:::info
@@ -477,12 +493,12 @@ curl --location 'http://0.0.0.0:4000/budget/new' \
```
-#### Step 2. Create `End-User` with Budget
+#### Step 2. Create `Customer` with Budget
-We use `budget_id="free-tier"` from Step 1 when creating this new end user
+We use `budget_id="free-tier"` from Step 1 when creating this new customers
```shell
-curl --location 'http://0.0.0.0:4000/end_user/new' \
+curl --location 'http://0.0.0.0:4000/customer/new' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
@@ -492,7 +508,7 @@ curl --location 'http://0.0.0.0:4000/end_user/new' \
```
-#### Step 3. Pass end user id in `/chat/completions` requests
+#### Step 3. Pass `user_id` id in `/chat/completions` requests
Pass the `user_id` from Step 2 as `user="palantir"`
diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md
index 5ba3221c9..d91912644 100644
--- a/docs/my-website/docs/routing.md
+++ b/docs/my-website/docs/routing.md
@@ -713,26 +713,43 @@ response = router.completion(model="gpt-3.5-turbo", messages=messages)
print(f"response: {response}")
```
-#### Retries based on Error Type
+### [Advanced]: Custom Retries, Cooldowns based on Error Type
-Use `RetryPolicy` if you want to set a `num_retries` based on the Exception receieved
+- Use `RetryPolicy` if you want to set a `num_retries` based on the Exception receieved
+- Use `AllowedFailsPolicy` to set a custom number of `allowed_fails`/minute before cooling down a deployment
Example:
-- 4 retries for `ContentPolicyViolationError`
-- 0 retries for `RateLimitErrors`
+
+```python
+retry_policy = RetryPolicy(
+ ContentPolicyViolationErrorRetries=3, # run 3 retries for ContentPolicyViolationErrors
+ AuthenticationErrorRetries=0, # run 0 retries for AuthenticationErrorRetries
+)
+
+allowed_fails_policy = AllowedFailsPolicy(
+ ContentPolicyViolationErrorAllowedFails=1000, # Allow 1000 ContentPolicyViolationError before cooling down a deployment
+ RateLimitErrorAllowedFails=100, # Allow 100 RateLimitErrors before cooling down a deployment
+)
+```
Example Usage
```python
-from litellm.router import RetryPolicy
+from litellm.router import RetryPolicy, AllowedFailsPolicy
+
retry_policy = RetryPolicy(
- ContentPolicyViolationErrorRetries=3, # run 3 retries for ContentPolicyViolationErrors
- AuthenticationErrorRetries=0, # run 0 retries for AuthenticationErrorRetries
+ ContentPolicyViolationErrorRetries=3, # run 3 retries for ContentPolicyViolationErrors
+ AuthenticationErrorRetries=0, # run 0 retries for AuthenticationErrorRetries
BadRequestErrorRetries=1,
TimeoutErrorRetries=2,
RateLimitErrorRetries=3,
)
+allowed_fails_policy = AllowedFailsPolicy(
+ ContentPolicyViolationErrorAllowedFails=1000, # Allow 1000 ContentPolicyViolationError before cooling down a deployment
+ RateLimitErrorAllowedFails=100, # Allow 100 RateLimitErrors before cooling down a deployment
+)
+
router = litellm.Router(
model_list=[
{
@@ -755,6 +772,7 @@ router = litellm.Router(
},
],
retry_policy=retry_policy,
+ allowed_fails_policy=allowed_fails_policy,
)
response = await router.acompletion(
diff --git a/docs/my-website/docs/scheduler.md b/docs/my-website/docs/scheduler.md
new file mode 100644
index 000000000..e7943c459
--- /dev/null
+++ b/docs/my-website/docs/scheduler.md
@@ -0,0 +1,175 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# [BETA] Request Prioritization
+
+:::info
+
+Beta feature. Use for testing only.
+
+[Help us improve this](https://github.com/BerriAI/litellm/issues)
+:::
+
+Prioritize LLM API requests in high-traffic.
+
+- Add request to priority queue
+- Poll queue, to check if request can be made. Returns 'True':
+ * if there's healthy deployments
+ * OR if request is at top of queue
+- Priority - The lower the number, the higher the priority:
+ * e.g. `priority=0` > `priority=2000`
+
+## Quick Start
+
+```python
+from litellm import Router
+
+router = Router(
+ model_list=[
+ {
+ "model_name": "gpt-3.5-turbo",
+ "litellm_params": {
+ "model": "gpt-3.5-turbo",
+ "mock_response": "Hello world this is Macintosh!", # fakes the LLM API call
+ "rpm": 1,
+ },
+ },
+ ],
+ timeout=2, # timeout request if takes > 2s
+ routing_strategy="usage-based-routing-v2",
+ polling_interval=0.03 # poll queue every 3ms if no healthy deployments
+)
+
+try:
+ _response = await router.schedule_acompletion( # 👈 ADDS TO QUEUE + POLLS + MAKES CALL
+ model="gpt-3.5-turbo",
+ messages=[{"role": "user", "content": "Hey!"}],
+ priority=0, # 👈 LOWER IS BETTER
+ )
+except Exception as e:
+ print("didn't make request")
+```
+
+## LiteLLM Proxy
+
+To prioritize requests on LiteLLM Proxy call our beta openai-compatible `http://localhost:4000/queue` endpoint.
+
+
+
+
+```curl
+curl -X POST 'http://localhost:4000/queue/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-D '{
+ "model": "gpt-3.5-turbo-fake-model",
+ "messages": [
+ {
+ "role": "user",
+ "content": "what is the meaning of the universe? 1234"
+ }],
+ "priority": 0 👈 SET VALUE HERE
+}'
+```
+
+
+
+
+```python
+import openai
+client = openai.OpenAI(
+ api_key="anything",
+ base_url="http://0.0.0.0:4000"
+)
+
+# request sent to model set on litellm proxy, `litellm --model`
+response = client.chat.completions.create(
+ model="gpt-3.5-turbo",
+ messages = [
+ {
+ "role": "user",
+ "content": "this is a test request, write a short poem"
+ }
+ ],
+ extra_body={
+ "priority": 0 👈 SET VALUE HERE
+ }
+)
+
+print(response)
+```
+
+
+
+
+## Advanced - Redis Caching
+
+Use redis caching to do request prioritization across multiple instances of LiteLLM.
+
+### SDK
+```python
+from litellm import Router
+
+router = Router(
+ model_list=[
+ {
+ "model_name": "gpt-3.5-turbo",
+ "litellm_params": {
+ "model": "gpt-3.5-turbo",
+ "mock_response": "Hello world this is Macintosh!", # fakes the LLM API call
+ "rpm": 1,
+ },
+ },
+ ],
+ ### REDIS PARAMS ###
+ redis_host=os.environ["REDIS_HOST"],
+ redis_password=os.environ["REDIS_PASSWORD"],
+ redis_port=os.environ["REDIS_PORT"],
+)
+
+try:
+ _response = await router.schedule_acompletion( # 👈 ADDS TO QUEUE + POLLS + MAKES CALL
+ model="gpt-3.5-turbo",
+ messages=[{"role": "user", "content": "Hey!"}],
+ priority=0, # 👈 LOWER IS BETTER
+ )
+except Exception as e:
+ print("didn't make request")
+```
+
+### PROXY
+
+```yaml
+model_list:
+ - model_name: gpt-3.5-turbo-fake-model
+ litellm_params:
+ model: gpt-3.5-turbo
+ mock_response: "hello world!"
+ api_key: my-good-key
+
+router_settings:
+ redis_host; os.environ/REDIS_HOST
+ redis_password: os.environ/REDIS_PASSWORD
+ redis_port: os.environ/REDIS_PORT
+```
+
+```bash
+$ litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000s
+```
+
+```bash
+curl -X POST 'http://localhost:4000/queue/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-D '{
+ "model": "gpt-3.5-turbo-fake-model",
+ "messages": [
+ {
+ "role": "user",
+ "content": "what is the meaning of the universe? 1234"
+ }],
+ "priority": 0 👈 SET VALUE HERE
+}'
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/secret.md b/docs/my-website/docs/secret.md
index 2b945837a..08c2e89d1 100644
--- a/docs/my-website/docs/secret.md
+++ b/docs/my-website/docs/secret.md
@@ -1,11 +1,31 @@
# Secret Manager
LiteLLM supports reading secrets from Azure Key Vault and Infisical
+- AWS Key Managemenet Service
+- AWS Secret Manager
- [Azure Key Vault](#azure-key-vault)
- Google Key Management Service
- [Infisical Secret Manager](#infisical-secret-manager)
- [.env Files](#env-files)
+## AWS Key Management Service
+
+Use AWS KMS to storing a hashed copy of your Proxy Master Key in the environment.
+
+```bash
+export LITELLM_MASTER_KEY="djZ9xjVaZ..." # 👈 ENCRYPTED KEY
+export AWS_REGION_NAME="us-west-2"
+```
+
+```yaml
+general_settings:
+ key_management_system: "aws_kms"
+ key_management_settings:
+ hosted_keys: ["LITELLM_MASTER_KEY"] # 👈 WHICH KEYS ARE STORED ON KMS
+```
+
+[**See Decryption Code**](https://github.com/BerriAI/litellm/blob/a2da2a8f168d45648b61279d4795d647d94f90c9/litellm/utils.py#L10182)
+
## AWS Secret Manager
Store your proxy keys in AWS Secret Manager.
diff --git a/docs/my-website/docs/text_to_speech.md b/docs/my-website/docs/text_to_speech.md
new file mode 100644
index 000000000..f4adf15eb
--- /dev/null
+++ b/docs/my-website/docs/text_to_speech.md
@@ -0,0 +1,87 @@
+# Text to Speech
+
+## Quick Start
+
+```python
+from pathlib import Path
+from litellm import speech
+import os
+
+os.environ["OPENAI_API_KEY"] = "sk-.."
+
+speech_file_path = Path(__file__).parent / "speech.mp3"
+response = speech(
+ model="openai/tts-1",
+ voice="alloy",
+ input="the quick brown fox jumped over the lazy dogs",
+ api_base=None,
+ api_key=None,
+ organization=None,
+ project=None,
+ max_retries=1,
+ timeout=600,
+ client=None,
+ optional_params={},
+ )
+response.stream_to_file(speech_file_path)
+```
+
+## Async Usage
+
+```python
+from litellm import aspeech
+from pathlib import Path
+import os, asyncio
+
+os.environ["OPENAI_API_KEY"] = "sk-.."
+
+async def test_async_speech():
+ speech_file_path = Path(__file__).parent / "speech.mp3"
+ response = await litellm.aspeech(
+ model="openai/tts-1",
+ voice="alloy",
+ input="the quick brown fox jumped over the lazy dogs",
+ api_base=None,
+ api_key=None,
+ organization=None,
+ project=None,
+ max_retries=1,
+ timeout=600,
+ client=None,
+ optional_params={},
+ )
+ response.stream_to_file(speech_file_path)
+
+asyncio.run(test_async_speech())
+```
+
+## Proxy Usage
+
+LiteLLM provides an openai-compatible `/audio/speech` endpoint for Text-to-speech calls.
+
+```bash
+curl http://0.0.0.0:4000/v1/audio/speech \
+ -H "Authorization: Bearer sk-1234" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "tts-1",
+ "input": "The quick brown fox jumped over the lazy dog.",
+ "voice": "alloy"
+ }' \
+ --output speech.mp3
+```
+
+**Setup**
+
+```bash
+- model_name: tts
+ litellm_params:
+ model: openai/tts-1
+ api_key: os.environ/OPENAI_API_KEY
+```
+
+```bash
+litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md
index 75a610e0c..3ca57a570 100644
--- a/docs/my-website/docs/troubleshoot.md
+++ b/docs/my-website/docs/troubleshoot.md
@@ -9,12 +9,3 @@ Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
[](https://wa.link/huol9n) [](https://discord.gg/wuPM9dRgDw)
-## Stable Version
-
-If you're running into problems with installation / Usage
-Use the stable version of litellm
-
-```shell
-pip install litellm==0.1.819
-```
-
diff --git a/docs/my-website/docs/tutorials/finetuned_chat_gpt.md b/docs/my-website/docs/tutorials/finetuned_chat_gpt.md
index 641c45b5f..5dde3b3ff 100644
--- a/docs/my-website/docs/tutorials/finetuned_chat_gpt.md
+++ b/docs/my-website/docs/tutorials/finetuned_chat_gpt.md
@@ -1,8 +1,8 @@
# Using Fine-Tuned gpt-3.5-turbo
LiteLLM allows you to call `completion` with your fine-tuned gpt-3.5-turbo models
-If you're trying to create your custom finetuned gpt-3.5-turbo model following along on this tutorial: https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset
+If you're trying to create your custom fine-tuned gpt-3.5-turbo model following along on this tutorial: https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset
-Once you've created your fine tuned model, you can call it with `litellm.completion()`
+Once you've created your fine-tuned model, you can call it with `litellm.completion()`
## Usage
```python
diff --git a/docs/my-website/img/add_internal_user.png b/docs/my-website/img/add_internal_user.png
new file mode 100644
index 000000000..eb6c68b2b
Binary files /dev/null and b/docs/my-website/img/add_internal_user.png differ
diff --git a/docs/my-website/img/admin_ui_spend.png b/docs/my-website/img/admin_ui_spend.png
new file mode 100644
index 000000000..6a7196f83
Binary files /dev/null and b/docs/my-website/img/admin_ui_spend.png differ
diff --git a/docs/my-website/img/create_budget_modal.png b/docs/my-website/img/create_budget_modal.png
new file mode 100644
index 000000000..0e307be5e
Binary files /dev/null and b/docs/my-website/img/create_budget_modal.png differ
diff --git a/docs/my-website/img/debug_langfuse.png b/docs/my-website/img/debug_langfuse.png
new file mode 100644
index 000000000..8768fcd09
Binary files /dev/null and b/docs/my-website/img/debug_langfuse.png differ
diff --git a/docs/my-website/img/invitation_link.png b/docs/my-website/img/invitation_link.png
new file mode 100644
index 000000000..e65767327
Binary files /dev/null and b/docs/my-website/img/invitation_link.png differ
diff --git a/docs/my-website/img/model_hub.png b/docs/my-website/img/model_hub.png
new file mode 100644
index 000000000..1aafc993a
Binary files /dev/null and b/docs/my-website/img/model_hub.png differ
diff --git a/docs/my-website/img/ui_clean_login.png b/docs/my-website/img/ui_clean_login.png
new file mode 100644
index 000000000..62c65d4ae
Binary files /dev/null and b/docs/my-website/img/ui_clean_login.png differ
diff --git a/docs/my-website/img/ui_self_serve_create_key.png b/docs/my-website/img/ui_self_serve_create_key.png
new file mode 100644
index 000000000..4b83e9abf
Binary files /dev/null and b/docs/my-website/img/ui_self_serve_create_key.png differ
diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json
index 0738c4d09..48b541986 100644
--- a/docs/my-website/package-lock.json
+++ b/docs/my-website/package-lock.json
@@ -5975,9 +5975,9 @@
}
},
"node_modules/caniuse-lite": {
- "version": "1.0.30001519",
- "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001519.tgz",
- "integrity": "sha512-0QHgqR+Jv4bxHMp8kZ1Kn8CH55OikjKJ6JmKkZYP1F3D7w+lnFXF70nG5eNfsZS89jadi5Ywy5UCSKLAglIRkg==",
+ "version": "1.0.30001629",
+ "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001629.tgz",
+ "integrity": "sha512-c3dl911slnQhmxUIT4HhYzT7wnBK/XYpGnYLOj4nJBaRiw52Ibe7YxlDaAeRECvA786zCuExhxIUJ2K7nHMrBw==",
"funding": [
{
"type": "opencollective",
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 330a3c550..ff110bb62 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -36,23 +36,26 @@ const sidebars = {
label: "📖 All Endpoints (Swagger)",
href: "https://litellm-api.up.railway.app/",
},
+ "proxy/enterprise",
"proxy/demo",
"proxy/configs",
"proxy/reliability",
"proxy/cost_tracking",
+ "proxy/self_serve",
"proxy/users",
+ "proxy/customers",
"proxy/billing",
"proxy/user_keys",
- "proxy/enterprise",
"proxy/virtual_keys",
"proxy/alerting",
{
type: "category",
- label: "Logging",
+ label: "🪢 Logging",
items: ["proxy/logging", "proxy/streaming_logging"],
},
"proxy/ui",
"proxy/email",
+ "proxy/multiple_admins",
"proxy/team_based_routing",
"proxy/customer_routing",
"proxy/token_auth",
@@ -99,13 +102,16 @@ const sidebars = {
},
{
type: "category",
- label: "Embedding(), Moderation(), Image Generation(), Audio Transcriptions()",
+ label: "Embedding(), Image Generation(), Assistants(), Moderation(), Audio Transcriptions(), TTS(), Batches()",
items: [
"embedding/supported_embedding",
"embedding/async_embedding",
"embedding/moderation",
"image_generation",
- "audio_transcription"
+ "audio_transcription",
+ "text_to_speech",
+ "assistants",
+ "batches",
],
},
{
@@ -163,6 +169,7 @@ const sidebars = {
},
"proxy/custom_pricing",
"routing",
+ "scheduler",
"rules",
"set_keys",
"budget_manager",
@@ -248,6 +255,7 @@ const sidebars = {
"projects/GPT Migrate",
"projects/YiVal",
"projects/LiteLLM Proxy",
+ "projects/llm_cord",
],
},
],
diff --git a/docs/my-website/yarn.lock b/docs/my-website/yarn.lock
index 3acc48153..e96d90e26 100644
--- a/docs/my-website/yarn.lock
+++ b/docs/my-website/yarn.lock
@@ -84,7 +84,7 @@
"@algolia/requester-common" "4.19.1"
"@algolia/transporter" "4.19.1"
-"@algolia/client-search@4.19.1":
+"@algolia/client-search@>= 4.9.1 < 6", "@algolia/client-search@4.19.1":
version "4.19.1"
resolved "https://registry.npmjs.org/@algolia/client-search/-/client-search-4.19.1.tgz"
integrity sha512-mBecfMFS4N+yK/p0ZbK53vrZbL6OtWMk8YmnOv1i0LXx4pelY8TFhqKoTit3NPVPwoSNN0vdSN9dTu1xr1XOVw==
@@ -146,13 +146,6 @@
"@jridgewell/gen-mapping" "^0.3.0"
"@jridgewell/trace-mapping" "^0.3.9"
-"@babel/code-frame@7.10.4", "@babel/code-frame@^7.5.5":
- version "7.10.4"
- resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.10.4.tgz"
- integrity sha512-vG6SvB6oYEhvgisZNFRmRCUkLz11c7rp+tbNTynGqc6mS1d5ATd/sGyV6W0KZZnXRKMTzZDRgQT3Ou9jhpAfUg==
- dependencies:
- "@babel/highlight" "^7.10.4"
-
"@babel/code-frame@^7.0.0", "@babel/code-frame@^7.10.4", "@babel/code-frame@^7.16.0", "@babel/code-frame@^7.22.10", "@babel/code-frame@^7.22.13", "@babel/code-frame@^7.8.3":
version "7.22.13"
resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.13.tgz"
@@ -161,11 +154,39 @@
"@babel/highlight" "^7.22.13"
chalk "^2.4.2"
+"@babel/code-frame@^7.5.5", "@babel/code-frame@7.10.4":
+ version "7.10.4"
+ resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.10.4.tgz"
+ integrity sha512-vG6SvB6oYEhvgisZNFRmRCUkLz11c7rp+tbNTynGqc6mS1d5ATd/sGyV6W0KZZnXRKMTzZDRgQT3Ou9jhpAfUg==
+ dependencies:
+ "@babel/highlight" "^7.10.4"
+
"@babel/compat-data@^7.22.5", "@babel/compat-data@^7.22.6", "@babel/compat-data@^7.22.9":
version "7.22.9"
resolved "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.22.9.tgz"
integrity sha512-5UamI7xkUcJ3i9qVDS+KFDEK8/7oJ55/sJMB1Ge7IEapr7KfdfV/HErR+koZwOfd+SgtFKOKRhRakdg++DcJpQ==
+"@babel/core@^7.0.0", "@babel/core@^7.0.0-0", "@babel/core@^7.0.0-0 || ^8.0.0-0 <8.0.0", "@babel/core@^7.11.6", "@babel/core@^7.12.0", "@babel/core@^7.12.3", "@babel/core@^7.13.0", "@babel/core@^7.18.6", "@babel/core@^7.19.6", "@babel/core@^7.4.0 || ^8.0.0-0 <8.0.0":
+ version "7.22.10"
+ resolved "https://registry.npmjs.org/@babel/core/-/core-7.22.10.tgz"
+ integrity sha512-fTmqbbUBAwCcre6zPzNngvsI0aNrPZe77AeqvDxWM9Nm+04RrJ3CAmGHA9f7lJQY6ZMhRztNemy4uslDxTX4Qw==
+ dependencies:
+ "@ampproject/remapping" "^2.2.0"
+ "@babel/code-frame" "^7.22.10"
+ "@babel/generator" "^7.22.10"
+ "@babel/helper-compilation-targets" "^7.22.10"
+ "@babel/helper-module-transforms" "^7.22.9"
+ "@babel/helpers" "^7.22.10"
+ "@babel/parser" "^7.22.10"
+ "@babel/template" "^7.22.5"
+ "@babel/traverse" "^7.22.10"
+ "@babel/types" "^7.22.10"
+ convert-source-map "^1.7.0"
+ debug "^4.1.0"
+ gensync "^1.0.0-beta.2"
+ json5 "^2.2.2"
+ semver "^6.3.1"
+
"@babel/core@7.12.9":
version "7.12.9"
resolved "https://registry.npmjs.org/@babel/core/-/core-7.12.9.tgz"
@@ -188,27 +209,6 @@
semver "^5.4.1"
source-map "^0.5.0"
-"@babel/core@^7.12.3", "@babel/core@^7.18.6", "@babel/core@^7.19.6":
- version "7.22.10"
- resolved "https://registry.npmjs.org/@babel/core/-/core-7.22.10.tgz"
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- "@babel/generator" "^7.22.10"
- "@babel/helper-compilation-targets" "^7.22.10"
- "@babel/helper-module-transforms" "^7.22.9"
- "@babel/helpers" "^7.22.10"
- "@babel/parser" "^7.22.10"
- "@babel/template" "^7.22.5"
- "@babel/traverse" "^7.22.10"
- "@babel/types" "^7.22.10"
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- debug "^4.1.0"
- gensync "^1.0.0-beta.2"
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version "7.23.3"
resolved "https://registry.npmjs.org/@babel/generator/-/generator-7.23.3.tgz"
@@ -331,16 +331,16 @@
dependencies:
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- resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.10.4.tgz"
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+"@babel/helper-plugin-utils@7.10.4":
+ version "7.10.4"
+ resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.10.4.tgz"
+ integrity sha512-O4KCvQA6lLiMU9l2eawBPMf1xPP8xPfB3iEQw150hOVTqj/rfXz0ThTb4HEzqQfs2Bmo5Ay8BzxfzVtBrr9dVg==
+
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version "7.22.9"
resolved "https://registry.npmjs.org/@babel/helper-remap-async-to-generator/-/helper-remap-async-to-generator-7.22.9.tgz"
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"@babel/helper-plugin-utils" "^7.18.6"
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resolved "https://registry.npmjs.org/@babel/plugin-proposal-object-rest-spread/-/plugin-proposal-object-rest-spread-7.12.1.tgz"
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@@ -528,13 +528,6 @@
dependencies:
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-"@babel/plugin-syntax-jsx@7.12.1":
- version "7.12.1"
- resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.12.1.tgz"
- integrity sha512-1yRi7yAtB0ETgxdY9ti/p2TivUxJkTdhu/ZbF9MshVGqOx1TdB3b7xCXs49Fupgg50N45KcAsRP/ZqWjs9SRjg==
- dependencies:
- "@babel/helper-plugin-utils" "^7.10.4"
-
"@babel/plugin-syntax-jsx@^7.22.5":
version "7.22.5"
resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.22.5.tgz"
@@ -542,6 +535,13 @@
dependencies:
"@babel/helper-plugin-utils" "^7.22.5"
+"@babel/plugin-syntax-jsx@7.12.1":
+ version "7.12.1"
+ resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.12.1.tgz"
+ integrity sha512-1yRi7yAtB0ETgxdY9ti/p2TivUxJkTdhu/ZbF9MshVGqOx1TdB3b7xCXs49Fupgg50N45KcAsRP/ZqWjs9SRjg==
+ dependencies:
+ "@babel/helper-plugin-utils" "^7.10.4"
+
"@babel/plugin-syntax-logical-assignment-operators@^7.10.4":
version "7.10.4"
resolved "https://registry.npmjs.org/@babel/plugin-syntax-logical-assignment-operators/-/plugin-syntax-logical-assignment-operators-7.10.4.tgz"
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@@ -1279,7 +1279,7 @@
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algoliasearch "^4.0.0"
-"@docusaurus/core@2.4.1":
+"@docusaurus/core@^2.0.0-alpha.60 || ^2.0.0", "@docusaurus/core@2.4.1":
version "2.4.1"
resolved "https://registry.npmjs.org/@docusaurus/core/-/core-2.4.1.tgz"
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@@ -1502,7 +1502,7 @@
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tslib "^2.4.0"
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resolved "https://registry.npmjs.org/@docusaurus/plugin-google-gtag/-/plugin-google-gtag-2.4.1.tgz"
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@@ -1573,7 +1573,7 @@
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"@docusaurus/types" "2.4.1"
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resolved "https://registry.npmjs.org/@docusaurus/react-loadable/-/react-loadable-5.5.2.tgz"
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@@ -1671,7 +1671,7 @@
fs-extra "^10.1.0"
tslib "^2.4.0"
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version "2.4.1"
resolved "https://registry.npmjs.org/@docusaurus/types/-/types-2.4.1.tgz"
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@@ -1857,16 +1857,16 @@
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run-parallel "^1.1.9"
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- version "2.0.5"
- resolved "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-2.0.5.tgz"
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-
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+"@nodelib/fs.stat@^2.0.2", "@nodelib/fs.stat@2.0.5":
+ version "2.0.5"
+ resolved "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-2.0.5.tgz"
+ integrity sha512-RkhPPp2zrqDAQA/2jNhnztcPAlv64XdhIp7a7454A5ovI7Bukxgt7MX7udwAu3zg1DcpPU0rz3VV1SeaqvY4+A==
+
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version "1.2.8"
resolved "https://registry.npmjs.org/@nodelib/fs.walk/-/fs.walk-1.2.8.tgz"
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resolved "https://registry.npmjs.org/@svgr/core/-/core-6.5.1.tgz"
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@@ -2248,7 +2248,7 @@
"@types/history" "^4.7.11"
"@types/react" "*"
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+"@types/react@*", "@types/react@>= 16.8.0 < 19.0.0":
version "18.2.20"
resolved "https://registry.npmjs.org/@types/react/-/react-18.2.20.tgz"
integrity sha512-WKNtmsLWJM/3D5mG4U84cysVY31ivmyw85dE84fOCk5Hx78wezB/XEjVPWl2JTZ5FkEeaTJf+VgUAUn3PE7Isw==
@@ -2329,7 +2329,7 @@
dependencies:
"@types/yargs-parser" "*"
-"@webassemblyjs/ast@1.11.6", "@webassemblyjs/ast@^1.11.5":
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version "1.11.6"
resolved "https://registry.npmjs.org/@webassemblyjs/ast/-/ast-1.11.6.tgz"
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@@ -2430,7 +2430,7 @@
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"@webassemblyjs/wasm-parser" "1.11.6"
-"@webassemblyjs/wasm-parser@1.11.6", "@webassemblyjs/wasm-parser@^1.11.5":
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integrity sha512-k+iyHEuPgSw6SbuDpGQM+06HQUa04DZ3o+F6CSzXMvvI5KMvnaEqXe+YVe555R9nn6GPt404fos4wcgpw12SDA==
-acorn@^8.0.4, acorn@^8.7.1, acorn@^8.8.2:
+acorn@^8, acorn@^8.0.4, acorn@^8.7.1, acorn@^8.8.2:
version "8.10.0"
resolved "https://registry.npmjs.org/acorn/-/acorn-8.10.0.tgz"
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- resolved "https://registry.npmjs.org/address/-/address-1.1.2.tgz"
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resolved "https://registry.npmjs.org/address/-/address-1.2.2.tgz"
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+address@1.1.2:
+ version "1.1.2"
+ resolved "https://registry.npmjs.org/address/-/address-1.1.2.tgz"
+ integrity sha512-aT6camzM4xEA54YVJYSqxz1kv4IHnQZRtThJJHhUMRExaU5spC7jX5ugSwTaTgJliIgs4VhZOk7htClvQ/LmRA==
+
aggregate-error@^3.0.0:
version "3.1.0"
resolved "https://registry.npmjs.org/aggregate-error/-/aggregate-error-3.1.0.tgz"
@@ -2540,7 +2540,7 @@ ajv-keywords@^5.1.0:
dependencies:
fast-deep-equal "^3.1.3"
-ajv@^6.12.2, ajv@^6.12.3, ajv@^6.12.4, ajv@^6.12.5:
+ajv@^6.12.2, ajv@^6.12.3, ajv@^6.12.4, ajv@^6.12.5, ajv@^6.9.1:
version "6.12.6"
resolved "https://registry.npmjs.org/ajv/-/ajv-6.12.6.tgz"
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@@ -2550,7 +2550,17 @@ ajv@^6.12.2, ajv@^6.12.3, ajv@^6.12.4, ajv@^6.12.5:
json-schema-traverse "^0.4.1"
uri-js "^4.2.2"
-ajv@^8.0.0, ajv@^8.9.0:
+ajv@^8.0.0:
+ version "8.12.0"
+ resolved "https://registry.npmjs.org/ajv/-/ajv-8.12.0.tgz"
+ integrity sha512-sRu1kpcO9yLtYxBKvqfTeh9KzZEwO3STyX1HT+4CaDzC6HpTGYhIhPIzj9XuKU7KYDwnaeh5hcOwjy1QuJzBPA==
+ dependencies:
+ fast-deep-equal "^3.1.1"
+ json-schema-traverse "^1.0.0"
+ require-from-string "^2.0.2"
+ uri-js "^4.2.2"
+
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version "8.12.0"
resolved "https://registry.npmjs.org/ajv/-/ajv-8.12.0.tgz"
integrity sha512-sRu1kpcO9yLtYxBKvqfTeh9KzZEwO3STyX1HT+4CaDzC6HpTGYhIhPIzj9XuKU7KYDwnaeh5hcOwjy1QuJzBPA==
@@ -2567,7 +2577,7 @@ algoliasearch-helper@^3.10.0:
dependencies:
"@algolia/events" "^4.0.1"
-algoliasearch@^4.0.0, algoliasearch@^4.13.1:
+algoliasearch@^4.0.0, algoliasearch@^4.13.1, "algoliasearch@>= 3.1 < 6", "algoliasearch@>= 4.9.1 < 6":
version "4.19.1"
resolved "https://registry.npmjs.org/algoliasearch/-/algoliasearch-4.19.1.tgz"
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@@ -2725,16 +2735,16 @@ array-find-index@^1.0.1:
resolved "https://registry.npmjs.org/array-find-index/-/array-find-index-1.0.2.tgz"
integrity sha512-M1HQyIXcBGtVywBt8WVdim+lrNaK7VHp99Qt5pSNziXznKHViIBbXWtfRTpEFpF/c4FdfxNAsCCwPp5phBYJtw==
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- resolved "https://registry.npmjs.org/array-flatten/-/array-flatten-1.1.1.tgz"
- integrity sha512-PCVAQswWemu6UdxsDFFX/+gVeYqKAod3D3UVm91jHwynguOwAvYPhx8nNlM++NqRcK6CxxpUafjmhIdKiHibqg==
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version "2.1.2"
resolved "https://registry.npmjs.org/array-flatten/-/array-flatten-2.1.2.tgz"
integrity sha512-hNfzcOV8W4NdualtqBFPyVO+54DSJuZGY9qT4pRroB6S9e3iiido2ISIC5h9R2sPJ8H3FHCIiEnsv1lPXO3KtQ==
+array-flatten@1.1.1:
+ version "1.1.1"
+ resolved "https://registry.npmjs.org/array-flatten/-/array-flatten-1.1.1.tgz"
+ integrity sha512-PCVAQswWemu6UdxsDFFX/+gVeYqKAod3D3UVm91jHwynguOwAvYPhx8nNlM++NqRcK6CxxpUafjmhIdKiHibqg==
+
array-union@^1.0.1:
version "1.0.2"
resolved "https://registry.npmjs.org/array-union/-/array-union-1.0.2.tgz"
@@ -2829,7 +2839,7 @@ asn1@~0.2.3:
dependencies:
safer-buffer "~2.1.0"
-assert-plus@1.0.0, assert-plus@^1.0.0:
+assert-plus@^1.0.0, assert-plus@1.0.0:
version "1.0.0"
resolved "https://registry.npmjs.org/assert-plus/-/assert-plus-1.0.0.tgz"
integrity sha512-NfJ4UzBCcQGLDlQq7nHxH+tv3kyZ0hHQqF5BO6J7tNJeP5do1llPr8dZ8zHonfhAu0PHAdMkSo+8o0wxg9lZWw==
@@ -3005,16 +3015,6 @@ balanced-match@^1.0.0:
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integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
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- version "1.0.0"
- resolved "https://registry.npmjs.org/base16/-/base16-1.0.0.tgz"
- integrity sha512-pNdYkNPiJUnEhnfXV56+sQy8+AaPcG3POZAUnwr4EeqCUZFz4u2PePbo3e5Gj4ziYPCWGUZT9RHisvJKnwFuBQ==
-
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- version "1.5.1"
- resolved "https://registry.npmjs.org/base64-js/-/base64-js-1.5.1.tgz"
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-
base@^0.11.1:
version "0.11.2"
resolved "https://registry.npmjs.org/base/-/base-0.11.2.tgz"
@@ -3028,6 +3028,16 @@ base@^0.11.1:
mixin-deep "^1.2.0"
pascalcase "^0.1.1"
+base16@^1.0.0:
+ version "1.0.0"
+ resolved "https://registry.npmjs.org/base16/-/base16-1.0.0.tgz"
+ integrity sha512-pNdYkNPiJUnEhnfXV56+sQy8+AaPcG3POZAUnwr4EeqCUZFz4u2PePbo3e5Gj4ziYPCWGUZT9RHisvJKnwFuBQ==
+
+base64-js@^1.3.1:
+ version "1.5.1"
+ resolved "https://registry.npmjs.org/base64-js/-/base64-js-1.5.1.tgz"
+ integrity sha512-AKpaYlHn8t4SVbOHCy+b5+KKgvR4vrsD8vbvrbiQJps7fKDTkjkDry6ji0rUJjC0kzbNePLwzxq8iypo41qeWA==
+
batch@0.6.1:
version "0.6.1"
resolved "https://registry.npmjs.org/batch/-/batch-0.6.1.tgz"
@@ -3140,7 +3150,7 @@ bluebird@~3.4.1:
body-parser@1.20.2:
version "1.20.2"
- resolved "https://registry.yarnpkg.com/body-parser/-/body-parser-1.20.2.tgz#6feb0e21c4724d06de7ff38da36dad4f57a747fd"
+ resolved "https://registry.npmjs.org/body-parser/-/body-parser-1.20.2.tgz"
integrity sha512-ml9pReCu3M61kGlqoTm2umSXTlRTuGTx0bfYj+uIUKKYycG5NtSbeetV3faSU6R7ajOPw0g/J1PvK4qNy7s5bA==
dependencies:
bytes "3.1.2"
@@ -3240,17 +3250,7 @@ braces@^3.0.2, braces@~3.0.2:
dependencies:
fill-range "^7.0.1"
-browserslist@4.14.2, browserslist@^4.12.0:
- version "4.14.2"
- resolved "https://registry.npmjs.org/browserslist/-/browserslist-4.14.2.tgz"
- integrity sha512-HI4lPveGKUR0x2StIz+2FXfDk9SfVMrxn6PLh1JeGUwcuoDkdKZebWiyLRJ68iIPDpMI4JLVDf7S7XzslgWOhw==
- dependencies:
- caniuse-lite "^1.0.30001125"
- electron-to-chromium "^1.3.564"
- escalade "^3.0.2"
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node-releases "^2.0.13"
update-browserslist-db "^1.0.11"
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+ version "4.14.2"
+ resolved "https://registry.npmjs.org/browserslist/-/browserslist-4.14.2.tgz"
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+ dependencies:
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+ electron-to-chromium "^1.3.564"
+ escalade "^3.0.2"
+ node-releases "^1.1.61"
+
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resolved "https://registry.npmjs.org/buffer-alloc-unsafe/-/buffer-alloc-unsafe-1.1.0.tgz"
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+ version "1.0.30001629"
+ resolved "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001629.tgz"
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dependencies:
traverse ">=0.3.0 <0.4"
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resolved "https://registry.npmjs.org/chalk/-/chalk-1.1.3.tgz"
@@ -3493,6 +3494,24 @@ chalk@^1.0.0:
strip-ansi "^3.0.0"
supports-color "^2.0.0"
+chalk@^2.4.1:
+ version "2.4.2"
+ resolved "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz"
+ integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==
+ dependencies:
+ ansi-styles "^3.2.1"
+ escape-string-regexp "^1.0.5"
+ supports-color "^5.3.0"
+
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+ version "2.4.2"
+ resolved "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz"
+ integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==
+ dependencies:
+ ansi-styles "^3.2.1"
+ escape-string-regexp "^1.0.5"
+ supports-color "^5.3.0"
+
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version "3.0.0"
resolved "https://registry.npmjs.org/chalk/-/chalk-3.0.0.tgz"
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ansi-styles "^4.1.0"
supports-color "^7.1.0"
+chalk@2.4.2:
+ version "2.4.2"
+ resolved "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz"
+ integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==
+ dependencies:
+ ansi-styles "^3.2.1"
+ escape-string-regexp "^1.0.5"
+ supports-color "^5.3.0"
+
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version "1.1.4"
resolved "https://registry.npmjs.org/character-entities-legacy/-/character-entities-legacy-1.1.4.tgz"
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domhandler "^5.0.3"
domutils "^3.0.1"
+cheerio@^1.0.0-rc.12, cheerio@^1.0.0-rc.3:
+ version "1.0.0-rc.12"
+ resolved "https://registry.npmjs.org/cheerio/-/cheerio-1.0.0-rc.12.tgz"
+ integrity sha512-VqR8m68vM46BNnuZ5NtnGBKIE/DfN0cRIzg9n40EIq9NOv90ayxLBXA8fXC5gquFRGJSTRqBq25Jt2ECLR431Q==
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+ dom-serializer "^2.0.0"
+ domhandler "^5.0.3"
+ domutils "^3.0.1"
+ htmlparser2 "^8.0.1"
+ parse5 "^7.0.0"
+ parse5-htmlparser2-tree-adapter "^7.0.0"
+
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resolved "https://registry.npmjs.org/cheerio/-/cheerio-0.22.0.tgz"
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lodash.some "^4.4.0"
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resolved "https://registry.npmjs.org/chokidar/-/chokidar-3.5.3.tgz"
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shallow-clone "^3.0.0"
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resolved "https://registry.npmjs.org/clone-response/-/clone-response-1.0.3.tgz"
@@ -3675,6 +3696,13 @@ clone-response@^1.0.2:
dependencies:
mimic-response "^1.0.0"
+clone-response@1.0.2:
+ version "1.0.2"
+ resolved "https://registry.npmjs.org/clone-response/-/clone-response-1.0.2.tgz"
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+ dependencies:
+ mimic-response "^1.0.0"
+
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version "1.2.1"
resolved "https://registry.npmjs.org/clsx/-/clsx-1.2.1.tgz"
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dependencies:
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+color-name@1.1.3:
+ version "1.1.3"
+ resolved "https://registry.npmjs.org/color-name/-/color-name-1.1.3.tgz"
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+
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version "1.9.1"
resolved "https://registry.npmjs.org/color-string/-/color-string-1.9.1.tgz"
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-commander@^2.19.0, commander@^2.20.0, commander@^2.8.1:
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+ version "2.20.3"
+ resolved "https://registry.npmjs.org/commander/-/commander-2.20.3.tgz"
+ integrity sha512-GpVkmM8vF2vQUkj2LvZmD35JxeJOLCwJ9cUkugyk2nuhbv3+mJvpLYYt+0+USMxE+oj+ey/lJEnhZw75x/OMcQ==
+
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+ version "2.20.3"
+ resolved "https://registry.npmjs.org/commander/-/commander-2.20.3.tgz"
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+content-disposition@^0.5.2, content-disposition@0.5.2:
version "0.5.2"
resolved "https://registry.npmjs.org/content-disposition/-/content-disposition-0.5.2.tgz"
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@@ -3943,7 +3981,7 @@ cookie-signature@1.0.6:
cookie@0.6.0:
version "0.6.0"
- resolved "https://registry.yarnpkg.com/cookie/-/cookie-0.6.0.tgz#2798b04b071b0ecbff0dbb62a505a8efa4e19051"
+ resolved "https://registry.npmjs.org/cookie/-/cookie-0.6.0.tgz"
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+core-util-is@1.0.2:
+ version "1.0.2"
+ resolved "https://registry.npmjs.org/core-util-is/-/core-util-is-1.0.2.tgz"
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+
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resolved "https://registry.npmjs.org/cosmiconfig/-/cosmiconfig-5.2.1.tgz"
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dependencies:
node-fetch "^2.6.12"
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resolved "https://registry.npmjs.org/cross-spawn/-/cross-spawn-5.1.0.tgz"
@@ -4078,6 +4107,15 @@ cross-spawn@^6.0.0:
shebang-command "^1.2.0"
which "^1.2.9"
+cross-spawn@^7.0.3, cross-spawn@7.0.3:
+ version "7.0.3"
+ resolved "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.3.tgz"
+ integrity sha512-iRDPJKUPVEND7dHPO8rkbOnPpyDygcDFtWjpeWNCgy8WP2rXcxXL8TskReQl6OrB2G7+UJrags1q15Fudc7G6w==
+ dependencies:
+ path-key "^3.1.0"
+ shebang-command "^2.0.0"
+ which "^2.0.1"
+
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version "0.3.0"
resolved "https://registry.npmjs.org/crowdin-cli/-/crowdin-cli-0.3.0.tgz"
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version "0.0.4"
resolved "https://registry.npmjs.org/css-color-names/-/css-color-names-0.0.4.tgz"
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resolved "https://registry.npmjs.org/css-tree/-/css-tree-1.1.3.tgz"
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source-map "^0.6.1"
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+css-tree@1.0.0-alpha.37:
+ version "1.0.0-alpha.37"
+ resolved "https://registry.npmjs.org/css-tree/-/css-tree-1.0.0-alpha.37.tgz"
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+ dependencies:
+ mdn-data "2.0.4"
+ source-map "^0.6.1"
css-what@^3.2.1:
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+ resolved "https://registry.npmjs.org/css-what/-/css-what-2.1.3.tgz"
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version "3.0.0"
resolved "https://registry.npmjs.org/cssesc/-/cssesc-3.0.0.tgz"
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dependencies:
assert-plus "^1.0.0"
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dependencies:
ms "2.0.0"
-debug@4, debug@^4.1.0, debug@^4.1.1:
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+ dependencies:
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dependencies:
ms "2.1.2"
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+ resolved "https://registry.npmjs.org/debug/-/debug-2.6.9.tgz"
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+ dependencies:
+ ms "2.0.0"
+
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resolved "https://registry.npmjs.org/debug/-/debug-4.3.1.tgz"
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resolved "https://registry.npmjs.org/decamelize/-/decamelize-1.2.0.tgz"
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+
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resolved "https://registry.npmjs.org/destroy/-/destroy-1.2.0.tgz"
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resolved "https://registry.npmjs.org/detect-port-alt/-/detect-port-alt-1.1.6.tgz"
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+ version "3.0.1"
+ resolved "https://registry.npmjs.org/dir-glob/-/dir-glob-3.0.1.tgz"
+ integrity sha512-WkrWp9GR4KXfKGYzOLmTuGVi1UWFfws377n9cc55/tb6DuqyF6pcQ5AbiHEshaDpY9v6oaSr2XCDidGmMwdzIA==
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+
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resolved "https://registry.npmjs.org/dir-glob/-/dir-glob-2.0.0.tgz"
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arrify "^1.0.1"
path-type "^3.0.0"
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resolved "https://registry.npmjs.org/direction/-/direction-1.0.4.tgz"
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dependencies:
utila "~0.4"
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- entities "^2.0.0"
-
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version "1.4.1"
resolved "https://registry.npmjs.org/dom-serializer/-/dom-serializer-1.4.1.tgz"
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domelementtype "^1.3.0"
entities "^1.1.1"
-domelementtype@1, domelementtype@^1.3.0, domelementtype@^1.3.1:
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+ version "0.2.2"
+ resolved "https://registry.npmjs.org/dom-serializer/-/dom-serializer-0.2.2.tgz"
+ integrity sha512-2/xPb3ORsQ42nHYiSunXkDjPLBaEj/xTwUO4B7XCZQTRk7EBtTOPaygh10YAAh2OI1Qrp6NWfpAhzswj0ydt9g==
+ dependencies:
+ domelementtype "^2.0.1"
+ entities "^2.0.0"
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resolved "https://registry.npmjs.org/domelementtype/-/domelementtype-1.3.1.tgz"
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dependencies:
domelementtype "^2.3.0"
-domutils@1.5.1, domutils@^1.5.1:
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resolved "https://registry.npmjs.org/domutils/-/domutils-1.5.1.tgz"
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p-event "^2.1.0"
pify "^3.0.0"
+duplexer@^0.1.1, duplexer@^0.1.2:
+ version "0.1.2"
+ resolved "https://registry.npmjs.org/duplexer/-/duplexer-0.1.2.tgz"
+ integrity sha512-jtD6YG370ZCIi/9GTaJKQxWTZD045+4R4hTk/x1UyoqadyJ9x9CgSi1RlVDQF8U2sxLLSnFkCaMihqljHIWgMg==
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resolved "https://registry.npmjs.org/duplexer2/-/duplexer2-0.1.4.tgz"
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resolved "https://registry.npmjs.org/eastasianwidth/-/eastasianwidth-0.2.0.tgz"
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has "^1.0.3"
object-is "^1.1.5"
-enzyme@^3.10.0:
+enzyme@^3.0.0, enzyme@^3.10.0:
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resolved "https://registry.npmjs.org/enzyme/-/enzyme-3.11.0.tgz"
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-
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resolved "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz"
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+escape-string-regexp@^1.0.5:
+ version "1.0.5"
+ resolved "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz"
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+
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+ version "2.0.0"
+ resolved "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-2.0.0.tgz"
+ integrity sha512-UpzcLCXolUWcNu5HtVMHYdXJjArjsF9C0aNnquZYY4uW/Vu0miy5YoWvbV345HauVvcAUnpRuhMMcqTcGOY2+w==
+
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version "4.0.0"
resolved "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-4.0.0.tgz"
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express@^4.17.1, express@^4.17.3:
version "4.19.2"
- resolved "https://registry.yarnpkg.com/express/-/express-4.19.2.tgz#e25437827a3aa7f2a827bc8171bbbb664a356465"
+ resolved "https://registry.npmjs.org/express/-/express-4.19.2.tgz"
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dependencies:
accepts "~1.3.8"
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dependencies:
is-extendable "^0.1.0"
-extend-shallow@^3.0.0, extend-shallow@^3.0.2:
+extend-shallow@^3.0.0:
+ version "3.0.2"
+ resolved "https://registry.npmjs.org/extend-shallow/-/extend-shallow-3.0.2.tgz"
+ integrity sha512-BwY5b5Ql4+qZoefgMj2NUmx+tehVTH/Kf4k1ZEtOHNFcm2wSxMRo992l6X3TIgni2eZVTZ85xMOjF31fwZAj6Q==
+ dependencies:
+ assign-symbols "^1.0.0"
+ is-extendable "^1.0.1"
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resolved "https://registry.npmjs.org/extend-shallow/-/extend-shallow-3.0.2.tgz"
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snapdragon "^0.8.1"
to-regex "^3.0.1"
-extsprintf@1.3.0, extsprintf@^1.2.0:
+extsprintf@^1.2.0, extsprintf@1.3.0:
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resolved "https://registry.npmjs.org/extsprintf/-/extsprintf-1.3.0.tgz"
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escape-string-regexp "^1.0.5"
object-assign "^4.1.0"
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version "6.2.0"
resolved "https://registry.npmjs.org/file-loader/-/file-loader-6.2.0.tgz"
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loader-utils "^2.0.0"
schema-utils "^3.0.0"
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- version "5.2.0"
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version "10.11.0"
resolved "https://registry.npmjs.org/file-type/-/file-type-10.11.0.tgz"
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+file-type@^5.2.0:
+ version "5.2.0"
+ resolved "https://registry.npmjs.org/file-type/-/file-type-5.2.0.tgz"
+ integrity sha512-Iq1nJ6D2+yIO4c8HHg4fyVb8mAJieo1Oloy1mLLaB2PvezNedhBVm+QU7g0qM42aiMbRXTxKKwGD17rjKNJYVQ==
+
file-type@^6.1.0:
version "6.2.0"
resolved "https://registry.npmjs.org/file-type/-/file-type-6.2.0.tgz"
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+file-type@5.2.0:
+ version "5.2.0"
+ resolved "https://registry.npmjs.org/file-type/-/file-type-5.2.0.tgz"
+ integrity sha512-Iq1nJ6D2+yIO4c8HHg4fyVb8mAJieo1Oloy1mLLaB2PvezNedhBVm+QU7g0qM42aiMbRXTxKKwGD17rjKNJYVQ==
+
filename-reserved-regex@^2.0.0:
version "2.0.0"
resolved "https://registry.npmjs.org/filename-reserved-regex/-/filename-reserved-regex-2.0.0.tgz"
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strip-outer "^1.0.0"
trim-repeated "^1.0.0"
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- version "6.1.0"
- resolved "https://registry.npmjs.org/filesize/-/filesize-6.1.0.tgz"
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+filesize@6.1.0:
+ version "6.1.0"
+ resolved "https://registry.npmjs.org/filesize/-/filesize-6.1.0.tgz"
+ integrity sha512-LpCHtPQ3sFx67z+uh2HnSyWSLLu5Jxo21795uRDuar/EOuYWXib5EmPaGIBuSnRqH2IODiKA2k5re/K9OnN/Yg==
+
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version "2.2.4"
resolved "https://registry.npmjs.org/fill-range/-/fill-range-2.2.4.tgz"
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make-dir "^3.0.2"
pkg-dir "^4.1.0"
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- version "4.1.0"
- resolved "https://registry.npmjs.org/find-up/-/find-up-4.1.0.tgz"
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- path-exists "^4.0.0"
-
find-up@^1.0.0:
version "1.1.2"
resolved "https://registry.npmjs.org/find-up/-/find-up-1.1.2.tgz"
@@ -5686,6 +5762,14 @@ find-up@^3.0.0:
dependencies:
locate-path "^3.0.0"
+find-up@^4.0.0, find-up@4.1.0:
+ version "4.1.0"
+ resolved "https://registry.npmjs.org/find-up/-/find-up-4.1.0.tgz"
+ integrity sha512-PpOwAdQ/YlXQ2vj8a3h8IipDuYRi3wceVQQGYWxNINccq40Anw7BlsEXCMbt1Zt+OLA6Fq9suIpIWD0OsnISlw==
+ dependencies:
+ locate-path "^5.0.0"
+ path-exists "^4.0.0"
+
find-up@^5.0.0:
version "5.0.0"
resolved "https://registry.npmjs.org/find-up/-/find-up-5.0.0.tgz"
@@ -5711,7 +5795,7 @@ flux@^4.0.1:
follow-redirects@^1.0.0, follow-redirects@^1.14.7:
version "1.15.6"
- resolved "https://registry.yarnpkg.com/follow-redirects/-/follow-redirects-1.15.6.tgz#7f815c0cda4249c74ff09e95ef97c23b5fd0399b"
+ resolved "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.15.6.tgz"
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for-each@^0.3.3:
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- resolved "https://registry.npmjs.org/fork-ts-checker-webpack-plugin/-/fork-ts-checker-webpack-plugin-4.1.6.tgz"
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- minimatch "^3.0.4"
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version "6.5.3"
resolved "https://registry.npmjs.org/fork-ts-checker-webpack-plugin/-/fork-ts-checker-webpack-plugin-6.5.3.tgz"
@@ -5763,6 +5834,19 @@ fork-ts-checker-webpack-plugin@^6.5.0:
semver "^7.3.2"
tapable "^1.0.0"
+fork-ts-checker-webpack-plugin@4.1.6:
+ version "4.1.6"
+ resolved "https://registry.npmjs.org/fork-ts-checker-webpack-plugin/-/fork-ts-checker-webpack-plugin-4.1.6.tgz"
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+ dependencies:
+ "@babel/code-frame" "^7.5.5"
+ chalk "^2.4.1"
+ micromatch "^3.1.10"
+ minimatch "^3.0.4"
+ semver "^5.6.0"
+ tapable "^1.0.0"
+ worker-rpc "^0.1.0"
+
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resolved "https://registry.npmjs.org/form-data/-/form-data-2.3.3.tgz"
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jsonfile "^6.0.1"
universalify "^2.0.0"
-fs-extra@^9.0.0, fs-extra@^9.0.1:
+fs-extra@^9.0.0:
+ version "9.1.0"
+ resolved "https://registry.npmjs.org/fs-extra/-/fs-extra-9.1.0.tgz"
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+ dependencies:
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+ graceful-fs "^4.2.0"
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resolved "https://registry.npmjs.org/get-stream/-/get-stream-2.3.1.tgz"
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object-assign "^4.0.1"
pinkie-promise "^2.0.0"
+get-stream@^3.0.0, get-stream@3.0.0:
+ version "3.0.0"
+ resolved "https://registry.npmjs.org/get-stream/-/get-stream-3.0.0.tgz"
+ integrity sha512-GlhdIUuVakc8SJ6kK0zAFbiGzRFzNnY4jUuEbV9UROo4Y+0Ny4fjvcZFVTeDA4odpFyOQzaw6hXukJSq/f28sQ==
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resolved "https://registry.npmjs.org/get-stream/-/get-stream-4.1.0.tgz"
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dependencies:
ini "2.0.0"
-global-modules@2.0.0, global-modules@^2.0.0:
+global-modules@^2.0.0, global-modules@2.0.0:
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- resolved "https://registry.npmjs.org/globby/-/globby-11.0.1.tgz"
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resolved "https://registry.npmjs.org/globby/-/globby-11.1.0.tgz"
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pify "^3.0.0"
slash "^1.0.0"
+globby@11.0.1:
+ version "11.0.1"
+ resolved "https://registry.npmjs.org/globby/-/globby-11.0.1.tgz"
+ integrity sha512-iH9RmgwCmUJHi2z5o2l3eTtGBtXek1OYlHrbcxOYugyHLmAsZrPj43OtHThd62Buh/Vv6VyCBD2bdyWcGNQqoQ==
+ dependencies:
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+ dir-glob "^3.0.1"
+ fast-glob "^3.1.1"
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+ merge2 "^1.3.0"
+ slash "^3.0.0"
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resolved "https://registry.npmjs.org/globule/-/globule-1.3.4.tgz"
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lodash.template "^4.4.0"
through2 "^2.0.0"
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+ version "6.0.0"
+ resolved "https://registry.npmjs.org/gzip-size/-/gzip-size-6.0.0.tgz"
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+ dependencies:
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+
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resolved "https://registry.npmjs.org/gzip-size/-/gzip-size-5.1.1.tgz"
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+ resolved "https://registry.npmjs.org/http-cache-semantics/-/http-cache-semantics-3.8.1.tgz"
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+http-errors@~1.6.2:
+ version "1.6.3"
+ resolved "https://registry.npmjs.org/http-errors/-/http-errors-1.6.3.tgz"
+ integrity sha512-lks+lVC8dgGyh97jxvxeYTWQFvh4uw4yC12gVl63Cg30sjPX4wuGcdkICVXDAESr6OJGjqGA8Iz5mkeN6zlD7A==
+ dependencies:
+ depd "~1.1.2"
+ inherits "2.0.3"
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resolved "https://registry.npmjs.org/http-errors/-/http-errors-2.0.0.tgz"
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+ resolved "https://registry.npmjs.org/immer/-/immer-8.0.1.tgz"
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resolved "https://registry.npmjs.org/import-fresh/-/import-fresh-2.0.0.tgz"
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+ini@2.0.0:
+ version "2.0.0"
+ resolved "https://registry.npmjs.org/ini/-/ini-2.0.0.tgz"
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resolved "https://registry.npmjs.org/inline-style-parser/-/inline-style-parser-0.1.1.tgz"
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+ version "1.9.1"
+ resolved "https://registry.npmjs.org/ipaddr.js/-/ipaddr.js-1.9.1.tgz"
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resolved "https://registry.npmjs.org/is-absolute-url/-/is-absolute-url-2.1.0.tgz"
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dependencies:
hasown "^2.0.0"
-is-alphabetical@1.0.4, is-alphabetical@^1.0.0:
+is-alphabetical@^1.0.0, is-alphabetical@1.0.4:
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-is-plain-obj@^1.0.0, is-plain-obj@^1.1.0:
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+ version "1.1.0"
+ resolved "https://registry.npmjs.org/is-plain-obj/-/is-plain-obj-1.1.0.tgz"
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-is-root@2.1.0, is-root@^2.1.0:
+is-root@^2.1.0, is-root@2.1.0:
version "2.1.0"
resolved "https://registry.npmjs.org/is-root/-/is-root-2.1.0.tgz"
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dependencies:
call-bind "^1.0.2"
-is-stream@^1.0.0, is-stream@^1.1.0:
+is-stream@^1.0.0:
+ version "1.1.0"
+ resolved "https://registry.npmjs.org/is-stream/-/is-stream-1.1.0.tgz"
+ integrity sha512-uQPm8kcs47jx38atAcWTVxyltQYoPT68y9aWYdV6yWXSyW8mzSat0TL6CiWdZeCdF3KrAvpVtnHbTv4RN+rqdQ==
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@@ -7409,21 +7513,26 @@ is2@^2.0.6:
ip-regex "^4.1.0"
is-url "^1.2.4"
+isarray@^2.0.5:
+ version "2.0.5"
+ resolved "https://registry.npmjs.org/isarray/-/isarray-2.0.5.tgz"
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+ resolved "https://registry.npmjs.org/isarray/-/isarray-1.0.0.tgz"
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resolved "https://registry.npmjs.org/isarray/-/isarray-0.0.1.tgz"
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resolved "https://registry.npmjs.org/isexe/-/isexe-2.0.0.tgz"
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-js-yaml@^3.13.1, js-yaml@^3.8.1:
+js-yaml@^3.13.1:
+ version "3.14.1"
+ resolved "https://registry.npmjs.org/js-yaml/-/js-yaml-3.14.1.tgz"
+ integrity sha512-okMH7OXXJ7YrN9Ok3/SXrnu4iX9yOk+25nqX4imS2npuvTYDmo/QEZoqwZkYaIDk3jVvBOTOIEgEhaLOynBS9g==
+ dependencies:
+ argparse "^1.0.7"
+ esprima "^4.0.0"
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version "3.14.1"
resolved "https://registry.npmjs.org/js-yaml/-/js-yaml-3.14.1.tgz"
integrity sha512-okMH7OXXJ7YrN9Ok3/SXrnu4iX9yOk+25nqX4imS2npuvTYDmo/QEZoqwZkYaIDk3jVvBOTOIEgEhaLOynBS9g==
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json-schema "0.4.0"
verror "1.10.0"
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- dependencies:
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-
keyv@^3.0.0:
version "3.1.0"
resolved "https://registry.npmjs.org/keyv/-/keyv-3.1.0.tgz"
@@ -7618,7 +7728,28 @@ keyv@^3.0.0:
dependencies:
json-buffer "3.0.0"
-kind-of@^3.0.2, kind-of@^3.0.3, kind-of@^3.2.0:
+keyv@3.0.0:
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dependencies:
brace-expansion "^1.1.7"
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dependencies:
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+ dependencies:
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resolved "https://registry.npmjs.org/moo/-/moo-0.5.2.tgz"
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+ dependencies:
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+ sort-keys "^2.0.0"
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resolved "https://registry.npmjs.org/not/-/not-0.1.0.tgz"
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resolved "https://registry.npmjs.org/nth-check/-/nth-check-1.0.2.tgz"
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dependencies:
boolbase "^1.0.0"
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+ dependencies:
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resolved "https://registry.npmjs.org/path-to-regexp/-/path-to-regexp-0.1.7.tgz"
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resolved "https://registry.npmjs.org/path-type/-/path-type-1.1.0.tgz"
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-pify@^2.0.0, pify@^2.2.0, pify@^2.3.0:
+pify@^2.0.0:
+ version "2.3.0"
+ resolved "https://registry.npmjs.org/pify/-/pify-2.3.0.tgz"
+ integrity sha512-udgsAY+fTnvv7kI7aaxbqwWNb0AHiB0qBO89PZKPkoTmGOgdbrHDKD+0B2X4uTfJ/FT1R09r9gTsjUjNJotuog==
+
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+ version "2.3.0"
+ resolved "https://registry.npmjs.org/pify/-/pify-2.3.0.tgz"
+ integrity sha512-udgsAY+fTnvv7kI7aaxbqwWNb0AHiB0qBO89PZKPkoTmGOgdbrHDKD+0B2X4uTfJ/FT1R09r9gTsjUjNJotuog==
+
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resolved "https://registry.npmjs.org/pify/-/pify-2.3.0.tgz"
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dependencies:
find-up "^4.0.0"
-pkg-up@3.1.0, pkg-up@^3.1.0:
+pkg-up@^3.1.0, pkg-up@3.1.0:
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resolved "https://registry.npmjs.org/pkg-up/-/pkg-up-3.1.0.tgz"
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- resolved "https://registry.npmjs.org/postcss/-/postcss-7.0.39.tgz"
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- source-map "^0.6.1"
-
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picocolors "^1.0.0"
source-map-js "^1.0.2"
+postcss@^7.0.0, postcss@^7.0.1, postcss@^7.0.23, postcss@^7.0.27, postcss@^7.0.32:
+ version "7.0.39"
+ resolved "https://registry.npmjs.org/postcss/-/postcss-7.0.39.tgz"
+ integrity sha512-yioayjNbHn6z1/Bywyb2Y4s3yvDAeXGOyxqD+LnVOinq6Mdmd++SW2wUNVzavyyHxd6+DxzWGIuosg6P1Rj8uA==
+ dependencies:
+ picocolors "^0.2.1"
+ source-map "^0.6.1"
+
prebuild-install@^7.1.1:
version "7.1.1"
resolved "https://registry.npmjs.org/prebuild-install/-/prebuild-install-7.1.1.tgz"
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dependencies:
asap "~2.0.3"
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- version "2.4.0"
- resolved "https://registry.npmjs.org/prompts/-/prompts-2.4.0.tgz"
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- dependencies:
- kleur "^3.0.3"
- sisteransi "^1.0.5"
-
prompts@^2.4.2:
version "2.4.2"
resolved "https://registry.npmjs.org/prompts/-/prompts-2.4.2.tgz"
@@ -9757,6 +9951,14 @@ prompts@^2.4.2:
kleur "^3.0.3"
sisteransi "^1.0.5"
+prompts@2.4.0:
+ version "2.4.0"
+ resolved "https://registry.npmjs.org/prompts/-/prompts-2.4.0.tgz"
+ integrity sha512-awZAKrk3vN6CroQukBL+R9051a4R3zCZBlJm/HBfrSZ8iTpYix3VX1vU4mveiLpiwmOJT4wokTF9m6HUk4KqWQ==
+ dependencies:
+ kleur "^3.0.3"
+ sisteransi "^1.0.5"
+
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version "1.2.0"
resolved "https://registry.npmjs.org/prop-types-exact/-/prop-types-exact-1.2.0.tgz"
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object.assign "^4.1.0"
reflect.ownkeys "^0.2.0"
-prop-types@^15.0.0, prop-types@^15.6.2, prop-types@^15.7.2, prop-types@^15.8.1:
+prop-types@^15.0.0, prop-types@^15.6.2, prop-types@^15.7.2, prop-types@^15.8.1, prop-types@>=15:
version "15.8.1"
resolved "https://registry.npmjs.org/prop-types/-/prop-types-15.8.1.tgz"
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-punycode@^2.1.0, punycode@^2.1.1:
+punycode@^2.1.0:
+ version "2.3.1"
+ resolved "https://registry.npmjs.org/punycode/-/punycode-2.3.1.tgz"
+ integrity sha512-vYt7UD1U9Wg6138shLtLOvdAu+8DsC/ilFtEVHcH+wydcSpNE20AfSOduf6MkRFahL5FY7X1oU7nKVZFtfq8Fg==
+
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resolved "https://registry.npmjs.org/punycode/-/punycode-2.3.1.tgz"
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+qs@^6.4.0, qs@6.11.0:
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resolved "https://registry.npmjs.org/qs/-/qs-6.11.0.tgz"
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@@ -9914,25 +10121,20 @@ randombytes@^2.1.0:
dependencies:
safe-buffer "^5.1.0"
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- version "1.2.0"
- resolved "https://registry.npmjs.org/range-parser/-/range-parser-1.2.0.tgz"
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-
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+range-parser@^1.2.1:
version "1.2.1"
resolved "https://registry.npmjs.org/range-parser/-/range-parser-1.2.1.tgz"
integrity sha512-Hrgsx+orqoygnmhFbKaHE6c296J+HTAQXoxEF6gNupROmmGJRoyzfG3ccAveqCBrwr/2yxQ5BVd/GTl5agOwSg==
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- version "2.5.2"
- resolved "https://registry.yarnpkg.com/raw-body/-/raw-body-2.5.2.tgz#99febd83b90e08975087e8f1f9419a149366b68a"
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- dependencies:
- bytes "3.1.2"
- http-errors "2.0.0"
- iconv-lite "0.4.24"
- unpipe "1.0.0"
+range-parser@~1.2.1:
+ version "1.2.1"
+ resolved "https://registry.npmjs.org/range-parser/-/range-parser-1.2.1.tgz"
+ integrity sha512-Hrgsx+orqoygnmhFbKaHE6c296J+HTAQXoxEF6gNupROmmGJRoyzfG3ccAveqCBrwr/2yxQ5BVd/GTl5agOwSg==
+
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+ version "1.2.0"
+ resolved "https://registry.npmjs.org/range-parser/-/range-parser-1.2.0.tgz"
+ integrity sha512-kA5WQoNVo4t9lNx2kQNFCxKeBl5IbbSNBl1M/tLkw9WCn+hxNBAW5Qh8gdhs63CJnhjJ2zQWFoqPJP2sK1AV5A==
raw-body@~1.1.0:
version "1.1.7"
@@ -9942,7 +10144,17 @@ raw-body@~1.1.0:
bytes "1"
string_decoder "0.10"
-rc@1.2.8, rc@^1.2.7, rc@^1.2.8:
+raw-body@2.5.2:
+ version "2.5.2"
+ resolved "https://registry.npmjs.org/raw-body/-/raw-body-2.5.2.tgz"
+ integrity sha512-8zGqypfENjCIqGhgXToC8aB2r7YrBX+AQAfIPs/Mlk+BtPTztOvTS01NRW/3Eh60J+a48lt8qsCzirQ6loCVfA==
+ dependencies:
+ bytes "3.1.2"
+ http-errors "2.0.0"
+ iconv-lite "0.4.24"
+ unpipe "1.0.0"
+
+rc@^1.2.7, rc@^1.2.8, rc@1.2.8:
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resolved "https://registry.npmjs.org/rc/-/rc-1.2.8.tgz"
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@@ -10022,7 +10234,16 @@ react-dev-utils@^12.0.1:
strip-ansi "^6.0.1"
text-table "^0.2.0"
-react-dom@^16.8.4:
+react-dom@*, "react-dom@^16.6.0 || ^17.0.0 || ^18.0.0", "react-dom@^16.8.4 || ^17", "react-dom@^16.8.4 || ^17.0.0", "react-dom@^17.0.0 || ^16.3.0 || ^15.5.4", react-dom@^17.0.2, "react-dom@>= 16.8.0 < 19.0.0":
+ version "17.0.2"
+ resolved "https://registry.npmjs.org/react-dom/-/react-dom-17.0.2.tgz"
+ integrity sha512-s4h96KtLDUQlsENhMn1ar8t2bEa+q/YAtj8pPPdIjPDGBDIVNsrD9aXNWqspUe6AzKCIG0C1HZZLqLV7qpOBGA==
+ dependencies:
+ loose-envify "^1.1.0"
+ object-assign "^4.1.1"
+ scheduler "^0.20.2"
+
+react-dom@^16.0.0-0, react-dom@^16.8.4:
version "16.14.0"
resolved "https://registry.npmjs.org/react-dom/-/react-dom-16.14.0.tgz"
integrity sha512-1gCeQXDLoIqMgqD3IO2Ah9bnf0w9kzhwN5q4FGnHZ67hBm9yePzB5JJAIQCc8x3pFnNlwFq4RidZggNAAkzWWw==
@@ -10032,15 +10253,6 @@ react-dom@^16.8.4:
prop-types "^15.6.2"
scheduler "^0.19.1"
-react-dom@^17.0.2:
- version "17.0.2"
- resolved "https://registry.npmjs.org/react-dom/-/react-dom-17.0.2.tgz"
- integrity sha512-s4h96KtLDUQlsENhMn1ar8t2bEa+q/YAtj8pPPdIjPDGBDIVNsrD9aXNWqspUe6AzKCIG0C1HZZLqLV7qpOBGA==
- dependencies:
- loose-envify "^1.1.0"
- object-assign "^4.1.1"
- scheduler "^0.20.2"
-
react-error-overlay@^6.0.11, react-error-overlay@^6.0.9:
version "6.0.11"
resolved "https://registry.npmjs.org/react-error-overlay/-/react-error-overlay-6.0.11.tgz"
@@ -10094,6 +10306,14 @@ react-loadable-ssr-addon-v5-slorber@^1.0.1:
dependencies:
"@babel/runtime" "^7.10.3"
+react-loadable@*, "react-loadable@npm:@docusaurus/react-loadable@5.5.2":
+ version "5.5.2"
+ resolved "https://registry.npmjs.org/@docusaurus/react-loadable/-/react-loadable-5.5.2.tgz"
+ integrity sha512-A3dYjdBGuy0IGT+wyLIGIKLRE+sAk1iNk0f1HjNDysO7u8lhL4N3VEm+FAubmJbAztn94F7MxBTPmnixbiyFdQ==
+ dependencies:
+ "@types/react" "*"
+ prop-types "^15.6.2"
+
react-router-config@^5.1.1:
version "5.1.1"
resolved "https://registry.npmjs.org/react-router-config/-/react-router-config-5.1.1.tgz"
@@ -10114,7 +10334,7 @@ react-router-dom@^5.3.3:
tiny-invariant "^1.0.2"
tiny-warning "^1.0.0"
-react-router@5.3.4, react-router@^5.3.3:
+react-router@^5.3.3, react-router@>=5, react-router@5.3.4:
version "5.3.4"
resolved "https://registry.npmjs.org/react-router/-/react-router-5.3.4.tgz"
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@@ -10148,7 +10368,7 @@ react-textarea-autosize@^8.3.2:
use-composed-ref "^1.3.0"
use-latest "^1.2.1"
-react-waypoint@^10.3.0:
+react-waypoint@^10.3.0, react-waypoint@>=9.0.2:
version "10.3.0"
resolved "https://registry.npmjs.org/react-waypoint/-/react-waypoint-10.3.0.tgz"
integrity sha512-iF1y2c1BsoXuEGz08NoahaLFIGI9gTUAAOKip96HUmylRT6DUtpgoBPjk/Y8dfcFVmfVDvUzWjNXpZyKTOV0SQ==
@@ -10158,7 +10378,15 @@ react-waypoint@^10.3.0:
prop-types "^15.0.0"
react-is "^17.0.1 || ^18.0.0"
-react@^16.8.4:
+react@*, "react@^15.0.2 || ^16.0.0 || ^17.0.0", "react@^15.3.0 || ^16.0.0 || ^17.0.0 || ^18.0.0", "react@^16.13.1 || ^17.0.0", "react@^16.6.0 || ^17.0.0 || ^18.0.0", "react@^16.8.0 || ^17.0.0 || ^18.0.0", "react@^16.8.4 || ^17", "react@^16.8.4 || ^17.0.0", "react@^17.0.0 || ^16.3.0 || ^15.5.4", react@^17.0.2, "react@>= 16.8.0 < 19.0.0", react@>=0.14.9, react@>=0.14.x, react@>=15, react@17.0.2:
+ version "17.0.2"
+ resolved "https://registry.npmjs.org/react/-/react-17.0.2.tgz"
+ integrity sha512-gnhPt75i/dq/z3/6q/0asP78D0u592D5L1pd7M8P+dck6Fu/jJeL6iVVK23fptSUZj8Vjf++7wXA8UNclGQcbA==
+ dependencies:
+ loose-envify "^1.1.0"
+ object-assign "^4.1.1"
+
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object-assign "^4.1.1"
prop-types "^15.6.2"
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- version "17.0.2"
- resolved "https://registry.npmjs.org/react/-/react-17.0.2.tgz"
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- dependencies:
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- object-assign "^4.1.1"
-
read-pkg-up@^1.0.1:
version "1.0.1"
resolved "https://registry.npmjs.org/read-pkg-up/-/read-pkg-up-1.0.1.tgz"
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normalize-package-data "^2.3.2"
path-type "^1.0.0"
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+readable-stream@^2.0.0:
+ version "2.3.8"
+ resolved "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.8.tgz"
+ integrity sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA==
+ dependencies:
+ core-util-is "~1.0.0"
+ inherits "~2.0.3"
+ isarray "~1.0.0"
+ process-nextick-args "~2.0.0"
+ safe-buffer "~5.1.1"
+ string_decoder "~1.1.1"
+ util-deprecate "~1.0.1"
+
+readable-stream@^2.0.1:
+ version "2.3.8"
+ resolved "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.8.tgz"
+ integrity sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA==
+ dependencies:
+ core-util-is "~1.0.0"
+ inherits "~2.0.3"
+ isarray "~1.0.0"
+ process-nextick-args "~2.0.0"
+ safe-buffer "~5.1.1"
+ string_decoder "~1.1.1"
+ util-deprecate "~1.0.1"
+
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+ version "2.3.8"
+ resolved "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.8.tgz"
+ integrity sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA==
+ dependencies:
+ core-util-is "~1.0.0"
+ inherits "~2.0.3"
+ isarray "~1.0.0"
+ process-nextick-args "~2.0.0"
+ safe-buffer "~5.1.1"
+ string_decoder "~1.1.1"
+ util-deprecate "~1.0.1"
+
+readable-stream@^2.2.2:
+ version "2.3.8"
+ resolved "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.8.tgz"
+ integrity sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA==
+ dependencies:
+ core-util-is "~1.0.0"
+ inherits "~2.0.3"
+ isarray "~1.0.0"
+ process-nextick-args "~2.0.0"
+ safe-buffer "~5.1.1"
+ string_decoder "~1.1.1"
+ util-deprecate "~1.0.1"
+
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version "2.3.8"
resolved "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.8.tgz"
integrity sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA==
@@ -10214,6 +10486,19 @@ readable-stream@^3.0.6, readable-stream@^3.1.1, readable-stream@^3.4.0:
string_decoder "^1.1.1"
util-deprecate "^1.0.1"
+readable-stream@~2.3.6:
+ version "2.3.8"
+ resolved "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.8.tgz"
+ integrity sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA==
+ dependencies:
+ core-util-is "~1.0.0"
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+ isarray "~1.0.0"
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resolved "https://registry.npmjs.org/redent/-/redent-1.0.0.tgz"
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supports-preserve-symlinks-flag "^1.0.0"
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resolved "https://registry.npmjs.org/rst-selector-parser/-/rst-selector-parser-2.2.3.tgz"
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ajv-keywords "^5.1.0"
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resolved "https://registry.npmjs.org/sharp/-/sharp-0.30.7.tgz"
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+ dependencies:
+ emoji-regex "^8.0.0"
+ is-fullwidth-code-point "^3.0.0"
+ strip-ansi "^6.0.1"
+
+string-width@^4.0.0, string-width@^4.1.0, string-width@^4.2.2:
+ version "4.2.3"
+ resolved "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz"
+ integrity sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==
+ dependencies:
+ emoji-regex "^8.0.0"
+ is-fullwidth-code-point "^3.0.0"
+ strip-ansi "^6.0.1"
+
+string-width@^4.2.0:
+ version "4.2.3"
+ resolved "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz"
+ integrity sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==
+ dependencies:
+ emoji-regex "^8.0.0"
+ is-fullwidth-code-point "^3.0.0"
+ strip-ansi "^6.0.1"
+
+string-width@^4.2.3:
version "4.2.3"
resolved "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz"
integrity sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==
@@ -11349,25 +11755,6 @@ string.prototype.trimstart@^1.0.7:
define-properties "^1.2.0"
es-abstract "^1.22.1"
-string_decoder@0.10:
- version "0.10.31"
- resolved "https://registry.npmjs.org/string_decoder/-/string_decoder-0.10.31.tgz"
- integrity sha512-ev2QzSzWPYmy9GuqfIVildA4OdcGLeFZQrq5ys6RtiuF+RQQiZWr8TZNyAcuVXyQRYfEO+MsoB/1BuQVhOJuoQ==
-
-string_decoder@^1.1.1:
- version "1.3.0"
- resolved "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz"
- integrity sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA==
- dependencies:
- safe-buffer "~5.2.0"
-
-string_decoder@~1.1.1:
- version "1.1.1"
- resolved "https://registry.npmjs.org/string_decoder/-/string_decoder-1.1.1.tgz"
- integrity sha512-n/ShnvDi6FHbbVfviro+WojiFzv+s8MPMHBczVePfUpDJLwoLT0ht1l4YwBCbi8pJAveEEdnkHyPyTP/mzRfwg==
- dependencies:
- safe-buffer "~5.1.0"
-
stringify-object@^3.3.0:
version "3.3.0"
resolved "https://registry.npmjs.org/stringify-object/-/stringify-object-3.3.0.tgz"
@@ -11377,13 +11764,6 @@ stringify-object@^3.3.0:
is-obj "^1.0.1"
is-regexp "^1.0.0"
-strip-ansi@6.0.0:
- version "6.0.0"
- resolved "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.0.tgz"
- integrity sha512-AuvKTrTfQNYNIctbR1K/YGTR1756GycPsg7b9bdV9Duqur4gv6aKqHXah67Z8ImS7WEz5QVcOtlfW2rZEugt6w==
- dependencies:
- ansi-regex "^5.0.0"
-
strip-ansi@^3.0.0:
version "3.0.1"
resolved "https://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz"
@@ -11405,6 +11785,13 @@ strip-ansi@^7.0.1:
dependencies:
ansi-regex "^6.0.1"
+strip-ansi@6.0.0:
+ version "6.0.0"
+ resolved "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.0.tgz"
+ integrity sha512-AuvKTrTfQNYNIctbR1K/YGTR1756GycPsg7b9bdV9Duqur4gv6aKqHXah67Z8ImS7WEz5QVcOtlfW2rZEugt6w==
+ dependencies:
+ ansi-regex "^5.0.0"
+
strip-bom-string@^1.0.0:
version "1.0.0"
resolved "https://registry.npmjs.org/strip-bom-string/-/strip-bom-string-1.0.0.tgz"
@@ -11468,7 +11855,7 @@ strnum@^1.0.5:
resolved "https://registry.npmjs.org/strnum/-/strnum-1.0.5.tgz"
integrity sha512-J8bbNyKKXl5qYcR36TIO8W3mVGVHrmmxsd5PAItGkmyzwJvybiw2IVq5nqd0i4LSNSkB/sx9VHllbfFdr9k1JA==
-style-to-object@0.3.0, style-to-object@^0.3.0:
+style-to-object@^0.3.0, style-to-object@0.3.0:
version "0.3.0"
resolved "https://registry.npmjs.org/style-to-object/-/style-to-object-0.3.0.tgz"
integrity sha512-CzFnRRXhzWIdItT3OmF8SQfWyahHhjq3HwcMNCNLn+N7klOOqPjMeG/4JSu77D7ypZdGvSzvkrbyeTMizz2VrA==
@@ -11528,7 +11915,26 @@ svg-parser@^2.0.4:
resolved "https://registry.npmjs.org/svg-parser/-/svg-parser-2.0.4.tgz"
integrity sha512-e4hG1hRwoOdRb37cIMSgzNsxyzKfayW6VOflrwvR+/bzrkyxY/31WkbgnQpgtrNp1SdpJvpUAGTa/ZoiPNDuRQ==
-svgo@^1.0.0, svgo@^1.3.2:
+svgo@^1.0.0:
+ version "1.3.2"
+ resolved "https://registry.npmjs.org/svgo/-/svgo-1.3.2.tgz"
+ integrity sha512-yhy/sQYxR5BkC98CY7o31VGsg014AKLEPxdfhora76l36hD9Rdy5NZA/Ocn6yayNPgSamYdtX2rFJdcv07AYVw==
+ dependencies:
+ chalk "^2.4.1"
+ coa "^2.0.2"
+ css-select "^2.0.0"
+ css-select-base-adapter "^0.1.1"
+ css-tree "1.0.0-alpha.37"
+ csso "^4.0.2"
+ js-yaml "^3.13.1"
+ mkdirp "~0.5.1"
+ object.values "^1.1.0"
+ sax "~1.2.4"
+ stable "^0.1.8"
+ unquote "~1.1.1"
+ util.promisify "~1.0.0"
+
+svgo@^1.3.2:
version "1.3.2"
resolved "https://registry.npmjs.org/svgo/-/svgo-1.3.2.tgz"
integrity sha512-yhy/sQYxR5BkC98CY7o31VGsg014AKLEPxdfhora76l36hD9Rdy5NZA/Ocn6yayNPgSamYdtX2rFJdcv07AYVw==
@@ -11664,11 +12070,16 @@ terser@^5.10.0, terser@^5.16.8:
commander "^2.20.0"
source-map-support "~0.5.20"
-text-table@0.2.0, text-table@^0.2.0:
+text-table@^0.2.0, text-table@0.2.0:
version "0.2.0"
resolved "https://registry.npmjs.org/text-table/-/text-table-0.2.0.tgz"
integrity sha512-N+8UisAXDGk8PFXP4HAzVR9nbfmVJ3zYLAWiTIoqC5v5isinhr+r5uaO8+7r3BMfuNIufIsA7RdpVgacC2cSpw==
+through@^2.3.8:
+ version "2.3.8"
+ resolved "https://registry.npmjs.org/through/-/through-2.3.8.tgz"
+ integrity sha512-w89qg7PI8wAdvX60bMDP+bFoD5Dvhm9oLheFp5O4a2QF0cSBGsBX4qZmadPMvVqlLJBBci+WqGGOAPvcDeNSVg==
+
through2@^2.0.0:
version "2.0.5"
resolved "https://registry.npmjs.org/through2/-/through2-2.0.5.tgz"
@@ -11677,11 +12088,6 @@ through2@^2.0.0:
readable-stream "~2.3.6"
xtend "~4.0.1"
-through@^2.3.8:
- version "2.3.8"
- resolved "https://registry.npmjs.org/through/-/through-2.3.8.tgz"
- integrity sha512-w89qg7PI8wAdvX60bMDP+bFoD5Dvhm9oLheFp5O4a2QF0cSBGsBX4qZmadPMvVqlLJBBci+WqGGOAPvcDeNSVg==
-
thunky@^1.0.2:
version "1.1.0"
resolved "https://registry.npmjs.org/thunky/-/thunky-1.1.0.tgz"
@@ -11944,6 +12350,11 @@ typedarray@^0.0.6:
resolved "https://registry.npmjs.org/typedarray/-/typedarray-0.0.6.tgz"
integrity sha512-/aCDEGatGvZ2BIk+HmLf4ifCJFwvKFNb9/JeZPMulfgFracn9QFcAf5GO8B/mweUjSoblS5In0cWhqpfs/5PQA==
+"typescript@>= 2.7":
+ version "5.1.6"
+ resolved "https://registry.npmjs.org/typescript/-/typescript-5.1.6.tgz"
+ integrity sha512-zaWCozRZ6DLEWAWFrVDz1H6FVXzUSfTy5FUMWsQlU8Ym5JP9eO4xkTIROFCQvhQf61z6O/G6ugw3SgAnvvm+HA==
+
ua-parser-js@^1.0.35:
version "1.0.35"
resolved "https://registry.npmjs.org/ua-parser-js/-/ua-parser-js-1.0.35.tgz"
@@ -11998,10 +12409,10 @@ unicode-property-aliases-ecmascript@^2.0.0:
resolved "https://registry.npmjs.org/unicode-property-aliases-ecmascript/-/unicode-property-aliases-ecmascript-2.1.0.tgz"
integrity sha512-6t3foTQI9qne+OZoVQB/8x8rk2k1eVy1gRXhV3oFQ5T6R1dqQ1xtin3XqSlx3+ATBkliTaR/hHyJBm+LVPNM8w==
-unified@9.2.0:
- version "9.2.0"
- resolved "https://registry.npmjs.org/unified/-/unified-9.2.0.tgz"
- integrity sha512-vx2Z0vY+a3YoTj8+pttM3tiJHCwY5UFbYdiWrwBEbHmK8pvsPj2rtAX2BFfgXen8T39CJWblWRDT4L5WGXtDdg==
+unified@^9.0.0, unified@^9.2.2:
+ version "9.2.2"
+ resolved "https://registry.npmjs.org/unified/-/unified-9.2.2.tgz"
+ integrity sha512-Sg7j110mtefBD+qunSLO1lqOEKdrwBFBrR6Qd8f4uwkhWNlbkaqwHse6e7QvD3AP/MNoJdEDLaf8OxYyoWgorQ==
dependencies:
bail "^1.0.0"
extend "^3.0.0"
@@ -12010,10 +12421,10 @@ unified@9.2.0:
trough "^1.0.0"
vfile "^4.0.0"
-unified@^9.0.0, unified@^9.2.2:
- version "9.2.2"
- resolved "https://registry.npmjs.org/unified/-/unified-9.2.2.tgz"
- integrity sha512-Sg7j110mtefBD+qunSLO1lqOEKdrwBFBrR6Qd8f4uwkhWNlbkaqwHse6e7QvD3AP/MNoJdEDLaf8OxYyoWgorQ==
+unified@9.2.0:
+ version "9.2.0"
+ resolved "https://registry.npmjs.org/unified/-/unified-9.2.0.tgz"
+ integrity sha512-vx2Z0vY+a3YoTj8+pttM3tiJHCwY5UFbYdiWrwBEbHmK8pvsPj2rtAX2BFfgXen8T39CJWblWRDT4L5WGXtDdg==
dependencies:
bail "^1.0.0"
extend "^3.0.0"
@@ -12049,7 +12460,7 @@ unique-string@^2.0.0:
dependencies:
crypto-random-string "^2.0.0"
-unist-builder@2.0.3, unist-builder@^2.0.0:
+unist-builder@^2.0.0, unist-builder@2.0.3:
version "2.0.3"
resolved "https://registry.npmjs.org/unist-builder/-/unist-builder-2.0.3.tgz"
integrity sha512-f98yt5pnlMWlzP539tPc4grGMsFaQQlP/vM396b00jngsiINumNmsY8rkXjfoi1c6QaM8nQ3vaGDuoKWbe/1Uw==
@@ -12105,7 +12516,7 @@ unist-util-visit-parents@^3.0.0:
"@types/unist" "^2.0.0"
unist-util-is "^4.0.0"
-unist-util-visit@2.0.3, unist-util-visit@^2.0.0, unist-util-visit@^2.0.3:
+unist-util-visit@^2.0.0, unist-util-visit@^2.0.3, unist-util-visit@2.0.3:
version "2.0.3"
resolved "https://registry.npmjs.org/unist-util-visit/-/unist-util-visit-2.0.3.tgz"
integrity sha512-iJ4/RczbJMkD0712mGktuGpm/U4By4FfDonL7N/9tATGIF4imikjOuagyMY53tnZq3NP6BcmlrHhEKAfGWjh7Q==
@@ -12119,7 +12530,7 @@ universalify@^2.0.0:
resolved "https://registry.npmjs.org/universalify/-/universalify-2.0.0.tgz"
integrity sha512-hAZsKq7Yy11Zu1DE0OzWjw7nnLZmJZYTDZZyEFHZdUhV8FkH5MCfoU1XMaxXovpyW5nq5scPqq0ZDP9Zyl04oQ==
-unpipe@1.0.0, unpipe@~1.0.0:
+unpipe@~1.0.0, unpipe@1.0.0:
version "1.0.0"
resolved "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz"
integrity sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ==
@@ -12278,7 +12689,12 @@ utils-merge@1.0.1:
resolved "https://registry.npmjs.org/utils-merge/-/utils-merge-1.0.1.tgz"
integrity sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA==
-uuid@^3.0.1, uuid@^3.3.2:
+uuid@^3.0.1:
+ version "3.4.0"
+ resolved "https://registry.npmjs.org/uuid/-/uuid-3.4.0.tgz"
+ integrity sha512-HjSDRw6gZE5JMggctHBcjVak08+KEVhSIiDzFnT9S9aegmp85S/bReBVTb4QTFaRNptJ9kuYaNhnbNEOkbKb/A==
+
+uuid@^3.3.2:
version "3.4.0"
resolved "https://registry.npmjs.org/uuid/-/uuid-3.4.0.tgz"
integrity sha512-HjSDRw6gZE5JMggctHBcjVak08+KEVhSIiDzFnT9S9aegmp85S/bReBVTb4QTFaRNptJ9kuYaNhnbNEOkbKb/A==
@@ -12407,7 +12823,7 @@ webpack-bundle-analyzer@^4.5.0:
webpack-dev-middleware@^5.3.1:
version "5.3.4"
- resolved "https://registry.yarnpkg.com/webpack-dev-middleware/-/webpack-dev-middleware-5.3.4.tgz#eb7b39281cbce10e104eb2b8bf2b63fce49a3517"
+ resolved "https://registry.npmjs.org/webpack-dev-middleware/-/webpack-dev-middleware-5.3.4.tgz"
integrity sha512-BVdTqhhs+0IfoeAf7EoH5WE+exCmqGerHfDM0IL096Px60Tq2Mn9MAbnaGUe6HiMa41KMCYF19gyzZmBcq/o4Q==
dependencies:
colorette "^2.0.10"
@@ -12465,7 +12881,7 @@ webpack-sources@^3.2.2, webpack-sources@^3.2.3:
resolved "https://registry.npmjs.org/webpack-sources/-/webpack-sources-3.2.3.tgz"
integrity sha512-/DyMEOrDgLKKIG0fmvtz+4dUX/3Ghozwgm6iPp8KRhvn+eQf9+Q7GWxVNMk3+uCPWfdXYC4ExGBckIXdFEfH1w==
-webpack@^5.73.0:
+"webpack@^4.0.0 || ^5.0.0", "webpack@^4.37.0 || ^5.0.0", webpack@^5.0.0, webpack@^5.1.0, webpack@^5.20.0, webpack@^5.73.0, "webpack@>= 4", webpack@>=2, "webpack@>=4.41.1 || 5.x", "webpack@3 || 4 || 5":
version "5.88.2"
resolved "https://registry.npmjs.org/webpack/-/webpack-5.88.2.tgz"
integrity sha512-JmcgNZ1iKj+aiR0OvTYtWQqJwq37Pf683dY9bVORwVbUrDhLhdn/PlO2sHsFHPkj7sHNQF3JwaAkp49V+Sq1tQ==
@@ -12505,7 +12921,7 @@ webpackbar@^5.0.2:
pretty-time "^1.1.0"
std-env "^3.0.1"
-websocket-driver@>=0.5.1, websocket-driver@^0.7.4:
+websocket-driver@^0.7.4, websocket-driver@>=0.5.1:
version "0.7.4"
resolved "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz"
integrity sha512-b17KeDIQVjvb0ssuSDF2cYXSg2iztliJ4B9WdsuB6J952qCPKmnVq4DyW5motImXHDC1cBT/1UezrJVsKw5zjg==
@@ -12558,7 +12974,14 @@ which-typed-array@^1.1.11, which-typed-array@^1.1.13:
gopd "^1.0.1"
has-tostringtag "^1.0.0"
-which@^1.2.9, which@^1.3.1:
+which@^1.2.9:
+ version "1.3.1"
+ resolved "https://registry.npmjs.org/which/-/which-1.3.1.tgz"
+ integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ==
+ dependencies:
+ isexe "^2.0.0"
+
+which@^1.3.1:
version "1.3.1"
resolved "https://registry.npmjs.org/which/-/which-1.3.1.tgz"
integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ==
diff --git a/enterprise/enterprise_callbacks/example_logging_api.py b/enterprise/enterprise_callbacks/example_logging_api.py
index 57ea99a67..c3d3f5e63 100644
--- a/enterprise/enterprise_callbacks/example_logging_api.py
+++ b/enterprise/enterprise_callbacks/example_logging_api.py
@@ -18,10 +18,6 @@ async def log_event(request: Request):
return {"message": "Request received successfully"}
except Exception as e:
- print(f"Error processing request: {str(e)}")
- import traceback
-
- traceback.print_exc()
raise HTTPException(status_code=500, detail="Internal Server Error")
diff --git a/enterprise/enterprise_callbacks/generic_api_callback.py b/enterprise/enterprise_callbacks/generic_api_callback.py
index cf1d22e8f..ba189b149 100644
--- a/enterprise/enterprise_callbacks/generic_api_callback.py
+++ b/enterprise/enterprise_callbacks/generic_api_callback.py
@@ -120,6 +120,5 @@ class GenericAPILogger:
)
return response
except Exception as e:
- traceback.print_exc()
- verbose_logger.debug(f"Generic - {str(e)}\n{traceback.format_exc()}")
+ verbose_logger.error(f"Generic - {str(e)}\n{traceback.format_exc()}")
pass
diff --git a/enterprise/enterprise_hooks/banned_keywords.py b/enterprise/enterprise_hooks/banned_keywords.py
index acd390d79..4cf68b2fd 100644
--- a/enterprise/enterprise_hooks/banned_keywords.py
+++ b/enterprise/enterprise_hooks/banned_keywords.py
@@ -82,7 +82,7 @@ class _ENTERPRISE_BannedKeywords(CustomLogger):
except HTTPException as e:
raise e
except Exception as e:
- traceback.print_exc()
+ verbose_proxy_logger.error(traceback.format_exc())
async def async_post_call_success_hook(
self,
diff --git a/enterprise/enterprise_hooks/blocked_user_list.py b/enterprise/enterprise_hooks/blocked_user_list.py
index cbc14d2c2..8e642a026 100644
--- a/enterprise/enterprise_hooks/blocked_user_list.py
+++ b/enterprise/enterprise_hooks/blocked_user_list.py
@@ -118,4 +118,4 @@ class _ENTERPRISE_BlockedUserList(CustomLogger):
except HTTPException as e:
raise e
except Exception as e:
- traceback.print_exc()
+ verbose_proxy_logger.error(traceback.format_exc())
diff --git a/enterprise/enterprise_hooks/llm_guard.py b/enterprise/enterprise_hooks/llm_guard.py
index 3a15ca52b..9db10cf79 100644
--- a/enterprise/enterprise_hooks/llm_guard.py
+++ b/enterprise/enterprise_hooks/llm_guard.py
@@ -92,7 +92,7 @@ class _ENTERPRISE_LLMGuard(CustomLogger):
},
)
except Exception as e:
- traceback.print_exc()
+ verbose_proxy_logger.error(traceback.format_exc())
raise e
def should_proceed(self, user_api_key_dict: UserAPIKeyAuth, data: dict) -> bool:
diff --git a/enterprise/utils.py b/enterprise/utils.py
index 90b14314c..b8f660927 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)
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 3c78c9b27..e92ae355e 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -5,8 +5,15 @@ 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,
+)
from litellm.proxy._types import (
KeyManagementSystem,
KeyManagementSettings,
@@ -53,6 +60,8 @@ _async_failure_callback: List[Callable] = (
pre_call_rules: List[Callable] = []
post_call_rules: List[Callable] = []
turn_off_message_logging: Optional[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] = (
@@ -95,7 +104,9 @@ common_cloud_provider_auth_params: dict = {
}
use_client: bool = False
ssl_verify: bool = True
+ssl_certificate: Optional[str] = None
disable_streaming_logging: bool = False
+in_memory_llm_clients_cache: dict = {}
### GUARDRAILS ###
llamaguard_model_name: Optional[str] = None
openai_moderations_model_name: Optional[str] = None
@@ -221,7 +232,9 @@ default_team_settings: Optional[List] = None
max_user_budget: Optional[float] = None
max_end_user_budget: Optional[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
@@ -298,6 +311,7 @@ api_base = None
headers = None
api_version = None
organization = None
+project = None
config_path = None
####### COMPLETION MODELS ###################
open_ai_chat_completion_models: List = []
@@ -695,6 +709,7 @@ all_embedding_models = (
openai_image_generation_models = ["dall-e-2", "dall-e-3"]
from .timeout import timeout
+from .cost_calculator import completion_cost
from .utils import (
client,
exception_type,
@@ -704,7 +719,6 @@ from .utils import (
create_pretrained_tokenizer,
create_tokenizer,
cost_per_token,
- completion_cost,
supports_function_calling,
supports_parallel_function_calling,
supports_vision,
@@ -754,7 +768,7 @@ 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
from .llms.bedrock import (
AmazonTitanConfig,
AmazonAI21Config,
@@ -772,7 +786,11 @@ from .llms.openai import (
MistralConfig,
DeepInfraConfig,
)
-from .llms.azure import AzureOpenAIConfig, AzureOpenAIError
+from .llms.azure import (
+ AzureOpenAIConfig,
+ AzureOpenAIError,
+ AzureOpenAIAssistantsAPIConfig,
+)
from .llms.watsonx import IBMWatsonXAIConfig
from .main import * # type: ignore
from .integrations import *
@@ -792,8 +810,13 @@ from .exceptions import (
APIConnectionError,
APIResponseValidationError,
UnprocessableEntityError,
+ InternalServerError,
+ LITELLM_EXCEPTION_TYPES,
)
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 .scheduler import *
+from .cost_calculator import response_cost_calculator
diff --git a/litellm/_logging.py b/litellm/_logging.py
index 0759ad51e..ab7a08f97 100644
--- a/litellm/_logging.py
+++ b/litellm/_logging.py
@@ -1,5 +1,6 @@
import logging, os, json
from logging import Formatter
+import traceback
set_verbose = False
json_logs = bool(os.getenv("JSON_LOGS", False))
@@ -39,6 +40,23 @@ 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 dc6f35642..f2edb403e 100644
--- a/litellm/_service_logger.py
+++ b/litellm/_service_logger.py
@@ -1,10 +1,18 @@
-import litellm, traceback
+from datetime import datetime
+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 typing import Union, Optional, TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+ from opentelemetry.trace import Span as _Span
+
+ Span = _Span
+else:
+ Span = Any
class ServiceLogging(CustomLogger):
@@ -40,7 +48,13 @@ 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[datetime] = None,
+ end_time: Optional[datetime] = None,
):
"""
- For counting if the redis, postgres call is successful
@@ -61,12 +75,25 @@ 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_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,
service: ServiceTypes,
duration: float,
error: Union[str, Exception],
call_type: str,
+ parent_otel_span: Optional[Span] = None,
+ start_time: Optional[datetime] = None,
+ end_time: Optional[datetime] = None,
):
"""
- For counting if the redis, postgres call is unsuccessful
@@ -95,6 +122,16 @@ 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/assistants/main.py b/litellm/assistants/main.py
index 25d2433d7..eff9adfb2 100644
--- a/litellm/assistants/main.py
+++ b/litellm/assistants/main.py
@@ -1,27 +1,83 @@
# What is this?
## Main file for assistants API logic
from typing import Iterable
-import os
+from functools import partial
+import os, asyncio, contextvars
import litellm
-from openai import OpenAI
+from openai import OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI
from litellm import client
-from litellm.utils import supports_httpx_timeout
+from litellm.utils import (
+ supports_httpx_timeout,
+ exception_type,
+ get_llm_provider,
+ get_secret,
+)
from ..llms.openai import OpenAIAssistantsAPI
+from ..llms.azure import AzureAssistantsAPI
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 +116,7 @@ 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 +124,43 @@ 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(
@@ -87,8 +181,43 @@ def get_assistants(
### 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 +246,7 @@ def create_thread(
)
```
"""
+ acreate_thread = kwargs.get("acreate_thread", None)
optional_params = GenericLiteLLMParams(**kwargs)
### TIMEOUT LOGIC ###
@@ -165,7 +295,49 @@ 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 +351,55 @@ 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 +439,7 @@ 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 +448,47 @@ 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 +503,90 @@ 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 +633,45 @@ 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 +687,61 @@ 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 +789,44 @@ 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 +842,21 @@ 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 +865,79 @@ 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 +977,7 @@ 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 +993,53 @@ 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 +1053,4 @@ 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 000000000..ca5a1293d
--- /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 000000000..4043606d5
--- /dev/null
+++ b/litellm/batches/main.py
@@ -0,0 +1,589 @@
+"""
+Main File for Batches API implementation
+
+https://platform.openai.com/docs/api-reference/batch
+
+- create_batch()
+- retrieve_batch()
+- cancel_batch()
+- list_batch()
+
+"""
+
+import os
+import asyncio
+from functools import partial
+import contextvars
+from typing import Literal, Optional, Dict, Coroutine, Any, Union
+import httpx
+
+import litellm
+from litellm import client
+from litellm.utils import supports_httpx_timeout
+from ..types.router import *
+from ..llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI
+from ..types.llms.openai import (
+ CreateBatchRequest,
+ RetrieveBatchRequest,
+ CancelBatchRequest,
+ CreateFileRequest,
+ FileTypes,
+ FileObject,
+ Batch,
+ FileContentRequest,
+ HttpxBinaryResponseContent,
+)
+
+####### ENVIRONMENT VARIABLES ###################
+openai_batches_instance = OpenAIBatchesAPI()
+openai_files_instance = OpenAIFilesAPI()
+#################################################
+
+
+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
+
+
+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 c8c1736d8..497dd2371 100644
--- a/litellm/caching.py
+++ b/litellm/caching.py
@@ -26,6 +26,16 @@ 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
@@ -233,6 +243,9 @@ 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 +259,9 @@ 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 +269,6 @@ 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 +292,8 @@ 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 +308,8 @@ class RedisCache(BaseCache):
duration=_duration,
error=e,
call_type="async_scan_iter",
+ start_time=start_time,
+ end_time=end_time,
)
)
raise e
@@ -304,7 +323,12 @@ 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),
)
)
# NON blocking - notify users Redis is throwing an exception
@@ -313,7 +337,6 @@ 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 +356,9 @@ 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 +370,9 @@ 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 +381,8 @@ 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 +420,9 @@ 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 +436,9 @@ 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 +447,6 @@ class RedisCache(BaseCache):
str(e),
cache_value,
)
- traceback.print_exc()
async def batch_cache_write(self, key, value, **kwargs):
print_verbose(
@@ -438,6 +471,9 @@ 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 +487,9 @@ 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 +497,6 @@ class RedisCache(BaseCache):
str(e),
value,
)
- traceback.print_exc()
raise e
async def flush_cache_buffer(self):
@@ -495,8 +533,9 @@ 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 +583,9 @@ 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 +599,9 @@ 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 +632,8 @@ class RedisCache(BaseCache):
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_batch_get_cache",
+ start_time=start_time,
+ end_time=end_time,
)
)
@@ -612,6 +659,8 @@ 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 +695,9 @@ 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 +731,9 @@ 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):
@@ -1138,22 +1185,23 @@ 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.error(
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 +1282,7 @@ 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 +1309,7 @@ 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 +1342,7 @@ 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 +1375,7 @@ 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 +1415,7 @@ 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 +1428,8 @@ 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 +1448,11 @@ 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
@@ -1429,8 +1476,8 @@ 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)}")
+ verbose_logger.debug(traceback.format_exc())
raise e
def flush_cache(self):
@@ -1846,8 +1893,8 @@ 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 +1911,8 @@ 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 +1944,8 @@ 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 000000000..d1e2dab52
--- /dev/null
+++ b/litellm/cost_calculator.py
@@ -0,0 +1,352 @@
+# What is this?
+## File for 'response_cost' calculation in Logging
+from typing import Optional, Union, Literal, List
+import litellm._logging
+from litellm.utils import (
+ ModelResponse,
+ EmbeddingResponse,
+ ImageResponse,
+ TranscriptionResponse,
+ TextCompletionResponse,
+ CallTypes,
+ cost_per_token,
+ print_verbose,
+ CostPerToken,
+ token_counter,
+)
+import litellm
+from litellm import verbose_logger
+
+
+# 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
+ completion_tokens = 0
+ custom_llm_provider = None
+ if completion_response is not None:
+ # 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 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 (
+ 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"
+ )
+ # 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}"
+ )
+
+ (
+ 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,
+ )
+ _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,
+ ],
+ model: str,
+ custom_llm_provider: 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]:
+ try:
+ response_cost: float = 0.0
+ if cache_hit is not None and cache_hit is True:
+ response_cost = 0.0
+ else:
+ 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
+ and custom_pricing is not None
+ and custom_llm_provider 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
diff --git a/litellm/exceptions.py b/litellm/exceptions.py
index d189b7ebe..edc17133d 100644
--- a/litellm/exceptions.py
+++ b/litellm/exceptions.py
@@ -20,18 +20,44 @@ 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.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 +66,44 @@ 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.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__(
@@ -61,6 +113,8 @@ 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
@@ -73,10 +127,28 @@ 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__(
@@ -86,20 +158,46 @@ 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.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__(
@@ -110,10 +208,25 @@ class Timeout(openai.APITimeoutError): # type: ignore
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 +237,36 @@ 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.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 +274,48 @@ 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.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
@@ -174,8 +337,25 @@ class ContextWindowExceededError(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
+
# sub class of bad request error - meant to help us catch guardrails-related errors on proxy.
class RejectedRequestError(BadRequestError): # type: ignore
@@ -200,8 +380,25 @@ 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'}}
@@ -223,8 +420,25 @@ class ContentPolicyViolationError(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 ServiceUnavailableError(openai.APIStatusError): # type: ignore
def __init__(
@@ -232,18 +446,97 @@ 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.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 = 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
@@ -255,14 +548,34 @@ class APIError(openai.APIError): # type: ignore
model,
request: httpx.Request,
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.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, 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
@@ -273,19 +586,45 @@ 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.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.llm_provider = llm_provider
@@ -293,8 +632,26 @@ class APIResponseValidationError(openai.APIResponseValidationError): # type: ig
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):
@@ -309,11 +666,33 @@ class OpenAIError(openai.OpenAIError): # type: ignore
self.llm_provider = "openai"
+LITELLM_EXCEPTION_TYPES = [
+ AuthenticationError,
+ NotFoundError,
+ BadRequestError,
+ UnprocessableEntityError,
+ Timeout,
+ PermissionDeniedError,
+ RateLimitError,
+ ContextWindowExceededError,
+ RejectedRequestError,
+ ContentPolicyViolationError,
+ InternalServerError,
+ ServiceUnavailableError,
+ APIError,
+ APIConnectionError,
+ APIResponseValidationError,
+ OpenAIError,
+ InternalServerError,
+]
+
+
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)
diff --git a/litellm/integrations/aispend.py b/litellm/integrations/aispend.py
index 2fe8ea0df..ca284e62e 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 7d30b706c..d428fb54d 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 0c38b8626..f8b6b1bbf 100644
--- a/litellm/integrations/clickhouse.py
+++ b/litellm/integrations/clickhouse.py
@@ -297,6 +297,5 @@ 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 e192cdaea..1d447da1f 100644
--- a/litellm/integrations/custom_logger.py
+++ b/litellm/integrations/custom_logger.py
@@ -115,7 +115,6 @@ 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 +129,6 @@ 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 +144,6 @@ 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 +160,5 @@ 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 6d5e08faf..d835b3d67 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 21ccabe4b..847f930ec 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_templates/templates.py b/litellm/integrations/email_templates/templates.py
new file mode 100644
index 000000000..7029e8ce1
--- /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},
+ 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} Hi {recipient_email}, Get Started here
Hi {recipient_email},
- 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} Hi {user_name}, )`) or in env - `PREDIBASE_TENANT_ID`."
+ )
headers = {
"content-type": "application/json",
"Authorization": "Bearer {}".format(api_key),
@@ -210,12 +243,12 @@ 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"
@@ -233,7 +266,7 @@ 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(
@@ -243,7 +276,7 @@ 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,
)
@@ -253,10 +286,8 @@ class PredibaseChatCompletion(BaseLLM):
## 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", "")
@@ -299,15 +330,17 @@ 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 +350,7 @@ 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 +394,9 @@ 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 +411,7 @@ class PredibaseChatCompletion(BaseLLM):
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=headers,
+ timeout=timeout,
) # type: ignore
else:
### ASYNC COMPLETION
@@ -396,10 +430,11 @@ 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 +455,6 @@ class PredibaseChatCompletion(BaseLLM):
headers=headers,
data=json.dumps(data),
)
-
return self.process_response(
model=model,
response=response,
@@ -448,16 +482,26 @@ 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 - {}".format(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 +527,25 @@ 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 41ecb486c..6bf03b52d 100644
--- a/litellm/llms/prompt_templates/factory.py
+++ b/litellm/llms/prompt_templates/factory.py
@@ -3,14 +3,7 @@ import requests, traceback
import json, re, xml.etree.ElementTree as ET
from jinja2 import Template, exceptions, meta, BaseLoader
from jinja2.sandbox import ImmutableSandboxedEnvironment
-from typing import (
- Any,
- List,
- Mapping,
- MutableMapping,
- Optional,
- Sequence,
-)
+from typing import Any, List, Mapping, MutableMapping, Optional, Sequence, Tuple
import litellm
import litellm.types
from litellm.types.completion import (
@@ -24,7 +17,7 @@ from litellm.types.completion import (
import litellm.types.llms
from litellm.types.llms.anthropic import *
import uuid
-
+from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock
import litellm.types.llms.vertex_ai
@@ -833,7 +826,7 @@ 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"]
)
@@ -1224,7 +1217,7 @@ def anthropic_messages_pt(messages: list):
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"])
)
@@ -1460,9 +1453,7 @@ 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 +1604,380 @@ def azure_text_pt(messages: list):
return prompt
+###### AMAZON BEDROCK #######
+
+from litellm.types.llms.bedrock import (
+ ToolResultContentBlock as BedrockToolResultContentBlock,
+ ToolResultBlock as BedrockToolResultBlock,
+ ToolConfigBlock as BedrockToolConfigBlock,
+ ToolUseBlock as BedrockToolUseBlock,
+ ImageSourceBlock as BedrockImageSourceBlock,
+ ImageBlock as BedrockImageBlock,
+ ContentBlock as BedrockContentBlock,
+ ToolInputSchemaBlock as BedrockToolInputSchemaBlock,
+ ToolSpecBlock as BedrockToolSpecBlock,
+ ToolBlock as BedrockToolBlock,
+ ToolChoiceValuesBlock as BedrockToolChoiceValuesBlock,
+)
+
+
+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, 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) -> 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 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 Exception(
+ "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
+ messages[msg_i]
+ )
+ )
+
+ 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:"""
diff --git a/litellm/llms/replicate.py b/litellm/llms/replicate.py
index 386d24f59..ce62e51e9 100644
--- a/litellm/llms/replicate.py
+++ b/litellm/llms/replicate.py
@@ -251,7 +251,7 @@ 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()
diff --git a/litellm/llms/vertex_ai.py b/litellm/llms/vertex_ai.py
index dc185aef9..bd9cfaa8d 100644
--- a/litellm/llms/vertex_ai.py
+++ b/litellm/llms/vertex_ai.py
@@ -3,7 +3,7 @@ import json
from enum import Enum
import requests # type: ignore
import time
-from typing import Callable, Optional, Union, List, Literal
+from typing import Callable, Optional, Union, List, Literal, Any
from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
import litellm, uuid
import httpx, inspect # type: ignore
@@ -12,6 +12,7 @@ from litellm.llms.prompt_templates.factory import (
convert_to_gemini_tool_call_result,
convert_to_gemini_tool_call_invoke,
)
+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
class VertexAIError(Exception):
@@ -297,24 +298,31 @@ 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)
+ # Figure out file type
+ extension_with_dot = os.path.splitext(image_url)[-1] # Ex: ".png"
+ extension = extension_with_dot[1:] # Ex: "png"
+
+ file_type = get_file_type_from_extension(extension)
+
+ # 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}")
+
+ 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:
- # 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)
+
+ # Base64 encoding
elif "base64" in image_url:
- # Case 4: Images with base64 encoding
import base64, re
# base 64 is passed as data:image/jpeg;base64,
@@ -390,7 +398,7 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
assistant_content.extend(_parts)
elif messages[msg_i].get(
"tool_calls", []
- ): # support assistant tool invoke convertion
+ ): # support assistant tool invoke conversion
assistant_content.extend(
convert_to_gemini_tool_call_invoke(messages[msg_i]["tool_calls"])
)
@@ -421,110 +429,17 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
return contents
-def _gemini_vision_convert_messages(messages: list):
- """
- Converts given messages for GPT-4 Vision to Gemini format.
+def _get_client_cache_key(model: str, vertex_project: str, vertex_location: str):
+ _cache_key = f"{model}-{vertex_project}-{vertex_location}"
+ return _cache_key
- 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).
+def _get_client_from_cache(client_cache_key: str):
+ return litellm.in_memory_llm_clients_cache.get(client_cache_key, None)
- 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,
- )
-
- # 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
-
- # 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)
- 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
- except Exception as e:
- raise e
+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(
@@ -580,23 +495,32 @@ def completion(
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 +544,9 @@ 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 +563,27 @@ 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":
@@ -1034,6 +962,15 @@ 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)
):
diff --git a/litellm/main.py b/litellm/main.py
index 37fc1db8f..2c906e990 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -79,7 +79,7 @@ from .llms.anthropic import AnthropicChatCompletion
from .llms.anthropic_text import AnthropicTextCompletion
from .llms.huggingface_restapi import Huggingface
from .llms.predibase import PredibaseChatCompletion
-from .llms.bedrock_httpx import BedrockLLM
+from .llms.bedrock_httpx import BedrockLLM, BedrockConverseLLM
from .llms.vertex_httpx import VertexLLM
from .llms.triton import TritonChatCompletion
from .llms.prompt_templates.factory import (
@@ -92,6 +92,7 @@ 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 .types.llms.openai import HttpxBinaryResponseContent
encoding = tiktoken.get_encoding("cl100k_base")
from litellm.utils import (
@@ -121,6 +122,7 @@ huggingface = Huggingface()
predibase_chat_completions = PredibaseChatCompletion()
triton_chat_completions = TritonChatCompletion()
bedrock_chat_completion = BedrockLLM()
+bedrock_converse_chat_completion = BedrockConverseLLM()
vertex_chat_completion = VertexLLM()
####### COMPLETION ENDPOINTS ################
@@ -223,7 +225,7 @@ 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)
@@ -294,6 +296,7 @@ async def acompletion(
"api_version": api_version,
"api_key": api_key,
"model_list": model_list,
+ "extra_headers": extra_headers,
"acompletion": True, # assuming this is a required parameter
}
if custom_llm_provider is None:
@@ -338,6 +341,8 @@ async def acompletion(
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
@@ -360,6 +365,10 @@ 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 - {}".format(str(e))
+ )
+ verbose_logger.debug(traceback.format_exc())
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
@@ -423,12 +432,16 @@ def mock_completion(
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
+ 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", "openai"), # type: ignore
model=model, # type: ignore
request=httpx.Request(method="POST", url="https://api.openai.com/v1/"),
)
+ time_delay = kwargs.get("mock_delay", None)
+ if time_delay is not None:
+ time.sleep(time_delay)
+
model_response = ModelResponse(stream=stream)
if stream is True:
# don't try to access stream object,
@@ -459,16 +472,25 @@ 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")
@@ -679,6 +701,7 @@ def completion(
"region_name",
"allowed_model_region",
"model_config",
+ "fastest_response",
]
default_params = openai_params + litellm_params
@@ -828,6 +851,7 @@ def completion(
logprobs=logprobs,
top_logprobs=top_logprobs,
extra_headers=extra_headers,
+ api_version=api_version,
**non_default_params,
)
@@ -878,6 +902,7 @@ def completion(
mock_response=mock_response,
logging=logging,
acompletion=acompletion,
+ mock_delay=kwargs.get("mock_delay", None),
)
if custom_llm_provider == "azure":
# azure configs
@@ -1924,7 +1949,8 @@ 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")
@@ -1952,12 +1978,13 @@ 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
@@ -2085,22 +2112,40 @@ def completion(
logging_obj=logging,
)
else:
- response = bedrock_chat_completion.completion(
- model=model,
- messages=messages,
- custom_prompt_dict=custom_prompt_dict,
- model_response=model_response,
- print_verbose=print_verbose,
- optional_params=optional_params,
- litellm_params=litellm_params,
- logger_fn=logger_fn,
- encoding=encoding,
- logging_obj=logging,
- extra_headers=extra_headers,
- timeout=timeout,
- acompletion=acompletion,
- client=client,
- )
+ if model.startswith("anthropic"):
+ response = bedrock_converse_chat_completion.completion(
+ model=model,
+ messages=messages,
+ custom_prompt_dict=custom_prompt_dict,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ encoding=encoding,
+ logging_obj=logging,
+ extra_headers=extra_headers,
+ timeout=timeout,
+ acompletion=acompletion,
+ client=client,
+ )
+ else:
+ response = bedrock_chat_completion.completion(
+ model=model,
+ messages=messages,
+ custom_prompt_dict=custom_prompt_dict,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ encoding=encoding,
+ logging_obj=logging,
+ extra_headers=extra_headers,
+ timeout=timeout,
+ acompletion=acompletion,
+ client=client,
+ )
if optional_params.get("stream", False):
## LOGGING
logging.post_call(
@@ -2403,6 +2448,7 @@ def completion(
"top_k": kwargs.get("top_k", 40),
},
},
+ verify=litellm.ssl_verify,
)
response_json = resp.json()
"""
@@ -3712,7 +3758,7 @@ 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.
@@ -3745,6 +3791,8 @@ 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
@@ -3780,7 +3828,7 @@ def image_generation(
litellm_logging_obj=None,
custom_llm_provider=None,
**kwargs,
-):
+) -> ImageResponse:
"""
Maps the https://api.openai.com/v1/images/generations endpoint.
@@ -4112,7 +4160,7 @@ 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,
@@ -4129,6 +4177,24 @@ def transcription(
max_retries=max_retries,
)
elif custom_llm_provider == "openai":
+ 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,
@@ -4138,6 +4204,139 @@ def transcription(
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,
+ 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
+
+ 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 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,
+ )
+
+ 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
@@ -4170,6 +4369,10 @@ 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":
@@ -4260,7 +4463,10 @@ 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]
@@ -4366,7 +4572,7 @@ 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")
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index aab9c9af1..f2b292c92 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -380,6 +380,18 @@
"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,
@@ -692,8 +704,8 @@
"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 +713,8 @@
"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 +723,8 @@
"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 +760,8 @@
"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 +770,63 @@
"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"
},
@@ -1128,6 +1177,24 @@
"supports_tool_choice": 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_token": 0,
+ "output_cost_per_token": 0,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "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-preview-0514": {
"max_tokens": 8192,
"max_input_tokens": 1000000,
@@ -1146,6 +1213,18 @@
"supports_vision": 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,
+ "litellm_provider": "vertex_ai-language-models",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_tool_choice": 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,
@@ -1265,8 +1344,8 @@
"max_tokens": 4096,
"max_input_tokens": 200000,
"max_output_tokens": 4096,
- "input_cost_per_token": 0.0000015,
- "output_cost_per_token": 0.0000075,
+ "input_cost_per_token": 0.000015,
+ "output_cost_per_token": 0.000075,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
@@ -1421,7 +1500,7 @@
"max_pdf_size_mb": 30,
"input_cost_per_token": 0,
"output_cost_per_token": 0,
- "litellm_provider": "vertex_ai-language-models",
+ "litellm_provider": "gemini",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
@@ -2930,32 +3009,37 @@
"litellm_provider": "sagemaker",
"mode": "chat"
},
- "together-ai-up-to-3b": {
+ "together-ai-up-to-4b": {
"input_cost_per_token": 0.0000001,
"output_cost_per_token": 0.0000001,
"litellm_provider": "together_ai"
},
- "together-ai-3.1b-7b": {
+ "together-ai-4.1b-8b": {
"input_cost_per_token": 0.0000002,
"output_cost_per_token": 0.0000002,
"litellm_provider": "together_ai"
},
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t,n;n={"equation-command":{pattern:t=/\\(?:[^a-z()[\]]|[a-z*]+)/i,alias:"regex"}},e.languages.latex={comment:/%.*/,cdata:{pattern:/(\\begin\{((?:lstlisting|verbatim)\*?)\})[\s\S]*?(?=\\end\{\2\})/,lookbehind:!0},equation:[{pattern:/\$\$(?:\\[\s\S]|[^\\$])+\$\$|\$(?:\\[\s\S]|[^\\$])+\$|\\\([\s\S]*?\\\)|\\\[[\s\S]*?\\\]/,inside:n,alias:"string"},{pattern:/(\\begin\{((?:align|eqnarray|equation|gather|math|multline)\*?)\})[\s\S]*?(?=\\end\{\2\})/,lookbehind:!0,inside:n,alias:"string"}],keyword:{pattern:/(\\(?:begin|cite|documentclass|end|label|ref|usepackage)(?:\[[^\]]+\])?\{)[^}]+(?=\})/,lookbehind:!0},url:{pattern:/(\\url\{)[^}]+(?=\})/,lookbehind:!0},headline:{pattern:/(\\(?:chapter|frametitle|paragraph|part|section|subparagraph|subsection|subsubparagraph|subsubsection|subsubsubparagraph)\*?(?:\[[^\]]+\])?\{)[^}]+(?=\})/,lookbehind:!0,alias:"class-name"},function:{pattern:t,alias:"selector"},punctuation:/[[\]{}&]/},e.languages.tex=e.languages.latex,e.languages.context=e.languages.latex}e.exports=t,t.displayName="latex",t.aliases=["tex","context"]},94137:function(e,t,n){var a=n(26483),r=n(12925);function i(e){var 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t(e){e.languages.less=e.languages.extend("css",{comment:[/\/\*[\s\S]*?\*\//,{pattern:/(^|[^\\])\/\/.*/,lookbehind:!0}],atrule:{pattern:/@[\w-](?:\((?:[^(){}]|\([^(){}]*\))*\)|[^(){};\s]|\s+(?!\s))*?(?=\s*\{)/,inside:{punctuation:/[:()]/}},selector:{pattern:/(?:@\{[\w-]+\}|[^{};\s@])(?:@\{[\w-]+\}|\((?:[^(){}]|\([^(){}]*\))*\)|[^(){};@\s]|\s+(?!\s))*?(?=\s*\{)/,inside:{variable:/@+[\w-]+/}},property:/(?:@\{[\w-]+\}|[\w-])+(?:\+_?)?(?=\s*:)/,operator:/[+\-*\/]/}),e.languages.insertBefore("less","property",{variable:[{pattern:/@[\w-]+\s*:/,inside:{punctuation:/:/}},/@@?[\w-]+/],"mixin-usage":{pattern:/([{;]\s*)[.#](?!\d)[\w-].*?(?=[(;])/,lookbehind:!0,alias:"function"}})}e.exports=t,t.displayName="less",t.aliases=[]},72968:function(e,t,n){var a=n(30876);function r(e){e.register(a),function(e){for(var t=/\((?:[^();"#\\]|\\[\s\S]|;.*(?!.)|"(?:[^"\\]|\\.)*"|#(?:\{(?:(?!#\})[\s\S])*#\}|[^{])|)*\)/.source,n=0;n<5;n++)t=t.replace(//g,function(){return t});t=t.replace(//g,/[^\s\S]/.source);var 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