response.choices = choices_list
+# except Exception as e:
+# verbose_logger.error("LiteLLM.gemini.py: Exception occured - {}".format(str(e)))
+# verbose_logger.debug(traceback.format_exc())
+# raise GeminiError(
+# message=traceback.format_exc(), status_code=response.status_code
+# )
- try:
- completion_response = model_response["choices"][0]["message"].get("content")
- if completion_response is None:
- raise Exception
- except:
- original_response = f"response: {response}"
- if hasattr(response, "candidates"):
- original_response = f"response: {response.candidates}"
- if "SAFETY" in original_response:
- original_response += (
- "\nThe candidate content was flagged for safety reasons."
- )
- elif "RECITATION" in original_response:
- original_response += (
- "\nThe candidate content was flagged for recitation reasons."
- )
- raise GeminiError(
- status_code=400,
- message=f"No response received. Original response - {original_response}",
- )
+# try:
+# completion_response = model_response["choices"][0]["message"].get("content")
+# if completion_response is None:
+# raise Exception
+# except:
+# original_response = f"response: {response}"
+# if hasattr(response, "candidates"):
+# original_response = f"response: {response.candidates}"
+# if "SAFETY" in original_response:
+# original_response += (
+# "\nThe candidate content was flagged for safety reasons."
+# )
+# elif "RECITATION" in original_response:
+# original_response += (
+# "\nThe candidate content was flagged for recitation reasons."
+# )
+# raise GeminiError(
+# status_code=400,
+# message=f"No response received. Original response - {original_response}",
+# )
- ## CALCULATING USAGE
- prompt_str = ""
- for m in messages:
- if isinstance(m["content"], str):
- prompt_str += m["content"]
- elif isinstance(m["content"], list):
- for content in m["content"]:
- if content["type"] == "text":
- prompt_str += content["text"]
- prompt_tokens = len(encoding.encode(prompt_str))
- completion_tokens = len(
- encoding.encode(model_response["choices"][0]["message"].get("content", ""))
- )
+# ## CALCULATING USAGE
+# prompt_str = ""
+# for m in messages:
+# if isinstance(m["content"], str):
+# prompt_str += m["content"]
+# elif isinstance(m["content"], list):
+# for content in m["content"]:
+# if content["type"] == "text":
+# prompt_str += content["text"]
+# prompt_tokens = len(encoding.encode(prompt_str))
+# completion_tokens = len(
+# encoding.encode(model_response["choices"][0]["message"].get("content", ""))
+# )
- model_response["created"] = int(time.time())
- model_response["model"] = "gemini/" + model
- usage = Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
- )
- setattr(model_response, "usage", usage)
- return model_response
+# model_response.created = int(time.time())
+# model_response.model = "gemini/" + model
+# usage = Usage(
+# prompt_tokens=prompt_tokens,
+# completion_tokens=completion_tokens,
+# total_tokens=prompt_tokens + completion_tokens,
+# )
+# setattr(model_response, "usage", usage)
+# return model_response
-async def async_completion(
- _model,
- model,
- prompt,
- inference_params,
- safety_settings,
- logging_obj,
- print_verbose,
- model_response,
- messages,
- encoding,
-):
- import google.generativeai as genai # type: ignore
+# async def async_completion(
+# _model,
+# model,
+# prompt,
+# inference_params,
+# safety_settings,
+# logging_obj,
+# print_verbose,
+# model_response,
+# messages,
+# encoding,
+# ):
+# import google.generativeai as genai # type: ignore
- response = await _model.generate_content_async(
- contents=prompt,
- generation_config=genai.types.GenerationConfig(**inference_params),
- safety_settings=safety_settings,
- )
+# response = await _model.generate_content_async(
+# contents=prompt,
+# generation_config=genai.types.GenerationConfig(**inference_params),
+# safety_settings=safety_settings,
+# )
- ## LOGGING
- logging_obj.post_call(
- input=prompt,
- api_key="",
- original_response=response,
- additional_args={"complete_input_dict": {}},
- )
- print_verbose(f"raw model_response: {response}")
- ## RESPONSE OBJECT
- completion_response = response
- try:
- choices_list = []
- for idx, item in enumerate(completion_response.candidates):
- if len(item.content.parts) > 0:
- message_obj = Message(content=item.content.parts[0].text)
- else:
- message_obj = Message(content=None)
- choice_obj = Choices(index=idx, message=message_obj)
- choices_list.append(choice_obj)
- model_response["choices"] = choices_list
- except Exception as e:
- traceback.print_exc()
- raise GeminiError(
- message=traceback.format_exc(), status_code=response.status_code
- )
+# ## LOGGING
+# logging_obj.post_call(
+# input=prompt,
+# api_key="",
+# original_response=response,
+# additional_args={"complete_input_dict": {}},
+# )
+# print_verbose(f"raw model_response: {response}")
+# ## RESPONSE OBJECT
+# completion_response = response
+# try:
+# choices_list = []
+# for idx, item in enumerate(completion_response.candidates):
+# if len(item.content.parts) > 0:
+# message_obj = Message(content=item.content.parts[0].text)
+# else:
+# message_obj = Message(content=None)
+# choice_obj = Choices(index=idx, message=message_obj)
+# choices_list.append(choice_obj)
+# model_response["choices"] = choices_list
+# except Exception as e:
+# verbose_logger.error("LiteLLM.gemini.py: Exception occured - {}".format(str(e)))
+# verbose_logger.debug(traceback.format_exc())
+# raise GeminiError(
+# message=traceback.format_exc(), status_code=response.status_code
+# )
- try:
- completion_response = model_response["choices"][0]["message"].get("content")
- if completion_response is None:
- raise Exception
- except:
- original_response = f"response: {response}"
- if hasattr(response, "candidates"):
- original_response = f"response: {response.candidates}"
- if "SAFETY" in original_response:
- original_response += (
- "\nThe candidate content was flagged for safety reasons."
- )
- elif "RECITATION" in original_response:
- original_response += (
- "\nThe candidate content was flagged for recitation reasons."
- )
- raise GeminiError(
- status_code=400,
- message=f"No response received. Original response - {original_response}",
- )
+# try:
+# completion_response = model_response["choices"][0]["message"].get("content")
+# if completion_response is None:
+# raise Exception
+# except:
+# original_response = f"response: {response}"
+# if hasattr(response, "candidates"):
+# original_response = f"response: {response.candidates}"
+# if "SAFETY" in original_response:
+# original_response += (
+# "\nThe candidate content was flagged for safety reasons."
+# )
+# elif "RECITATION" in original_response:
+# original_response += (
+# "\nThe candidate content was flagged for recitation reasons."
+# )
+# raise GeminiError(
+# status_code=400,
+# message=f"No response received. Original response - {original_response}",
+# )
- ## CALCULATING USAGE
- prompt_str = ""
- for m in messages:
- if isinstance(m["content"], str):
- prompt_str += m["content"]
- elif isinstance(m["content"], list):
- for content in m["content"]:
- if content["type"] == "text":
- prompt_str += content["text"]
- prompt_tokens = len(encoding.encode(prompt_str))
- completion_tokens = len(
- encoding.encode(model_response["choices"][0]["message"].get("content", ""))
- )
+# ## CALCULATING USAGE
+# prompt_str = ""
+# for m in messages:
+# if isinstance(m["content"], str):
+# prompt_str += m["content"]
+# elif isinstance(m["content"], list):
+# for content in m["content"]:
+# if content["type"] == "text":
+# prompt_str += content["text"]
+# prompt_tokens = len(encoding.encode(prompt_str))
+# completion_tokens = len(
+# encoding.encode(model_response["choices"][0]["message"].get("content", ""))
+# )
- model_response["created"] = int(time.time())
- model_response["model"] = "gemini/" + model
- usage = Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
- )
- model_response.usage = usage
- return model_response
+# model_response["created"] = int(time.time())
+# model_response["model"] = "gemini/" + model
+# usage = Usage(
+# prompt_tokens=prompt_tokens,
+# completion_tokens=completion_tokens,
+# total_tokens=prompt_tokens + completion_tokens,
+# )
+# model_response.usage = usage
+# return model_response
-def embedding():
- # logic for parsing in - calling - parsing out model embedding calls
- pass
+# def embedding():
+# # logic for parsing in - calling - parsing out model embedding calls
+# pass
diff --git a/litellm/llms/huggingface_restapi.py b/litellm/llms/huggingface_restapi.py
index c54dba75f1..8b755e2bb7 100644
--- a/litellm/llms/huggingface_restapi.py
+++ b/litellm/llms/huggingface_restapi.py
@@ -1,17 +1,22 @@
## Uses the huggingface text generation inference API
-import os, copy, types
-import json
-from enum import Enum
-import httpx, requests
-from .base import BaseLLM
-import time
-import litellm
-from typing import Callable, Dict, List, Any, Literal, Tuple
-from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, Usage
-from typing import Optional
-from .prompt_templates.factory import prompt_factory, custom_prompt
-from litellm.types.completion import ChatCompletionMessageToolCallParam
+import copy
import enum
+import json
+import os
+import time
+import types
+from enum import Enum
+from typing import Any, Callable, Dict, List, Literal, Optional, Tuple
+
+import httpx
+import requests
+
+import litellm
+from litellm.types.completion import ChatCompletionMessageToolCallParam
+from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage
+
+from .base import BaseLLM
+from .prompt_templates.factory import custom_prompt, prompt_factory
class HuggingfaceError(Exception):
@@ -269,7 +274,7 @@ class Huggingface(BaseLLM):
def convert_to_model_response_object(
self,
completion_response,
- model_response,
+ model_response: litellm.ModelResponse,
task: hf_tasks,
optional_params,
encoding,
@@ -278,11 +283,9 @@ class Huggingface(BaseLLM):
):
if task == "conversational":
if len(completion_response["generated_text"]) > 0: # type: ignore
- model_response["choices"][0]["message"][
- "content"
- ] = completion_response[
+ model_response.choices[0].message.content = completion_response[ # type: ignore
"generated_text"
- ] # type: ignore
+ ]
elif task == "text-generation-inference":
if (
not isinstance(completion_response, list)
@@ -295,7 +298,7 @@ class Huggingface(BaseLLM):
)
if len(completion_response[0]["generated_text"]) > 0:
- model_response["choices"][0]["message"]["content"] = output_parser(
+ model_response.choices[0].message.content = output_parser( # type: ignore
completion_response[0]["generated_text"]
)
## GETTING LOGPROBS + FINISH REASON
@@ -310,7 +313,7 @@ class Huggingface(BaseLLM):
for token in completion_response[0]["details"]["tokens"]:
if token["logprob"] != None:
sum_logprob += token["logprob"]
- model_response["choices"][0]["message"]._logprob = sum_logprob
+ setattr(model_response.choices[0].message, "_logprob", sum_logprob) # type: ignore
if "best_of" in optional_params and optional_params["best_of"] > 1:
if (
"details" in completion_response[0]
@@ -337,14 +340,14 @@ class Huggingface(BaseLLM):
message=message_obj,
)
choices_list.append(choice_obj)
- model_response["choices"].extend(choices_list)
+ model_response.choices.extend(choices_list)
elif task == "text-classification":
- model_response["choices"][0]["message"]["content"] = json.dumps(
+ model_response.choices[0].message.content = json.dumps( # type: ignore
completion_response
)
else:
if len(completion_response[0]["generated_text"]) > 0:
- model_response["choices"][0]["message"]["content"] = output_parser(
+ model_response.choices[0].message.content = output_parser( # type: ignore
completion_response[0]["generated_text"]
)
## CALCULATING USAGE
@@ -371,14 +374,14 @@ class Huggingface(BaseLLM):
else:
completion_tokens = 0
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
- model_response.usage = usage
+ setattr(model_response, "usage", usage)
model_response._hidden_params["original_response"] = completion_response
return model_response
@@ -763,10 +766,10 @@ class Huggingface(BaseLLM):
self,
model: str,
input: list,
+ model_response: litellm.EmbeddingResponse,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
logging_obj=None,
- model_response=None,
encoding=None,
):
super().embedding()
@@ -867,15 +870,21 @@ class Huggingface(BaseLLM):
], # flatten list returned from hf
}
)
- model_response["object"] = "list"
- model_response["data"] = output_data
- model_response["model"] = model
+ model_response.object = "list"
+ model_response.data = output_data
+ model_response.model = model
input_tokens = 0
for text in input:
input_tokens += len(encoding.encode(text))
- model_response["usage"] = {
- "prompt_tokens": input_tokens,
- "total_tokens": input_tokens,
- }
+ setattr(
+ model_response,
+ "usage",
+ litellm.Usage(
+ **{
+ "prompt_tokens": input_tokens,
+ "total_tokens": input_tokens,
+ }
+ ),
+ )
return model_response
diff --git a/litellm/llms/maritalk.py b/litellm/llms/maritalk.py
index dfe53e9df0..c2eb663826 100644
--- a/litellm/llms/maritalk.py
+++ b/litellm/llms/maritalk.py
@@ -1,11 +1,15 @@
-import os, types
import json
+import os
+import time
+import traceback
+import types
from enum import Enum
+from typing import Callable, List, Optional
+
import requests # type: ignore
-import time, traceback
-from typing import Callable, Optional, List
-from litellm.utils import ModelResponse, Choices, Message, Usage
+
import litellm
+from litellm.utils import Choices, Message, ModelResponse, Usage
class MaritalkError(Exception):
@@ -152,9 +156,9 @@ def completion(
else:
try:
if len(completion_response["answer"]) > 0:
- model_response["choices"][0]["message"]["content"] = (
- completion_response["answer"]
- )
+ model_response.choices[0].message.content = completion_response[ # type: ignore
+ "answer"
+ ]
except Exception as e:
raise MaritalkError(
message=response.text, status_code=response.status_code
@@ -167,8 +171,8 @@ def completion(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/nlp_cloud.py b/litellm/llms/nlp_cloud.py
index cd5f17a90b..84908f26b6 100644
--- a/litellm/llms/nlp_cloud.py
+++ b/litellm/llms/nlp_cloud.py
@@ -1,9 +1,12 @@
-import os, types
import json
-from enum import Enum
-import requests # type: ignore
+import os
import time
+import types
+from enum import Enum
from typing import Callable, Optional
+
+import requests # type: ignore
+
import litellm
from litellm.utils import ModelResponse, Usage
@@ -185,7 +188,7 @@ def completion(
else:
try:
if len(completion_response["generated_text"]) > 0:
- model_response["choices"][0]["message"]["content"] = (
+ model_response.choices[0].message.content = ( # type: ignore
completion_response["generated_text"]
)
except:
@@ -198,8 +201,8 @@ def completion(
prompt_tokens = completion_response["nb_input_tokens"]
completion_tokens = completion_response["nb_generated_tokens"]
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/nvidia_nim.py b/litellm/llms/nvidia_nim.py
new file mode 100644
index 0000000000..6d2e4316b2
--- /dev/null
+++ b/litellm/llms/nvidia_nim.py
@@ -0,0 +1,138 @@
+"""
+Nvidia NIM endpoint: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer
+
+This is OpenAI compatible
+
+This file only contains param mapping logic
+
+API calling is done using the OpenAI SDK with an api_base
+"""
+
+import types
+from typing import Optional, Union
+
+
+class NvidiaNimConfig:
+ """
+ Reference: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer
+
+ The class `NvidiaNimConfig` provides configuration for the Nvidia NIM's Chat Completions API interface. Below are the parameters:
+ """
+
+ temperature: Optional[int] = None
+ top_p: Optional[int] = None
+ frequency_penalty: Optional[int] = None
+ presence_penalty: Optional[int] = None
+ max_tokens: Optional[int] = None
+ stop: Optional[Union[str, list]] = None
+
+ def __init__(
+ self,
+ temperature: Optional[int] = None,
+ top_p: Optional[int] = None,
+ frequency_penalty: Optional[int] = None,
+ presence_penalty: Optional[int] = None,
+ max_tokens: Optional[int] = None,
+ stop: Optional[Union[str, list]] = None,
+ ) -> None:
+ locals_ = locals().copy()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self, model: str) -> list:
+ """
+ Get the supported OpenAI params for the given model
+
+
+ Updated on July 5th, 2024 - based on https://docs.api.nvidia.com/nim/reference
+ """
+ if model in [
+ "google/recurrentgemma-2b",
+ "google/gemma-2-27b-it",
+ "google/gemma-2-9b-it",
+ "gemma-2-9b-it",
+ ]:
+ return ["stream", "temperature", "top_p", "max_tokens", "stop", "seed"]
+ elif model == "nvidia/nemotron-4-340b-instruct":
+ return [
+ "stream",
+ "temperature",
+ "top_p",
+ "max_tokens",
+ ]
+ elif model == "nvidia/nemotron-4-340b-reward":
+ return [
+ "stream",
+ ]
+ elif model in ["google/codegemma-1.1-7b"]:
+ # most params - but no 'seed' :(
+ return [
+ "stream",
+ "temperature",
+ "top_p",
+ "frequency_penalty",
+ "presence_penalty",
+ "max_tokens",
+ "stop",
+ ]
+ else:
+ # DEFAULT Case - The vast majority of Nvidia NIM Models lie here
+ # "upstage/solar-10.7b-instruct",
+ # "snowflake/arctic",
+ # "seallms/seallm-7b-v2.5",
+ # "nvidia/llama3-chatqa-1.5-8b",
+ # "nvidia/llama3-chatqa-1.5-70b",
+ # "mistralai/mistral-large",
+ # "mistralai/mixtral-8x22b-instruct-v0.1",
+ # "mistralai/mixtral-8x7b-instruct-v0.1",
+ # "mistralai/mistral-7b-instruct-v0.3",
+ # "mistralai/mistral-7b-instruct-v0.2",
+ # "mistralai/codestral-22b-instruct-v0.1",
+ # "microsoft/phi-3-small-8k-instruct",
+ # "microsoft/phi-3-small-128k-instruct",
+ # "microsoft/phi-3-mini-4k-instruct",
+ # "microsoft/phi-3-mini-128k-instruct",
+ # "microsoft/phi-3-medium-4k-instruct",
+ # "microsoft/phi-3-medium-128k-instruct",
+ # "meta/llama3-70b-instruct",
+ # "meta/llama3-8b-instruct",
+ # "meta/llama2-70b",
+ # "meta/codellama-70b",
+ return [
+ "stream",
+ "temperature",
+ "top_p",
+ "frequency_penalty",
+ "presence_penalty",
+ "max_tokens",
+ "stop",
+ "seed",
+ ]
+
+ def map_openai_params(
+ self, model: str, non_default_params: dict, optional_params: dict
+ ) -> dict:
+ supported_openai_params = self.get_supported_openai_params(model=model)
+ for param, value in non_default_params.items():
+ if param in supported_openai_params:
+ optional_params[param] = value
+ return optional_params
diff --git a/litellm/llms/ollama.py b/litellm/llms/ollama.py
index 9c9b5e8981..7b15582f48 100644
--- a/litellm/llms/ollama.py
+++ b/litellm/llms/ollama.py
@@ -1,11 +1,21 @@
-from itertools import chain
-import requests, types, time # type: ignore
-import json, uuid
+import asyncio
+import json
+import time
import traceback
-from typing import Optional
+import types
+import uuid
+from itertools import chain
+from typing import List, Optional
+
+import aiohttp
+import httpx # type: ignore
+import requests # type: ignore
+
import litellm
-import httpx, aiohttp, asyncio # type: ignore
-from .prompt_templates.factory import prompt_factory, custom_prompt
+from litellm import verbose_logger
+from litellm.types.utils import ProviderField
+
+from .prompt_templates.factory import custom_prompt, prompt_factory
class OllamaError(Exception):
@@ -45,6 +55,8 @@ class OllamaConfig:
- `temperature` (float): The temperature of the model. Increasing the temperature will make the model answer more creatively. Default: 0.8. Example usage: temperature 0.7
+ - `seed` (int): Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. Example usage: seed 42
+
- `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:"
- `tfs_z` (float): Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. Default: 1. Example usage: tfs_z 1
@@ -65,10 +77,12 @@ class OllamaConfig:
mirostat_tau: Optional[float] = None
num_ctx: Optional[int] = None
num_gqa: Optional[int] = None
+ num_gpu: Optional[int] = None
num_thread: Optional[int] = None
repeat_last_n: Optional[int] = None
repeat_penalty: Optional[float] = None
temperature: Optional[float] = None
+ seed: Optional[int] = None
stop: Optional[list] = (
None # stop is a list based on this - https://github.com/ollama/ollama/pull/442
)
@@ -86,10 +100,12 @@ class OllamaConfig:
mirostat_tau: Optional[float] = None,
num_ctx: Optional[int] = None,
num_gqa: Optional[int] = None,
+ num_gpu: Optional[int] = None,
num_thread: Optional[int] = None,
repeat_last_n: Optional[int] = None,
repeat_penalty: Optional[float] = None,
temperature: Optional[float] = None,
+ seed: Optional[int] = None,
stop: Optional[list] = None,
tfs_z: Optional[float] = None,
num_predict: Optional[int] = None,
@@ -121,16 +137,69 @@ class OllamaConfig:
and v is not None
}
+ def get_required_params(self) -> List[ProviderField]:
+ """For a given provider, return it's required fields with a description"""
+ return [
+ ProviderField(
+ field_name="base_url",
+ field_type="string",
+ field_description="Your Ollama API Base",
+ field_value="http://10.10.11.249:11434",
+ )
+ ]
+
+ def get_supported_openai_params(
+ self,
+ ):
+ return [
+ "max_tokens",
+ "stream",
+ "top_p",
+ "temperature",
+ "seed",
+ "frequency_penalty",
+ "stop",
+ "response_format",
+ ]
+
+
+# ollama wants plain base64 jpeg/png files as images. strip any leading dataURI
+# and convert to jpeg if necessary.
+def _convert_image(image):
+ import base64
+ import io
+
+ try:
+ from PIL import Image
+ except:
+ raise Exception(
+ "ollama image conversion failed please run `pip install Pillow`"
+ )
+
+ orig = image
+ if image.startswith("data:"):
+ image = image.split(",")[-1]
+ try:
+ image_data = Image.open(io.BytesIO(base64.b64decode(image)))
+ if image_data.format in ["JPEG", "PNG"]:
+ return image
+ except:
+ return orig
+ jpeg_image = io.BytesIO()
+ image_data.convert("RGB").save(jpeg_image, "JPEG")
+ jpeg_image.seek(0)
+ return base64.b64encode(jpeg_image.getvalue()).decode("utf-8")
+
# ollama implementation
def get_ollama_response(
+ model_response: litellm.ModelResponse,
api_base="http://localhost:11434",
model="llama2",
prompt="Why is the sky blue?",
optional_params=None,
logging_obj=None,
acompletion: bool = False,
- model_response=None,
encoding=None,
):
if api_base.endswith("/api/generate"):
@@ -158,7 +227,7 @@ def get_ollama_response(
if format is not None:
data["format"] = format
if images is not None:
- data["images"] = images
+ data["images"] = [_convert_image(image) for image in images]
## LOGGING
logging_obj.pre_call(
@@ -212,7 +281,7 @@ def get_ollama_response(
response_json = response.json()
## RESPONSE OBJECT
- model_response["choices"][0]["finish_reason"] = "stop"
+ model_response.choices[0].finish_reason = "stop"
if data.get("format", "") == "json":
function_call = json.loads(response_json["response"])
message = litellm.Message(
@@ -228,20 +297,24 @@ def get_ollama_response(
}
],
)
- model_response["choices"][0]["message"] = message
- model_response["choices"][0]["finish_reason"] = "tool_calls"
+ model_response.choices[0].message = message # type: ignore
+ model_response.choices[0].finish_reason = "tool_calls"
else:
- model_response["choices"][0]["message"]["content"] = response_json["response"]
- model_response["created"] = int(time.time())
- model_response["model"] = "ollama/" + model
+ model_response.choices[0].message.content = response_json["response"] # type: ignore
+ model_response.created = int(time.time())
+ model_response.model = "ollama/" + model
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt, disallowed_special=()))) # type: ignore
completion_tokens = response_json.get(
"eval_count", len(response_json.get("message", dict()).get("content", ""))
)
- model_response["usage"] = litellm.Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
+ setattr(
+ model_response,
+ "usage",
+ litellm.Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ ),
)
return model_response
@@ -287,8 +360,8 @@ def ollama_completion_stream(url, data, logging_obj):
],
)
model_response = first_chunk
- model_response["choices"][0]["delta"] = delta
- model_response["choices"][0]["finish_reason"] = "tool_calls"
+ model_response.choices[0].delta = delta # type: ignore
+ model_response.choices[0].finish_reason = "tool_calls"
yield model_response
else:
for transformed_chunk in streamwrapper:
@@ -342,18 +415,26 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
],
)
model_response = first_chunk
- model_response["choices"][0]["delta"] = delta
- model_response["choices"][0]["finish_reason"] = "tool_calls"
+ model_response.choices[0].delta = delta # type: ignore
+ model_response.choices[0].finish_reason = "tool_calls"
yield model_response
else:
async for transformed_chunk in streamwrapper:
yield transformed_chunk
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error(
+ "LiteLLM.ollama.py::ollama_async_streaming(): Exception occured - {}".format(
+ str(e)
+ )
+ )
+ verbose_logger.debug(traceback.format_exc())
+
raise e
-async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
+async def ollama_acompletion(
+ url, data, model_response: litellm.ModelResponse, encoding, logging_obj
+):
data["stream"] = False
try:
timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) # 10 minutes
@@ -377,7 +458,7 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
response_json = await resp.json()
## RESPONSE OBJECT
- model_response["choices"][0]["finish_reason"] = "stop"
+ model_response.choices[0].finish_reason = "stop"
if data.get("format", "") == "json":
function_call = json.loads(response_json["response"])
message = litellm.Message(
@@ -386,34 +467,43 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
- "name": function_call["name"],
+ "name": function_call.get(
+ "name", function_call.get("function", None)
+ ),
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
)
- model_response["choices"][0]["message"] = message
- model_response["choices"][0]["finish_reason"] = "tool_calls"
+ model_response.choices[0].message = message # type: ignore
+ model_response.choices[0].finish_reason = "tool_calls"
else:
- model_response["choices"][0]["message"]["content"] = response_json[
- "response"
- ]
- model_response["created"] = int(time.time())
- model_response["model"] = "ollama/" + data["model"]
+ model_response.choices[0].message.content = response_json["response"] # type: ignore
+ model_response.created = int(time.time())
+ model_response.model = "ollama/" + data["model"]
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"], disallowed_special=()))) # type: ignore
completion_tokens = response_json.get(
"eval_count",
len(response_json.get("message", dict()).get("content", "")),
)
- model_response["usage"] = litellm.Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
+ setattr(
+ model_response,
+ "usage",
+ litellm.Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ ),
)
return model_response
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error(
+ "LiteLLM.ollama.py::ollama_acompletion(): Exception occured - {}".format(
+ str(e)
+ )
+ )
+ verbose_logger.debug(traceback.format_exc())
raise e
@@ -421,9 +511,9 @@ async def ollama_aembeddings(
api_base: str,
model: str,
prompts: list,
+ model_response: litellm.EmbeddingResponse,
optional_params=None,
logging_obj=None,
- model_response=None,
encoding=None,
):
if api_base.endswith("/api/embeddings"):
@@ -484,13 +574,19 @@ async def ollama_aembeddings(
input_tokens = len(encoding.encode(prompt))
total_input_tokens += input_tokens
- model_response["object"] = "list"
- model_response["data"] = output_data
- model_response["model"] = model
- model_response["usage"] = {
- "prompt_tokens": total_input_tokens,
- "total_tokens": total_input_tokens,
- }
+ model_response.object = "list"
+ model_response.data = output_data
+ model_response.model = model
+ setattr(
+ model_response,
+ "usage",
+ litellm.Usage(
+ **{
+ "prompt_tokens": total_input_tokens,
+ "total_tokens": total_input_tokens,
+ }
+ ),
+ )
return model_response
diff --git a/litellm/llms/ollama_chat.py b/litellm/llms/ollama_chat.py
index d1ff4953fa..ebd0f22fb7 100644
--- a/litellm/llms/ollama_chat.py
+++ b/litellm/llms/ollama_chat.py
@@ -1,11 +1,17 @@
-from itertools import chain
-import requests, types, time
-import json, uuid
+import json
+import time
import traceback
+import types
+import uuid
+from itertools import chain
from typing import Optional
+
+import aiohttp
+import httpx
+import requests
+
import litellm
-import httpx, aiohttp, asyncio
-from .prompt_templates.factory import prompt_factory, custom_prompt
+from litellm import verbose_logger
class OllamaError(Exception):
@@ -45,6 +51,8 @@ class OllamaChatConfig:
- `temperature` (float): The temperature of the model. Increasing the temperature will make the model answer more creatively. Default: 0.8. Example usage: temperature 0.7
+ - `seed` (int): Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. Example usage: seed 42
+
- `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:"
- `tfs_z` (float): Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. Default: 1. Example usage: tfs_z 1
@@ -69,6 +77,7 @@ class OllamaChatConfig:
repeat_last_n: Optional[int] = None
repeat_penalty: Optional[float] = None
temperature: Optional[float] = None
+ seed: Optional[int] = None
stop: Optional[list] = (
None # stop is a list based on this - https://github.com/ollama/ollama/pull/442
)
@@ -90,6 +99,7 @@ class OllamaChatConfig:
repeat_last_n: Optional[int] = None,
repeat_penalty: Optional[float] = None,
temperature: Optional[float] = None,
+ seed: Optional[int] = None,
stop: Optional[list] = None,
tfs_z: Optional[float] = None,
num_predict: Optional[int] = None,
@@ -130,6 +140,7 @@ class OllamaChatConfig:
"stream",
"top_p",
"temperature",
+ "seed",
"frequency_penalty",
"stop",
"tools",
@@ -146,6 +157,8 @@ class OllamaChatConfig:
optional_params["stream"] = value
if param == "temperature":
optional_params["temperature"] = value
+ if param == "seed":
+ optional_params["seed"] = value
if param == "top_p":
optional_params["top_p"] = value
if param == "frequency_penalty":
@@ -184,6 +197,7 @@ class OllamaChatConfig:
# ollama implementation
def get_ollama_response(
+ model_response: litellm.ModelResponse,
api_base="http://localhost:11434",
api_key: Optional[str] = None,
model="llama2",
@@ -191,7 +205,6 @@ def get_ollama_response(
optional_params=None,
logging_obj=None,
acompletion: bool = False,
- model_response=None,
encoding=None,
):
if api_base.endswith("/api/chat"):
@@ -284,7 +297,7 @@ def get_ollama_response(
response_json = response.json()
## RESPONSE OBJECT
- model_response["choices"][0]["finish_reason"] = "stop"
+ model_response.choices[0].finish_reason = "stop"
if data.get("format", "") == "json":
function_call = json.loads(response_json["message"]["content"])
message = litellm.Message(
@@ -292,25 +305,32 @@ def get_ollama_response(
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
- "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+ "function": {
+ "name": function_call["name"],
+ "arguments": json.dumps(function_call["arguments"]),
+ },
"type": "function",
}
],
)
- model_response["choices"][0]["message"] = message
- model_response["choices"][0]["finish_reason"] = "tool_calls"
+ model_response.choices[0].message = message # type: ignore
+ model_response.choices[0].finish_reason = "tool_calls"
else:
- model_response["choices"][0]["message"]["content"] = response_json["message"]["content"]
- model_response["created"] = int(time.time())
- model_response["model"] = "ollama/" + model
+ model_response.choices[0].message.content = response_json["message"]["content"] # type: ignore
+ model_response.created = int(time.time())
+ model_response.model = "ollama/" + model
prompt_tokens = response_json.get("prompt_eval_count", litellm.token_counter(messages=messages)) # type: ignore
completion_tokens = response_json.get(
"eval_count", litellm.token_counter(text=response_json["message"]["content"])
)
- model_response["usage"] = litellm.Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
+ setattr(
+ model_response,
+ "usage",
+ litellm.Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ ),
)
return model_response
@@ -354,14 +374,17 @@ def ollama_completion_stream(url, api_key, data, logging_obj):
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
- "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+ "function": {
+ "name": function_call["name"],
+ "arguments": json.dumps(function_call["arguments"]),
+ },
"type": "function",
}
],
)
model_response = first_chunk
- model_response["choices"][0]["delta"] = delta
- model_response["choices"][0]["finish_reason"] = "tool_calls"
+ model_response.choices[0].delta = delta # type: ignore
+ model_response.choices[0].finish_reason = "tool_calls"
yield model_response
else:
for transformed_chunk in streamwrapper:
@@ -403,9 +426,10 @@ async def ollama_async_streaming(
first_chunk_content = first_chunk.choices[0].delta.content or ""
response_content = first_chunk_content + "".join(
[
- chunk.choices[0].delta.content
- async for chunk in streamwrapper
- if chunk.choices[0].delta.content]
+ chunk.choices[0].delta.content
+ async for chunk in streamwrapper
+ if chunk.choices[0].delta.content
+ ]
)
function_call = json.loads(response_content)
delta = litellm.utils.Delta(
@@ -413,27 +437,33 @@ async def ollama_async_streaming(
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
- "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+ "function": {
+ "name": function_call.get(
+ "name", function_call.get("function", None)
+ ),
+ "arguments": json.dumps(function_call["arguments"]),
+ },
"type": "function",
}
],
)
model_response = first_chunk
- model_response["choices"][0]["delta"] = delta
- model_response["choices"][0]["finish_reason"] = "tool_calls"
+ model_response.choices[0].delta = delta # type: ignore
+ model_response.choices[0].finish_reason = "tool_calls"
yield model_response
else:
async for transformed_chunk in streamwrapper:
yield transformed_chunk
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error("LiteLLM.gemini(): Exception occured - {}".format(str(e)))
+ verbose_logger.debug(traceback.format_exc())
async def ollama_acompletion(
url,
api_key: Optional[str],
data,
- model_response,
+ model_response: litellm.ModelResponse,
encoding,
logging_obj,
function_name,
@@ -468,7 +498,7 @@ async def ollama_acompletion(
)
## RESPONSE OBJECT
- model_response["choices"][0]["finish_reason"] = "stop"
+ model_response.choices[0].finish_reason = "stop"
if data.get("format", "") == "json":
function_call = json.loads(response_json["message"]["content"])
message = litellm.Message(
@@ -476,18 +506,27 @@ async def ollama_acompletion(
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
- "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+ "function": {
+ "name": function_call.get("name", function_name),
+ "arguments": json.dumps(
+ function_call.get("arguments", function_call)
+ ),
+ },
"type": "function",
}
],
)
- model_response["choices"][0]["message"] = message
- model_response["choices"][0]["finish_reason"] = "tool_calls"
+ model_response.choices[0].message = message # type: ignore
+ model_response.choices[0].finish_reason = "tool_calls"
else:
- model_response["choices"][0]["message"]["content"] = response_json["message"]["content"]
+ model_response.choices[0].message.content = response_json[ # type: ignore
+ "message"
+ ][
+ "content"
+ ]
- model_response["created"] = int(time.time())
- model_response["model"] = "ollama_chat/" + data["model"]
+ model_response.created = int(time.time())
+ model_response.model = "ollama_chat/" + data["model"]
prompt_tokens = response_json.get("prompt_eval_count", litellm.token_counter(messages=data["messages"])) # type: ignore
completion_tokens = response_json.get(
"eval_count",
@@ -495,12 +534,20 @@ async def ollama_acompletion(
text=response_json["message"]["content"], count_response_tokens=True
),
)
- model_response["usage"] = litellm.Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
+ setattr(
+ model_response,
+ "usage",
+ litellm.Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ ),
)
return model_response
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error(
+ "LiteLLM.ollama_acompletion(): Exception occured - {}".format(str(e))
+ )
+ verbose_logger.debug(traceback.format_exc())
+
raise e
diff --git a/litellm/llms/oobabooga.py b/litellm/llms/oobabooga.py
index f8f32e0fe4..79d918667c 100644
--- a/litellm/llms/oobabooga.py
+++ b/litellm/llms/oobabooga.py
@@ -1,11 +1,14 @@
-import os
import json
-from enum import Enum
-import requests # type: ignore
+import os
import time
+from enum import Enum
from typing import Callable, Optional
-from litellm.utils import ModelResponse, Usage
-from .prompt_templates.factory import prompt_factory, custom_prompt
+
+import requests # type: ignore
+
+from litellm.utils import EmbeddingResponse, ModelResponse, Usage
+
+from .prompt_templates.factory import custom_prompt, prompt_factory
class OobaboogaError(Exception):
@@ -99,17 +102,15 @@ def completion(
)
else:
try:
- model_response["choices"][0]["message"]["content"] = (
- completion_response["choices"][0]["message"]["content"]
- )
+ model_response.choices[0].message.content = completion_response["choices"][0]["message"]["content"] # type: ignore
except:
raise OobaboogaError(
message=json.dumps(completion_response),
status_code=response.status_code,
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=completion_response["usage"]["prompt_tokens"],
completion_tokens=completion_response["usage"]["completion_tokens"],
@@ -122,10 +123,10 @@ def completion(
def embedding(
model: str,
input: list,
+ model_response: EmbeddingResponse,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
logging_obj=None,
- model_response=None,
optional_params=None,
encoding=None,
):
@@ -166,7 +167,7 @@ def embedding(
)
# Process response data
- model_response["data"] = [
+ model_response.data = [
{
"embedding": completion_response["data"][0]["embedding"],
"index": 0,
@@ -176,8 +177,12 @@ def embedding(
num_tokens = len(completion_response["data"][0]["embedding"])
# Adding metadata to response
- model_response.usage = Usage(prompt_tokens=num_tokens, total_tokens=num_tokens)
- model_response["object"] = "list"
- model_response["model"] = model
+ setattr(
+ model_response,
+ "usage",
+ Usage(prompt_tokens=num_tokens, total_tokens=num_tokens),
+ )
+ model_response.object = "list"
+ model_response.model = model
return model_response
diff --git a/litellm/llms/openai.py b/litellm/llms/openai.py
index 05e6566ffa..0a40ab3fe9 100644
--- a/litellm/llms/openai.py
+++ b/litellm/llms/openai.py
@@ -1,31 +1,44 @@
+import hashlib
+import json
+import time
+import traceback
+import types
from typing import (
- Optional,
- Union,
Any,
BinaryIO,
- Literal,
+ Callable,
+ Coroutine,
Iterable,
+ Literal,
+ Optional,
+ Union,
)
-from typing_extensions import override
-from pydantic import BaseModel
-import types, time, json, traceback
+
import httpx
-from .base import BaseLLM
-from litellm.utils import (
- ModelResponse,
- Choices,
- Message,
- CustomStreamWrapper,
- convert_to_model_response_object,
- Usage,
- TranscriptionResponse,
- TextCompletionResponse,
-)
-from typing import Callable, Optional
+import openai
+from openai import AsyncOpenAI, OpenAI
+from openai.types.beta.assistant_deleted import AssistantDeleted
+from openai.types.file_deleted import FileDeleted
+from pydantic import BaseModel
+from typing_extensions import overload, override
+
import litellm
-from .prompt_templates.factory import prompt_factory, custom_prompt
-from openai import OpenAI, AsyncOpenAI
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.types.utils import ProviderField
+from litellm.utils import (
+ Choices,
+ CustomStreamWrapper,
+ Message,
+ ModelResponse,
+ TextCompletionResponse,
+ TranscriptionResponse,
+ Usage,
+ convert_to_model_response_object,
+)
+
from ..types.llms.openai import *
+from .base import BaseLLM
+from .prompt_templates.factory import custom_prompt, prompt_factory
class OpenAIError(Exception):
@@ -162,6 +175,68 @@ class MistralConfig:
return optional_params
+class MistralEmbeddingConfig:
+ """
+ Reference: https://docs.mistral.ai/api/#operation/createEmbedding
+ """
+
+ def __init__(
+ self,
+ ) -> None:
+ locals_ = locals().copy()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self):
+ return [
+ "encoding_format",
+ ]
+
+ def map_openai_params(self, non_default_params: dict, optional_params: dict):
+ for param, value in non_default_params.items():
+ if param == "encoding_format":
+ optional_params["encoding_format"] = value
+ return optional_params
+
+
+class AzureAIStudioConfig:
+ def get_required_params(self) -> List[ProviderField]:
+ """For a given provider, return it's required fields with a description"""
+ return [
+ ProviderField(
+ field_name="api_key",
+ field_type="string",
+ field_description="Your Azure AI Studio API Key.",
+ field_value="zEJ...",
+ ),
+ ProviderField(
+ field_name="api_base",
+ field_type="string",
+ field_description="Your Azure AI Studio API Base.",
+ field_value="https://Mistral-serverless.",
+ ),
+ ]
+
+
class DeepInfraConfig:
"""
Reference: https://deepinfra.com/docs/advanced/openai_api
@@ -241,8 +316,12 @@ class DeepInfraConfig:
]
def map_openai_params(
- self, non_default_params: dict, optional_params: dict, model: str
- ):
+ self,
+ non_default_params: dict,
+ optional_params: dict,
+ model: str,
+ drop_params: bool,
+ ) -> dict:
supported_openai_params = self.get_supported_openai_params()
for param, value in non_default_params.items():
if (
@@ -251,7 +330,120 @@ class DeepInfraConfig:
and model == "mistralai/Mistral-7B-Instruct-v0.1"
): # this model does no support temperature == 0
value = 0.0001 # close to 0
+ if param == "tool_choice":
+ if (
+ value != "auto" and value != "none"
+ ): # https://deepinfra.com/docs/advanced/function_calling
+ ## UNSUPPORTED TOOL CHOICE VALUE
+ if litellm.drop_params is True or drop_params is True:
+ value = None
+ else:
+ raise litellm.utils.UnsupportedParamsError(
+ message="Deepinfra doesn't support tool_choice={}. To drop unsupported openai params from the call, set `litellm.drop_params = True`".format(
+ value
+ ),
+ status_code=400,
+ )
if param in supported_openai_params:
+ if value is not None:
+ optional_params[param] = value
+ return optional_params
+
+
+class GroqConfig:
+ """
+ Reference: https://deepinfra.com/docs/advanced/openai_api
+
+ The class `DeepInfra` provides configuration for the DeepInfra's Chat Completions API interface. Below are the parameters:
+ """
+
+ frequency_penalty: Optional[int] = None
+ function_call: Optional[Union[str, dict]] = None
+ functions: Optional[list] = None
+ logit_bias: Optional[dict] = None
+ max_tokens: Optional[int] = None
+ n: Optional[int] = None
+ presence_penalty: Optional[int] = None
+ stop: Optional[Union[str, list]] = None
+ temperature: Optional[int] = None
+ top_p: Optional[int] = None
+ response_format: Optional[dict] = None
+ tools: Optional[list] = None
+ tool_choice: Optional[Union[str, dict]] = None
+
+ def __init__(
+ self,
+ frequency_penalty: Optional[int] = None,
+ function_call: Optional[Union[str, dict]] = None,
+ functions: Optional[list] = None,
+ logit_bias: Optional[dict] = None,
+ max_tokens: Optional[int] = None,
+ n: Optional[int] = None,
+ presence_penalty: Optional[int] = None,
+ stop: Optional[Union[str, list]] = None,
+ temperature: Optional[int] = None,
+ top_p: Optional[int] = None,
+ response_format: Optional[dict] = None,
+ tools: Optional[list] = None,
+ tool_choice: Optional[Union[str, dict]] = None,
+ ) -> None:
+ locals_ = locals().copy()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params_stt(self):
+ return [
+ "prompt",
+ "response_format",
+ "temperature",
+ "language",
+ ]
+
+ def get_supported_openai_response_formats_stt(self) -> List[str]:
+ return ["json", "verbose_json", "text"]
+
+ def map_openai_params_stt(
+ self,
+ non_default_params: dict,
+ optional_params: dict,
+ model: str,
+ drop_params: bool,
+ ) -> dict:
+ response_formats = self.get_supported_openai_response_formats_stt()
+ for param, value in non_default_params.items():
+ if param == "response_format":
+ if value in response_formats:
+ optional_params[param] = value
+ else:
+ if litellm.drop_params is True or drop_params is True:
+ pass
+ else:
+ raise litellm.utils.UnsupportedParamsError(
+ message="Groq doesn't support response_format={}. To drop unsupported openai params from the call, set `litellm.drop_params = True`".format(
+ value
+ ),
+ status_code=400,
+ )
+ else:
optional_params[param] = value
return optional_params
@@ -349,11 +541,11 @@ class OpenAIConfig:
"top_p",
"tools",
"tool_choice",
- "user",
"function_call",
"functions",
"max_retries",
"extra_headers",
+ "parallel_tool_calls",
] # works across all models
model_specific_params = []
@@ -362,6 +554,12 @@ class OpenAIConfig:
): # gpt-4 does not support 'response_format'
model_specific_params.append("response_format")
+ if (
+ model in litellm.open_ai_chat_completion_models
+ ) or model in litellm.open_ai_text_completion_models:
+ model_specific_params.append(
+ "user"
+ ) # user is not a param supported by all openai-compatible endpoints - e.g. azure ai
return base_params + model_specific_params
def map_openai_params(
@@ -498,6 +696,94 @@ class OpenAIChatCompletion(BaseLLM):
def __init__(self) -> None:
super().__init__()
+ def _get_openai_client(
+ self,
+ is_async: bool,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ timeout: Union[float, httpx.Timeout] = httpx.Timeout(None),
+ max_retries: Optional[int] = 2,
+ organization: Optional[str] = None,
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ):
+ args = locals()
+ if client is None:
+ if not isinstance(max_retries, int):
+ raise OpenAIError(
+ status_code=422,
+ message="max retries must be an int. Passed in value: {}".format(
+ max_retries
+ ),
+ )
+ # Creating a new OpenAI Client
+ # check in memory cache before creating a new one
+ # Convert the API key to bytes
+ hashed_api_key = None
+ if api_key is not None:
+ hash_object = hashlib.sha256(api_key.encode())
+ # Hexadecimal representation of the hash
+ hashed_api_key = hash_object.hexdigest()
+
+ _cache_key = f"hashed_api_key={hashed_api_key},api_base={api_base},timeout={timeout},max_retries={max_retries},organization={organization},is_async={is_async}"
+
+ if _cache_key in litellm.in_memory_llm_clients_cache:
+ return litellm.in_memory_llm_clients_cache[_cache_key]
+ if is_async:
+ _new_client: Union[OpenAI, AsyncOpenAI] = AsyncOpenAI(
+ api_key=api_key,
+ base_url=api_base,
+ http_client=litellm.aclient_session,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ )
+ else:
+ _new_client = OpenAI(
+ api_key=api_key,
+ base_url=api_base,
+ http_client=litellm.client_session,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ )
+
+ ## SAVE CACHE KEY
+ litellm.in_memory_llm_clients_cache[_cache_key] = _new_client
+ return _new_client
+
+ else:
+ return client
+
+ async def make_openai_chat_completion_request(
+ self,
+ openai_aclient: AsyncOpenAI,
+ data: dict,
+ timeout: Union[float, httpx.Timeout],
+ ):
+ """
+ Helper to:
+ - call chat.completions.create.with_raw_response when litellm.return_response_headers is True
+ - call chat.completions.create by default
+ """
+ try:
+ if litellm.return_response_headers is True:
+ raw_response = (
+ await openai_aclient.chat.completions.with_raw_response.create(
+ **data, timeout=timeout
+ )
+ )
+
+ headers = dict(raw_response.headers)
+ response = raw_response.parse()
+ return headers, response
+ else:
+ response = await openai_aclient.chat.completions.create(
+ **data, timeout=timeout
+ )
+ return None, response
+ except Exception as e:
+ raise e
+
def completion(
self,
model_response: ModelResponse,
@@ -524,17 +810,17 @@ class OpenAIChatCompletion(BaseLLM):
if headers:
optional_params["extra_headers"] = headers
if model is None or messages is None:
- raise OpenAIError(status_code=422, message=f"Missing model or messages")
+ raise OpenAIError(status_code=422, message="Missing model or messages")
if not isinstance(timeout, float) and not isinstance(
timeout, httpx.Timeout
):
raise OpenAIError(
status_code=422,
- message=f"Timeout needs to be a float or httpx.Timeout",
+ message="Timeout needs to be a float or httpx.Timeout",
)
- if custom_llm_provider != "openai":
+ if custom_llm_provider is not None and custom_llm_provider != "openai":
model_response.model = f"{custom_llm_provider}/{model}"
# process all OpenAI compatible provider logic here
if custom_llm_provider == "mistral":
@@ -604,17 +890,16 @@ class OpenAIChatCompletion(BaseLLM):
raise OpenAIError(
status_code=422, message="max retries must be an int"
)
- if client is None:
- openai_client = OpenAI(
- api_key=api_key,
- base_url=api_base,
- http_client=litellm.client_session,
- timeout=timeout,
- max_retries=max_retries,
- organization=organization,
- )
- else:
- openai_client = client
+
+ openai_client = self._get_openai_client(
+ is_async=False,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
## LOGGING
logging_obj.pre_call(
@@ -668,6 +953,12 @@ class OpenAIChatCompletion(BaseLLM):
new_messages = messages
new_messages.append({"role": "user", "content": ""})
messages = new_messages
+ elif (
+ "unknown field: parameter index is not a valid field" in str(e)
+ ) and "tools" in data:
+ litellm.remove_index_from_tool_calls(
+ tool_calls=data["tools"], messages=messages
+ )
else:
raise e
except OpenAIError as e:
@@ -683,28 +974,26 @@ class OpenAIChatCompletion(BaseLLM):
self,
data: dict,
model_response: ModelResponse,
+ logging_obj: LiteLLMLoggingObj,
timeout: Union[float, httpx.Timeout],
api_key: Optional[str] = None,
api_base: Optional[str] = None,
organization: Optional[str] = None,
client=None,
max_retries=None,
- logging_obj=None,
headers=None,
):
response = None
try:
- if client is None:
- openai_aclient = AsyncOpenAI(
- api_key=api_key,
- base_url=api_base,
- http_client=litellm.aclient_session,
- timeout=timeout,
- max_retries=max_retries,
- organization=organization,
- )
- else:
- openai_aclient = client
+ openai_aclient = self._get_openai_client(
+ is_async=True,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
## LOGGING
logging_obj.pre_call(
@@ -718,8 +1007,8 @@ class OpenAIChatCompletion(BaseLLM):
},
)
- response = await openai_aclient.chat.completions.create(
- **data, timeout=timeout
+ headers, response = await self.make_openai_chat_completion_request(
+ openai_aclient=openai_aclient, data=data, timeout=timeout
)
stringified_response = response.model_dump()
logging_obj.post_call(
@@ -728,9 +1017,11 @@ class OpenAIChatCompletion(BaseLLM):
original_response=stringified_response,
additional_args={"complete_input_dict": data},
)
+ logging_obj.model_call_details["response_headers"] = headers
return convert_to_model_response_object(
response_object=stringified_response,
model_response_object=model_response,
+ hidden_params={"headers": headers},
)
except Exception as e:
raise e
@@ -748,17 +1039,15 @@ class OpenAIChatCompletion(BaseLLM):
max_retries=None,
headers=None,
):
- if client is None:
- openai_client = OpenAI(
- api_key=api_key,
- base_url=api_base,
- http_client=litellm.client_session,
- timeout=timeout,
- max_retries=max_retries,
- organization=organization,
- )
- else:
- openai_client = client
+ openai_client = self._get_openai_client(
+ is_async=False,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
## LOGGING
logging_obj.pre_call(
input=data["messages"],
@@ -782,10 +1071,10 @@ class OpenAIChatCompletion(BaseLLM):
async def async_streaming(
self,
- logging_obj,
timeout: Union[float, httpx.Timeout],
data: dict,
model: str,
+ logging_obj: LiteLLMLoggingObj,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
organization: Optional[str] = None,
@@ -795,17 +1084,15 @@ class OpenAIChatCompletion(BaseLLM):
):
response = None
try:
- if client is None:
- openai_aclient = AsyncOpenAI(
- api_key=api_key,
- base_url=api_base,
- http_client=litellm.aclient_session,
- timeout=timeout,
- max_retries=max_retries,
- organization=organization,
- )
- else:
- openai_aclient = client
+ openai_aclient = self._get_openai_client(
+ is_async=True,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
## LOGGING
logging_obj.pre_call(
input=data["messages"],
@@ -818,9 +1105,10 @@ class OpenAIChatCompletion(BaseLLM):
},
)
- response = await openai_aclient.chat.completions.create(
- **data, timeout=timeout
+ headers, response = await self.make_openai_chat_completion_request(
+ openai_aclient=openai_aclient, data=data, timeout=timeout
)
+ logging_obj.model_call_details["response_headers"] = headers
streamwrapper = CustomStreamWrapper(
completion_stream=response,
model=model,
@@ -845,31 +1133,58 @@ class OpenAIChatCompletion(BaseLLM):
else:
raise OpenAIError(status_code=500, message=f"{str(e)}")
+ # Embedding
+ async def make_openai_embedding_request(
+ self,
+ openai_aclient: AsyncOpenAI,
+ data: dict,
+ timeout: Union[float, httpx.Timeout],
+ ):
+ """
+ Helper to:
+ - call embeddings.create.with_raw_response when litellm.return_response_headers is True
+ - call embeddings.create by default
+ """
+ try:
+ if litellm.return_response_headers is True:
+ raw_response = await openai_aclient.embeddings.with_raw_response.create(
+ **data, timeout=timeout
+ ) # type: ignore
+ headers = dict(raw_response.headers)
+ response = raw_response.parse()
+ return headers, response
+ else:
+ response = await openai_aclient.embeddings.create(**data, timeout=timeout) # type: ignore
+ return None, response
+ except Exception as e:
+ raise e
+
async def aembedding(
self,
input: list,
data: dict,
- model_response: ModelResponse,
+ model_response: litellm.utils.EmbeddingResponse,
timeout: float,
+ logging_obj: LiteLLMLoggingObj,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
- client=None,
+ client: Optional[AsyncOpenAI] = None,
max_retries=None,
- logging_obj=None,
):
response = None
try:
- if client is None:
- openai_aclient = AsyncOpenAI(
- api_key=api_key,
- base_url=api_base,
- http_client=litellm.aclient_session,
- timeout=timeout,
- max_retries=max_retries,
- )
- else:
- openai_aclient = client
- response = await openai_aclient.embeddings.create(**data, timeout=timeout) # type: ignore
+ openai_aclient = self._get_openai_client(
+ is_async=True,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+ headers, response = await self.make_openai_embedding_request(
+ openai_aclient=openai_aclient, data=data, timeout=timeout
+ )
+ logging_obj.model_call_details["response_headers"] = headers
stringified_response = response.model_dump()
## LOGGING
logging_obj.post_call(
@@ -894,9 +1209,9 @@ class OpenAIChatCompletion(BaseLLM):
input: list,
timeout: float,
logging_obj,
+ model_response: litellm.utils.EmbeddingResponse,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
- model_response: Optional[litellm.utils.EmbeddingResponse] = None,
optional_params=None,
client=None,
aembedding=None,
@@ -916,19 +1231,28 @@ class OpenAIChatCompletion(BaseLLM):
additional_args={"complete_input_dict": data, "api_base": api_base},
)
- if aembedding == True:
- response = self.aembedding(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) # type: ignore
- return response
- if client is None:
- openai_client = OpenAI(
+ if aembedding is True:
+ response = self.aembedding(
+ data=data,
+ input=input,
+ logging_obj=logging_obj,
+ model_response=model_response,
+ api_base=api_base,
api_key=api_key,
- base_url=api_base,
- http_client=litellm.client_session,
timeout=timeout,
+ client=client,
max_retries=max_retries,
)
- else:
- openai_client = client
+ return response
+
+ openai_client = self._get_openai_client(
+ is_async=False,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
## COMPLETION CALL
response = openai_client.embeddings.create(**data, timeout=timeout) # type: ignore
@@ -964,16 +1288,16 @@ class OpenAIChatCompletion(BaseLLM):
):
response = None
try:
- if client is None:
- openai_aclient = AsyncOpenAI(
- api_key=api_key,
- base_url=api_base,
- http_client=litellm.aclient_session,
- timeout=timeout,
- max_retries=max_retries,
- )
- else:
- openai_aclient = client
+
+ openai_aclient = self._get_openai_client(
+ is_async=True,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
response = await openai_aclient.images.generate(**data, timeout=timeout) # type: ignore
stringified_response = response.model_dump()
## LOGGING
@@ -1018,16 +1342,14 @@ class OpenAIChatCompletion(BaseLLM):
response = self.aimage_generation(data=data, prompt=prompt, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) # type: ignore
return response
- if client is None:
- openai_client = OpenAI(
- api_key=api_key,
- base_url=api_base,
- http_client=litellm.client_session,
- timeout=timeout,
- max_retries=max_retries,
- )
- else:
- openai_client = client
+ openai_client = self._get_openai_client(
+ is_async=False,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
## LOGGING
logging_obj.pre_call(
@@ -1077,6 +1399,34 @@ class OpenAIChatCompletion(BaseLLM):
else:
raise OpenAIError(status_code=500, message=str(e))
+ # Audio Transcriptions
+ async def make_openai_audio_transcriptions_request(
+ self,
+ openai_aclient: AsyncOpenAI,
+ data: dict,
+ timeout: Union[float, httpx.Timeout],
+ ):
+ """
+ Helper to:
+ - call openai_aclient.audio.transcriptions.with_raw_response when litellm.return_response_headers is True
+ - call openai_aclient.audio.transcriptions.create by default
+ """
+ try:
+ if litellm.return_response_headers is True:
+ raw_response = (
+ await openai_aclient.audio.transcriptions.with_raw_response.create(
+ **data, timeout=timeout
+ )
+ ) # type: ignore
+ headers = dict(raw_response.headers)
+ response = raw_response.parse()
+ return headers, response
+ else:
+ response = await openai_aclient.audio.transcriptions.create(**data, timeout=timeout) # type: ignore
+ return None, response
+ except Exception as e:
+ raise e
+
def audio_transcriptions(
self,
model: str,
@@ -1085,14 +1435,14 @@ class OpenAIChatCompletion(BaseLLM):
model_response: TranscriptionResponse,
timeout: float,
max_retries: int,
- api_key: Optional[str] = None,
- api_base: Optional[str] = None,
+ api_key: Optional[str],
+ api_base: Optional[str],
client=None,
logging_obj=None,
atranscription: bool = False,
):
data = {"model": model, "file": audio_file, **optional_params}
- if atranscription == True:
+ if atranscription is True:
return self.async_audio_transcriptions(
audio_file=audio_file,
data=data,
@@ -1104,21 +1454,23 @@ class OpenAIChatCompletion(BaseLLM):
max_retries=max_retries,
logging_obj=logging_obj,
)
- if client is None:
- openai_client = OpenAI(
- api_key=api_key,
- base_url=api_base,
- http_client=litellm.client_session,
- timeout=timeout,
- max_retries=max_retries,
- )
- else:
- openai_client = client
+
+ openai_client = self._get_openai_client(
+ is_async=False,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ )
response = openai_client.audio.transcriptions.create(
**data, timeout=timeout # type: ignore
)
- stringified_response = response.model_dump()
+ if isinstance(response, BaseModel):
+ stringified_response = response.model_dump()
+ else:
+ stringified_response = TranscriptionResponse(text=response).model_dump()
+
## LOGGING
logging_obj.post_call(
input=audio_file.name,
@@ -1136,28 +1488,32 @@ class OpenAIChatCompletion(BaseLLM):
data: dict,
model_response: TranscriptionResponse,
timeout: float,
+ logging_obj: LiteLLMLoggingObj,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
client=None,
max_retries=None,
- logging_obj=None,
):
- response = None
try:
- if client is None:
- openai_aclient = AsyncOpenAI(
- api_key=api_key,
- base_url=api_base,
- http_client=litellm.aclient_session,
- timeout=timeout,
- max_retries=max_retries,
- )
+ openai_aclient = self._get_openai_client(
+ is_async=True,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ headers, response = await self.make_openai_audio_transcriptions_request(
+ openai_aclient=openai_aclient,
+ data=data,
+ timeout=timeout,
+ )
+ logging_obj.model_call_details["response_headers"] = headers
+ if isinstance(response, BaseModel):
+ stringified_response = response.model_dump()
else:
- openai_aclient = client
- response = await openai_aclient.audio.transcriptions.create(
- **data, timeout=timeout
- ) # type: ignore
- stringified_response = response.model_dump()
+ stringified_response = TranscriptionResponse(text=response).model_dump()
## LOGGING
logging_obj.post_call(
input=audio_file.name,
@@ -1176,6 +1532,87 @@ class OpenAIChatCompletion(BaseLLM):
)
raise e
+ def audio_speech(
+ self,
+ model: str,
+ input: str,
+ voice: str,
+ optional_params: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ organization: Optional[str],
+ project: Optional[str],
+ max_retries: int,
+ timeout: Union[float, httpx.Timeout],
+ aspeech: Optional[bool] = None,
+ client=None,
+ ) -> HttpxBinaryResponseContent:
+
+ if aspeech is not None and aspeech is True:
+ return self.async_audio_speech(
+ model=model,
+ input=input,
+ voice=voice,
+ optional_params=optional_params,
+ api_key=api_key,
+ api_base=api_base,
+ organization=organization,
+ project=project,
+ max_retries=max_retries,
+ timeout=timeout,
+ client=client,
+ ) # type: ignore
+
+ openai_client = self._get_openai_client(
+ is_async=False,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ response = openai_client.audio.speech.create(
+ model=model,
+ voice=voice, # type: ignore
+ input=input,
+ **optional_params,
+ )
+ return response
+
+ async def async_audio_speech(
+ self,
+ model: str,
+ input: str,
+ voice: str,
+ optional_params: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ organization: Optional[str],
+ project: Optional[str],
+ max_retries: int,
+ timeout: Union[float, httpx.Timeout],
+ client=None,
+ ) -> HttpxBinaryResponseContent:
+
+ openai_client = self._get_openai_client(
+ is_async=True,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ client=client,
+ )
+
+ response = await openai_client.audio.speech.create(
+ model=model,
+ voice=voice, # type: ignore
+ input=input,
+ **optional_params,
+ )
+
+ return response
+
async def ahealth_check(
self,
model: Optional[str],
@@ -1268,9 +1705,9 @@ class OpenAITextCompletion(BaseLLM):
model: str,
messages: list,
timeout: float,
+ logging_obj: LiteLLMLoggingObj,
print_verbose: Optional[Callable] = None,
api_base: Optional[str] = None,
- logging_obj=None,
acompletion: bool = False,
optional_params=None,
litellm_params=None,
@@ -1357,6 +1794,7 @@ class OpenAITextCompletion(BaseLLM):
response = openai_client.completions.create(**data) # type: ignore
response_json = response.model_dump()
+
## LOGGING
logging_obj.post_call(
input=prompt,
@@ -1497,6 +1935,467 @@ class OpenAITextCompletion(BaseLLM):
yield transformed_chunk
+class OpenAIFilesAPI(BaseLLM):
+ """
+ OpenAI methods to support for batches
+ - create_file()
+ - retrieve_file()
+ - list_files()
+ - delete_file()
+ - file_content()
+ - update_file()
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+
+ def get_openai_client(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ _is_async: bool = False,
+ ) -> Optional[Union[OpenAI, AsyncOpenAI]]:
+ received_args = locals()
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = None
+ if client is None:
+ data = {}
+ for k, v in received_args.items():
+ if k == "self" or k == "client" or k == "_is_async":
+ pass
+ elif k == "api_base" and v is not None:
+ data["base_url"] = v
+ elif v is not None:
+ data[k] = v
+ if _is_async is True:
+ openai_client = AsyncOpenAI(**data)
+ else:
+ openai_client = OpenAI(**data) # type: ignore
+ else:
+ openai_client = client
+
+ return openai_client
+
+ async def acreate_file(
+ self,
+ create_file_data: CreateFileRequest,
+ openai_client: AsyncOpenAI,
+ ) -> FileObject:
+ response = await openai_client.files.create(**create_file_data)
+ return response
+
+ def create_file(
+ self,
+ _is_async: bool,
+ create_file_data: CreateFileRequest,
+ api_base: str,
+ api_key: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ) -> Union[FileObject, Coroutine[Any, Any, FileObject]]:
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.acreate_file( # type: ignore
+ create_file_data=create_file_data, openai_client=openai_client
+ )
+ response = openai_client.files.create(**create_file_data)
+ return response
+
+ async def afile_content(
+ self,
+ file_content_request: FileContentRequest,
+ openai_client: AsyncOpenAI,
+ ) -> HttpxBinaryResponseContent:
+ response = await openai_client.files.content(**file_content_request)
+ return response
+
+ def file_content(
+ self,
+ _is_async: bool,
+ file_content_request: FileContentRequest,
+ api_base: str,
+ api_key: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ) -> Union[
+ HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]
+ ]:
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.afile_content( # type: ignore
+ file_content_request=file_content_request,
+ openai_client=openai_client,
+ )
+ response = openai_client.files.content(**file_content_request)
+
+ return response
+
+ async def aretrieve_file(
+ self,
+ file_id: str,
+ openai_client: AsyncOpenAI,
+ ) -> FileObject:
+ response = await openai_client.files.retrieve(file_id=file_id)
+ return response
+
+ def retrieve_file(
+ self,
+ _is_async: bool,
+ file_id: str,
+ api_base: str,
+ api_key: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ):
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.aretrieve_file( # type: ignore
+ file_id=file_id,
+ openai_client=openai_client,
+ )
+ response = openai_client.files.retrieve(file_id=file_id)
+
+ return response
+
+ async def adelete_file(
+ self,
+ file_id: str,
+ openai_client: AsyncOpenAI,
+ ) -> FileDeleted:
+ response = await openai_client.files.delete(file_id=file_id)
+ return response
+
+ def delete_file(
+ self,
+ _is_async: bool,
+ file_id: str,
+ api_base: str,
+ api_key: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ):
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.adelete_file( # type: ignore
+ file_id=file_id,
+ openai_client=openai_client,
+ )
+ response = openai_client.files.delete(file_id=file_id)
+
+ return response
+
+ async def alist_files(
+ self,
+ openai_client: AsyncOpenAI,
+ purpose: Optional[str] = None,
+ ):
+ if isinstance(purpose, str):
+ response = await openai_client.files.list(purpose=purpose)
+ else:
+ response = await openai_client.files.list()
+ return response
+
+ def list_files(
+ self,
+ _is_async: bool,
+ api_base: str,
+ api_key: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ purpose: Optional[str] = None,
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ):
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.alist_files( # type: ignore
+ purpose=purpose,
+ openai_client=openai_client,
+ )
+
+ if isinstance(purpose, str):
+ response = openai_client.files.list(purpose=purpose)
+ else:
+ response = openai_client.files.list()
+
+ return response
+
+
+class OpenAIBatchesAPI(BaseLLM):
+ """
+ OpenAI methods to support for batches
+ - create_batch()
+ - retrieve_batch()
+ - cancel_batch()
+ - list_batch()
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+
+ def get_openai_client(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ _is_async: bool = False,
+ ) -> Optional[Union[OpenAI, AsyncOpenAI]]:
+ received_args = locals()
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = None
+ if client is None:
+ data = {}
+ for k, v in received_args.items():
+ if k == "self" or k == "client" or k == "_is_async":
+ pass
+ elif k == "api_base" and v is not None:
+ data["base_url"] = v
+ elif v is not None:
+ data[k] = v
+ if _is_async is True:
+ openai_client = AsyncOpenAI(**data)
+ else:
+ openai_client = OpenAI(**data) # type: ignore
+ else:
+ openai_client = client
+
+ return openai_client
+
+ async def acreate_batch(
+ self,
+ create_batch_data: CreateBatchRequest,
+ openai_client: AsyncOpenAI,
+ ) -> Batch:
+ response = await openai_client.batches.create(**create_batch_data)
+ return response
+
+ def create_batch(
+ self,
+ _is_async: bool,
+ create_batch_data: CreateBatchRequest,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+ ) -> Union[Batch, Coroutine[Any, Any, Batch]]:
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.acreate_batch( # type: ignore
+ create_batch_data=create_batch_data, openai_client=openai_client
+ )
+ response = openai_client.batches.create(**create_batch_data)
+ return response
+
+ async def aretrieve_batch(
+ self,
+ retrieve_batch_data: RetrieveBatchRequest,
+ openai_client: AsyncOpenAI,
+ ) -> Batch:
+ response = await openai_client.batches.retrieve(**retrieve_batch_data)
+ return response
+
+ def retrieve_batch(
+ self,
+ _is_async: bool,
+ retrieve_batch_data: RetrieveBatchRequest,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[OpenAI] = None,
+ ):
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+
+ if _is_async is True:
+ if not isinstance(openai_client, AsyncOpenAI):
+ raise ValueError(
+ "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+ )
+ return self.aretrieve_batch( # type: ignore
+ retrieve_batch_data=retrieve_batch_data, openai_client=openai_client
+ )
+ response = openai_client.batches.retrieve(**retrieve_batch_data)
+ return response
+
+ def cancel_batch(
+ self,
+ _is_async: bool,
+ cancel_batch_data: CancelBatchRequest,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[OpenAI] = None,
+ ):
+ openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ _is_async=_is_async,
+ )
+ if openai_client is None:
+ raise ValueError(
+ "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+ )
+ response = openai_client.batches.cancel(**cancel_batch_data)
+ return response
+
+ # def list_batch(
+ # self,
+ # list_batch_data: ListBatchRequest,
+ # api_key: Optional[str],
+ # api_base: Optional[str],
+ # timeout: Union[float, httpx.Timeout],
+ # max_retries: Optional[int],
+ # organization: Optional[str],
+ # client: Optional[OpenAI] = None,
+ # ):
+ # openai_client: OpenAI = self.get_openai_client(
+ # api_key=api_key,
+ # api_base=api_base,
+ # timeout=timeout,
+ # max_retries=max_retries,
+ # organization=organization,
+ # client=client,
+ # )
+ # response = openai_client.batches.list(**list_batch_data)
+ # return response
+
+
class OpenAIAssistantsAPI(BaseLLM):
def __init__(self) -> None:
super().__init__()
@@ -1526,8 +2425,85 @@ class OpenAIAssistantsAPI(BaseLLM):
return openai_client
+ def async_get_openai_client(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI] = None,
+ ) -> AsyncOpenAI:
+ received_args = locals()
+ if client is None:
+ data = {}
+ for k, v in received_args.items():
+ if k == "self" or k == "client":
+ pass
+ elif k == "api_base" and v is not None:
+ data["base_url"] = v
+ elif v is not None:
+ data[k] = v
+ openai_client = AsyncOpenAI(**data) # type: ignore
+ else:
+ openai_client = client
+
+ return openai_client
+
### ASSISTANTS ###
+ async def async_get_assistants(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI],
+ ) -> AsyncCursorPage[Assistant]:
+ openai_client = self.async_get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+
+ response = await openai_client.beta.assistants.list()
+
+ return response
+
+ # fmt: off
+
+ @overload
+ def get_assistants(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI],
+ aget_assistants: Literal[True],
+ ) -> Coroutine[None, None, AsyncCursorPage[Assistant]]:
+ ...
+
+ @overload
+ def get_assistants(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[OpenAI],
+ aget_assistants: Optional[Literal[False]],
+ ) -> SyncCursorPage[Assistant]:
+ ...
+
+ # fmt: on
+
def get_assistants(
self,
api_key: Optional[str],
@@ -1535,8 +2511,18 @@ class OpenAIAssistantsAPI(BaseLLM):
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
organization: Optional[str],
- client: Optional[OpenAI],
- ) -> SyncCursorPage[Assistant]:
+ client=None,
+ aget_assistants=None,
+ ):
+ if aget_assistants is not None and aget_assistants == True:
+ return self.async_get_assistants(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
openai_client = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@@ -1550,20 +2536,211 @@ class OpenAIAssistantsAPI(BaseLLM):
return response
- ### MESSAGES ###
-
- def add_message(
+ # Create Assistant
+ async def async_create_assistants(
self,
- thread_id: str,
- message_data: MessageData,
api_key: Optional[str],
api_base: Optional[str],
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
organization: Optional[str],
- client: Optional[OpenAI] = None,
- ) -> OpenAIMessage:
+ client: Optional[AsyncOpenAI],
+ create_assistant_data: dict,
+ ) -> Assistant:
+ openai_client = self.async_get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+ response = await openai_client.beta.assistants.create(**create_assistant_data)
+
+ return response
+
+ def create_assistants(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ create_assistant_data: dict,
+ client=None,
+ async_create_assistants=None,
+ ):
+ if async_create_assistants is not None and async_create_assistants == True:
+ return self.async_create_assistants(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ create_assistant_data=create_assistant_data,
+ )
+ openai_client = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+
+ response = openai_client.beta.assistants.create(**create_assistant_data)
+ return response
+
+ # Delete Assistant
+ async def async_delete_assistant(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI],
+ assistant_id: str,
+ ) -> AssistantDeleted:
+ openai_client = self.async_get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+
+ response = await openai_client.beta.assistants.delete(assistant_id=assistant_id)
+
+ return response
+
+ def delete_assistant(
+ self,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ assistant_id: str,
+ client=None,
+ async_delete_assistants=None,
+ ):
+ if async_delete_assistants is not None and async_delete_assistants == True:
+ return self.async_delete_assistant(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ assistant_id=assistant_id,
+ )
+ openai_client = self.get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+
+ response = openai_client.beta.assistants.delete(assistant_id=assistant_id)
+ return response
+
+ ### MESSAGES ###
+
+ async def a_add_message(
+ self,
+ thread_id: str,
+ message_data: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI] = None,
+ ) -> OpenAIMessage:
+ openai_client = self.async_get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+
+ thread_message: OpenAIMessage = await openai_client.beta.threads.messages.create( # type: ignore
+ thread_id, **message_data # type: ignore
+ )
+
+ response_obj: Optional[OpenAIMessage] = None
+ if getattr(thread_message, "status", None) is None:
+ thread_message.status = "completed"
+ response_obj = OpenAIMessage(**thread_message.dict())
+ else:
+ response_obj = OpenAIMessage(**thread_message.dict())
+ return response_obj
+
+ # fmt: off
+
+ @overload
+ def add_message(
+ self,
+ thread_id: str,
+ message_data: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI],
+ a_add_message: Literal[True],
+ ) -> Coroutine[None, None, OpenAIMessage]:
+ ...
+
+ @overload
+ def add_message(
+ self,
+ thread_id: str,
+ message_data: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[OpenAI],
+ a_add_message: Optional[Literal[False]],
+ ) -> OpenAIMessage:
+ ...
+
+ # fmt: on
+
+ def add_message(
+ self,
+ thread_id: str,
+ message_data: dict,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client=None,
+ a_add_message: Optional[bool] = None,
+ ):
+ if a_add_message is not None and a_add_message == True:
+ return self.a_add_message(
+ thread_id=thread_id,
+ message_data=message_data,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
openai_client = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@@ -1585,6 +2762,61 @@ class OpenAIAssistantsAPI(BaseLLM):
response_obj = OpenAIMessage(**thread_message.dict())
return response_obj
+ async def async_get_messages(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI] = None,
+ ) -> AsyncCursorPage[OpenAIMessage]:
+ openai_client = self.async_get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+
+ response = await openai_client.beta.threads.messages.list(thread_id=thread_id)
+
+ return response
+
+ # fmt: off
+
+ @overload
+ def get_messages(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI],
+ aget_messages: Literal[True],
+ ) -> Coroutine[None, None, AsyncCursorPage[OpenAIMessage]]:
+ ...
+
+ @overload
+ def get_messages(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[OpenAI],
+ aget_messages: Optional[Literal[False]],
+ ) -> SyncCursorPage[OpenAIMessage]:
+ ...
+
+ # fmt: on
+
def get_messages(
self,
thread_id: str,
@@ -1593,8 +2825,19 @@ class OpenAIAssistantsAPI(BaseLLM):
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
organization: Optional[str],
- client: Optional[OpenAI] = None,
- ) -> SyncCursorPage[OpenAIMessage]:
+ client=None,
+ aget_messages=None,
+ ):
+ if aget_messages is not None and aget_messages == True:
+ return self.async_get_messages(
+ thread_id=thread_id,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
openai_client = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@@ -1610,6 +2853,70 @@ class OpenAIAssistantsAPI(BaseLLM):
### THREADS ###
+ async def async_create_thread(
+ self,
+ metadata: Optional[dict],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI],
+ messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+ ) -> Thread:
+ openai_client = self.async_get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+
+ data = {}
+ if messages is not None:
+ data["messages"] = messages # type: ignore
+ if metadata is not None:
+ data["metadata"] = metadata # type: ignore
+
+ message_thread = await openai_client.beta.threads.create(**data) # type: ignore
+
+ return Thread(**message_thread.dict())
+
+ # fmt: off
+
+ @overload
+ def create_thread(
+ self,
+ metadata: Optional[dict],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+ client: Optional[AsyncOpenAI],
+ acreate_thread: Literal[True],
+ ) -> Coroutine[None, None, Thread]:
+ ...
+
+ @overload
+ def create_thread(
+ self,
+ metadata: Optional[dict],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+ client: Optional[OpenAI],
+ acreate_thread: Optional[Literal[False]],
+ ) -> Thread:
+ ...
+
+ # fmt: on
+
def create_thread(
self,
metadata: Optional[dict],
@@ -1618,9 +2925,10 @@ class OpenAIAssistantsAPI(BaseLLM):
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
organization: Optional[str],
- client: Optional[OpenAI],
messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
- ) -> Thread:
+ client=None,
+ acreate_thread=None,
+ ):
"""
Here's an example:
```
@@ -1631,6 +2939,17 @@ class OpenAIAssistantsAPI(BaseLLM):
openai_api.create_thread(messages=[message])
```
"""
+ if acreate_thread is not None and acreate_thread == True:
+ return self.async_create_thread(
+ metadata=metadata,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ messages=messages,
+ )
openai_client = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@@ -1650,6 +2969,61 @@ class OpenAIAssistantsAPI(BaseLLM):
return Thread(**message_thread.dict())
+ async def async_get_thread(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI],
+ ) -> Thread:
+ openai_client = self.async_get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+
+ response = await openai_client.beta.threads.retrieve(thread_id=thread_id)
+
+ return Thread(**response.dict())
+
+ # fmt: off
+
+ @overload
+ def get_thread(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI],
+ aget_thread: Literal[True],
+ ) -> Coroutine[None, None, Thread]:
+ ...
+
+ @overload
+ def get_thread(
+ self,
+ thread_id: str,
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[OpenAI],
+ aget_thread: Optional[Literal[False]],
+ ) -> Thread:
+ ...
+
+ # fmt: on
+
def get_thread(
self,
thread_id: str,
@@ -1658,8 +3032,19 @@ class OpenAIAssistantsAPI(BaseLLM):
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
organization: Optional[str],
- client: Optional[OpenAI],
- ) -> Thread:
+ client=None,
+ aget_thread=None,
+ ):
+ if aget_thread is not None and aget_thread == True:
+ return self.async_get_thread(
+ thread_id=thread_id,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
openai_client = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@@ -1678,6 +3063,142 @@ class OpenAIAssistantsAPI(BaseLLM):
### RUNS ###
+ async def arun_thread(
+ self,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ stream: Optional[bool],
+ tools: Optional[Iterable[AssistantToolParam]],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client: Optional[AsyncOpenAI],
+ ) -> Run:
+ openai_client = self.async_get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+
+ response = await openai_client.beta.threads.runs.create_and_poll( # type: ignore
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ tools=tools,
+ )
+
+ return response
+
+ def async_run_thread_stream(
+ self,
+ client: AsyncOpenAI,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ tools: Optional[Iterable[AssistantToolParam]],
+ event_handler: Optional[AssistantEventHandler],
+ ) -> AsyncAssistantStreamManager[AsyncAssistantEventHandler]:
+ data = {
+ "thread_id": thread_id,
+ "assistant_id": assistant_id,
+ "additional_instructions": additional_instructions,
+ "instructions": instructions,
+ "metadata": metadata,
+ "model": model,
+ "tools": tools,
+ }
+ if event_handler is not None:
+ data["event_handler"] = event_handler
+ return client.beta.threads.runs.stream(**data) # type: ignore
+
+ def run_thread_stream(
+ self,
+ client: OpenAI,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ tools: Optional[Iterable[AssistantToolParam]],
+ event_handler: Optional[AssistantEventHandler],
+ ) -> AssistantStreamManager[AssistantEventHandler]:
+ data = {
+ "thread_id": thread_id,
+ "assistant_id": assistant_id,
+ "additional_instructions": additional_instructions,
+ "instructions": instructions,
+ "metadata": metadata,
+ "model": model,
+ "tools": tools,
+ }
+ if event_handler is not None:
+ data["event_handler"] = event_handler
+ return client.beta.threads.runs.stream(**data) # type: ignore
+
+ # fmt: off
+
+ @overload
+ def run_thread(
+ self,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ stream: Optional[bool],
+ tools: Optional[Iterable[AssistantToolParam]],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client,
+ arun_thread: Literal[True],
+ event_handler: Optional[AssistantEventHandler],
+ ) -> Coroutine[None, None, Run]:
+ ...
+
+ @overload
+ def run_thread(
+ self,
+ thread_id: str,
+ assistant_id: str,
+ additional_instructions: Optional[str],
+ instructions: Optional[str],
+ metadata: Optional[object],
+ model: Optional[str],
+ stream: Optional[bool],
+ tools: Optional[Iterable[AssistantToolParam]],
+ api_key: Optional[str],
+ api_base: Optional[str],
+ timeout: Union[float, httpx.Timeout],
+ max_retries: Optional[int],
+ organization: Optional[str],
+ client,
+ arun_thread: Optional[Literal[False]],
+ event_handler: Optional[AssistantEventHandler],
+ ) -> Run:
+ ...
+
+ # fmt: on
+
def run_thread(
self,
thread_id: str,
@@ -1693,8 +3214,47 @@ class OpenAIAssistantsAPI(BaseLLM):
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
organization: Optional[str],
- client: Optional[OpenAI],
- ) -> Run:
+ client=None,
+ arun_thread=None,
+ event_handler: Optional[AssistantEventHandler] = None,
+ ):
+ if arun_thread is not None and arun_thread == True:
+ if stream is not None and stream == True:
+ _client = self.async_get_openai_client(
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
+ return self.async_run_thread_stream(
+ client=_client,
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ tools=tools,
+ event_handler=event_handler,
+ )
+ return self.arun_thread(
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ stream=stream,
+ tools=tools,
+ api_key=api_key,
+ api_base=api_base,
+ timeout=timeout,
+ max_retries=max_retries,
+ organization=organization,
+ client=client,
+ )
openai_client = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@@ -1704,6 +3264,19 @@ class OpenAIAssistantsAPI(BaseLLM):
client=client,
)
+ if stream is not None and stream == True:
+ return self.run_thread_stream(
+ client=openai_client,
+ thread_id=thread_id,
+ assistant_id=assistant_id,
+ additional_instructions=additional_instructions,
+ instructions=instructions,
+ metadata=metadata,
+ model=model,
+ tools=tools,
+ event_handler=event_handler,
+ )
+
response = openai_client.beta.threads.runs.create_and_poll( # type: ignore
thread_id=thread_id,
assistant_id=assistant_id,
diff --git a/litellm/llms/palm.py b/litellm/llms/palm.py
index f15be43db4..b750b800bb 100644
--- a/litellm/llms/palm.py
+++ b/litellm/llms/palm.py
@@ -1,11 +1,14 @@
-import os, types, traceback, copy
-import json
-from enum import Enum
+import copy
import time
+import traceback
+import types
from typing import Callable, Optional
-from litellm.utils import ModelResponse, get_secret, Choices, Message, Usage
+
+import httpx
+
import litellm
-import sys, httpx
+from litellm import verbose_logger
+from litellm.utils import Choices, Message, ModelResponse, Usage
class PalmError(Exception):
@@ -163,9 +166,12 @@ def completion(
message_obj = Message(content=None)
choice_obj = Choices(index=idx + 1, message=message_obj)
choices_list.append(choice_obj)
- model_response["choices"] = choices_list
+ model_response.choices = choices_list # type: ignore
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error(
+ "litellm.llms.palm.py::completion(): Exception occured - {}".format(str(e))
+ )
+ verbose_logger.debug(traceback.format_exc())
raise PalmError(
message=traceback.format_exc(), status_code=response.status_code
)
@@ -184,8 +190,8 @@ def completion(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
)
- model_response["created"] = int(time.time())
- model_response["model"] = "palm/" + model
+ model_response.created = int(time.time())
+ model_response.model = "palm/" + model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/petals.py b/litellm/llms/petals.py
index 334b80d388..be9f92f071 100644
--- a/litellm/llms/petals.py
+++ b/litellm/llms/petals.py
@@ -1,12 +1,16 @@
-import os, types
import json
-from enum import Enum
-import requests # type: ignore
+import os
import time
+import types
+from enum import Enum
from typing import Callable, Optional
+
+import requests # type: ignore
+
import litellm
from litellm.utils import ModelResponse, Usage
-from .prompt_templates.factory import prompt_factory, custom_prompt
+
+from .prompt_templates.factory import custom_prompt, prompt_factory
class PetalsError(Exception):
@@ -151,8 +155,8 @@ def completion(
else:
try:
import torch
- from transformers import AutoTokenizer
from petals import AutoDistributedModelForCausalLM # type: ignore
+ from transformers import AutoTokenizer
except:
raise Exception(
"Importing torch, transformers, petals failed\nTry pip installing petals \npip install git+https://github.com/bigscience-workshop/petals"
@@ -189,15 +193,15 @@ def completion(
output_text = tokenizer.decode(outputs[0])
if len(output_text) > 0:
- model_response["choices"][0]["message"]["content"] = output_text
+ model_response.choices[0].message.content = output_text # type: ignore
prompt_tokens = len(encoding.encode(prompt))
completion_tokens = len(
encoding.encode(model_response["choices"][0]["message"].get("content"))
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/predibase.py b/litellm/llms/predibase.py
index 1e7e1d3348..d028cb1074 100644
--- a/litellm/llms/predibase.py
+++ b/litellm/llms/predibase.py
@@ -1,26 +1,28 @@
# What is this?
## Controller file for Predibase Integration - https://predibase.com/
-
-import os, types
+import copy
import json
-from enum import Enum
-import requests, copy # type: ignore
+import os
import time
-from typing import Callable, Optional, List, Literal, Union
-from litellm.utils import (
- ModelResponse,
- Usage,
- map_finish_reason,
- CustomStreamWrapper,
- Message,
- Choices,
-)
-import litellm
-from .prompt_templates.factory import prompt_factory, custom_prompt
-from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
-from .base import BaseLLM
+import traceback
+import types
+from enum import Enum
+from functools import partial
+from typing import Callable, List, Literal, Optional, Union
+
import httpx # type: ignore
+import requests # type: ignore
+
+import litellm
+import litellm.litellm_core_utils
+import litellm.litellm_core_utils.litellm_logging
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage
+
+from .base import BaseLLM
+from .prompt_templates.factory import custom_prompt, prompt_factory
class PredibaseError(Exception):
@@ -51,6 +53,32 @@ class PredibaseError(Exception):
) # Call the base class constructor with the parameters it needs
+async def make_call(
+ client: AsyncHTTPHandler,
+ api_base: str,
+ headers: dict,
+ data: str,
+ model: str,
+ messages: list,
+ logging_obj,
+):
+ response = await client.post(api_base, headers=headers, data=data, stream=True)
+
+ if response.status_code != 200:
+ raise PredibaseError(status_code=response.status_code, message=response.text)
+
+ completion_stream = response.aiter_lines()
+ # LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response=completion_stream, # Pass the completion stream for logging
+ additional_args={"complete_input_dict": data},
+ )
+
+ return completion_stream
+
+
class PredibaseConfig:
"""
Reference: https://docs.predibase.com/user-guide/inference/rest_api
@@ -119,18 +147,66 @@ class PredibaseConfig:
}
def get_supported_openai_params(self):
- return ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
+ return [
+ "stream",
+ "temperature",
+ "max_tokens",
+ "top_p",
+ "stop",
+ "n",
+ "response_format",
+ ]
+
+ def map_openai_params(self, non_default_params: dict, optional_params: dict):
+ for param, value in non_default_params.items():
+ # temperature, top_p, n, stream, stop, max_tokens, n, presence_penalty default to None
+ if param == "temperature":
+ if value == 0.0 or value == 0:
+ # hugging face exception raised when temp==0
+ # Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive
+ value = 0.01
+ optional_params["temperature"] = value
+ if param == "top_p":
+ optional_params["top_p"] = value
+ if param == "n":
+ optional_params["best_of"] = value
+ optional_params["do_sample"] = (
+ True # Need to sample if you want best of for hf inference endpoints
+ )
+ if param == "stream":
+ optional_params["stream"] = value
+ if param == "stop":
+ optional_params["stop"] = value
+ if param == "max_tokens":
+ # HF TGI raises the following exception when max_new_tokens==0
+ # Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive
+ if value == 0:
+ value = 1
+ optional_params["max_new_tokens"] = value
+ if param == "echo":
+ # https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation.decoder_input_details
+ # Return the decoder input token logprobs and ids. You must set details=True as well for it to be taken into account. Defaults to False
+ optional_params["decoder_input_details"] = True
+ if param == "response_format":
+ optional_params["response_format"] = value
+ return optional_params
class PredibaseChatCompletion(BaseLLM):
def __init__(self) -> None:
super().__init__()
- def _validate_environment(self, api_key: Optional[str], user_headers: dict) -> dict:
+ def _validate_environment(
+ self, api_key: Optional[str], user_headers: dict, tenant_id: Optional[str]
+ ) -> dict:
if api_key is None:
raise ValueError(
"Missing Predibase API Key - A call is being made to predibase but no key is set either in the environment variables or via params"
)
+ if tenant_id is None:
+ raise ValueError(
+ "Missing Predibase Tenant ID - Required for making the request. Set dynamically (e.g. `completion(..tenant_id=)`) or in env - `PREDIBASE_TENANT_ID`."
+ )
headers = {
"content-type": "application/json",
"Authorization": "Bearer {}".format(api_key),
@@ -165,7 +241,7 @@ class PredibaseChatCompletion(BaseLLM):
response: Union[requests.Response, httpx.Response],
model_response: ModelResponse,
stream: bool,
- logging_obj: litellm.utils.Logging,
+ logging_obj: litellm.litellm_core_utils.litellm_logging.Logging,
optional_params: dict,
api_key: str,
data: Union[dict, str],
@@ -192,17 +268,18 @@ class PredibaseChatCompletion(BaseLLM):
status_code=response.status_code,
)
else:
- if (
- not isinstance(completion_response, dict)
- or "generated_text" not in completion_response
- ):
+ if not isinstance(completion_response, dict):
raise PredibaseError(
status_code=422,
- message=f"response is not in expected format - {completion_response}",
+ message=f"'completion_response' is not a dictionary - {completion_response}",
+ )
+ elif "generated_text" not in completion_response:
+ raise PredibaseError(
+ status_code=422,
+ message=f"'generated_text' is not a key response dictionary - {completion_response}",
)
-
if len(completion_response["generated_text"]) > 0:
- model_response["choices"][0]["message"]["content"] = self.output_parser(
+ model_response.choices[0].message.content = self.output_parser( # type: ignore
completion_response["generated_text"]
)
## GETTING LOGPROBS + FINISH REASON
@@ -210,17 +287,17 @@ class PredibaseChatCompletion(BaseLLM):
"details" in completion_response
and "tokens" in completion_response["details"]
):
- model_response.choices[0].finish_reason = completion_response[
- "details"
- ]["finish_reason"]
+ model_response.choices[0].finish_reason = map_finish_reason(
+ completion_response["details"]["finish_reason"]
+ )
sum_logprob = 0
for token in completion_response["details"]["tokens"]:
- if token["logprob"] != None:
+ if token["logprob"] is not None:
sum_logprob += token["logprob"]
- model_response["choices"][0][
- "message"
- ]._logprob = (
- sum_logprob # [TODO] move this to using the actual logprobs
+ setattr(
+ model_response.choices[0].message, # type: ignore
+ "_logprob",
+ sum_logprob, # [TODO] move this to using the actual logprobs
)
if "best_of" in optional_params and optional_params["best_of"] > 1:
if (
@@ -233,7 +310,7 @@ class PredibaseChatCompletion(BaseLLM):
):
sum_logprob = 0
for token in item["tokens"]:
- if token["logprob"] != None:
+ if token["logprob"] is not None:
sum_logprob += token["logprob"]
if len(item["generated_text"]) > 0:
message_obj = Message(
@@ -243,20 +320,18 @@ class PredibaseChatCompletion(BaseLLM):
else:
message_obj = Message(content=None)
choice_obj = Choices(
- finish_reason=item["finish_reason"],
+ finish_reason=map_finish_reason(item["finish_reason"]),
index=idx + 1,
message=message_obj,
)
choices_list.append(choice_obj)
- model_response["choices"].extend(choices_list)
+ model_response.choices.extend(choices_list)
## CALCULATING USAGE
prompt_tokens = 0
try:
- prompt_tokens = len(
- encoding.encode(model_response["choices"][0]["message"]["content"])
- ) ##[TODO] use a model-specific tokenizer here
- except:
+ prompt_tokens = litellm.token_counter(messages=messages)
+ except Exception:
# this should remain non blocking we should not block a response returning if calculating usage fails
pass
output_text = model_response["choices"][0]["message"].get("content", "")
@@ -276,8 +351,8 @@ class PredibaseChatCompletion(BaseLLM):
total_tokens = prompt_tokens + completion_tokens
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@@ -299,15 +374,17 @@ class PredibaseChatCompletion(BaseLLM):
logging_obj,
optional_params: dict,
tenant_id: str,
+ timeout: Union[float, httpx.Timeout],
acompletion=None,
litellm_params=None,
logger_fn=None,
headers: dict = {},
) -> Union[ModelResponse, CustomStreamWrapper]:
- headers = self._validate_environment(api_key, headers)
+ headers = self._validate_environment(api_key, headers, tenant_id=tenant_id)
completion_url = ""
input_text = ""
base_url = "https://serving.app.predibase.com"
+
if "https" in model:
completion_url = model
elif api_base:
@@ -317,7 +394,7 @@ class PredibaseChatCompletion(BaseLLM):
completion_url = f"{base_url}/{tenant_id}/deployments/v2/llms/{model}"
- if optional_params.get("stream", False) == True:
+ if optional_params.get("stream", False) is True:
completion_url += "/generate_stream"
else:
completion_url += "/generate"
@@ -361,9 +438,9 @@ class PredibaseChatCompletion(BaseLLM):
},
)
## COMPLETION CALL
- if acompletion == True:
+ if acompletion is True:
### ASYNC STREAMING
- if stream == True:
+ if stream is True:
return self.async_streaming(
model=model,
messages=messages,
@@ -378,6 +455,7 @@ class PredibaseChatCompletion(BaseLLM):
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=headers,
+ timeout=timeout,
) # type: ignore
else:
### ASYNC COMPLETION
@@ -396,10 +474,11 @@ class PredibaseChatCompletion(BaseLLM):
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=headers,
+ timeout=timeout,
) # type: ignore
### SYNC STREAMING
- if stream == True:
+ if stream is True:
response = requests.post(
completion_url,
headers=headers,
@@ -420,7 +499,6 @@ class PredibaseChatCompletion(BaseLLM):
headers=headers,
data=json.dumps(data),
)
-
return self.process_response(
model=model,
response=response,
@@ -448,16 +526,28 @@ class PredibaseChatCompletion(BaseLLM):
stream,
data: dict,
optional_params: dict,
+ timeout: Union[float, httpx.Timeout],
litellm_params=None,
logger_fn=None,
headers={},
) -> ModelResponse:
- self.async_handler = AsyncHTTPHandler(
- timeout=httpx.Timeout(timeout=600.0, connect=5.0)
- )
- response = await self.async_handler.post(
- api_base, headers=headers, data=json.dumps(data)
- )
+
+ async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=timeout))
+ try:
+ response = await async_handler.post(
+ api_base, headers=headers, data=json.dumps(data)
+ )
+ except httpx.HTTPStatusError as e:
+ raise PredibaseError(
+ status_code=e.response.status_code,
+ message="HTTPStatusError - received status_code={}, error_message={}".format(
+ e.response.status_code, e.response.text
+ ),
+ )
+ except Exception as e:
+ raise PredibaseError(
+ status_code=500, message="{}\n{}".format(str(e), traceback.format_exc())
+ )
return self.process_response(
model=model,
response=response,
@@ -483,31 +573,25 @@ class PredibaseChatCompletion(BaseLLM):
api_key,
logging_obj,
data: dict,
+ timeout: Union[float, httpx.Timeout],
optional_params=None,
litellm_params=None,
logger_fn=None,
headers={},
) -> CustomStreamWrapper:
- self.async_handler = AsyncHTTPHandler(
- timeout=httpx.Timeout(timeout=600.0, connect=5.0)
- )
data["stream"] = True
- response = await self.async_handler.post(
- url=api_base,
- headers=headers,
- data=json.dumps(data),
- stream=True,
- )
-
- if response.status_code != 200:
- raise PredibaseError(
- status_code=response.status_code, message=response.text
- )
-
- completion_stream = response.aiter_lines()
streamwrapper = CustomStreamWrapper(
- completion_stream=completion_stream,
+ completion_stream=None,
+ make_call=partial(
+ make_call,
+ api_base=api_base,
+ headers=headers,
+ data=json.dumps(data),
+ model=model,
+ messages=messages,
+ logging_obj=logging_obj,
+ ),
model=model,
custom_llm_provider="predibase",
logging_obj=logging_obj,
diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py
index 41ecb486ce..99ffcfbf41 100644
--- a/litellm/llms/prompt_templates/factory.py
+++ b/litellm/llms/prompt_templates/factory.py
@@ -1,31 +1,30 @@
+import json
+import re
+import traceback
+import uuid
+import xml.etree.ElementTree as ET
from enum import Enum
-import requests, traceback
-import json, re, xml.etree.ElementTree as ET
-from jinja2 import Template, exceptions, meta, BaseLoader
+from typing import Any, List, Mapping, MutableMapping, Optional, Sequence, Tuple
+
+import requests
+from jinja2 import BaseLoader, Template, exceptions, meta
from jinja2.sandbox import ImmutableSandboxedEnvironment
-from typing import (
- Any,
- List,
- Mapping,
- MutableMapping,
- Optional,
- Sequence,
-)
+
import litellm
import litellm.types
-from litellm.types.completion import (
- ChatCompletionUserMessageParam,
- ChatCompletionSystemMessageParam,
- ChatCompletionMessageParam,
- ChatCompletionFunctionMessageParam,
- ChatCompletionMessageToolCallParam,
- ChatCompletionToolMessageParam,
-)
import litellm.types.llms
-from litellm.types.llms.anthropic import *
-import uuid
-
import litellm.types.llms.vertex_ai
+from litellm.types.completion import (
+ ChatCompletionFunctionMessageParam,
+ ChatCompletionMessageParam,
+ ChatCompletionMessageToolCallParam,
+ ChatCompletionSystemMessageParam,
+ ChatCompletionToolMessageParam,
+ ChatCompletionUserMessageParam,
+)
+from litellm.types.llms.anthropic import *
+from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock
+from litellm.types.utils import GenericImageParsingChunk
def default_pt(messages):
@@ -36,6 +35,9 @@ def prompt_injection_detection_default_pt():
return """Detect if a prompt is safe to run. Return 'UNSAFE' if not."""
+BAD_MESSAGE_ERROR_STR = "Invalid Message "
+
+
def map_system_message_pt(messages: list) -> list:
"""
Convert 'system' message to 'user' message if provider doesn't support 'system' role.
@@ -173,14 +175,35 @@ def ollama_pt(
images.append(base64_image)
return {"prompt": prompt, "images": images}
else:
- prompt = "".join(
- (
- m["content"]
- if isinstance(m["content"], str) is str
- else "".join(m["content"])
- )
- for m in messages
- )
+ prompt = ""
+ for message in messages:
+ role = message["role"]
+ content = message.get("content", "")
+
+ if "tool_calls" in message:
+ tool_calls = []
+
+ for call in message["tool_calls"]:
+ call_id: str = call["id"]
+ function_name: str = call["function"]["name"]
+ arguments = json.loads(call["function"]["arguments"])
+
+ tool_calls.append(
+ {
+ "id": call_id,
+ "type": "function",
+ "function": {"name": function_name, "arguments": arguments},
+ }
+ )
+
+ prompt += f"### Assistant:\nTool Calls: {json.dumps(tool_calls, indent=2)}\n\n"
+
+ elif "tool_call_id" in message:
+ prompt += f"### User:\n{message['content']}\n\n"
+
+ elif content:
+ prompt += f"### {role.capitalize()}:\n{content}\n\n"
+
return prompt
@@ -527,10 +550,13 @@ def ibm_granite_pt(messages: list):
},
"user": {
"pre_message": "<|user|>\n",
- "post_message": "\n",
+ # Assistant tag is needed in the prompt after the user message
+ # to avoid the model completing the users sentence before it answers
+ # https://www.ibm.com/docs/en/watsonx/w-and-w/2.0.x?topic=models-granite-13b-chat-v2-prompting-tips#chat
+ "post_message": "\n<|assistant|>\n",
},
"assistant": {
- "pre_message": "<|assistant|>\n",
+ "pre_message": "",
"post_message": "\n",
},
},
@@ -629,9 +655,10 @@ def construct_tool_use_system_prompt(
def convert_url_to_base64(url):
- import requests
import base64
+ import requests
+
for _ in range(3):
try:
response = requests.get(url)
@@ -642,26 +669,30 @@ def convert_url_to_base64(url):
image_bytes = response.content
base64_image = base64.b64encode(image_bytes).decode("utf-8")
- img_type = url.split(".")[-1].lower()
- if img_type == "jpg" or img_type == "jpeg":
- img_type = "image/jpeg"
- elif img_type == "png":
- img_type = "image/png"
- elif img_type == "gif":
- img_type = "image/gif"
- elif img_type == "webp":
- img_type = "image/webp"
+ image_type = response.headers.get("Content-Type", None)
+ if image_type is not None:
+ img_type = image_type
else:
- raise Exception(
- f"Error: Unsupported image format. Format={img_type}. Supported types = ['image/jpeg', 'image/png', 'image/gif', 'image/webp']"
- )
+ img_type = url.split(".")[-1].lower()
+ if img_type == "jpg" or img_type == "jpeg":
+ img_type = "image/jpeg"
+ elif img_type == "png":
+ img_type = "image/png"
+ elif img_type == "gif":
+ img_type = "image/gif"
+ elif img_type == "webp":
+ img_type = "image/webp"
+ else:
+ raise Exception(
+ f"Error: Unsupported image format. Format={img_type}. Supported types = ['image/jpeg', 'image/png', 'image/gif', 'image/webp']"
+ )
return f"data:{img_type};base64,{base64_image}"
else:
raise Exception(f"Error: Unable to fetch image from URL. url={url}")
-def convert_to_anthropic_image_obj(openai_image_url: str):
+def convert_to_anthropic_image_obj(openai_image_url: str) -> GenericImageParsingChunk:
"""
Input:
"image_url": "data:image/jpeg;base64,{base64_image}",
@@ -676,22 +707,20 @@ def convert_to_anthropic_image_obj(openai_image_url: str):
try:
if openai_image_url.startswith("http"):
openai_image_url = convert_url_to_base64(url=openai_image_url)
- # Extract the base64 image data
- base64_data = openai_image_url.split("data:image/")[1].split(";base64,")[1]
+ # Extract the media type and base64 data
+ media_type, base64_data = openai_image_url.split("data:")[1].split(";base64,")
+ media_type = media_type.replace("\\/", "/")
- # Infer image format from the URL
- image_format = openai_image_url.split("data:image/")[1].split(";base64,")[0]
-
- return {
- "type": "base64",
- "media_type": f"image/{image_format}",
- "data": base64_data,
- }
+ return GenericImageParsingChunk(
+ type="base64",
+ media_type=media_type,
+ data=base64_data,
+ )
except Exception as e:
if "Error: Unable to fetch image from URL" in str(e):
raise e
raise Exception(
- """Image url not in expected format. Example Expected input - "image_url": "data:image/jpeg;base64,{base64_image}". Supported formats - ['image/jpeg', 'image/png', 'image/gif', 'image/webp'] """
+ """Image url not in expected format. Example Expected input - "image_url": "data:image/jpeg;base64,{base64_image}". Supported formats - ['image/jpeg', 'image/png', 'image/gif', 'image/webp']."""
)
@@ -710,7 +739,7 @@ def convert_to_anthropic_tool_result_xml(message: dict) -> str:
"""
Anthropic tool_results look like:
-
+
[Successful results]
@@ -833,7 +862,7 @@ def anthropic_messages_pt_xml(messages: list):
) # either string or none
if messages[msg_i].get(
"tool_calls", []
- ): # support assistant tool invoke convertion
+ ): # support assistant tool invoke conversion
assistant_text += convert_to_anthropic_tool_invoke_xml( # type: ignore
messages[msg_i]["tool_calls"]
)
@@ -997,16 +1026,17 @@ def convert_to_gemini_tool_call_invoke(
def convert_to_gemini_tool_call_result(
message: dict,
+ last_message_with_tool_calls: Optional[dict],
) -> litellm.types.llms.vertex_ai.PartType:
"""
OpenAI message with a tool result looks like:
{
"tool_call_id": "tool_1",
"role": "tool",
- "name": "get_current_weather",
"content": "function result goes here",
},
+ # NOTE: Function messages have been deprecated
OpenAI message with a function call result looks like:
{
"role": "function",
@@ -1015,7 +1045,23 @@ def convert_to_gemini_tool_call_result(
}
"""
content = message.get("content", "")
- name = message.get("name", "")
+ name = ""
+
+ # Recover name from last message with tool calls
+ if last_message_with_tool_calls:
+ tools = last_message_with_tool_calls.get("tool_calls", [])
+ msg_tool_call_id = message.get("tool_call_id", None)
+ for tool in tools:
+ prev_tool_call_id = tool.get("id", None)
+ if (
+ msg_tool_call_id
+ and prev_tool_call_id
+ and msg_tool_call_id == prev_tool_call_id
+ ):
+ name = tool.get("function", {}).get("name", "")
+
+ if not name:
+ raise Exception("Missing corresponding tool call for tool response message")
# We can't determine from openai message format whether it's a successful or
# error call result so default to the successful result template
@@ -1036,7 +1082,7 @@ def convert_to_gemini_tool_call_result(
return _part
-def convert_to_anthropic_tool_result(message: dict) -> dict:
+def convert_to_anthropic_tool_result(message: dict) -> AnthropicMessagesToolResultParam:
"""
OpenAI message with a tool result looks like:
{
@@ -1069,44 +1115,50 @@ def convert_to_anthropic_tool_result(message: dict) -> dict:
}
"""
if message["role"] == "tool":
- tool_call_id = message.get("tool_call_id")
- content = message.get("content")
+ tool_call_id: str = message.get("tool_call_id") # type: ignore
+ content: str = message.get("content") # type: ignore
# We can't determine from openai message format whether it's a successful or
# error call result so default to the successful result template
- anthropic_tool_result = {
- "type": "tool_result",
- "tool_use_id": tool_call_id,
- "content": content,
- }
+ anthropic_tool_result = AnthropicMessagesToolResultParam(
+ type="tool_result", tool_use_id=tool_call_id, content=content
+ )
return anthropic_tool_result
- elif message["role"] == "function":
- content = message.get("content")
- anthropic_tool_result = {
- "type": "tool_result",
- "tool_use_id": str(uuid.uuid4()),
- "content": content,
- }
+ if message["role"] == "function":
+ content = message.get("content") # type: ignore
+ anthropic_tool_result = AnthropicMessagesToolResultParam(
+ type="tool_result", tool_use_id=str(uuid.uuid4()), content=content
+ )
+
return anthropic_tool_result
- return {}
+ else:
+ raise Exception(
+ "Invalid role={}. Only 'tool' or 'function' are accepted for tool result blocks.".format(
+ message.get("content")
+ )
+ )
-def convert_function_to_anthropic_tool_invoke(function_call):
+def convert_function_to_anthropic_tool_invoke(
+ function_call,
+) -> List[AnthropicMessagesToolUseParam]:
try:
anthropic_tool_invoke = [
- {
- "type": "tool_use",
- "id": str(uuid.uuid4()),
- "name": get_attribute_or_key(function_call, "name"),
- "input": json.loads(get_attribute_or_key(function_call, "arguments")),
- }
+ AnthropicMessagesToolUseParam(
+ type="tool_use",
+ id=str(uuid.uuid4()),
+ name=get_attribute_or_key(function_call, "name"),
+ input=json.loads(get_attribute_or_key(function_call, "arguments")),
+ )
]
return anthropic_tool_invoke
except Exception as e:
raise e
-def convert_to_anthropic_tool_invoke(tool_calls: list) -> list:
+def convert_to_anthropic_tool_invoke(
+ tool_calls: list,
+) -> List[AnthropicMessagesToolUseParam]:
"""
OpenAI tool invokes:
{
@@ -1144,18 +1196,16 @@ def convert_to_anthropic_tool_invoke(tool_calls: list) -> list:
}
"""
anthropic_tool_invoke = [
- {
- "type": "tool_use",
- "id": get_attribute_or_key(tool, "id"),
- "name": get_attribute_or_key(
- get_attribute_or_key(tool, "function"), "name"
- ),
- "input": json.loads(
+ AnthropicMessagesToolUseParam(
+ type="tool_use",
+ id=get_attribute_or_key(tool, "id"),
+ name=get_attribute_or_key(get_attribute_or_key(tool, "function"), "name"),
+ input=json.loads(
get_attribute_or_key(
get_attribute_or_key(tool, "function"), "arguments"
)
),
- }
+ )
for tool in tool_calls
if get_attribute_or_key(tool, "type") == "function"
]
@@ -1163,7 +1213,16 @@ def convert_to_anthropic_tool_invoke(tool_calls: list) -> list:
return anthropic_tool_invoke
-def anthropic_messages_pt(messages: list):
+def anthropic_messages_pt(
+ messages: list,
+ model: str,
+ llm_provider: str,
+) -> List[
+ Union[
+ AnthropicMessagesUserMessageParam,
+ AnthopicMessagesAssistantMessageParam,
+ ]
+]:
"""
format messages for anthropic
1. Anthropic supports roles like "user" and "assistant" (system prompt sent separately)
@@ -1176,24 +1235,33 @@ def anthropic_messages_pt(messages: list):
# add role=tool support to allow function call result/error submission
user_message_types = {"user", "tool", "function"}
# reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them.
- new_messages: list = []
+ new_messages: List[
+ Union[
+ AnthropicMessagesUserMessageParam,
+ AnthopicMessagesAssistantMessageParam,
+ ]
+ ] = []
msg_i = 0
- tool_use_param = False
while msg_i < len(messages):
- user_content = []
+ user_content: List[AnthropicMessagesUserMessageValues] = []
init_msg_i = msg_i
## MERGE CONSECUTIVE USER CONTENT ##
while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types:
if isinstance(messages[msg_i]["content"], list):
for m in messages[msg_i]["content"]:
if m.get("type", "") == "image_url":
+ image_chunk = convert_to_anthropic_image_obj(
+ m["image_url"]["url"]
+ )
user_content.append(
- {
- "type": "image",
- "source": convert_to_anthropic_image_obj(
- m["image_url"]["url"]
+ AnthropicMessagesImageParam(
+ type="image",
+ source=AnthropicImageParamSource(
+ type="base64",
+ media_type=image_chunk["media_type"],
+ data=image_chunk["data"],
),
- }
+ )
)
elif m.get("type", "") == "text":
user_content.append({"type": "text", "text": m["text"]})
@@ -1213,18 +1281,34 @@ def anthropic_messages_pt(messages: list):
if user_content:
new_messages.append({"role": "user", "content": user_content})
- assistant_content = []
+ assistant_content: List[AnthropicMessagesAssistantMessageValues] = []
## MERGE CONSECUTIVE ASSISTANT CONTENT ##
while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
- assistant_text = (
- messages[msg_i].get("content") or ""
- ) # either string or none
- if assistant_text:
- assistant_content.append({"type": "text", "text": assistant_text})
+ if "content" in messages[msg_i] and isinstance(
+ messages[msg_i]["content"], list
+ ):
+ for m in messages[msg_i]["content"]:
+ # handle text
+ if (
+ m.get("type", "") == "text" and len(m.get("text", "")) > 0
+ ): # don't pass empty text blocks. anthropic api raises errors.
+ anthropic_message = AnthropicMessagesTextParam(
+ type="text", text=m.get("text")
+ )
+ assistant_content.append(anthropic_message)
+ elif (
+ "content" in messages[msg_i]
+ and isinstance(messages[msg_i]["content"], str)
+ and len(messages[msg_i]["content"])
+ > 0 # don't pass empty text blocks. anthropic api raises errors.
+ ):
+ assistant_content.append(
+ {"type": "text", "text": messages[msg_i]["content"]}
+ )
if messages[msg_i].get(
"tool_calls", []
- ): # support assistant tool invoke convertion
+ ): # support assistant tool invoke conversion
assistant_content.extend(
convert_to_anthropic_tool_invoke(messages[msg_i]["tool_calls"])
)
@@ -1242,10 +1326,10 @@ def anthropic_messages_pt(messages: list):
new_messages.append({"role": "assistant", "content": assistant_content})
if msg_i == init_msg_i: # prevent infinite loops
- raise Exception(
- "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
- messages[msg_i]
- )
+ raise litellm.BadRequestError(
+ message=BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}",
+ model=model,
+ llm_provider=llm_provider,
)
if not new_messages or new_messages[0]["role"] != "user":
if litellm.modify_params:
@@ -1254,7 +1338,9 @@ def anthropic_messages_pt(messages: list):
)
else:
raise Exception(
- "Invalid first message. Should always start with 'role'='user' for Anthropic. System prompt is sent separately for Anthropic. set 'litellm.modify_params = True' or 'litellm_settings:modify_params = True' on proxy, to insert a placeholder user message - '.' as the first message, "
+ "Invalid first message={}. Should always start with 'role'='user' for Anthropic. System prompt is sent separately for Anthropic. set 'litellm.modify_params = True' or 'litellm_settings:modify_params = True' on proxy, to insert a placeholder user message - '.' as the first message, ".format(
+ new_messages
+ )
)
if new_messages[-1]["role"] == "assistant":
@@ -1368,16 +1454,37 @@ def convert_to_documents(
return documents
-def convert_openai_message_to_cohere_tool_result(message):
+from litellm.types.llms.cohere import (
+ CallObject,
+ ChatHistory,
+ ChatHistoryChatBot,
+ ChatHistorySystem,
+ ChatHistoryToolResult,
+ ChatHistoryUser,
+ ToolCallObject,
+ ToolResultObject,
+)
+
+
+def convert_openai_message_to_cohere_tool_result(
+ message, tool_calls: List
+) -> ToolResultObject:
"""
OpenAI message with a tool result looks like:
{
"tool_call_id": "tool_1",
"role": "tool",
- "name": "get_current_weather",
"content": {"location": "San Francisco, CA", "unit": "fahrenheit", "temperature": "72"},
},
"""
+ """
+ OpenAI message with a function call looks like:
+ {
+ "role": "function",
+ "name": "get_current_weather",
+ "content": "function result goes here",
+ }
+ """
"""
Cohere tool_results look like:
@@ -1387,7 +1494,6 @@ def convert_openai_message_to_cohere_tool_result(message):
"parameters": {
"day": "2023-09-29"
},
- "generation_id": "4807c924-9003-4d6b-8069-eda03962c465"
},
"outputs": [
{
@@ -1397,30 +1503,257 @@ def convert_openai_message_to_cohere_tool_result(message):
]
},
"""
+ content_str: str = message.get("content", "")
+ if len(content_str) > 0:
+ try:
+ content = json.loads(content_str)
+ except json.JSONDecodeError:
+ content = {"result": content_str}
+ else:
+ content = {}
+ name = ""
+ arguments = {}
+ # Recover name from last message with tool calls
+ if len(tool_calls) > 0:
+ tools = tool_calls
+ msg_tool_call_id = message.get("tool_call_id", None)
+ for tool in tools:
+ prev_tool_call_id = tool.get("id", None)
+ if (
+ msg_tool_call_id
+ and prev_tool_call_id
+ and msg_tool_call_id == prev_tool_call_id
+ ):
+ name = tool.get("function", {}).get("name", "")
+ arguments_str = tool.get("function", {}).get("arguments", "")
+ if arguments_str is not None and len(arguments_str) > 0:
+ arguments = json.loads(arguments_str)
- tool_call_id = message.get("tool_call_id")
- name = message.get("name")
- content = message.get("content")
+ if message["role"] == "function":
+ name = message.get("name")
+ cohere_tool_result: ToolResultObject = {
+ "call": CallObject(name=name, parameters=arguments),
+ "outputs": [content],
+ }
+ return cohere_tool_result
+ else:
+ # We can't determine from openai message format whether it's a successful or
+ # error call result so default to the successful result template
- # Create the Cohere tool_result dictionary
- cohere_tool_result = {
- "call": {
- "name": name,
- "parameters": {"location": "San Francisco, CA"},
- "generation_id": tool_call_id,
- },
- "outputs": convert_to_documents(content),
+ cohere_tool_result = {
+ "call": CallObject(name=name, parameters=arguments),
+ "outputs": [content],
+ }
+ return cohere_tool_result
+
+
+def get_all_tool_calls(messages: List) -> List:
+ """
+ Returns extracted list of `tool_calls`.
+
+ Done to handle openai no longer returning tool call 'name' in tool results.
+ """
+ tool_calls: List = []
+ for m in messages:
+ if m.get("tool_calls", None) is not None:
+ if isinstance(m["tool_calls"], list):
+ tool_calls.extend(m["tool_calls"])
+
+ return tool_calls
+
+
+def convert_to_cohere_tool_invoke(tool_calls: list) -> List[ToolCallObject]:
+ """
+ OpenAI tool invokes:
+ {
+ "role": "assistant",
+ "content": null,
+ "tool_calls": [
+ {
+ "id": "call_abc123",
+ "type": "function",
+ "function": {
+ "name": "get_current_weather",
+ "arguments": "{\n\"location\": \"Boston, MA\"\n}"
+ }
+ }
+ ]
+ },
+ """
+
+ """
+ Cohere tool invokes:
+ {
+ "role": "CHATBOT",
+ "tool_calls": [{"name": "get_weather", "parameters": {"location": "San Francisco, CA"}}]
}
- return cohere_tool_result
+ """
+
+ cohere_tool_invoke: List[ToolCallObject] = [
+ {
+ "name": get_attribute_or_key(
+ get_attribute_or_key(tool, "function"), "name"
+ ),
+ "parameters": json.loads(
+ get_attribute_or_key(
+ get_attribute_or_key(tool, "function"), "arguments"
+ )
+ ),
+ }
+ for tool in tool_calls
+ if get_attribute_or_key(tool, "type") == "function"
+ ]
+
+ return cohere_tool_invoke
+
+
+def cohere_messages_pt_v2(
+ messages: List,
+ model: str,
+ llm_provider: str,
+) -> Tuple[Union[str, ToolResultObject], ChatHistory]:
+ """
+ Returns a tuple(Union[tool_result, message], chat_history)
+
+ - if last message is tool result -> return 'tool_result'
+ - if last message is text -> return message (str)
+
+ - return preceding messages as 'chat_history'
+
+ Note:
+ - cannot specify message if the last entry in chat history contains tool results
+ - message must be at least 1 token long or tool results must be specified.
+ """
+ tool_calls: List = get_all_tool_calls(messages=messages)
+
+ ## GET MOST RECENT MESSAGE
+ most_recent_message = messages.pop(-1)
+ returned_message: Union[ToolResultObject, str] = ""
+ if (
+ most_recent_message.get("role", "") is not None
+ and most_recent_message["role"] == "tool"
+ ):
+ # tool result
+ returned_message = convert_openai_message_to_cohere_tool_result(
+ most_recent_message, tool_calls
+ )
+ else:
+ content: Union[str, List] = most_recent_message.get("content")
+ if isinstance(content, str):
+ returned_message = content
+ else:
+ for chunk in content:
+ if chunk.get("type") == "text":
+ returned_message += chunk.get("text")
+
+ ## CREATE CHAT HISTORY
+ user_message_types = {"user"}
+ tool_message_types = {"tool", "function"}
+ # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them.
+ new_messages: ChatHistory = []
+ msg_i = 0
+
+ while msg_i < len(messages):
+ user_content: str = ""
+ init_msg_i = msg_i
+ ## MERGE CONSECUTIVE USER CONTENT ##
+ while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types:
+ if isinstance(messages[msg_i]["content"], list):
+ for m in messages[msg_i]["content"]:
+ if m.get("type", "") == "text":
+ user_content += m["text"]
+ else:
+ user_content += messages[msg_i]["content"]
+ msg_i += 1
+
+ if len(user_content) > 0:
+ new_messages.append(ChatHistoryUser(role="USER", message=user_content))
+
+ system_content: str = ""
+ ## MERGE CONSECUTIVE SYSTEM CONTENT ##
+ while msg_i < len(messages) and messages[msg_i]["role"] == "system":
+ if isinstance(messages[msg_i]["content"], list):
+ for m in messages[msg_i]["content"]:
+ if m.get("type", "") == "text":
+ system_content += m["text"]
+ else:
+ system_content += messages[msg_i]["content"]
+ msg_i += 1
+
+ if len(system_content) > 0:
+ new_messages.append(
+ ChatHistorySystem(role="SYSTEM", message=system_content)
+ )
+
+ assistant_content: str = ""
+ assistant_tool_calls: List[ToolCallObject] = []
+ ## MERGE CONSECUTIVE ASSISTANT CONTENT ##
+ while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
+ assistant_text = (
+ messages[msg_i].get("content") or ""
+ ) # either string or none
+ if assistant_text:
+ assistant_content += assistant_text
+
+ if messages[msg_i].get(
+ "tool_calls", []
+ ): # support assistant tool invoke conversion
+ assistant_tool_calls.extend(
+ convert_to_cohere_tool_invoke(messages[msg_i]["tool_calls"])
+ )
+
+ if messages[msg_i].get("function_call"):
+ assistant_tool_calls.extend(
+ convert_to_cohere_tool_invoke(messages[msg_i]["function_call"])
+ )
+
+ msg_i += 1
+
+ if len(assistant_content) > 0:
+ new_messages.append(
+ ChatHistoryChatBot(
+ role="CHATBOT",
+ message=assistant_content,
+ tool_calls=assistant_tool_calls,
+ )
+ )
+
+ ## MERGE CONSECUTIVE TOOL RESULTS
+ tool_results: List[ToolResultObject] = []
+ while msg_i < len(messages) and messages[msg_i]["role"] in tool_message_types:
+ tool_results.append(
+ convert_openai_message_to_cohere_tool_result(
+ messages[msg_i], tool_calls
+ )
+ )
+
+ msg_i += 1
+
+ if len(tool_results) > 0:
+ new_messages.append(
+ ChatHistoryToolResult(role="TOOL", tool_results=tool_results)
+ )
+
+ if msg_i == init_msg_i: # prevent infinite loops
+ raise litellm.BadRequestError(
+ message=BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}",
+ model=model,
+ llm_provider=llm_provider,
+ )
+
+ return returned_message, new_messages
def cohere_message_pt(messages: list):
+ tool_calls: List = get_all_tool_calls(messages=messages)
prompt = ""
tool_results = []
for message in messages:
# check if this is a tool_call result
if message["role"] == "tool":
- tool_result = convert_openai_message_to_cohere_tool_result(message)
+ tool_result = convert_openai_message_to_cohere_tool_result(
+ message, tool_calls=tool_calls
+ )
tool_results.append(tool_result)
elif message.get("content"):
prompt += message["content"] + "\n\n"
@@ -1460,9 +1793,7 @@ def _load_image_from_url(image_url):
try:
from PIL import Image
except:
- raise Exception(
- "gemini image conversion failed please run `pip install Pillow`"
- )
+ raise Exception("image conversion failed please run `pip install Pillow`")
from io import BytesIO
try:
@@ -1613,6 +1944,410 @@ def azure_text_pt(messages: list):
return prompt
+###### AZURE AI #######
+def stringify_json_tool_call_content(messages: List) -> List:
+ """
+
+ - Check 'content' in tool role -> convert to dict (if not) -> stringify
+
+ Done for azure_ai/cohere calls to handle results of a tool call
+ """
+
+ for m in messages:
+ if m["role"] == "tool" and isinstance(m["content"], str):
+ # check if content is a valid json object
+ try:
+ json.loads(m["content"])
+ except json.JSONDecodeError:
+ m["content"] = json.dumps({"result": m["content"]})
+
+ return messages
+
+
+###### AMAZON BEDROCK #######
+
+from litellm.types.llms.bedrock import ContentBlock as BedrockContentBlock
+from litellm.types.llms.bedrock import ImageBlock as BedrockImageBlock
+from litellm.types.llms.bedrock import ImageSourceBlock as BedrockImageSourceBlock
+from litellm.types.llms.bedrock import ToolBlock as BedrockToolBlock
+from litellm.types.llms.bedrock import (
+ ToolChoiceValuesBlock as BedrockToolChoiceValuesBlock,
+)
+from litellm.types.llms.bedrock import ToolConfigBlock as BedrockToolConfigBlock
+from litellm.types.llms.bedrock import (
+ ToolInputSchemaBlock as BedrockToolInputSchemaBlock,
+)
+from litellm.types.llms.bedrock import ToolResultBlock as BedrockToolResultBlock
+from litellm.types.llms.bedrock import (
+ ToolResultContentBlock as BedrockToolResultContentBlock,
+)
+from litellm.types.llms.bedrock import ToolSpecBlock as BedrockToolSpecBlock
+from litellm.types.llms.bedrock import ToolUseBlock as BedrockToolUseBlock
+
+
+def get_image_details(image_url) -> Tuple[str, str]:
+ try:
+ import base64
+
+ # Send a GET request to the image URL
+ response = requests.get(image_url)
+ response.raise_for_status() # Raise an exception for HTTP errors
+
+ # Check the response's content type to ensure it is an image
+ content_type = response.headers.get("content-type")
+ if not content_type or "image" not in content_type:
+ raise ValueError(
+ f"URL does not point to a valid image (content-type: {content_type})"
+ )
+
+ # Convert the image content to base64 bytes
+ base64_bytes = base64.b64encode(response.content).decode("utf-8")
+
+ # Get mime-type
+ mime_type = content_type.split("/")[
+ 1
+ ] # Extract mime-type from content-type header
+
+ return base64_bytes, mime_type
+
+ except requests.RequestException as e:
+ raise Exception(f"Request failed: {e}")
+ except Exception as e:
+ raise e
+
+
+def _process_bedrock_converse_image_block(image_url: str) -> BedrockImageBlock:
+ if "base64" in image_url:
+ # Case 1: Images with base64 encoding
+ import base64
+ import re
+
+ # base 64 is passed as data:image/jpeg;base64,
+ image_metadata, img_without_base_64 = image_url.split(",")
+
+ # read mime_type from img_without_base_64=data:image/jpeg;base64
+ # Extract MIME type using regular expression
+ mime_type_match = re.match(r"data:(.*?);base64", image_metadata)
+ if mime_type_match:
+ mime_type = mime_type_match.group(1)
+ image_format = mime_type.split("/")[1]
+ else:
+ mime_type = "image/jpeg"
+ image_format = "jpeg"
+ _blob = BedrockImageSourceBlock(bytes=img_without_base_64)
+ supported_image_formats = (
+ litellm.AmazonConverseConfig().get_supported_image_types()
+ )
+ if image_format in supported_image_formats:
+ return BedrockImageBlock(source=_blob, format=image_format) # type: ignore
+ else:
+ # Handle the case when the image format is not supported
+ raise ValueError(
+ "Unsupported image format: {}. Supported formats: {}".format(
+ image_format, supported_image_formats
+ )
+ )
+ elif "https:/" in image_url:
+ # Case 2: Images with direct links
+ image_bytes, image_format = get_image_details(image_url)
+ _blob = BedrockImageSourceBlock(bytes=image_bytes)
+ supported_image_formats = (
+ litellm.AmazonConverseConfig().get_supported_image_types()
+ )
+ if image_format in supported_image_formats:
+ return BedrockImageBlock(source=_blob, format=image_format) # type: ignore
+ else:
+ # Handle the case when the image format is not supported
+ raise ValueError(
+ "Unsupported image format: {}. Supported formats: {}".format(
+ image_format, supported_image_formats
+ )
+ )
+ else:
+ raise ValueError(
+ "Unsupported image type. Expected either image url or base64 encoded string - \
+ e.g. 'data:image/jpeg;base64,'"
+ )
+
+
+def _convert_to_bedrock_tool_call_invoke(
+ tool_calls: list,
+) -> List[BedrockContentBlock]:
+ """
+ OpenAI tool invokes:
+ {
+ "role": "assistant",
+ "content": null,
+ "tool_calls": [
+ {
+ "id": "call_abc123",
+ "type": "function",
+ "function": {
+ "name": "get_current_weather",
+ "arguments": "{\n\"location\": \"Boston, MA\"\n}"
+ }
+ }
+ ]
+ },
+ """
+ """
+ Bedrock tool invokes:
+ [
+ {
+ "role": "assistant",
+ "toolUse": {
+ "input": {"location": "Boston, MA", ..},
+ "name": "get_current_weather",
+ "toolUseId": "call_abc123"
+ }
+ }
+ ]
+ """
+ """
+ - json.loads argument
+ - extract name
+ - extract id
+ """
+
+ try:
+ _parts_list: List[BedrockContentBlock] = []
+ for tool in tool_calls:
+ if "function" in tool:
+ id = tool["id"]
+ name = tool["function"].get("name", "")
+ arguments = tool["function"].get("arguments", "")
+ arguments_dict = json.loads(arguments)
+ bedrock_tool = BedrockToolUseBlock(
+ input=arguments_dict, name=name, toolUseId=id
+ )
+ bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool)
+ _parts_list.append(bedrock_content_block)
+ return _parts_list
+ except Exception as e:
+ raise Exception(
+ "Unable to convert openai tool calls={} to bedrock tool calls. Received error={}".format(
+ tool_calls, str(e)
+ )
+ )
+
+
+def _convert_to_bedrock_tool_call_result(
+ message: dict,
+) -> BedrockMessageBlock:
+ """
+ OpenAI message with a tool result looks like:
+ {
+ "tool_call_id": "tool_1",
+ "role": "tool",
+ "name": "get_current_weather",
+ "content": "function result goes here",
+ },
+
+ OpenAI message with a function call result looks like:
+ {
+ "role": "function",
+ "name": "get_current_weather",
+ "content": "function result goes here",
+ }
+ """
+ """
+ Bedrock result looks like this:
+ {
+ "role": "user",
+ "content": [
+ {
+ "toolResult": {
+ "toolUseId": "tooluse_kZJMlvQmRJ6eAyJE5GIl7Q",
+ "content": [
+ {
+ "json": {
+ "song": "Elemental Hotel",
+ "artist": "8 Storey Hike"
+ }
+ }
+ ]
+ }
+ }
+ ]
+ }
+ """
+ """
+ -
+ """
+ content = message.get("content", "")
+ name = message.get("name", "")
+ id = message.get("tool_call_id", str(uuid.uuid4()))
+
+ tool_result_content_block = BedrockToolResultContentBlock(text=content)
+ tool_result = BedrockToolResultBlock(
+ content=[tool_result_content_block],
+ toolUseId=id,
+ )
+ content_block = BedrockContentBlock(toolResult=tool_result)
+
+ return BedrockMessageBlock(role="user", content=[content_block])
+
+
+def _bedrock_converse_messages_pt(
+ messages: List,
+ model: str,
+ llm_provider: str,
+) -> List[BedrockMessageBlock]:
+ """
+ Converts given messages from OpenAI format to Bedrock format
+
+ - Roles must alternate b/w 'user' and 'model' (same as anthropic -> merge consecutive roles)
+ - Please ensure that function response turn comes immediately after a function call turn
+ """
+
+ contents: List[BedrockMessageBlock] = []
+ msg_i = 0
+ while msg_i < len(messages):
+ user_content: List[BedrockContentBlock] = []
+ init_msg_i = msg_i
+ ## MERGE CONSECUTIVE USER CONTENT ##
+ while msg_i < len(messages) and messages[msg_i]["role"] == "user":
+ if isinstance(messages[msg_i]["content"], list):
+ _parts: List[BedrockContentBlock] = []
+ for element in messages[msg_i]["content"]:
+ if isinstance(element, dict):
+ if element["type"] == "text":
+ _part = BedrockContentBlock(text=element["text"])
+ _parts.append(_part)
+ elif element["type"] == "image_url":
+ image_url = element["image_url"]["url"]
+ _part = _process_bedrock_converse_image_block( # type: ignore
+ image_url=image_url
+ )
+ _parts.append(BedrockContentBlock(image=_part)) # type: ignore
+ user_content.extend(_parts)
+ else:
+ _part = BedrockContentBlock(text=messages[msg_i]["content"])
+ user_content.append(_part)
+
+ msg_i += 1
+
+ if user_content:
+ contents.append(BedrockMessageBlock(role="user", content=user_content))
+ assistant_content: List[BedrockContentBlock] = []
+ ## MERGE CONSECUTIVE ASSISTANT CONTENT ##
+ while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
+ if messages[msg_i].get("content", None) is not None and isinstance(
+ messages[msg_i]["content"], list
+ ):
+ assistants_parts: List[BedrockContentBlock] = []
+ for element in messages[msg_i]["content"]:
+ if isinstance(element, dict):
+ if element["type"] == "text":
+ assistants_part = BedrockContentBlock(text=element["text"])
+ assistants_parts.append(assistants_part)
+ elif element["type"] == "image_url":
+ image_url = element["image_url"]["url"]
+ assistants_part = _process_bedrock_converse_image_block( # type: ignore
+ image_url=image_url
+ )
+ assistants_parts.append(
+ BedrockContentBlock(image=assistants_part) # type: ignore
+ )
+ assistant_content.extend(assistants_parts)
+ elif messages[msg_i].get(
+ "tool_calls", []
+ ): # support assistant tool invoke convertion
+ assistant_content.extend(
+ _convert_to_bedrock_tool_call_invoke(messages[msg_i]["tool_calls"])
+ )
+ else:
+ assistant_text = (
+ messages[msg_i].get("content") or ""
+ ) # either string or none
+ if assistant_text:
+ assistant_content.append(BedrockContentBlock(text=assistant_text))
+
+ msg_i += 1
+
+ if assistant_content:
+ contents.append(
+ BedrockMessageBlock(role="assistant", content=assistant_content)
+ )
+
+ ## APPEND TOOL CALL MESSAGES ##
+ if msg_i < len(messages) and messages[msg_i]["role"] == "tool":
+ tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i])
+ contents.append(tool_call_result)
+ msg_i += 1
+ if msg_i == init_msg_i: # prevent infinite loops
+ raise litellm.BadRequestError(
+ message=BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}",
+ model=model,
+ llm_provider=llm_provider,
+ )
+ return contents
+
+
+def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]:
+ """
+ OpenAI tools looks like:
+ tools = [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_current_weather",
+ "description": "Get the current weather in a given location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
+ },
+ "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+ },
+ "required": ["location"],
+ },
+ }
+ }
+ ]
+ """
+ """
+ Bedrock toolConfig looks like:
+ "tools": [
+ {
+ "toolSpec": {
+ "name": "top_song",
+ "description": "Get the most popular song played on a radio station.",
+ "inputSchema": {
+ "json": {
+ "type": "object",
+ "properties": {
+ "sign": {
+ "type": "string",
+ "description": "The call sign for the radio station for which you want the most popular song. Example calls signs are WZPZ, and WKRP."
+ }
+ },
+ "required": [
+ "sign"
+ ]
+ }
+ }
+ }
+ }
+ ]
+ """
+ tool_block_list: List[BedrockToolBlock] = []
+ for tool in tools:
+ parameters = tool.get("function", {}).get("parameters", None)
+ name = tool.get("function", {}).get("name", "")
+ description = tool.get("function", {}).get("description", "")
+ tool_input_schema = BedrockToolInputSchemaBlock(json=parameters)
+ tool_spec = BedrockToolSpecBlock(
+ inputSchema=tool_input_schema, name=name, description=description
+ )
+ tool_block = BedrockToolBlock(toolSpec=tool_spec)
+ tool_block_list.append(tool_block)
+
+ return tool_block_list
+
+
# Function call template
def function_call_prompt(messages: list, functions: list):
function_prompt = """Produce JSON OUTPUT ONLY! Adhere to this format {"name": "function_name", "arguments":{"argument_name": "argument_value"}} The following functions are available to you:"""
@@ -1631,6 +2366,50 @@ def function_call_prompt(messages: list, functions: list):
return messages
+def response_schema_prompt(model: str, response_schema: dict) -> str:
+ """
+ Decides if a user-defined custom prompt or default needs to be used
+
+ Returns the prompt str that's passed to the model as a user message
+ """
+ custom_prompt_details: Optional[dict] = None
+ response_schema_as_message = [
+ {"role": "user", "content": "{}".format(response_schema)}
+ ]
+ if f"{model}/response_schema_prompt" in litellm.custom_prompt_dict:
+
+ custom_prompt_details = litellm.custom_prompt_dict[
+ f"{model}/response_schema_prompt"
+ ] # allow user to define custom response schema prompt by model
+ elif "response_schema_prompt" in litellm.custom_prompt_dict:
+ custom_prompt_details = litellm.custom_prompt_dict["response_schema_prompt"]
+
+ if custom_prompt_details is not None:
+ return custom_prompt(
+ role_dict=custom_prompt_details["roles"],
+ initial_prompt_value=custom_prompt_details["initial_prompt_value"],
+ final_prompt_value=custom_prompt_details["final_prompt_value"],
+ messages=response_schema_as_message,
+ )
+ else:
+ return default_response_schema_prompt(response_schema=response_schema)
+
+
+def default_response_schema_prompt(response_schema: dict) -> str:
+ """
+ Used if provider/model doesn't support 'response_schema' param.
+
+ This is the default prompt. Allow user to override this with a custom_prompt.
+ """
+ prompt_str = """Use this JSON schema:
+ ```json
+ {}
+ ```""".format(
+ response_schema
+ )
+ return prompt_str
+
+
# Custom prompt template
def custom_prompt(
role_dict: dict,
@@ -1661,7 +2440,16 @@ def custom_prompt(
if role in role_dict and "post_message" in role_dict[role]
else ""
)
- prompt += pre_message_str + message["content"] + post_message_str
+ if isinstance(message["content"], str):
+ prompt += pre_message_str + message["content"] + post_message_str
+ elif isinstance(message["content"], list):
+ text_str = ""
+ for content in message["content"]:
+ if content.get("text", None) is not None and isinstance(
+ content["text"], str
+ ):
+ text_str += content["text"]
+ prompt += pre_message_str + text_str + post_message_str
if role == "assistant":
prompt += eos_token
@@ -1684,7 +2472,9 @@ def prompt_factory(
elif custom_llm_provider == "anthropic":
if model == "claude-instant-1" or model == "claude-2":
return anthropic_pt(messages=messages)
- return anthropic_messages_pt(messages=messages)
+ return anthropic_messages_pt(
+ messages=messages, model=model, llm_provider=custom_llm_provider
+ )
elif custom_llm_provider == "anthropic_xml":
return anthropic_messages_pt_xml(messages=messages)
elif custom_llm_provider == "together_ai":
diff --git a/litellm/llms/replicate.py b/litellm/llms/replicate.py
index 386d24f59b..1dd29fd7d6 100644
--- a/litellm/llms/replicate.py
+++ b/litellm/llms/replicate.py
@@ -1,13 +1,18 @@
-import os, types
+import asyncio
import json
-import requests # type: ignore
+import os
import time
-from typing import Callable, Optional, Union, Tuple, Any
-from litellm.utils import ModelResponse, Usage, CustomStreamWrapper
-import litellm, asyncio
+import types
+from typing import Any, Callable, Optional, Tuple, Union
+
import httpx # type: ignore
-from .prompt_templates.factory import prompt_factory, custom_prompt
+import requests # type: ignore
+
+import litellm
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
+
+from .prompt_templates.factory import custom_prompt, prompt_factory
class ReplicateError(Exception):
@@ -251,7 +256,7 @@ async def async_handle_prediction_response(
logs = ""
while True and (status not in ["succeeded", "failed", "canceled"]):
print_verbose(f"replicate: polling endpoint: {prediction_url}")
- await asyncio.sleep(0.5)
+ await asyncio.sleep(0.5) # prevent replicate rate limit errors
response = await http_handler.get(prediction_url, headers=headers)
if response.status_code == 200:
response_data = response.json()
@@ -290,7 +295,15 @@ def handle_prediction_response_streaming(prediction_url, api_token, print_verbos
response_data = response.json()
status = response_data["status"]
if "output" in response_data:
- output_string = "".join(response_data["output"])
+ try:
+ output_string = "".join(response_data["output"])
+ except Exception as e:
+ raise ReplicateError(
+ status_code=422,
+ message="Unable to parse response. Got={}".format(
+ response_data["output"]
+ ),
+ )
new_output = output_string[len(previous_output) :]
print_verbose(f"New chunk: {new_output}")
yield {"output": new_output, "status": status}
@@ -329,7 +342,15 @@ async def async_handle_prediction_response_streaming(
response_data = response.json()
status = response_data["status"]
if "output" in response_data:
- output_string = "".join(response_data["output"])
+ try:
+ output_string = "".join(response_data["output"])
+ except Exception as e:
+ raise ReplicateError(
+ status_code=422,
+ message="Unable to parse response. Got={}".format(
+ response_data["output"]
+ ),
+ )
new_output = output_string[len(previous_output) :]
print_verbose(f"New chunk: {new_output}")
yield {"output": new_output, "status": status}
@@ -367,7 +388,7 @@ def process_response(
## Building RESPONSE OBJECT
if len(result) > 1:
- model_response["choices"][0]["message"]["content"] = result
+ model_response.choices[0].message.content = result # type: ignore
# Calculate usage
prompt_tokens = len(encoding.encode(prompt, disallowed_special=()))
@@ -377,7 +398,7 @@ def process_response(
disallowed_special=(),
)
)
- model_response["model"] = "replicate/" + model
+ model_response.model = "replicate/" + model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@@ -477,7 +498,7 @@ def completion(
## Step1: Start Prediction: gets a prediction url
## Step2: Poll prediction url for response
## Step2: is handled with and without streaming
- model_response["created"] = int(
+ model_response.created = int(
time.time()
) # for pricing this must remain right before calling api
diff --git a/litellm/llms/sagemaker.py b/litellm/llms/sagemaker.py
index 8e75428bb7..d16d2bd11b 100644
--- a/litellm/llms/sagemaker.py
+++ b/litellm/llms/sagemaker.py
@@ -1,15 +1,21 @@
-import os, types, traceback
-from enum import Enum
+import io
import json
-import requests # type: ignore
-import time
-from typing import Callable, Optional, Any
-import litellm
-from litellm.utils import ModelResponse, EmbeddingResponse, get_secret, Usage
+import os
import sys
+import time
+import traceback
+import types
from copy import deepcopy
+from enum import Enum
+from typing import Any, Callable, Optional
+
import httpx # type: ignore
-from .prompt_templates.factory import prompt_factory, custom_prompt
+import requests # type: ignore
+
+import litellm
+from litellm.utils import EmbeddingResponse, ModelResponse, Usage, get_secret
+
+from .prompt_templates.factory import custom_prompt, prompt_factory
class SagemakerError(Exception):
@@ -25,10 +31,6 @@ class SagemakerError(Exception):
) # Call the base class constructor with the parameters it needs
-import io
-import json
-
-
class TokenIterator:
def __init__(self, stream, acompletion: bool = False):
if acompletion == False:
@@ -185,7 +187,8 @@ def completion(
# I assume majority of users use .env for auth
region_name = (
get_secret("AWS_REGION_NAME")
- or "us-west-2" # default to us-west-2 if user not specified
+ or aws_region_name # get region from config file if specified
+ or "us-west-2" # default to us-west-2 if region not specified
)
client = boto3.client(
service_name="sagemaker-runtime",
@@ -379,7 +382,7 @@ def completion(
if completion_output.startswith(prompt) and "" in prompt:
completion_output = completion_output.replace(prompt, "", 1)
- model_response["choices"][0]["message"]["content"] = completion_output
+ model_response.choices[0].message.content = completion_output # type: ignore
except:
raise SagemakerError(
message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}",
@@ -392,8 +395,8 @@ def completion(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@@ -439,7 +442,8 @@ async def async_streaming(
# I assume majority of users use .env for auth
region_name = (
get_secret("AWS_REGION_NAME")
- or "us-west-2" # default to us-west-2 if user not specified
+ or aws_region_name # get region from config file if specified
+ or "us-west-2" # default to us-west-2 if region not specified
)
_client = session.client(
service_name="sagemaker-runtime",
@@ -506,7 +510,8 @@ async def async_completion(
# I assume majority of users use .env for auth
region_name = (
get_secret("AWS_REGION_NAME")
- or "us-west-2" # default to us-west-2 if user not specified
+ or aws_region_name # get region from config file if specified
+ or "us-west-2" # default to us-west-2 if region not specified
)
_client = session.client(
service_name="sagemaker-runtime",
@@ -597,7 +602,7 @@ async def async_completion(
if completion_output.startswith(data["inputs"]) and "" in data["inputs"]:
completion_output = completion_output.replace(data["inputs"], "", 1)
- model_response["choices"][0]["message"]["content"] = completion_output
+ model_response.choices[0].message.content = completion_output # type: ignore
except:
raise SagemakerError(
message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}",
@@ -610,8 +615,8 @@ async def async_completion(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@@ -661,7 +666,8 @@ def embedding(
# I assume majority of users use .env for auth
region_name = (
get_secret("AWS_REGION_NAME")
- or "us-west-2" # default to us-west-2 if user not specified
+ or aws_region_name # get region from config file if specified
+ or "us-west-2" # default to us-west-2 if region not specified
)
client = boto3.client(
service_name="sagemaker-runtime",
@@ -740,16 +746,20 @@ def embedding(
{"object": "embedding", "index": idx, "embedding": embedding}
)
- model_response["object"] = "list"
- model_response["data"] = output_data
- model_response["model"] = model
+ model_response.object = "list"
+ model_response.data = output_data
+ model_response.model = model
input_tokens = 0
for text in input:
input_tokens += len(encoding.encode(text))
- model_response["usage"] = Usage(
- prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
+ setattr(
+ model_response,
+ "usage",
+ Usage(
+ prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
+ ),
)
return model_response
diff --git a/litellm/llms/text_completion_codestral.py b/litellm/llms/text_completion_codestral.py
new file mode 100644
index 0000000000..e732706b4a
--- /dev/null
+++ b/litellm/llms/text_completion_codestral.py
@@ -0,0 +1,532 @@
+# What is this?
+## Controller file for TextCompletionCodestral Integration - https://codestral.com/
+
+from functools import partial
+import os, types
+import traceback
+import json
+from enum import Enum
+import requests, copy # type: ignore
+import time
+from typing import Callable, Optional, List, Literal, Union
+from litellm.utils import (
+ TextCompletionResponse,
+ Usage,
+ CustomStreamWrapper,
+ Message,
+ Choices,
+)
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.types.llms.databricks import GenericStreamingChunk
+import litellm
+from .prompt_templates.factory import prompt_factory, custom_prompt
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from .base import BaseLLM
+import httpx # type: ignore
+
+
+class TextCompletionCodestralError(Exception):
+ def __init__(
+ self,
+ status_code,
+ message,
+ request: Optional[httpx.Request] = None,
+ response: Optional[httpx.Response] = None,
+ ):
+ self.status_code = status_code
+ self.message = message
+ if request is not None:
+ self.request = request
+ else:
+ self.request = httpx.Request(
+ method="POST",
+ url="https://docs.codestral.com/user-guide/inference/rest_api",
+ )
+ if response is not None:
+ self.response = response
+ else:
+ self.response = httpx.Response(
+ status_code=status_code, request=self.request
+ )
+ super().__init__(
+ self.message
+ ) # Call the base class constructor with the parameters it needs
+
+
+async def make_call(
+ client: AsyncHTTPHandler,
+ api_base: str,
+ headers: dict,
+ data: str,
+ model: str,
+ messages: list,
+ logging_obj,
+):
+ response = await client.post(api_base, headers=headers, data=data, stream=True)
+
+ if response.status_code != 200:
+ raise TextCompletionCodestralError(
+ status_code=response.status_code, message=response.text
+ )
+
+ completion_stream = response.aiter_lines()
+ # LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response=completion_stream, # Pass the completion stream for logging
+ additional_args={"complete_input_dict": data},
+ )
+
+ return completion_stream
+
+
+class MistralTextCompletionConfig:
+ """
+ Reference: https://docs.mistral.ai/api/#operation/createFIMCompletion
+ """
+
+ suffix: Optional[str] = None
+ temperature: Optional[int] = None
+ top_p: Optional[float] = None
+ max_tokens: Optional[int] = None
+ min_tokens: Optional[int] = None
+ stream: Optional[bool] = None
+ random_seed: Optional[int] = None
+ stop: Optional[str] = None
+
+ def __init__(
+ self,
+ suffix: Optional[str] = None,
+ temperature: Optional[int] = None,
+ top_p: Optional[float] = None,
+ max_tokens: Optional[int] = None,
+ min_tokens: Optional[int] = None,
+ stream: Optional[bool] = None,
+ random_seed: Optional[int] = None,
+ stop: Optional[str] = None,
+ ) -> None:
+ locals_ = locals().copy()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self):
+ return [
+ "suffix",
+ "temperature",
+ "top_p",
+ "max_tokens",
+ "stream",
+ "seed",
+ "stop",
+ ]
+
+ def map_openai_params(self, non_default_params: dict, optional_params: dict):
+ for param, value in non_default_params.items():
+ if param == "suffix":
+ optional_params["suffix"] = value
+ if param == "temperature":
+ optional_params["temperature"] = value
+ if param == "top_p":
+ optional_params["top_p"] = value
+ if param == "max_tokens":
+ optional_params["max_tokens"] = value
+ if param == "stream" and value == True:
+ optional_params["stream"] = value
+ if param == "stop":
+ optional_params["stop"] = value
+ if param == "seed":
+ optional_params["random_seed"] = value
+ if param == "min_tokens":
+ optional_params["min_tokens"] = value
+
+ return optional_params
+
+ def _chunk_parser(self, chunk_data: str) -> GenericStreamingChunk:
+ text = ""
+ is_finished = False
+ finish_reason = None
+ logprobs = None
+
+ chunk_data = chunk_data.replace("data:", "")
+ chunk_data = chunk_data.strip()
+ if len(chunk_data) == 0 or chunk_data == "[DONE]":
+ return {
+ "text": "",
+ "is_finished": is_finished,
+ "finish_reason": finish_reason,
+ }
+ chunk_data_dict = json.loads(chunk_data)
+ original_chunk = litellm.ModelResponse(**chunk_data_dict, stream=True)
+ _choices = chunk_data_dict.get("choices", []) or []
+ _choice = _choices[0]
+ text = _choice.get("delta", {}).get("content", "")
+
+ if _choice.get("finish_reason") is not None:
+ is_finished = True
+ finish_reason = _choice.get("finish_reason")
+ logprobs = _choice.get("logprobs")
+
+ return GenericStreamingChunk(
+ text=text,
+ original_chunk=original_chunk,
+ is_finished=is_finished,
+ finish_reason=finish_reason,
+ logprobs=logprobs,
+ )
+
+
+class CodestralTextCompletion(BaseLLM):
+ def __init__(self) -> None:
+ super().__init__()
+
+ def _validate_environment(
+ self,
+ api_key: Optional[str],
+ user_headers: dict,
+ ) -> dict:
+ if api_key is None:
+ raise ValueError(
+ "Missing CODESTRAL_API_Key - Please add CODESTRAL_API_Key to your environment variables"
+ )
+ headers = {
+ "content-type": "application/json",
+ "Authorization": "Bearer {}".format(api_key),
+ }
+ if user_headers is not None and isinstance(user_headers, dict):
+ headers = {**headers, **user_headers}
+ return headers
+
+ def output_parser(self, generated_text: str):
+ """
+ Parse the output text to remove any special characters. In our current approach we just check for ChatML tokens.
+
+ Initial issue that prompted this - https://github.com/BerriAI/litellm/issues/763
+ """
+ chat_template_tokens = [
+ "<|assistant|>",
+ "<|system|>",
+ "<|user|>",
+ "",
+ "",
+ ]
+ for token in chat_template_tokens:
+ if generated_text.strip().startswith(token):
+ generated_text = generated_text.replace(token, "", 1)
+ if generated_text.endswith(token):
+ generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1]
+ return generated_text
+
+ def process_text_completion_response(
+ self,
+ model: str,
+ response: Union[requests.Response, httpx.Response],
+ model_response: TextCompletionResponse,
+ stream: bool,
+ logging_obj: litellm.litellm_core_utils.litellm_logging.Logging,
+ optional_params: dict,
+ api_key: str,
+ data: Union[dict, str],
+ messages: list,
+ print_verbose,
+ encoding,
+ ) -> TextCompletionResponse:
+ ## LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key=api_key,
+ original_response=response.text,
+ additional_args={"complete_input_dict": data},
+ )
+ print_verbose(f"codestral api: raw model_response: {response.text}")
+ ## RESPONSE OBJECT
+ if response.status_code != 200:
+ raise TextCompletionCodestralError(
+ message=str(response.text),
+ status_code=response.status_code,
+ )
+ try:
+ completion_response = response.json()
+ except:
+ raise TextCompletionCodestralError(message=response.text, status_code=422)
+
+ _original_choices = completion_response.get("choices", [])
+ _choices: List[litellm.utils.TextChoices] = []
+ for choice in _original_choices:
+ # This is what 1 choice looks like from codestral API
+ # {
+ # "index": 0,
+ # "message": {
+ # "role": "assistant",
+ # "content": "\n assert is_odd(1)\n assert",
+ # "tool_calls": null
+ # },
+ # "finish_reason": "length",
+ # "logprobs": null
+ # }
+ _finish_reason = None
+ _index = 0
+ _text = None
+ _logprobs = None
+
+ _choice_message = choice.get("message", {})
+ _choice = litellm.utils.TextChoices(
+ finish_reason=choice.get("finish_reason"),
+ index=choice.get("index"),
+ text=_choice_message.get("content"),
+ logprobs=choice.get("logprobs"),
+ )
+
+ _choices.append(_choice)
+
+ _response = litellm.TextCompletionResponse(
+ id=completion_response.get("id"),
+ choices=_choices,
+ created=completion_response.get("created"),
+ model=completion_response.get("model"),
+ usage=completion_response.get("usage"),
+ stream=False,
+ object=completion_response.get("object"),
+ )
+ return _response
+
+ def completion(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ custom_prompt_dict: dict,
+ model_response: TextCompletionResponse,
+ print_verbose: Callable,
+ encoding,
+ api_key: str,
+ logging_obj,
+ optional_params: dict,
+ timeout: Union[float, httpx.Timeout],
+ acompletion=None,
+ litellm_params=None,
+ logger_fn=None,
+ headers: dict = {},
+ ) -> Union[TextCompletionResponse, CustomStreamWrapper]:
+ headers = self._validate_environment(api_key, headers)
+
+ completion_url = api_base or "https://codestral.mistral.ai/v1/fim/completions"
+
+ if model in custom_prompt_dict:
+ # check if the model has a registered custom prompt
+ model_prompt_details = custom_prompt_dict[model]
+ prompt = custom_prompt(
+ role_dict=model_prompt_details["roles"],
+ initial_prompt_value=model_prompt_details["initial_prompt_value"],
+ final_prompt_value=model_prompt_details["final_prompt_value"],
+ messages=messages,
+ )
+ else:
+ prompt = prompt_factory(model=model, messages=messages)
+
+ ## Load Config
+ config = litellm.MistralTextCompletionConfig.get_config()
+ for k, v in config.items():
+ if (
+ k not in optional_params
+ ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+ optional_params[k] = v
+
+ stream = optional_params.pop("stream", False)
+
+ data = {
+ "prompt": prompt,
+ **optional_params,
+ }
+ input_text = prompt
+ ## LOGGING
+ logging_obj.pre_call(
+ input=input_text,
+ api_key=api_key,
+ additional_args={
+ "complete_input_dict": data,
+ "headers": headers,
+ "api_base": completion_url,
+ "acompletion": acompletion,
+ },
+ )
+ ## COMPLETION CALL
+ if acompletion is True:
+ ### ASYNC STREAMING
+ if stream is True:
+ return self.async_streaming(
+ model=model,
+ messages=messages,
+ data=data,
+ api_base=completion_url,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ api_key=api_key,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ headers=headers,
+ timeout=timeout,
+ ) # type: ignore
+ else:
+ ### ASYNC COMPLETION
+ return self.async_completion(
+ model=model,
+ messages=messages,
+ data=data,
+ api_base=completion_url,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ api_key=api_key,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ stream=False,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ headers=headers,
+ timeout=timeout,
+ ) # type: ignore
+
+ ### SYNC STREAMING
+ if stream is True:
+ response = requests.post(
+ completion_url,
+ headers=headers,
+ data=json.dumps(data),
+ stream=stream,
+ )
+ _response = CustomStreamWrapper(
+ response.iter_lines(),
+ model,
+ custom_llm_provider="codestral",
+ logging_obj=logging_obj,
+ )
+ return _response
+ ### SYNC COMPLETION
+ else:
+ response = requests.post(
+ url=completion_url,
+ headers=headers,
+ data=json.dumps(data),
+ )
+ return self.process_text_completion_response(
+ model=model,
+ response=response,
+ model_response=model_response,
+ stream=optional_params.get("stream", False),
+ logging_obj=logging_obj, # type: ignore
+ optional_params=optional_params,
+ api_key=api_key,
+ data=data,
+ messages=messages,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ )
+
+ async def async_completion(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ model_response: TextCompletionResponse,
+ print_verbose: Callable,
+ encoding,
+ api_key,
+ logging_obj,
+ stream,
+ data: dict,
+ optional_params: dict,
+ timeout: Union[float, httpx.Timeout],
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+ ) -> TextCompletionResponse:
+
+ async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=timeout))
+ try:
+ response = await async_handler.post(
+ api_base, headers=headers, data=json.dumps(data)
+ )
+ except httpx.HTTPStatusError as e:
+ raise TextCompletionCodestralError(
+ status_code=e.response.status_code,
+ message="HTTPStatusError - {}".format(e.response.text),
+ )
+ except Exception as e:
+ raise TextCompletionCodestralError(
+ status_code=500, message="{}\n{}".format(str(e), traceback.format_exc())
+ )
+ return self.process_text_completion_response(
+ model=model,
+ response=response,
+ model_response=model_response,
+ stream=stream,
+ logging_obj=logging_obj,
+ api_key=api_key,
+ data=data,
+ messages=messages,
+ print_verbose=print_verbose,
+ optional_params=optional_params,
+ encoding=encoding,
+ )
+
+ async def async_streaming(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ model_response: TextCompletionResponse,
+ print_verbose: Callable,
+ encoding,
+ api_key,
+ logging_obj,
+ data: dict,
+ timeout: Union[float, httpx.Timeout],
+ optional_params=None,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+ ) -> CustomStreamWrapper:
+ data["stream"] = True
+
+ streamwrapper = CustomStreamWrapper(
+ completion_stream=None,
+ make_call=partial(
+ make_call,
+ api_base=api_base,
+ headers=headers,
+ data=json.dumps(data),
+ model=model,
+ messages=messages,
+ logging_obj=logging_obj,
+ ),
+ model=model,
+ custom_llm_provider="text-completion-codestral",
+ logging_obj=logging_obj,
+ )
+ return streamwrapper
+
+ def embedding(self, *args, **kwargs):
+ pass
diff --git a/litellm/llms/together_ai.py b/litellm/llms/together_ai.py
index 47453ca885..3adbcae37e 100644
--- a/litellm/llms/together_ai.py
+++ b/litellm/llms/together_ai.py
@@ -3,16 +3,20 @@ Deprecated. We now do together ai calls via the openai client.
Reference: https://docs.together.ai/docs/openai-api-compatibility
"""
-import os, types
import json
-from enum import Enum
-import requests # type: ignore
+import os
import time
+import types
+from enum import Enum
from typing import Callable, Optional
-import litellm
+
import httpx # type: ignore
+import requests # type: ignore
+
+import litellm
from litellm.utils import ModelResponse, Usage
-from .prompt_templates.factory import prompt_factory, custom_prompt
+
+from .prompt_templates.factory import custom_prompt, prompt_factory
class TogetherAIError(Exception):
@@ -91,145 +95,145 @@ class TogetherAIConfig:
}
-def validate_environment(api_key):
- if api_key is None:
- raise ValueError(
- "Missing TogetherAI API Key - A call is being made to together_ai but no key is set either in the environment variables or via params"
- )
- headers = {
- "accept": "application/json",
- "content-type": "application/json",
- "Authorization": "Bearer " + api_key,
- }
- return headers
+# def validate_environment(api_key):
+# if api_key is None:
+# raise ValueError(
+# "Missing TogetherAI API Key - A call is being made to together_ai but no key is set either in the environment variables or via params"
+# )
+# headers = {
+# "accept": "application/json",
+# "content-type": "application/json",
+# "Authorization": "Bearer " + api_key,
+# }
+# return headers
-def completion(
- model: str,
- messages: list,
- api_base: str,
- model_response: ModelResponse,
- print_verbose: Callable,
- encoding,
- api_key,
- logging_obj,
- custom_prompt_dict={},
- optional_params=None,
- litellm_params=None,
- logger_fn=None,
-):
- headers = validate_environment(api_key)
+# def completion(
+# model: str,
+# messages: list,
+# api_base: str,
+# model_response: ModelResponse,
+# print_verbose: Callable,
+# encoding,
+# api_key,
+# logging_obj,
+# custom_prompt_dict={},
+# optional_params=None,
+# litellm_params=None,
+# logger_fn=None,
+# ):
+# headers = validate_environment(api_key)
- ## Load Config
- config = litellm.TogetherAIConfig.get_config()
- for k, v in config.items():
- if (
- k not in optional_params
- ): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in
- optional_params[k] = v
+# ## Load Config
+# config = litellm.TogetherAIConfig.get_config()
+# for k, v in config.items():
+# if (
+# k not in optional_params
+# ): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in
+# optional_params[k] = v
- print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}")
- if model in custom_prompt_dict:
- # check if the model has a registered custom prompt
- model_prompt_details = custom_prompt_dict[model]
- prompt = custom_prompt(
- role_dict=model_prompt_details.get("roles", {}),
- initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
- final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
- bos_token=model_prompt_details.get("bos_token", ""),
- eos_token=model_prompt_details.get("eos_token", ""),
- messages=messages,
- )
- else:
- prompt = prompt_factory(
- model=model,
- messages=messages,
- api_key=api_key,
- custom_llm_provider="together_ai",
- ) # api key required to query together ai model list
+# print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}")
+# if model in custom_prompt_dict:
+# # check if the model has a registered custom prompt
+# model_prompt_details = custom_prompt_dict[model]
+# prompt = custom_prompt(
+# role_dict=model_prompt_details.get("roles", {}),
+# initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
+# final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
+# bos_token=model_prompt_details.get("bos_token", ""),
+# eos_token=model_prompt_details.get("eos_token", ""),
+# messages=messages,
+# )
+# else:
+# prompt = prompt_factory(
+# model=model,
+# messages=messages,
+# api_key=api_key,
+# custom_llm_provider="together_ai",
+# ) # api key required to query together ai model list
- data = {
- "model": model,
- "prompt": prompt,
- "request_type": "language-model-inference",
- **optional_params,
- }
+# data = {
+# "model": model,
+# "prompt": prompt,
+# "request_type": "language-model-inference",
+# **optional_params,
+# }
- ## LOGGING
- logging_obj.pre_call(
- input=prompt,
- api_key=api_key,
- additional_args={
- "complete_input_dict": data,
- "headers": headers,
- "api_base": api_base,
- },
- )
- ## COMPLETION CALL
- if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True:
- response = requests.post(
- api_base,
- headers=headers,
- data=json.dumps(data),
- stream=optional_params["stream_tokens"],
- )
- return response.iter_lines()
- else:
- response = requests.post(api_base, headers=headers, data=json.dumps(data))
- ## LOGGING
- logging_obj.post_call(
- input=prompt,
- api_key=api_key,
- original_response=response.text,
- additional_args={"complete_input_dict": data},
- )
- print_verbose(f"raw model_response: {response.text}")
- ## RESPONSE OBJECT
- if response.status_code != 200:
- raise TogetherAIError(
- status_code=response.status_code, message=response.text
- )
- completion_response = response.json()
+# ## LOGGING
+# logging_obj.pre_call(
+# input=prompt,
+# api_key=api_key,
+# additional_args={
+# "complete_input_dict": data,
+# "headers": headers,
+# "api_base": api_base,
+# },
+# )
+# ## COMPLETION CALL
+# if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True:
+# response = requests.post(
+# api_base,
+# headers=headers,
+# data=json.dumps(data),
+# stream=optional_params["stream_tokens"],
+# )
+# return response.iter_lines()
+# else:
+# response = requests.post(api_base, headers=headers, data=json.dumps(data))
+# ## LOGGING
+# logging_obj.post_call(
+# input=prompt,
+# api_key=api_key,
+# original_response=response.text,
+# additional_args={"complete_input_dict": data},
+# )
+# print_verbose(f"raw model_response: {response.text}")
+# ## RESPONSE OBJECT
+# if response.status_code != 200:
+# raise TogetherAIError(
+# status_code=response.status_code, message=response.text
+# )
+# completion_response = response.json()
- if "error" in completion_response:
- raise TogetherAIError(
- message=json.dumps(completion_response),
- status_code=response.status_code,
- )
- elif "error" in completion_response["output"]:
- raise TogetherAIError(
- message=json.dumps(completion_response["output"]),
- status_code=response.status_code,
- )
+# if "error" in completion_response:
+# raise TogetherAIError(
+# message=json.dumps(completion_response),
+# status_code=response.status_code,
+# )
+# elif "error" in completion_response["output"]:
+# raise TogetherAIError(
+# message=json.dumps(completion_response["output"]),
+# status_code=response.status_code,
+# )
- if len(completion_response["output"]["choices"][0]["text"]) >= 0:
- model_response["choices"][0]["message"]["content"] = completion_response[
- "output"
- ]["choices"][0]["text"]
+# if len(completion_response["output"]["choices"][0]["text"]) >= 0:
+# model_response.choices[0].message.content = completion_response["output"][
+# "choices"
+# ][0]["text"]
- ## CALCULATING USAGE
- print_verbose(
- f"CALCULATING TOGETHERAI TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}"
- )
- prompt_tokens = len(encoding.encode(prompt))
- completion_tokens = len(
- encoding.encode(model_response["choices"][0]["message"].get("content", ""))
- )
- if "finish_reason" in completion_response["output"]["choices"][0]:
- model_response.choices[0].finish_reason = completion_response["output"][
- "choices"
- ][0]["finish_reason"]
- model_response["created"] = int(time.time())
- model_response["model"] = "together_ai/" + model
- usage = Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
- )
- setattr(model_response, "usage", usage)
- return model_response
+# ## CALCULATING USAGE
+# print_verbose(
+# f"CALCULATING TOGETHERAI TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}"
+# )
+# prompt_tokens = len(encoding.encode(prompt))
+# completion_tokens = len(
+# encoding.encode(model_response["choices"][0]["message"].get("content", ""))
+# )
+# if "finish_reason" in completion_response["output"]["choices"][0]:
+# model_response.choices[0].finish_reason = completion_response["output"][
+# "choices"
+# ][0]["finish_reason"]
+# model_response["created"] = int(time.time())
+# model_response["model"] = "together_ai/" + model
+# usage = Usage(
+# prompt_tokens=prompt_tokens,
+# completion_tokens=completion_tokens,
+# total_tokens=prompt_tokens + completion_tokens,
+# )
+# setattr(model_response, "usage", usage)
+# return model_response
-def embedding():
- # logic for parsing in - calling - parsing out model embedding calls
- pass
+# def embedding():
+# # logic for parsing in - calling - parsing out model embedding calls
+# pass
diff --git a/litellm/llms/tokenizers/fb374d419588a4632f3f557e76b4b70aebbca790 b/litellm/llms/tokenizers/fb374d419588a4632f3f557e76b4b70aebbca790
new file mode 100644
index 0000000000..fbc14c2e1e
--- /dev/null
+++ b/litellm/llms/tokenizers/fb374d419588a4632f3f557e76b4b70aebbca790
@@ -0,0 +1,199998 @@
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+IOCwpOCxhuCwsuCxgeCwuOCxjeCwpA== 193448
+INC90LDQv9GA0Y/QvNGD0Y4= 193449
+LlNtYWxs 193450
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+IOCqieCqmuCrjeCqmg== 193454
+UXRk 193455
+IGhhcmltbw== 193456
+IHByb2Zp 193457
+X0RlcA== 193458
+2LXZhNin2K0= 193459
+IGFyYmVqZGVy 193460
+64w= 193461
+T25kZQ== 193462
+IHZpc29y 193463
+cG9ydGFibGU= 193464
+INC/0LDQtg== 193465
+4YOQ4YOi4YOY4YOV 193466
+IENm 193467
+ZG9r 193468
+4LKi 193469
+IHNpc3TDqG0= 193470
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+IOCmquCnjeCmsOCmpg== 193474
+Lmxk 193475
+IMO8c3TDvG5saWs= 193476
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+IGNo4bqzbmc= 193478
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+IEJvdGFu 193480
+IGVuc2F5bw== 193481
+LnZhbGlkYXRvcnM= 193482
+IGVtb3M= 193483
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+LWNvbmRpdGlvbmluZw== 193487
+b2xpZGF5 193488
+IE1lcnNp 193489
+a3VoYQ== 193490
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+YW5nc3Ryb20= 193492
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+xYJlZ28= 193494
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+dXJpb24= 193496
+IHN0YW5vdw== 193497
+VVJH 193498
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+cmFueQ== 193500
+INGB0L7QtNCw 193501
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+INC60LXQu9GW0YE= 193503
+b21hcA== 193504
+INi52YTYp9mF2KfYqg== 193505
+CQkJCQkJCQkJCQkJCQkJCQkJCQk= 193506
+IG1hdXQ= 193507
+cmlvbA== 193508
+INmB2YbYp9mI2LHbjA== 193509
+INin2YHYsw== 193510
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+4Lit4Lix4LiZ4LiU4Lix4Lia 193517
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+IGJhbmp1cg== 193520
+IEt1eQ== 193521
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+b3hpYQ== 193528
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+QmF0bWFu 193533
+YXJpYWxlcw== 193534
+Li4uLi4uLi4uLi4u 193535
+X0xJVEVSQUw= 193536
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+LnBvc2l0aW9ucw== 193538
+LlRlY2g= 193539
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+IGNvbnZhaW5jcmU= 193541
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+IFVnbHk= 193545
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+xpA= 193548
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+IOCqneCqoeCqquCrgA== 193550
+IHF1YW50aXTDoA== 193551
+INmI2YfZhA== 193552
+IGVkYXI= 193553
+IHByZXV2ZXM= 193554
+IOOAgOOAgOOAgA== 193555
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+cHJvamVr 193557
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+IOCkuOCkvuCkhw== 193559
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+IHZhamFk 193561
+IOCmpuCnsOCnhw== 193562
+LnhodG1s 193563
+4Ki+4Kih4KmH 193564
+INOZ0YHQutOZ0YA= 193565
+IOCqj+CqqOCrgA== 193566
+X1RPVUNI 193567
+INKz0LDRgg== 193568
+IGxpdGVyYWxz 193569
+IGdhcm5pc2g= 193570
+IOCuquCvi+CusuCvgA== 193571
+IOS4iuS8oA== 193572
+IHLDqWpvdQ== 193573
+In0seyI= 193574
+Lm9uZXJyb3I= 193575
+IGknZA== 193576
+IHbFr2JlYw== 193577
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+INil2YLYp9mF2Kk= 193579
+IOCkuOClgOCkp+Clhw== 193580
+Lnpr 193581
+YWxzZWx0 193582
+15XXlNeZ150= 193583
+LmFuc3dlcnM= 193584
+U0lO 193585
+cmlzdG8= 193586
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+IFRvb24= 193588
+IOCqruCrgeCqguCqrOCqiA== 193589
+IFF1ZWxz 193590
+Q2xhaXJl 193591
+IOWFqOawkeW9qeelqA== 193592
+aXRoaW9s 193593
+5rqq5Y6/ 193594
+TElORVM= 193595
+X3BhZ28= 193596
+IEdvbw== 193597
+4KS/4KSH 193598
+4Z6b4Z+B4Z6B 193599
+IHRlcnJvcmlzbW8= 193600
+IGRpc2N1bA== 193601
+IGNhdXph 193602
+IFNjaGF0eg== 193603
+CXBhcnQ= 193604
+6Kej57uR6ZO26KGM5Y2h 193605
+IHRhbWFudXQ= 193606
+INGP0LrRltC8 193607
+IEdPVkVSTg== 193608
+0LXQstC40Yc= 193609
+YWtob25h 193610
+IG5vdHdlbmRpZ2Vu 193611
+Y3R1cmU= 193612
+xbNqxbM= 193613
+SW1wb3J0YW5jZQ== 193614
+IGRlbW9jcsOhdGljbw== 193615
+INGF0L7RgNC+0YjQuNC1 193616
+IFRyYWR1 193617
+IEJha8Sx 193618
+aWZpY28= 193619
+4oCcVGhlc2U= 193620
+IE9yZW8= 193621
+IHZhbGd0 193622
+IENyw6lkaXQ= 193623
+zr7Osc69 193624
+IHBlcmd1bnRhcg== 193625
+0LTQsNGC0YzRgdGP 193626
+1bzVuQ== 193627
+INC/0LDQstC10LTQsNC8 193628
+IFNlbXVh 193629
+INKa0KA= 193630
+IOaYn+acnw== 193631
+IHN1cGVyY29uZHVjdA== 193632
+L3Jlc3BvbmQ= 193633
+4LmC4Lia4LiZ4Lix4Liq 193634
+IOCwteCxjeCwr+CwvuCwluCxjeCwrw== 193635
+2YHYsdip 193636
+w61nZW5v 193637
+cmlkZGVu 193638
+cGhyaW5l 193639
+IOCml+CngQ== 193640
+CWNsdXN0ZXI= 193641
+ZmpvcmQ= 193642
+IOCyheCyreCyv+CyqA== 193643
+IGluZHVzdHJpeWE= 193644
+IEtyaXN0dQ== 193645
+IFTDvHJlbg== 193646
+IEpvYW5uZQ== 193647
+IGNvbWVkaWFucw== 193648
+IHLDqWFjdGlvbnM= 193649
+IGNodXA= 193650
+dW5pbmdkZWs= 193651
+IFBvbHNrYQ== 193652
+INmF24zYp9i02Ko= 193653
+zrrOv8+C 193654
+IOCqieCqpOCrjeCqpOCqsA== 193655
+IGNoaWVmcw== 193656
+IHBoYXJtYWNpc3Rz 193657
+CXRleHR1cmU= 193658
+IGFkZWN1YWRvcw== 193659
+INeZ15XXk9ei15nXnQ== 193660
+IFJlcXVlc3RlZA== 193661
+z4fOtc+EzrU= 193662
+IHBvbm92bm8= 193663
+IEVpbmtvbW1lbg== 193664
+IGFjaHRlcmthbnQ= 193665
+IFNJVA== 193666
+4KSC4KSW4KSy4KS+ 193667
+cGFja2Vy 193668
+Z2Vmw7xndA== 193669
+CU1hdA== 193670
+IOCmqOCmv+CmnOCnsA== 193671
+IEV4b3RpYw== 193672
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+RW5jb3Jl 193674
+IEdyb8OfZQ== 193675
+4Lio4Li24LiB4Lip4Liy 193676
+IGRlZ2VuZQ== 193677
+IFBlZ2FzdXM= 193678
+J10iLA== 193679
+IExldXRlbg== 193680
+TUFTVEVS 193681
+IGFkbW9u 193682
+X2V4YW0= 193683
+b3VuZQ== 193684
+aXJpa2FyZQ== 193685
+IG1ldGFzdGF0aWM= 193686
+INmE2KfYqNiv 193687
+2KzYp9mG2Kg= 193688
+ZW5nZWw= 193689
+emVsbA== 193690
+IGJsb2tr 193691
+RWE= 193692
+X3BpY2tsZQ== 193693
+IOGDkOGDpuGDnOGDmOGDqOGDnOGDkA== 193694
+INC/0LDRgtGA0LDQsQ== 193695
+IE1hdGxhYg== 193696
+IOCmquCnjeCmsOCmpOCmv+Cmt+CnjeCmoOCmvuCmqA== 193697
+0LLQuNGA 193698
+IHBvcXVpdG8= 193699
+IGRhdWVyaGFmdA== 193700
+UGVi 193701
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+INC80LDRgdGB0YM= 193704
+INi12YTYp9ip 193705
+QE9uZQ== 193706
+IOCkteClgOCksA== 193707
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+IOCqruCqvg== 193710
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+bW9zaXM= 193717
+IHRydWRubw== 193718
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+INC/0L7Rng== 193720
+0L7QvdCw0YA= 193721
+L3N0YXI= 193722
+IOCuheCuruCviOCumuCvjeCumuCusOCvjQ== 193723
+4KS/4KSV4KS+4KSw4KS/4KSV 193724
+INCz0YDQuNCy 193725
+0rXQsNCw0YDQsNC00YvRgA== 193726
+z4TPgw== 193727
+IHNlbGVjaW9uYXI= 193728
+R2Vmw6RsbHQ= 193729
+IERFRw== 193730
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+IG9kbw== 193733
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+IHRla3Vy 193739
+IHDFmWV2 193740
+IMSNw6FzdGk= 193741
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+IOCkteCljeCkr+CkvuCkquCklQ== 193743
+Vlg= 193744
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+IEhvcml6b25z 193749
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+IGtpc2U= 193751
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+b3JhYQ== 193755
+J2Fzc3VyZXI= 193756
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+IGRpZ2k= 193758
+IGFyZWEncw== 193759
+IHNlbnNpYmlsaWRhZA== 193760
+X18pOwov 193761
+IGtlbmRhcmFhbg== 193762
+VE9PTFM= 193763
+IFhlcm94 193764
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+IFZhbGVuY2lhbmE= 193766
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+IHRlbmRyZQ== 193769
+IHlhag== 193770
+bWxhZW4= 193771
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+IEp1bmhv 193773
+IGFic29sdg== 193774
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+IGluYXVndXJhdGVk 193776
+IHBvdXA= 193777
+IHByZWNpc2F2YQ== 193778
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+ZW5kcg== 193780
+INin2YTYpdiv 193781
+INKb0LDQt9GW0YDQs9GW 193782
+OmlubmVu 193783
+INC20YPRgNC90LDQu9GW 193784
+INm+2KfbjNmH 193785
+IGZvb2RpZQ== 193786
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+4LK/4LKV4LK+ 193788
+INmI2LrZitix2Yc= 193789
+bWV0ZW9y 193790
+2YjYo9i02KfYsQ== 193791
+IOyInOqwhA== 193792
+IGNvbmp1bmN0 193793
+IOCkteCksOCljeCkow== 193794
+IFRlbXBvcmFs 193795
+0YPRhg== 193796
+INqr2rw= 193797
+IHN0cmFpZ2h0ZW4= 193798
+IMOBbmdlbGVz 193799
+IMOkaG5saWNoZQ== 193800
+IOC4leC5iOC4sw== 193801
+bGFzaHQ= 193802
+z4bOsc69 193803
+KHJv 193804
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+IEthbw== 193806
+IOCupOCvhuCusOCuv+CuteCuv+CupOCvjeCupOCvgeCus+CvjeCus+CuvuCusOCvjQ== 193807
+INmE24zZhtqp 193808
+IEJvd21hbg== 193809
+LDw= 193810
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+IEJsb29taW5ndG9u 193824
+5Lit5Zu954m56Imy56S+5Lya5Li75LmJ 193825
+IGF5aQ== 193826
+emluYQ== 193827
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+IGdlc3VuZGhlaXQ= 193829
+IGFkYXB0ZQ== 193830
+YW3DoWw= 193831
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+IOC4iuC4seC5ieC4mQ== 193835
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+X0JST1dTRVI= 193837
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+INWh1oDVvtWl1b3Vvw== 193839
+IGFraXllc2k= 193840
+INGB0LLQsNGW0YU= 193841
+4KS+4KSi4KS8 193842
+bHlneW55 193843
+aHVuZw== 193844
+X25hbg== 193845
+IGNvaG9ydHM= 193846
+IGFudsOkbmRz 193847
+amVyZW4= 193848
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+IGVuY2Vy 193850
+4Kq+4Kqq4KuN4Kqw 193851
+IGNyaWF0dXJhcw== 193852
+IOCkoeClh+Ckn+Ckvg== 193853
+Lmp2bQ== 193854
+IEJyaWdncw== 193855
+PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 193856
+IOGDq+GDmuGDmOGDlOGDoOGDmA== 193857
+dW50YXI= 193858
+Pj4p 193859
+4Z6T4Z+S4Z6S 193860
+INC00L7QsdGA0L7Qsg== 193861
+INem15XXldeq 193862
+X3Byb2R1aw== 193863
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+IOC4nuC4o+C4sA== 193865
+bWlzZWw= 193866
+cGN0 193867
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+Y29udGVuaWRv 193869
+IE5EQQ== 193870
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+IEFydGVtaXM= 193872
+w6lyZW50 193873
+IEtvbXBsZQ== 193874
+IGZobw== 193875
+IHJlemVwdA== 193876
+IM+Az4HOv863zrM= 193877
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+IOCyleCysOCzhg== 193884
+IOCmsOCmv+CmquCni+CmsOCnjeCmnw== 193885
+4KS44KWB 193886
+IG1lbXBlcnQ= 193887
+IENvbnRyYXJ5 193888
+IHVkZQ== 193889
+IGNhcmVmcmVl 193890
+IGVuZXJqaQ== 193891
+w6RydGVu 193892
+IGdlY29tYmluZWVyZA== 193893
+IE1FTUJFUlM= 193894
+TmF0aW9uYWxpdHk= 193895
+IOC4quC4ueC4leC4o+C4muC4suC4hOC4suC4o+C5iOC4sg== 193896
+c3ByaXRlcw== 193897
+IHZpZWxtZWhy 193898
+KFVJbnQ= 193899
+Iteq 193900
+ZGVueQ== 193901
+aXNhcQ== 193902
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+IEFsdmlu 193904
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+IGdyb3VuZHdvcms= 193906
+IEZFUg== 193907
+SUJD 193908
+TGlnYQ== 193909
+IERlYWY= 193910
+IHBvbGljaWVycw== 193911
+IEVpbnN0aWVn 193912
+IHNwZWxhcg== 193913
+IHBhcXVldGVz 193914
+cmlhbWFuaXRyYQ== 193915
+IOCmqOCmnA== 193916
+INC30LTQsNGA 193917
+4YOY4YOc4YOd4YOh 193918
+cGlwZXM= 193919
+INGC0LXQu9C10Lo= 193920
+IHF1ZWlyYQ== 193921
+TUJB 193922
+IHByb2plY3Rv 193923
+IEdyZWVucGVhY2U= 193924
+UFJPTQ== 193925
+IOCknOCksOCkv+Ckjw== 193926
+KENvbW1lbnQ= 193927
+IGxhdG8= 193928
+0YPRjtGC0YzRgdGP 193929
+yZlkyZluaQ== 193930
+4KS14KWH4KSv4KSw 193931
+IGluc3BpcmFkbw== 193932
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+IFZpYg== 193934
+QWxsb3dz 193935
+IGNvbm9zY2VyZQ== 193936
+IGRhbmdsaW5n 193937
+LXZlc20= 193938
+IHBhbGF1dA== 193939
+IHBlbWJlcg== 193940
+dW1tZWw= 193941
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+INC+0YjQvtC90LTQvtC5 193943
+Z2FuaQ== 193944
+IGFuZ3Vpc2g= 193945
+T2JzZXJ2ZWQ= 193946
+IOCkruCkv+CktuCljeCksA== 193947
+IEplZG5haw== 193948
+15nXodeZ15XXnw== 193949
+Z2Nj 193950
+5Lic5YyX 193951
+4LOL4LKw4LON4LKf4LON 193952
+44Gu5ZWG5ZOB 193953
+bXl6ecWI 193954
+X0Rv 193955
+aWZmeQ== 193956
+IGTDqXBsYWNlbWVudHM= 193957
+INGC05nRiNC60LjQu9Cw0YLQuA== 193958
+IGFza2Vy 193959
+IGNvbmR1Y3Rpb24= 193960
+IOCkuOCliOCkqA== 193961
+CQkJCQkJCQkgIA== 193962
+SGVhdGhlcg== 193963
+aXlhaw== 193964
+bGF1Z2g= 193965
+IEh3 193966
+INC/0YDQtdCy0L7RgQ== 193967
+5b6q546v 193968
+QnVjaw== 193969
+IHdlacOfZW4= 193970
+bGVucw== 193971
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+IExhbmRzY2FwaW5n 193973
+IHN0w6Rya2Vu 193974
+X0ludGVnZXI= 193975
+IOC4luC4ueC4gQ== 193976
+IHR1dHU= 193977
+INin2YTYo9iz2LHYqQ== 193978
+IHThu5Fj 193979
+IGNvbnRpbnVhcsOh 193980
+INCf0LXQvQ== 193981
+IGdvcmF1 193982
+dWNodGV0 193983
+IGJhcmNvcw== 193984
+IEplbm9zaWRl 193985
+ZGXFnw== 193986
+0LLRi9GP 193987
+IOC0ieC0r+C0sOC1jQ== 193988
+4LK+4LKw4LK/4LKX4LOG 193989
+IG11aGlpbQ== 193990
+1LXVqdWl 193991
+QUJE 193992
+INC60L7QvNC/0LDQvdC40Y4= 193993
+ZW5nd2E= 193994
+IGRlZ3JhZGVk 193995
+0YnQuNGC 193996
+ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAK 193997
+4YOQ4YOg4YOp4YOU4YOV4YOc 193998
+IGVuc2Ft 193999
+YW5jb2Rl 194000
+ZWFyZW4= 194001
+IG1vxI0= 194002
+PicpOw0K 194003
+INeU15DXpNep16g= 194004
+IE1hZGFt 194005
+IGV4dHJhaXQ= 194006
+bMmZcmluaW4= 194007
+0q3QsNGF0YM= 194008
+INm+2KfYsdin2YY= 194009
+INC+0YLQutGA0YvRgtC40Y8= 194010
+Vml0YWxz 194011
+w6F0aWxlcw== 194012
+IEV4ZW1wbA== 194013
+IGFnaWw= 194014
+INeg15nXlQ== 194015
+5Lic5Y6/ 194016
+IHRlb3Jp 194017
+IOCqr+Cri+CqnOCqqOCqvg== 194018
+IGNyZcOz 194019
+LnZp 194020
+INix2KfYqA== 194021
+X2J1bGs= 194022
+IOC4q+C5ieC4reC4hw== 194023
+IOCqteCqsOCrjeCqt+CqqOCrgA== 194024
+Pih7Cg== 194025
+IDwqPg== 194026
+IG1pZW4= 194027
+IOWcqOe3mg== 194028
+YWRvdQ== 194029
+IHNlbmR0 194030
+L2Zyb20= 194031
+INij2LLZhdip 194032
+L3ByZWZlcmVuY2Vz 194033
+a2FzdGVu 194034
+IHNwb29m 194035
+IOC0heC0qOC1geC0teC0pg== 194036
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+IOCuleCvgeCuseCuv+CuquCvjeCuquCuv+Cunw== 194372
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+IFNjb3BlZA== 194551
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+IHRyYWRpdGlvbmVsbGU= 194558
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+IOCupOCusuCviOCurg== 194588
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+IEhpZXJ2b29y 194600
+IOCmsOCmvuCmnOCmp+CmvuCmqA== 194601
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+IOCmrOCmv+CmreCmvuCml+Cnh+CmsA== 194610
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+b2xhcm1lbnRl 194630
+INmE24zYqg== 194631
+aW50emE= 194632
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+ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICA= 195732
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+66W06rOg 196434
+IGZlbmQ= 196435
+X0FMUkVBRFk= 196436
+QVJUSUNMRQ== 196437
+IGFhbnRyZWtrZWxpamtl 196438
+Ono= 196439
+IGNvbXByb21pcw== 196440
+IFNjdWxwdHVyZQ== 196441
+IGJpb21hcmtlcnM= 196442
+cmVzdHJpYWw= 196443
+SWRpb21h 196444
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+IHNlbGVzYWk= 196446
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+KHBlcmNlbnQ= 196449
+2YrYttin 196450
+IOGDoeGDkOGDm+GDruGDoA== 196451
+Li4uJyw= 196452
+IOCqleCqsOCrgOCqjw== 196453
+aW5pc2k= 196454
+IEppZw== 196455
+Lkdlbw== 196456
+4KSt4KWC4KSu 196457
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+IHZlY2thbg== 196460
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+INC60ZbQvA== 196464
+bWFuYWdlYWJsZQ== 196465
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+IOWkqeWkqeS4reW9qeelqOW9qemHkQ== 196469
+QUFQ 196470
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+XERi 196473
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+IHRhbGxl 196475
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+IGZpc3Rz 196487
+IHwt 196488
+IFByaW1v 196489
+6K6p5oiR 196490
+LVNF 196491
+IEvDpHI= 196492
+IGFwYXJ0aGVpZA== 196493
+IGJhb3M= 196494
+IHRpbnRlZA== 196495
+J10iKTsK 196496
+INWp1ac= 196497
+IGNhZMOhdmVy 196498
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+4LCq4LGN4LCw4LCm4LGH4LC24LGN 196503
+INGB0Y/RgNGN0LQ= 196504
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+J0dD 196507
+IE1hc2N1bA== 196508
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+IHNwb3RsZXNz 196534
+IExPU1Q= 196535
+INGH0LjQsw== 196536
+INC+0YLQu9C40YfQvdGL0Lk= 196537
+INC20LDRgdGC0LDRgA== 196538
+LUp1bA== 196539
+IHNpZ25hbGxpbmc= 196540
+IGjDoXJvbQ== 196541
+IOWFreWSjOW9qQ== 196542
+IGFkbWlyZXI= 196543
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+IHZydWNodA== 196545
+INCy0LXRgNGI 196546
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+b3RvcmVz 196548
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+4Li04LiB4Liy4Lii4LiZ 196551
+IHNlbnRlbsOnYQ== 196552
+IEtpZGQ= 196553
+IG9zdGVucw== 196554
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+YWZi 196558
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+IEhlYnJld3M= 196560
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+IGfDvG5l 196578
+IOGDmeGDneGDoOGDneGDnOGDkOGDleGDmOGDoOGDo+GDoQ== 196579
+IHNleGlzdA== 196580
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+2LHZitin2YY= 196583
+ID8iKTsK 196584
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+IMW+bW9n 196586
+RXNwYcOxYQ== 196587
+INC60LjRgNOZ0Lo= 196588
+b2h5 196589
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+wq1tYQ== 196591
+JykKCi8v 196592
+ZXNoaQ== 196593
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+IGFmbG9hdA== 196595
+IFlvcmtlcnM= 196596
+IE51Z2dldHM= 196597
+IFRhaXdhbmVzZQ== 196598
+IGJ1bHVubWFrdGFkxLFy 196599
+IGluZmxhw6fDo28= 196600
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+IHNjaGVua2Vu 196602
+y50= 196603
+IG1heGltYQ== 196604
+INC40LDRhg== 196605
+IHphbWk= 196606
+IE9tcm9w 196607
+bnVldm8= 196608
+4LSo4LS/4LSV4LWN4LSV4LWN 196609
+IGNvbXBldGl0aWU= 196610
+0L3Ri9Kz05k= 196611
+INeU16nXpw== 196612
+IG1hbGluZw== 196613
+UU5hbWU= 196614
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+5YCh 196632
+b2dnaQ== 196633
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+IOWkqeWkqeW9qeelqOi9r+S7tg== 196638
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+IHp1dmVybMOkc3NpZw== 196641
+IGludGVybmF6aW9uYWxl 196642
+w61uY2lh 196643
+INin2YTYrdmI2Ks= 196644
+IHRzb2E= 196645
+SW50ZWdyYXRvcg== 196646
+2K/Ysdis2Kk= 196647
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+IFJpdGU= 196649
+IGVtcHJlZ2Fkbw== 196650
+xLBS 196651
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+a2xhbg== 196654
+IGRlc2VydHM= 196655
+INW+1aXWgNWh1aTVodaA1bE= 196656
+IOCmquCnjeCmsOCmr+CngeCmleCnjeCmpA== 196657
+IFNpdHp1bmc= 196658
+INGB0YrQtA== 196659
+aXJnw60= 196660
+IGJhZG1pbnRvbg== 196661
+56eY5Lmm 196662
+bmFubw== 196663
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+IOCouOCovuCouQ== 196670
+CXJvb20= 196671
+aXJkcw== 196672
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+IFJhY2V3YXk= 196674
+IEdlaWdl 196675
+IEVzcMOtcml0dQ== 196676
+Q2hhbmc= 196677
+IENvbnN0YW50aW5l 196678
+IGtlaW5lbQ== 196679
+XHZpZXdz 196680
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+IEJsYW5rZXQ= 196683
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+IEJlcmlrdXQ= 196686
+IG1lbsOpZQ== 196687
+IM+AzrnOuM6xzr0= 196688
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+IEZpbGk= 196710
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+IOCqpeCqpOCqvuCqgg== 196740
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+IEZhbGxvbg== 196752
+xbxlxYQ= 196753
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+IHVuYmxvY2s= 196755
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+INCi0LDQvQ== 196759
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+IOCmheCmnuCnjeCmmuCmsg== 196762
+YXJiZXRl 196763
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+INqv2LHbjA== 196765
+IGRpw6FtZXRybw== 196766
+IGNvbXBhZ25vbg== 196767
+IFN3ZWVwc3Rha2Vz 196768
+IFNlbWFwaG9yZQ== 196769
+INCz0LDQt9C10YI= 196770
+LWZsZWRnZWQ= 196771
+YmFueWVnaOG7iw== 196772
+IHN1cmZlcg== 196773
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+TmltYnVz 196775
+YWNyaXQ= 196776
+YWJzY2hsdXNz 196777
+QW5o 196778
+INW61aHWgNWm1aHVutWl1b0= 196779
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+INmI2YrZg9mK2KjZitiv2YrYpw== 196784
+IGZvb3RwcmludHM= 196785
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+KGNsaWVudGU= 196787
+IGJvbm5ldA== 196788
+TW9udG8= 196789
+X3N0cmVuZ3Ro 196790
+LlN5bnRheA== 196791
+KioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioK 196792
+IMOhYnlyZw== 196793
+IGZyb250YWdl 196794
+IOC0uOC0guC0teC0v+C0p+C0vuC0rw== 196795
+IGF0dWFsaXphZG8= 196796
+IM6W 196797
+cGFkcw== 196798
+ZW1x 196799
+IOCwquCwqOCwvw== 196800
+IOGDoeGDkOGDqOGDo+GDkOGDmuGDlOGDkeGDkOGDoQ== 196801
+INC00LXQvdC10LbQvdGL0YU= 196802
+INC30LDRgdC7 196803
+IOGemg== 196804
+IFplYnJh 196805
+IG5vb2w= 196806
+ImRlZmF1bHQ= 196807
+IOGDmuGDmOGDkg== 196808
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+IFNlZ3U= 196824
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+a29jaA== 196828
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+INCx0L7RgNCx0L7RgA== 196856
+ZHE= 196857
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+IExpZWJlcg== 196859
+Q0hBSU4= 196860
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+IHVuZGVyc2VydmVk 196876
+IOS6uuS6uuS4reW9qeelqA== 196877
+IGJsYWc= 196878
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+b2J1dHRvbg== 196881
+JyI7DQo= 196882
+IGNhdGFseXN0cw== 196883
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+0JLQoQ== 196888
+IOCqq+Crh+CqsA== 196889
+IOWkp+mHkQ== 196890
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+IFZvbGQ= 196892
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+IOWkqeWkqeS4reW9qeelqOiFvuiurw== 196894
+IEt5aXY= 196895
+INqr2LE= 196896
+INiy2KfYptiv 196897
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+YnJpZ2h0bmVzcw== 196899
+Kj4oJg== 196900
+IOCyleCyvuCyrg== 196901
+IEZlZGVyYWFsa2E= 196902
+INC20YvRhtGG0ZE= 196903
+IHNhbnRvcw== 196904
+4oCYemJla2lzdG9u 196905
+cm9vZw== 196906
+IExvdXJkZXM= 196907
+IGNsYXJpZGFk 196908
+IG9jY2FzaW9uZQ== 196909
+IOS4vuaKpQ== 196910
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+Z3Jvc3M= 196912
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+IHRvcnNkYWc= 196918
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+Lk1lcmdl 196923
+IEN1cnNvcw== 196924
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+IGp1csOtZGljYXM= 196927
+5qih5p2/ 196928
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+zr3Ov868zrE= 196931
+IGN1YWRyb3M= 196932
+IOCqpuCrh+CqtuCqruCqvuCqgg== 196933
+0YHRgtGA0Lg= 196934
+INiv2YjYrw== 196935
+bmllanN6eWNo 196936
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+IOaci+WFiw== 196938
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+IG3DtW5lZA== 196944
+KGdvYWw= 196945
+IGh1Z2dpbmc= 196946
+IEzDq3R6 196947
+IHlhYmE= 196948
+IGVuaWdtYXRpYw== 196949
+INCa0YDQsNGB0L3QvtC00LDRgA== 196950
+IHbDpG50 196951
+IMOpbMOpZ2FudA== 196952
+IGV1cm9wZXU= 196953
+IFNoZWx0b24= 196954
+IEhvbWJyZQ== 196955
+YWhlcg== 196956
+ZXhjbHVzaXZl 196957
+Ij4l 196958
+IGdhYmk= 196959
+dmlzaW9uYWw= 196960
+IGVzdMOpcw== 196961
+IGdlw69udGVncmU= 196962
+INC/0L7Qu9GD0YfQuNC70L7RgdGM 196963
+IFJJUw== 196964
+44OL44Oz44Kw 196965
+4Ka/4Kef4Ka+4Kao 196966
+Q292ZXJlZA== 196967
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+INC60LDRgtCw0LvQvtCz 196969
+zIFz 196970
+IEdhcmZpZWxk 196971
+R2VuZXJhdG9ycw== 196972
+KHZj 196973
+4LGG4LCC4LCs4LCw4LGN 196974
+4Ka+4Kaw4KeN4Ka4 196975
+INuL2KfZgtiq2Yk= 196976
+4bmjaeG5o+G6uQ== 196977
+J10/PiI= 196978
+IHRhYW1hdHV0 196979
+LnBvc3Rlcg== 196980
+INC/0YDRi9C8 196981
+IGVubGV2ZXI= 196982
+ZWRm 196983
+IOGDmeGDmOGDl+GDruGDlQ== 196984
+INC/0LjRgtC+0Lw= 196985
+Pz4KCgo= 196986
+L2Jhbm5lcg== 196987
+4KS/4KSw4KS54KWH4KSV4KS+ 196988
+IGNvbnRyYXJp 196989
+LmRpc2NvdmVyeQ== 196990
+CWd1aQ== 196991
+LmZhbHNl 196992
+44GV44KM44Gm44GE44G+44GZ 196993
+IExldHRyZQ== 196994
+IHBlcmNlbnR1YWw= 196995
+IGlibg== 196996
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+IHN0b3J5dGVsbGVy 196998
+T250YXJpbw== 196999
+bWFrcw== 197000
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+IHRldHQ= 197002
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+INWv1bjWgtW21aXVtg== 197005
+IFdhcnA= 197006
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+4oCZYWlkZXI= 197008
+SW5kaWNl 197009
+IOeQhg== 197010
+aXR1ZW4= 197011
+O1RaSUQ= 197012
+IGt1c2VtYQ== 197013
+IFNsaWNr 197014
+16DXmdeU15U= 197015
+IGluaGFsZQ== 197016
+IHVtZ2VoZW4= 197017
+INqp2KfYqA== 197018
+IHByemVjaQ== 197019
+INKT0LDRgdGL0YA= 197020
+IGFkbg== 197021
+IHdhcmRyb2Jlcw== 197022
+IFRn 197023
+X2xpbnV4 197024
+CXBt 197025
+INCz0LvQsNCy0L3Ri9C8 197026
+INGB0LDSm9GC0LDRgw== 197027
+INix2K3ZhNip 197028
+Il1dLAo= 197029
+IHBhcsOn 197030
+INC30LDQtdC8 197031
+IHsv 197032
+LnRlbGw= 197033
+z4POsc68zrU= 197034
+IFJldmVhbA== 197035
+4KSs4KWI 197036
+IGJlbGlydA== 197037
+IHdob2xlaGVhcnRlZGx5 197038
+bm9zdGlj 197039
+a8OpaG8= 197040
+bGlrYQ== 197041
+XFRyYWl0cw== 197042
+IGthbG9y 197043
+V29yc3Q= 197044
+0LLQsNGO0YbRhtCw 197045
+LmdvbGQ= 197046
+UElSRUQ= 197047
+IGdyYXRpZnlpbmc= 197048
+KElTZXJ2aWNl 197049
+IHZ1b2Q= 197050
+IGh2w7hy 197051
+IGxpY2hhYW1z 197052
+cmFjaw== 197053
+IGRpc2NyaW1pbmF0b3I= 197054
+IOCwteCwmuCxjeCwmuCxhw== 197055
+RWxlcw== 197056
+IGluaGliaXRvcnk= 197057
+INC/0YDQvtGB0LzQvtGC0YA= 197058
+IGxldXRl 197059
+LUZyaWRheQ== 197060
+U2Vydmlkb3I= 197061
+LlBVQkxJQw== 197062
+IOCmpuCmv+CmqOCnhw== 197063
+INC006nRgA== 197064
+IGtsZXVycg== 197065
+INGA0LDSsw== 197066
+IEbDqWxpeA== 197067
+IEJ1cm1lc2U= 197068
+IGphaHI= 197069
+nuGDmOGDoOGDmOGDoeGDng== 197070
+4KWB4KSV4KWL 197071
+SGVhbGluZw== 197072
+IHphaHRldg== 197073
+cHRhYmxl 197074
+0LfQsg== 197075
+INmD2YrZhNmI 197076
+IOWkqeWkqeS4reW9qeelqOWFheWAvA== 197077
+IHRyb3ZhdG8= 197078
+JSk7Cg== 197079
+INiq2LTZitix 197080
+IGdhbXV0 197081
+54i454i4 197082
+INmF2YLYp9mE 197083
+4oCZYXJ0aXN0ZQ== 197084
+LW5vd3JhcA== 197085
+LXJlYWRhYmxl 197086
+IHZpcmFy 197087
+WWVy 197088
+SGFzaGVy 197089
+fX1dLAo= 197090
+KG10 197091
+IGl6cmF6 197092
+R2ly 197093
+IGhhdW0= 197094
+0LXQvNC+0L0= 197095
+IOGDluGDneGDkuGDmA== 197096
+IERlYmlkbw== 197097
+CWN0cmw= 197098
+Lm9mZmljZQ== 197099
+c2Vydm8= 197100
+LXZpc2libGU= 197101
+INeq15XXk9eU 197102
+IHJlbW9yc2U= 197103
+YW1paW4= 197104
+S2F6 197105
+IEJPUg== 197106
+IEFpcnBvcnRz 197107
+IHBvc3RhZ2Vt 197108
+IHNhaGE= 197109
+S3Vyeg== 197110
+IEVpbmthdWZz 197111
+IHNjaGxlY2g= 197112
+IHNlZGF0aW9u 197113
+IGxlYWZsZXQ= 197114
+dHJpcw== 197115
+IHBpdGvDpA== 197116
+5Y2i 197117
+IGFqw6Fu 197118
+2LLZitin2KE= 197119
+INC40L3RgdGC0YDRg9C60YbQuNGP 197120
+IGNvZ25pdA== 197121
+0LDQvdCw0Lw= 197122
+L0Fib3V0 197123
+INGH0LXRgdGC0Yw= 197124
+xaFuaQ== 197125
+CVByb2Nlc3M= 197126
+IFBrdw== 197127
+IFZhcA== 197128
+5a656YeP 197129
+IH19Ij48 197130
+ICghWw== 197131
+IEJ1cmd1bmR5 197132
+IHRhd3M= 197133
+IEJheXM= 197134
+IGxhbmdsZWI= 197135
+cHJldMOp 197136
+IHN0cmVudW91cw== 197137
+IEViZW5zbw== 197138
+w6F2YW1vcw== 197139
+4oCZZXNwZXI= 197140
+INGP0YHQvdC+ 197141
+IOCkquClgeCkow== 197142
+INm+2LHZhdiu2Ko= 197143
+IOaJi+acuueJiOWkqeWkqeS4reW9qeelqA== 197144
+LW1vdXRo 197145
+KFJvdXRl 197146
+IHByZWNlag== 197147
+INi62LDYp9uM24w= 197148
+cmlmZmU= 197149
+IOygnOyjvA== 197150
+YW5jaXM= 197151
+IEFHUg== 197152
+INGA0LXQt9C60L4= 197153
+IHRlYXRyYWw= 197154
+IOam 197155
+IHNob3J0ZW5pbmc= 197156
+INeV15TXqA== 197157
+IFJhaXNpbmc= 197158
+IAkJCQkJCQ== 197159
+IHRyYXVyaWc= 197160
+yZl6aQ== 197161
+cGVyc29vbg== 197162
+IGJ5Z2dlcg== 197163
+IGV4ZXJjZQ== 197164
+IFBERQ== 197165
+IFNraWxsZWQ= 197166
+IGvDpnI= 197167
+0LjRgNC+0LLQutGD 197168
+IFdPUg== 197169
+IFRlcHA= 197170
+YmFrYW4= 197171
+IFJlYWN0b3I= 197172
+IHJldW5pw7M= 197173
+IFNCTQ== 197174
+IG5hc2ls 197175
+IOS4h+WIqQ== 197176
+X0ludmFsaWQ= 197177
+IHV5Z3VsYW4= 197178
+IHZpZGVy 197179
+IOCwquCxjeCwsOCwmg== 197180
+INC40L3QtNC10LrRgQ== 197181
+IGNvbnRhdG9z 197182
+INGB0YLQtdC/0LXQvdGM 197183
+X3lvdQ== 197184
+b3JkaW5hdGV1cg== 197185
+5LiK57q/ 197186
+IGNvdXZyaXI= 197187
+IHJ1c28= 197188
+LnNvZnRtYXg= 197189
+INio2LHYtNmE2YjZhtip 197190
+4LSk4LWN4LSk4LS/4LSo4LWB 197191
+IOCqteCqv+Cql+CqpOCriw== 197192
+IFBpeGFiYXk= 197193
+IHBoacOqbg== 197194
+IEJFTkVG 197195
+2LfYpw== 197196
+IHd5asSFdGs= 197197
+cmVzYXI= 197198
+IG5hYmk= 197199
+IHBvZHA= 197200
+IOCmtuCmvuCmuQ== 197201
+IHNhYXZhZA== 197202
+IEVxdWl2YWxlbnQ= 197203
+IGV0cmVl 197204
+5om26LSr 197205
+Q3VkYQ== 197206
+24zaqdin2LE= 197207
+IGJhbGxhc3Q= 197208
+IFNBRQ== 197209
+dHJpemVz 197210
+IGRldGFpbmU= 197211
+4oCZaXI= 197212
+IHRhw71kYW4= 197213
+0JPQkA== 197214
+0q3QtdC4 197215
+INeV15zXog== 197216
+LVdpdGg= 197217
+QVJSSUVS 197218
+IG1lZWs= 197219
+INm+2LHZiNmG 197220
+J2F1dGFudA== 197221
+cG9zdGk= 197222
+X3Jlc2VydmVk 197223
+0LDQutGB 197224
+7JWk 197225
+IE5JQ0U= 197226
+INin2YTYrNmF2YfZiNix 197227
+IGVyemllbHQ= 197228
+IGdlY29udHJvbGU= 197229
+1aHWgdW41oLVtNWo 197230
+INC+0LzQsA== 197231
+44CL44CCCgo= 197232
+QURWRVJUSVNFTUVOVA== 197233
+IGN1cnJpY3VsYQ== 197234
+IEF1w58= 197235
+TEVHRQ== 197236
+INGA0ZbRiNC10L3QvdGP 197237
+IGNvbWFuZG9z 197238
+INec15TXmw== 197239
+0LHRgNC1 197240
+b3NpdmU= 197241
+Rm9uZA== 197242
+IGFpc2xhbWllbnRv 197243
+X3JlbHU= 197244
+IFRTUg== 197245
+IExpbmllbg== 197246
+IHJhbWU= 197247
+IEVudmVsb3Bl 197248
+IGluamVjdGFibGU= 197249
+bGljZW5zZXM= 197250
+IGRyZWhlbg== 197251
+RGFr 197252
+ZsO4cmVy 197253
+INC/0L7QstC10LTQtdC90LjRjw== 197254
+IE51bWVyaWNhbA== 197255
+IG15YWth 197256
+IHZ6dA== 197257
+IHdoYWthdGE= 197258
+IFN0w6Rya2U= 197259
+Z2hh 197260
+INec16TXqg== 197261
+IHBvbW1l 197262
+IGRhYQ== 197263
+LXBhaW50ZWQ= 197264
+5p2l5Lmf 197265
+SVJJVA== 197266
+LWFuZw== 197267
+IHRyaHU= 197268
+IWltcG9ydGFudA== 197269
+RmFx 197270
+IELDvG5k 197271
+4LK/4LK44LOB4LKk4LON4LKk4LKm4LOG 197272
+IFNwaXR6 197273
+IHRha3k= 197274
+RGVjb21waWxlcg== 197275
+KSc6 197276
+bGllZ2VuZGVu 197277
+IGF04bq5 197278
+77yM5L6L5aaC 197279
+w7ZobnQ= 197280
+IOCkh+CksuCkvuCkleClhw== 197281
+IHRpbHRhaw== 197282
+IHRhbmd1 197283
+IOCyrOCysOCzgeCypOCzjeCypA== 197284
+bnNrZQ== 197285
+INi12YbYp9uM2Lk= 197286
+IOCkquClgeCksOCkvuCkqOClhw== 197287
+Z2V0d2lqZmVsZA== 197288
+IEFjY3JlZGl0ZWQ= 197289
+4oCZbmRl 197290
+0LvQvtCz0L4= 197291
+IG1pY3JvcA== 197292
+IHN2b2c= 197293
+X2R1ZQ== 197294
+0LvQvtGJ0LDQtA== 197295
+IERvcm4= 197296
+IOCymuCyv+CyleCyv+CypOCzjeCyuOCzhg== 197297
+IOCoqOCovg== 197298
+INek16DXmQ== 197299
+IGdlcsOnZWt0ZW4= 197300
+INGA0LDQt9Cy0LXQtA== 197301
+w6lwZQ== 197302
+IGhzdg== 197303
+IENsZWFyd2F0ZXI= 197304
+IG5hcQ== 197305
+5ouJ54m5 197306
+IGTDqXRvdXI= 197307
+X0xlbmd0aA== 197308
+IFpvZHJh 197309
+IGNhc3VhbHR5 197310
+IHp1c2FtbWVuc2No 197311
+IG1hbmRp 197312
+IOGDqOGDlOGDoeGDkOGDq+GDmuGDlOGDkeGDlOGDmuGDmOGDkA== 197313
+IFRyYXZlbHM= 197314
+YXRpa2E= 197315
+IOCuqOCvgOCupA== 197316
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+IHByZXNlbnRhbW9z 197318
+IGhpbmdlZA== 197319
+IFJvdG9y 197320
+IOGDrOGDp+GDkOGDmg== 197321
+TsOt 197322
+IHNleHZpZGVv 197323
+INC60YDQuNGC0LXRgA== 197324
+LVNoYWJhYWI= 197325
+IOqzteyXsA== 197326
+X3BvcHVsYXRpb24= 197327
+IEF6aXo= 197328
+54KJ 197329
+INeZ15zXkw== 197330
+IGthYmFu 197331
+IGTDqXB1dMOp 197332
+IHRyZW1ibGluZw== 197333
+44CB56ys 197334
+INi52KfZhdin2Ys= 197335
+IEJheWVzaWFu 197336
+IOmhtemdog== 197337
+4Ka+4KaB4KaT 197338
+IEV0YXQ= 197339
+IGdlw6dpcm1law== 197340
+INCR0rE= 197341
+LmVmZmVjdHM= 197342
+QVBL 197343
+IGd3YW1uYXRpbg== 197344
+IHN1amVpdG9z 197345
+IOCyuOCyvuCylw== 197346
+IOCkrOCknOCkvuCkrw== 197347
+IPCfjQ== 197348
+INio2K/Ypw== 197349
+IFJhY2Vy 197350
+emt5 197351
+IOa5mA== 197352
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+INGB0L7QvtCx0YnQtdC90LjQuQ== 197354
+IENocmlzdGVuc2Vu 197355
+INW21bfVpdaB 197356
+IEVtZXJ5 197357
+LmJlcg== 197358
+INC+0pPQvtC3 197359
+INiz2YTYt9in2YY= 197360
+cHTEg20= 197361
+INin2YTYsdit2YrZhQ== 197362
+INeX15XXkdeU 197363
+4YOQ4YOh4YOQ 197364
+IG5ldXpl 197365
+IOGDoeGDmOGDp+GDleGDkOGDoOGDo+GDmg== 197366
+IGFlcm9wb3J0 197367
+c2VydmV6 197368
+IG5ldWc= 197369
+IOC0quC0sOC0v+C0tuC1gOC0sg== 197370
+IHJlc3BvbnNhYmlsaXTDqXM= 197371
+xLFrbMSx 197372
+IOGDkOGDnOGDkuGDkOGDoOGDmOGDqA== 197373
+LlRoZW4= 197374
+X2Fubm90YXRpb25z 197375
+IOCkruCkvuCkpOCljeCksOCliA== 197376
+IE5vcm1hbmR5 197377
+IHp1c2FtbWVudA== 197378
+L3JlbmRlcg== 197379
+IHBhcnRpY2lwYXJhbQ== 197380
+Z2VtZWluc2NoYWZ0 197381
+IG1hYXlv 197382
+LkF0dGFjaA== 197383
+Lk1hZw== 197384
+INGB0L7Qu9Kz0L7QuA== 197385
+Q2FydGVzaWFu 197386
+LlB1bGw= 197387
+IGFydGljdWxhdGlvbg== 197388
+IHRhaXI= 197389
+X1N1cmZhY2U= 197390
+cHRpb3Vz 197391
+IOS8geS4mg== 197392
+aW50w6TDpG4= 197393
+INWq1bjVstW41b7WgNWk1as= 197394
+X2NvdXBvbg== 197395
+zrnOus63 197396
+IGVnaW5nbw== 197397
+IHDFq25hZXdlbGU= 197398
+7LCN 197399
+IENETA== 197400
+Inw= 197401
+INiz2LHahg== 197402
+IGl0ZW0ncw== 197403
+IGNocm9ub2xvZ3k= 197404
+IGZhY2VsaWZ0 197405
+IHN0dXJk 197406
+IE1hdGhpZXU= 197407
+b2NjdXI= 197408
+anVibA== 197409
+ICIuCg== 197410
+2LnYr9mE 197411
+CQkgICAgICAgICAgICA= 197412
+IFBJVA== 197413
+INin2YTYrNmI 197414
+IOCkmuCksuClgA== 197415
+X01hc3Rlcg== 197416
+VHJlYXM= 197417
+VVBMT0FE 197418
+IHB1YmxpY291 197419
+IERldmlu 197420
+IGNhdGVnb3JpemU= 197421
+IFJpaw== 197422
+IHByb3FyYW0= 197423
+INC+0YLQstC10YLQsA== 197424
+IOGDoeGDkOGDmg== 197425
+IOC4oeC4geC4o+C4suC4hOC4oQ== 197426
+IHNpbXBsaWZ5aW5n 197427
+IOWogeWwvOaWr+S6ug== 197428
+INCT0LDQs9GA0LA= 197429
+IOCujuCuqeCvjeCuseCvgeCuruCvjQ== 197430
+IOCqruCqgg== 197431
+Lw0KDQo= 197432
+IHBhZ2FuZG8= 197433
+IG11cmRlcm91cw== 197434
+LldvcmtmbG93 197435
+aWV0YW4= 197436
+4LKt4LON4LKv 197437
+xIg= 197438
+KGFuaW0= 197439
+PyE/IQ== 197440
+XCIiKTsK 197441
+ICEhISEh 197442
+IENhbGlicmF0aW9u 197443
+CQkgICAgCg== 197444
+KCItIik7Cg== 197445
+0LDQu9Cw0YDQsA== 197446
+IFBpYW4= 197447
+4YOQ4YOb4YOq 197448
+IGludGw= 197449
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+IyMjIyMj 197451
+IHRhaWZh 197452
+INi02YfYp9iv 197453
+IGVsZXZhZG9z 197454
+acOwaXM= 197455
+d2hvc2U= 197456
+IGRoaW0= 197457
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+4Lik4Lio4LiI4Li04LiB4Liy4Lii4LiZ 197459
+LnBhcnR5 197460
+IGtyZWF0aXZl 197461
+aWdlYWNo 197462
+QmF0aXM= 197463
+IENlbQ== 197464
+IHN5bW1ldHJpY2Fs 197465
+INio2LHYp9mK 197466
+VEVBTQ== 197467
+IHNlc3PDtWVz 197468
+INGD0YfQsNGB0YLQsg== 197469
+IENBTkNFTA== 197470
+IG1vd2luZw== 197471
+INCS0LjQutC40L8= 197472
+ID8+Ij4NCg== 197473
+IG9kbWFo 197474
+X0hJTlQ= 197475
+IGVyc3RhdW4= 197476
+IG5hY2hoYWx0aWc= 197477
+4YOQ4YOg4YOd4YOT 197478
+aW1lbmRl 197479
+4LiX4Li14LmI4LiI4Liw 197480
+IE1lZXR1cA== 197481
+INi52YTYp9mC2Yjaug== 197482
+YW5nYWpl 197483
+X2FuZ2xlcw== 197484
+IOCwj+CwsOCxjeCwquCwvuCwn+CxgQ== 197485
+IG1ldG9kb2xvZ8OtYQ== 197486
+J2h0dHBz 197487
+IG9yZ3VsbG8= 197488
+4K6/4K6p4K6/4K6u 197489
+IHNyZWRzdHZh 197490
+IGdlcmluZ2Vy 197491
+INGD0LnSk9GD0YDQu9Cw0YDSk9Cw 197492
+XFs= 197493
+44Kr44Or 197494
+INen15DWtw== 197495
+IGdsb3NzYXJ5 197496
+IG1hbmlmZXN0YcOnw7Vlcw== 197497
+4YOU4YOc4YOi4YOU4YOR4YOY 197498
+IGFncmljb2xlcw== 197499
+IHN0dWZmcw== 197500
+IOCmmOCmn+CmqOCmvuCnnw== 197501
+INC+0LrQsNC30YvQstCw0LXRgg== 197502
+4Z624Z6c 197503
+INCg0LDQuQ== 197504
+15XXktee15A= 197505
+IGjDs24= 197506
+IOCkueCkv+CksA== 197507
+emlucw== 197508
+IHNwZWVsZ29lZA== 197509
+7ZWc64uk6rOg 197510
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+IFBlcnNpYQ== 197512
+77yg44GK44O844G344KT 197513
+bHlneW55xYg= 197514
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+IGF1c3RyYWw= 197516
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+IFJlc2lkZW5jeQ== 197518
+INGE0L7QvdC00LA= 197519
+IGp1cms= 197520
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+IGRlcm1hdG9sb2dpc3Q= 197522
+6K235aOr 197523
+TmFtdW4= 197524
+IFRlcnJpdG9yaWVz 197525
+INi12KfYr9ix2KfYqg== 197526
+VG91cnM= 197527
+INC+0LrQsNC30YvQstCw0LXRgtGB0Y8= 197528
+IGJhbWJpbm8= 197529
+IHJvYnVzdG5lc3M= 197530
+IHNlY29uZGx5 197531
+IOiCoeelqA== 197532
+IGd1ZXN0cm9vbXM= 197533
+INmC2KjYtg== 197534
+4LWN4LSw4LSu 197535
+IHNvbnVjdQ== 197536
+IGjGoWk= 197537
+IGNvbnNpZ2E= 197538
+IHNjaG5laWRlbg== 197539
+QWR1bHRz 197540
+YW5nZ2lo 197541
+RVJUQQ== 197542
+YWt0eQ== 197543
+UFJJU0U= 197544
+INC60YDQsNGB0LjQstC+ 197545
+IEpM 197546
+IGFhcXFpc3N1 197547
+INqG24zZgQ== 197548
+4LKj4LKm4LKy4LON4LKy4LK/ 197549
+U3ViZGl2aXNpb24= 197550
+15nXpteY 197551
+IFZvcnN0ZWxsdW5nZW4= 197552
+INC80YPQt9GL0LrRgw== 197553
+IGRyw7xja2Vu 197554
+0L7RgNCy 197555
+IHN3aW1tZXI= 197556
+0L3QtdGB0YI= 197557
+IHRyw6Ru 197558
+RGVubmlz 197559
+IHllcmRl 197560
+aW9pZ2E= 197561
+IG3DvHE= 197562
+IG9uZGVyaGFuZA== 197563
+IOu2hOychA== 197564
+IMWha29seQ== 197565
+5aSp5aSp5Lit5aWW 197566
+Y3VhbmRv 197567
+KFN0b3JhZ2U= 197568
+dGVyaW9ycw== 197569
+IGNoaWxscw== 197570
+IHByb3NzaW1v 197571
+X3NodWZmbGU= 197572
+Lk1JTlVURQ== 197573
+INWh1bzVodW71bbVuNaA1aQ= 197574
+7J6Q66as 197575
+66O4 197576
+0YbQuNC0 197577
+IHVydWg= 197578
+c2plZA== 197579
+INGE0LjQt9C40YfQtdGB0LrQuNGF 197580
+aWpha2Fu 197581
+IHNlZ3VlbQ== 197582
+TG9ja3M= 197583
+LWVsbGVz 197584
+5r2Y 197585
+IGZvcmw= 197586
+I2Fh 197587
+IEFrYQ== 197588
+IOC5gOC4geC4reC4o+C5jA== 197589
+IHp2aXJp 197590
+INW91avWgNW41oLVtA== 197591
+IEFuYWx5dGljYWw= 197592
+aWRybw== 197593
+2KfYptmG2Kc= 197594
+16DXoteo 197595
+RXhlcmNpc2Vz 197596
+LG5w 197597
+IEFVUw== 197598
+SU5FVA== 197599
+56qB5Ye6 197600
+IHByw6lzZW50YW50 197601
+IOC0juC0n+C1geC0pOC1jeC0pA== 197602
+IOC0rOC1hg== 197603
+INGC0Y/RhQ== 197604
+IHVyc3Byw7xuZ2xpY2g= 197605
+cmljaHRlcg== 197606
+IFByb3ZlcmJz 197607
+IHN6aw== 197608
+IFdvcmtlZA== 197609
+IOyXhuyWtA== 197610
+IHpiaXI= 197611
+IGFhbnU= 197612
+IEVyb3Rpc2tl 197613
+IGx1Y3R1cw== 197614
+0LvQuNC80Lg= 197615
+Y2hhcHBlcg== 197616
+IE15c2VsZg== 197617
+4oCUZXZlbg== 197618
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+IGltcGVkaXQ= 197621
+5Li75L2T 197622
+LmZpcmViYXNlaW8= 197623
+INio2LHYr9in2LTYqg== 197624
+QWNhZA== 197625
+INin2YTYpdix2YfYp9io 197626
+YXZpcnVz 197627
+4YOU4YOY4YOa 197628
+IFN0ZWxsYXI= 197629
+IHNvYnJlbQ== 197630
+b2ZhbmlyYQ== 197631
+S29tbWVudGFyZQ== 197632
+IGNvbXBsZXRhcw== 197633
+IG91dmVydHM= 197634
+LnRoZW0= 197635
+IHJpZ2lkaXR5 197636
+IOCmj+CmuOCnh+Cmm+Cnhw== 197637
+INCw0LTQsNC80L3Ri9Kj 197638
+IOWkmuS5kA== 197639
+IElsaGE= 197640
+INeo16TXldeQ 197641
+IGPDqW4= 197642
+KGNhdGVnb3JpZXM= 197643
+IMO6ZGFq 197644
+4Ki/4KiX 197645
+Y2h0dW5n 197646
+IGNhb3A= 197647
+dWxla2U= 197648
+INio2LHZgtix2KfYsQ== 197649
+INmI2YLZiNi5 197650
+IG11dg== 197651
+0L3RltGI 197652
+IFN0YWZmaW5n 197653
+IFBhbGF2cmE= 197654
+IFBBUkFNRVRFUlM= 197655
+IGdhbGx3Y2g= 197656
+IEF1dGV1cg== 197657
+IGZyaXZvbA== 197658
+IHNldHRpbWFuZQ== 197659
+IGVuY2xhdmU= 197660
+IG9zdm9q 197661
+bGVzenQ= 197662
+INmI2KfYttit2Kk= 197663
+15XXkdeZ 197664
+IFBSRVNJREVOVA== 197665
+YW1hbQ== 197666
+5qm5 197667
+4Liq4Li54LiV4Lij 197668
+IOCwteCxh+Cwsg== 197669
+LWNoYXZl 197670
+IHF14bqjbmc= 197671
+5LiI5aSr 197672
+IFBDUA== 197673
+RHJvcHM= 197674
+U3VjdXJzYWw= 197675
+TU9R 197676
+INC+0YHQstGW 197677
+INC60q/RgtOZ0YA= 197678
+bW91c2VlbnRlcg== 197679
+IERpdmluZw== 197680
+bsOhdg== 197681
+INKX05nRgA== 197682
+ID09PT0= 197683
+aXNtZXQ= 197684
+c3RyYWlnaHQ= 197685
+INC40LTQsNGA 197686
+IG1uaWVq 197687
+zqDOsQ== 197688
+IOCkhuCksuCliw== 197689
+KCIuIik7Cg== 197690
+IGJhbHQ= 197691
+dmVyc2NobHVzcw== 197692
+IOCkueCkvuCksuCkpA== 197693
+aW5zcA== 197694
+X0hFQURFUlM= 197695
+INC/0YDQuNGF0L7QtNC40YI= 197696
+xaFrZWdh 197697
+dXNzaW9ucw== 197698
+Qm9va2Vk 197699
+IGZ1ZGdl 197700
+YmJj 197701
+IGdyYXNhcw== 197702
+INCy0LDSodGL0YLRgtCw 197703
+IOGDmOGDkuGDmOGDleGDlA== 197704
+IHRhYmxv 197705
+IFRJRw== 197706
+IOCmleCmruCmv+Cmtg== 197707
+IEFqdXN0 197708
+IGRldmFz 197709
+IGFrdGl2aWVydA== 197710
+INKz0LDQuw== 197711
+INi12YbYpw== 197712
+LW1haWxhZHJlcw== 197713
+LXJlYWRpbmc= 197714
+IO2DgOyehQ== 197715
+IGhhaW5iYXQ= 197716
+IHBvdMWZ 197717
+4KSJ4KSk4KWN4KSk4KSw 197718
+INqB2YQ= 197719
+IOCmhuCmqw== 197720
+INWp1aXVrw== 197721
+QXV0b21hdGljYWxseQ== 197722
+INeT15DWt9eo 197723
+Q2FjaGVz 197724
+IHB1bHNh 197725
+IFRPVA== 197726
+5pyA5paw6K+E6K66 197727
+dW1zdW0= 197728
+IGJpY2g= 197729
+ZXRvcA== 197730
+IOCqhuCqquCqow== 197731
+bXlw 197732
+IOCkrOCli+CksuClhw== 197733
+IFJ1aHI= 197734
+UGlubmVk 197735
+2YrZi9in 197736
+5pu+6YGT5Lq6 197737
+IOC4i+C4t+C5ieC4rQ== 197738
+IMO8Y3JldA== 197739
+INKb0L7Sk9Cw0LzQtNGL0ps= 197740
+IGV5ZWxhc2hlcw== 197741
+LVBDUg== 197742
+INmF2KfYr9ix 197743
+IGluaWw= 197744
+IG53YW554buL 197745
+IHN5cmluZ2U= 197746
+IEF2ZW50 197747
+INiz2YrYp9iz2Kk= 197748
+IHd5cG9zYcW8 197749
+X3d3dw== 197750
+PmQ= 197751
+LkVtcA== 197752
+IHPDsm5yYWljaHRl 197753
+LmZ1dHVyZQ== 197754
+6LaK5p2l6LaK 197755
+IHRoYWly 197756
+aXp1bmd1bXph 197757
+INGB0L7QstGA0LXQvNC10L3QvdGL0LU= 197758
+IGRpY3RhdG9yc2hpcA== 197759
+KENoYW5nZQ== 197760
+IGhpa2Vycw== 197761
+4LK+4LKC4LKk4LKw 197762
+INCx0LjQu9C00q/RgNCz05nQvQ== 197763
+4YOb4YOn4YOd4YOk 197764
+INGY0LDQsg== 197765
+ZWRlbnQ= 197766
+IGRpc3Byb3BvcnRpb25hdGVseQ== 197767
+4LK+4LKm4LON 197768
+IG5ld2ZvdW5k 197769
+54Ot5LmF5LmF57K+5ZOB 197770
+IEFjY29yZGlvbg== 197771
+INiu2KjYsdmK 197772
+IFdlc2Vu 197773
+4oCZZW8= 197774
+IGNvcnRpY2Fs 197775
+6KOF5aSH 197776
+IFdlaXRlcmJpbGR1bmc= 197777
+VW5uYW1lZA== 197778
+IFBhY2g= 197779
+IGNvbGxhYm9yYXRvcg== 197780
+S2Fuc2Fz 197781
+INmI2YXYs9iq 197782
+IGNvbnNjaWVudGlvdXM= 197783
+INC80LDSm9C+0LzQvtGC0Lg= 197784
+INiz2YfZhNip 197785
+fHJlcXVpcmVk 197786
+cGxvaXQ= 197787
+Q2FyZ2E= 197788
+INCz0LDQt9GA0YvQvQ== 197789
+X3Rha2U= 197790
+IHNjaGlsZA== 197791
+IHRpZGVz 197792
+L2xvdW5nZQ== 197793
+INC/0LXRgNCy0L7QvdCw0Yc= 197794
+aW52YWxpZGF0ZQ== 197795
+LkNyZWF0b3I= 197796
+IGRlaWxpZ2U= 197797
+4oie 197798
+cmFtaW4= 197799
+IFdST05H 197800
+IOCql+CrjeCqsOCqvuCqrg== 197801
+INWw1aHVtNW41aY= 197802
+IGthcmRl 197803
+IMWbcmVk 197804
+cGFra2Vu 197805
+44CRIeOAkA== 197806
+IM+AzrHPgc6/z4XPg865zqw= 197807
+4KeB4Ka34KeN4Kaf 197808
+IGJyb29t 197809
+4KSc4KWN4KSc 197810
+IOCqrOCqqOCqvuCqteCqteCqvg== 197811
+0L/QtdGA0LDRgtC+0YA= 197812
+IOCkrOCljeCksuClieCklw== 197813
+4KeH4Kaw4KeH 197814
+UXVhcnR6 197815
+ZmVsZGVy 197816
+CXRlc3Rz 197817
+INGB0YPRgNCz 197818
+IGN1cmF0ZQ== 197819
+VUxTRQ== 197820
+IHNoaWRh 197821
+dWl0YXJ5 197822
+IOydtOyDgeydmA== 197823
+IHZlbGw= 197824
+IG9yZWQ= 197825
+IGFtYWRv 197826
+X2hkbA== 197827
+KHN5bmM= 197828
+INmG2qnYsdiv 197829
+772F 197830
+IFZhbHVhYmxl 197831
+IG5o4buNcuG7jQ== 197832
+IEJyb2tlcmFnZQ== 197833
+16HXmNef 197834
+IOCksOCli+Cksg== 197835
+IGJvbGV0bw== 197836
+44CC44GT44Gu 197837
+4Ka54Kao 197838
+IG1hbGFtYW4= 197839
+IGVnZW50bGlnZW4= 197840
+0q/Qs9Kv 197841
+77yM5YWo5Zu9 197842
+PGFycg== 197843
+IGJyb2FkY2FzdGVycw== 197844
+44CM44GC 197845
+IOG7jXfhu40= 197846
+IHdpanM= 197847
+aWRlbnRpZnk= 197848
+IG1hZHVyYXM= 197849
+IOCkleCksOCljeCkruCkmuCkvuCksA== 197850
+IHBpdGFuanU= 197851
+IHBhbGlnaWQ= 197852
+IEVyZ28= 197853
+IGJhbmNhZGE= 197854
+IHNtYXJ0eQ== 197855
+IGRhbXU= 197856
+xJNqaQ== 197857
+ZsO2cmRlcg== 197858
+ZGlzcG9zZWQ= 197859
+PFVzdWFyaW8= 197860
+IOGDkuGDmw== 197861
+IHPDpGw= 197862
+LXN0YXJz 197863
+IGdhcm90bw== 197864
+QU5HTEVT 197865
+IOCyquCysOCyv+Cytg== 197866
+IOC0uOC1jeC0teC0vuC0l+C0pA== 197867
+IGluZ2V2 197868
+Z2xlZA== 197869
+IGdoYQ== 197870
+IEd1Zw== 197871
+Vk9D 197872
+INm+2YjagQ== 197873
+IGV0aGljYWxseQ== 197874
+15nXmdeY16LXqA== 197875
+IOGDquGDlOGDnOGDouGDoA== 197876
+INC+0LHRgA== 197877
+IOCmpOCmvuCmgeCmpuCnh+CmsA== 197878
+INCX0LXQu9C10L0= 197879
+IHN5bXB0b21hdGlj 197880
+IHBpeg== 197881
+cGVuZXRy 197882
+INin2YTZhdiz2LE= 197883
+bGly 197884
+0J/Qng== 197885
+5Y2n 197886
+INeS16LXkg== 197887
+IOCwuOCwv+CwpuCxjeCwpw== 197888
+IE1haGpvbmc= 197889
+YXplbG8= 197890
+IHLDqWFsaXNhdGV1cg== 197891
+b2JveWU= 197892
+eWtlcw== 197893
+77yf77yB 197894
+IGNhcm9z 197895
+IExJUw== 197896
+YWxhcsSxbsSxbg== 197897
+IG96Ymls 197898
+dGVraWo= 197899
+INep15nXoNeV15k= 197900
+IGFic3VyZG8= 197901
+IG3DvMmZbGw= 197902
+IGhvcHBlZA== 197903
+INeU15fXqA== 197904
+INGP0LnRhtCw 197905
+IGJ1dHRlcnk= 197906
+IFdvcnNl 197907
+IEhBTEY= 197908
+IHRpYXA= 197909
+4Kio4KmN4Ki54Ki+4KiC 197910
+IGF0dGlyZXI= 197911
+IGFubGVkbmluZw== 197912
+YWJpbmc= 197913
+IENvbnZlcnRlZA== 197914
+IGZldWdpYXQ= 197915
+IOC0huC0pOC1jeC0rg== 197916
+b25lc2E= 197917
+b2doaQ== 197918
+INGH0YvQvdCw0Lw= 197919
+PWNlbnRlcg== 197920
+IHRjaA== 197921
+IGJlbcO8 197922
+IEVtcGZhbmc= 197923
+L3NwYW4= 197924
+IHBpY2NvbGk= 197925
+ZWNvcw== 197926
+INC70LXRh9C10L3QuNC4 197927
+IGlyZWxhbmQ= 197928
+QnJlZg== 197929
+5rCn 197930
+YWdleHQ= 197931
+5pel44Gr 197932
+INCv0LrRidC+ 197933
+IFlvbw== 197934
+IG9obmVoaW4= 197935
+5rCR5Li7 197936
+IHdvaG5lbg== 197937
+INCz0L7RgNC+0LTRgdC60L7QuQ== 197938
+INC+0LrQtQ== 197939
+IM61zrLOtM6/ 197940
+INCf0YDQvtCy 197941
+IGVwbw== 197942
+1bjWgtWi 197943
+IEJVRg== 197944
+IOCkieCkqOCkruClh+Ckgg== 197945
+IGZvdMOzZ3JhZm8= 197946
+IE3DqWRpY28= 197947
+IHRpZGVy 197948
+ZmZpbg== 197949
+X3Zpc2liaWxpdHk= 197950
+INmF2LXYt9mB 197951
+IFRodW5kZXJiaXJk 197952
+IOCyieCysw== 197953
+IEJlbGxv 197954
+IGvDvG5zdA== 197955
+IGNvbXBldMOqbmNpYXM= 197956
+INmK2LTYudix 197957
+INin2YTYqNmI2YQ= 197958
+4Liq4Lin4LiZ 197959
+IGZzY2FuZg== 197960
+w61kaW9z 197961
+IOyYiOuwqQ== 197962
+INqv2LLbjNmG2Yc= 197963
+TG92ZWQ= 197964
+IGlnYmVzaQ== 197965
+INCh0L7RhA== 197966
+IOCkruClgeCkuOCljeCksuCkv+Ckrg== 197967
+IteV 197968
+8J+SlQ== 197969
+15nXnteV150= 197970
+LmRlbQ== 197971
+d2FybmE= 197972
+Y3Jp 197973
+INiq2LXYqNit 197974
+J2VmZmV0 197975
+IGhhbmRsZWJhcnM= 197976
+IERpcmV0b3I= 197977
+ZGl0Y2g= 197978
+2qnYsdin 197979
+IOS8l+mRqw== 197980
+2LHYqNmK2YY= 197981
+IGdlcmVhbA== 197982
+L29jdGV0 197983
+IGNvcm91dGluZQ== 197984
+IGtpbmRqZQ== 197985
+LUtleQ== 197986
+INin2YTYqtmG2YHZitiw2Yo= 197987
+IG1pZXNpxJk= 197988
+44K344Oq44O844K6 197989
+4Liy4Lij4Li1 197990
+b3JhZG9z 197991
+IGJyem8= 197992
+2YrZhdmK2YY= 197993
+IHZlcndlcmtpbmc= 197994
+IOCkreCkrw== 197995
+Y3JlZQ== 197996
+IE5hdWdodHk= 197997
+IElTQw== 197998
+4Lix4LiI4LiI 197999
+0LXQu9C00LU= 198000
+IGNoYWxldXJldXg= 198001
+IHRpZXRv 198002
+IEdlcsOkdGVu 198003
+INGA05nRhQ== 198004
+IGt0b3LDrQ== 198005
+w4FyZWE= 198006
+INis2YTZiNqv24zYsduM 198007
+INCw0YLQsNC6 198008
+15HXqNeZ16c= 198009
+w63FoQ== 198010
+X1JQQw== 198011
+IGFiYXo= 198012
+aGlwc3Rlcg== 198013
+IGFuc2F0dGU= 198014
+IGZvcmV0 198015
+a29tZm9ydA== 198016
+IERpc3BvbmlibGU= 198017
+IEZPUkVJR04= 198018
+zrTOrA== 198019
+IHNlbnNpdGl2 198020
+Wmo= 198021
+4YOQ4YOo4YOQ4YOj4YOa 198022
+IEFkdmlzZXI= 198023
+X3NlY3VyZQ== 198024
+KGFsYnVt 198025
+IGZvcmNpYmx5 198026
+IEFkc2Vuc2U= 198027
+IEF1dGhlbnRpY2F0ZQ== 198028
+IOCyteCziOCysA== 198029
+XV1d 198030
+4oiZ 198031
+IGNvbmZpcm1hw6fDo28= 198032
+IGhpZXJ6dQ== 198033
+KFN5bWJvbA== 198034
+IE5hZGlh 198035
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+IOW6lw== 198037
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+5aix5LmQ5bmz5Y+w5Li7566h 198041
+RG9ybQ== 198042
+KCl9LAo= 198043
+IG3hu7k= 198044
+INmC2YjZhdmK 198045
+IGZpbGk= 198046
+IGdlYmV1cnRlbmlzc2Vu 198047
+IGNvbnNpZGVyZQ== 198048
+IGRlc2N1YnJl 198049
+b21vZA== 198050
+2aw= 198051
+IEVpbmxhZHVuZw== 198052
+VElO 198053
+IGdhbmhvcw== 198054
+Lmd2 198055
+IGRpc3RyaWs= 198056
+TWFzYw== 198057
+LWZlZWQ= 198058
+w6dpbGlnaQ== 198059
+IG5ueW8= 198060
+INC80q/RiA== 198061
+IENvbmNlbGxv 198062
+IOCkrOCkv+CksuCljeCkleClgeCksg== 198063
+X3Bhcw== 198064
+4LOD4LK5 198065
+IHdlaWNo 198066
+YWNvcg== 198067
+4YOd4YOR4YOQ4YOW4YOU 198068
+Y2FyZWVy 198069
+X3hsaW0= 198070
+INGF0L7Rh9Cw 198071
+IHNhbHVkYWJsZXM= 198072
+IFNvYmFsZA== 198073
+4K+N4K6V4K+B 198074
+IHJ1bmF3YXk= 198075
+IGNpdmlsaXplZA== 198076
+QU5USQ== 198077
+IGhhdHRh 198078
+2YjYp9mE24w= 198079
+w5FB 198080
+IG1tb2w= 198081
+YmFuZQ== 198082
+aW5udQ== 198083
+IGdsYXVidA== 198084
+IGPhuqV1 198085
+IFdhbm5hbg== 198086
+INWs1aHVtdW2 198087
+INGB0LDQsdCw0ps= 198088
+YXRhd2Fn 198089
+IGNvbW1lcmNpYWxpemF0aW9u 198090
+IGt1ZWxl 198091
+aGlr 198092
+INC/0YDQvtGI0LvQvg== 198093
+IHNldWxlcw== 198094
+X3RyYWl0 198095
+b2J5bA== 198096
+aWlpaQ== 198097
+c2hpcnRz 198098
+LUlW 198099
+4KWH4KSw4KWA 198100
+cG9sYXI= 198101
+xJ9peg== 198102
+KGZyaWVuZA== 198103
+IGNyaXA= 198104
+IOWkqeWkqeS4reW9qeelqOS4jeiDveS5sA== 198105
+IGFwcHJlY2lhdGluZw== 198106
+QG4= 198107
+IHNlbnNvcmVz 198108
+4Ka+4Kae4KeN4Kaa 198109
+aW5zdGFudGlhdGU= 198110
+X252 198111
+dWxuZXJhYmlsaXR5 198112
+xaVhxb4= 198113
+IHByb3NlY3V0ZQ== 198114
+w6lpbg== 198115
+IHNjcmFwYm9vaw== 198116
+4oCZaW5ub3ZhdGlvbg== 198117
+IGZhaWdvZmll 198118
+IERPQg== 198119
+dnJpamU= 198120
+4LSZ4LWN4LSZ4LS/ 198121
+0LjRgtC10LvRjNC90L7QvA== 198122
+IG1hcnF1ZWU= 198123
+IGNvbXBsYWlucw== 198124
+INW21bfVodW21aHVr9W41oLVtA== 198125
+IHBhcGly 198126
+XV0NCg== 198127
+UsOpc3Vtw6k= 198128
+INio2KfZhNix2YrYp9i2 198129
+IOW8tQ== 198130
+INin2YTYudmF2YrZhA== 198131
+YWRkb25z 198132
+IERhbWlhbg== 198133
+IOC0uOC0qOC1jeC0pA== 198134
+IG1vdG9yd2F5 198135
+4K6w4K6/4K6y4K+N 198136
+IHVwcHNr 198137
+IGluZHVzdHJpYWlz 198138
+IGltcHJlZ24= 198139
+SGVuY2U= 198140
+IHdhc20= 198141
+z4TOrs68zrHPhM6x 198142
+IHVpbGU= 198143
+IGVpc2Vy 198144
+IGhldXR6dXRhZ2U= 198145
+INGA0YPRgdC40LnTmQ== 198146
+b2Nhbm5hYmlubw== 198147
+IOCkluClgeCksuClhw== 198148
+CQkJICAgICAgICAgICAgICAg 198149
+IHBva2E= 198150
+Lnh4eA== 198151
+IHJlZ2lvbmU= 198152
+IOCyruCyn+CzjeCynw== 198153
+IEJveGVy 198154
+YWRkaXRpb24= 198155
+IOCmuOCmrOCmmuCnh+Cnn+Cnhw== 198156
+Q2hlcXVl 198157
+INGI0L4= 198158
+VG9wbw== 198159
+ZGF0YXRhYmxl 198160
+INC30LjQtA== 198161
+IHNjaGF1dA== 198162
+IGjhu6U= 198163
+IHZlc3o= 198164
+IGFkaGFydA== 198165
+INGB0LvQvtCy0L3Qvg== 198166
+KGF1dGhlbnRpY2F0aW9u 198167
+IOuFuOuPmQ== 198168
+IENhbGFicmlh 198169
+YXbkuI3ljaHlhY3otLnmkq3mlL4= 198170
+IHdjemXFm25pZWo= 198171
+IGdyZW5hZGU= 198172
+IEdCUg== 198173
+INqv2LPYqg== 198174
+IGl0aw== 198175
+IOCkh+CksuClh+CkleCljeCkn+CljeCksA== 198176
+IHJhZHk= 198177
+KGV4Y2hhbmdl 198178
+IGFlcm9keW5hbWlj 198179
+IGt1bw== 198180
+YWRlbWlj 198181
+eWx5 198182
+IOa7oeWggg== 198183
+UHJpdmlsZWdlcw== 198184
+0q3QsNC6 198185
+5oC75L2T 198186
+LnNoYWRlcg== 198187
+IHBhcmVudCdz 198188
+0LTQsNGA0LA= 198189
+IGRuw60= 198190
+IEZlaWVydA== 198191
+IGdyYXBoaQ== 198192
+IGF1Z2U= 198193
+77+8 198194
+IExpc3A= 198195
+YXBhdGFsaw== 198196
+X3Jvb21z 198197
+IOCmhuCmmg== 198198
+IEFyYml0cmF0aW9u 198199
+IGx1eHVyaQ== 198200
+INC40YDQsA== 198201
+IEtvbnplbnRy 198202
+IENSUw== 198203
+YW1hbGFtYQ== 198204
+IFByb3Rvbg== 198205
+QXBwYXJ0ZW1lbnQ= 198206
+IOCmquCmvuCmuQ== 198207
+IGFsaXF1ZXQ= 198208
+IGVrc2lzdGVy 198209
+4KeA4Kam 198210
+IGFsYXBqw6Fu 198211
+TWVkaWNhdGlvbg== 198212
+ZG9uYWxk 198213
+z4HOs86szr0= 198214
+4YOU4YOc4YOY4YOa4YOY 198215
+IGF0cmFr 198216
+IFN1cHByZXNzYW50 198217
+IMOhdWRpbw== 198218
+IGRpcGVyY2F5YQ== 198219
+5YWs572R 198220
+IG3Em3M= 198221
+INC/0YDQuNGH0LjQvdCw0Lw= 198222
+a2lsbGVy 198223
+INCx0LXQudKX0LjSow== 198224
+X3Jvcw== 198225
+IEhhaXRpYW4= 198226
+LVByb2R1 198227
+IOe+juWciw== 198228
+dG9x 198229
+IGtvbGVt 198230
+IGRldGVybWluYcOnw6Nv 198231
+IGthcnRv 198232
+INC00L7RgdGC0YPQv9C90Ys= 198233
+IOCqreCqvuCqsOCqpOCqruCqvuCqgg== 198234
+INC80YPRgdGD0LvQvNCw0L0= 198235
+ZXRzaw== 198236
+INmF2KfZg9mK2YbYqQ== 198237
+IOCkpOCkgQ== 198238
+aW11cg== 198239
+KCk9PSI= 198240
+IOCwuOCwguCwluCxjeCwrw== 198241
+IGRldGVjdGFibGU= 198242
+4KS/4KSu4KS+4KSX 198243
+IOyngOuPhA== 198244
+IGVyaGU= 198245
+cmV0d2VldGVk 198246
+IGNvbnN0aXR1aQ== 198247
+IHR1cmlzdGE= 198248
+KCIiKTs= 198249
+ICcpDQo= 198250
+0LXRgdGC0ZY= 198251
+IGNob2NvbGFkZQ== 198252
+fSE= 198253
+4KiV4KmA 198254
+INGA0LjRgdC60LA= 198255
+UU9iamVjdA== 198256
+INis2LHZhQ== 198257
+IG1lc21lcml6aW5n 198258
+KS4uLgoK 198259
+IOC0muC0sOC0v+C0pOC1jeC0sA== 198260
+4LmA4LiK4Li14Lii4LiH 198261
+0LvQsNGC0YM= 198262
+LeG7jQ== 198263
+W109ew== 198264
+IOCkhuCkiOCkquClgA== 198265
+IG1ldWJlbHM= 198266
+IG5vdGlm 198267
+IGJhc2llcnQ= 198268
+IEJhbmdvcg== 198269
+IE7Emw== 198270
+IOCyheCyqOCzjeCyqA== 198271
+IHRodcOq 198272
+IHlhcMSx 198273
+ZmFseg== 198274
+IERvY3VtZW50bw== 198275
+44CB5Ymv 198276
+INC/0YDQuNC90LjQvA== 198277
+0YLSr9GB0YLRltC6 198278
+4LmA4LiC4LmJ4Liy4Liq 198279
+IFB1cmNoYXNlZA== 198280
+INGC06nRgNOp0Ls= 198281
+IENhbXBlcg== 198282
+INec157XoQ== 198283
+INi12KfYrQ== 198284
+IEVMRg== 198285
+IE1hcmluZXJz 198286
+IHN0YXJ0bGVk 198287
+IHJlbGVudGxlc3NseQ== 198288
+IOeGig== 198289
+U05T 198290
+LUV1cm8= 198291
+0L3QsNC60Lg= 198292
+Py4u 198293
+IENsYXA= 198294
+Oko= 198295
+IHZhbGFibGU= 198296
+PSgt 198297
+INiw2KfYqtmH 198298
+IGZhaWxp 198299
+0YLQtdGA0LA= 198300
+INC90LDQv9GA0LDQstC70LXQvdC40LU= 198301
+5pys6YeR 198302
+IFNlY3JldGFyaW8= 198303
+INOp0LrSr9C7 198304
+IOCqheCqreCqv+CqqOCrhw== 198305
+0YPQvNC10L3RgtGL 198306
+IHBhcnRpaw== 198307
+SW5jbHVz 198308
+w612aW8= 198309
+4LmC4LiK 198310
+4LKk4LK/4LKv 198311
+2K7ZiNin2YfYrw== 198312
+IEZpbG1lcw== 198313
+IFl1c3Vm 198314
+44G+44GB 198315
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+IHN0b3JpY28= 198317
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+IGVudHJldA== 198319
+INis2YXYudmK2Kk= 198320
+2YbYr9ix2YrYqQ== 198321
+LUNsYXVkZQ== 198322
+IGVsw6Q= 198323
+0L/RgNC+0YHRgg== 198324
+U2xpY2Vz 198325
+4Kqq4KqV4Kqh 198326
+15vXoNeV158= 198327
+IM6RzrjOrg== 198328
+OnN0YXJ0 198329
+bWVo 198330
+IGNyYWNrZXI= 198331
+5Zyo57q/57K+5ZOB6KeG6aKR 198332
+IGfDqm4= 198333
+INmK2LnYt9mK 198334
+IFNjaHVsdGVy 198335
+bGlzdGFz 198336
+UVJDb2Rl 198337
+SUZVTA== 198338
+INC00L7Rh9GM 198339
+X3NpZ251cA== 198340
+IFN1bm5p 198341
+IOGDo+GDrg== 198342
+Lmh5cGVy 198343
+IHNvbGlkZXM= 198344
+IOC0h+C0guC0l+C1jeC0sg== 198345
+IEJpc2E= 198346
+IOCysOCyvuCyt+CzjeCyn+CzjeCysOCzgOCyrw== 198347
+44K544K/44OD44OV 198348
+aWNvbnQ= 198349
+TWlsbGlvbnM= 198350
+YnVsYXM= 198351
+eXpkYQ== 198352
+44OG44O844Oe 198353
+4LSx4LSj 198354
+xbg= 198355
+WlU= 198356
+4Kq+4Kqo4KuN4Kq4 198357
+xLFzxLF5bGE= 198358
+IGludGVydmp1 198359
+77yJLA== 198360
+R3Jhdw== 198361
+IGNhdXNhZGE= 198362
+IGNvbnZleW9ycw== 198363
+0YbQuNGP0YLQsA== 198364
+IHJvdWxhbnQ= 198365
+IOCwquCwsOCxgeCwlw== 198366
+IEJvZ2E= 198367
+KGlucGxhY2U= 198368
+U0VNQg== 198369
+IOC4o+C4sOC4muC4mg== 198370
+IGFzc3VyaW5n 198371
+YWNoYWVs 198372
+IHTDoW1vZ2F0 198373
+IGVsZWl0b3I= 198374
+INW61aHVv9Wl1oDVodWm1bTVqw== 198375
+IHVsaW0= 198376
+15XXqNeQ 198377
+IMW+aXZsamVuamU= 198378
+aWdpYQ== 198379
+IEdlcnM= 198380
+INiq2LXYp9mI24zYsQ== 198381
+TG9hbnM= 198382
+INGF0YPRgdGD0YE= 198383
+4Kyb 198384
+aXphY2ph 198385
+UHVlcnRv 198386
+44CB5a6J5YWo 198387
+IDsKLy8= 198388
+LmV1bGVy 198389
+LlZlaGljbGU= 198390
+IGlzaXQ= 198391
+IEJyaXRhbm5pY2E= 198392
+X3BlcmY= 198393
+IGNvbm5lY3RpdmU= 198394
+IG5pdG9yaW5hYQ== 198395
+IE1VTA== 198396
+IikrIg== 198397
+ZW1ibw== 198398
+IFJlY2s= 198399
+IFJlZGVz 198400
+IEtvbnRleHQ= 198401
+aHBw 198402
+CXJlcGx5 198403
+IGdsYXVjb21h 198404
+aXNzYW5p 198405
+INmI2LHagduQ 198406
+2LXYsdmK 198407
+Z2FyaA== 198408
+b3ZhbsO9Y2g= 198409
+ZGV0ZXJt 198410
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+IOCwpOCwrw== 198412
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+4LSV4LWN4LSV4LWB4LSo4LWN4LSo 198414
+IGJhdG9u 198415
+0LDSm9OZ0LXQuA== 198416
+IOGDlOGDoOGDl+GDm+GDkOGDnOGDlOGDl+GDoQ== 198417
+IyMjIwo= 198418
+SMOkcg== 198419
+INCc0LXQvA== 198420
+LUNPTg== 198421
+IFJlc3VycmVjdGlvbg== 198422
+INCa0YA= 198423
+IGxsZWdhcsOh 198424
+IEV1cm9wZSdz 198425
+IE1lbG8= 198426
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+IHByYXdh 198428
+IGNsdXN0ZXJlZA== 198429
+IOCmuOCmvuCmleCnjeCmtw== 198430
+IGxvdW5naW5n 198431
+INWl1oDVr9aA1bbVpdaA1as= 198432
+INGB0L7RgNGC0LA= 198433
+KGZsYXQ= 198434
+IGJsb2NrYWRl 198435
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+IOC5geC4mQ== 198437
+IGtydWlz 198438
+INmE2YXYsw== 198439
+4LSq4LWN4LSq4LWG4LSf4LWB4LSk4LWN4LSk4LS/4LSv 198440
+IG9keg== 198441
+IOS5heS5heeDrQ== 198442
+aGF2YQ== 198443
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+IHVuZm9y 198447
+IEF0dGVtcHRz 198448
+ZGVjbHR5cGU= 198449
+YW1iaXNh 198450
+IGdlZGVlbHQ= 198451
+IG1hbmdhcw== 198452
+IGtvamVn 198453
+IGphdW5lcw== 198454
+IEpOSQ== 198455
+157XoteY 198456
+IG1hc2N1bGlu 198457
+cXFhYXQ= 198458
+IHBsbA== 198459
+YWxwaGFiZXQ= 198460
+IOCujuCuqeCvjeCuquCupOCviA== 198461
+IGdlcmluZ2U= 198462
+INiz2YjYsduM 198463
+IEluc3BpcmF0aW9ucw== 198464
+YXZpZXN0 198465
+IHJlbGF0YQ== 198466
+IG5hY2s= 198467
+IHRlamlkb3M= 198468
+IGluZGlrYXRvcg== 198469
+ZG9w 198470
+IFNjaHdhbno= 198471
+b3dhbmk= 198472
+IHRlcmFzYQ== 198473
+2ZHZiQ== 198474
+IEZNSQ== 198475
+IGt1c2hv 198476
+IOC0quC0tA== 198477
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+IHNrYg== 198480
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+4K+B4K6V4K+N4K6V4K6z4K+N 198483
+IGludGVyc2VjdHM= 198484
+IGVtb3o= 198485
+IHJveWF1bWU= 198486
+ICgpLAo= 198487
+IOaoqg== 198488
+QkND 198489
+IGVvZg== 198490
+OmdyaWQ= 198491
+4LKX4LK+4LKX4LK/ 198492
+4LS+4LSv4LWN 198493
+UHJldA== 198494
+IGluY3Jlw61ibGVz 198495
+dWx0bw== 198496
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+KHJlbGF0aXZl 198498
+IGJicQ== 198499
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+INGC0LDRgNC4 198503
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+IHZvcmdlbm9tbWVu 198505
+IGdlZ2FyYW5kZQ== 198506
+IHJlZnVyYmlzaG1lbnQ= 198507
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+IERPSg== 198510
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+INit24w= 198517
+IGFwYXJhdA== 198518
+IGhhbXN0ZXI= 198519
+IOCqm+Cri+CqoQ== 198520
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+IOCmheCmuOCnjeCmpA== 198522
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+KEh0bWw= 198525
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+IOC0leC0peC0vuC0quC0vuC0pOC1jeC0sA== 198531
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+INGD0LHRgNCw0YLRjA== 198533
+b21zbml0dA== 198534
+IOCyruCyvuCyoeCyv+CyleCziuCyguCyoQ== 198535
+INGI0LDQsNGA0LQ= 198536
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+IOyYpOuluA== 198538
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+INC60LjRgtC+0LE= 198542
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+IHphdGE= 198552
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+IG9uZGVyem9la2Vycw== 198554
+X0lNRVRIT0Q= 198555
+IGRpc2FncmVlbWVudHM= 198556
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+SG0= 198558
+QWNjZXB0YW5jZQ== 198559
+IGVudHdpY2tlbHRl 198560
+IG5kaW0= 198561
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+0YPRgNGL0YE= 198563
+IOCkreCkvuCkteCkqOCkvg== 198564
+INC60L7QvNC/0LDQvdC40LXQuQ== 198565
+IGhvbW9maWxl 198566
+0LzRi9C5 198567
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+cW1s 198569
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+IGJlaGVyZW4= 198574
+IM+AzrvOtc+F 198575
+IGVzaWdlbnpl 198576
+4K+K4K6f 198577
+IGNyb3Q= 198578
+INmF2LnYqtio2LE= 198579
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+IHJhbm8= 198583
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+IHJlY29tYmluYW50 198585
+X0VTVA== 198586
+1aHVttW3 198587
+W3Jlc3VsdA== 198588
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+5LiN5Y2h5peg56CB 198590
+INC30LDRh9Cw0YHRgg== 198591
+INC30LDRhdC+0YI= 198592
+1aHVttWk1aHVrw== 198593
+4YCQ4YC94YCA4YC6 198594
+IGRpYWxvZ3M= 198595
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+IOW4gg== 198597
+bGlzaQ== 198598
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+IGFkb3B0YXI= 198602
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+IGFhbmdldmVu 198615
+IEJhaGthbg== 198616
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+IHBpbGFyZXM= 198619
+0YHQtdC90LTRlg== 198620
+IOC0nOC0v+C0suC1jeC0suC0r+C0v+C0suC1hg== 198621
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+IHZhY3VuYXM= 198639
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+zrrPhQ== 198643
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+2Kjavg== 198649
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+IM+Azr/Pgw== 198654
+IOCquOCqguCqquCqsOCrjeCqlQ== 198655
+IOuvvOyjvA== 198656
+INC60LXQvdOZ 198657
+OndlYg== 198658
+IHN1YnRlcnI= 198659
+cHJvY2Vzc29ycw== 198660
+dGVuc29yZmxvdw== 198661
+IGNvbmZpcm1vdQ== 198662
+SG9yYXM= 198663
+1aHVo9Wh1bA= 198664
+IEZvcm1hdHRpbmc= 198665
+X3NpbWlsYXJpdHk= 198666
+KE1vdmll 198667
+IGFzc2ltaWxhdGlvbg== 198668
+6IO95Lmw5ZCX 198669
+IM+Azr/PhM6t 198670
+IER1YXJ0ZQ== 198671
+IGt1cnVsdcWf 198672
+U1RBTkRBUkQ= 198673
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+LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLQo= 198676
+CVNv 198677
+IHNvbmlkb3M= 198678
+IOyeiOydjA== 198679
+IEpTUA== 198680
+IFRjeA== 198681
+IENhc3RlbG8= 198682
+IExpbmtpbmc= 198683
+IGVucXVpcmU= 198684
+YW1hbm8= 198685
+RmVldA== 198686
+V2FsdGVy 198687
+INiz2YjYoQ== 198688
+aG9vdGVy 198689
+IEZhYmlhbg== 198690
+dW5nZWxl 198691
+2YjZhdiq2LE= 198692
+IOCuj+CuseCvjeCuquCun+CvjeCunw== 198693
+IGNyZWF0aXZv 198694
+T0dB 198695
+IEFwcm8= 198696
+ZW5kaWRvcw== 198697
+Y3Jpcw== 198698
+IEZBUk0= 198699
+IEdvbGRjbw== 198700
+LHsK 198701
+IE7Dsw== 198702
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+IM+Az4HPjM6zz4HOsc68zrzOsQ== 198704
+UGxhbm8= 198705
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+dGhhd2k= 198707
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+IOCwqOCwv+CwsOCxjeCwruCwvuCwpA== 198709
+aGV0c2VuZw== 198710
+5o2u5LqG6Kej 198711
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+5Yqb5bqm 198717
+w6lyYWxlbWVudA== 198718
+INGF0LDQu9GL0pvQsNGA0LDQu9GL0ps= 198719
+INGD0LTQvtCy0L7Qu9GM0YHRgtCy0LjQtdC8 198720
+LXRvbw== 198721
+IEthcnRvZmY= 198722
+IM6zzr3Pjg== 198723
+4oCZYXk= 198724
+IOCmuOCmleCnjeCmt+Cmrg== 198725
+IHBvbnU= 198726
+IHNob3ZlZA== 198727
+INCz0LXQutGC 198728
+IGxlZmF0c2hlbmc= 198729
+IOCuqOCun+CuteCunw== 198730
+INGB0YLRgNCw0YLQtdCz0LjQuA== 198731
+QkVD 198732
+IGludGVyZXNzZXI= 198733
+IEpNUw== 198734
+4KS24KWN4KSw4KWH4KS34KWN4KSg 198735
+5LqS5Yqo 198736
+IFFVRVNUSU9OUw== 198737
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+IEFwcHJlY2lhdGlvbg== 198739
+TGluZWpvaW4= 198740
+IOuTseyepQ== 198741
+IE1pbGVhZ2U= 198742
+IFR1bmE= 198743
+IGVzY3JldmU= 198744
+Rml6eg== 198745
+IHV5YQ== 198746
+IOCqhuCqguCqpA== 198747
+4Kav4Ka84KeH4Kaw 198748
+IHJlY29yZMOz 198749
+IG9samU= 198750
+IHNpdGVyZQ== 198751
+eW1tZW4= 198752
+IGRpYXBocmFnbQ== 198753
+IGV0ZGl5aQ== 198754
+INeT15XXnNeo 198755
+ICcpJw== 198756
+IENhc3NpZHk= 198757
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+0YzRi9GA 198762
+IHpodg== 198763
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+0YvQstCw0LXRgg== 198765
+IGhvaWQ= 198766
+IGVudGVydGFpbmVycw== 198767
+IFRleHRv 198768
+YWR2aWVz 198769
+IGtlemVsw6lz 198770
+IHBhcGlsbA== 198771
+INi52KjYp9ix2Ko= 198772
+INin2YTZiNir 198773
+INCy0LDQttC90YvRhQ== 198774
+INij2K3Yr9in2Ks= 198775
+LWJ1ZGdldA== 198776
+INeY15XXmA== 198777
+LnNjYWxhcg== 198778
+INix2LPZhduM 198779
+IFdheGFhbg== 198780
+QFw= 198781
+zrrPjM+D 198782
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+INC/0L7QutCw0LfQsNGC0LXQu9GM 198784
+IGJvcQ== 198785
+4Z6J4Z+S4Z6H 198786
+IHBlcmln 198787
+IGFmd2lqaw== 198788
+IFBhcm1h 198789
+ZXRlbQ== 198790
+IHJlZ2VsZ2V2aW5n 198791
+7J2066m0 198792
+IGF1c2dlc3Byb2NoZW4= 198793
+KCcuJyk7Cg== 198794
+X3dyaXR0ZW4= 198795
+KHRvZ2dsZQ== 198796
+IGF1bWVudGFkbw== 198797
+IGpvcm5haXM= 198798
+5bCk54mp 198799
+INC90LDQvNC+0Y/QvQ== 198800
+2YjYr9ix 198801
+V2hpdA== 198802
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+IElnbml0ZQ== 198805
+IElJUw== 198806
+YWplbg== 198807
+IGxldHRlbg== 198808
+IHNob3BwZW4= 198809
+IOCkh+CkpOCkqOClhw== 198810
+bWFya3Vw 198811
+IGJzcA== 198812
+IHByb21lc3Nh 198813
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+2YPYtNmB 199408
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+K+WkqeWkqeS4reW9qeelqA== 199410
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+IHp3acSZa3M= 199412
+IHnEsWzEsQ== 199413
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+IExEUw== 199417
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+INin2YbYqtuB2KfYptuM 199423
+4Kqk4Kq/4KqV 199424
+4KWM4KSk 199425
+IG1vxb5lbW8= 199426
+IHByw6ls 199427
+IHZlcnRpY2FsZQ== 199428
+IEFkZW0= 199429
+6L+b6KGM5LqG 199430
+IHNhbGFyaW9z 199431
+IHZldQ== 199432
+IGFwYXJlY2nDsw== 199433
+IOGDleGDmOGDp+GDkOGDleGDmA== 199434
+2KrZiNix 199435
+IFBhcGVs 199436
+INW61aHVsNWr1bY= 199437
+INCU0YPQvNCw 199438
+INmE2KjYudi2 199439
+ID8i 199440
+IGdvc2g= 199441
+RG9ubmVsbA== 199442
+bGppdmU= 199443
+IOqyveywsA== 199444
+am9pbnM= 199445
+aGVhdGVk 199446
+L3BvbHk= 199447
+INGY0LXRgA== 199448
+UnRj 199449
+IGRhdWVybg== 199450
+4KWN4KSy4KWH 199451
+INio2K/Yp9mG 199452
+ZXllbg== 199453
+IHNwb29s 199454
+X3N0YQ== 199455
+X1RJTEU= 199456
+w6lkaXRldXI= 199457
+U3Bpa2U= 199458
+d2FuZGxl 199459
+IG3DqWlk 199460
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+IOC0pOC1gOC0rw== 199469
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+IGTDvHpnw7xu 199478
+IHByemVi 199479
+IOCqq+CrjeCqsg== 199480
+IGNyaW5nZQ== 199481
+IGtsaWVudMOzdw== 199482
+IHNtcnRp 199483
+YXJwYQ== 199484
+4Kq+4Kqv4Kqk 199485
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+dGl0b3I= 199487
+IOC5gOC4o+C4t+C5iOC4reC4hw== 199488
+0LjRgNGL 199489
+INCf0LDQuw== 199490
+ZWxsYWFu 199491
+IGZsd3lkZHlu 199492
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+IFRyb24= 199494
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+IHBoaWxhbnRocm9w 199496
+dHJhdGU= 199497
+INin2YTZg9in2YXZhA== 199498
+UHVibGljaWRhZGU= 199499
+IHJvb21tYXRlcw== 199500
+IGFkdWx0YQ== 199501
+6ams5YWL 199502
+IOCkq+CkvuCkr+CkpuCkvg== 199503
+IHNpbGhvdWV0dGVz 199504
+56eR5a2m6Zmi 199505
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+4LCw4LCV4LGB 199507
+dXJsaWprZQ== 199508
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+INGE0LDRgNC3 199510
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+IGFhbmdlbg== 199514
+IGhhZW0= 199515
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+IGNvcnJlc3BvbmRlbnRl 199520
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+X3RyaWFscw== 199555
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+IHZlcmR3ZW5lbg== 199560
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+L1RlbXBsYXRlcw== 199562
+IOCkmuCksuCkpOCkvg== 199563
+IGFhbmdla29uZA== 199564
+IGJlc2NoZXJtZA== 199565
+Z2V2b2Vs 199566
+INC40YfQuNC90LTQtQ== 199567
+IGTDqWxpY2lldXg= 199568
+P3w= 199569
+X1NJREU= 199570
+ZXN0YXQ= 199571
+IG11bHRhcw== 199572
+ZWln 199573
+INC80LDQutGB0LjQvNGD0Lw= 199574
+IFNhdGVsbA== 199575
+dmFybmE= 199576
+LnRyYW5zZm9ybXM= 199577
+IFN1cnZleXM= 199578
+IGltcGV0dXM= 199579
+IG5ha2l0YQ== 199580
+IGF1ZGl0aW9ucw== 199581
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+IOGDnuGDoOGDkOGDpeGDog== 199586
+YXJ0dXNz 199587
+0L/RgNC+ 199588
+dW1wdWxhbg== 199589
+IGFxdcOy 199590
+IOGDlOGDlw== 199591
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+UHV0dGluZw== 199593
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+IGNvbnNlcXXDqm5jaWE= 199604
+IHVubG9hZGVk 199605
+LuCujg== 199606
+IOeIteWjqw== 199607
+IHByZWZlcmU= 199608
+IGFwcGFyaXRpb24= 199609
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+QXBwbGljYW50cw== 199619
+IGNhcGVsbGk= 199620
+cHJk 199621
+IGNhcHRhcg== 199622
+INin2YTYqti2 199623
+cm9zaQ== 199624
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+IEdhZWxpYw== 199632
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+LWluZGVwZW5kZW50 199635
+R0FS 199636
+INCy0ZbQudGB0Yw= 199637
+IGNvbmNpbGk= 199638
+KC4p 199639
+c2Vscw== 199640
+INC60q/RgNOZ 199641
+IHNvbHRl 199642
+UGhlbg== 199643
+IHp1eg== 199644
+IHF1YW5kdQ== 199645
+VU1JTg== 199646
+INWv1aHVrQ== 199647
+IFN1cHJh 199648
+44CB5ZCN54Sh44GX 199649
+INeX15nXnA== 199650
+IG1hc3F1ZXI= 199651
+INC90L7QstC40Yc= 199652
+IGF1Z21lbnTDqQ== 199653
+0LjRgNC+0LLQsNC90L3QvtC5 199654
+LnByYWN0aWNl 199655
+IHBhc2llbg== 199656
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+IGNhcmV0YWtlcg== 199658
+YXJteQ== 199659
+IGluc3BpcmFjacOzbg== 199660
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+dWdibw== 199666
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+Ymllcg== 199669
+IHByb3Rlc3RlZA== 199670
+IOCkteCkv+CksOCkvuCknw== 199671
+INGA0LDRgdGF0L7QtNC+0LI= 199672
+IC4uLgoKCg== 199673
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+LUFsbA== 199675
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+IGluY3LDrXZlaXM= 199678
+INC40LzQutC+ 199679
+IGZvbGlnYQ== 199680
+IEJhbGFuY2Vk 199681
+4YCh4YCe 199682
+IHNjaGxlY2h0ZW4= 199683
+IM6xzrvOu86xzrM= 199684
+INC20LDRgdCw0L8= 199685
+IFPGoW4= 199686
+0LjRgNCw0Lg= 199687
+4YOY4YOc4YOU 199688
+IG1pbGlvYW5l 199689
+RU1V 199690
+4oCZw6l2aXRlcg== 199691
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+IGhhbGxhcg== 199694
+IHRhYmxhcw== 199695
+X0xvbmc= 199696
+PENlbGw= 199697
+IGludGVyYWN0ZWQ= 199698
+IGZpZ3NpemU= 199699
+IOGDluGDpg== 199700
+INGH0L7QstC10Lo= 199701
+IENvZGlnbw== 199702
+IGRlZGljYcOnw6Nv 199703
+IHN0b2xldA== 199704
+IE9ydGVu 199705
+UGhpbGlw 199706
+IMOpbWlzc2lvbg== 199707
+4Liy4Lir4Lil4Li1 199708
+IGJlYXJiZWl0ZW4= 199709
+IG1pZXJkYQ== 199710
+aG9sZGU= 199711
+0YPQstCw0LI= 199712
+PENvbXBvbmVudA== 199713
+IFdQQQ== 199714
+16bXmdec15XXnQ== 199715
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+IEFsbGllcw== 199717
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+X1NNUw== 199720
+IOCkmOClguCkrg== 199721
+IEZpeHR1cmVz 199722
+IEJhc2s= 199723
+IOW+kA== 199724
+IHRyYWR1w6fDo28= 199725
+YmxpY2tl 199726
+IGhhbW1lcmVk 199727
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+INCx0LjQsw== 199729
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+IGNpZXI= 199734
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+INC30LDQutC+0L3QvtC00LDRgtC10LvRjNGB0YLQstCw 199737
+IOGDnuGDkOGDoeGDo+GDruGDmOGDoeGDm+GDkg== 199738
+LW9wZXJhdGVk 199739
+4LWK4LSq4LWN4LSq4LSC 199740
+IGhhc3NsZXM= 199741
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+0LDQvdCw0LPQsNGA0LA= 199743
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+SmFwZ29sbHk= 199745
+IGRvZWxlaW5kZW4= 199746
+INC/0LXRgNC10LnRgtC4 199747
+IHRlbXNpbA== 199748
+IG9sw61tcA== 199749
+IFNwYXppZXI= 199750
+INin2YTYs9mF2Kc= 199751
+IHByZXNzaW9uZQ== 199752
+IGZpc2NhaXM= 199753
+bMmZcmluaQ== 199754
+IGFib2Rl 199755
+IHRhbnRyaWM= 199756
+KCldLA== 199757
+cmVsbG8= 199758
+INC30LDRh9Cw0YHRgtGD0Y4= 199759
+5b6u6L2v6ZuF6buR 199760
+2LPYsdin2KbZitmE2YrYqQ== 199761
+IHVpdHplbmRpbmc= 199762
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+IHNsaW1tZXI= 199776
+bWlsaw== 199777
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+IENoYXVmZg== 199782
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+0LDRhtCw0YDRgg== 199784
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+IFZCQQ== 199791
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+IGJyaW5jYXI= 199800
+IEFNT0xFRA== 199801
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+IGF5YWFu 199803
+IHBob3NwaG9yeWxhdGlvbg== 199804
+IOCmheCmquCnh+CmleCnjeCmtw== 199805
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+aW5nZ2k= 199810
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+IGFsbW9o 199812
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+IE1hZ2F6aW4= 199814
+IEFybWU= 199815
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+IGHFn2s= 199817
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+5YWs5bmz 199820
+IGt2w6RsbA== 199821
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+IOCkruCkqOCljeCkpOCljeCksOClgA== 199823
+4YOQ4YOV4YOY4YOQ 199824
+IHRla3M= 199825
+IGx1bWVh 199826
+IGxlZ3llbg== 199827
+IHp3aW5n 199828
+CWJvb3N0 199829
+IErDug== 199830
+5b2p56Wo6K665Z2b 199831
+INC00Y0= 199832
+INC+0YjQuNCx0L7Qug== 199833
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+4Lik4Lip4Lig4Liy4LiE4Lih 199835
+ZWJ3YQ== 199836
+IHRpcHU= 199837
+IGRvbWluaQ== 199838
+IEd1cmdhb24= 199839
+Y29tYmF0 199840
+IFJlc2lzdA== 199841
+IOCksOCli+CknOCkl+CkvuCksA== 199842
+c3RyYXRlZ2ll 199843
+4KiX4KmA 199844
+KGxvY2F0b3I= 199845
+TU9OVEg= 199846
+IGF0bGlr 199847
+IOuenA== 199848
+2KfZhNiz2Yo= 199849
+IHJ6ZWN6 199850
+IFfDpA== 199851
+IGRlcG9ydGl2b3M= 199852
+IEZhY2lsaXQ= 199853
+LWFibGU= 199854
+IGV2aXRhcmU= 199855
+IHRob25n 199856
+IOa0uw== 199857
+66y066OM 199858
+IG1hY2U= 199859
+IEV4dHJhb3JkaW5hcnk= 199860
+4KSn4KS+ 199861
+IGtvbmZsaWs= 199862
+IGxpdmVsaWhvb2Rz 199863
+INC60L7QvNC/0L7QvdC10L3RgtC+0LI= 199864
+IGt1aXRlbmthYW4= 199865
+5bm46L+Q6aOe6ImH 199866
+Q29ycHVz 199867
+IGJlc2Now6Rk 199868
+INeT15XXkw== 199869
+IGRlbnRhbnQ= 199870
+IG53ZXRh 199871
+IGhlYXRo 199872
+IOCktuClgeCkleCljeCksOCkrOCkvuCksA== 199873
+IG1uZW1vbmlj 199874
+LkZpbHRlcnM= 199875
+JywnIi4k 199876
+IOCkheCkuOCljeCkpA== 199877
+aXNpdGU= 199878
+IGFzYXdh 199879
+5bm05pyI 199880
+IGdvdGFz 199881
+IOCwquCwqOCwv+Cwmg== 199882
+RGVr 199883
+IFBhY3Q= 199884
+J2FnZW5jZQ== 199885
+ZmVpw6dv 199886
+IELDtnlsZQ== 199887
+dmVudGVlbg== 199888
+bm90aWY= 199889
+INCQ0L3QtNGA0LXQuQ== 199890
+5aSa5bm0 199891
+bWl0dGVkbHk= 199892
+6LKh5biD 199893
+INC60L7QvNC/0LDQutGC 199894
+dHJr 199895
+4KSo4KWN4KSm4KWN4KSw4KWA4KSv 199896
+IG1pZXN0bw== 199897
+IEthaXQ= 199898
+IOCyrOCyuA== 199899
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+IEluamVjdG9y 199901
+IGxvZ2FyaXRo 199902
+ZW50aW9ucw== 199903
+INGH0LXQuw== 199904
+Q29sbGF0ZXJhbA== 199905
+IGNvbmZlcw== 199906
+4KS/4KS54KSw 199907
+IHN1ZnJpw7M= 199908
+Q1JJ 199909
+IHR1Z2U= 199910
+dXJsbGli 199911
+IGvEvA== 199912
+IHZlcnBsZWVn 199913
+IGFwcGxpY2F0aW9uJ3M= 199914
+KENvbnN0cnVjdG9y 199915
+4Ka+4Kay4Ka+ 199916
+IHZ1bHB1dGF0ZQ== 199917
+INC+0L/Rg9GF 199918
+IE5vcmRyaGVpbg== 199919
+YWxsYWdl 199920
+w61zaW1hcw== 199921
+b3JuYWRv 199922
+0LjQtNC10YA= 199923
+bcOpbnllaw== 199924
+UXVh 199925
+IHBlbGljdWxh 199926
+7ISx6rO8 199927
+44CRLQ== 199928
+IGhlbWk= 199929
+IHRvdXJpc3Rlcw== 199930
+IGFic2VudGVl 199931
+IE5ldGhlcg== 199932
+LlBBR0U= 199933
+IGnFn2fDpHI= 199934
+IGVmZWN0aXZvcw== 199935
+IHNhYXY= 199936
+CQkJCSAgICAgICAgICAg 199937
+Zmxhc2hkYXRh 199938
+IHNlbWJsZXI= 199939
+IGlrdWt1 199940
+IOCmn+CmvuCmhw== 199941
+IOKAnQo= 199942
+5b2p56Wo5bmz5Y+w5byA5oi3 199943
+5peB 199944
+LtCT 199945
+dWNrbGVz 199946
+INin2YTYrtin2LE= 199947
+0LLQvtC70Yw= 199948
+INC90LXQstC+0LfQvNC+0LY= 199949
+LmdyYWRpZW50 199950
+IGViaWxl 199951
+IEF0aGxldGU= 199952
+INGC0rHRgNKT0LDQvQ== 199953
+IFN5cmll 199954
+LnByb2Nlc3Npbmc= 199955
+IENvbnN1bHRhbmN5 199956
+IHRocmlsbHM= 199957
+2YLYsdin2KE= 199958
+KHZhcmFyZ2lu 199959
+a8O8bmZ0ZQ== 199960
+IOW9rQ== 199961
+LWtuaXQ= 199962
+cmllZGVuaGVpdA== 199963
+IHTDpG5rYQ== 199964
+J10vLw== 199965
+dWRpZXM= 199966
+aGFjZXI= 199967
+IFRow6nDonRyZQ== 199968
+YXbnlLXlvbE= 199969
+IEFybWFkYQ== 199970
+LXRlY2hu 199971
+aWJyaXM= 199972
+dmVsaQ== 199973
+IHJhemxvZ2E= 199974
+LUhvcA== 199975
+INee15fXk9ep 199976
+IEJsb2dnZXJz 199977
+IGNvbnRyb3ZlcnNpZXM= 199978
+IOCwmuCwsOCxjeCwrw== 199979
+IOC0quC1jeC0sOC0ruC1geC0lg== 199980
+X3VuYw== 199981
+d2VyZmVu 199982
+INC70L7QvA== 199983
+INC/0L7QutGD0L/QsNGC0LXQu9C10Lk= 199984
+INCz0LvQsNC30LDQvNC4 199985
+INeR16LXldeT 199986
+IG5hbm90 199987
+CXdz 199988
+INC60LXQt9C00LXRgQ== 199989
+KToo 199990
+IFByb2R1aXQ= 199991
+QWlyY3JhZnQ= 199992
+aWZmZW4= 199993
+IHBhdHJvbmVz 199994
+IHBhcsOibWV0cm9z 199995
+Q3Vyc29z 199996
+IGNvY29z 199997
diff --git a/litellm/llms/triton.py b/litellm/llms/triton.py
index 95cf38f1fc..7708989495 100644
--- a/litellm/llms/triton.py
+++ b/litellm/llms/triton.py
@@ -61,17 +61,25 @@ class TritonChatCompletion(BaseLLM):
logging_obj.post_call(original_response=_text_response)
_json_response = response.json()
+ _embedding_output = []
_outputs = _json_response["outputs"]
- _output_data = [output["data"] for output in _outputs]
- _embedding_output = {
- "object": "embedding",
- "index": 0,
- "embedding": _output_data,
- }
+ for output in _outputs:
+ _shape = output["shape"]
+ _data = output["data"]
+ _split_output_data = self.split_embedding_by_shape(_data, _shape)
+
+ for idx, embedding in enumerate(_split_output_data):
+ _embedding_output.append(
+ {
+ "object": "embedding",
+ "index": idx,
+ "embedding": embedding,
+ }
+ )
model_response.model = _json_response.get("model_name", "None")
- model_response.data = [_embedding_output]
+ model_response.data = _embedding_output
return model_response
@@ -92,7 +100,7 @@ class TritonChatCompletion(BaseLLM):
"inputs": [
{
"name": "input_text",
- "shape": [len(input)], # size of the input data
+ "shape": [len(input)],
"datatype": "BYTES",
"data": input,
}
@@ -315,3 +323,14 @@ class TritonChatCompletion(BaseLLM):
Choices(index=0, message=Message(content=_json_response["outputs"]))
]
return model_response
+
+ @staticmethod
+ def split_embedding_by_shape(
+ data: List[float], shape: List[int]
+ ) -> List[List[float]]:
+ if len(shape) != 2:
+ raise ValueError("Shape must be of length 2.")
+ embedding_size = shape[1]
+ return [
+ data[i * embedding_size : (i + 1) * embedding_size] for i in range(shape[0])
+ ]
diff --git a/litellm/llms/vertex_ai.py b/litellm/llms/vertex_ai.py
index dc185aef9d..c891a86eeb 100644
--- a/litellm/llms/vertex_ai.py
+++ b/litellm/llms/vertex_ai.py
@@ -1,17 +1,32 @@
-import os, types
+import inspect
import json
-from enum import Enum
-import requests # type: ignore
+import os
import time
-from typing import Callable, Optional, Union, List, Literal
-from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
-import litellm, uuid
-import httpx, inspect # type: ignore
-from litellm.types.llms.vertex_ai import *
+import types
+import uuid
+from enum import Enum
+from typing import Any, Callable, List, Literal, Optional, Union
+
+import httpx # type: ignore
+import requests # type: ignore
+from pydantic import BaseModel
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
from litellm.llms.prompt_templates.factory import (
- convert_to_gemini_tool_call_result,
+ convert_to_anthropic_image_obj,
convert_to_gemini_tool_call_invoke,
+ convert_to_gemini_tool_call_result,
)
+from litellm.types.files import (
+ get_file_mime_type_for_file_type,
+ get_file_type_from_extension,
+ is_gemini_1_5_accepted_file_type,
+ is_video_file_type,
+)
+from litellm.types.llms.vertex_ai import *
+from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
class VertexAIError(Exception):
@@ -140,6 +155,7 @@ class VertexAIConfig:
"response_format",
"n",
"stop",
+ "extra_headers",
]
def map_openai_params(self, non_default_params: dict, optional_params: dict):
@@ -266,28 +282,6 @@ def _get_image_bytes_from_url(image_url: str) -> bytes:
raise Exception(f"An exception occurs with this image - {str(e)}")
-def _load_image_from_url(image_url: str):
- """
- Loads an image from a URL.
-
- Args:
- image_url (str): The URL of the image.
-
- Returns:
- Image: The loaded image.
- """
- from vertexai.preview.generative_models import (
- GenerativeModel,
- Part,
- GenerationConfig,
- Image,
- )
-
- image_bytes = _get_image_bytes_from_url(image_url)
-
- return Image.from_bytes(data=image_bytes)
-
-
def _convert_gemini_role(role: str) -> Literal["user", "model"]:
if role == "user":
return "user"
@@ -297,39 +291,27 @@ def _convert_gemini_role(role: str) -> Literal["user", "model"]:
def _process_gemini_image(image_url: str) -> PartType:
try:
+ # GCS URIs
if "gs://" in image_url:
- # Case 1: Images with Cloud Storage URIs
- # The supported MIME types for images include image/png and image/jpeg.
- part_mime = "image/png" if "png" in image_url else "image/jpeg"
- _file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
- return PartType(file_data=_file_data)
- elif "https:/" in image_url:
- # Case 2: Images with direct links
- image = _load_image_from_url(image_url)
- _blob = BlobType(data=image.data, mime_type=image._mime_type)
- return PartType(inline_data=_blob)
- elif ".mp4" in image_url and "gs://" in image_url:
- # Case 3: Videos with Cloud Storage URIs
- part_mime = "video/mp4"
- _file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
- return PartType(file_data=_file_data)
- elif "base64" in image_url:
- # Case 4: Images with base64 encoding
- import base64, re
+ # Figure out file type
+ extension_with_dot = os.path.splitext(image_url)[-1] # Ex: ".png"
+ extension = extension_with_dot[1:] # Ex: "png"
- # base 64 is passed as data:image/jpeg;base64,
- image_metadata, img_without_base_64 = image_url.split(",")
+ file_type = get_file_type_from_extension(extension)
- # read mime_type from img_without_base_64=data:image/jpeg;base64
- # Extract MIME type using regular expression
- mime_type_match = re.match(r"data:(.*?);base64", image_metadata)
+ # Validate the file type is supported by Gemini
+ if not is_gemini_1_5_accepted_file_type(file_type):
+ raise Exception(f"File type not supported by gemini - {file_type}")
- if mime_type_match:
- mime_type = mime_type_match.group(1)
- else:
- mime_type = "image/jpeg"
- decoded_img = base64.b64decode(img_without_base_64)
- _blob = BlobType(data=decoded_img, mime_type=mime_type)
+ mime_type = get_file_mime_type_for_file_type(file_type)
+ file_data = FileDataType(mime_type=mime_type, file_uri=image_url)
+
+ return PartType(file_data=file_data)
+
+ # Direct links
+ elif "https:/" in image_url or "base64" in image_url:
+ image = convert_to_anthropic_image_obj(image_url)
+ _blob = BlobType(data=image["data"], mime_type=image["media_type"])
return PartType(inline_data=_blob)
raise Exception("Invalid image received - {}".format(image_url))
except Exception as e:
@@ -347,186 +329,109 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
user_message_types = {"user", "system"}
contents: List[ContentType] = []
+ last_message_with_tool_calls = None
+
msg_i = 0
- while msg_i < len(messages):
- user_content: List[PartType] = []
- init_msg_i = msg_i
- ## MERGE CONSECUTIVE USER CONTENT ##
- while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types:
- if isinstance(messages[msg_i]["content"], list):
- _parts: List[PartType] = []
- for element in messages[msg_i]["content"]:
- if isinstance(element, dict):
- if element["type"] == "text":
- _part = PartType(text=element["text"])
- _parts.append(_part)
- elif element["type"] == "image_url":
- image_url = element["image_url"]["url"]
- _part = _process_gemini_image(image_url=image_url)
- _parts.append(_part) # type: ignore
- user_content.extend(_parts)
- else:
- _part = PartType(text=messages[msg_i]["content"])
- user_content.append(_part)
-
- msg_i += 1
-
- if user_content:
- contents.append(ContentType(role="user", parts=user_content))
- assistant_content = []
- ## MERGE CONSECUTIVE ASSISTANT CONTENT ##
- while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
- if isinstance(messages[msg_i]["content"], list):
- _parts = []
- for element in messages[msg_i]["content"]:
- if isinstance(element, dict):
- if element["type"] == "text":
- _part = PartType(text=element["text"])
- _parts.append(_part)
- elif element["type"] == "image_url":
- image_url = element["image_url"]["url"]
- _part = _process_gemini_image(image_url=image_url)
- _parts.append(_part) # type: ignore
- assistant_content.extend(_parts)
- elif messages[msg_i].get(
- "tool_calls", []
- ): # support assistant tool invoke convertion
- assistant_content.extend(
- convert_to_gemini_tool_call_invoke(messages[msg_i]["tool_calls"])
- )
- else:
- assistant_text = (
- messages[msg_i].get("content") or ""
- ) # either string or none
- if assistant_text:
- assistant_content.append(PartType(text=assistant_text))
-
- msg_i += 1
-
- if assistant_content:
- contents.append(ContentType(role="model", parts=assistant_content))
-
- ## APPEND TOOL CALL MESSAGES ##
- if msg_i < len(messages) and messages[msg_i]["role"] == "tool":
- _part = convert_to_gemini_tool_call_result(messages[msg_i])
- contents.append(ContentType(parts=[_part])) # type: ignore
- msg_i += 1
- if msg_i == init_msg_i: # prevent infinite loops
- raise Exception(
- "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
- messages[msg_i]
- )
- )
-
- return contents
-
-
-def _gemini_vision_convert_messages(messages: list):
- """
- Converts given messages for GPT-4 Vision to Gemini format.
-
- Args:
- messages (list): The messages to convert. Each message can be a dictionary with a "content" key. The content can be a string or a list of elements. If it is a string, it will be concatenated to the prompt. If it is a list, each element will be processed based on its type:
- - If the element is a dictionary with a "type" key equal to "text", its "text" value will be concatenated to the prompt.
- - If the element is a dictionary with a "type" key equal to "image_url", its "image_url" value will be added to the list of images.
-
- Returns:
- tuple: A tuple containing the prompt (a string) and the processed images (a list of objects representing the images).
-
- Raises:
- VertexAIError: If the import of the 'vertexai' module fails, indicating that 'google-cloud-aiplatform' needs to be installed.
- Exception: If any other exception occurs during the execution of the function.
-
- Note:
- This function is based on the code from the 'gemini/getting-started/intro_gemini_python.ipynb' notebook in the 'generative-ai' repository on GitHub.
- The supported MIME types for images include 'image/png' and 'image/jpeg'.
-
- Examples:
- >>> messages = [
- ... {"content": "Hello, world!"},
- ... {"content": [{"type": "text", "text": "This is a text message."}, {"type": "image_url", "image_url": "example.com/image.png"}]},
- ... ]
- >>> _gemini_vision_convert_messages(messages)
- ('Hello, world!This is a text message.', [, ])
- """
try:
- import vertexai
- except:
- raise VertexAIError(
- status_code=400,
- message="vertexai import failed please run `pip install google-cloud-aiplatform`",
- )
- try:
- from vertexai.preview.language_models import (
- ChatModel,
- CodeChatModel,
- InputOutputTextPair,
- )
- from vertexai.language_models import TextGenerationModel, CodeGenerationModel
- from vertexai.preview.generative_models import (
- GenerativeModel,
- Part,
- GenerationConfig,
- Image,
- )
+ while msg_i < len(messages):
+ user_content: List[PartType] = []
+ init_msg_i = msg_i
+ ## MERGE CONSECUTIVE USER CONTENT ##
+ while (
+ msg_i < len(messages) and messages[msg_i]["role"] in user_message_types
+ ):
+ if isinstance(messages[msg_i]["content"], list):
+ _parts: List[PartType] = []
+ for element in messages[msg_i]["content"]:
+ if isinstance(element, dict):
+ if element["type"] == "text" and len(element["text"]) > 0:
+ _part = PartType(text=element["text"])
+ _parts.append(_part)
+ elif element["type"] == "image_url":
+ image_url = element["image_url"]["url"]
+ _part = _process_gemini_image(image_url=image_url)
+ _parts.append(_part) # type: ignore
+ user_content.extend(_parts)
+ elif (
+ isinstance(messages[msg_i]["content"], str)
+ and len(messages[msg_i]["content"]) > 0
+ ):
+ _part = PartType(text=messages[msg_i]["content"])
+ user_content.append(_part)
- # given messages for gpt-4 vision, convert them for gemini
- # https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/getting-started/intro_gemini_python.ipynb
- prompt = ""
- images = []
- for message in messages:
- if isinstance(message["content"], str):
- prompt += message["content"]
- elif isinstance(message["content"], list):
- # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models
- for element in message["content"]:
- if isinstance(element, dict):
- if element["type"] == "text":
- prompt += element["text"]
- elif element["type"] == "image_url":
- image_url = element["image_url"]["url"]
- images.append(image_url)
- # processing images passed to gemini
- processed_images = []
- for img in images:
- if "gs://" in img:
- # Case 1: Images with Cloud Storage URIs
- # The supported MIME types for images include image/png and image/jpeg.
- part_mime = "image/png" if "png" in img else "image/jpeg"
- google_clooud_part = Part.from_uri(img, mime_type=part_mime)
- processed_images.append(google_clooud_part)
- elif "https:/" in img:
- # Case 2: Images with direct links
- image = _load_image_from_url(img)
- processed_images.append(image)
- elif ".mp4" in img and "gs://" in img:
- # Case 3: Videos with Cloud Storage URIs
- part_mime = "video/mp4"
- google_clooud_part = Part.from_uri(img, mime_type=part_mime)
- processed_images.append(google_clooud_part)
- elif "base64" in img:
- # Case 4: Images with base64 encoding
- import base64, re
+ msg_i += 1
- # base 64 is passed as data:image/jpeg;base64,
- image_metadata, img_without_base_64 = img.split(",")
-
- # read mime_type from img_without_base_64=data:image/jpeg;base64
- # Extract MIME type using regular expression
- mime_type_match = re.match(r"data:(.*?);base64", image_metadata)
-
- if mime_type_match:
- mime_type = mime_type_match.group(1)
+ if user_content:
+ contents.append(ContentType(role="user", parts=user_content))
+ assistant_content = []
+ ## MERGE CONSECUTIVE ASSISTANT CONTENT ##
+ while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
+ if messages[msg_i].get("content", None) is not None and isinstance(
+ messages[msg_i]["content"], list
+ ):
+ _parts = []
+ for element in messages[msg_i]["content"]:
+ if isinstance(element, dict):
+ if element["type"] == "text":
+ _part = PartType(text=element["text"])
+ _parts.append(_part)
+ elif element["type"] == "image_url":
+ image_url = element["image_url"]["url"]
+ _part = _process_gemini_image(image_url=image_url)
+ _parts.append(_part) # type: ignore
+ assistant_content.extend(_parts)
+ elif messages[msg_i].get(
+ "tool_calls", []
+ ): # support assistant tool invoke conversion
+ assistant_content.extend(
+ convert_to_gemini_tool_call_invoke(
+ messages[msg_i]["tool_calls"]
+ )
+ )
+ last_message_with_tool_calls = messages[msg_i]
else:
- mime_type = "image/jpeg"
- decoded_img = base64.b64decode(img_without_base_64)
- processed_image = Part.from_data(data=decoded_img, mime_type=mime_type)
- processed_images.append(processed_image)
- return prompt, processed_images
+ assistant_text = (
+ messages[msg_i].get("content") or ""
+ ) # either string or none
+ if assistant_text:
+ assistant_content.append(PartType(text=assistant_text))
+
+ msg_i += 1
+
+ if assistant_content:
+ contents.append(ContentType(role="model", parts=assistant_content))
+
+ ## APPEND TOOL CALL MESSAGES ##
+ if msg_i < len(messages) and messages[msg_i]["role"] == "tool":
+ _part = convert_to_gemini_tool_call_result(
+ messages[msg_i], last_message_with_tool_calls
+ )
+ contents.append(ContentType(parts=[_part])) # type: ignore
+ msg_i += 1
+ if msg_i == init_msg_i: # prevent infinite loops
+ raise Exception(
+ "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
+ messages[msg_i]
+ )
+ )
+ return contents
except Exception as e:
raise e
+def _get_client_cache_key(model: str, vertex_project: str, vertex_location: str):
+ _cache_key = f"{model}-{vertex_project}-{vertex_location}"
+ return _cache_key
+
+
+def _get_client_from_cache(client_cache_key: str):
+ return litellm.in_memory_llm_clients_cache.get(client_cache_key, None)
+
+
+def _set_client_in_cache(client_cache_key: str, vertex_llm_model: Any):
+ litellm.in_memory_llm_clients_cache[client_cache_key] = vertex_llm_model
+
+
def completion(
model: str,
messages: list,
@@ -547,7 +452,7 @@ def completion(
except:
raise VertexAIError(
status_code=400,
- message="vertexai import failed please run `pip install google-cloud-aiplatform`",
+ message="vertexai import failed please run `pip install google-cloud-aiplatform`. This is required for the 'vertex_ai/' route on LiteLLM",
)
if not (
@@ -558,45 +463,56 @@ def completion(
message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""",
)
try:
+ import google.auth # type: ignore
+ import proto # type: ignore
+ from google.cloud import aiplatform # type: ignore
+ from google.cloud.aiplatform_v1beta1.types import (
+ content as gapic_content_types, # type: ignore
+ )
+ from google.protobuf import json_format # type: ignore
+ from google.protobuf.struct_pb2 import Value # type: ignore
+ from vertexai.language_models import CodeGenerationModel, TextGenerationModel
+ from vertexai.preview.generative_models import (
+ GenerationConfig,
+ GenerativeModel,
+ Part,
+ )
from vertexai.preview.language_models import (
ChatModel,
CodeChatModel,
InputOutputTextPair,
)
- from vertexai.language_models import TextGenerationModel, CodeGenerationModel
- from vertexai.preview.generative_models import (
- GenerativeModel,
- Part,
- GenerationConfig,
- )
- from google.cloud import aiplatform # type: ignore
- from google.protobuf import json_format # type: ignore
- from google.protobuf.struct_pb2 import Value # type: ignore
- from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types # type: ignore
- import google.auth # type: ignore
- import proto # type: ignore
## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744
print_verbose(
f"VERTEX AI: vertex_project={vertex_project}; vertex_location={vertex_location}"
)
- if vertex_credentials is not None and isinstance(vertex_credentials, str):
- import google.oauth2.service_account
- json_obj = json.loads(vertex_credentials)
+ _cache_key = _get_client_cache_key(
+ model=model, vertex_project=vertex_project, vertex_location=vertex_location
+ )
+ _vertex_llm_model_object = _get_client_from_cache(client_cache_key=_cache_key)
- creds = google.oauth2.service_account.Credentials.from_service_account_info(
- json_obj,
- scopes=["https://www.googleapis.com/auth/cloud-platform"],
+ if _vertex_llm_model_object is None:
+ if vertex_credentials is not None and isinstance(vertex_credentials, str):
+ import google.oauth2.service_account
+
+ json_obj = json.loads(vertex_credentials)
+
+ creds = (
+ google.oauth2.service_account.Credentials.from_service_account_info(
+ json_obj,
+ scopes=["https://www.googleapis.com/auth/cloud-platform"],
+ )
+ )
+ else:
+ creds, _ = google.auth.default(quota_project_id=vertex_project)
+ print_verbose(
+ f"VERTEX AI: creds={creds}; google application credentials: {os.getenv('GOOGLE_APPLICATION_CREDENTIALS')}"
+ )
+ vertexai.init(
+ project=vertex_project, location=vertex_location, credentials=creds
)
- else:
- creds, _ = google.auth.default(quota_project_id=vertex_project)
- print_verbose(
- f"VERTEX AI: creds={creds}; google application credentials: {os.getenv('GOOGLE_APPLICATION_CREDENTIALS')}"
- )
- vertexai.init(
- project=vertex_project, location=vertex_location, credentials=creds
- )
## Load Config
config = litellm.VertexAIConfig.get_config()
@@ -620,9 +536,9 @@ def completion(
prompt = " ".join(
[
- message["content"]
+ message.get("content")
for message in messages
- if isinstance(message["content"], str)
+ if isinstance(message.get("content", None), str)
]
)
@@ -639,23 +555,27 @@ def completion(
model in litellm.vertex_language_models
or model in litellm.vertex_vision_models
):
- llm_model = GenerativeModel(model)
+ llm_model = _vertex_llm_model_object or GenerativeModel(model)
mode = "vision"
request_str += f"llm_model = GenerativeModel({model})\n"
elif model in litellm.vertex_chat_models:
- llm_model = ChatModel.from_pretrained(model)
+ llm_model = _vertex_llm_model_object or ChatModel.from_pretrained(model)
mode = "chat"
request_str += f"llm_model = ChatModel.from_pretrained({model})\n"
elif model in litellm.vertex_text_models:
- llm_model = TextGenerationModel.from_pretrained(model)
+ llm_model = _vertex_llm_model_object or TextGenerationModel.from_pretrained(
+ model
+ )
mode = "text"
request_str += f"llm_model = TextGenerationModel.from_pretrained({model})\n"
elif model in litellm.vertex_code_text_models:
- llm_model = CodeGenerationModel.from_pretrained(model)
+ llm_model = _vertex_llm_model_object or CodeGenerationModel.from_pretrained(
+ model
+ )
mode = "text"
request_str += f"llm_model = CodeGenerationModel.from_pretrained({model})\n"
elif model in litellm.vertex_code_chat_models: # vertex_code_llm_models
- llm_model = CodeChatModel.from_pretrained(model)
+ llm_model = _vertex_llm_model_object or CodeChatModel.from_pretrained(model)
mode = "chat"
request_str += f"llm_model = CodeChatModel.from_pretrained({model})\n"
elif model == "private":
@@ -683,7 +603,7 @@ def completion(
llm_model = None
# NOTE: async prediction and streaming under "private" mode isn't supported by aiplatform right now
- if acompletion == True:
+ if acompletion is True:
data = {
"llm_model": llm_model,
"mode": mode,
@@ -715,7 +635,7 @@ def completion(
tools = optional_params.pop("tools", None)
content = _gemini_convert_messages_with_history(messages=messages)
stream = optional_params.pop("stream", False)
- if stream == True:
+ if stream is True:
request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), safety_settings={safety_settings}, stream={stream})\n"
logging_obj.pre_call(
input=prompt,
@@ -932,16 +852,14 @@ def completion(
## RESPONSE OBJECT
if isinstance(completion_response, litellm.Message):
- model_response["choices"][0]["message"] = completion_response
+ model_response.choices[0].message = completion_response # type: ignore
elif len(str(completion_response)) > 0:
- model_response["choices"][0]["message"]["content"] = str(
- completion_response
- )
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.choices[0].message.content = str(completion_response) # type: ignore
+ model_response.created = int(time.time())
+ model_response.model = model
## CALCULATING USAGE
if model in litellm.vertex_language_models and response_obj is not None:
- model_response["choices"][0].finish_reason = map_finish_reason(
+ model_response.choices[0].finish_reason = map_finish_reason(
response_obj.candidates[0].finish_reason.name
)
usage = Usage(
@@ -992,7 +910,7 @@ async def async_completion(
request_str: str,
print_verbose: Callable,
logging_obj,
- encoding=None,
+ encoding,
client_options=None,
instances=None,
vertex_project=None,
@@ -1034,6 +952,15 @@ async def async_completion(
tools=tools,
)
+ _cache_key = _get_client_cache_key(
+ model=model,
+ vertex_project=vertex_project,
+ vertex_location=vertex_location,
+ )
+ _set_client_in_cache(
+ client_cache_key=_cache_key, vertex_llm_model=llm_model
+ )
+
if tools is not None and bool(
getattr(response.candidates[0].content.parts[0], "function_call", None)
):
@@ -1159,16 +1086,16 @@ async def async_completion(
## RESPONSE OBJECT
if isinstance(completion_response, litellm.Message):
- model_response["choices"][0]["message"] = completion_response
+ model_response.choices[0].message = completion_response # type: ignore
elif len(str(completion_response)) > 0:
- model_response["choices"][0]["message"]["content"] = str(
+ model_response.choices[0].message.content = str( # type: ignore
completion_response
)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
## CALCULATING USAGE
if model in litellm.vertex_language_models and response_obj is not None:
- model_response["choices"][0].finish_reason = map_finish_reason(
+ model_response.choices[0].finish_reason = map_finish_reason(
response_obj.candidates[0].finish_reason.name
)
usage = Usage(
@@ -1356,19 +1283,108 @@ async def async_streaming(
return streamwrapper
+class VertexAITextEmbeddingConfig(BaseModel):
+ """
+ Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#TextEmbeddingInput
+
+ Args:
+ auto_truncate: Optional(bool) If True, will truncate input text to fit within the model's max input length.
+ task_type: Optional(str) The type of task to be performed. The default is "RETRIEVAL_QUERY".
+ title: Optional(str) The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).
+ """
+
+ auto_truncate: Optional[bool] = None
+ task_type: Optional[
+ Literal[
+ "RETRIEVAL_QUERY",
+ "RETRIEVAL_DOCUMENT",
+ "SEMANTIC_SIMILARITY",
+ "CLASSIFICATION",
+ "CLUSTERING",
+ "QUESTION_ANSWERING",
+ "FACT_VERIFICATION",
+ ]
+ ] = None
+ title: Optional[str] = None
+
+ def __init__(
+ self,
+ auto_truncate: Optional[bool] = None,
+ task_type: Optional[
+ Literal[
+ "RETRIEVAL_QUERY",
+ "RETRIEVAL_DOCUMENT",
+ "SEMANTIC_SIMILARITY",
+ "CLASSIFICATION",
+ "CLUSTERING",
+ "QUESTION_ANSWERING",
+ "FACT_VERIFICATION",
+ ]
+ ] = None,
+ title: Optional[str] = None,
+ ) -> None:
+ locals_ = locals()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self):
+ return [
+ "dimensions",
+ ]
+
+ def map_openai_params(self, non_default_params: dict, optional_params: dict):
+ for param, value in non_default_params.items():
+ if param == "dimensions":
+ optional_params["output_dimensionality"] = value
+ return optional_params
+
+ def get_mapped_special_auth_params(self) -> dict:
+ """
+ Common auth params across bedrock/vertex_ai/azure/watsonx
+ """
+ return {"project": "vertex_project", "region_name": "vertex_location"}
+
+ def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
+ mapped_params = self.get_mapped_special_auth_params()
+
+ for param, value in non_default_params.items():
+ if param in mapped_params:
+ optional_params[mapped_params[param]] = value
+ return optional_params
+
+
def embedding(
model: str,
input: Union[list, str],
+ print_verbose,
+ model_response: litellm.EmbeddingResponse,
+ optional_params: dict,
api_key: Optional[str] = None,
logging_obj=None,
- model_response=None,
- optional_params=None,
encoding=None,
vertex_project=None,
vertex_location=None,
vertex_credentials=None,
aembedding=False,
- print_verbose=None,
):
# logic for parsing in - calling - parsing out model embedding calls
try:
@@ -1379,8 +1395,8 @@ def embedding(
message="vertexai import failed please run `pip install google-cloud-aiplatform`",
)
- from vertexai.language_models import TextEmbeddingModel
import google.auth # type: ignore
+ from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744
try:
@@ -1410,6 +1426,16 @@ def embedding(
if isinstance(input, str):
input = [input]
+ if optional_params is not None and isinstance(optional_params, dict):
+ if optional_params.get("task_type") or optional_params.get("title"):
+ # if user passed task_type or title, cast to TextEmbeddingInput
+ _task_type = optional_params.pop("task_type", None)
+ _title = optional_params.pop("title", None)
+ input = [
+ TextEmbeddingInput(text=x, task_type=_task_type, title=_title)
+ for x in input
+ ]
+
try:
llm_model = TextEmbeddingModel.from_pretrained(model)
except Exception as e:
@@ -1426,7 +1452,8 @@ def embedding(
encoding=encoding,
)
- request_str = f"""embeddings = llm_model.get_embeddings({input})"""
+ _input_dict = {"texts": input, **optional_params}
+ request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
## LOGGING PRE-CALL
logging_obj.pre_call(
input=input,
@@ -1438,7 +1465,7 @@ def embedding(
)
try:
- embeddings = llm_model.get_embeddings(input)
+ embeddings = llm_model.get_embeddings(**_input_dict)
except Exception as e:
raise VertexAIError(status_code=500, message=str(e))
@@ -1446,6 +1473,7 @@ def embedding(
logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
## Populate OpenAI compliant dictionary
embedding_response = []
+ input_tokens: int = 0
for idx, embedding in enumerate(embeddings):
embedding_response.append(
{
@@ -1454,19 +1482,15 @@ def embedding(
"embedding": embedding.values,
}
)
- model_response["object"] = "list"
- model_response["data"] = embedding_response
- model_response["model"] = model
- input_tokens = 0
-
- input_str = "".join(input)
-
- input_tokens += len(encoding.encode(input_str))
+ input_tokens += embedding.statistics.token_count # type: ignore
+ model_response.object = "list"
+ model_response.data = embedding_response
+ model_response.model = model
usage = Usage(
prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
)
- model_response.usage = usage
+ setattr(model_response, "usage", usage)
return model_response
@@ -1474,8 +1498,8 @@ def embedding(
async def async_embedding(
model: str,
input: Union[list, str],
+ model_response: litellm.EmbeddingResponse,
logging_obj=None,
- model_response=None,
optional_params=None,
encoding=None,
client=None,
@@ -1483,7 +1507,8 @@ async def async_embedding(
"""
Async embedding implementation
"""
- request_str = f"""embeddings = llm_model.get_embeddings({input})"""
+ _input_dict = {"texts": input, **optional_params}
+ request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
## LOGGING PRE-CALL
logging_obj.pre_call(
input=input,
@@ -1495,7 +1520,7 @@ async def async_embedding(
)
try:
- embeddings = await client.get_embeddings_async(input)
+ embeddings = await client.get_embeddings_async(**_input_dict)
except Exception as e:
raise VertexAIError(status_code=500, message=str(e))
@@ -1503,6 +1528,7 @@ async def async_embedding(
logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
## Populate OpenAI compliant dictionary
embedding_response = []
+ input_tokens: int = 0
for idx, embedding in enumerate(embeddings):
embedding_response.append(
{
@@ -1511,18 +1537,13 @@ async def async_embedding(
"embedding": embedding.values,
}
)
- model_response["object"] = "list"
- model_response["data"] = embedding_response
- model_response["model"] = model
- input_tokens = 0
-
- input_str = "".join(input)
-
- input_tokens += len(encoding.encode(input_str))
+ input_tokens += embedding.statistics.token_count
+ model_response.object = "list"
+ model_response.data = embedding_response
+ model_response.model = model
usage = Usage(
prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
)
- model_response.usage = usage
-
+ setattr(model_response, "usage", usage)
return model_response
diff --git a/litellm/llms/vertex_ai_anthropic.py b/litellm/llms/vertex_ai_anthropic.py
index 0652942801..900e7795f7 100644
--- a/litellm/llms/vertex_ai_anthropic.py
+++ b/litellm/llms/vertex_ai_anthropic.py
@@ -1,23 +1,40 @@
# What is this?
## Handler file for calling claude-3 on vertex ai
-import os, types
+import copy
import json
+import os
+import time
+import types
+import uuid
from enum import Enum
-import requests, copy # type: ignore
-import time, uuid
-from typing import Callable, Optional, List
-from litellm.utils import ModelResponse, Usage, map_finish_reason, CustomStreamWrapper
+from typing import Any, Callable, List, Optional, Tuple, Union
+
+import httpx # type: ignore
+import requests # type: ignore
+
import litellm
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.llms.anthropic import (
+ AnthropicMessagesTool,
+ AnthropicMessagesToolChoice,
+)
+from litellm.types.llms.openai import (
+ ChatCompletionToolParam,
+ ChatCompletionToolParamFunctionChunk,
+)
+from litellm.types.utils import ResponseFormatChunk
+from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
+
from .prompt_templates.factory import (
- contains_tag,
- prompt_factory,
- custom_prompt,
construct_tool_use_system_prompt,
+ contains_tag,
+ custom_prompt,
extract_between_tags,
parse_xml_params,
+ prompt_factory,
+ response_schema_prompt,
)
-import httpx # type: ignore
class VertexAIError(Exception):
@@ -103,6 +120,7 @@ class VertexAIAnthropicConfig:
"stop",
"temperature",
"top_p",
+ "response_format",
]
def map_openai_params(self, non_default_params: dict, optional_params: dict):
@@ -111,6 +129,17 @@ class VertexAIAnthropicConfig:
optional_params["max_tokens"] = value
if param == "tools":
optional_params["tools"] = value
+ if param == "tool_choice":
+ _tool_choice: Optional[AnthropicMessagesToolChoice] = None
+ if value == "auto":
+ _tool_choice = {"type": "auto"}
+ elif value == "required":
+ _tool_choice = {"type": "any"}
+ elif isinstance(value, dict):
+ _tool_choice = {"type": "tool", "name": value["function"]["name"]}
+
+ if _tool_choice is not None:
+ optional_params["tool_choice"] = _tool_choice
if param == "stream":
optional_params["stream"] = value
if param == "stop":
@@ -119,6 +148,28 @@ class VertexAIAnthropicConfig:
optional_params["temperature"] = value
if param == "top_p":
optional_params["top_p"] = value
+ if param == "response_format" and "response_schema" in value:
+ """
+ When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode
+ - You usually want to provide a single tool
+ - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool
+ - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective.
+ """
+ _tool_choice = None
+ _tool_choice = {"name": "json_tool_call", "type": "tool"}
+
+ _tool = AnthropicMessagesTool(
+ name="json_tool_call",
+ input_schema={
+ "type": "object",
+ "properties": {"values": value["response_schema"]}, # type: ignore
+ },
+ )
+
+ optional_params["tools"] = [_tool]
+ optional_params["tool_choice"] = _tool_choice
+ optional_params["json_mode"] = True
+
return optional_params
@@ -128,7 +179,6 @@ class VertexAIAnthropicConfig:
"""
-# makes headers for API call
def refresh_auth(
credentials,
) -> str: # used when user passes in credentials as json string
@@ -143,6 +193,52 @@ def refresh_auth(
return credentials.token
+def get_vertex_client(
+ client: Any,
+ vertex_project: Optional[str],
+ vertex_location: Optional[str],
+ vertex_credentials: Optional[str],
+) -> Tuple[Any, Optional[str]]:
+ args = locals()
+ from litellm.llms.vertex_httpx import VertexLLM
+
+ try:
+ from anthropic import AnthropicVertex
+ except Exception:
+ raise VertexAIError(
+ status_code=400,
+ message="""vertexai import failed please run `pip install -U google-cloud-aiplatform "anthropic[vertex]"`""",
+ )
+
+ access_token: Optional[str] = None
+
+ if client is None:
+ _credentials, cred_project_id = VertexLLM().load_auth(
+ credentials=vertex_credentials, project_id=vertex_project
+ )
+
+ vertex_ai_client = AnthropicVertex(
+ project_id=vertex_project or cred_project_id,
+ region=vertex_location or "us-central1",
+ access_token=_credentials.token,
+ )
+ access_token = _credentials.token
+ else:
+ vertex_ai_client = client
+ access_token = client.access_token
+
+ return vertex_ai_client, access_token
+
+
+def create_vertex_anthropic_url(
+ vertex_location: str, vertex_project: str, model: str, stream: bool
+) -> str:
+ if stream is True:
+ return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/anthropic/models/{model}:streamRawPredict"
+ else:
+ return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/anthropic/models/{model}:rawPredict"
+
+
def completion(
model: str,
messages: list,
@@ -150,10 +246,13 @@ def completion(
print_verbose: Callable,
encoding,
logging_obj,
+ optional_params: dict,
+ custom_prompt_dict: dict,
+ headers: Optional[dict],
+ timeout: Union[float, httpx.Timeout],
vertex_project=None,
vertex_location=None,
vertex_credentials=None,
- optional_params=None,
litellm_params=None,
logger_fn=None,
acompletion: bool = False,
@@ -162,6 +261,9 @@ def completion(
try:
import vertexai
from anthropic import AnthropicVertex
+
+ from litellm.llms.anthropic import AnthropicChatCompletion
+ from litellm.llms.vertex_httpx import VertexLLM
except:
raise VertexAIError(
status_code=400,
@@ -177,180 +279,60 @@ def completion(
)
try:
+ vertex_httpx_logic = VertexLLM()
+
+ access_token, project_id = vertex_httpx_logic._ensure_access_token(
+ credentials=vertex_credentials, project_id=vertex_project
+ )
+
+ anthropic_chat_completions = AnthropicChatCompletion()
+
## Load Config
config = litellm.VertexAIAnthropicConfig.get_config()
for k, v in config.items():
if k not in optional_params:
optional_params[k] = v
- ## Format Prompt
- _is_function_call = False
- messages = copy.deepcopy(messages)
- optional_params = copy.deepcopy(optional_params)
- # Separate system prompt from rest of message
- system_prompt_indices = []
- system_prompt = ""
- for idx, message in enumerate(messages):
- if message["role"] == "system":
- system_prompt += message["content"]
- system_prompt_indices.append(idx)
- if len(system_prompt_indices) > 0:
- for idx in reversed(system_prompt_indices):
- messages.pop(idx)
- if len(system_prompt) > 0:
- optional_params["system"] = system_prompt
- # Format rest of message according to anthropic guidelines
- try:
- messages = prompt_factory(
- model=model, messages=messages, custom_llm_provider="anthropic_xml"
- )
- except Exception as e:
- raise VertexAIError(status_code=400, message=str(e))
+ ## CONSTRUCT API BASE
+ stream = optional_params.get("stream", False)
- ## Handle Tool Calling
- if "tools" in optional_params:
- _is_function_call = True
- tool_calling_system_prompt = construct_tool_use_system_prompt(
- tools=optional_params["tools"]
- )
- optional_params["system"] = (
- optional_params.get("system", "\n") + tool_calling_system_prompt
- ) # add the anthropic tool calling prompt to the system prompt
- optional_params.pop("tools")
-
- stream = optional_params.pop("stream", None)
-
- data = {
- "model": model,
- "messages": messages,
- **optional_params,
- }
- print_verbose(f"_is_function_call: {_is_function_call}")
-
- ## Completion Call
-
- print_verbose(
- f"VERTEX AI: vertex_project={vertex_project}; vertex_location={vertex_location}; vertex_credentials={vertex_credentials}"
+ api_base = create_vertex_anthropic_url(
+ vertex_location=vertex_location or "us-central1",
+ vertex_project=vertex_project or project_id,
+ model=model,
+ stream=stream,
)
- access_token = None
- if client is None:
- if vertex_credentials is not None and isinstance(vertex_credentials, str):
- import google.oauth2.service_account
- json_obj = json.loads(vertex_credentials)
-
- creds = (
- google.oauth2.service_account.Credentials.from_service_account_info(
- json_obj,
- scopes=["https://www.googleapis.com/auth/cloud-platform"],
- )
- )
- ### CHECK IF ACCESS
- access_token = refresh_auth(credentials=creds)
-
- vertex_ai_client = AnthropicVertex(
- project_id=vertex_project,
- region=vertex_location,
- access_token=access_token,
- )
+ if headers is not None:
+ vertex_headers = headers
else:
- vertex_ai_client = client
+ vertex_headers = {}
- if acompletion == True:
- """
- - async streaming
- - async completion
- """
- if stream is not None and stream == True:
- return async_streaming(
- model=model,
- messages=messages,
- data=data,
- print_verbose=print_verbose,
- model_response=model_response,
- logging_obj=logging_obj,
- vertex_project=vertex_project,
- vertex_location=vertex_location,
- optional_params=optional_params,
- client=client,
- access_token=access_token,
- )
- else:
- return async_completion(
- model=model,
- messages=messages,
- data=data,
- print_verbose=print_verbose,
- model_response=model_response,
- logging_obj=logging_obj,
- vertex_project=vertex_project,
- vertex_location=vertex_location,
- optional_params=optional_params,
- client=client,
- access_token=access_token,
- )
- if stream is not None and stream == True:
- ## LOGGING
- logging_obj.pre_call(
- input=messages,
- api_key=None,
- additional_args={
- "complete_input_dict": optional_params,
- },
- )
- response = vertex_ai_client.messages.create(**data, stream=True) # type: ignore
- return response
+ vertex_headers.update({"Authorization": "Bearer {}".format(access_token)})
- ## LOGGING
- logging_obj.pre_call(
- input=messages,
- api_key=None,
- additional_args={
- "complete_input_dict": optional_params,
- },
+ optional_params.update(
+ {"anthropic_version": "vertex-2023-10-16", "is_vertex_request": True}
)
- message = vertex_ai_client.messages.create(**data) # type: ignore
- text_content = message.content[0].text
- ## TOOL CALLING - OUTPUT PARSE
- if text_content is not None and contains_tag("invoke", text_content):
- function_name = extract_between_tags("tool_name", text_content)[0]
- function_arguments_str = extract_between_tags("invoke", text_content)[
- 0
- ].strip()
- function_arguments_str = f"{function_arguments_str}"
- function_arguments = parse_xml_params(function_arguments_str)
- _message = litellm.Message(
- tool_calls=[
- {
- "id": f"call_{uuid.uuid4()}",
- "type": "function",
- "function": {
- "name": function_name,
- "arguments": json.dumps(function_arguments),
- },
- }
- ],
- content=None,
- )
- model_response.choices[0].message = _message # type: ignore
- else:
- model_response.choices[0].message.content = text_content # type: ignore
- model_response.choices[0].finish_reason = map_finish_reason(message.stop_reason)
-
- ## CALCULATING USAGE
- prompt_tokens = message.usage.input_tokens
- completion_tokens = message.usage.output_tokens
-
- model_response["created"] = int(time.time())
- model_response["model"] = model
- usage = Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
+ return anthropic_chat_completions.completion(
+ model=model,
+ messages=messages,
+ api_base=api_base,
+ custom_prompt_dict=custom_prompt_dict,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ api_key=access_token,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ acompletion=acompletion,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ headers=vertex_headers,
+ client=client,
+ timeout=timeout,
)
- setattr(model_response, "usage", usage)
- return model_response
+
except Exception as e:
raise VertexAIError(status_code=500, message=str(e))
@@ -415,8 +397,8 @@ async def async_completion(
prompt_tokens = message.usage.input_tokens
completion_tokens = message.usage.output_tokens
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/vertex_httpx.py b/litellm/llms/vertex_httpx.py
index b8c698c901..03b0a56ccd 100644
--- a/litellm/llms/vertex_httpx.py
+++ b/litellm/llms/vertex_httpx.py
@@ -1,16 +1,546 @@
-import os, types
+# What is this?
+## httpx client for vertex ai calls
+## Initial implementation - covers gemini + image gen calls
+import inspect
import json
-from enum import Enum
-import requests # type: ignore
+import os
import time
-from typing import Callable, Optional, Union, List, Any, Tuple
-from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
-import litellm, uuid
-import httpx, inspect # type: ignore
+import types
+import uuid
+from enum import Enum
+from functools import partial
+from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
+
+import httpx # type: ignore
+import requests # type: ignore
+
+import litellm
+import litellm.litellm_core_utils
+import litellm.litellm_core_utils.litellm_logging
+from litellm import verbose_logger
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.llms.prompt_templates.factory import (
+ convert_url_to_base64,
+ response_schema_prompt,
+)
+from litellm.llms.vertex_ai import _gemini_convert_messages_with_history
+from litellm.types.llms.openai import (
+ ChatCompletionResponseMessage,
+ ChatCompletionToolCallChunk,
+ ChatCompletionToolCallFunctionChunk,
+ ChatCompletionUsageBlock,
+)
+from litellm.types.llms.vertex_ai import (
+ ContentType,
+ FunctionCallingConfig,
+ FunctionDeclaration,
+ GenerateContentResponseBody,
+ GenerationConfig,
+ PartType,
+ RequestBody,
+ SafetSettingsConfig,
+ SystemInstructions,
+ ToolConfig,
+ Tools,
+)
+from litellm.types.utils import GenericStreamingChunk
+from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
+
from .base import BaseLLM
+class GoogleAIStudioGeminiConfig: # key diff from VertexAI - 'frequency_penalty' and 'presence_penalty' not supported
+ """
+ Reference: https://ai.google.dev/api/rest/v1beta/GenerationConfig
+
+ The class `GoogleAIStudioGeminiConfig` provides configuration for the Google AI Studio's Gemini API interface. Below are the parameters:
+
+ - `temperature` (float): This controls the degree of randomness in token selection.
+
+ - `max_output_tokens` (integer): This sets the limitation for the maximum amount of token in the text output. In this case, the default value is 256.
+
+ - `top_p` (float): The tokens are selected from the most probable to the least probable until the sum of their probabilities equals the `top_p` value. Default is 0.95.
+
+ - `top_k` (integer): The value of `top_k` determines how many of the most probable tokens are considered in the selection. For example, a `top_k` of 1 means the selected token is the most probable among all tokens. The default value is 40.
+
+ - `response_mime_type` (str): The MIME type of the response. The default value is 'text/plain'. Other values - `application/json`.
+
+ - `response_schema` (dict): Optional. Output response schema of the generated candidate text when response mime type can have schema. Schema can be objects, primitives or arrays and is a subset of OpenAPI schema. If set, a compatible response_mime_type must also be set. Compatible mimetypes: application/json: Schema for JSON response.
+
+ - `candidate_count` (int): Number of generated responses to return.
+
+ - `stop_sequences` (List[str]): The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
+
+ Note: Please make sure to modify the default parameters as required for your use case.
+ """
+
+ temperature: Optional[float] = None
+ max_output_tokens: Optional[int] = None
+ top_p: Optional[float] = None
+ top_k: Optional[int] = None
+ response_mime_type: Optional[str] = None
+ response_schema: Optional[dict] = None
+ candidate_count: Optional[int] = None
+ stop_sequences: Optional[list] = None
+
+ def __init__(
+ self,
+ temperature: Optional[float] = None,
+ max_output_tokens: Optional[int] = None,
+ top_p: Optional[float] = None,
+ top_k: Optional[int] = None,
+ response_mime_type: Optional[str] = None,
+ response_schema: Optional[dict] = None,
+ candidate_count: Optional[int] = None,
+ stop_sequences: Optional[list] = None,
+ ) -> None:
+ locals_ = locals()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self):
+ return [
+ "temperature",
+ "top_p",
+ "max_tokens",
+ "stream",
+ "tools",
+ "tool_choice",
+ "response_format",
+ "n",
+ "stop",
+ ]
+
+ def map_tool_choice_values(
+ self, model: str, tool_choice: Union[str, dict]
+ ) -> Optional[ToolConfig]:
+ if tool_choice == "none":
+ return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="NONE"))
+ elif tool_choice == "required":
+ return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="ANY"))
+ elif tool_choice == "auto":
+ return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="AUTO"))
+ elif isinstance(tool_choice, dict):
+ # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
+ name = tool_choice.get("function", {}).get("name", "")
+ return ToolConfig(
+ functionCallingConfig=FunctionCallingConfig(
+ mode="ANY", allowed_function_names=[name]
+ )
+ )
+ else:
+ raise litellm.utils.UnsupportedParamsError(
+ message="VertexAI doesn't support tool_choice={}. Supported tool_choice values=['auto', 'required', json object]. To drop it from the call, set `litellm.drop_params = True.".format(
+ tool_choice
+ ),
+ status_code=400,
+ )
+
+ def map_openai_params(
+ self,
+ model: str,
+ non_default_params: dict,
+ optional_params: dict,
+ ):
+ for param, value in non_default_params.items():
+ if param == "temperature":
+ optional_params["temperature"] = value
+ if param == "top_p":
+ optional_params["top_p"] = value
+ if (
+ param == "stream" and value is True
+ ): # sending stream = False, can cause it to get passed unchecked and raise issues
+ optional_params["stream"] = value
+ if param == "n":
+ optional_params["candidate_count"] = value
+ if param == "stop":
+ if isinstance(value, str):
+ optional_params["stop_sequences"] = [value]
+ elif isinstance(value, list):
+ optional_params["stop_sequences"] = value
+ if param == "max_tokens":
+ optional_params["max_output_tokens"] = value
+ if param == "response_format" and value["type"] == "json_object": # type: ignore
+ optional_params["response_mime_type"] = "application/json"
+ if param == "tools" and isinstance(value, list):
+ gtool_func_declarations = []
+ for tool in value:
+ _parameters = tool.get("function", {}).get("parameters", {})
+ _properties = _parameters.get("properties", {})
+ if isinstance(_properties, dict):
+ for _, _property in _properties.items():
+ if "enum" in _property and "format" not in _property:
+ _property["format"] = "enum"
+
+ gtool_func_declaration = FunctionDeclaration(
+ name=tool["function"]["name"],
+ description=tool["function"].get("description", ""),
+ parameters=_parameters,
+ )
+ gtool_func_declarations.append(gtool_func_declaration)
+ optional_params["tools"] = [
+ Tools(function_declarations=gtool_func_declarations)
+ ]
+ if param == "tool_choice" and (
+ isinstance(value, str) or isinstance(value, dict)
+ ):
+ _tool_choice_value = self.map_tool_choice_values(
+ model=model, tool_choice=value # type: ignore
+ )
+ if _tool_choice_value is not None:
+ optional_params["tool_choice"] = _tool_choice_value
+ return optional_params
+
+ def get_mapped_special_auth_params(self) -> dict:
+ """
+ Common auth params across bedrock/vertex_ai/azure/watsonx
+ """
+ return {"project": "vertex_project", "region_name": "vertex_location"}
+
+ def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
+ mapped_params = self.get_mapped_special_auth_params()
+
+ for param, value in non_default_params.items():
+ if param in mapped_params:
+ optional_params[mapped_params[param]] = value
+ return optional_params
+
+ def get_flagged_finish_reasons(self) -> Dict[str, str]:
+ """
+ Return Dictionary of finish reasons which indicate response was flagged
+
+ and what it means
+ """
+ return {
+ "SAFETY": "The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output.",
+ "RECITATION": "The token generation was stopped as the response was flagged for unauthorized citations.",
+ "BLOCKLIST": "The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist.",
+ "PROHIBITED_CONTENT": "The token generation was stopped as the response was flagged for the prohibited contents.",
+ "SPII": "The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents.",
+ }
+
+
+class VertexGeminiConfig:
+ """
+ Reference: https://cloud.google.com/vertex-ai/docs/generative-ai/chat/test-chat-prompts
+ Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+
+ The class `VertexAIConfig` provides configuration for the VertexAI's API interface. Below are the parameters:
+
+ - `temperature` (float): This controls the degree of randomness in token selection.
+
+ - `max_output_tokens` (integer): This sets the limitation for the maximum amount of token in the text output. In this case, the default value is 256.
+
+ - `top_p` (float): The tokens are selected from the most probable to the least probable until the sum of their probabilities equals the `top_p` value. Default is 0.95.
+
+ - `top_k` (integer): The value of `top_k` determines how many of the most probable tokens are considered in the selection. For example, a `top_k` of 1 means the selected token is the most probable among all tokens. The default value is 40.
+
+ - `response_mime_type` (str): The MIME type of the response. The default value is 'text/plain'.
+
+ - `candidate_count` (int): Number of generated responses to return.
+
+ - `stop_sequences` (List[str]): The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
+
+ - `frequency_penalty` (float): This parameter is used to penalize the model from repeating the same output. The default value is 0.0.
+
+ - `presence_penalty` (float): This parameter is used to penalize the model from generating the same output as the input. The default value is 0.0.
+
+ - `seed` (int): The seed value is used to help generate the same output for the same input. The default value is None.
+
+ Note: Please make sure to modify the default parameters as required for your use case.
+ """
+
+ temperature: Optional[float] = None
+ max_output_tokens: Optional[int] = None
+ top_p: Optional[float] = None
+ top_k: Optional[int] = None
+ response_mime_type: Optional[str] = None
+ candidate_count: Optional[int] = None
+ stop_sequences: Optional[list] = None
+ frequency_penalty: Optional[float] = None
+ presence_penalty: Optional[float] = None
+ seed: Optional[int] = None
+
+ def __init__(
+ self,
+ temperature: Optional[float] = None,
+ max_output_tokens: Optional[int] = None,
+ top_p: Optional[float] = None,
+ top_k: Optional[int] = None,
+ response_mime_type: Optional[str] = None,
+ candidate_count: Optional[int] = None,
+ stop_sequences: Optional[list] = None,
+ frequency_penalty: Optional[float] = None,
+ presence_penalty: Optional[float] = None,
+ seed: Optional[int] = None,
+ ) -> None:
+ locals_ = locals()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self):
+ return [
+ "temperature",
+ "top_p",
+ "max_tokens",
+ "stream",
+ "tools",
+ "tool_choice",
+ "response_format",
+ "n",
+ "stop",
+ "frequency_penalty",
+ "presence_penalty",
+ "seed",
+ ]
+
+ def map_tool_choice_values(
+ self, model: str, tool_choice: Union[str, dict]
+ ) -> Optional[ToolConfig]:
+ if tool_choice == "none":
+ return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="NONE"))
+ elif tool_choice == "required":
+ return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="ANY"))
+ elif tool_choice == "auto":
+ return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="AUTO"))
+ elif isinstance(tool_choice, dict):
+ # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
+ name = tool_choice.get("function", {}).get("name", "")
+ return ToolConfig(
+ functionCallingConfig=FunctionCallingConfig(
+ mode="ANY", allowed_function_names=[name]
+ )
+ )
+ else:
+ raise litellm.utils.UnsupportedParamsError(
+ message="VertexAI doesn't support tool_choice={}. Supported tool_choice values=['auto', 'required', json object]. To drop it from the call, set `litellm.drop_params = True.".format(
+ tool_choice
+ ),
+ status_code=400,
+ )
+
+ def map_openai_params(
+ self,
+ model: str,
+ non_default_params: dict,
+ optional_params: dict,
+ drop_params: bool,
+ ):
+ for param, value in non_default_params.items():
+ if param == "temperature":
+ optional_params["temperature"] = value
+ if param == "top_p":
+ optional_params["top_p"] = value
+ if (
+ param == "stream" and value is True
+ ): # sending stream = False, can cause it to get passed unchecked and raise issues
+ optional_params["stream"] = value
+ if param == "n":
+ optional_params["candidate_count"] = value
+ if param == "stop":
+ if isinstance(value, str):
+ optional_params["stop_sequences"] = [value]
+ elif isinstance(value, list):
+ optional_params["stop_sequences"] = value
+ if param == "max_tokens":
+ optional_params["max_output_tokens"] = value
+ if param == "response_format" and isinstance(value, dict): # type: ignore
+ if value["type"] == "json_object":
+ optional_params["response_mime_type"] = "application/json"
+ elif value["type"] == "text":
+ optional_params["response_mime_type"] = "text/plain"
+ if "response_schema" in value:
+ optional_params["response_schema"] = value["response_schema"]
+ if param == "frequency_penalty":
+ optional_params["frequency_penalty"] = value
+ if param == "presence_penalty":
+ optional_params["presence_penalty"] = value
+ if param == "tools" and isinstance(value, list):
+ gtool_func_declarations = []
+ googleSearchRetrieval: Optional[dict] = None
+ provider_specific_tools: List[dict] = []
+ for tool in value:
+ # check if grounding
+ try:
+ gtool_func_declaration = FunctionDeclaration(
+ name=tool["function"]["name"],
+ description=tool["function"].get("description", ""),
+ parameters=tool["function"].get("parameters", {}),
+ )
+ gtool_func_declarations.append(gtool_func_declaration)
+ except KeyError:
+ # assume it's a provider-specific param
+ verbose_logger.warning(
+ "Got KeyError parsing tool={}. Assuming it's a provider-specific param. Use `litellm.set_verbose` or `litellm --detailed_debug` to see raw request."
+ )
+ if tool.get("googleSearchRetrieval", None) is not None:
+ googleSearchRetrieval = tool["googleSearchRetrieval"]
+ _tools = Tools(
+ function_declarations=gtool_func_declarations,
+ )
+ if googleSearchRetrieval is not None:
+ _tools["googleSearchRetrieval"] = googleSearchRetrieval
+ optional_params["tools"] = [_tools] + provider_specific_tools
+ if param == "tool_choice" and (
+ isinstance(value, str) or isinstance(value, dict)
+ ):
+ _tool_choice_value = self.map_tool_choice_values(
+ model=model, tool_choice=value # type: ignore
+ )
+ if _tool_choice_value is not None:
+ optional_params["tool_choice"] = _tool_choice_value
+ if param == "seed":
+ optional_params["seed"] = value
+ return optional_params
+
+ def get_mapped_special_auth_params(self) -> dict:
+ """
+ Common auth params across bedrock/vertex_ai/azure/watsonx
+ """
+ return {"project": "vertex_project", "region_name": "vertex_location"}
+
+ def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
+ mapped_params = self.get_mapped_special_auth_params()
+
+ for param, value in non_default_params.items():
+ if param in mapped_params:
+ optional_params[mapped_params[param]] = value
+ return optional_params
+
+ def get_eu_regions(self) -> List[str]:
+ """
+ Source: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations#available-regions
+ """
+ return [
+ "europe-central2",
+ "europe-north1",
+ "europe-southwest1",
+ "europe-west1",
+ "europe-west2",
+ "europe-west3",
+ "europe-west4",
+ "europe-west6",
+ "europe-west8",
+ "europe-west9",
+ ]
+
+ def get_flagged_finish_reasons(self) -> Dict[str, str]:
+ """
+ Return Dictionary of finish reasons which indicate response was flagged
+
+ and what it means
+ """
+ return {
+ "SAFETY": "The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output.",
+ "RECITATION": "The token generation was stopped as the response was flagged for unauthorized citations.",
+ "BLOCKLIST": "The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist.",
+ "PROHIBITED_CONTENT": "The token generation was stopped as the response was flagged for the prohibited contents.",
+ "SPII": "The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents.",
+ }
+
+
+async def make_call(
+ client: Optional[AsyncHTTPHandler],
+ api_base: str,
+ headers: dict,
+ data: str,
+ model: str,
+ messages: list,
+ logging_obj,
+):
+ if client is None:
+ client = AsyncHTTPHandler() # Create a new client if none provided
+
+ response = await client.post(api_base, headers=headers, data=data, stream=True)
+
+ if response.status_code != 200:
+ raise VertexAIError(status_code=response.status_code, message=response.text)
+
+ completion_stream = ModelResponseIterator(
+ streaming_response=response.aiter_lines(), sync_stream=False
+ )
+ # LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response="first stream response received",
+ additional_args={"complete_input_dict": data},
+ )
+
+ return completion_stream
+
+
+def make_sync_call(
+ client: Optional[HTTPHandler],
+ api_base: str,
+ headers: dict,
+ data: str,
+ model: str,
+ messages: list,
+ logging_obj,
+):
+ if client is None:
+ client = HTTPHandler() # Create a new client if none provided
+
+ response = client.post(api_base, headers=headers, data=data, stream=True)
+
+ if response.status_code != 200:
+ raise VertexAIError(status_code=response.status_code, message=response.read())
+
+ completion_stream = ModelResponseIterator(
+ streaming_response=response.iter_lines(), sync_stream=True
+ )
+
+ # LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response="first stream response received",
+ additional_args={"complete_input_dict": data},
+ )
+
+ return completion_stream
+
+
class VertexAIError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
@@ -33,16 +563,235 @@ class VertexLLM(BaseLLM):
self.project_id: Optional[str] = None
self.async_handler: Optional[AsyncHTTPHandler] = None
- def load_auth(self) -> Tuple[Any, str]:
- from google.auth.transport.requests import Request # type: ignore[import-untyped]
- from google.auth.credentials import Credentials # type: ignore[import-untyped]
- import google.auth as google_auth
+ def _process_response(
+ self,
+ model: str,
+ response: httpx.Response,
+ model_response: ModelResponse,
+ logging_obj: litellm.litellm_core_utils.litellm_logging.Logging,
+ optional_params: dict,
+ api_key: str,
+ data: Union[dict, str],
+ messages: List,
+ print_verbose,
+ encoding,
+ ) -> ModelResponse:
- credentials, project_id = google_auth.default(
- scopes=["https://www.googleapis.com/auth/cloud-platform"],
+ ## LOGGING
+ logging_obj.post_call(
+ input=messages,
+ api_key="",
+ original_response=response.text,
+ additional_args={"complete_input_dict": data},
)
- credentials.refresh(Request())
+ print_verbose(f"raw model_response: {response.text}")
+
+ ## RESPONSE OBJECT
+ try:
+ completion_response = GenerateContentResponseBody(**response.json()) # type: ignore
+ except Exception as e:
+ raise VertexAIError(
+ message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format(
+ response.text, str(e)
+ ),
+ status_code=422,
+ )
+
+ ## GET MODEL ##
+ model_response.model = model
+
+ ## CHECK IF RESPONSE FLAGGED
+ if "promptFeedback" in completion_response:
+ if "blockReason" in completion_response["promptFeedback"]:
+ # If set, the prompt was blocked and no candidates are returned. Rephrase your prompt
+ model_response.choices[0].finish_reason = "content_filter"
+
+ chat_completion_message: ChatCompletionResponseMessage = {
+ "role": "assistant",
+ "content": None,
+ }
+
+ choice = litellm.Choices(
+ finish_reason="content_filter",
+ index=0,
+ message=chat_completion_message, # type: ignore
+ logprobs=None,
+ enhancements=None,
+ )
+
+ model_response.choices = [choice]
+
+ ## GET USAGE ##
+ usage = litellm.Usage(
+ prompt_tokens=completion_response["usageMetadata"].get(
+ "promptTokenCount", 0
+ ),
+ completion_tokens=completion_response["usageMetadata"].get(
+ "candidatesTokenCount", 0
+ ),
+ total_tokens=completion_response["usageMetadata"].get(
+ "totalTokenCount", 0
+ ),
+ )
+
+ setattr(model_response, "usage", usage)
+
+ return model_response
+
+ if len(completion_response["candidates"]) > 0:
+ content_policy_violations = (
+ VertexGeminiConfig().get_flagged_finish_reasons()
+ )
+ if (
+ "finishReason" in completion_response["candidates"][0]
+ and completion_response["candidates"][0]["finishReason"]
+ in content_policy_violations.keys()
+ ):
+ ## CONTENT POLICY VIOLATION ERROR
+ model_response.choices[0].finish_reason = "content_filter"
+
+ chat_completion_message = {
+ "role": "assistant",
+ "content": None,
+ }
+
+ choice = litellm.Choices(
+ finish_reason="content_filter",
+ index=0,
+ message=chat_completion_message, # type: ignore
+ logprobs=None,
+ enhancements=None,
+ )
+
+ model_response.choices = [choice]
+
+ ## GET USAGE ##
+ usage = litellm.Usage(
+ prompt_tokens=completion_response["usageMetadata"].get(
+ "promptTokenCount", 0
+ ),
+ completion_tokens=completion_response["usageMetadata"].get(
+ "candidatesTokenCount", 0
+ ),
+ total_tokens=completion_response["usageMetadata"].get(
+ "totalTokenCount", 0
+ ),
+ )
+
+ setattr(model_response, "usage", usage)
+
+ return model_response
+
+ model_response.choices = [] # type: ignore
+
+ try:
+ ## CHECK IF GROUNDING METADATA IN REQUEST
+ grounding_metadata: List[dict] = []
+ ## GET TEXT ##
+ chat_completion_message = {"role": "assistant"}
+ content_str = ""
+ tools: List[ChatCompletionToolCallChunk] = []
+ for idx, candidate in enumerate(completion_response["candidates"]):
+ if "content" not in candidate:
+ continue
+
+ if "groundingMetadata" in candidate:
+ grounding_metadata.append(candidate["groundingMetadata"])
+
+ if "text" in candidate["content"]["parts"][0]:
+ content_str = candidate["content"]["parts"][0]["text"]
+
+ if "functionCall" in candidate["content"]["parts"][0]:
+ _function_chunk = ChatCompletionToolCallFunctionChunk(
+ name=candidate["content"]["parts"][0]["functionCall"]["name"],
+ arguments=json.dumps(
+ candidate["content"]["parts"][0]["functionCall"]["args"]
+ ),
+ )
+ _tool_response_chunk = ChatCompletionToolCallChunk(
+ id=f"call_{str(uuid.uuid4())}",
+ type="function",
+ function=_function_chunk,
+ index=candidate.get("index", idx),
+ )
+ tools.append(_tool_response_chunk)
+
+ chat_completion_message["content"] = content_str
+ chat_completion_message["tool_calls"] = tools
+
+ choice = litellm.Choices(
+ finish_reason=candidate.get("finishReason", "stop"),
+ index=candidate.get("index", idx),
+ message=chat_completion_message, # type: ignore
+ logprobs=None,
+ enhancements=None,
+ )
+
+ model_response.choices.append(choice)
+
+ ## GET USAGE ##
+ usage = litellm.Usage(
+ prompt_tokens=completion_response["usageMetadata"].get(
+ "promptTokenCount", 0
+ ),
+ completion_tokens=completion_response["usageMetadata"].get(
+ "candidatesTokenCount", 0
+ ),
+ total_tokens=completion_response["usageMetadata"].get(
+ "totalTokenCount", 0
+ ),
+ )
+
+ setattr(model_response, "usage", usage)
+
+ ## ADD GROUNDING METADATA ##
+ model_response._hidden_params["vertex_ai_grounding_metadata"] = (
+ grounding_metadata
+ )
+ except Exception as e:
+ raise VertexAIError(
+ message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format(
+ completion_response, str(e)
+ ),
+ status_code=422,
+ )
+
+ return model_response
+
+ def get_vertex_region(self, vertex_region: Optional[str]) -> str:
+ return vertex_region or "us-central1"
+
+ def load_auth(
+ self, credentials: Optional[str], project_id: Optional[str]
+ ) -> Tuple[Any, str]:
+ import google.auth as google_auth
+ from google.auth.credentials import Credentials # type: ignore[import-untyped]
+ from google.auth.transport.requests import (
+ Request, # type: ignore[import-untyped]
+ )
+
+ if credentials is not None and isinstance(credentials, str):
+ import google.oauth2.service_account
+
+ json_obj = json.loads(credentials)
+
+ creds = google.oauth2.service_account.Credentials.from_service_account_info(
+ json_obj,
+ scopes=["https://www.googleapis.com/auth/cloud-platform"],
+ )
+
+ if project_id is None:
+ project_id = creds.project_id
+ else:
+ creds, creds_project_id = google_auth.default(
+ quota_project_id=project_id,
+ scopes=["https://www.googleapis.com/auth/cloud-platform"],
+ )
+ if project_id is None:
+ project_id = creds_project_id
+
+ creds.refresh(Request())
if not project_id:
raise ValueError("Could not resolve project_id")
@@ -52,59 +801,433 @@ class VertexLLM(BaseLLM):
f"Expected project_id to be a str but got {type(project_id)}"
)
- return credentials, project_id
+ return creds, project_id
def refresh_auth(self, credentials: Any) -> None:
- from google.auth.transport.requests import Request # type: ignore[import-untyped]
+ from google.auth.transport.requests import (
+ Request, # type: ignore[import-untyped]
+ )
credentials.refresh(Request())
- def _prepare_request(self, request: httpx.Request) -> None:
- access_token = self._ensure_access_token()
-
- if request.headers.get("Authorization"):
- # already authenticated, nothing for us to do
- return
-
- request.headers["Authorization"] = f"Bearer {access_token}"
-
- def _ensure_access_token(self) -> str:
- if self.access_token is not None:
- return self.access_token
+ def _ensure_access_token(
+ self, credentials: Optional[str], project_id: Optional[str]
+ ) -> Tuple[str, str]:
+ """
+ Returns auth token and project id
+ """
+ if self.access_token is not None and self.project_id is not None:
+ return self.access_token, self.project_id
if not self._credentials:
- self._credentials, project_id = self.load_auth()
+ self._credentials, cred_project_id = self.load_auth(
+ credentials=credentials, project_id=project_id
+ )
if not self.project_id:
- self.project_id = project_id
+ self.project_id = project_id or cred_project_id
else:
self.refresh_auth(self._credentials)
- if not self._credentials.token:
+ if not self.project_id:
+ self.project_id = self._credentials.project_id
+
+ if not self.project_id:
+ raise ValueError("Could not resolve project_id")
+
+ if not self._credentials or not self._credentials.token:
raise RuntimeError("Could not resolve API token from the environment")
- assert isinstance(self._credentials.token, str)
- return self._credentials.token
+ return self._credentials.token, self.project_id
+
+ def _get_token_and_url(
+ self,
+ model: str,
+ gemini_api_key: Optional[str],
+ vertex_project: Optional[str],
+ vertex_location: Optional[str],
+ vertex_credentials: Optional[str],
+ stream: Optional[bool],
+ custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"],
+ api_base: Optional[str],
+ ) -> Tuple[Optional[str], str]:
+ """
+ Internal function. Returns the token and url for the call.
+
+ Handles logic if it's google ai studio vs. vertex ai.
+
+ Returns
+ token, url
+ """
+ if custom_llm_provider == "gemini":
+ _gemini_model_name = "models/{}".format(model)
+ auth_header = None
+ endpoint = "generateContent"
+ if stream is True:
+ endpoint = "streamGenerateContent"
+ url = "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}&alt=sse".format(
+ _gemini_model_name, endpoint, gemini_api_key
+ )
+ else:
+ url = "https://generativelanguage.googleapis.com/v1beta/{}:{}?key={}".format(
+ _gemini_model_name, endpoint, gemini_api_key
+ )
+ else:
+ auth_header, vertex_project = self._ensure_access_token(
+ credentials=vertex_credentials, project_id=vertex_project
+ )
+ vertex_location = self.get_vertex_region(vertex_region=vertex_location)
+
+ ### SET RUNTIME ENDPOINT ###
+ endpoint = "generateContent"
+ if stream is True:
+ endpoint = "streamGenerateContent"
+ url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}?alt=sse"
+ else:
+ url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}"
+
+ if (
+ api_base is not None
+ ): # for cloudflare ai gateway - https://github.com/BerriAI/litellm/issues/4317
+ if custom_llm_provider == "gemini":
+ url = "{}/{}".format(api_base, endpoint)
+ auth_header = (
+ gemini_api_key # cloudflare expects api key as bearer token
+ )
+ else:
+ url = "{}:{}".format(api_base, endpoint)
+
+ if stream is True:
+ url = url + "?alt=sse"
+
+ return auth_header, url
+
+ async def async_streaming(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ data: str,
+ timeout: Optional[Union[float, httpx.Timeout]],
+ encoding,
+ logging_obj,
+ stream,
+ optional_params: dict,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+ client: Optional[AsyncHTTPHandler] = None,
+ ) -> CustomStreamWrapper:
+ streaming_response = CustomStreamWrapper(
+ completion_stream=None,
+ make_call=partial(
+ make_call,
+ client=client,
+ api_base=api_base,
+ headers=headers,
+ data=data,
+ model=model,
+ messages=messages,
+ logging_obj=logging_obj,
+ ),
+ model=model,
+ custom_llm_provider="vertex_ai_beta",
+ logging_obj=logging_obj,
+ )
+ return streaming_response
+
+ async def async_completion(
+ self,
+ model: str,
+ messages: list,
+ api_base: str,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ data: str,
+ timeout: Optional[Union[float, httpx.Timeout]],
+ encoding,
+ logging_obj,
+ stream,
+ optional_params: dict,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+ client: Optional[AsyncHTTPHandler] = None,
+ ) -> Union[ModelResponse, CustomStreamWrapper]:
+ if client is None:
+ _params = {}
+ if timeout is not None:
+ if isinstance(timeout, float) or isinstance(timeout, int):
+ timeout = httpx.Timeout(timeout)
+ _params["timeout"] = timeout
+ client = AsyncHTTPHandler(**_params) # type: ignore
+ else:
+ client = client # type: ignore
+
+ try:
+ response = await client.post(api_base, headers=headers, json=data) # type: ignore
+ response.raise_for_status()
+ except httpx.HTTPStatusError as err:
+ error_code = err.response.status_code
+ raise VertexAIError(status_code=error_code, message=err.response.text)
+ except httpx.TimeoutException:
+ raise VertexAIError(status_code=408, message="Timeout error occurred.")
+
+ return self._process_response(
+ model=model,
+ response=response,
+ model_response=model_response,
+ logging_obj=logging_obj,
+ api_key="",
+ data=data,
+ messages=messages,
+ print_verbose=print_verbose,
+ optional_params=optional_params,
+ encoding=encoding,
+ )
+
+ def completion(
+ self,
+ model: str,
+ messages: list,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ custom_llm_provider: Literal[
+ "vertex_ai", "vertex_ai_beta", "gemini"
+ ], # if it's vertex_ai or gemini (google ai studio)
+ encoding,
+ logging_obj,
+ optional_params: dict,
+ acompletion: bool,
+ timeout: Optional[Union[float, httpx.Timeout]],
+ vertex_project: Optional[str],
+ vertex_location: Optional[str],
+ vertex_credentials: Optional[str],
+ gemini_api_key: Optional[str],
+ litellm_params=None,
+ logger_fn=None,
+ extra_headers: Optional[dict] = None,
+ client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
+ api_base: Optional[str] = None,
+ ) -> Union[ModelResponse, CustomStreamWrapper]:
+ stream: Optional[bool] = optional_params.pop("stream", None) # type: ignore
+
+ auth_header, url = self._get_token_and_url(
+ model=model,
+ gemini_api_key=gemini_api_key,
+ vertex_project=vertex_project,
+ vertex_location=vertex_location,
+ vertex_credentials=vertex_credentials,
+ stream=stream,
+ custom_llm_provider=custom_llm_provider,
+ api_base=api_base,
+ )
+
+ ## TRANSFORMATION ##
+ try:
+ _custom_llm_provider = custom_llm_provider
+ if custom_llm_provider == "vertex_ai_beta":
+ _custom_llm_provider = "vertex_ai"
+ supports_system_message = litellm.supports_system_messages(
+ model=model, custom_llm_provider=_custom_llm_provider
+ )
+ except Exception as e:
+ verbose_logger.error(
+ "Unable to identify if system message supported. Defaulting to 'False'. Received error message - {}\nAdd it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json".format(
+ str(e)
+ )
+ )
+ supports_system_message = False
+ # Separate system prompt from rest of message
+ system_prompt_indices = []
+ system_content_blocks: List[PartType] = []
+ if supports_system_message is True:
+ for idx, message in enumerate(messages):
+ if message["role"] == "system":
+ _system_content_block = PartType(text=message["content"])
+ system_content_blocks.append(_system_content_block)
+ system_prompt_indices.append(idx)
+ if len(system_prompt_indices) > 0:
+ for idx in reversed(system_prompt_indices):
+ messages.pop(idx)
+
+ # Checks for 'response_schema' support - if passed in
+ if "response_schema" in optional_params:
+ supports_response_schema = litellm.supports_response_schema(
+ model=model, custom_llm_provider="vertex_ai"
+ )
+ if supports_response_schema is False:
+ user_response_schema_message = response_schema_prompt(
+ model=model, response_schema=optional_params.get("response_schema") # type: ignore
+ )
+ messages.append(
+ {"role": "user", "content": user_response_schema_message}
+ )
+ optional_params.pop("response_schema")
+
+ try:
+ content = _gemini_convert_messages_with_history(messages=messages)
+ tools: Optional[Tools] = optional_params.pop("tools", None)
+ tool_choice: Optional[ToolConfig] = optional_params.pop("tool_choice", None)
+ safety_settings: Optional[List[SafetSettingsConfig]] = optional_params.pop(
+ "safety_settings", None
+ ) # type: ignore
+ cached_content: Optional[str] = optional_params.pop("cached_content", None)
+ generation_config: Optional[GenerationConfig] = GenerationConfig(
+ **optional_params
+ )
+ data = RequestBody(contents=content)
+ if len(system_content_blocks) > 0:
+ system_instructions = SystemInstructions(parts=system_content_blocks)
+ data["system_instruction"] = system_instructions
+ if tools is not None:
+ data["tools"] = tools
+ if tool_choice is not None:
+ data["toolConfig"] = tool_choice
+ if safety_settings is not None:
+ data["safetySettings"] = safety_settings
+ if generation_config is not None:
+ data["generationConfig"] = generation_config
+ if cached_content is not None:
+ data["cachedContent"] = cached_content
+
+ headers = {
+ "Content-Type": "application/json",
+ }
+ if auth_header is not None:
+ headers["Authorization"] = f"Bearer {auth_header}"
+ if extra_headers is not None:
+ headers.update(extra_headers)
+ except Exception as e:
+ raise e
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=messages,
+ api_key="",
+ additional_args={
+ "complete_input_dict": data,
+ "api_base": url,
+ "headers": headers,
+ },
+ )
+
+ ### ROUTING (ASYNC, STREAMING, SYNC)
+ if acompletion:
+ ### ASYNC STREAMING
+ if stream is True:
+ return self.async_streaming(
+ model=model,
+ messages=messages,
+ data=json.dumps(data), # type: ignore
+ api_base=url,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ stream=stream,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ headers=headers,
+ timeout=timeout,
+ client=client, # type: ignore
+ )
+ ### ASYNC COMPLETION
+ return self.async_completion(
+ model=model,
+ messages=messages,
+ data=data, # type: ignore
+ api_base=url,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ stream=stream,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ headers=headers,
+ timeout=timeout,
+ client=client, # type: ignore
+ )
+
+ ## SYNC STREAMING CALL ##
+ if stream is not None and stream is True:
+ streaming_response = CustomStreamWrapper(
+ completion_stream=None,
+ make_call=partial(
+ make_sync_call,
+ client=None,
+ api_base=url,
+ headers=headers, # type: ignore
+ data=json.dumps(data),
+ model=model,
+ messages=messages,
+ logging_obj=logging_obj,
+ ),
+ model=model,
+ custom_llm_provider="vertex_ai_beta",
+ logging_obj=logging_obj,
+ )
+
+ return streaming_response
+ ## COMPLETION CALL ##
+ if client is None or isinstance(client, AsyncHTTPHandler):
+ _params = {}
+ if timeout is not None:
+ if isinstance(timeout, float) or isinstance(timeout, int):
+ timeout = httpx.Timeout(timeout)
+ _params["timeout"] = timeout
+ client = HTTPHandler(**_params) # type: ignore
+ else:
+ client = client
+
+ try:
+ response = client.post(url=url, headers=headers, json=data) # type: ignore
+ response.raise_for_status()
+ except httpx.HTTPStatusError as err:
+ error_code = err.response.status_code
+ raise VertexAIError(status_code=error_code, message=response.text)
+ except httpx.TimeoutException:
+ raise VertexAIError(status_code=408, message="Timeout error occurred.")
+
+ return self._process_response(
+ model=model,
+ response=response,
+ model_response=model_response,
+ logging_obj=logging_obj,
+ optional_params=optional_params,
+ api_key="",
+ data=data, # type: ignore
+ messages=messages,
+ print_verbose=print_verbose,
+ encoding=encoding,
+ )
def image_generation(
self,
prompt: str,
- vertex_project: str,
- vertex_location: str,
+ vertex_project: Optional[str],
+ vertex_location: Optional[str],
+ vertex_credentials: Optional[str],
+ model_response: litellm.ImageResponse,
model: Optional[
str
] = "imagegeneration", # vertex ai uses imagegeneration as the default model
- client: Optional[AsyncHTTPHandler] = None,
+ client: Optional[Any] = None,
optional_params: Optional[dict] = None,
timeout: Optional[int] = None,
logging_obj=None,
- model_response=None,
aimg_generation=False,
):
- if aimg_generation == True:
- response = self.aimage_generation(
+ if aimg_generation is True:
+ return self.aimage_generation(
prompt=prompt,
vertex_project=vertex_project,
vertex_location=vertex_location,
+ vertex_credentials=vertex_credentials,
model=model,
client=client,
optional_params=optional_params,
@@ -112,13 +1235,99 @@ class VertexLLM(BaseLLM):
logging_obj=logging_obj,
model_response=model_response,
)
- return response
+
+ if client is None:
+ _params = {}
+ if timeout is not None:
+ if isinstance(timeout, float) or isinstance(timeout, int):
+ _httpx_timeout = httpx.Timeout(timeout)
+ _params["timeout"] = _httpx_timeout
+ else:
+ _params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
+
+ sync_handler: HTTPHandler = HTTPHandler(**_params) # type: ignore
+ else:
+ sync_handler = client # type: ignore
+
+ url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:predict"
+
+ auth_header, _ = self._ensure_access_token(
+ credentials=vertex_credentials, project_id=vertex_project
+ )
+ optional_params = optional_params or {
+ "sampleCount": 1
+ } # default optional params
+
+ request_data = {
+ "instances": [{"prompt": prompt}],
+ "parameters": optional_params,
+ }
+
+ request_str = f"\n curl -X POST \\\n -H \"Authorization: Bearer {auth_header[:10] + 'XXXXXXXXXX'}\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d {request_data} \\\n \"{url}\""
+ logging_obj.pre_call(
+ input=prompt,
+ api_key=None,
+ additional_args={
+ "complete_input_dict": optional_params,
+ "request_str": request_str,
+ },
+ )
+
+ logging_obj.pre_call(
+ input=prompt,
+ api_key=None,
+ additional_args={
+ "complete_input_dict": optional_params,
+ "request_str": request_str,
+ },
+ )
+
+ response = sync_handler.post(
+ url=url,
+ headers={
+ "Content-Type": "application/json; charset=utf-8",
+ "Authorization": f"Bearer {auth_header}",
+ },
+ data=json.dumps(request_data),
+ )
+
+ if response.status_code != 200:
+ raise Exception(f"Error: {response.status_code} {response.text}")
+ """
+ Vertex AI Image generation response example:
+ {
+ "predictions": [
+ {
+ "bytesBase64Encoded": "BASE64_IMG_BYTES",
+ "mimeType": "image/png"
+ },
+ {
+ "mimeType": "image/png",
+ "bytesBase64Encoded": "BASE64_IMG_BYTES"
+ }
+ ]
+ }
+ """
+
+ _json_response = response.json()
+ _predictions = _json_response["predictions"]
+
+ _response_data: List[litellm.ImageObject] = []
+ for _prediction in _predictions:
+ _bytes_base64_encoded = _prediction["bytesBase64Encoded"]
+ image_object = litellm.ImageObject(b64_json=_bytes_base64_encoded)
+ _response_data.append(image_object)
+
+ model_response.data = _response_data
+
+ return model_response
async def aimage_generation(
self,
prompt: str,
- vertex_project: str,
- vertex_location: str,
+ vertex_project: Optional[str],
+ vertex_location: Optional[str],
+ vertex_credentials: Optional[str],
model_response: litellm.ImageResponse,
model: Optional[
str
@@ -163,7 +1372,9 @@ class VertexLLM(BaseLLM):
} \
"https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration:predict"
"""
- auth_header = self._ensure_access_token()
+ auth_header, _ = self._ensure_access_token(
+ credentials=vertex_credentials, project_id=vertex_project
+ )
optional_params = optional_params or {
"sampleCount": 1
} # default optional params
@@ -222,3 +1433,137 @@ class VertexLLM(BaseLLM):
model_response.data = _response_data
return model_response
+
+
+class ModelResponseIterator:
+ def __init__(self, streaming_response, sync_stream: bool):
+ self.streaming_response = streaming_response
+
+ def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
+ try:
+ processed_chunk = GenerateContentResponseBody(**chunk) # type: ignore
+
+ text = ""
+ tool_use: Optional[ChatCompletionToolCallChunk] = None
+ is_finished = False
+ finish_reason = ""
+ usage: Optional[ChatCompletionUsageBlock] = None
+
+ gemini_chunk = processed_chunk["candidates"][0]
+
+ if "content" in gemini_chunk:
+ if "text" in gemini_chunk["content"]["parts"][0]:
+ text = gemini_chunk["content"]["parts"][0]["text"]
+ elif "functionCall" in gemini_chunk["content"]["parts"][0]:
+ function_call = ChatCompletionToolCallFunctionChunk(
+ name=gemini_chunk["content"]["parts"][0]["functionCall"][
+ "name"
+ ],
+ arguments=json.dumps(
+ gemini_chunk["content"]["parts"][0]["functionCall"]["args"]
+ ),
+ )
+ tool_use = ChatCompletionToolCallChunk(
+ id=str(uuid.uuid4()),
+ type="function",
+ function=function_call,
+ index=0,
+ )
+
+ if "finishReason" in gemini_chunk:
+ finish_reason = map_finish_reason(
+ finish_reason=gemini_chunk["finishReason"]
+ )
+ ## DO NOT SET 'is_finished' = True
+ ## GEMINI SETS FINISHREASON ON EVERY CHUNK!
+
+ if "usageMetadata" in processed_chunk:
+ usage = ChatCompletionUsageBlock(
+ prompt_tokens=processed_chunk["usageMetadata"].get(
+ "promptTokenCount", 0
+ ),
+ completion_tokens=processed_chunk["usageMetadata"].get(
+ "candidatesTokenCount", 0
+ ),
+ total_tokens=processed_chunk["usageMetadata"].get(
+ "totalTokenCount", 0
+ ),
+ )
+
+ returned_chunk = GenericStreamingChunk(
+ text=text,
+ tool_use=tool_use,
+ is_finished=False,
+ finish_reason=finish_reason,
+ usage=usage,
+ index=0,
+ )
+ return returned_chunk
+ except json.JSONDecodeError:
+ raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
+
+ # Sync iterator
+ def __iter__(self):
+ self.response_iterator = self.streaming_response
+ return self
+
+ def __next__(self):
+ try:
+ chunk = self.response_iterator.__next__()
+ except StopIteration:
+ raise StopIteration
+ except ValueError as e:
+ raise RuntimeError(f"Error receiving chunk from stream: {e}")
+
+ try:
+ chunk = chunk.replace("data:", "")
+ chunk = chunk.strip()
+ if len(chunk) > 0:
+ json_chunk = json.loads(chunk)
+ return self.chunk_parser(chunk=json_chunk)
+ else:
+ return GenericStreamingChunk(
+ text="",
+ is_finished=False,
+ finish_reason="",
+ usage=None,
+ index=0,
+ tool_use=None,
+ )
+ except StopIteration:
+ raise StopIteration
+ except ValueError as e:
+ raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
+
+ # Async iterator
+ def __aiter__(self):
+ self.async_response_iterator = self.streaming_response.__aiter__()
+ return self
+
+ async def __anext__(self):
+ try:
+ chunk = await self.async_response_iterator.__anext__()
+ except StopAsyncIteration:
+ raise StopAsyncIteration
+ except ValueError as e:
+ raise RuntimeError(f"Error receiving chunk from stream: {e}")
+
+ try:
+ chunk = chunk.replace("data:", "")
+ chunk = chunk.strip()
+ if len(chunk) > 0:
+ json_chunk = json.loads(chunk)
+ return self.chunk_parser(chunk=json_chunk)
+ else:
+ return GenericStreamingChunk(
+ text="",
+ is_finished=False,
+ finish_reason="",
+ usage=None,
+ index=0,
+ tool_use=None,
+ )
+ except StopAsyncIteration:
+ raise StopAsyncIteration
+ except ValueError as e:
+ raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
diff --git a/litellm/llms/vllm.py b/litellm/llms/vllm.py
index b2a9dd54db..f261b7297c 100644
--- a/litellm/llms/vllm.py
+++ b/litellm/llms/vllm.py
@@ -1,11 +1,15 @@
-import os
import json
+import os
+import time # type: ignore
from enum import Enum
+from typing import Any, Callable
+
+import httpx
import requests # type: ignore
-import time, httpx # type: ignore
-from typing import Callable, Any
+
from litellm.utils import ModelResponse, Usage
-from .prompt_templates.factory import prompt_factory, custom_prompt
+
+from .prompt_templates.factory import custom_prompt, prompt_factory
llm = None
@@ -91,14 +95,14 @@ def completion(
)
print_verbose(f"raw model_response: {outputs}")
## RESPONSE OBJECT
- model_response["choices"][0]["message"]["content"] = outputs[0].outputs[0].text
+ model_response.choices[0].message.content = outputs[0].outputs[0].text # type: ignore
## CALCULATING USAGE
prompt_tokens = len(outputs[0].prompt_token_ids)
completion_tokens = len(outputs[0].outputs[0].token_ids)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
@@ -173,14 +177,14 @@ def batch_completions(
for output in outputs:
model_response = ModelResponse()
## RESPONSE OBJECT
- model_response["choices"][0]["message"]["content"] = output.outputs[0].text
+ model_response.choices[0].message.content = output.outputs[0].text # type: ignore
## CALCULATING USAGE
prompt_tokens = len(output.prompt_token_ids)
completion_tokens = len(output.outputs[0].token_ids)
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.created = int(time.time())
+ model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
diff --git a/litellm/llms/volcengine.py b/litellm/llms/volcengine.py
new file mode 100644
index 0000000000..eb289d1c49
--- /dev/null
+++ b/litellm/llms/volcengine.py
@@ -0,0 +1,87 @@
+import types
+from typing import Literal, Optional, Union
+
+import litellm
+
+
+class VolcEngineConfig:
+ frequency_penalty: Optional[int] = None
+ function_call: Optional[Union[str, dict]] = None
+ functions: Optional[list] = None
+ logit_bias: Optional[dict] = None
+ max_tokens: Optional[int] = None
+ n: Optional[int] = None
+ presence_penalty: Optional[int] = None
+ stop: Optional[Union[str, list]] = None
+ temperature: Optional[int] = None
+ top_p: Optional[int] = None
+ response_format: Optional[dict] = None
+
+ def __init__(
+ self,
+ frequency_penalty: Optional[int] = None,
+ function_call: Optional[Union[str, dict]] = None,
+ functions: Optional[list] = None,
+ logit_bias: Optional[dict] = None,
+ max_tokens: Optional[int] = None,
+ n: Optional[int] = None,
+ presence_penalty: Optional[int] = None,
+ stop: Optional[Union[str, list]] = None,
+ temperature: Optional[int] = None,
+ top_p: Optional[int] = None,
+ response_format: Optional[dict] = None,
+ ) -> None:
+ locals_ = locals().copy()
+ for key, value in locals_.items():
+ if key != "self" and value is not None:
+ setattr(self.__class__, key, value)
+
+ @classmethod
+ def get_config(cls):
+ return {
+ k: v
+ for k, v in cls.__dict__.items()
+ if not k.startswith("__")
+ and not isinstance(
+ v,
+ (
+ types.FunctionType,
+ types.BuiltinFunctionType,
+ classmethod,
+ staticmethod,
+ ),
+ )
+ and v is not None
+ }
+
+ def get_supported_openai_params(self, model: str) -> list:
+ return [
+ "frequency_penalty",
+ "logit_bias",
+ "logprobs",
+ "top_logprobs",
+ "max_tokens",
+ "n",
+ "presence_penalty",
+ "seed",
+ "stop",
+ "stream",
+ "stream_options",
+ "temperature",
+ "top_p",
+ "tools",
+ "tool_choice",
+ "function_call",
+ "functions",
+ "max_retries",
+ "extra_headers",
+ ] # works across all models
+
+ def map_openai_params(
+ self, non_default_params: dict, optional_params: dict, model: str
+ ) -> dict:
+ supported_openai_params = self.get_supported_openai_params(model)
+ for param, value in non_default_params.items():
+ if param in supported_openai_params:
+ optional_params[param] = value
+ return optional_params
diff --git a/litellm/llms/watsonx.py b/litellm/llms/watsonx.py
index 34176a23a4..c01efd8adc 100644
--- a/litellm/llms/watsonx.py
+++ b/litellm/llms/watsonx.py
@@ -1,26 +1,37 @@
-from enum import Enum
-import json, types, time # noqa: E401
+import asyncio
+import json # noqa: E401
+import time
+import types
from contextlib import asynccontextmanager, contextmanager
+from datetime import datetime
+from enum import Enum
from typing import (
+ Any,
+ AsyncContextManager,
+ AsyncGenerator,
+ AsyncIterator,
Callable,
+ ContextManager,
Dict,
Generator,
- AsyncGenerator,
Iterator,
- AsyncIterator,
- Optional,
- Any,
- Union,
List,
- ContextManager,
- AsyncContextManager,
+ Optional,
+ Union,
)
import httpx # type: ignore
import requests # type: ignore
+
import litellm
-from litellm.utils import ModelResponse, Usage, get_secret
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from litellm.utils import (
+ EmbeddingResponse,
+ ModelResponse,
+ Usage,
+ get_secret,
+ map_finish_reason,
+)
from .base import BaseLLM
from .prompt_templates import factory as ptf
@@ -285,7 +296,10 @@ class IBMWatsonXAI(BaseLLM):
)
def _get_api_params(
- self, params: dict, print_verbose: Optional[Callable] = None
+ self,
+ params: dict,
+ print_verbose: Optional[Callable] = None,
+ generate_token: Optional[bool] = True,
) -> dict:
"""
Find watsonx.ai credentials in the params or environment variables and return the headers for authentication.
@@ -365,7 +379,7 @@ class IBMWatsonXAI(BaseLLM):
status_code=401,
message="Error: Watsonx URL not set. Set WX_URL in environment variables or pass in as a parameter.",
)
- if token is None and api_key is not None:
+ if token is None and api_key is not None and generate_token:
# generate the auth token
if print_verbose is not None:
print_verbose("Generating IAM token for Watsonx.ai")
@@ -393,6 +407,37 @@ class IBMWatsonXAI(BaseLLM):
"api_version": api_version,
}
+ def _process_text_gen_response(
+ self, json_resp: dict, model_response: Union[ModelResponse, None] = None
+ ) -> ModelResponse:
+ if "results" not in json_resp:
+ raise WatsonXAIError(
+ status_code=500,
+ message=f"Error: Invalid response from Watsonx.ai API: {json_resp}",
+ )
+ if model_response is None:
+ model_response = ModelResponse(model=json_resp.get("model_id", None))
+ generated_text = json_resp["results"][0]["generated_text"]
+ prompt_tokens = json_resp["results"][0]["input_token_count"]
+ completion_tokens = json_resp["results"][0]["generated_token_count"]
+ model_response.choices[0].message.content = generated_text # type: ignore
+ model_response.choices[0].finish_reason = map_finish_reason(
+ json_resp["results"][0]["stop_reason"]
+ )
+ if json_resp.get("created_at"):
+ model_response.created = int(
+ datetime.fromisoformat(json_resp["created_at"]).timestamp()
+ )
+ else:
+ model_response.created = int(time.time())
+ usage = Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ )
+ setattr(model_response, "usage", usage)
+ return model_response
+
def completion(
self,
model: str,
@@ -426,27 +471,7 @@ class IBMWatsonXAI(BaseLLM):
prompt = convert_messages_to_prompt(
model, messages, provider, custom_prompt_dict
)
-
- def process_text_gen_response(json_resp: dict) -> ModelResponse:
- if "results" not in json_resp:
- raise WatsonXAIError(
- status_code=500,
- message=f"Error: Invalid response from Watsonx.ai API: {json_resp}",
- )
- generated_text = json_resp["results"][0]["generated_text"]
- prompt_tokens = json_resp["results"][0]["input_token_count"]
- completion_tokens = json_resp["results"][0]["generated_token_count"]
- model_response["choices"][0]["message"]["content"] = generated_text
- model_response["finish_reason"] = json_resp["results"][0]["stop_reason"]
- model_response["created"] = int(time.time())
- model_response["model"] = model
- usage = Usage(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
- )
- setattr(model_response, "usage", usage)
- return model_response
+ model_response.model = model
def process_stream_response(
stream_resp: Union[Iterator[str], AsyncIterator],
@@ -470,7 +495,7 @@ class IBMWatsonXAI(BaseLLM):
) as resp:
json_resp = resp.json()
- return process_text_gen_response(json_resp)
+ return self._process_text_gen_response(json_resp, model_response)
async def handle_text_request_async(request_params: dict) -> ModelResponse:
async with self.request_manager.async_request(
@@ -479,7 +504,7 @@ class IBMWatsonXAI(BaseLLM):
timeout=timeout,
) as resp:
json_resp = resp.json()
- return process_text_gen_response(json_resp)
+ return self._process_text_gen_response(json_resp, model_response)
def handle_stream_request(request_params: dict) -> litellm.CustomStreamWrapper:
# stream the response - generated chunks will be handled
@@ -493,7 +518,9 @@ class IBMWatsonXAI(BaseLLM):
streamwrapper = process_stream_response(resp.iter_lines())
return streamwrapper
- async def handle_stream_request_async(request_params: dict) -> litellm.CustomStreamWrapper:
+ async def handle_stream_request_async(
+ request_params: dict,
+ ) -> litellm.CustomStreamWrapper:
# stream the response - generated chunks will be handled
# by litellm.utils.CustomStreamWrapper.handle_watsonx_stream
async with self.request_manager.async_request(
@@ -520,7 +547,7 @@ class IBMWatsonXAI(BaseLLM):
elif stream:
# streaming text generation
return handle_stream_request(req_params)
- elif (acompletion is True):
+ elif acompletion is True:
# async text generation
return handle_text_request_async(req_params)
else:
@@ -531,15 +558,45 @@ class IBMWatsonXAI(BaseLLM):
except Exception as e:
raise WatsonXAIError(status_code=500, message=str(e))
+ def _process_embedding_response(
+ self, json_resp: dict, model_response: Optional[EmbeddingResponse] = None
+ ) -> EmbeddingResponse:
+ if model_response is None:
+ model_response = EmbeddingResponse(model=json_resp.get("model_id", None))
+ results = json_resp.get("results", [])
+ embedding_response = []
+ for idx, result in enumerate(results):
+ embedding_response.append(
+ {
+ "object": "embedding",
+ "index": idx,
+ "embedding": result["embedding"],
+ }
+ )
+ model_response.object = "list"
+ model_response.data = embedding_response
+ input_tokens = json_resp.get("input_token_count", 0)
+ setattr(
+ model_response,
+ "usage",
+ Usage(
+ prompt_tokens=input_tokens,
+ completion_tokens=0,
+ total_tokens=input_tokens,
+ ),
+ )
+ return model_response
+
def embedding(
self,
model: str,
input: Union[list, str],
+ model_response: litellm.EmbeddingResponse,
api_key: Optional[str] = None,
logging_obj=None,
- model_response=None,
optional_params=None,
encoding=None,
+ print_verbose=None,
aembedding=None,
):
"""
@@ -553,6 +610,8 @@ class IBMWatsonXAI(BaseLLM):
if k not in optional_params:
optional_params[k] = v
+ model_response.model = model
+
# Load auth variables from environment variables
if isinstance(input, str):
input = [input]
@@ -584,43 +643,23 @@ class IBMWatsonXAI(BaseLLM):
}
request_manager = RequestManager(logging_obj)
- def process_embedding_response(json_resp: dict) -> ModelResponse:
- results = json_resp.get("results", [])
- embedding_response = []
- for idx, result in enumerate(results):
- embedding_response.append(
- {
- "object": "embedding",
- "index": idx,
- "embedding": result["embedding"],
- }
- )
- model_response["object"] = "list"
- model_response["data"] = embedding_response
- model_response["model"] = model
- input_tokens = json_resp.get("input_token_count", 0)
- model_response.usage = Usage(
- prompt_tokens=input_tokens,
- completion_tokens=0,
- total_tokens=input_tokens,
- )
- return model_response
-
- def handle_embedding(request_params: dict) -> ModelResponse:
+ def handle_embedding(request_params: dict) -> EmbeddingResponse:
with request_manager.request(request_params, input=input) as resp:
json_resp = resp.json()
- return process_embedding_response(json_resp)
+ return self._process_embedding_response(json_resp, model_response)
- async def handle_aembedding(request_params: dict) -> ModelResponse:
- async with request_manager.async_request(request_params, input=input) as resp:
+ async def handle_aembedding(request_params: dict) -> EmbeddingResponse:
+ async with request_manager.async_request(
+ request_params, input=input
+ ) as resp:
json_resp = resp.json()
- return process_embedding_response(json_resp)
+ return self._process_embedding_response(json_resp, model_response)
try:
if aembedding is True:
- return handle_embedding(req_params)
- else:
return handle_aembedding(req_params)
+ else:
+ return handle_embedding(req_params)
except WatsonXAIError as e:
raise e
except Exception as e:
@@ -663,128 +702,137 @@ class IBMWatsonXAI(BaseLLM):
return json_resp
return [res["model_id"] for res in json_resp["resources"]]
+
class RequestManager:
+ """
+ A class to handle sync/async HTTP requests to the IBM Watsonx.ai API.
+
+ Usage:
+ ```python
+ request_params = dict(method="POST", url="https://api.example.com", headers={"Authorization" : "Bearer token"}, json={"key": "value"})
+ request_manager = RequestManager(logging_obj=logging_obj)
+ with request_manager.request(request_params) as resp:
+ ...
+ # or
+ async with request_manager.async_request(request_params) as resp:
+ ...
+ ```
+ """
+
+ def __init__(self, logging_obj=None):
+ self.logging_obj = logging_obj
+
+ def pre_call(
+ self,
+ request_params: dict,
+ input: Optional[Any] = None,
+ is_async: Optional[bool] = False,
+ ):
+ if self.logging_obj is None:
+ return
+ request_str = (
+ f"response = {'await ' if is_async else ''}{request_params['method']}(\n"
+ f"\turl={request_params['url']},\n"
+ f"\tjson={request_params.get('json')},\n"
+ f")"
+ )
+ self.logging_obj.pre_call(
+ input=input,
+ api_key=request_params["headers"].get("Authorization"),
+ additional_args={
+ "complete_input_dict": request_params.get("json"),
+ "request_str": request_str,
+ },
+ )
+
+ def post_call(self, resp, request_params):
+ if self.logging_obj is None:
+ return
+ self.logging_obj.post_call(
+ input=input,
+ api_key=request_params["headers"].get("Authorization"),
+ original_response=json.dumps(resp.json()),
+ additional_args={
+ "status_code": resp.status_code,
+ "complete_input_dict": request_params.get(
+ "data", request_params.get("json")
+ ),
+ },
+ )
+
+ @contextmanager
+ def request(
+ self,
+ request_params: dict,
+ stream: bool = False,
+ input: Optional[Any] = None,
+ timeout=None,
+ ) -> Generator[requests.Response, None, None]:
"""
- Returns a context manager that manages the response from the request.
- if async_ is True, returns an async context manager, otherwise returns a regular context manager.
-
- Usage:
- ```python
- request_params = dict(method="POST", url="https://api.example.com", headers={"Authorization" : "Bearer token"}, json={"key": "value"})
- request_manager = RequestManager(logging_obj=logging_obj)
- async with request_manager.request(request_params) as resp:
- ...
- # or
- with request_manager.async_request(request_params) as resp:
- ...
- ```
+ Returns a context manager that yields the response from the request.
"""
-
- def __init__(self, logging_obj=None):
- self.logging_obj = logging_obj
-
- def pre_call(
- self,
- request_params: dict,
- input: Optional[Any] = None,
- ):
- if self.logging_obj is None:
- return
- request_str = (
- f"response = {request_params['method']}(\n"
- f"\turl={request_params['url']},\n"
- f"\tjson={request_params.get('json')},\n"
- f")"
- )
- self.logging_obj.pre_call(
- input=input,
- api_key=request_params["headers"].get("Authorization"),
- additional_args={
- "complete_input_dict": request_params.get("json"),
- "request_str": request_str,
- },
- )
-
- def post_call(self, resp, request_params):
- if self.logging_obj is None:
- return
- self.logging_obj.post_call(
- input=input,
- api_key=request_params["headers"].get("Authorization"),
- original_response=json.dumps(resp.json()),
- additional_args={
- "status_code": resp.status_code,
- "complete_input_dict": request_params.get(
- "data", request_params.get("json")
- ),
- },
- )
-
- @contextmanager
- def request(
- self,
- request_params: dict,
- stream: bool = False,
- input: Optional[Any] = None,
- timeout=None,
- ) -> Generator[requests.Response, None, None]:
- """
- Returns a context manager that yields the response from the request.
- """
- self.pre_call(request_params, input)
- if timeout:
- request_params["timeout"] = timeout
- if stream:
- request_params["stream"] = stream
- try:
- resp = requests.request(**request_params)
- if not resp.ok:
- raise WatsonXAIError(
- status_code=resp.status_code,
- message=f"Error {resp.status_code} ({resp.reason}): {resp.text}",
- )
- yield resp
- except Exception as e:
- raise WatsonXAIError(status_code=500, message=str(e))
- if not stream:
- self.post_call(resp, request_params)
-
- @asynccontextmanager
- async def async_request(
- self,
- request_params: dict,
- stream: bool = False,
- input: Optional[Any] = None,
- timeout=None,
- ) -> AsyncGenerator[httpx.Response, None]:
- self.pre_call(request_params, input)
- if timeout:
- request_params["timeout"] = timeout
- if stream:
- request_params["stream"] = stream
- try:
- # async with AsyncHTTPHandler(timeout=timeout) as client:
- self.async_handler = AsyncHTTPHandler(
- timeout=httpx.Timeout(
- timeout=request_params.pop("timeout", 600.0), connect=5.0
- ),
+ self.pre_call(request_params, input)
+ if timeout:
+ request_params["timeout"] = timeout
+ if stream:
+ request_params["stream"] = stream
+ try:
+ resp = requests.request(**request_params)
+ if not resp.ok:
+ raise WatsonXAIError(
+ status_code=resp.status_code,
+ message=f"Error {resp.status_code} ({resp.reason}): {resp.text}",
)
- # async_handler.client.verify = False
- if "json" in request_params:
- request_params["data"] = json.dumps(request_params.pop("json", {}))
- method = request_params.pop("method")
+ yield resp
+ except Exception as e:
+ raise WatsonXAIError(status_code=500, message=str(e))
+ if not stream:
+ self.post_call(resp, request_params)
+
+ @asynccontextmanager
+ async def async_request(
+ self,
+ request_params: dict,
+ stream: bool = False,
+ input: Optional[Any] = None,
+ timeout=None,
+ ) -> AsyncGenerator[httpx.Response, None]:
+ self.pre_call(request_params, input, is_async=True)
+ if timeout:
+ request_params["timeout"] = timeout
+ if stream:
+ request_params["stream"] = stream
+ try:
+ self.async_handler = AsyncHTTPHandler(
+ timeout=httpx.Timeout(
+ timeout=request_params.pop("timeout", 600.0), connect=5.0
+ ),
+ )
+ if "json" in request_params:
+ request_params["data"] = json.dumps(request_params.pop("json", {}))
+ method = request_params.pop("method")
+ retries = 0
+ while retries < 3:
if method.upper() == "POST":
resp = await self.async_handler.post(**request_params)
else:
resp = await self.async_handler.get(**request_params)
- if resp.status_code not in [200, 201]:
- raise WatsonXAIError(
- status_code=resp.status_code,
- message=f"Error {resp.status_code} ({resp.reason}): {resp.text}",
- )
- yield resp
- # await async_handler.close()
- except Exception as e:
- raise WatsonXAIError(status_code=500, message=str(e))
- if not stream:
- self.post_call(resp, request_params)
\ No newline at end of file
+ if resp.status_code in [429, 503, 504, 520]:
+ # to handle rate limiting and service unavailable errors
+ # see: ibm_watsonx_ai.foundation_models.inference.base_model_inference.BaseModelInference._send_inference_payload
+ await asyncio.sleep(2**retries)
+ retries += 1
+ else:
+ break
+ if resp.is_error:
+ raise WatsonXAIError(
+ status_code=resp.status_code,
+ message=f"Error {resp.status_code} ({resp.reason}): {resp.text}",
+ )
+ yield resp
+ # await async_handler.close()
+ except Exception as e:
+ raise e
+ raise WatsonXAIError(status_code=500, message=str(e))
+ if not stream:
+ self.post_call(resp, request_params)
diff --git a/litellm/main.py b/litellm/main.py
index 06d1abf829..65c0db880c 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -7,106 +7,138 @@
#
# Thank you ! We ❤️ you! - Krrish & Ishaan
-import os, openai, sys, json, inspect, uuid, datetime, threading
-from typing import Any, Literal, Union, BinaryIO
-from typing_extensions import overload
-from functools import partial
-import dotenv, traceback, random, asyncio, time, contextvars
+import asyncio
+import contextvars
+import datetime
+import inspect
+import json
+import os
+import random
+import sys
+import threading
+import time
+import traceback
+import uuid
+from concurrent.futures import ThreadPoolExecutor
from copy import deepcopy
+from functools import partial
+from typing import (
+ Any,
+ BinaryIO,
+ Callable,
+ Dict,
+ List,
+ Literal,
+ Mapping,
+ Optional,
+ Union,
+)
+
+import dotenv
import httpx
+import openai
+import tiktoken
+from pydantic import BaseModel
+from typing_extensions import overload
import litellm
-from ._logging import verbose_logger
from litellm import ( # type: ignore
+ Logging,
client,
exception_type,
- get_optional_params,
get_litellm_params,
- Logging,
+ get_optional_params,
)
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.utils import (
- get_secret,
CustomStreamWrapper,
- read_config_args,
- completion_with_fallbacks,
- get_llm_provider,
- get_api_key,
- mock_completion_streaming_obj,
+ Usage,
async_mock_completion_streaming_obj,
+ completion_with_fallbacks,
convert_to_model_response_object,
- token_counter,
create_pretrained_tokenizer,
create_tokenizer,
- Usage,
+ get_api_key,
+ get_llm_provider,
get_optional_params_embeddings,
get_optional_params_image_gen,
+ get_optional_params_transcription,
+ get_secret,
+ mock_completion_streaming_obj,
+ read_config_args,
supports_httpx_timeout,
+ token_counter,
)
+
+from ._logging import verbose_logger
+from .caching import disable_cache, enable_cache, update_cache
from .llms import (
- anthropic_text,
- together_ai,
ai21,
- sagemaker,
- bedrock,
- triton,
- huggingface_restapi,
- replicate,
aleph_alpha,
- nlp_cloud,
+ anthropic_text,
baseten,
- vllm,
- ollama,
- ollama_chat,
- cloudflare,
+ bedrock,
clarifai,
+ cloudflare,
cohere,
cohere_chat,
- petals,
+ gemini,
+ huggingface_restapi,
+ maritalk,
+ nlp_cloud,
+ ollama,
+ ollama_chat,
oobabooga,
openrouter,
palm,
- gemini,
+ petals,
+ replicate,
+ sagemaker,
+ together_ai,
+ triton,
vertex_ai,
vertex_ai_anthropic,
- maritalk,
+ vllm,
watsonx,
)
-from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion
-from .llms.azure import AzureChatCompletion
-from .llms.databricks import DatabricksChatCompletion
-from .llms.azure_text import AzureTextCompletion
from .llms.anthropic import AnthropicChatCompletion
from .llms.anthropic_text import AnthropicTextCompletion
+from .llms.azure import AzureChatCompletion
+from .llms.azure_text import AzureTextCompletion
+from .llms.bedrock_httpx import BedrockConverseLLM, BedrockLLM
+from .llms.databricks import DatabricksChatCompletion
from .llms.huggingface_restapi import Huggingface
+from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion
from .llms.predibase import PredibaseChatCompletion
-from .llms.bedrock_httpx import BedrockLLM
-from .llms.vertex_httpx import VertexLLM
-from .llms.triton import TritonChatCompletion
from .llms.prompt_templates.factory import (
- prompt_factory,
custom_prompt,
function_call_prompt,
map_system_message_pt,
+ prompt_factory,
+ stringify_json_tool_call_content,
)
-import tiktoken
-from concurrent.futures import ThreadPoolExecutor
-from typing import Callable, List, Optional, Dict, Union, Mapping
-from .caching import enable_cache, disable_cache, update_cache
+from .llms.text_completion_codestral import CodestralTextCompletion
+from .llms.triton import TritonChatCompletion
+from .llms.vertex_httpx import VertexLLM
+from .llms.watsonx import IBMWatsonXAI
+from .types.llms.openai import HttpxBinaryResponseContent
+from .types.utils import ChatCompletionMessageToolCall
encoding = tiktoken.get_encoding("cl100k_base")
from litellm.utils import (
- get_secret,
+ Choices,
CustomStreamWrapper,
- TextCompletionStreamWrapper,
- ModelResponse,
- TextCompletionResponse,
- TextChoices,
EmbeddingResponse,
ImageResponse,
- read_config_args,
- Choices,
Message,
+ ModelResponse,
+ TextChoices,
+ TextCompletionResponse,
+ TextCompletionStreamWrapper,
TranscriptionResponse,
+ get_secret,
+ read_config_args,
)
####### ENVIRONMENT VARIABLES ###################
@@ -119,9 +151,12 @@ azure_chat_completions = AzureChatCompletion()
azure_text_completions = AzureTextCompletion()
huggingface = Huggingface()
predibase_chat_completions = PredibaseChatCompletion()
+codestral_text_completions = CodestralTextCompletion()
triton_chat_completions = TritonChatCompletion()
bedrock_chat_completion = BedrockLLM()
+bedrock_converse_chat_completion = BedrockConverseLLM()
vertex_chat_completion = VertexLLM()
+watsonxai = IBMWatsonXAI()
####### COMPLETION ENDPOINTS ################
@@ -212,6 +247,7 @@ async def acompletion(
seed: Optional[int] = None,
tools: Optional[List] = None,
tool_choice: Optional[str] = None,
+ parallel_tool_calls: Optional[bool] = None,
logprobs: Optional[bool] = None,
top_logprobs: Optional[int] = None,
deployment_id=None,
@@ -223,7 +259,7 @@ async def acompletion(
extra_headers: Optional[dict] = None,
# Optional liteLLM function params
**kwargs,
-):
+) -> Union[ModelResponse, CustomStreamWrapper]:
"""
Asynchronously executes a litellm.completion() call for any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly)
@@ -287,6 +323,7 @@ async def acompletion(
"seed": seed,
"tools": tools,
"tool_choice": tool_choice,
+ "parallel_tool_calls": parallel_tool_calls,
"logprobs": logprobs,
"top_logprobs": top_logprobs,
"deployment_id": deployment_id,
@@ -320,6 +357,10 @@ async def acompletion(
or custom_llm_provider == "deepinfra"
or custom_llm_provider == "perplexity"
or custom_llm_provider == "groq"
+ or custom_llm_provider == "nvidia_nim"
+ or custom_llm_provider == "volcengine"
+ or custom_llm_provider == "codestral"
+ or custom_llm_provider == "text-completion-codestral"
or custom_llm_provider == "deepseek"
or custom_llm_provider == "text-completion-openai"
or custom_llm_provider == "huggingface"
@@ -327,6 +368,7 @@ async def acompletion(
or custom_llm_provider == "ollama_chat"
or custom_llm_provider == "replicate"
or custom_llm_provider == "vertex_ai"
+ or custom_llm_provider == "vertex_ai_beta"
or custom_llm_provider == "gemini"
or custom_llm_provider == "sagemaker"
or custom_llm_provider == "anthropic"
@@ -334,21 +376,26 @@ async def acompletion(
or custom_llm_provider == "bedrock"
or custom_llm_provider == "databricks"
or custom_llm_provider == "triton"
+ or custom_llm_provider == "clarifai"
+ or custom_llm_provider == "watsonx"
or custom_llm_provider in litellm.openai_compatible_providers
): # currently implemented aiohttp calls for just azure, openai, hf, ollama, vertex ai soon all.
init_response = await loop.run_in_executor(None, func_with_context)
if isinstance(init_response, dict) or isinstance(
init_response, ModelResponse
): ## CACHING SCENARIO
+ if isinstance(init_response, dict):
+ response = ModelResponse(**init_response)
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response
else:
response = init_response # type: ignore
- if custom_llm_provider == "text-completion-openai" and isinstance(
- response, TextCompletionResponse
- ):
+ if (
+ custom_llm_provider == "text-completion-openai"
+ or custom_llm_provider == "text-completion-codestral"
+ ) and isinstance(response, TextCompletionResponse):
response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object(
response_object=response,
model_response_object=litellm.ModelResponse(),
@@ -362,6 +409,12 @@ async def acompletion(
) # sets the logging event loop if the user does sync streaming (e.g. on proxy for sagemaker calls)
return response
except Exception as e:
+ verbose_logger.error(
+ "litellm.acompletion(): Exception occured - {}\n{}".format(
+ str(e), traceback.format_exc()
+ )
+ )
+ verbose_logger.debug(traceback.format_exc())
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
@@ -388,8 +441,11 @@ def mock_completion(
model: str,
messages: List,
stream: Optional[bool] = False,
- mock_response: Union[str, Exception] = "This is a mock request",
+ n: Optional[int] = None,
+ mock_response: Union[str, Exception, dict] = "This is a mock request",
+ mock_tool_calls: Optional[List] = None,
logging=None,
+ custom_llm_provider=None,
**kwargs,
):
"""
@@ -424,33 +480,69 @@ def mock_completion(
if isinstance(mock_response, Exception):
if isinstance(mock_response, openai.APIError):
raise mock_response
- raise litellm.APIError(
- status_code=500, # type: ignore
- message=str(mock_response),
- llm_provider="openai", # type: ignore
+ raise litellm.MockException(
+ status_code=getattr(mock_response, "status_code", 500), # type: ignore
+ message=getattr(mock_response, "text", str(mock_response)),
+ llm_provider=getattr(mock_response, "llm_provider", custom_llm_provider or "openai"), # type: ignore
model=model, # type: ignore
request=httpx.Request(method="POST", url="https://api.openai.com/v1/"),
)
+ elif (
+ isinstance(mock_response, str) and mock_response == "litellm.RateLimitError"
+ ):
+ raise litellm.RateLimitError(
+ message="this is a mock rate limit error",
+ status_code=getattr(mock_response, "status_code", 429), # type: ignore
+ llm_provider=getattr(mock_response, "llm_provider", custom_llm_provider or "openai"), # type: ignore
+ model=model,
+ )
+ time_delay = kwargs.get("mock_delay", None)
+ if time_delay is not None:
+ time.sleep(time_delay)
+
+ if isinstance(mock_response, dict):
+ return ModelResponse(**mock_response)
+
model_response = ModelResponse(stream=stream)
if stream is True:
# don't try to access stream object,
if kwargs.get("acompletion", False) == True:
return CustomStreamWrapper(
completion_stream=async_mock_completion_streaming_obj(
- model_response, mock_response=mock_response, model=model
+ model_response, mock_response=mock_response, model=model, n=n
),
model=model,
custom_llm_provider="openai",
logging_obj=logging,
)
response = mock_completion_streaming_obj(
- model_response, mock_response=mock_response, model=model
+ model_response,
+ mock_response=mock_response,
+ model=model,
+ n=n,
)
return response
+ if n is None:
+ model_response.choices[0].message.content = mock_response # type: ignore
+ else:
+ _all_choices = []
+ for i in range(n):
+ _choice = litellm.utils.Choices(
+ index=i,
+ message=litellm.utils.Message(
+ content=mock_response, role="assistant"
+ ),
+ )
+ _all_choices.append(_choice)
+ model_response.choices = _all_choices # type: ignore
+ model_response.created = int(time.time())
+ model_response.model = model
- model_response["choices"][0]["message"]["content"] = mock_response
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ if mock_tool_calls:
+ model_response.choices[0].message.tool_calls = [ # type: ignore
+ ChatCompletionMessageToolCall(**tool_call)
+ for tool_call in mock_tool_calls
+ ]
setattr(
model_response,
@@ -461,16 +553,25 @@ def mock_completion(
try:
_, custom_llm_provider, _, _ = litellm.utils.get_llm_provider(model=model)
model_response._hidden_params["custom_llm_provider"] = custom_llm_provider
- except:
+ except Exception:
# dont let setting a hidden param block a mock_respose
pass
+ if logging is not None:
+ logging.post_call(
+ input=messages,
+ api_key="my-secret-key",
+ original_response="my-original-response",
+ )
return model_response
except Exception as e:
if isinstance(e, openai.APIError):
raise e
- traceback.print_exc()
+ verbose_logger.error(
+ "litellm.mock_completion(): Exception occured - {}".format(str(e))
+ )
+ verbose_logger.debug(traceback.format_exc())
raise Exception("Mock completion response failed")
@@ -498,6 +599,7 @@ def completion(
tool_choice: Optional[Union[str, dict]] = None,
logprobs: Optional[bool] = None,
top_logprobs: Optional[int] = None,
+ parallel_tool_calls: Optional[bool] = None,
deployment_id=None,
extra_headers: Optional[dict] = None,
# soon to be deprecated params by OpenAI
@@ -556,6 +658,7 @@ def completion(
args = locals()
api_base = kwargs.get("api_base", None)
mock_response = kwargs.get("mock_response", None)
+ mock_tool_calls = kwargs.get("mock_tool_calls", None)
force_timeout = kwargs.get("force_timeout", 600) ## deprecated
logger_fn = kwargs.get("logger_fn", None)
verbose = kwargs.get("verbose", False)
@@ -569,6 +672,7 @@ def completion(
headers = kwargs.get("headers", None) or extra_headers
num_retries = kwargs.get("num_retries", None) ## deprecated
max_retries = kwargs.get("max_retries", None)
+ cooldown_time = kwargs.get("cooldown_time", None)
context_window_fallback_dict = kwargs.get("context_window_fallback_dict", None)
organization = kwargs.get("organization", None)
### CUSTOM MODEL COST ###
@@ -593,6 +697,8 @@ def completion(
client = kwargs.get("client", None)
### Admin Controls ###
no_log = kwargs.get("no-log", False)
+ ### COPY MESSAGES ### - related issue https://github.com/BerriAI/litellm/discussions/4489
+ messages = deepcopy(messages)
######## end of unpacking kwargs ###########
openai_params = [
"functions",
@@ -623,12 +729,14 @@ def completion(
"tools",
"tool_choice",
"max_retries",
+ "parallel_tool_calls",
"logprobs",
"top_logprobs",
"extra_headers",
]
litellm_params = [
"metadata",
+ "tags",
"acompletion",
"atext_completion",
"text_completion",
@@ -681,6 +789,8 @@ def completion(
"region_name",
"allowed_model_region",
"model_config",
+ "fastest_response",
+ "cooldown_time",
]
default_params = openai_params + litellm_params
@@ -795,7 +905,7 @@ def completion(
if (
supports_system_message is not None
and isinstance(supports_system_message, bool)
- and supports_system_message == False
+ and supports_system_message is False
):
messages = map_system_message_pt(messages=messages)
model_api_key = get_api_key(
@@ -830,6 +940,8 @@ def completion(
logprobs=logprobs,
top_logprobs=top_logprobs,
extra_headers=extra_headers,
+ api_version=api_version,
+ parallel_tool_calls=parallel_tool_calls,
**non_default_params,
)
@@ -864,6 +976,7 @@ def completion(
input_cost_per_token=input_cost_per_token,
output_cost_per_second=output_cost_per_second,
output_cost_per_token=output_cost_per_token,
+ cooldown_time=cooldown_time,
)
logging.update_environment_variables(
model=model,
@@ -872,15 +985,20 @@ def completion(
litellm_params=litellm_params,
custom_llm_provider=custom_llm_provider,
)
- if mock_response:
+ if mock_response or mock_tool_calls:
return mock_completion(
model,
messages,
stream=stream,
+ n=n,
mock_response=mock_response,
+ mock_tool_calls=mock_tool_calls,
logging=logging,
acompletion=acompletion,
+ mock_delay=kwargs.get("mock_delay", None),
+ custom_llm_provider=custom_llm_provider,
)
+
if custom_llm_provider == "azure":
# azure configs
api_type = get_secret("AZURE_API_TYPE") or "azure"
@@ -934,7 +1052,7 @@ def completion(
client=client, # pass AsyncAzureOpenAI, AzureOpenAI client
)
- if optional_params.get("stream", False) or acompletion == True:
+ if optional_params.get("stream", False):
## LOGGING
logging.post_call(
input=messages,
@@ -1011,40 +1129,18 @@ def completion(
"api_base": api_base,
},
)
- elif (
- model in litellm.open_ai_chat_completion_models
- or custom_llm_provider == "custom_openai"
- or custom_llm_provider == "deepinfra"
- or custom_llm_provider == "perplexity"
- or custom_llm_provider == "groq"
- or custom_llm_provider == "deepseek"
- or custom_llm_provider == "anyscale"
- or custom_llm_provider == "mistral"
- or custom_llm_provider == "openai"
- or custom_llm_provider == "together_ai"
- or custom_llm_provider in litellm.openai_compatible_providers
- or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo
- ): # allow user to make an openai call with a custom base
- # note: if a user sets a custom base - we should ensure this works
- # allow for the setting of dynamic and stateful api-bases
+ elif custom_llm_provider == "azure_ai":
api_base = (
- api_base # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+ api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
or litellm.api_base
- or get_secret("OPENAI_API_BASE")
- or "https://api.openai.com/v1"
- )
- openai.organization = (
- organization
- or litellm.organization
- or get_secret("OPENAI_ORGANIZATION")
- or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ or get_secret("AZURE_AI_API_BASE")
)
# set API KEY
api_key = (
api_key
- or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
- or get_secret("OPENAI_API_KEY")
+ or get_secret("AZURE_AI_API_KEY")
)
headers = headers or litellm.headers
@@ -1057,6 +1153,10 @@ def completion(
): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
+ ## FOR COHERE
+ if "command-r" in model: # make sure tool call in messages are str
+ messages = stringify_json_tool_call_content(messages=messages)
+
## COMPLETION CALL
try:
response = openai_chat_completions.completion(
@@ -1179,6 +1279,95 @@ def completion(
additional_args={"headers": headers},
)
response = _response
+
+ elif (
+ model in litellm.open_ai_chat_completion_models
+ or custom_llm_provider == "custom_openai"
+ or custom_llm_provider == "deepinfra"
+ or custom_llm_provider == "perplexity"
+ or custom_llm_provider == "groq"
+ or custom_llm_provider == "nvidia_nim"
+ or custom_llm_provider == "volcengine"
+ or custom_llm_provider == "codestral"
+ or custom_llm_provider == "deepseek"
+ or custom_llm_provider == "anyscale"
+ or custom_llm_provider == "mistral"
+ or custom_llm_provider == "openai"
+ or custom_llm_provider == "together_ai"
+ or custom_llm_provider in litellm.openai_compatible_providers
+ or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo
+ ): # allow user to make an openai call with a custom base
+ # note: if a user sets a custom base - we should ensure this works
+ # allow for the setting of dynamic and stateful api-bases
+ api_base = (
+ api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
+ or litellm.api_base
+ or get_secret("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ )
+ openai.organization = (
+ organization
+ or litellm.organization
+ or get_secret("OPENAI_ORGANIZATION")
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or get_secret("OPENAI_API_KEY")
+ )
+
+ headers = headers or litellm.headers
+
+ ## LOAD CONFIG - if set
+ config = litellm.OpenAIConfig.get_config()
+ for k, v in config.items():
+ if (
+ k not in optional_params
+ ): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in
+ optional_params[k] = v
+
+ ## COMPLETION CALL
+ try:
+ response = openai_chat_completions.completion(
+ model=model,
+ messages=messages,
+ headers=headers,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ api_key=api_key,
+ api_base=api_base,
+ acompletion=acompletion,
+ logging_obj=logging,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ timeout=timeout, # type: ignore
+ custom_prompt_dict=custom_prompt_dict,
+ client=client, # pass AsyncOpenAI, OpenAI client
+ organization=organization,
+ custom_llm_provider=custom_llm_provider,
+ )
+ except Exception as e:
+ ## LOGGING - log the original exception returned
+ logging.post_call(
+ input=messages,
+ api_key=api_key,
+ original_response=str(e),
+ additional_args={"headers": headers},
+ )
+ raise e
+
+ if optional_params.get("stream", False):
+ ## LOGGING
+ logging.post_call(
+ input=messages,
+ api_key=api_key,
+ original_response=response,
+ additional_args={"headers": headers},
+ )
elif (
"replicate" in model
or custom_llm_provider == "replicate"
@@ -1341,6 +1530,8 @@ def completion(
api_key=api_key,
logging_obj=logging,
headers=headers,
+ timeout=timeout,
+ client=client,
)
if optional_params.get("stream", False) or acompletion == True:
## LOGGING
@@ -1733,6 +1924,7 @@ def completion(
logging_obj=logging,
acompletion=acompletion,
timeout=timeout, # type: ignore
+ custom_llm_provider="openrouter",
)
## LOGGING
logging.post_call(
@@ -1746,51 +1938,7 @@ def completion(
"""
Deprecated. We now do together ai calls via the openai client - https://docs.together.ai/docs/openai-api-compatibility
"""
- custom_llm_provider = "together_ai"
- together_ai_key = (
- api_key
- or litellm.togetherai_api_key
- or get_secret("TOGETHER_AI_TOKEN")
- or get_secret("TOGETHERAI_API_KEY")
- or litellm.api_key
- )
-
- api_base = (
- api_base
- or litellm.api_base
- or get_secret("TOGETHERAI_API_BASE")
- or "https://api.together.xyz/inference"
- )
-
- custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
-
- model_response = together_ai.completion(
- model=model,
- messages=messages,
- api_base=api_base,
- model_response=model_response,
- print_verbose=print_verbose,
- optional_params=optional_params,
- litellm_params=litellm_params,
- logger_fn=logger_fn,
- encoding=encoding,
- api_key=together_ai_key,
- logging_obj=logging,
- custom_prompt_dict=custom_prompt_dict,
- )
- if (
- "stream_tokens" in optional_params
- and optional_params["stream_tokens"] == True
- ):
- # don't try to access stream object,
- response = CustomStreamWrapper(
- model_response,
- model,
- custom_llm_provider="together_ai",
- logging_obj=logging,
- )
- return response
- response = model_response
+ pass
elif custom_llm_provider == "palm":
palm_api_key = api_key or get_secret("PALM_API_KEY") or litellm.api_key
@@ -1816,7 +1964,25 @@ def completion(
)
return response
response = model_response
- elif custom_llm_provider == "gemini":
+ elif custom_llm_provider == "vertex_ai_beta" or custom_llm_provider == "gemini":
+ vertex_ai_project = (
+ optional_params.pop("vertex_project", None)
+ or optional_params.pop("vertex_ai_project", None)
+ or litellm.vertex_project
+ or get_secret("VERTEXAI_PROJECT")
+ )
+ vertex_ai_location = (
+ optional_params.pop("vertex_location", None)
+ or optional_params.pop("vertex_ai_location", None)
+ or litellm.vertex_location
+ or get_secret("VERTEXAI_LOCATION")
+ )
+ vertex_credentials = (
+ optional_params.pop("vertex_credentials", None)
+ or optional_params.pop("vertex_ai_credentials", None)
+ or get_secret("VERTEXAI_CREDENTIALS")
+ )
+
gemini_api_key = (
api_key
or get_secret("GEMINI_API_KEY")
@@ -1824,34 +1990,29 @@ def completion(
or litellm.api_key
)
- # palm does not support streaming as yet :(
- model_response = gemini.completion(
+ new_params = deepcopy(optional_params)
+ response = vertex_chat_completion.completion( # type: ignore
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
- optional_params=optional_params,
+ optional_params=new_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding,
- api_key=gemini_api_key,
+ vertex_location=vertex_ai_location,
+ vertex_project=vertex_ai_project,
+ vertex_credentials=vertex_credentials,
+ gemini_api_key=gemini_api_key,
logging_obj=logging,
acompletion=acompletion,
- custom_prompt_dict=custom_prompt_dict,
+ timeout=timeout,
+ custom_llm_provider=custom_llm_provider,
+ client=client,
+ api_base=api_base,
+ extra_headers=extra_headers,
)
- if (
- "stream" in optional_params
- and optional_params["stream"] == True
- and acompletion == False
- ):
- response = CustomStreamWrapper(
- iter(model_response),
- model,
- custom_llm_provider="gemini",
- logging_obj=logging,
- )
- return response
- response = model_response
+
elif custom_llm_provider == "vertex_ai":
vertex_ai_project = (
optional_params.pop("vertex_project", None)
@@ -1870,6 +2031,7 @@ def completion(
or optional_params.pop("vertex_ai_credentials", None)
or get_secret("VERTEXAI_CREDENTIALS")
)
+
new_params = deepcopy(optional_params)
if "claude-3" in model:
model_response = vertex_ai_anthropic.completion(
@@ -1886,7 +2048,12 @@ def completion(
vertex_credentials=vertex_credentials,
logging_obj=logging,
acompletion=acompletion,
+ headers=headers,
+ custom_prompt_dict=custom_prompt_dict,
+ timeout=timeout,
+ client=client,
)
+
else:
model_response = vertex_ai.completion(
model=model,
@@ -1904,18 +2071,18 @@ def completion(
acompletion=acompletion,
)
- if (
- "stream" in optional_params
- and optional_params["stream"] == True
- and acompletion == False
- ):
- response = CustomStreamWrapper(
- model_response,
- model,
- custom_llm_provider="vertex_ai",
- logging_obj=logging,
- )
- return response
+ if (
+ "stream" in optional_params
+ and optional_params["stream"] == True
+ and acompletion == False
+ ):
+ response = CustomStreamWrapper(
+ model_response,
+ model,
+ custom_llm_provider="vertex_ai",
+ logging_obj=logging,
+ )
+ return response
response = model_response
elif custom_llm_provider == "predibase":
tenant_id = (
@@ -1926,7 +2093,8 @@ def completion(
)
api_base = (
- optional_params.pop("api_base", None)
+ api_base
+ or optional_params.pop("api_base", None)
or optional_params.pop("base_url", None)
or litellm.api_base
or get_secret("PREDIBASE_API_BASE")
@@ -1954,12 +2122,53 @@ def completion(
custom_prompt_dict=custom_prompt_dict,
api_key=api_key,
tenant_id=tenant_id,
+ timeout=timeout,
)
if (
"stream" in optional_params
- and optional_params["stream"] == True
- and acompletion == False
+ and optional_params["stream"] is True
+ and acompletion is False
+ ):
+ return _model_response
+ response = _model_response
+ elif custom_llm_provider == "text-completion-codestral":
+
+ api_base = (
+ api_base
+ or optional_params.pop("api_base", None)
+ or optional_params.pop("base_url", None)
+ or litellm.api_base
+ or "https://codestral.mistral.ai/v1/fim/completions"
+ )
+
+ api_key = api_key or litellm.api_key or get_secret("CODESTRAL_API_KEY")
+
+ text_completion_model_response = litellm.TextCompletionResponse(
+ stream=stream
+ )
+
+ _model_response = codestral_text_completions.completion( # type: ignore
+ model=model,
+ messages=messages,
+ model_response=text_completion_model_response,
+ print_verbose=print_verbose,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ encoding=encoding,
+ logging_obj=logging,
+ acompletion=acompletion,
+ api_base=api_base,
+ custom_prompt_dict=custom_prompt_dict,
+ api_key=api_key,
+ timeout=timeout,
+ )
+
+ if (
+ "stream" in optional_params
+ and optional_params["stream"] is True
+ and acompletion is False
):
return _model_response
response = _model_response
@@ -2048,13 +2257,33 @@ def completion(
# boto3 reads keys from .env
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
- if (
- "aws_bedrock_client" in optional_params
- ): # use old bedrock flow for aws_bedrock_client users.
- response = bedrock.completion(
+ if "aws_bedrock_client" in optional_params:
+ verbose_logger.warning(
+ "'aws_bedrock_client' is a deprecated param. Please move to another auth method - https://docs.litellm.ai/docs/providers/bedrock#boto3---authentication."
+ )
+ # Extract credentials for legacy boto3 client and pass thru to httpx
+ aws_bedrock_client = optional_params.pop("aws_bedrock_client")
+ creds = aws_bedrock_client._get_credentials().get_frozen_credentials()
+
+ if creds.access_key:
+ optional_params["aws_access_key_id"] = creds.access_key
+ if creds.secret_key:
+ optional_params["aws_secret_access_key"] = creds.secret_key
+ if creds.token:
+ optional_params["aws_session_token"] = creds.token
+ if (
+ "aws_region_name" not in optional_params
+ or optional_params["aws_region_name"] is None
+ ):
+ optional_params["aws_region_name"] = (
+ aws_bedrock_client.meta.region_name
+ )
+
+ if model in litellm.BEDROCK_CONVERSE_MODELS:
+ response = bedrock_converse_chat_completion.completion(
model=model,
messages=messages,
- custom_prompt_dict=litellm.custom_prompt_dict,
+ custom_prompt_dict=custom_prompt_dict,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
@@ -2064,28 +2293,9 @@ def completion(
logging_obj=logging,
extra_headers=extra_headers,
timeout=timeout,
+ acompletion=acompletion,
+ client=client,
)
-
- if (
- "stream" in optional_params
- and optional_params["stream"] == True
- and not isinstance(response, CustomStreamWrapper)
- ):
- # don't try to access stream object,
- if "ai21" in model:
- response = CustomStreamWrapper(
- response,
- model,
- custom_llm_provider="bedrock",
- logging_obj=logging,
- )
- else:
- response = CustomStreamWrapper(
- iter(response),
- model,
- custom_llm_provider="bedrock",
- logging_obj=logging,
- )
else:
response = bedrock_chat_completion.completion(
model=model,
@@ -2103,6 +2313,7 @@ def completion(
acompletion=acompletion,
client=client,
)
+
if optional_params.get("stream", False):
## LOGGING
logging.post_call(
@@ -2115,7 +2326,7 @@ def completion(
response = response
elif custom_llm_provider == "watsonx":
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
- response = watsonx.IBMWatsonXAI().completion(
+ response = watsonxai.completion(
model=model,
messages=messages,
custom_prompt_dict=custom_prompt_dict,
@@ -2127,6 +2338,7 @@ def completion(
encoding=encoding,
logging_obj=logging,
timeout=timeout, # type: ignore
+ acompletion=acompletion,
)
if (
"stream" in optional_params
@@ -2212,10 +2424,10 @@ def completion(
## LOGGING
generator = ollama.get_ollama_response(
- api_base,
- model,
- prompt,
- optional_params,
+ api_base=api_base,
+ model=model,
+ prompt=prompt,
+ optional_params=optional_params,
logging_obj=logging,
acompletion=acompletion,
model_response=model_response,
@@ -2241,11 +2453,11 @@ def completion(
)
## LOGGING
generator = ollama_chat.get_ollama_response(
- api_base,
- api_key,
- model,
- messages,
- optional_params,
+ api_base=api_base,
+ api_key=api_key,
+ model=model,
+ messages=messages,
+ optional_params=optional_params,
logging_obj=logging,
acompletion=acompletion,
model_response=model_response,
@@ -2427,6 +2639,7 @@ def completion(
"top_k": kwargs.get("top_k", 40),
},
},
+ verify=litellm.ssl_verify,
)
response_json = resp.json()
"""
@@ -2444,9 +2657,9 @@ def completion(
"""
string_response = response_json["data"][0]["output"][0]
## RESPONSE OBJECT
- model_response["choices"][0]["message"]["content"] = string_response
- model_response["created"] = int(time.time())
- model_response["model"] = model
+ model_response.choices[0].message.content = string_response # type: ignore
+ model_response.created = int(time.time())
+ model_response.model = model
response = model_response
else:
raise ValueError(
@@ -2807,11 +3020,14 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse:
or custom_llm_provider == "deepinfra"
or custom_llm_provider == "perplexity"
or custom_llm_provider == "groq"
+ or custom_llm_provider == "nvidia_nim"
+ or custom_llm_provider == "volcengine"
or custom_llm_provider == "deepseek"
or custom_llm_provider == "fireworks_ai"
or custom_llm_provider == "ollama"
or custom_llm_provider == "vertex_ai"
or custom_llm_provider == "databricks"
+ or custom_llm_provider == "watsonx"
): # currently implemented aiohttp calls for just azure and openai, soon all.
# Await normally
init_response = await loop.run_in_executor(None, func_with_context)
@@ -2886,6 +3102,7 @@ def embedding(
client = kwargs.pop("client", None)
rpm = kwargs.pop("rpm", None)
tpm = kwargs.pop("tpm", None)
+ cooldown_time = kwargs.get("cooldown_time", None)
max_parallel_requests = kwargs.pop("max_parallel_requests", None)
model_info = kwargs.get("model_info", None)
metadata = kwargs.get("metadata", None)
@@ -2961,6 +3178,8 @@ def embedding(
"region_name",
"allowed_model_region",
"model_config",
+ "cooldown_time",
+ "tags",
]
default_params = openai_params + litellm_params
non_default_params = {
@@ -3021,6 +3240,7 @@ def embedding(
"aembedding": aembedding,
"preset_cache_key": None,
"stream_response": {},
+ "cooldown_time": cooldown_time,
},
)
if azure == True or custom_llm_provider == "azure":
@@ -3231,7 +3451,7 @@ def embedding(
or api_base
or get_secret("OLLAMA_API_BASE")
or "http://localhost:11434"
- )
+ ) # type: ignore
if isinstance(input, str):
input = [input]
if not all(isinstance(item, str) for item in input):
@@ -3241,9 +3461,11 @@ def embedding(
llm_provider="ollama", # type: ignore
)
ollama_embeddings_fn = (
- ollama.ollama_aembeddings if aembedding else ollama.ollama_embeddings
+ ollama.ollama_aembeddings
+ if aembedding is True
+ else ollama.ollama_embeddings
)
- response = ollama_embeddings_fn(
+ response = ollama_embeddings_fn( # type: ignore
api_base=api_base,
model=model,
prompts=input,
@@ -3316,13 +3538,14 @@ def embedding(
aembedding=aembedding,
)
elif custom_llm_provider == "watsonx":
- response = watsonx.IBMWatsonXAI().embedding(
+ response = watsonxai.embedding(
model=model,
input=input,
encoding=encoding,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
+ aembedding=aembedding,
)
else:
args = locals()
@@ -3348,7 +3571,9 @@ def embedding(
###### Text Completion ################
@client
-async def atext_completion(*args, **kwargs):
+async def atext_completion(
+ *args, **kwargs
+) -> Union[TextCompletionResponse, TextCompletionStreamWrapper]:
"""
Implemented to handle async streaming for the text completion endpoint
"""
@@ -3380,6 +3605,9 @@ async def atext_completion(*args, **kwargs):
or custom_llm_provider == "deepinfra"
or custom_llm_provider == "perplexity"
or custom_llm_provider == "groq"
+ or custom_llm_provider == "nvidia_nim"
+ or custom_llm_provider == "volcengine"
+ or custom_llm_provider == "text-completion-codestral"
or custom_llm_provider == "deepseek"
or custom_llm_provider == "fireworks_ai"
or custom_llm_provider == "text-completion-openai"
@@ -3641,6 +3869,7 @@ def text_completion(
custom_llm_provider == "openai"
or custom_llm_provider == "azure"
or custom_llm_provider == "azure_text"
+ or custom_llm_provider == "text-completion-codestral"
or custom_llm_provider == "text-completion-openai"
)
and isinstance(prompt, list)
@@ -3657,6 +3886,12 @@ def text_completion(
)
kwargs.pop("prompt", None)
+
+ if model is not None and model.startswith(
+ "openai/"
+ ): # for openai compatible endpoints - e.g. vllm, call the native /v1/completions endpoint for text completion calls
+ model = model.replace("openai/", "text-completion-openai/")
+
kwargs["text_completion"] = True
response = completion(
model=model,
@@ -3698,6 +3933,63 @@ def text_completion(
return text_completion_response
+###### Adapter Completion ################
+
+
+async def aadapter_completion(*, adapter_id: str, **kwargs) -> Optional[BaseModel]:
+ """
+ Implemented to handle async calls for adapter_completion()
+ """
+ try:
+ translation_obj: Optional[CustomLogger] = None
+ for item in litellm.adapters:
+ if item["id"] == adapter_id:
+ translation_obj = item["adapter"]
+
+ if translation_obj is None:
+ raise ValueError(
+ "No matching adapter given. Received 'adapter_id'={}, litellm.adapters={}".format(
+ adapter_id, litellm.adapters
+ )
+ )
+
+ new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs)
+
+ response: ModelResponse = await acompletion(**new_kwargs) # type: ignore
+
+ translated_response = translation_obj.translate_completion_output_params(
+ response=response
+ )
+
+ return translated_response
+ except Exception as e:
+ raise e
+
+
+def adapter_completion(*, adapter_id: str, **kwargs) -> Optional[BaseModel]:
+ translation_obj: Optional[CustomLogger] = None
+ for item in litellm.adapters:
+ if item["id"] == adapter_id:
+ translation_obj = item["adapter"]
+
+ if translation_obj is None:
+ raise ValueError(
+ "No matching adapter given. Received 'adapter_id'={}, litellm.adapters={}".format(
+ adapter_id, litellm.adapters
+ )
+ )
+
+ new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs)
+
+ response: ModelResponse = completion(**new_kwargs) # type: ignore
+
+ translated_response = translation_obj.translate_completion_output_params(
+ response=response
+ )
+
+ return translated_response
+
+
##### Moderation #######################
@@ -3720,14 +4012,20 @@ def moderation(
@client
-async def amoderation(input: str, model: str, api_key: Optional[str] = None, **kwargs):
+async def amoderation(
+ input: str, model: Optional[str] = None, api_key: Optional[str] = None, **kwargs
+):
# only supports open ai for now
api_key = (
api_key or litellm.api_key or litellm.openai_key or get_secret("OPENAI_API_KEY")
)
openai_client = kwargs.get("client", None)
if openai_client is None:
- openai_client = openai.AsyncOpenAI(
+
+ # call helper to get OpenAI client
+ # _get_openai_client maintains in-memory caching logic for OpenAI clients
+ openai_client = openai_chat_completions._get_openai_client(
+ is_async=True,
api_key=api_key,
)
response = await openai_client.moderations.create(input=input, model=model)
@@ -3736,7 +4034,7 @@ async def amoderation(input: str, model: str, api_key: Optional[str] = None, **k
##### Image Generation #######################
@client
-async def aimage_generation(*args, **kwargs):
+async def aimage_generation(*args, **kwargs) -> ImageResponse:
"""
Asynchronously calls the `image_generation` function with the given arguments and keyword arguments.
@@ -3769,6 +4067,8 @@ async def aimage_generation(*args, **kwargs):
if isinstance(init_response, dict) or isinstance(
init_response, ImageResponse
): ## CACHING SCENARIO
+ if isinstance(init_response, dict):
+ init_response = ImageResponse(**init_response)
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response
@@ -3804,7 +4104,7 @@ def image_generation(
litellm_logging_obj=None,
custom_llm_provider=None,
**kwargs,
-):
+) -> ImageResponse:
"""
Maps the https://api.openai.com/v1/images/generations endpoint.
@@ -3817,6 +4117,7 @@ def image_generation(
proxy_server_request = kwargs.get("proxy_server_request", None)
model_info = kwargs.get("model_info", None)
metadata = kwargs.get("metadata", {})
+ client = kwargs.get("client", None)
model_response = litellm.utils.ImageResponse()
if model is not None or custom_llm_provider is not None:
@@ -3955,6 +4256,7 @@ def image_generation(
model_response=model_response,
api_version=api_version,
aimg_generation=aimg_generation,
+ client=client,
)
elif custom_llm_provider == "openai":
model_response = openai_chat_completions.image_generation(
@@ -3967,6 +4269,7 @@ def image_generation(
optional_params=optional_params,
model_response=model_response,
aimg_generation=aimg_generation,
+ client=client,
)
elif custom_llm_provider == "bedrock":
if model is None:
@@ -4007,6 +4310,7 @@ def image_generation(
model_response=model_response,
vertex_project=vertex_ai_project,
vertex_location=vertex_ai_location,
+ vertex_credentials=vertex_credentials,
aimg_generation=aimg_generation,
)
@@ -4026,7 +4330,7 @@ def image_generation(
@client
-async def atranscription(*args, **kwargs):
+async def atranscription(*args, **kwargs) -> TranscriptionResponse:
"""
Calls openai + azure whisper endpoints.
@@ -4051,9 +4355,9 @@ async def atranscription(*args, **kwargs):
# Await normally
init_response = await loop.run_in_executor(None, func_with_context)
- if isinstance(init_response, dict) or isinstance(
- init_response, TranscriptionResponse
- ): ## CACHING SCENARIO
+ if isinstance(init_response, dict):
+ response = TranscriptionResponse(**init_response)
+ elif isinstance(init_response, TranscriptionResponse): ## CACHING SCENARIO
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response
@@ -4090,10 +4394,10 @@ def transcription(
api_base: Optional[str] = None,
api_version: Optional[str] = None,
max_retries: Optional[int] = None,
- litellm_logging_obj=None,
+ litellm_logging_obj: Optional[LiteLLMLoggingObj] = None,
custom_llm_provider=None,
**kwargs,
-):
+) -> TranscriptionResponse:
"""
Calls openai + azure whisper endpoints.
@@ -4105,6 +4409,21 @@ def transcription(
proxy_server_request = kwargs.get("proxy_server_request", None)
model_info = kwargs.get("model_info", None)
metadata = kwargs.get("metadata", {})
+ tags = kwargs.pop("tags", [])
+
+ drop_params = kwargs.get("drop_params", None)
+ client: Optional[
+ Union[
+ openai.AsyncOpenAI,
+ openai.OpenAI,
+ openai.AzureOpenAI,
+ openai.AsyncAzureOpenAI,
+ ]
+ ] = kwargs.pop("client", None)
+
+ if litellm_logging_obj:
+ litellm_logging_obj.model_call_details["client"] = str(client)
+
if max_retries is None:
max_retries = openai.DEFAULT_MAX_RETRIES
@@ -4112,12 +4431,24 @@ def transcription(
model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore
- optional_params = {
- "language": language,
- "prompt": prompt,
- "response_format": response_format,
- "temperature": None, # openai defaults this to 0
- }
+ if dynamic_api_key is not None:
+ api_key = dynamic_api_key
+
+ optional_params = get_optional_params_transcription(
+ model=model,
+ language=language,
+ prompt=prompt,
+ response_format=response_format,
+ temperature=temperature,
+ custom_llm_provider=custom_llm_provider,
+ drop_params=drop_params,
+ )
+ # optional_params = {
+ # "language": language,
+ # "prompt": prompt,
+ # "response_format": response_format,
+ # "temperature": None, # openai defaults this to 0
+ # }
if custom_llm_provider == "azure":
# azure configs
@@ -4136,7 +4467,7 @@ def transcription(
or litellm.api_key
or litellm.azure_key
or get_secret("AZURE_API_KEY")
- )
+ ) # type: ignore
response = azure_chat_completions.audio_transcriptions(
model=model,
@@ -4144,6 +4475,7 @@ def transcription(
optional_params=optional_params,
model_response=model_response,
atranscription=atranscription,
+ client=client,
timeout=timeout,
logging_obj=litellm_logging_obj,
api_base=api_base,
@@ -4152,16 +4484,209 @@ def transcription(
azure_ad_token=azure_ad_token,
max_retries=max_retries,
)
- elif custom_llm_provider == "openai":
+ elif custom_llm_provider == "openai" or custom_llm_provider == "groq":
+ api_base = (
+ api_base
+ or litellm.api_base
+ or get_secret("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ ) # type: ignore
+ openai.organization = (
+ litellm.organization
+ or get_secret("OPENAI_ORGANIZATION")
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ )
+ # set API KEY
+ api_key = (
+ api_key
+ or litellm.api_key
+ or litellm.openai_key
+ or get_secret("OPENAI_API_KEY")
+ ) # type: ignore
response = openai_chat_completions.audio_transcriptions(
model=model,
audio_file=file,
optional_params=optional_params,
model_response=model_response,
atranscription=atranscription,
+ client=client,
timeout=timeout,
logging_obj=litellm_logging_obj,
max_retries=max_retries,
+ api_base=api_base,
+ api_key=api_key,
+ )
+ return response
+
+
+@client
+async def aspeech(*args, **kwargs) -> HttpxBinaryResponseContent:
+ """
+ Calls openai tts endpoints.
+ """
+ loop = asyncio.get_event_loop()
+ model = args[0] if len(args) > 0 else kwargs["model"]
+ ### PASS ARGS TO Image Generation ###
+ kwargs["aspeech"] = True
+ custom_llm_provider = kwargs.get("custom_llm_provider", None)
+ try:
+ # Use a partial function to pass your keyword arguments
+ func = partial(speech, *args, **kwargs)
+
+ # Add the context to the function
+ ctx = contextvars.copy_context()
+ func_with_context = partial(ctx.run, func)
+
+ _, custom_llm_provider, _, _ = get_llm_provider(
+ model=model, api_base=kwargs.get("api_base", None)
+ )
+
+ # Await normally
+ init_response = await loop.run_in_executor(None, func_with_context)
+ if asyncio.iscoroutine(init_response):
+ response = await init_response
+ else:
+ # Call the synchronous function using run_in_executor
+ response = await loop.run_in_executor(None, func_with_context)
+ return response # type: ignore
+ except Exception as e:
+ custom_llm_provider = custom_llm_provider or "openai"
+ raise exception_type(
+ model=model,
+ custom_llm_provider=custom_llm_provider,
+ original_exception=e,
+ completion_kwargs=args,
+ extra_kwargs=kwargs,
+ )
+
+
+@client
+def speech(
+ model: str,
+ input: str,
+ voice: str,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ api_version: Optional[str] = None,
+ organization: Optional[str] = None,
+ project: Optional[str] = None,
+ max_retries: Optional[int] = None,
+ metadata: Optional[dict] = None,
+ timeout: Optional[Union[float, httpx.Timeout]] = None,
+ response_format: Optional[str] = None,
+ speed: Optional[int] = None,
+ client=None,
+ headers: Optional[dict] = None,
+ custom_llm_provider: Optional[str] = None,
+ aspeech: Optional[bool] = None,
+ **kwargs,
+) -> HttpxBinaryResponseContent:
+
+ model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore
+ tags = kwargs.pop("tags", [])
+
+ optional_params = {}
+ if response_format is not None:
+ optional_params["response_format"] = response_format
+ if speed is not None:
+ optional_params["speed"] = speed # type: ignore
+
+ if timeout is None:
+ timeout = litellm.request_timeout
+
+ if max_retries is None:
+ max_retries = litellm.num_retries or openai.DEFAULT_MAX_RETRIES
+ response: Optional[HttpxBinaryResponseContent] = None
+ if custom_llm_provider == "openai":
+ api_base = (
+ api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
+ or litellm.api_base
+ or get_secret("OPENAI_API_BASE")
+ or "https://api.openai.com/v1"
+ ) # type: ignore
+ # set API KEY
+ api_key = (
+ api_key
+ or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+ or litellm.openai_key
+ or get_secret("OPENAI_API_KEY")
+ ) # type: ignore
+
+ organization = (
+ organization
+ or litellm.organization
+ or get_secret("OPENAI_ORGANIZATION")
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ ) # type: ignore
+
+ project = (
+ project
+ or litellm.project
+ or get_secret("OPENAI_PROJECT")
+ or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+ ) # type: ignore
+
+ headers = headers or litellm.headers
+
+ response = openai_chat_completions.audio_speech(
+ model=model,
+ input=input,
+ voice=voice,
+ optional_params=optional_params,
+ api_key=api_key,
+ api_base=api_base,
+ organization=organization,
+ project=project,
+ max_retries=max_retries,
+ timeout=timeout,
+ client=client, # pass AsyncOpenAI, OpenAI client
+ aspeech=aspeech,
+ )
+ elif custom_llm_provider == "azure":
+ # azure configs
+ api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore
+
+ api_version = (
+ api_version or litellm.api_version or get_secret("AZURE_API_VERSION")
+ ) # type: ignore
+
+ api_key = (
+ api_key
+ or litellm.api_key
+ or litellm.azure_key
+ or get_secret("AZURE_OPENAI_API_KEY")
+ or get_secret("AZURE_API_KEY")
+ ) # type: ignore
+
+ azure_ad_token: Optional[str] = optional_params.get("extra_body", {}).pop( # type: ignore
+ "azure_ad_token", None
+ ) or get_secret(
+ "AZURE_AD_TOKEN"
+ )
+
+ headers = headers or litellm.headers
+
+ response = azure_chat_completions.audio_speech(
+ model=model,
+ input=input,
+ voice=voice,
+ optional_params=optional_params,
+ api_key=api_key,
+ api_base=api_base,
+ api_version=api_version,
+ azure_ad_token=azure_ad_token,
+ organization=organization,
+ max_retries=max_retries,
+ timeout=timeout,
+ client=client, # pass AsyncOpenAI, OpenAI client
+ aspeech=aspeech,
+ )
+
+ if response is None:
+ raise Exception(
+ "Unable to map the custom llm provider={} to a known provider={}.".format(
+ custom_llm_provider, litellm.provider_list
+ )
)
return response
@@ -4194,6 +4719,10 @@ async def ahealth_check(
mode = litellm.model_cost[model]["mode"]
model, custom_llm_provider, _, _ = get_llm_provider(model=model)
+
+ if model in litellm.model_cost and mode is None:
+ mode = litellm.model_cost[model]["mode"]
+
mode = mode or "chat" # default to chat completion calls
if custom_llm_provider == "azure":
@@ -4284,7 +4813,10 @@ async def ahealth_check(
response = {} # args like remaining ratelimit etc.
return response
except Exception as e:
- traceback.print_exc()
+ verbose_logger.error(
+ "litellm.ahealth_check(): Exception occured - {}".format(str(e))
+ )
+ verbose_logger.debug(traceback.format_exc())
stack_trace = traceback.format_exc()
if isinstance(stack_trace, str):
stack_trace = stack_trace[:1000]
@@ -4390,7 +4922,7 @@ def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List]
def stream_chunk_builder(
chunks: list, messages: Optional[list] = None, start_time=None, end_time=None
-):
+) -> Union[ModelResponse, TextCompletionResponse]:
model_response = litellm.ModelResponse()
### SORT CHUNKS BASED ON CREATED ORDER ##
print_verbose("Goes into checking if chunk has hiddden created at param")
@@ -4559,14 +5091,22 @@ def stream_chunk_builder(
else:
completion_output = ""
# # Update usage information if needed
+ prompt_tokens = 0
+ completion_tokens = 0
+ for chunk in chunks:
+ if "usage" in chunk:
+ if "prompt_tokens" in chunk["usage"]:
+ prompt_tokens = chunk["usage"].get("prompt_tokens", 0) or 0
+ if "completion_tokens" in chunk["usage"]:
+ completion_tokens = chunk["usage"].get("completion_tokens", 0) or 0
try:
- response["usage"]["prompt_tokens"] = token_counter(
+ response["usage"]["prompt_tokens"] = prompt_tokens or token_counter(
model=model, messages=messages
)
except: # don't allow this failing to block a complete streaming response from being returned
print_verbose(f"token_counter failed, assuming prompt tokens is 0")
response["usage"]["prompt_tokens"] = 0
- response["usage"]["completion_tokens"] = token_counter(
+ response["usage"]["completion_tokens"] = completion_tokens or token_counter(
model=model,
text=completion_output,
count_response_tokens=True, # count_response_tokens is a Flag to tell token counter this is a response, No need to add extra tokens we do for input messages
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index aab9c9af17..5b11b8360e 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -21,6 +21,30 @@
"supports_parallel_function_calling": true,
"supports_vision": true
},
+ "gpt-4o-mini": {
+ "max_tokens": 4096,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.00000015,
+ "output_cost_per_token": 0.00000060,
+ "litellm_provider": "openai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": true
+ },
+ "gpt-4o-mini-2024-07-18": {
+ "max_tokens": 4096,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.00000015,
+ "output_cost_per_token": 0.00000060,
+ "litellm_provider": "openai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": true
+ },
"gpt-4o-2024-05-13": {
"max_tokens": 4096,
"max_input_tokens": 128000,
@@ -234,6 +258,30 @@
"litellm_provider": "openai",
"mode": "chat"
},
+ "ft:gpt-4-0613": {
+ "max_tokens": 4096,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.00003,
+ "output_cost_per_token": 0.00006,
+ "litellm_provider": "openai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "source": "OpenAI needs to add pricing for this ft model, will be updated when added by OpenAI. Defaulting to base model pricing"
+ },
+ "ft:gpt-4o-2024-05-13": {
+ "max_tokens": 4096,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.000005,
+ "output_cost_per_token": 0.000015,
+ "litellm_provider": "openai",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": true,
+ "source": "OpenAI needs to add pricing for this ft model, will be updated when added by OpenAI. Defaulting to base model pricing"
+ },
"ft:davinci-002": {
"max_tokens": 16384,
"max_input_tokens": 16384,
@@ -374,12 +422,44 @@
"output_cost_per_second": 0.0001,
"litellm_provider": "openai"
},
+ "tts-1": {
+ "mode": "audio_speech",
+ "input_cost_per_character": 0.000015,
+ "litellm_provider": "openai"
+ },
+ "tts-1-hd": {
+ "mode": "audio_speech",
+ "input_cost_per_character": 0.000030,
+ "litellm_provider": "openai"
+ },
+ "azure/tts-1": {
+ "mode": "audio_speech",
+ "input_cost_per_character": 0.000015,
+ "litellm_provider": "azure"
+ },
+ "azure/tts-1-hd": {
+ "mode": "audio_speech",
+ "input_cost_per_character": 0.000030,
+ "litellm_provider": "azure"
+ },
"azure/whisper-1": {
"mode": "audio_transcription",
"input_cost_per_second": 0,
"output_cost_per_second": 0.0001,
"litellm_provider": "azure"
},
+ "azure/gpt-4o": {
+ "max_tokens": 4096,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.000005,
+ "output_cost_per_token": 0.000015,
+ "litellm_provider": "azure",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": true
+ },
"azure/gpt-4-turbo-2024-04-09": {
"max_tokens": 4096,
"max_input_tokens": 128000,
@@ -487,7 +567,7 @@
"max_tokens": 4096,
"max_input_tokens": 16384,
"max_output_tokens": 4096,
- "input_cost_per_token": 0.0000015,
+ "input_cost_per_token": 0.000001,
"output_cost_per_token": 0.000002,
"litellm_provider": "azure",
"mode": "chat",
@@ -642,6 +722,44 @@
"litellm_provider": "azure",
"mode": "image_generation"
},
+ "azure_ai/jamba-instruct": {
+ "max_tokens": 4096,
+ "max_input_tokens": 70000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.0000005,
+ "output_cost_per_token": 0.0000007,
+ "litellm_provider": "azure_ai",
+ "mode": "chat"
+ },
+ "azure_ai/mistral-large": {
+ "max_tokens": 8191,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "input_cost_per_token": 0.000004,
+ "output_cost_per_token": 0.000012,
+ "litellm_provider": "azure_ai",
+ "mode": "chat",
+ "supports_function_calling": true
+ },
+ "azure_ai/mistral-small": {
+ "max_tokens": 8191,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "input_cost_per_token": 0.000001,
+ "output_cost_per_token": 0.000003,
+ "litellm_provider": "azure_ai",
+ "supports_function_calling": true,
+ "mode": "chat"
+ },
+ "azure_ai/Meta-Llama-3-70B-Instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0000011,
+ "output_cost_per_token": 0.00000037,
+ "litellm_provider": "azure_ai",
+ "mode": "chat"
+ },
"babbage-002": {
"max_tokens": 16384,
"max_input_tokens": 16384,
@@ -692,8 +810,8 @@
"max_tokens": 8191,
"max_input_tokens": 32000,
"max_output_tokens": 8191,
- "input_cost_per_token": 0.00000015,
- "output_cost_per_token": 0.00000046,
+ "input_cost_per_token": 0.00000025,
+ "output_cost_per_token": 0.00000025,
"litellm_provider": "mistral",
"mode": "chat"
},
@@ -701,8 +819,8 @@
"max_tokens": 8191,
"max_input_tokens": 32000,
"max_output_tokens": 8191,
- "input_cost_per_token": 0.000002,
- "output_cost_per_token": 0.000006,
+ "input_cost_per_token": 0.000001,
+ "output_cost_per_token": 0.000003,
"litellm_provider": "mistral",
"supports_function_calling": true,
"mode": "chat"
@@ -711,8 +829,8 @@
"max_tokens": 8191,
"max_input_tokens": 32000,
"max_output_tokens": 8191,
- "input_cost_per_token": 0.000002,
- "output_cost_per_token": 0.000006,
+ "input_cost_per_token": 0.000001,
+ "output_cost_per_token": 0.000003,
"litellm_provider": "mistral",
"supports_function_calling": true,
"mode": "chat"
@@ -748,8 +866,8 @@
"max_tokens": 8191,
"max_input_tokens": 32000,
"max_output_tokens": 8191,
- "input_cost_per_token": 0.000008,
- "output_cost_per_token": 0.000024,
+ "input_cost_per_token": 0.000004,
+ "output_cost_per_token": 0.000012,
"litellm_provider": "mistral",
"mode": "chat",
"supports_function_calling": true
@@ -758,26 +876,63 @@
"max_tokens": 8191,
"max_input_tokens": 32000,
"max_output_tokens": 8191,
- "input_cost_per_token": 0.000008,
- "output_cost_per_token": 0.000024,
+ "input_cost_per_token": 0.000004,
+ "output_cost_per_token": 0.000012,
"litellm_provider": "mistral",
"mode": "chat",
"supports_function_calling": true
},
+ "mistral/open-mistral-7b": {
+ "max_tokens": 8191,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "input_cost_per_token": 0.00000025,
+ "output_cost_per_token": 0.00000025,
+ "litellm_provider": "mistral",
+ "mode": "chat"
+ },
"mistral/open-mixtral-8x7b": {
"max_tokens": 8191,
"max_input_tokens": 32000,
"max_output_tokens": 8191,
+ "input_cost_per_token": 0.0000007,
+ "output_cost_per_token": 0.0000007,
+ "litellm_provider": "mistral",
+ "mode": "chat",
+ "supports_function_calling": true
+ },
+ "mistral/open-mixtral-8x22b": {
+ "max_tokens": 8191,
+ "max_input_tokens": 64000,
+ "max_output_tokens": 8191,
"input_cost_per_token": 0.000002,
"output_cost_per_token": 0.000006,
"litellm_provider": "mistral",
"mode": "chat",
"supports_function_calling": true
},
+ "mistral/codestral-latest": {
+ "max_tokens": 8191,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "input_cost_per_token": 0.000001,
+ "output_cost_per_token": 0.000003,
+ "litellm_provider": "mistral",
+ "mode": "chat"
+ },
+ "mistral/codestral-2405": {
+ "max_tokens": 8191,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "input_cost_per_token": 0.000001,
+ "output_cost_per_token": 0.000003,
+ "litellm_provider": "mistral",
+ "mode": "chat"
+ },
"mistral/mistral-embed": {
"max_tokens": 8192,
"max_input_tokens": 8192,
- "input_cost_per_token": 0.000000111,
+ "input_cost_per_token": 0.0000001,
"litellm_provider": "mistral",
"mode": "embedding"
},
@@ -790,9 +945,49 @@
"litellm_provider": "deepseek",
"mode": "chat"
},
+ "codestral/codestral-latest": {
+ "max_tokens": 8191,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "input_cost_per_token": 0.000000,
+ "output_cost_per_token": 0.000000,
+ "litellm_provider": "codestral",
+ "mode": "chat",
+ "source": "https://docs.mistral.ai/capabilities/code_generation/"
+ },
+ "codestral/codestral-2405": {
+ "max_tokens": 8191,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "input_cost_per_token": 0.000000,
+ "output_cost_per_token": 0.000000,
+ "litellm_provider": "codestral",
+ "mode": "chat",
+ "source": "https://docs.mistral.ai/capabilities/code_generation/"
+ },
+ "text-completion-codestral/codestral-latest": {
+ "max_tokens": 8191,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "input_cost_per_token": 0.000000,
+ "output_cost_per_token": 0.000000,
+ "litellm_provider": "text-completion-codestral",
+ "mode": "completion",
+ "source": "https://docs.mistral.ai/capabilities/code_generation/"
+ },
+ "text-completion-codestral/codestral-2405": {
+ "max_tokens": 8191,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8191,
+ "input_cost_per_token": 0.000000,
+ "output_cost_per_token": 0.000000,
+ "litellm_provider": "text-completion-codestral",
+ "mode": "completion",
+ "source": "https://docs.mistral.ai/capabilities/code_generation/"
+ },
"deepseek-coder": {
"max_tokens": 4096,
- "max_input_tokens": 16000,
+ "max_input_tokens": 128000,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000014,
"output_cost_per_token": 0.00000028,
@@ -813,8 +1008,8 @@
"max_tokens": 8192,
"max_input_tokens": 8192,
"max_output_tokens": 8192,
- "input_cost_per_token": 0.00000010,
- "output_cost_per_token": 0.00000010,
+ "input_cost_per_token": 0.00000005,
+ "output_cost_per_token": 0.00000008,
"litellm_provider": "groq",
"mode": "chat",
"supports_function_calling": true
@@ -823,8 +1018,8 @@
"max_tokens": 8192,
"max_input_tokens": 8192,
"max_output_tokens": 8192,
- "input_cost_per_token": 0.00000064,
- "output_cost_per_token": 0.00000080,
+ "input_cost_per_token": 0.00000059,
+ "output_cost_per_token": 0.00000079,
"litellm_provider": "groq",
"mode": "chat",
"supports_function_calling": true
@@ -833,8 +1028,8 @@
"max_tokens": 32768,
"max_input_tokens": 32768,
"max_output_tokens": 32768,
- "input_cost_per_token": 0.00000027,
- "output_cost_per_token": 0.00000027,
+ "input_cost_per_token": 0.00000024,
+ "output_cost_per_token": 0.00000024,
"litellm_provider": "groq",
"mode": "chat",
"supports_function_calling": true
@@ -843,12 +1038,62 @@
"max_tokens": 8192,
"max_input_tokens": 8192,
"max_output_tokens": 8192,
- "input_cost_per_token": 0.00000010,
- "output_cost_per_token": 0.00000010,
+ "input_cost_per_token": 0.00000007,
+ "output_cost_per_token": 0.00000007,
"litellm_provider": "groq",
"mode": "chat",
"supports_function_calling": true
},
+ "groq/llama3-groq-70b-8192-tool-use-preview": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00000089,
+ "output_cost_per_token": 0.00000089,
+ "litellm_provider": "groq",
+ "mode": "chat",
+ "supports_function_calling": true
+ },
+ "groq/llama3-groq-8b-8192-tool-use-preview": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00000019,
+ "output_cost_per_token": 0.00000019,
+ "litellm_provider": "groq",
+ "mode": "chat",
+ "supports_function_calling": true
+ },
+ "friendliai/mixtral-8x7b-instruct-v0-1": {
+ "max_tokens": 32768,
+ "max_input_tokens": 32768,
+ "max_output_tokens": 32768,
+ "input_cost_per_token": 0.0000004,
+ "output_cost_per_token": 0.0000004,
+ "litellm_provider": "friendliai",
+ "mode": "chat",
+ "supports_function_calling": true
+ },
+ "friendliai/meta-llama-3-8b-instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0000001,
+ "output_cost_per_token": 0.0000001,
+ "litellm_provider": "friendliai",
+ "mode": "chat",
+ "supports_function_calling": true
+ },
+ "friendliai/meta-llama-3-70b-instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0000008,
+ "output_cost_per_token": 0.0000008,
+ "litellm_provider": "friendliai",
+ "mode": "chat",
+ "supports_function_calling": true
+ },
"claude-instant-1.2": {
"max_tokens": 8191,
"max_input_tokens": 100000,
@@ -912,22 +1157,68 @@
"supports_vision": true,
"tool_use_system_prompt_tokens": 159
},
+ "claude-3-5-sonnet-20240620": {
+ "max_tokens": 4096,
+ "max_input_tokens": 200000,
+ "max_output_tokens": 4096,
+ "input_cost_per_token": 0.000003,
+ "output_cost_per_token": 0.000015,
+ "litellm_provider": "anthropic",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 159
+ },
"text-bison": {
- "max_tokens": 1024,
+ "max_tokens": 2048,
"max_input_tokens": 8192,
- "max_output_tokens": 1024,
- "input_cost_per_token": 0.000000125,
- "output_cost_per_token": 0.000000125,
+ "max_output_tokens": 2048,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-text-models",
"mode": "completion",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
},
"text-bison@001": {
+ "max_tokens": 1024,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 1024,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-text-models",
+ "mode": "completion",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "text-bison@002": {
+ "max_tokens": 1024,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 1024,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-text-models",
+ "mode": "completion",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "text-bison32k": {
"max_tokens": 1024,
"max_input_tokens": 8192,
"max_output_tokens": 1024,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-text-models",
+ "mode": "completion",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "text-bison32k@002": {
+ "max_tokens": 1024,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 1024,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-text-models",
"mode": "completion",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -958,6 +1249,8 @@
"max_output_tokens": 4096,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-chat-models",
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -968,6 +1261,8 @@
"max_output_tokens": 4096,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-chat-models",
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -978,6 +1273,8 @@
"max_output_tokens": 4096,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-chat-models",
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -988,6 +1285,20 @@
"max_output_tokens": 8192,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-chat-models",
+ "mode": "chat",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "chat-bison-32k@002": {
+ "max_tokens": 8192,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-chat-models",
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -998,6 +1309,8 @@
"max_output_tokens": 1024,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-code-text-models",
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -1008,6 +1321,44 @@
"max_output_tokens": 1024,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-code-text-models",
+ "mode": "completion",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "code-bison@002": {
+ "max_tokens": 1024,
+ "max_input_tokens": 6144,
+ "max_output_tokens": 1024,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-code-text-models",
+ "mode": "completion",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "code-bison32k": {
+ "max_tokens": 1024,
+ "max_input_tokens": 6144,
+ "max_output_tokens": 1024,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-code-text-models",
+ "mode": "completion",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "code-bison-32k@002": {
+ "max_tokens": 1024,
+ "max_input_tokens": 6144,
+ "max_output_tokens": 1024,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-code-text-models",
"mode": "completion",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -1042,12 +1393,36 @@
"mode": "completion",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
},
+ "code-gecko-latest": {
+ "max_tokens": 64,
+ "max_input_tokens": 2048,
+ "max_output_tokens": 64,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.000000125,
+ "litellm_provider": "vertex_ai-code-text-models",
+ "mode": "completion",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "codechat-bison@latest": {
+ "max_tokens": 1024,
+ "max_input_tokens": 6144,
+ "max_output_tokens": 1024,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-code-chat-models",
+ "mode": "chat",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
"codechat-bison": {
"max_tokens": 1024,
"max_input_tokens": 6144,
"max_output_tokens": 1024,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-code-chat-models",
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -1058,6 +1433,20 @@
"max_output_tokens": 1024,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-code-chat-models",
+ "mode": "chat",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "codechat-bison@002": {
+ "max_tokens": 1024,
+ "max_input_tokens": 6144,
+ "max_output_tokens": 1024,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-code-chat-models",
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -1068,6 +1457,20 @@
"max_output_tokens": 8192,
"input_cost_per_token": 0.000000125,
"output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
+ "litellm_provider": "vertex_ai-code-chat-models",
+ "mode": "chat",
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "codechat-bison-32k@002": {
+ "max_tokens": 8192,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.000000125,
+ "output_cost_per_token": 0.000000125,
+ "input_cost_per_character": 0.00000025,
+ "output_cost_per_character": 0.0000005,
"litellm_provider": "vertex_ai-code-chat-models",
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
@@ -1076,56 +1479,281 @@
"max_tokens": 8192,
"max_input_tokens": 32760,
"max_output_tokens": 8192,
- "input_cost_per_token": 0.00000025,
- "output_cost_per_token": 0.0000005,
+ "input_cost_per_image": 0.0025,
+ "input_cost_per_video_per_second": 0.002,
+ "input_cost_per_token": 0.0000005,
+ "input_cost_per_character": 0.000000125,
+ "output_cost_per_token": 0.0000015,
+ "output_cost_per_character": 0.000000375,
"litellm_provider": "vertex_ai-language-models",
"mode": "chat",
"supports_function_calling": true,
- "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
},
"gemini-1.0-pro": {
"max_tokens": 8192,
"max_input_tokens": 32760,
"max_output_tokens": 8192,
- "input_cost_per_token": 0.00000025,
- "output_cost_per_token": 0.0000005,
+ "input_cost_per_image": 0.0025,
+ "input_cost_per_video_per_second": 0.002,
+ "input_cost_per_token": 0.0000005,
+ "input_cost_per_character": 0.000000125,
+ "output_cost_per_token": 0.0000015,
+ "output_cost_per_character": 0.000000375,
"litellm_provider": "vertex_ai-language-models",
"mode": "chat",
"supports_function_calling": true,
- "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models"
},
"gemini-1.0-pro-001": {
"max_tokens": 8192,
"max_input_tokens": 32760,
"max_output_tokens": 8192,
- "input_cost_per_token": 0.00000025,
- "output_cost_per_token": 0.0000005,
+ "input_cost_per_image": 0.0025,
+ "input_cost_per_video_per_second": 0.002,
+ "input_cost_per_token": 0.0000005,
+ "input_cost_per_character": 0.000000125,
+ "output_cost_per_token": 0.0000015,
+ "output_cost_per_character": 0.000000375,
"litellm_provider": "vertex_ai-language-models",
"mode": "chat",
"supports_function_calling": true,
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
},
+ "gemini-1.0-ultra": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 2048,
+ "input_cost_per_image": 0.0025,
+ "input_cost_per_video_per_second": 0.002,
+ "input_cost_per_token": 0.0000005,
+ "input_cost_per_character": 0.000000125,
+ "output_cost_per_token": 0.0000015,
+ "output_cost_per_character": 0.000000375,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "gemini-1.0-ultra-001": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 2048,
+ "input_cost_per_image": 0.0025,
+ "input_cost_per_video_per_second": 0.002,
+ "input_cost_per_token": 0.0000005,
+ "input_cost_per_character": 0.000000125,
+ "output_cost_per_token": 0.0000015,
+ "output_cost_per_character": 0.000000375,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
"gemini-1.0-pro-002": {
"max_tokens": 8192,
"max_input_tokens": 32760,
"max_output_tokens": 8192,
- "input_cost_per_token": 0.00000025,
- "output_cost_per_token": 0.0000005,
+ "input_cost_per_image": 0.0025,
+ "input_cost_per_video_per_second": 0.002,
+ "input_cost_per_token": 0.0000005,
+ "input_cost_per_character": 0.000000125,
+ "output_cost_per_token": 0.0000015,
+ "output_cost_per_character": 0.000000375,
"litellm_provider": "vertex_ai-language-models",
"mode": "chat",
"supports_function_calling": true,
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
},
"gemini-1.5-pro": {
+ "max_tokens": 8192,
+ "max_input_tokens": 2097152,
+ "max_output_tokens": 8192,
+ "input_cost_per_image": 0.001315,
+ "input_cost_per_audio_per_second": 0.000125,
+ "input_cost_per_video_per_second": 0.001315,
+ "input_cost_per_token": 0.000005,
+ "input_cost_per_character": 0.00000125,
+ "input_cost_per_token_above_128k_tokens": 0.00001,
+ "input_cost_per_character_above_128k_tokens": 0.0000025,
+ "output_cost_per_token": 0.000015,
+ "output_cost_per_character": 0.00000375,
+ "output_cost_per_token_above_128k_tokens": 0.00003,
+ "output_cost_per_character_above_128k_tokens": 0.0000075,
+ "output_cost_per_image": 0.00263,
+ "output_cost_per_video_per_second": 0.00263,
+ "output_cost_per_audio_per_second": 0.00025,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "supports_system_messages": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "gemini-1.5-pro-001": {
"max_tokens": 8192,
"max_input_tokens": 1000000,
"max_output_tokens": 8192,
- "input_cost_per_token": 0.000000625,
- "output_cost_per_token": 0.000001875,
+ "input_cost_per_image": 0.001315,
+ "input_cost_per_audio_per_second": 0.000125,
+ "input_cost_per_video_per_second": 0.001315,
+ "input_cost_per_token": 0.000005,
+ "input_cost_per_character": 0.00000125,
+ "input_cost_per_token_above_128k_tokens": 0.00001,
+ "input_cost_per_character_above_128k_tokens": 0.0000025,
+ "output_cost_per_token": 0.000015,
+ "output_cost_per_character": 0.00000375,
+ "output_cost_per_token_above_128k_tokens": 0.00003,
+ "output_cost_per_character_above_128k_tokens": 0.0000075,
+ "output_cost_per_image": 0.00263,
+ "output_cost_per_video_per_second": 0.00263,
+ "output_cost_per_audio_per_second": 0.00025,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "supports_system_messages": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "gemini-1.5-pro-preview-0514": {
+ "max_tokens": 8192,
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 8192,
+ "input_cost_per_image": 0.001315,
+ "input_cost_per_audio_per_second": 0.000125,
+ "input_cost_per_video_per_second": 0.001315,
+ "input_cost_per_token": 0.000005,
+ "input_cost_per_character": 0.00000125,
+ "input_cost_per_token_above_128k_tokens": 0.00001,
+ "input_cost_per_character_above_128k_tokens": 0.0000025,
+ "output_cost_per_token": 0.000015,
+ "output_cost_per_character": 0.00000375,
+ "output_cost_per_token_above_128k_tokens": 0.00003,
+ "output_cost_per_character_above_128k_tokens": 0.0000075,
+ "output_cost_per_image": 0.00263,
+ "output_cost_per_video_per_second": 0.00263,
+ "output_cost_per_audio_per_second": 0.00025,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "supports_system_messages": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "gemini-1.5-pro-preview-0215": {
+ "max_tokens": 8192,
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 8192,
+ "input_cost_per_image": 0.001315,
+ "input_cost_per_audio_per_second": 0.000125,
+ "input_cost_per_video_per_second": 0.001315,
+ "input_cost_per_token": 0.000005,
+ "input_cost_per_character": 0.00000125,
+ "input_cost_per_token_above_128k_tokens": 0.00001,
+ "input_cost_per_character_above_128k_tokens": 0.0000025,
+ "output_cost_per_token": 0.000015,
+ "output_cost_per_character": 0.00000375,
+ "output_cost_per_token_above_128k_tokens": 0.00003,
+ "output_cost_per_character_above_128k_tokens": 0.0000075,
+ "output_cost_per_image": 0.00263,
+ "output_cost_per_video_per_second": 0.00263,
+ "output_cost_per_audio_per_second": 0.00025,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "supports_system_messages": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "gemini-1.5-pro-preview-0409": {
+ "max_tokens": 8192,
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 8192,
+ "input_cost_per_image": 0.001315,
+ "input_cost_per_audio_per_second": 0.000125,
+ "input_cost_per_video_per_second": 0.001315,
+ "input_cost_per_token": 0.000005,
+ "input_cost_per_character": 0.00000125,
+ "input_cost_per_token_above_128k_tokens": 0.00001,
+ "input_cost_per_character_above_128k_tokens": 0.0000025,
+ "output_cost_per_token": 0.000015,
+ "output_cost_per_character": 0.00000375,
+ "output_cost_per_token_above_128k_tokens": 0.00003,
+ "output_cost_per_character_above_128k_tokens": 0.0000075,
+ "output_cost_per_image": 0.00263,
+ "output_cost_per_video_per_second": 0.00263,
+ "output_cost_per_audio_per_second": 0.00025,
"litellm_provider": "vertex_ai-language-models",
"mode": "chat",
"supports_function_calling": true,
- "supports_tool_choice": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "gemini-1.5-flash": {
+ "max_tokens": 8192,
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 8192,
+ "max_images_per_prompt": 3000,
+ "max_videos_per_prompt": 10,
+ "max_video_length": 1,
+ "max_audio_length_hours": 8.4,
+ "max_audio_per_prompt": 1,
+ "max_pdf_size_mb": 30,
+ "input_cost_per_image": 0.0001315,
+ "input_cost_per_video_per_second": 0.0001315,
+ "input_cost_per_audio_per_second": 0.000125,
+ "input_cost_per_token": 0.0000005,
+ "input_cost_per_character": 0.000000125,
+ "input_cost_per_token_above_128k_tokens": 0.000001,
+ "input_cost_per_character_above_128k_tokens": 0.00000025,
+ "output_cost_per_token": 0.0000015,
+ "output_cost_per_character": 0.000000375,
+ "output_cost_per_token_above_128k_tokens": 0.000003,
+ "output_cost_per_character_above_128k_tokens": 0.00000075,
+ "output_cost_per_image": 0.000263,
+ "output_cost_per_video_per_second": 0.000263,
+ "output_cost_per_audio_per_second": 0.00025,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "supports_system_messages": true,
+ "supports_function_calling": true,
+ "supports_vision": true,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+ },
+ "gemini-1.5-flash-001": {
+ "max_tokens": 8192,
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 8192,
+ "max_images_per_prompt": 3000,
+ "max_videos_per_prompt": 10,
+ "max_video_length": 1,
+ "max_audio_length_hours": 8.4,
+ "max_audio_per_prompt": 1,
+ "max_pdf_size_mb": 30,
+ "input_cost_per_image": 0.0001315,
+ "input_cost_per_video_per_second": 0.0001315,
+ "input_cost_per_audio_per_second": 0.000125,
+ "input_cost_per_token": 0.0000005,
+ "input_cost_per_character": 0.000000125,
+ "input_cost_per_token_above_128k_tokens": 0.000001,
+ "input_cost_per_character_above_128k_tokens": 0.00000025,
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]*>)*/m,alias:"punctuation"},table:{pattern:RegExp("^"+r+i+"(?:"+r+")*","m"),inside:{"table-data-rows":{pattern:RegExp("^("+r+i+")(?:"+r+")*$"),lookbehind:!0,inside:{"table-data":{pattern:RegExp(a),inside:e.languages.markdown},punctuation:/\|/}},"table-line":{pattern:RegExp("^("+r+")"+i+"$"),lookbehind:!0,inside:{punctuation:/\||:?-{3,}:?/}},"table-header-row":{pattern:RegExp("^"+r+"$"),inside:{"table-header":{pattern:RegExp(a),alias:"important",inside:e.languages.markdown},punctuation:/\|/}}}},code:[{pattern:/((?:^|\n)[ \t]*\n|(?:^|\r\n?)[ \t]*\r\n?)(?: {4}|\t).+(?:(?:\n|\r\n?)(?: {4}|\t).+)*/,lookbehind:!0,alias:"keyword"},{pattern:/^```[\s\S]*?^```$/m,greedy:!0,inside:{"code-block":{pattern:/^(```.*(?:\n|\r\n?))[\s\S]+?(?=(?:\n|\r\n?)^```$)/m,lookbehind:!0},"code-language":{pattern:/^(```).+/,lookbehind:!0},punctuation:/```/}}],title:[{pattern:/\S.*(?:\n|\r\n?)(?:==+|--+)(?=[ \t]*$)/m,alias:"important",inside:{punctuation:/==+$|--+$/}},{pattern:/(^\s*)#.+/m,lookbehind:!0,alias:"important",inside:{punctuation:/^#+|#+$/}}],hr:{pattern:/(^\s*)([*-])(?:[\t ]*\2){2,}(?=\s*$)/m,lookbehind:!0,alias:"punctuation"},list:{pattern:/(^\s*)(?:[*+-]|\d+\.)(?=[\t ].)/m,lookbehind:!0,alias:"punctuation"},"url-reference":{pattern:/!?\[[^\]]+\]:[\t ]+(?:\S+|<(?:\\.|[^>\\])+>)(?:[\t ]+(?:"(?:\\.|[^"\\])*"|'(?:\\.|[^'\\])*'|\((?:\\.|[^)\\])*\)))?/,inside:{variable:{pattern:/^(!?\[)[^\]]+/,lookbehind:!0},string:/(?:"(?:\\.|[^"\\])*"|'(?:\\.|[^'\\])*'|\((?:\\.|[^)\\])*\))$/,punctuation:/^[\[\]!:]|[<>]/},alias:"url"},bold:{pattern:n(/\b__(?:(?!_)|_(?:(?!_))+_)+__\b|\*\*(?:(?!\*)|\*(?:(?!\*))+\*)+\*\*/.source),lookbehind:!0,greedy:!0,inside:{content:{pattern:/(^..)[\s\S]+(?=..$)/,lookbehind:!0,inside:{}},punctuation:/\*\*|__/}},italic:{pattern:n(/\b_(?:(?!_)|__(?:(?!_))+__)+_\b|\*(?:(?!\*)
@@ -119,6 +120,7 @@ from litellm import completion
## set env variables for logging tools
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
+os.environ["HELICONE_API_KEY"] = "your-helicone-auth-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["ATHINA_API_KEY"] = "your-athina-api-key"
@@ -126,7 +128,7 @@ os.environ["ATHINA_API_KEY"] = "your-athina-api-key"
os.environ["OPENAI_API_KEY"]
# set callbacks
-litellm.success_callback = ["lunary", "langfuse", "athina"] # log input/output to lunary, langfuse, supabase, athina etc
+litellm.success_callback = ["lunary", "langfuse", "athina", "helicone"] # log input/output to lunary, langfuse, supabase, athina, helicone etc
#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
@@ -147,6 +149,7 @@ The proxy provides:
## 📖 Proxy Endpoints - [Swagger Docs](https://litellm-api.up.railway.app/)
+
## Quick Start Proxy - CLI
```shell
@@ -179,6 +182,31 @@ print(response)
## Proxy Key Management ([Docs](https://docs.litellm.ai/docs/proxy/virtual_keys))
+Connect the proxy with a Postgres DB to create proxy keys
+
+```bash
+# Get the code
+git clone https://github.com/BerriAI/litellm
+
+# Go to folder
+cd litellm
+
+# Add the master key - you can change this after setup
+echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
+
+# Add the litellm salt key - you cannot change this after adding a model
+# It is used to encrypt / decrypt your LLM API Key credentials
+# We recommned - https://1password.com/password-generator/
+# password generator to get a random hash for litellm salt key
+echo 'LITELLM_SALT_KEY="sk-1234"' > .env
+
+source .env
+
+# Start
+docker-compose up
+```
+
+
UI on `/ui` on your proxy server
@@ -206,37 +234,39 @@ curl 'http://0.0.0.0:4000/key/generate' \
## Supported Providers ([Docs](https://docs.litellm.ai/docs/providers))
| Provider | [Completion](https://docs.litellm.ai/docs/#basic-usage) | [Streaming](https://docs.litellm.ai/docs/completion/stream#streaming-responses) | [Async Completion](https://docs.litellm.ai/docs/completion/stream#async-completion) | [Async Streaming](https://docs.litellm.ai/docs/completion/stream#async-streaming) | [Async Embedding](https://docs.litellm.ai/docs/embedding/supported_embedding) | [Async Image Generation](https://docs.litellm.ai/docs/image_generation) |
-| ----------------------------------------------------------------------------------- | ------------------------------------------------------- | ------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | ----------------------------------------------------------------------- |
-| [openai](https://docs.litellm.ai/docs/providers/openai) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [azure](https://docs.litellm.ai/docs/providers/azure) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [aws - sagemaker](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [aws - bedrock](https://docs.litellm.ai/docs/providers/bedrock) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [google - vertex_ai [Gemini]](https://docs.litellm.ai/docs/providers/vertex) | ✅ | ✅ | ✅ | ✅ |
-| [google - palm](https://docs.litellm.ai/docs/providers/palm) | ✅ | ✅ | ✅ | ✅ |
-| [google AI Studio - gemini](https://docs.litellm.ai/docs/providers/gemini) | ✅ | ✅ | ✅ | ✅ | |
-| [mistral ai api](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [cloudflare AI Workers](https://docs.litellm.ai/docs/providers/cloudflare_workers) | ✅ | ✅ | ✅ | ✅ |
-| [cohere](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [anthropic](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | ✅ |
-| [huggingface](https://docs.litellm.ai/docs/providers/huggingface) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [replicate](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | ✅ |
-| [together_ai](https://docs.litellm.ai/docs/providers/togetherai) | ✅ | ✅ | ✅ | ✅ |
-| [openrouter](https://docs.litellm.ai/docs/providers/openrouter) | ✅ | ✅ | ✅ | ✅ |
-| [ai21](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | ✅ |
-| [baseten](https://docs.litellm.ai/docs/providers/baseten) | ✅ | ✅ | ✅ | ✅ |
-| [vllm](https://docs.litellm.ai/docs/providers/vllm) | ✅ | ✅ | ✅ | ✅ |
-| [nlp_cloud](https://docs.litellm.ai/docs/providers/nlp_cloud) | ✅ | ✅ | ✅ | ✅ |
-| [aleph alpha](https://docs.litellm.ai/docs/providers/aleph_alpha) | ✅ | ✅ | ✅ | ✅ |
-| [petals](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | ✅ |
-| [ollama](https://docs.litellm.ai/docs/providers/ollama) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [deepinfra](https://docs.litellm.ai/docs/providers/deepinfra) | ✅ | ✅ | ✅ | ✅ |
-| [perplexity-ai](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | ✅ |
-| [Groq AI](https://docs.litellm.ai/docs/providers/groq) | ✅ | ✅ | ✅ | ✅ |
-| [Deepseek](https://docs.litellm.ai/docs/providers/deepseek) | ✅ | ✅ | ✅ | ✅ |
-| [anyscale](https://docs.litellm.ai/docs/providers/anyscale) | ✅ | ✅ | ✅ | ✅ |
-| [IBM - watsonx.ai](https://docs.litellm.ai/docs/providers/watsonx) | ✅ | ✅ | ✅ | ✅ | ✅
-| [voyage ai](https://docs.litellm.ai/docs/providers/voyage) | | | | | ✅ |
-| [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) | | | | | ✅ |
+|-------------------------------------------------------------------------------------|---------------------------------------------------------|---------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------|-------------------------------------------------------------------------|
+| [openai](https://docs.litellm.ai/docs/providers/openai) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [azure](https://docs.litellm.ai/docs/providers/azure) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [aws - sagemaker](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | ✅ | ✅ | |
+| [aws - bedrock](https://docs.litellm.ai/docs/providers/bedrock) | ✅ | ✅ | ✅ | ✅ | ✅ | |
+| [google - vertex_ai](https://docs.litellm.ai/docs/providers/vertex) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [google - palm](https://docs.litellm.ai/docs/providers/palm) | ✅ | ✅ | ✅ | ✅ | | |
+| [google AI Studio - gemini](https://docs.litellm.ai/docs/providers/gemini) | ✅ | ✅ | ✅ | ✅ | | |
+| [mistral ai api](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | ✅ | |
+| [cloudflare AI Workers](https://docs.litellm.ai/docs/providers/cloudflare_workers) | ✅ | ✅ | ✅ | ✅ | | |
+| [cohere](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | ✅ | ✅ | |
+| [anthropic](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | ✅ | | |
+| [empower](https://docs.litellm.ai/docs/providers/empower) | ✅ | ✅ | ✅ | ✅ |
+| [huggingface](https://docs.litellm.ai/docs/providers/huggingface) | ✅ | ✅ | ✅ | ✅ | ✅ | |
+| [replicate](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | ✅ | | |
+| [together_ai](https://docs.litellm.ai/docs/providers/togetherai) | ✅ | ✅ | ✅ | ✅ | | |
+| [openrouter](https://docs.litellm.ai/docs/providers/openrouter) | ✅ | ✅ | ✅ | ✅ | | |
+| [ai21](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | ✅ | | |
+| [baseten](https://docs.litellm.ai/docs/providers/baseten) | ✅ | ✅ | ✅ | ✅ | | |
+| [vllm](https://docs.litellm.ai/docs/providers/vllm) | ✅ | ✅ | ✅ | ✅ | | |
+| [nlp_cloud](https://docs.litellm.ai/docs/providers/nlp_cloud) | ✅ | ✅ | ✅ | ✅ | | |
+| [aleph alpha](https://docs.litellm.ai/docs/providers/aleph_alpha) | ✅ | ✅ | ✅ | ✅ | | |
+| [petals](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | ✅ | | |
+| [ollama](https://docs.litellm.ai/docs/providers/ollama) | ✅ | ✅ | ✅ | ✅ | ✅ | |
+| [deepinfra](https://docs.litellm.ai/docs/providers/deepinfra) | ✅ | ✅ | ✅ | ✅ | | |
+| [perplexity-ai](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | ✅ | | |
+| [Groq AI](https://docs.litellm.ai/docs/providers/groq) | ✅ | ✅ | ✅ | ✅ | | |
+| [Deepseek](https://docs.litellm.ai/docs/providers/deepseek) | ✅ | ✅ | ✅ | ✅ | | |
+| [anyscale](https://docs.litellm.ai/docs/providers/anyscale) | ✅ | ✅ | ✅ | ✅ | | |
+| [IBM - watsonx.ai](https://docs.litellm.ai/docs/providers/watsonx) | ✅ | ✅ | ✅ | ✅ | ✅ | |
+| [voyage ai](https://docs.litellm.ai/docs/providers/voyage) | | | | | ✅ | |
+| [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) | | | | | ✅ | |
+| [FriendliAI](https://docs.litellm.ai/docs/providers/friendliai) | ✅ | ✅ | ✅ | ✅ | | |
[**Read the Docs**](https://docs.litellm.ai/docs/)
diff --git a/check_file_length.py b/check_file_length.py
new file mode 100644
index 0000000000..f23b79add2
--- /dev/null
+++ b/check_file_length.py
@@ -0,0 +1,28 @@
+import sys
+
+
+def check_file_length(max_lines, filenames):
+ bad_files = []
+ for filename in filenames:
+ with open(filename, "r") as file:
+ lines = file.readlines()
+ if len(lines) > max_lines:
+ bad_files.append((filename, len(lines)))
+ return bad_files
+
+
+if __name__ == "__main__":
+ max_lines = int(sys.argv[1])
+ filenames = sys.argv[2:]
+
+ bad_files = check_file_length(max_lines, filenames)
+ if bad_files:
+ bad_files.sort(
+ key=lambda x: x[1], reverse=True
+ ) # Sort files by length in descending order
+ for filename, length in bad_files:
+ print(f"{filename}: {length} lines")
+
+ sys.exit(1)
+ else:
+ sys.exit(0)
diff --git a/cookbook/litellm_proxy_server/grafana_dashboard/dashboard_1/grafana_dashboard.json b/cookbook/litellm_proxy_server/grafana_dashboard/dashboard_1/grafana_dashboard.json
new file mode 100644
index 0000000000..17fef1ffda
--- /dev/null
+++ b/cookbook/litellm_proxy_server/grafana_dashboard/dashboard_1/grafana_dashboard.json
@@ -0,0 +1,594 @@
+{
+ "annotations": {
+ "list": [
+ {
+ "builtIn": 1,
+ "datasource": {
+ "type": "grafana",
+ "uid": "-- Grafana --"
+ },
+ "enable": true,
+ "hide": true,
+ "iconColor": "rgba(0, 211, 255, 1)",
+ "name": "Annotations & Alerts",
+ "target": {
+ "limit": 100,
+ "matchAny": false,
+ "tags": [],
+ "type": "dashboard"
+ },
+ "type": "dashboard"
+ }
+ ]
+ },
+ "description": "",
+ "editable": true,
+ "fiscalYearStartMonth": 0,
+ "graphTooltip": 0,
+ "id": 2039,
+ "links": [],
+ "liveNow": false,
+ "panels": [
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "fieldConfig": {
+ "defaults": {
+ "color": {
+ "mode": "palette-classic"
+ },
+ "custom": {
+ "axisCenteredZero": false,
+ "axisColorMode": "text",
+ "axisLabel": "",
+ "axisPlacement": "auto",
+ "barAlignment": 0,
+ "drawStyle": "line",
+ "fillOpacity": 0,
+ "gradientMode": "none",
+ "hideFrom": {
+ "legend": false,
+ "tooltip": false,
+ "viz": false
+ },
+ "lineInterpolation": "linear",
+ "lineWidth": 1,
+ "pointSize": 5,
+ "scaleDistribution": {
+ "type": "linear"
+ },
+ "showPoints": "auto",
+ "spanNulls": false,
+ "stacking": {
+ "group": "A",
+ "mode": "none"
+ },
+ "thresholdsStyle": {
+ "mode": "off"
+ }
+ },
+ "mappings": [],
+ "thresholds": {
+ "mode": "absolute",
+ "steps": [
+ {
+ "color": "green",
+ "value": null
+ },
+ {
+ "color": "red",
+ "value": 80
+ }
+ ]
+ },
+ "unit": "s"
+ },
+ "overrides": []
+ },
+ "gridPos": {
+ "h": 8,
+ "w": 12,
+ "x": 0,
+ "y": 0
+ },
+ "id": 10,
+ "options": {
+ "legend": {
+ "calcs": [],
+ "displayMode": "list",
+ "placement": "bottom",
+ "showLegend": true
+ },
+ "tooltip": {
+ "mode": "single",
+ "sort": "none"
+ }
+ },
+ "targets": [
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "editorMode": "code",
+ "expr": "histogram_quantile(0.99, sum(rate(litellm_self_latency_bucket{self=\"self\"}[1m])) by (le))",
+ "legendFormat": "Time to first token",
+ "range": true,
+ "refId": "A"
+ }
+ ],
+ "title": "Time to first token (latency)",
+ "type": "timeseries"
+ },
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "fieldConfig": {
+ "defaults": {
+ "color": {
+ "mode": "palette-classic"
+ },
+ "custom": {
+ "axisCenteredZero": false,
+ "axisColorMode": "text",
+ "axisLabel": "",
+ "axisPlacement": "auto",
+ "barAlignment": 0,
+ "drawStyle": "line",
+ "fillOpacity": 0,
+ "gradientMode": "none",
+ "hideFrom": {
+ "legend": false,
+ "tooltip": false,
+ "viz": false
+ },
+ "lineInterpolation": "linear",
+ "lineWidth": 1,
+ "pointSize": 5,
+ "scaleDistribution": {
+ "type": "linear"
+ },
+ "showPoints": "auto",
+ "spanNulls": false,
+ "stacking": {
+ "group": "A",
+ "mode": "none"
+ },
+ "thresholdsStyle": {
+ "mode": "off"
+ }
+ },
+ "mappings": [],
+ "thresholds": {
+ "mode": "absolute",
+ "steps": [
+ {
+ "color": "green",
+ "value": null
+ },
+ {
+ "color": "red",
+ "value": 80
+ }
+ ]
+ },
+ "unit": "currencyUSD"
+ },
+ "overrides": [
+ {
+ "matcher": {
+ "id": "byName",
+ "options": "7e4b0627fd32efdd2313c846325575808aadcf2839f0fde90723aab9ab73c78f"
+ },
+ "properties": [
+ {
+ "id": "displayName",
+ "value": "Translata"
+ }
+ ]
+ }
+ ]
+ },
+ "gridPos": {
+ "h": 8,
+ "w": 12,
+ "x": 0,
+ "y": 8
+ },
+ "id": 11,
+ "options": {
+ "legend": {
+ "calcs": [],
+ "displayMode": "list",
+ "placement": "bottom",
+ "showLegend": true
+ },
+ "tooltip": {
+ "mode": "single",
+ "sort": "none"
+ }
+ },
+ "targets": [
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "editorMode": "code",
+ "expr": "sum(increase(litellm_spend_metric_total[30d])) by (hashed_api_key)",
+ "legendFormat": "{{team}}",
+ "range": true,
+ "refId": "A"
+ }
+ ],
+ "title": "Spend by team",
+ "transformations": [],
+ "type": "timeseries"
+ },
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "fieldConfig": {
+ "defaults": {
+ "color": {
+ "mode": "palette-classic"
+ },
+ "custom": {
+ "axisCenteredZero": false,
+ "axisColorMode": "text",
+ "axisLabel": "",
+ "axisPlacement": "auto",
+ "barAlignment": 0,
+ "drawStyle": "line",
+ "fillOpacity": 0,
+ "gradientMode": "none",
+ "hideFrom": {
+ "legend": false,
+ "tooltip": false,
+ "viz": false
+ },
+ "lineInterpolation": "linear",
+ "lineWidth": 1,
+ "pointSize": 5,
+ "scaleDistribution": {
+ "type": "linear"
+ },
+ "showPoints": "auto",
+ "spanNulls": false,
+ "stacking": {
+ "group": "A",
+ "mode": "none"
+ },
+ "thresholdsStyle": {
+ "mode": "off"
+ }
+ },
+ "mappings": [],
+ "thresholds": {
+ "mode": "absolute",
+ "steps": [
+ {
+ "color": "green",
+ "value": null
+ },
+ {
+ "color": "red",
+ "value": 80
+ }
+ ]
+ }
+ },
+ "overrides": []
+ },
+ "gridPos": {
+ "h": 9,
+ "w": 12,
+ "x": 0,
+ "y": 16
+ },
+ "id": 2,
+ "options": {
+ "legend": {
+ "calcs": [],
+ "displayMode": "list",
+ "placement": "bottom",
+ "showLegend": true
+ },
+ "tooltip": {
+ "mode": "single",
+ "sort": "none"
+ }
+ },
+ "targets": [
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "editorMode": "code",
+ "expr": "sum by (model) (increase(litellm_requests_metric_total[5m]))",
+ "legendFormat": "{{model}}",
+ "range": true,
+ "refId": "A"
+ }
+ ],
+ "title": "Requests by model",
+ "type": "timeseries"
+ },
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "fieldConfig": {
+ "defaults": {
+ "color": {
+ "mode": "thresholds"
+ },
+ "mappings": [],
+ "noValue": "0",
+ "thresholds": {
+ "mode": "absolute",
+ "steps": [
+ {
+ "color": "green",
+ "value": null
+ },
+ {
+ "color": "red",
+ "value": 80
+ }
+ ]
+ }
+ },
+ "overrides": []
+ },
+ "gridPos": {
+ "h": 7,
+ "w": 3,
+ "x": 0,
+ "y": 25
+ },
+ "id": 8,
+ "options": {
+ "colorMode": "value",
+ "graphMode": "area",
+ "justifyMode": "auto",
+ "orientation": "auto",
+ "reduceOptions": {
+ "calcs": [
+ "lastNotNull"
+ ],
+ "fields": "",
+ "values": false
+ },
+ "textMode": "auto"
+ },
+ "pluginVersion": "9.4.17",
+ "targets": [
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "editorMode": "code",
+ "expr": "sum(increase(litellm_llm_api_failed_requests_metric_total[1h]))",
+ "legendFormat": "__auto",
+ "range": true,
+ "refId": "A"
+ }
+ ],
+ "title": "Faild Requests",
+ "type": "stat"
+ },
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "fieldConfig": {
+ "defaults": {
+ "color": {
+ "mode": "palette-classic"
+ },
+ "custom": {
+ "axisCenteredZero": false,
+ "axisColorMode": "text",
+ "axisLabel": "",
+ "axisPlacement": "auto",
+ "barAlignment": 0,
+ "drawStyle": "line",
+ "fillOpacity": 0,
+ "gradientMode": "none",
+ "hideFrom": {
+ "legend": false,
+ "tooltip": false,
+ "viz": false
+ },
+ "lineInterpolation": "linear",
+ "lineWidth": 1,
+ "pointSize": 5,
+ "scaleDistribution": {
+ "type": "linear"
+ },
+ "showPoints": "auto",
+ "spanNulls": false,
+ "stacking": {
+ "group": "A",
+ "mode": "none"
+ },
+ "thresholdsStyle": {
+ "mode": "off"
+ }
+ },
+ "mappings": [],
+ "thresholds": {
+ "mode": "absolute",
+ "steps": [
+ {
+ "color": "green",
+ "value": null
+ },
+ {
+ "color": "red",
+ "value": 80
+ }
+ ]
+ },
+ "unit": "currencyUSD"
+ },
+ "overrides": []
+ },
+ "gridPos": {
+ "h": 7,
+ "w": 3,
+ "x": 3,
+ "y": 25
+ },
+ "id": 6,
+ "options": {
+ "legend": {
+ "calcs": [],
+ "displayMode": "list",
+ "placement": "bottom",
+ "showLegend": true
+ },
+ "tooltip": {
+ "mode": "single",
+ "sort": "none"
+ }
+ },
+ "targets": [
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "editorMode": "code",
+ "expr": "sum(increase(litellm_spend_metric_total[30d])) by (model)",
+ "legendFormat": "{{model}}",
+ "range": true,
+ "refId": "A"
+ }
+ ],
+ "title": "Spend",
+ "type": "timeseries"
+ },
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "fieldConfig": {
+ "defaults": {
+ "color": {
+ "mode": "palette-classic"
+ },
+ "custom": {
+ "axisCenteredZero": false,
+ "axisColorMode": "text",
+ "axisLabel": "",
+ "axisPlacement": "auto",
+ "barAlignment": 0,
+ "drawStyle": "line",
+ "fillOpacity": 0,
+ "gradientMode": "none",
+ "hideFrom": {
+ "legend": false,
+ "tooltip": false,
+ "viz": false
+ },
+ "lineInterpolation": "linear",
+ "lineWidth": 1,
+ "pointSize": 5,
+ "scaleDistribution": {
+ "type": "linear"
+ },
+ "showPoints": "auto",
+ "spanNulls": false,
+ "stacking": {
+ "group": "A",
+ "mode": "none"
+ },
+ "thresholdsStyle": {
+ "mode": "off"
+ }
+ },
+ "mappings": [],
+ "thresholds": {
+ "mode": "absolute",
+ "steps": [
+ {
+ "color": "green",
+ "value": null
+ },
+ {
+ "color": "red",
+ "value": 80
+ }
+ ]
+ }
+ },
+ "overrides": []
+ },
+ "gridPos": {
+ "h": 7,
+ "w": 6,
+ "x": 6,
+ "y": 25
+ },
+ "id": 4,
+ "options": {
+ "legend": {
+ "calcs": [],
+ "displayMode": "list",
+ "placement": "bottom",
+ "showLegend": true
+ },
+ "tooltip": {
+ "mode": "single",
+ "sort": "none"
+ }
+ },
+ "targets": [
+ {
+ "datasource": {
+ "type": "prometheus",
+ "uid": "rMzWaBvIk"
+ },
+ "editorMode": "code",
+ "expr": "sum(increase(litellm_total_tokens_total[5m])) by (model)",
+ "legendFormat": "__auto",
+ "range": true,
+ "refId": "A"
+ }
+ ],
+ "title": "Tokens",
+ "type": "timeseries"
+ }
+ ],
+ "refresh": "1m",
+ "revision": 1,
+ "schemaVersion": 38,
+ "style": "dark",
+ "tags": [],
+ "templating": {
+ "list": []
+ },
+ "time": {
+ "from": "now-1h",
+ "to": "now"
+ },
+ "timepicker": {},
+ "timezone": "",
+ "title": "LLM Proxy",
+ "uid": "rgRrHxESz",
+ "version": 15,
+ "weekStart": ""
+ }
\ No newline at end of file
diff --git a/cookbook/litellm_proxy_server/grafana_dashboard/dashboard_1/readme.md b/cookbook/litellm_proxy_server/grafana_dashboard/dashboard_1/readme.md
new file mode 100644
index 0000000000..1f193aba70
--- /dev/null
+++ b/cookbook/litellm_proxy_server/grafana_dashboard/dashboard_1/readme.md
@@ -0,0 +1,6 @@
+## This folder contains the `json` for creating the following Grafana Dashboard
+
+### Pre-Requisites
+- Setup LiteLLM Proxy Prometheus Metrics https://docs.litellm.ai/docs/proxy/prometheus
+
+
diff --git a/cookbook/litellm_proxy_server/grafana_dashboard/readme.md b/cookbook/litellm_proxy_server/grafana_dashboard/readme.md
new file mode 100644
index 0000000000..fae1d792d2
--- /dev/null
+++ b/cookbook/litellm_proxy_server/grafana_dashboard/readme.md
@@ -0,0 +1,6 @@
+## Contains example Grafana Dashboard made for LiteLLM Proxy Server
+
+This folder contains the `json` for creating Grafana Dashboards
+
+### Pre-Requisites
+- Setup LiteLLM Proxy Prometheus Metrics https://docs.litellm.ai/docs/proxy/prometheus
\ No newline at end of file
diff --git a/cookbook/proxy-server/readme.md b/cookbook/litellm_proxy_server/readme.md
similarity index 100%
rename from cookbook/proxy-server/readme.md
rename to cookbook/litellm_proxy_server/readme.md
diff --git a/cookbook/misc/add_new_models.py b/cookbook/misc/add_new_models.py
new file mode 100644
index 0000000000..c9b5a91e30
--- /dev/null
+++ b/cookbook/misc/add_new_models.py
@@ -0,0 +1,72 @@
+import requests
+import json
+
+
+def get_initial_config():
+ proxy_base_url = input("Enter your proxy base URL (e.g., http://localhost:4000): ")
+ master_key = input("Enter your LITELLM_MASTER_KEY ")
+ return proxy_base_url, master_key
+
+
+def get_user_input():
+ model_name = input(
+ "Enter model_name (this is the 'model' passed in /chat/completions requests):"
+ )
+ model = input("litellm_params: Enter model eg. 'azure/