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Merge branch 'main' into litellm_fix_httpx_transport

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Krish Dholakia 2024-07-06 19:12:06 -07:00 • committed by GitHub
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10
.github/workflows/ghcr_deploy.yml vendored
View file

@ -289,7 +289,8 @@ jobs:
repo: context.repo.repo, repo: context.repo.repo,
release_id: process.env.RELEASE_ID, release_id: process.env.RELEASE_ID,
}); });
return response.data.body; const formattedBody = JSON.stringify(response.data.body).slice(1, -1);
return formattedBody;
} catch (error) { } catch (error) {
core.setFailed(error.message); core.setFailed(error.message);
} }
@ -302,14 +303,15 @@ jobs:
RELEASE_NOTES: ${{ steps.release-notes.outputs.result }} RELEASE_NOTES: ${{ steps.release-notes.outputs.result }}
run: | run: |
curl -H "Content-Type: application/json" -X POST -d '{ curl -H "Content-Type: application/json" -X POST -d '{
"content": "New LiteLLM release ${{ env.RELEASE_TAG }}", "content": "New LiteLLM release '"${RELEASE_TAG}"'",
"username": "Release Changelog", "username": "Release Changelog",
"avatar_url": "https://cdn.discordapp.com/avatars/487431320314576937/bd64361e4ba6313d561d54e78c9e7171.png", "avatar_url": "https://cdn.discordapp.com/avatars/487431320314576937/bd64361e4ba6313d561d54e78c9e7171.png",
"embeds": [ "embeds": [
{ {
"title": "Changelog for LiteLLM ${{ env.RELEASE_TAG }}", "title": "Changelog for LiteLLM '"${RELEASE_TAG}"'",
"description": "${{ env.RELEASE_NOTES }}", "description": "'"${RELEASE_NOTES}"'",
"color": 2105893 "color": 2105893
} }
] ]
}' $WEBHOOK_URL }' $WEBHOOK_URL

4
.pre-commit-config.yaml
View file

@ -25,6 +25,10 @@ repos:
exclude: ^litellm/tests/|^litellm/proxy/tests/ exclude: ^litellm/tests/|^litellm/proxy/tests/
additional_dependencies: [flake8-print] additional_dependencies: [flake8-print]
files: litellm/.*\.py files: litellm/.*\.py
- repo: https://github.com/python-poetry/poetry
rev: 1.8.0
hooks:
- id: poetry-check
- repo: local - repo: local
hooks: hooks:
- id: check-files-match - id: check-files-match

594
cookbook/litellm_proxy_server/grafana_dashboard/dashboard_1/grafana_dashboard.json Normal file
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@ -0,0 +1,594 @@
{
"annotations": {
"list": [
{
"builtIn": 1,
"datasource": {
"type": "grafana",
"uid": "-- Grafana --"
},
"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
"target": {
"limit": 100,
"matchAny": false,
"tags": [],
"type": "dashboard"
},
"type": "dashboard"
}
]
},
"description": "",
"editable": true,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"id": 2039,
"links": [],
"liveNow": false,
"panels": [
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "s"
},
"overrides": []
},
"gridPos": {
"h": 8,
"w": 12,
"x": 0,
"y": 0
},
"id": 10,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom",
"showLegend": true
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"editorMode": "code",
"expr": "histogram_quantile(0.99, sum(rate(litellm_self_latency_bucket{self=\"self\"}[1m])) by (le))",
"legendFormat": "Time to first token",
"range": true,
"refId": "A"
}
],
"title": "Time to first token (latency)",
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "currencyUSD"
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "7e4b0627fd32efdd2313c846325575808aadcf2839f0fde90723aab9ab73c78f"
},
"properties": [
{
"id": "displayName",
"value": "Translata"
}
]
}
]
},
"gridPos": {
"h": 8,
"w": 12,
"x": 0,
"y": 8
},
"id": 11,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom",
"showLegend": true
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"editorMode": "code",
"expr": "sum(increase(litellm_spend_metric_total[30d])) by (hashed_api_key)",
"legendFormat": "{{team}}",
"range": true,
"refId": "A"
}
],
"title": "Spend by team",
"transformations": [],
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 16
},
"id": 2,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom",
"showLegend": true
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"editorMode": "code",
"expr": "sum by (model) (increase(litellm_requests_metric_total[5m]))",
"legendFormat": "{{model}}",
"range": true,
"refId": "A"
}
],
"title": "Requests by model",
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"noValue": "0",
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
"overrides": []
},
"gridPos": {
"h": 7,
"w": 3,
"x": 0,
"y": 25
},
"id": 8,
"options": {
"colorMode": "value",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"textMode": "auto"
},
"pluginVersion": "9.4.17",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"editorMode": "code",
"expr": "sum(increase(litellm_llm_api_failed_requests_metric_total[1h]))",
"legendFormat": "__auto",
"range": true,
"refId": "A"
}
],
"title": "Faild Requests",
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "currencyUSD"
},
"overrides": []
},
"gridPos": {
"h": 7,
"w": 3,
"x": 3,
"y": 25
},
"id": 6,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom",
"showLegend": true
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"editorMode": "code",
"expr": "sum(increase(litellm_spend_metric_total[30d])) by (model)",
"legendFormat": "{{model}}",
"range": true,
"refId": "A"
}
],
"title": "Spend",
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 0,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineInterpolation": "linear",
"lineWidth": 1,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
"overrides": []
},
"gridPos": {
"h": 7,
"w": 6,
"x": 6,
"y": 25
},
"id": 4,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom",
"showLegend": true
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "rMzWaBvIk"
},
"editorMode": "code",
"expr": "sum(increase(litellm_total_tokens_total[5m])) by (model)",
"legendFormat": "__auto",
"range": true,
"refId": "A"
}
],
"title": "Tokens",
"type": "timeseries"
}
],
"refresh": "1m",
"revision": 1,
"schemaVersion": 38,
"style": "dark",
"tags": [],
"templating": {
"list": []
},
"time": {
"from": "now-1h",
"to": "now"
},
"timepicker": {},
"timezone": "",
"title": "LLM Proxy",
"uid": "rgRrHxESz",
"version": 15,
"weekStart": ""
}

6
cookbook/litellm_proxy_server/grafana_dashboard/dashboard_1/readme.md Normal file
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@ -0,0 +1,6 @@
## This folder contains the `json` for creating the following Grafana Dashboard
### Pre-Requisites
- Setup LiteLLM Proxy Prometheus Metrics https://docs.litellm.ai/docs/proxy/prometheus
![1716623265684](https://github.com/BerriAI/litellm/assets/29436595/0e12c57e-4a2d-4850-bd4f-e4294f87a814)

6
cookbook/litellm_proxy_server/grafana_dashboard/readme.md Normal file
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@ -0,0 +1,6 @@
## Contains example Grafana Dashboard made for LiteLLM Proxy Server
This folder contains the `json` for creating Grafana Dashboards
### Pre-Requisites
- Setup LiteLLM Proxy Prometheus Metrics https://docs.litellm.ai/docs/proxy/prometheus

0
cookbook/proxy-server/readme.md → cookbook/litellm_proxy_server/readme.md
View file

72
cookbook/misc/add_new_models.py Normal file
View file

@ -0,0 +1,72 @@
import requests
import json
def get_initial_config():
proxy_base_url = input("Enter your proxy base URL (e.g., http://localhost:4000): ")
master_key = input("Enter your LITELLM_MASTER_KEY ")
return proxy_base_url, master_key
def get_user_input():
model_name = input(
"Enter model_name (this is the 'model' passed in /chat/completions requests):"
)
model = input("litellm_params: Enter model eg. 'azure/<your-deployment-name>': ")
tpm = int(input("litellm_params: Enter tpm (tokens per minute): "))
rpm = int(input("litellm_params: Enter rpm (requests per minute): "))
api_key = input("litellm_params: Enter api_key: ")
api_base = input("litellm_params: Enter api_base: ")
api_version = input("litellm_params: Enter api_version: ")
timeout = int(input("litellm_params: Enter timeout (0 for default): "))
stream_timeout = int(
input("litellm_params: Enter stream_timeout (0 for default): ")
)
max_retries = int(input("litellm_params: Enter max_retries (0 for default): "))
return {
"model_name": model_name,
"litellm_params": {
"model": model,
"tpm": tpm,
"rpm": rpm,
"api_key": api_key,
"api_base": api_base,
"api_version": api_version,
"timeout": timeout,
"stream_timeout": stream_timeout,
"max_retries": max_retries,
},
}
def make_request(proxy_base_url, master_key, data):
url = f"{proxy_base_url}/model/new"
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {master_key}",
}
response = requests.post(url, headers=headers, json=data)
print(f"Status Code: {response.status_code}")
print(f"Response from adding model: {response.text}")
def main():
proxy_base_url, master_key = get_initial_config()
while True:
print("Adding new Model to your proxy server...")
data = get_user_input()
make_request(proxy_base_url, master_key, data)
add_another = input("Do you want to add another model? (yes/no): ").lower()
if add_another != "yes":
break
print("Script finished.")
if __name__ == "__main__":
main()

2
docs/my-website/docs/completion/vision.md
View file

@ -39,7 +39,7 @@ Use `litellm.supports_vision(model="")` -> returns `True` if model supports `vis
```python ```python
assert litellm.supports_vision(model="gpt-4-vision-preview") == True assert litellm.supports_vision(model="gpt-4-vision-preview") == True
assert litellm.supports_vision(model="gemini-1.0-pro-visionn") == True assert litellm.supports_vision(model="gemini-1.0-pro-vision") == True
assert litellm.supports_vision(model="gpt-3.5-turbo") == False assert litellm.supports_vision(model="gpt-3.5-turbo") == False
``` ```

20
docs/my-website/docs/enterprise.md
View file

@ -7,6 +7,17 @@ Interested in Enterprise? Schedule a meeting with us here 👉
::: :::
## [AWS Marketplace Listing](https://aws.amazon.com/marketplace/pp/prodview-gdm3gswgjhgjo?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)
Deploy managed LiteLLM Proxy within your VPC.
Includes all enterprise features.
[**View Listing**](https://aws.amazon.com/marketplace/pp/prodview-gdm3gswgjhgjo?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)
[**Get early access**](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
This covers: This covers:
- **Enterprise Features** - **Enterprise Features**
- **Security** - **Security**
@ -37,15 +48,6 @@ This covers:
## [COMING SOON] AWS Marketplace Support
Deploy managed LiteLLM Proxy within your VPC.
Includes all enterprise features.
[**Get early access**](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
## Frequently Asked Questions ## Frequently Asked Questions
### What topics does Professional support cover and what SLAs do you offer? ### What topics does Professional support cover and what SLAs do you offer?

17
docs/my-website/docs/providers/groq.md
View file

@ -158,3 +158,20 @@ if tool_calls:
) # get a new response from the model where it can see the function response ) # get a new response from the model where it can see the function response
print("second response\n", second_response) print("second response\n", second_response)
``` ```
## Speech to Text - Whisper
```python
os.environ["GROQ_API_KEY"] = ""
audio_file = open("/path/to/audio.mp3", "rb")
transcript = litellm.transcription(
model="groq/whisper-large-v3",
file=audio_file,
prompt="Specify context or spelling",
temperature=0,
response_format="json"
)
print("response=", transcript)
```

132
docs/my-website/docs/proxy/cost_tracking.md
View file

@ -151,12 +151,9 @@ Navigate to the Usage Tab on the LiteLLM UI (found on https://your-proxy-endpoin
</Tabs> </Tabs>
## ✨ (Enterprise) API Endpoints to get Spend ## ✨ (Enterprise) API Endpoints to get Spend
#### Getting Spend Reports - To Charge Other Teams, Customers #### Getting Spend Reports - To Charge Other Teams, Customers, Users
Use the `/global/spend/report` endpoint to get daily spend report per Use the `/global/spend/report` endpoint to get spend reports
- Team
- Customer [this is `user` passed to `/chat/completions` request](#how-to-track-spend-with-litellm)
- [LiteLLM API key](virtual_keys.md)
<Tabs> <Tabs>
@ -285,6 +282,16 @@ Output from script
<TabItem value="per customer" label="Spend Per Customer"> <TabItem value="per customer" label="Spend Per Customer">
:::info
Customer This is the value of `user_id` passed when calling [`/key/generate`](https://litellm-api.up.railway.app/#/key%20management/generate_key_fn_key_generate_post)
[this is `user` passed to `/chat/completions` request](#how-to-track-spend-with-litellm)
- [LiteLLM API key](virtual_keys.md)
:::
##### Example Request ##### Example Request
👉 Key Change: Specify `group_by=customer` 👉 Key Change: Specify `group_by=customer`
@ -341,14 +348,14 @@ curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end
</TabItem> </TabItem>
<TabItem value="per key" label="Spend Per API Key"> <TabItem value="per key" label="Spend for Specific API Key">
👉 Key Change: Specify `group_by=api_key` 👉 Key Change: Specify `api_key=sk-1234`
```shell ```shell
curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30&group_by=api_key' \ curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30&api_key=sk-1234' \
-H 'Authorization: Bearer sk-1234' -H 'Authorization: Bearer sk-1234'
``` ```
@ -357,32 +364,18 @@ curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end
```shell ```shell
[ [
{
"api_key": "ad64768847d05d978d62f623d872bff0f9616cc14b9c1e651c84d14fe3b9f539",
"total_cost": 0.0002157,
"total_input_tokens": 45.0,
"total_output_tokens": 1375.0,
"model_details": [
{
"model": "gpt-3.5-turbo",
"total_cost": 0.0001095,
"total_input_tokens": 9,
"total_output_tokens": 70
},
{
"model": "llama3-8b-8192",
"total_cost": 0.0001062,
"total_input_tokens": 36,
"total_output_tokens": 1305
}
]
},
{ {
"api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b", "api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
"total_cost": 0.00012924, "total_cost": 0.3201286305151999,
"total_input_tokens": 36.0, "total_input_tokens": 36.0,
"total_output_tokens": 1593.0, "total_output_tokens": 1593.0,
"model_details": [ "model_details": [
{
"model": "dall-e-3",
"total_cost": 0.31999939051519993,
"total_input_tokens": 0,
"total_output_tokens": 0
},
{ {
"model": "llama3-8b-8192", "model": "llama3-8b-8192",
"total_cost": 0.00012924, "total_cost": 0.00012924,
@ -396,6 +389,87 @@ curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end
</TabItem> </TabItem>
<TabItem value="per user" label="Spend for Internal User (Key Owner)">
:::info
Internal User (Key Owner): This is the value of `user_id` passed when calling [`/key/generate`](https://litellm-api.up.railway.app/#/key%20management/generate_key_fn_key_generate_post)
:::
👉 Key Change: Specify `internal_user_id=ishaan`
```shell
curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end_date=2024-12-30&internal_user_id=ishaan' \
-H 'Authorization: Bearer sk-1234'
```
##### Example Response
```shell
[
{
"api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
"total_cost": 0.00013132,
"total_input_tokens": 105.0,
"total_output_tokens": 872.0,
"model_details": [
{
"model": "gpt-3.5-turbo-instruct",
"total_cost": 5.85e-05,
"total_input_tokens": 15,
"total_output_tokens": 18
},
{
"model": "llama3-8b-8192",
"total_cost": 7.282000000000001e-05,
"total_input_tokens": 90,
"total_output_tokens": 854
}
]
},
{
"api_key": "151e85e46ab8c9c7fad090793e3fe87940213f6ae665b543ca633b0b85ba6dc6",
"total_cost": 5.2699999999999993e-05,
"total_input_tokens": 26.0,
"total_output_tokens": 27.0,
"model_details": [
{
"model": "gpt-3.5-turbo",
"total_cost": 5.2499999999999995e-05,
"total_input_tokens": 24,
"total_output_tokens": 27
},
{
"model": "text-embedding-ada-002",
"total_cost": 2e-07,
"total_input_tokens": 2,
"total_output_tokens": 0
}
]
},
{
"api_key": "60cb83a2dcbf13531bd27a25f83546ecdb25a1a6deebe62d007999dc00e1e32a",
"total_cost": 9.42e-06,
"total_input_tokens": 30.0,
"total_output_tokens": 99.0,
"model_details": [
{
"model": "llama3-8b-8192",
"total_cost": 9.42e-06,
"total_input_tokens": 30,
"total_output_tokens": 99
}
]
}
]
```
</TabItem>
</Tabs> </Tabs>
#### Allowing Non-Proxy Admins to access `/spend` endpoints #### Allowing Non-Proxy Admins to access `/spend` endpoints

88
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@ -28,6 +28,7 @@ Features:
- **Guardrails, PII Masking, Content Moderation** - **Guardrails, PII Masking, Content Moderation**
- ✅ [Content Moderation with LLM Guard, LlamaGuard, Secret Detection, Google Text Moderations](#content-moderation) - ✅ [Content Moderation with LLM Guard, LlamaGuard, Secret Detection, Google Text Moderations](#content-moderation)
- ✅ [Prompt Injection Detection (with LakeraAI API)](#prompt-injection-detection---lakeraai) - ✅ [Prompt Injection Detection (with LakeraAI API)](#prompt-injection-detection---lakeraai)
- ✅ [Switch LakeraAI on / off per request](guardrails#control-guardrails-onoff-per-request)
- ✅ Reject calls from Blocked User list - ✅ Reject calls from Blocked User list
- ✅ Reject calls (incoming / outgoing) with Banned Keywords (e.g. competitors) - ✅ Reject calls (incoming / outgoing) with Banned Keywords (e.g. competitors)
- **Custom Branding** - **Custom Branding**
@ -505,10 +506,7 @@ curl --request POST \
🎉 Expect this endpoint to work without an `Authorization / Bearer Token` 🎉 Expect this endpoint to work without an `Authorization / Bearer Token`
## Guardrails - Secret Detection/Redaction
## Content Moderation
### Content Moderation - Secret Detection
❓ Use this to REDACT API Keys, Secrets sent in requests to an LLM. ❓ Use this to REDACT API Keys, Secrets sent in requests to an LLM.
Example if you want to redact the value of `OPENAI_API_KEY` in the following request Example if you want to redact the value of `OPENAI_API_KEY` in the following request
@ -599,6 +597,77 @@ https://api.groq.com/openai/v1/ \
} }
``` ```
### Secret Detection On/Off per API Key
❓ Use this when you need to switch guardrails on/off per API Key
**Step 1** Create Key with `hide_secrets` Off
👉 Set `"permissions": {"hide_secrets": false}` with either `/key/generate` or `/key/update`
This means the `hide_secrets` guardrail is off for all requests from this API Key
<Tabs>
<TabItem value="/key/generate" label="/key/generate">
```shell
curl --location 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"permissions": {"hide_secrets": false}
}'
```
```shell
# {"permissions":{"hide_secrets":false},"key":"sk-jNm1Zar7XfNdZXp49Z1kSQ"}
```
</TabItem>
<TabItem value="/key/update" label="/key/update">
```shell
curl --location 'http://0.0.0.0:4000/key/update' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"key": "sk-jNm1Zar7XfNdZXp49Z1kSQ",
"permissions": {"hide_secrets": false}
}'
```
```shell
# {"permissions":{"hide_secrets":false},"key":"sk-jNm1Zar7XfNdZXp49Z1kSQ"}
```
</TabItem>
</Tabs>
**Step 2** Test it with new key
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-jNm1Zar7XfNdZXp49Z1kSQ' \
--header 'Content-Type: application/json' \
--data '{
"model": "llama3",
"messages": [
{
"role": "user",
"content": "does my openai key look well formatted OpenAI_API_KEY=sk-1234777"
}
]
}'
```
Expect to see `sk-1234777` in your server logs on your callback.
:::info
The `hide_secrets` guardrail check did not run on this request because api key=sk-jNm1Zar7XfNdZXp49Z1kSQ has `"permissions": {"hide_secrets": false}`
:::
## Content Moderation
### Content Moderation with LLM Guard ### Content Moderation with LLM Guard
Set the LLM Guard API Base in your environment Set the LLM Guard API Base in your environment
@ -876,6 +945,11 @@ curl --location 'http://localhost:4000/chat/completions' \
}' }'
``` ```
:::info
Need to control LakeraAI per Request ? Doc here 👉: [Switch LakerAI on / off per request](prompt_injection.md#✨-enterprise-switch-lakeraai-on--off-per-api-call)
:::
## Swagger Docs - Custom Routes + Branding ## Swagger Docs - Custom Routes + Branding
:::info :::info
@ -1046,12 +1120,14 @@ This is a beta feature, and subject to changes.
USE_AWS_KMS="True" USE_AWS_KMS="True"
``` ```
**Step 2.** Add `aws_kms/` to encrypted keys in env **Step 2.** Add `LITELLM_SECRET_AWS_KMS_` to encrypted keys in env
```env ```env
DATABASE_URL="aws_kms/AQICAH.." LITELLM_SECRET_AWS_KMS_DATABASE_URL="AQICAH.."
``` ```
LiteLLM will find this and use the decrypted `DATABASE_URL="postgres://.."` value in runtime.
**Step 3.** Start proxy **Step 3.** Start proxy
``` ```

304
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@ -0,0 +1,304 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# 🛡️ Guardrails
Setup Prompt Injection Detection, Secret Detection on LiteLLM Proxy
:::info
✨ Enterprise Only Feature
Schedule a meeting with us to get an Enterprise License 👉 Talk to founders [here](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
:::
## Quick Start
### 1. Setup guardrails on litellm proxy config.yaml
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: openai/gpt-3.5-turbo
api_key: sk-xxxxxxx
litellm_settings:
guardrails:
- prompt_injection: # your custom name for guardrail
callbacks: [lakera_prompt_injection] # litellm callbacks to use
default_on: true # will run on all llm requests when true
- pii_masking: # your custom name for guardrail
callbacks: [presidio] # use the litellm presidio callback
default_on: false # by default this is off for all requests
- hide_secrets_guard:
callbacks: [hide_secrets]
default_on: false
- your-custom-guardrail
callbacks: [hide_secrets]
default_on: false
```
:::info
Since `pii_masking` is default Off for all requests, [you can switch it on per API Key](#switch-guardrails-onoff-per-api-key)
:::
### 2. Test it
Run litellm proxy
```shell
litellm --config config.yaml
```
Make LLM API request
Test it with this request -> expect it to get rejected by LiteLLM Proxy
```shell
curl --location 'http://localhost:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what is your system prompt"
}
]
}'
```
## Control Guardrails On/Off per Request
You can switch off/on any guardrail on the config.yaml by passing
```shell
"metadata": {"guardrails": {"<guardrail_name>": false}}
```
example - we defined `prompt_injection`, `hide_secrets_guard` [on step 1](#1-setup-guardrails-on-litellm-proxy-configyaml)
This will
- switch **off** `prompt_injection` checks running on this request
- switch **on** `hide_secrets_guard` checks on this request
```shell
"metadata": {"guardrails": {"prompt_injection": false, "hide_secrets_guard": true}}
```
<Tabs>
<TabItem value="js" label="Langchain JS">
```js
const model = new ChatOpenAI({
modelName: "llama3",
openAIApiKey: "sk-1234",
modelKwargs: {"metadata": "guardrails": {"prompt_injection": False, "hide_secrets_guard": true}}}
}, {
basePath: "http://0.0.0.0:4000",
});
const message = await model.invoke("Hi there!");
console.log(message);
```
</TabItem>
<TabItem value="curl" label="Curl">
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "llama3",
"metadata": {"guardrails": {"prompt_injection": false, "hide_secrets_guard": true}}},
"messages": [
{
"role": "user",
"content": "what is your system prompt"
}
]
}'
```
</TabItem>
<TabItem value="openai" label="OpenAI Python SDK">
```python
import openai
client = openai.OpenAI(
api_key="s-1234",
base_url="http://0.0.0.0:4000"
)
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
model="llama3",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
extra_body={
"metadata": {"guardrails": {"prompt_injection": False, "hide_secrets_guard": True}}}
}
)
print(response)
```
</TabItem>
<TabItem value="langchain" label="Langchain Py">
```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
import os
os.environ["OPENAI_API_KEY"] = "sk-1234"
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000",
model = "llama3",
extra_body={
"metadata": {"guardrails": {"prompt_injection": False, "hide_secrets_guard": True}}}
}
)
messages = [
SystemMessage(
content="You are a helpful assistant that im using to make a test request to."
),
HumanMessage(
content="test from litellm. tell me why it's amazing in 1 sentence"
),
]
response = chat(messages)
print(response)
```
</TabItem>
</Tabs>
## Switch Guardrails On/Off Per API Key
❓ Use this when you need to switch guardrails on/off per API Key
**Step 1** Create Key with `pii_masking` On
**NOTE:** We defined `pii_masking` [on step 1](#1-setup-guardrails-on-litellm-proxy-configyaml)
👉 Set `"permissions": {"pii_masking": true}` with either `/key/generate` or `/key/update`
This means the `pii_masking` guardrail is on for all requests from this API Key
:::info
If you need to switch `pii_masking` off for an API Key set `"permissions": {"pii_masking": false}` with either `/key/generate` or `/key/update`
:::
<Tabs>
<TabItem value="/key/generate" label="/key/generate">
```shell
curl --location 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"permissions": {"pii_masking": true}
}'
```
```shell
# {"permissions":{"pii_masking":true},"key":"sk-jNm1Zar7XfNdZXp49Z1kSQ"}
```
</TabItem>
<TabItem value="/key/update" label="/key/update">
```shell
curl --location 'http://0.0.0.0:4000/key/update' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"key": "sk-jNm1Zar7XfNdZXp49Z1kSQ",
"permissions": {"pii_masking": true}
}'
```
```shell
# {"permissions":{"pii_masking":true},"key":"sk-jNm1Zar7XfNdZXp49Z1kSQ"}
```
</TabItem>
</Tabs>
**Step 2** Test it with new key
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-jNm1Zar7XfNdZXp49Z1kSQ' \
--header 'Content-Type: application/json' \
--data '{
"model": "llama3",
"messages": [
{
"role": "user",
"content": "does my phone number look correct - +1 412-612-9992"
}
]
}'
```
Expect to NOT see `+1 412-612-9992` in your server logs on your callback.
:::info
The `pii_masking` guardrail ran on this request because api key=sk-jNm1Zar7XfNdZXp49Z1kSQ has `"permissions": {"pii_masking": true}`
:::
## Spec for `guardrails` on litellm config
```yaml
litellm_settings:
guardrails:
- prompt_injection: # your custom name for guardrail
callbacks: [lakera_prompt_injection, hide_secrets, llmguard_moderations, llamaguard_moderations, google_text_moderation] # litellm callbacks to use
default_on: true # will run on all llm requests when true
- hide_secrets:
callbacks: [hide_secrets]
default_on: true
- your-custom-guardrail
callbacks: [hide_secrets]
default_on: false
```
### `guardrails`: List of guardrail configurations to be applied to LLM requests.
#### Guardrail: `prompt_injection`: Configuration for detecting and preventing prompt injection attacks.
- `callbacks`: List of LiteLLM callbacks used for this guardrail. [Can be one of `[lakera_prompt_injection, hide_secrets, presidio, llmguard_moderations, llamaguard_moderations, google_text_moderation]`](enterprise#content-moderation)
- `default_on`: Boolean flag determining if this guardrail runs on all LLM requests by default.
#### Guardrail: `your-custom-guardrail`: Configuration for a user-defined custom guardrail.
- `callbacks`: List of callbacks for this custom guardrail. Can be one of `[lakera_prompt_injection, hide_secrets, presidio, llmguard_moderations, llamaguard_moderations, google_text_moderation]`
- `default_on`: Boolean flag determining if this custom guardrail runs by default, set to false.

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@ -7,10 +7,13 @@ import TabItem from '@theme/TabItem';
Log Proxy Input, Output, Exceptions using Langfuse, OpenTelemetry, Custom Callbacks, DataDog, DynamoDB, s3 Bucket Log Proxy Input, Output, Exceptions using Langfuse, OpenTelemetry, Custom Callbacks, DataDog, DynamoDB, s3 Bucket
## Table of Contents
- [Logging to Langfuse](#logging-proxy-inputoutput---langfuse) - [Logging to Langfuse](#logging-proxy-inputoutput---langfuse)
- [Logging with OpenTelemetry (OpenTelemetry)](#logging-proxy-inputoutput-in-opentelemetry-format) - [Logging with OpenTelemetry (OpenTelemetry)](#logging-proxy-inputoutput-in-opentelemetry-format)
- [Async Custom Callbacks](#custom-callback-class-async) - [Async Custom Callbacks](#custom-callback-class-async)
- [Async Custom Callback APIs](#custom-callback-apis-async) - [Async Custom Callback APIs](#custom-callback-apis-async)
- [Logging to Galileo](#logging-llm-io-to-galileo)
- [Logging to OpenMeter](#logging-proxy-inputoutput---langfuse) - [Logging to OpenMeter](#logging-proxy-inputoutput---langfuse)
- [Logging to s3 Buckets](#logging-proxy-inputoutput---s3-buckets) - [Logging to s3 Buckets](#logging-proxy-inputoutput---s3-buckets)
- [Logging to DataDog](#logging-proxy-inputoutput---datadog) - [Logging to DataDog](#logging-proxy-inputoutput---datadog)
@ -1056,6 +1059,68 @@ litellm_settings:
Start the LiteLLM Proxy and make a test request to verify the logs reached your callback API Start the LiteLLM Proxy and make a test request to verify the logs reached your callback API
## Logging LLM IO to Galileo
[BETA]
Log LLM I/O on [www.rungalileo.io](https://www.rungalileo.io/)
:::info
Beta Integration
:::
**Required Env Variables**
```bash
export GALILEO_BASE_URL="" # For most users, this is the same as their console URL except with the word 'console' replaced by 'api' (e.g. http://www.console.galileo.myenterprise.com -> http://www.api.galileo.myenterprise.com)
export GALILEO_PROJECT_ID=""
export GALILEO_USERNAME=""
export GALILEO_PASSWORD=""
```
### Quick Start
1. Add to Config.yaml
```yaml
model_list:
- litellm_params:
api_base: https://exampleopenaiendpoint-production.up.railway.app/
api_key: my-fake-key
model: openai/my-fake-model
model_name: fake-openai-endpoint
litellm_settings:
success_callback: ["galileo"] # 👈 KEY CHANGE
```
2. Start Proxy
```
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "fake-openai-endpoint",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}
'
```
🎉 That's it - Expect to see your Logs on your Galileo Dashboard
## Logging Proxy Cost + Usage - OpenMeter ## Logging Proxy Cost + Usage - OpenMeter
Bill customers according to their LLM API usage with [OpenMeter](../observability/openmeter.md) Bill customers according to their LLM API usage with [OpenMeter](../observability/openmeter.md)

6
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@ -132,3 +132,9 @@ litellm_settings:
| `litellm_redis_latency` | histogram latency for redis calls | | `litellm_redis_latency` | histogram latency for redis calls |
| `litellm_redis_fails` | Number of failed redis calls | | `litellm_redis_fails` | Number of failed redis calls |
| `litellm_self_latency` | Histogram latency for successful litellm api call | | `litellm_self_latency` | Histogram latency for successful litellm api call |
## 🔥 Community Maintained Grafana Dashboards
Link to Grafana Dashboards made by LiteLLM community
https://github.com/BerriAI/litellm/tree/main/cookbook/litellm_proxy_server/grafana_dashboard

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@ -1,12 +1,15 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# 🕵️ Prompt Injection Detection # 🕵️ Prompt Injection Detection
LiteLLM Supports the following methods for detecting prompt injection attacks LiteLLM Supports the following methods for detecting prompt injection attacks
- [Using Lakera AI API](#lakeraai) - [Using Lakera AI API](#✨-enterprise-lakeraai)
- [Similarity Checks](#similarity-checking) - [Similarity Checks](#similarity-checking)
- [LLM API Call to check](#llm-api-checks) - [LLM API Call to check](#llm-api-checks)
## LakeraAI ## ✨ [Enterprise] LakeraAI
Use this if you want to reject /chat, /completions, /embeddings calls that have prompt injection attacks Use this if you want to reject /chat, /completions, /embeddings calls that have prompt injection attacks

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@ -152,11 +152,11 @@ litellm_remaining_team_budget_metric{team_alias="QA Prod Bot",team_id="de35b29e-
``` ```
### Dynamic TPM Allocation ### Dynamic TPM/RPM Allocation
Prevent projects from gobbling too much quota. Prevent projects from gobbling too much tpm/rpm.
Dynamically allocate TPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125) Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125)
1. Setup config.yaml 1. Setup config.yaml
@ -248,3 +248,89 @@ except RateLimitError as e:
``` ```
This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key=<hashed_token> over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}} This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key=<hashed_token> over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}}
``` ```
#### ✨ [BETA] Set Priority / Reserve Quota
Reserve tpm/rpm capacity for projects in prod.
:::tip
Reserving tpm/rpm on keys based on priority is a premium feature. Please [get an enterprise license](./enterprise.md) for it.
:::
1. Setup config.yaml
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: "gpt-3.5-turbo"
api_key: os.environ/OPENAI_API_KEY
rpm: 100
litellm_settings:
callbacks: ["dynamic_rate_limiter"]
priority_reservation: {"dev": 0, "prod": 1}
general_settings:
master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env
```
priority_reservation:
- Dict[str, float]
- str: can be any string
- float: from 0 to 1. Specify the % of tpm/rpm to reserve for keys of this priority.
**Start Proxy**
```
litellm --config /path/to/config.yaml
```
2. Create a key with that priority
```bash
curl -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer <your-master-key>' \
-H 'Content-Type: application/json' \
-D '{
"metadata": {"priority": "dev"} # 👈 KEY CHANGE
}'
```
**Expected Response**
```
{
...
"key": "sk-.."
}
```
3. Test it!
```bash
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: sk-...' \ # 👈 key from step 2.
-D '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
**Expected Response**
```
Key=... over available RPM=0. Model RPM=100, Active keys=None
```

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@ -1,7 +1,7 @@
import Tabs from '@theme/Tabs'; import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem'; import TabItem from '@theme/TabItem';
# Use with Langchain, OpenAI SDK, LlamaIndex, Curl # Use with Langchain, OpenAI SDK, LlamaIndex, Instructor, Curl
:::info :::info
@ -173,6 +173,37 @@ console.log(message);
``` ```
</TabItem>
<TabItem value="instructor" label="Instructor">
```python
from openai import OpenAI
import instructor
from pydantic import BaseModel
my_proxy_api_key = "" # e.g. sk-1234
my_proxy_base_url = "" # e.g. http://0.0.0.0:4000
# This enables response_model keyword
# from client.chat.completions.create
client = instructor.from_openai(OpenAI(api_key=my_proxy_api_key, base_url=my_proxy_base_url))
class UserDetail(BaseModel):
name: str
age: int
user = client.chat.completions.create(
model="gemini-pro-flash",
response_model=UserDetail,
messages=[
{"role": "user", "content": "Extract Jason is 25 years old"},
]
)
assert isinstance(user, UserDetail)
assert user.name == "Jason"
assert user.age == 25
```
</TabItem> </TabItem>
</Tabs> </Tabs>
@ -205,6 +236,97 @@ console.log(message);
``` ```
### Function Calling
Here's some examples of doing function calling with the proxy.
You can use the proxy for function calling with **any** openai-compatible project.
<Tabs>
<TabItem value="curl" label="curl">
```bash
curl http://0.0.0.0:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPTIONAL_YOUR_PROXY_KEY" \
-d '{
"model": "gpt-4-turbo",
"messages": [
{
"role": "user",
"content": "What'\''s the weather like in Boston today?"
}
],
"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"]
}
}
}
],
"tool_choice": "auto"
}'
```
</TabItem>
<TabItem value="sdk" label="SDK">
```python
from openai import OpenAI
client = OpenAI(
api_key="sk-1234", # [OPTIONAL] set if you set one on proxy, else set ""
base_url="http://0.0.0.0:4000",
)
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"],
},
}
}
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
completion = client.chat.completions.create(
model="gpt-4o", # use 'model_name' from config.yaml
messages=messages,
tools=tools,
tool_choice="auto"
)
print(completion)
```
</TabItem>
</Tabs>
## `/embeddings` ## `/embeddings`
### Request Format ### Request Format

1
docs/my-website/sidebars.js
View file

@ -48,6 +48,7 @@ const sidebars = {
"proxy/billing", "proxy/billing",
"proxy/user_keys", "proxy/user_keys",
"proxy/virtual_keys", "proxy/virtual_keys",
"proxy/guardrails",
"proxy/token_auth", "proxy/token_auth",
"proxy/alerting", "proxy/alerting",
{ {

21
enterprise/enterprise_hooks/lakera_ai.py
View file

@ -17,12 +17,9 @@ from litellm.proxy._types import UserAPIKeyAuth
from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.custom_logger import CustomLogger
from fastapi import HTTPException from fastapi import HTTPException
from litellm._logging import verbose_proxy_logger from litellm._logging import verbose_proxy_logger
from litellm.utils import ( from litellm.proxy.guardrails.init_guardrails import all_guardrails
ModelResponse, from litellm.proxy.guardrails.guardrail_helpers import should_proceed_based_on_metadata
EmbeddingResponse,
ImageResponse,
StreamingChoices,
)
from datetime import datetime from datetime import datetime
import aiohttp, asyncio import aiohttp, asyncio
from litellm._logging import verbose_proxy_logger from litellm._logging import verbose_proxy_logger
@ -32,6 +29,8 @@ import json
litellm.set_verbose = True litellm.set_verbose = True
GUARDRAIL_NAME = "lakera_prompt_injection"
class _ENTERPRISE_lakeraAI_Moderation(CustomLogger): class _ENTERPRISE_lakeraAI_Moderation(CustomLogger):
def __init__(self): def __init__(self):
@ -49,6 +48,16 @@ class _ENTERPRISE_lakeraAI_Moderation(CustomLogger):
user_api_key_dict: UserAPIKeyAuth, user_api_key_dict: UserAPIKeyAuth,
call_type: Literal["completion", "embeddings", "image_generation"], call_type: Literal["completion", "embeddings", "image_generation"],
): ):
if (
await should_proceed_based_on_metadata(
data=data,
guardrail_name=GUARDRAIL_NAME,
)
is False
):
return
if "messages" in data and isinstance(data["messages"], list): if "messages" in data and isinstance(data["messages"], list):
text = "" text = ""
for m in data["messages"]: # assume messages is a list for m in data["messages"]: # assume messages is a list

12
enterprise/enterprise_hooks/secret_detection.py
View file

@ -32,6 +32,7 @@ from litellm._logging import verbose_proxy_logger
litellm.set_verbose = True litellm.set_verbose = True
GUARDRAIL_NAME = "hide_secrets"
_custom_plugins_path = "file://" + os.path.join( _custom_plugins_path = "file://" + os.path.join(
os.path.dirname(os.path.abspath(__file__)), "secrets_plugins" os.path.dirname(os.path.abspath(__file__)), "secrets_plugins"
@ -464,6 +465,14 @@ class _ENTERPRISE_SecretDetection(CustomLogger):
return detected_secrets return detected_secrets
async def should_run_check(self, user_api_key_dict: UserAPIKeyAuth) -> bool:
if user_api_key_dict.permissions is not None:
if GUARDRAIL_NAME in user_api_key_dict.permissions:
if user_api_key_dict.permissions[GUARDRAIL_NAME] is False:
return False
return True
#### CALL HOOKS - proxy only #### #### CALL HOOKS - proxy only ####
async def async_pre_call_hook( async def async_pre_call_hook(
self, self,
@ -475,6 +484,9 @@ class _ENTERPRISE_SecretDetection(CustomLogger):
from detect_secrets import SecretsCollection from detect_secrets import SecretsCollection
from detect_secrets.settings import default_settings from detect_secrets.settings import default_settings
if await self.should_run_check(user_api_key_dict) is False:
return
if "messages" in data and isinstance(data["messages"], list): if "messages" in data and isinstance(data["messages"], list):
for message in data["messages"]: for message in data["messages"]:
if "content" in message and isinstance(message["content"], str): if "content" in message and isinstance(message["content"], str):

6
litellm/__init__.py
View file

@ -106,13 +106,15 @@ aleph_alpha_key: Optional[str] = None
nlp_cloud_key: Optional[str] = None nlp_cloud_key: Optional[str] = None
common_cloud_provider_auth_params: dict = { common_cloud_provider_auth_params: dict = {
"params": ["project", "region_name", "token"], "params": ["project", "region_name", "token"],
"providers": ["vertex_ai", "bedrock", "watsonx", "azure"], "providers": ["vertex_ai", "bedrock", "watsonx", "azure", "vertex_ai_beta"],
} }
use_client: bool = False use_client: bool = False
ssl_verify: bool = True ssl_verify: bool = True
ssl_certificate: Optional[str] = None ssl_certificate: Optional[str] = None
disable_streaming_logging: bool = False disable_streaming_logging: bool = False
in_memory_llm_clients_cache: dict = {} in_memory_llm_clients_cache: dict = {}
### DEFAULT AZURE API VERSION ###
AZURE_DEFAULT_API_VERSION = "2024-02-01" # this is updated to the latest
### GUARDRAILS ### ### GUARDRAILS ###
llamaguard_model_name: Optional[str] = None llamaguard_model_name: Optional[str] = None
openai_moderations_model_name: Optional[str] = None openai_moderations_model_name: Optional[str] = None
@ -240,6 +242,8 @@ default_user_params: Optional[Dict] = None
default_team_settings: Optional[List] = None default_team_settings: Optional[List] = None
max_user_budget: Optional[float] = None max_user_budget: Optional[float] = None
max_end_user_budget: Optional[float] = None max_end_user_budget: Optional[float] = None
#### REQUEST PRIORITIZATION ####
priority_reservation: Optional[Dict[str, float]] = None
#### RELIABILITY #### #### RELIABILITY ####
request_timeout: float = 6000 request_timeout: float = 6000
module_level_aclient = AsyncHTTPHandler(timeout=request_timeout) module_level_aclient = AsyncHTTPHandler(timeout=request_timeout)

2
litellm/_service_logger.py
View file

@ -75,7 +75,7 @@ class ServiceLogging(CustomLogger):
await self.prometheusServicesLogger.async_service_success_hook( await self.prometheusServicesLogger.async_service_success_hook(
payload=payload payload=payload
) )
elif callback == "otel":
from litellm.proxy.proxy_server import open_telemetry_logger from litellm.proxy.proxy_server import open_telemetry_logger
if parent_otel_span is not None and open_telemetry_logger is not None: if parent_otel_span is not None and open_telemetry_logger is not None:

8
litellm/caching.py
View file

@ -248,8 +248,14 @@ class RedisCache(BaseCache):
# asyncio.get_running_loop().create_task(self.ping()) # asyncio.get_running_loop().create_task(self.ping())
result = asyncio.get_running_loop().create_task(self.ping()) result = asyncio.get_running_loop().create_task(self.ping())
except Exception as e: except Exception as e:
if "no running event loop" in str(e):
verbose_logger.debug(
"Ignoring async redis ping. No running event loop."
)
else:
verbose_logger.error( verbose_logger.error(
"Error connecting to Async Redis client", extra={"error": str(e)} "Error connecting to Async Redis client - {}".format(str(e)),
extra={"error": str(e)},
) )
### SYNC HEALTH PING ### ### SYNC HEALTH PING ###

65
litellm/cost_calculator.py
View file

@ -4,6 +4,8 @@ import time
import traceback import traceback
from typing import List, Literal, Optional, Tuple, Union from typing import List, Literal, Optional, Tuple, Union
from pydantic import BaseModel
import litellm import litellm
import litellm._logging import litellm._logging
from litellm import verbose_logger from litellm import verbose_logger
@ -13,6 +15,10 @@ from litellm.litellm_core_utils.llm_cost_calc.google import (
from litellm.litellm_core_utils.llm_cost_calc.google import ( from litellm.litellm_core_utils.llm_cost_calc.google import (
cost_per_token as google_cost_per_token, cost_per_token as google_cost_per_token,
) )
from litellm.litellm_core_utils.llm_cost_calc.utils import _generic_cost_per_character
from litellm.types.llms.openai import HttpxBinaryResponseContent
from litellm.types.router import SPECIAL_MODEL_INFO_PARAMS
from litellm.utils import ( from litellm.utils import (
CallTypes, CallTypes,
CostPerToken, CostPerToken,
@ -62,6 +68,23 @@ def cost_per_token(
### CUSTOM PRICING ### ### CUSTOM PRICING ###
custom_cost_per_token: Optional[CostPerToken] = None, custom_cost_per_token: Optional[CostPerToken] = None,
custom_cost_per_second: Optional[float] = None, custom_cost_per_second: Optional[float] = None,
### CALL TYPE ###
call_type: Literal[
"embedding",
"aembedding",
"completion",
"acompletion",
"atext_completion",
"text_completion",
"image_generation",
"aimage_generation",
"moderation",
"amoderation",
"atranscription",
"transcription",
"aspeech",
"speech",
] = "completion",
) -> Tuple[float, float]: ) -> Tuple[float, float]:
""" """
Calculates the cost per token for a given model, prompt tokens, and completion tokens. Calculates the cost per token for a given model, prompt tokens, and completion tokens.
@ -76,6 +99,7 @@ def cost_per_token(
custom_llm_provider (str): The llm provider to whom the call was made (see init.py for full list) custom_llm_provider (str): The llm provider to whom the call was made (see init.py for full list)
custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call. custom_cost_per_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. custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
call_type: Optional[str]: the call type
Returns: Returns:
tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively. tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively.
@ -159,6 +183,27 @@ def cost_per_token(
prompt_tokens=prompt_tokens, prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens, completion_tokens=completion_tokens,
) )
elif call_type == "speech" or call_type == "aspeech":
prompt_cost, completion_cost = _generic_cost_per_character(
model=model_without_prefix,
custom_llm_provider=custom_llm_provider,
prompt_characters=prompt_characters,
completion_characters=completion_characters,
custom_prompt_cost=None,
custom_completion_cost=0,
)
if prompt_cost is None or completion_cost is None:
raise ValueError(
"cost for tts call is None. prompt_cost={}, completion_cost={}, model={}, custom_llm_provider={}, prompt_characters={}, completion_characters={}".format(
prompt_cost,
completion_cost,
model_without_prefix,
custom_llm_provider,
prompt_characters,
completion_characters,
)
)
return prompt_cost, completion_cost
elif model in model_cost_ref: elif model in model_cost_ref:
print_verbose(f"Success: model={model} in model_cost_map") print_verbose(f"Success: model={model} in model_cost_map")
print_verbose( print_verbose(
@ -289,7 +334,7 @@ def cost_per_token(
return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
else: else:
# if model is not in model_prices_and_context_window.json. Raise an exception-let users know # if model is not in model_prices_and_context_window.json. Raise an exception-let users know
error_str = f"Model not in model_prices_and_context_window.json. You passed model={model}. Register pricing for model - https://docs.litellm.ai/docs/proxy/custom_pricing\n" error_str = f"Model not in model_prices_and_context_window.json. You passed model={model}, custom_llm_provider={custom_llm_provider}. Register pricing for model - https://docs.litellm.ai/docs/proxy/custom_pricing\n"
raise litellm.exceptions.NotFoundError( # type: ignore raise litellm.exceptions.NotFoundError( # type: ignore
message=error_str, message=error_str,
model=model, model=model,
@ -429,7 +474,10 @@ def completion_cost(
prompt_characters = 0 prompt_characters = 0
completion_tokens = 0 completion_tokens = 0
completion_characters = 0 completion_characters = 0
if completion_response is not None: if completion_response is not None and (
isinstance(completion_response, BaseModel)
or isinstance(completion_response, dict)
): # tts returns a custom class
# get input/output tokens from completion_response # get input/output tokens from completion_response
prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0) prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0)
completion_tokens = completion_response.get("usage", {}).get( completion_tokens = completion_response.get("usage", {}).get(
@ -535,6 +583,11 @@ def completion_cost(
raise Exception( raise Exception(
f"Model={image_gen_model_name} not found in completion cost model map" f"Model={image_gen_model_name} not found in completion cost model map"
) )
elif (
call_type == CallTypes.speech.value or call_type == CallTypes.aspeech.value
):
prompt_characters = litellm.utils._count_characters(text=prompt)
# Calculate cost based on prompt_tokens, completion_tokens # Calculate cost based on prompt_tokens, completion_tokens
if ( if (
"togethercomputer" in model "togethercomputer" in model
@ -591,6 +644,7 @@ def completion_cost(
custom_cost_per_token=custom_cost_per_token, custom_cost_per_token=custom_cost_per_token,
prompt_characters=prompt_characters, prompt_characters=prompt_characters,
completion_characters=completion_characters, completion_characters=completion_characters,
call_type=call_type,
) )
_final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar _final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
print_verbose( print_verbose(
@ -608,6 +662,7 @@ def response_cost_calculator(
ImageResponse, ImageResponse,
TranscriptionResponse, TranscriptionResponse,
TextCompletionResponse, TextCompletionResponse,
HttpxBinaryResponseContent,
], ],
model: str, model: str,
custom_llm_provider: Optional[str], custom_llm_provider: Optional[str],
@ -641,6 +696,7 @@ def response_cost_calculator(
if cache_hit is not None and cache_hit is True: if cache_hit is not None and cache_hit is True:
response_cost = 0.0 response_cost = 0.0
else: else:
if isinstance(response_object, BaseModel):
response_object._hidden_params["optional_params"] = optional_params response_object._hidden_params["optional_params"] = optional_params
if isinstance(response_object, ImageResponse): if isinstance(response_object, ImageResponse):
response_cost = completion_cost( response_cost = completion_cost(
@ -651,12 +707,11 @@ def response_cost_calculator(
) )
else: else:
if ( if (
model in litellm.model_cost model in litellm.model_cost or custom_pricing is True
and custom_pricing is not None
and custom_llm_provider is True
): # override defaults if custom pricing is set ): # override defaults if custom pricing is set
base_model = model base_model = model
# base_model defaults to None if not set on model_info # base_model defaults to None if not set on model_info
response_cost = completion_cost( response_cost = completion_cost(
completion_response=response_object, completion_response=response_object,
call_type=call_type, call_type=call_type,

159
litellm/integrations/galileo.py Normal file
View file

@ -0,0 +1,159 @@
import os
from datetime import datetime
from typing import Any, Dict, List, Optional
import httpx
from pydantic import BaseModel, Field
import litellm
from litellm._logging import verbose_logger
from litellm.integrations.custom_logger import CustomLogger
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
# from here: https://docs.rungalileo.io/galileo/gen-ai-studio-products/galileo-observe/how-to/logging-data-via-restful-apis#structuring-your-records
class LLMResponse(BaseModel):
latency_ms: int
status_code: int
input_text: str
output_text: str
node_type: str
model: str
num_input_tokens: int
num_output_tokens: int
output_logprobs: Optional[Dict[str, Any]] = Field(
default=None,
description="Optional. When available, logprobs are used to compute Uncertainty.",
)
created_at: str = Field(
..., description='timestamp constructed in "%Y-%m-%dT%H:%M:%S" format'
)
tags: Optional[List[str]] = None
user_metadata: Optional[Dict[str, Any]] = None
class GalileoObserve(CustomLogger):
def __init__(self) -> None:
self.in_memory_records: List[dict] = []
self.batch_size = 1
self.base_url = os.getenv("GALILEO_BASE_URL", None)
self.project_id = os.getenv("GALILEO_PROJECT_ID", None)
self.headers = None
self.async_httpx_handler = AsyncHTTPHandler(
timeout=httpx.Timeout(timeout=600.0, connect=5.0)
)
pass
def set_galileo_headers(self):
# following https://docs.rungalileo.io/galileo/gen-ai-studio-products/galileo-observe/how-to/logging-data-via-restful-apis#logging-your-records
headers = {
"accept": "application/json",
"Content-Type": "application/x-www-form-urlencoded",
}
galileo_login_response = self.async_httpx_handler.post(
url=f"{self.base_url}/login",
headers=headers,
data={
"username": os.getenv("GALILEO_USERNAME"),
"password": os.getenv("GALILEO_PASSWORD"),
},
)
access_token = galileo_login_response.json()["access_token"]
self.headers = {
"accept": "application/json",
"Content-Type": "application/json",
"Authorization": f"Bearer {access_token}",
}
def get_output_str_from_response(self, response_obj, kwargs):
output = None
if response_obj is not None and (
kwargs.get("call_type", None) == "embedding"
or isinstance(response_obj, litellm.EmbeddingResponse)
):
output = None
elif response_obj is not None and isinstance(
response_obj, litellm.ModelResponse
):
output = response_obj["choices"][0]["message"].json()
elif response_obj is not None and isinstance(
response_obj, litellm.TextCompletionResponse
):
output = response_obj.choices[0].text
elif response_obj is not None and isinstance(
response_obj, litellm.ImageResponse
):
output = response_obj["data"]
return output
async def async_log_success_event(
self,
kwargs,
start_time,
end_time,
response_obj,
):
verbose_logger.debug(f"On Async Success")
_latency_ms = int((end_time - start_time).total_seconds() * 1000)
_call_type = kwargs.get("call_type", "litellm")
input_text = litellm.utils.get_formatted_prompt(
data=kwargs, call_type=_call_type
)
_usage = response_obj.get("usage", {}) or {}
num_input_tokens = _usage.get("prompt_tokens", 0)
num_output_tokens = _usage.get("completion_tokens", 0)
output_text = self.get_output_str_from_response(
response_obj=response_obj, kwargs=kwargs
)
request_record = LLMResponse(
latency_ms=_latency_ms,
status_code=200,
input_text=input_text,
output_text=output_text,
node_type=_call_type,
model=kwargs.get("model", "-"),
num_input_tokens=num_input_tokens,
num_output_tokens=num_output_tokens,
created_at=start_time.strftime(
"%Y-%m-%dT%H:%M:%S"
), # timestamp str constructed in "%Y-%m-%dT%H:%M:%S" format
)
# dump to dict
request_dict = request_record.model_dump()
self.in_memory_records.append(request_dict)
if len(self.in_memory_records) >= self.batch_size:
await self.flush_in_memory_records()
async def flush_in_memory_records(self):
verbose_logger.debug("flushing in memory records")
response = await self.async_httpx_handler.post(
url=f"{self.base_url}/projects/{self.project_id}/observe/ingest",
headers=self.headers,
json={"records": self.in_memory_records},
)
if response.status_code == 200:
verbose_logger.debug(
"Galileo Logger:successfully flushed in memory records"
)
self.in_memory_records = []
else:
verbose_logger.debug("Galileo Logger: failed to flush in memory records")
verbose_logger.debug(
"Galileo Logger error=%s, status code=%s",
response.text,
response.status_code,
)
async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
verbose_logger.debug(f"On Async Failure")

6
litellm/integrations/langfuse.py
View file

@ -32,6 +32,12 @@ class LangFuseLogger:
self.langfuse_host = langfuse_host or os.getenv( self.langfuse_host = langfuse_host or os.getenv(
"LANGFUSE_HOST", "https://cloud.langfuse.com" "LANGFUSE_HOST", "https://cloud.langfuse.com"
) )
if not (
self.langfuse_host.startswith("http://")
or self.langfuse_host.startswith("https://")
):
# add http:// if unset, assume communicating over private network - e.g. render
self.langfuse_host = "http://" + self.langfuse_host
self.langfuse_release = os.getenv("LANGFUSE_RELEASE") self.langfuse_release = os.getenv("LANGFUSE_RELEASE")
self.langfuse_debug = os.getenv("LANGFUSE_DEBUG") self.langfuse_debug = os.getenv("LANGFUSE_DEBUG")

12
litellm/integrations/opentelemetry.py
View file

@ -29,6 +29,7 @@ else:
LITELLM_TRACER_NAME = os.getenv("OTEL_TRACER_NAME", "litellm") LITELLM_TRACER_NAME = os.getenv("OTEL_TRACER_NAME", "litellm")
LITELLM_RESOURCE = { LITELLM_RESOURCE = {
"service.name": os.getenv("OTEL_SERVICE_NAME", "litellm"), "service.name": os.getenv("OTEL_SERVICE_NAME", "litellm"),
"deployment.environment": os.getenv("OTEL_ENVIRONMENT_NAME", "production"),
} }
RAW_REQUEST_SPAN_NAME = "raw_gen_ai_request" RAW_REQUEST_SPAN_NAME = "raw_gen_ai_request"
LITELLM_REQUEST_SPAN_NAME = "litellm_request" LITELLM_REQUEST_SPAN_NAME = "litellm_request"
@ -447,6 +448,7 @@ class OpenTelemetry(CustomLogger):
# cast sr -> dict # cast sr -> dict
import json import json
try:
_raw_response = json.loads(_raw_response) _raw_response = json.loads(_raw_response)
for param, val in _raw_response.items(): for param, val in _raw_response.items():
if not isinstance(val, str): if not isinstance(val, str):
@ -455,6 +457,16 @@ class OpenTelemetry(CustomLogger):
f"llm.{custom_llm_provider}.{param}", f"llm.{custom_llm_provider}.{param}",
val, val,
) )
except json.JSONDecodeError:
verbose_logger.debug(
"litellm.integrations.opentelemetry.py::set_raw_request_attributes() - raw_response not json string - {}".format(
_raw_response
)
)
span.set_attribute(
f"llm.{custom_llm_provider}.stringified_raw_response",
_raw_response,
)
pass pass

8
litellm/integrations/prometheus.py
View file

@ -34,6 +34,7 @@ class PrometheusLogger:
labelnames=[ labelnames=[
"end_user", "end_user",
"hashed_api_key", "hashed_api_key",
"api_key_alias",
"model", "model",
"team", "team",
"team_alias", "team_alias",
@ -47,6 +48,7 @@ class PrometheusLogger:
labelnames=[ labelnames=[
"end_user", "end_user",
"hashed_api_key", "hashed_api_key",
"api_key_alias",
"model", "model",
"team", "team",
"team_alias", "team_alias",
@ -61,6 +63,7 @@ class PrometheusLogger:
labelnames=[ labelnames=[
"end_user", "end_user",
"hashed_api_key", "hashed_api_key",
"api_key_alias",
"model", "model",
"team", "team",
"team_alias", "team_alias",
@ -75,6 +78,7 @@ class PrometheusLogger:
labelnames=[ labelnames=[
"end_user", "end_user",
"hashed_api_key", "hashed_api_key",
"api_key_alias",
"model", "model",
"team", "team",
"team_alias", "team_alias",
@ -204,6 +208,7 @@ class PrometheusLogger:
self.litellm_requests_metric.labels( self.litellm_requests_metric.labels(
end_user_id, end_user_id,
user_api_key, user_api_key,
user_api_key_alias,
model, model,
user_api_team, user_api_team,
user_api_team_alias, user_api_team_alias,
@ -212,6 +217,7 @@ class PrometheusLogger:
self.litellm_spend_metric.labels( self.litellm_spend_metric.labels(
end_user_id, end_user_id,
user_api_key, user_api_key,
user_api_key_alias,
model, model,
user_api_team, user_api_team,
user_api_team_alias, user_api_team_alias,
@ -220,6 +226,7 @@ class PrometheusLogger:
self.litellm_tokens_metric.labels( self.litellm_tokens_metric.labels(
end_user_id, end_user_id,
user_api_key, user_api_key,
user_api_key_alias,
model, model,
user_api_team, user_api_team,
user_api_team_alias, user_api_team_alias,
@ -243,6 +250,7 @@ class PrometheusLogger:
self.litellm_llm_api_failed_requests_metric.labels( self.litellm_llm_api_failed_requests_metric.labels(
end_user_id, end_user_id,
user_api_key, user_api_key,
user_api_key_alias,
model, model,
user_api_team, user_api_team,
user_api_team_alias, user_api_team_alias,

68
litellm/litellm_core_utils/litellm_logging.py
View file

@ -24,6 +24,8 @@ from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.redact_messages import ( from litellm.litellm_core_utils.redact_messages import (
redact_message_input_output_from_logging, redact_message_input_output_from_logging,
) )
from litellm.types.llms.openai import HttpxBinaryResponseContent
from litellm.types.router import SPECIAL_MODEL_INFO_PARAMS
from litellm.types.utils import ( from litellm.types.utils import (
CallTypes, CallTypes,
EmbeddingResponse, EmbeddingResponse,
@ -56,6 +58,7 @@ from ..integrations.clickhouse import ClickhouseLogger
from ..integrations.custom_logger import CustomLogger from ..integrations.custom_logger import CustomLogger
from ..integrations.datadog import DataDogLogger from ..integrations.datadog import DataDogLogger
from ..integrations.dynamodb import DyanmoDBLogger from ..integrations.dynamodb import DyanmoDBLogger
from ..integrations.galileo import GalileoObserve
from ..integrations.greenscale import GreenscaleLogger from ..integrations.greenscale import GreenscaleLogger
from ..integrations.helicone import HeliconeLogger from ..integrations.helicone import HeliconeLogger
from ..integrations.lago import LagoLogger from ..integrations.lago import LagoLogger
@ -153,11 +156,6 @@ class Logging:
langfuse_secret=None, langfuse_secret=None,
langfuse_host=None, langfuse_host=None,
): ):
if call_type not in [item.value for item in CallTypes]:
allowed_values = ", ".join([item.value for item in CallTypes])
raise ValueError(
f"Invalid call_type {call_type}. Allowed values: {allowed_values}"
)
if messages is not None: if messages is not None:
if isinstance(messages, str): if isinstance(messages, str):
messages = [ messages = [
@ -426,6 +424,7 @@ class Logging:
self.model_call_details["additional_args"] = additional_args self.model_call_details["additional_args"] = additional_args
self.model_call_details["log_event_type"] = "post_api_call" self.model_call_details["log_event_type"] = "post_api_call"
if json_logs:
verbose_logger.debug( verbose_logger.debug(
"RAW RESPONSE:\n{}\n\n".format( "RAW RESPONSE:\n{}\n\n".format(
self.model_call_details.get( self.model_call_details.get(
@ -433,6 +432,14 @@ class Logging:
) )
), ),
) )
else:
print_verbose(
"RAW RESPONSE:\n{}\n\n".format(
self.model_call_details.get(
"original_response", self.model_call_details
)
)
)
if self.logger_fn and callable(self.logger_fn): if self.logger_fn and callable(self.logger_fn):
try: try:
self.logger_fn( self.logger_fn(
@ -512,18 +519,20 @@ class Logging:
self.model_call_details["cache_hit"] = cache_hit self.model_call_details["cache_hit"] = cache_hit
## if model in model cost map - log the response cost ## if model in model cost map - log the response cost
## else set cost to None ## else set cost to None
verbose_logger.debug(f"Model={self.model};")
if ( if (
result is not None result is not None and self.stream is not True
and ( ): # handle streaming separately
if (
isinstance(result, ModelResponse) isinstance(result, ModelResponse)
or isinstance(result, EmbeddingResponse) or isinstance(result, EmbeddingResponse)
or isinstance(result, ImageResponse) or isinstance(result, ImageResponse)
or isinstance(result, TranscriptionResponse) or isinstance(result, TranscriptionResponse)
or isinstance(result, TextCompletionResponse) or isinstance(result, TextCompletionResponse)
or isinstance(result, HttpxBinaryResponseContent) # tts
):
custom_pricing = use_custom_pricing_for_model(
litellm_params=self.litellm_params
) )
and self.stream != True
): # handle streaming separately
self.model_call_details["response_cost"] = ( self.model_call_details["response_cost"] = (
litellm.response_cost_calculator( litellm.response_cost_calculator(
response_object=result, response_object=result,
@ -537,6 +546,7 @@ class Logging:
), ),
call_type=self.call_type, call_type=self.call_type,
optional_params=self.optional_params, optional_params=self.optional_params,
custom_pricing=custom_pricing,
) )
) )
else: # streaming chunks + image gen. else: # streaming chunks + image gen.
@ -595,8 +605,7 @@ class Logging:
verbose_logger.error( verbose_logger.error(
"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while building complete streaming response in success logging {}\n{}".format( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while building complete streaming response in success logging {}\n{}".format(
str(e), traceback.format_exc() str(e), traceback.format_exc()
), )
log_level="ERROR",
) )
complete_streaming_response = None complete_streaming_response = None
else: else:
@ -621,7 +630,11 @@ class Logging:
model_call_details=self.model_call_details model_call_details=self.model_call_details
), ),
call_type=self.call_type, call_type=self.call_type,
optional_params=self.optional_params, optional_params=(
self.optional_params
if hasattr(self, "optional_params")
else {}
),
) )
) )
if self.dynamic_success_callbacks is not None and isinstance( if self.dynamic_success_callbacks is not None and isinstance(
@ -1603,6 +1616,7 @@ class Logging:
) )
== False == False
): # custom logger class ): # custom logger class
callback.log_failure_event( callback.log_failure_event(
start_time=start_time, start_time=start_time,
end_time=end_time, end_time=end_time,
@ -1789,7 +1803,6 @@ def set_callbacks(callback_list, function_id=None):
try: try:
for callback in callback_list: for callback in callback_list:
print_verbose(f"init callback list: {callback}")
if callback == "sentry": if callback == "sentry":
try: try:
import sentry_sdk import sentry_sdk
@ -1920,6 +1933,15 @@ def _init_custom_logger_compatible_class(
_openmeter_logger = OpenMeterLogger() _openmeter_logger = OpenMeterLogger()
_in_memory_loggers.append(_openmeter_logger) _in_memory_loggers.append(_openmeter_logger)
return _openmeter_logger # type: ignore return _openmeter_logger # type: ignore
elif logging_integration == "galileo":
for callback in _in_memory_loggers:
if isinstance(callback, GalileoObserve):
return callback # type: ignore
galileo_logger = GalileoObserve()
_in_memory_loggers.append(galileo_logger)
return galileo_logger # type: ignore
elif logging_integration == "logfire": elif logging_integration == "logfire":
if "LOGFIRE_TOKEN" not in os.environ: if "LOGFIRE_TOKEN" not in os.environ:
raise ValueError("LOGFIRE_TOKEN not found in environment variables") raise ValueError("LOGFIRE_TOKEN not found in environment variables")
@ -1976,6 +1998,10 @@ def get_custom_logger_compatible_class(
for callback in _in_memory_loggers: for callback in _in_memory_loggers:
if isinstance(callback, OpenMeterLogger): if isinstance(callback, OpenMeterLogger):
return callback return callback
elif logging_integration == "galileo":
for callback in _in_memory_loggers:
if isinstance(callback, GalileoObserve):
return callback
elif logging_integration == "logfire": elif logging_integration == "logfire":
if "LOGFIRE_TOKEN" not in os.environ: if "LOGFIRE_TOKEN" not in os.environ:
raise ValueError("LOGFIRE_TOKEN not found in environment variables") raise ValueError("LOGFIRE_TOKEN not found in environment variables")
@ -1994,3 +2020,17 @@ def get_custom_logger_compatible_class(
if isinstance(callback, _PROXY_DynamicRateLimitHandler): if isinstance(callback, _PROXY_DynamicRateLimitHandler):
return callback # type: ignore return callback # type: ignore
return None return None
def use_custom_pricing_for_model(litellm_params: Optional[dict]) -> bool:
if litellm_params is None:
return False
metadata: Optional[dict] = litellm_params.get("metadata", {})
if metadata is None:
return False
model_info: Optional[dict] = metadata.get("model_info", {})
if model_info is not None:
for k, v in model_info.items():
if k in SPECIAL_MODEL_INFO_PARAMS:
return True
return False

85
litellm/litellm_core_utils/llm_cost_calc/utils.py Normal file
View file

@ -0,0 +1,85 @@
# What is this?
## Helper utilities for cost_per_token()
import traceback
from typing import List, Literal, Optional, Tuple
import litellm
from litellm import verbose_logger
def _generic_cost_per_character(
model: str,
custom_llm_provider: str,
prompt_characters: float,
completion_characters: float,
custom_prompt_cost: Optional[float],
custom_completion_cost: Optional[float],
) -> Tuple[Optional[float], Optional[float]]:
"""
Generic function to help calculate cost per character.
"""
"""
Calculates the cost per character for a given model, input messages, and response object.
Input:
- model: str, the model name without provider prefix
- custom_llm_provider: str, "vertex_ai-*"
- prompt_characters: float, the number of input characters
- completion_characters: float, the number of output characters
Returns:
Tuple[Optional[float], Optional[float]] - prompt_cost_in_usd, completion_cost_in_usd.
- returns None if not able to calculate cost.
Raises:
Exception if 'input_cost_per_character' or 'output_cost_per_character' is missing from model_info
"""
args = locals()
## GET MODEL INFO
model_info = litellm.get_model_info(
model=model, custom_llm_provider=custom_llm_provider
)
## CALCULATE INPUT COST
try:
if custom_prompt_cost is None:
assert (
"input_cost_per_character" in model_info
and model_info["input_cost_per_character"] is not None
), "model info for model={} does not have 'input_cost_per_character'-pricing\nmodel_info={}".format(
model, model_info
)
custom_prompt_cost = model_info["input_cost_per_character"]
prompt_cost = prompt_characters * custom_prompt_cost
except Exception as e:
verbose_logger.error(
"litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format(
str(e), traceback.format_exc()
)
)
prompt_cost = None
## CALCULATE OUTPUT COST
try:
if custom_completion_cost is None:
assert (
"output_cost_per_character" in model_info
and model_info["output_cost_per_character"] is not None
), "model info for model={} does not have 'output_cost_per_character'-pricing\nmodel_info={}".format(
model, model_info
)
custom_completion_cost = model_info["output_cost_per_character"]
completion_cost = completion_characters * custom_completion_cost
except Exception as e:
verbose_logger.error(
"litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format(
str(e), traceback.format_exc()
)
)
completion_cost = None
return prompt_cost, completion_cost

493
litellm/llms/anthropic.py
View file

@ -12,13 +12,27 @@ import requests # type: ignore
import litellm import litellm
import litellm.litellm_core_utils import litellm.litellm_core_utils
from litellm import verbose_logger
from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.litellm_core_utils.core_helpers import map_finish_reason
from litellm.llms.custom_httpx.http_handler import ( from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler, AsyncHTTPHandler,
_get_async_httpx_client, _get_async_httpx_client,
_get_httpx_client, _get_httpx_client,
) )
from litellm.types.llms.anthropic import AnthropicMessagesToolChoice from litellm.types.llms.anthropic import (
AnthropicMessagesToolChoice,
ContentBlockDelta,
ContentBlockStart,
MessageBlockDelta,
MessageStartBlock,
)
from litellm.types.llms.openai import (
ChatCompletionResponseMessage,
ChatCompletionToolCallChunk,
ChatCompletionToolCallFunctionChunk,
ChatCompletionUsageBlock,
)
from litellm.types.utils import GenericStreamingChunk
from litellm.utils import CustomStreamWrapper, ModelResponse, Usage from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
from .base import BaseLLM from .base import BaseLLM
@ -35,7 +49,7 @@ class AnthropicConstants(Enum):
class AnthropicError(Exception): class AnthropicError(Exception):
def __init__(self, status_code, message): def __init__(self, status_code, message):
self.status_code = status_code self.status_code = status_code
self.message = message self.message: str = message
self.request = httpx.Request( self.request = httpx.Request(
method="POST", url="https://api.anthropic.com/v1/messages" method="POST", url="https://api.anthropic.com/v1/messages"
) )
@ -198,7 +212,9 @@ async def make_call(
status_code=response.status_code, message=await response.aread() status_code=response.status_code, message=await response.aread()
) )
completion_stream = response.aiter_lines() completion_stream = ModelResponseIterator(
streaming_response=response.aiter_lines(), sync_stream=False
)
# LOGGING # LOGGING
logging_obj.post_call( logging_obj.post_call(
@ -215,120 +231,120 @@ class AnthropicChatCompletion(BaseLLM):
def __init__(self) -> None: def __init__(self) -> None:
super().__init__() super().__init__()
def process_streaming_response( # def process_streaming_response(
self, # self,
model: str, # model: str,
response: Union[requests.Response, httpx.Response], # response: Union[requests.Response, httpx.Response],
model_response: ModelResponse, # model_response: ModelResponse,
stream: bool, # stream: bool,
logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, # logging_obj: litellm.litellm_core_utils.litellm_logging.Logging,
optional_params: dict, # optional_params: dict,
api_key: str, # api_key: str,
data: Union[dict, str], # data: Union[dict, str],
messages: List, # messages: List,
print_verbose, # print_verbose,
encoding, # encoding,
) -> CustomStreamWrapper: # ) -> CustomStreamWrapper:
""" # """
Return stream object for tool-calling + streaming # Return stream object for tool-calling + streaming
""" # """
## LOGGING # ## LOGGING
logging_obj.post_call( # logging_obj.post_call(
input=messages, # input=messages,
api_key=api_key, # api_key=api_key,
original_response=response.text, # original_response=response.text,
additional_args={"complete_input_dict": data}, # additional_args={"complete_input_dict": data},
) # )
print_verbose(f"raw model_response: {response.text}") # print_verbose(f"raw model_response: {response.text}")
## RESPONSE OBJECT # ## RESPONSE OBJECT
try: # try:
completion_response = response.json() # completion_response = response.json()
except: # except:
raise AnthropicError( # raise AnthropicError(
message=response.text, status_code=response.status_code # message=response.text, status_code=response.status_code
) # )
text_content = "" # text_content = ""
tool_calls = [] # tool_calls = []
for content in completion_response["content"]: # for content in completion_response["content"]:
if content["type"] == "text": # if content["type"] == "text":
text_content += content["text"] # text_content += content["text"]
## TOOL CALLING # ## TOOL CALLING
elif content["type"] == "tool_use": # elif content["type"] == "tool_use":
tool_calls.append( # tool_calls.append(
{ # {
"id": content["id"], # "id": content["id"],
"type": "function", # "type": "function",
"function": { # "function": {
"name": content["name"], # "name": content["name"],
"arguments": json.dumps(content["input"]), # "arguments": json.dumps(content["input"]),
}, # },
} # }
) # )
if "error" in completion_response: # if "error" in completion_response:
raise AnthropicError( # raise AnthropicError(
message=str(completion_response["error"]), # message=str(completion_response["error"]),
status_code=response.status_code, # status_code=response.status_code,
) # )
_message = litellm.Message( # _message = litellm.Message(
tool_calls=tool_calls, # tool_calls=tool_calls,
content=text_content or None, # content=text_content or None,
) # )
model_response.choices[0].message = _message # type: ignore # model_response.choices[0].message = _message # type: ignore
model_response._hidden_params["original_response"] = completion_response[ # model_response._hidden_params["original_response"] = completion_response[
"content" # "content"
] # allow user to access raw anthropic tool calling response # ] # allow user to access raw anthropic tool calling response
model_response.choices[0].finish_reason = map_finish_reason( # model_response.choices[0].finish_reason = map_finish_reason(
completion_response["stop_reason"] # completion_response["stop_reason"]
) # )
print_verbose("INSIDE ANTHROPIC STREAMING TOOL CALLING CONDITION BLOCK") # print_verbose("INSIDE ANTHROPIC STREAMING TOOL CALLING CONDITION BLOCK")
# return an iterator # # return an iterator
streaming_model_response = ModelResponse(stream=True) # streaming_model_response = ModelResponse(stream=True)
streaming_model_response.choices[0].finish_reason = model_response.choices[ # type: ignore # streaming_model_response.choices[0].finish_reason = model_response.choices[ # type: ignore
0 # 0
].finish_reason # ].finish_reason
# streaming_model_response.choices = [litellm.utils.StreamingChoices()] # # streaming_model_response.choices = [litellm.utils.StreamingChoices()]
streaming_choice = litellm.utils.StreamingChoices() # streaming_choice = litellm.utils.StreamingChoices()
streaming_choice.index = model_response.choices[0].index # streaming_choice.index = model_response.choices[0].index
_tool_calls = [] # _tool_calls = []
print_verbose( # print_verbose(
f"type of model_response.choices[0]: {type(model_response.choices[0])}" # f"type of model_response.choices[0]: {type(model_response.choices[0])}"
) # )
print_verbose(f"type of streaming_choice: {type(streaming_choice)}") # print_verbose(f"type of streaming_choice: {type(streaming_choice)}")
if isinstance(model_response.choices[0], litellm.Choices): # if isinstance(model_response.choices[0], litellm.Choices):
if getattr( # if getattr(
model_response.choices[0].message, "tool_calls", None # model_response.choices[0].message, "tool_calls", None
) is not None and isinstance( # ) is not None and isinstance(
model_response.choices[0].message.tool_calls, list # model_response.choices[0].message.tool_calls, list
): # ):
for tool_call in model_response.choices[0].message.tool_calls: # for tool_call in model_response.choices[0].message.tool_calls:
_tool_call = {**tool_call.dict(), "index": 0} # _tool_call = {**tool_call.dict(), "index": 0}
_tool_calls.append(_tool_call) # _tool_calls.append(_tool_call)
delta_obj = litellm.utils.Delta( # delta_obj = litellm.utils.Delta(
content=getattr(model_response.choices[0].message, "content", None), # content=getattr(model_response.choices[0].message, "content", None),
role=model_response.choices[0].message.role, # role=model_response.choices[0].message.role,
tool_calls=_tool_calls, # tool_calls=_tool_calls,
) # )
streaming_choice.delta = delta_obj # streaming_choice.delta = delta_obj
streaming_model_response.choices = [streaming_choice] # streaming_model_response.choices = [streaming_choice]
completion_stream = ModelResponseIterator( # completion_stream = ModelResponseIterator(
model_response=streaming_model_response # model_response=streaming_model_response
) # )
print_verbose( # print_verbose(
"Returns anthropic CustomStreamWrapper with 'cached_response' streaming object" # "Returns anthropic CustomStreamWrapper with 'cached_response' streaming object"
) # )
return CustomStreamWrapper( # return CustomStreamWrapper(
completion_stream=completion_stream, # completion_stream=completion_stream,
model=model, # model=model,
custom_llm_provider="cached_response", # custom_llm_provider="cached_response",
logging_obj=logging_obj, # logging_obj=logging_obj,
) # )
else: # else:
raise AnthropicError( # raise AnthropicError(
status_code=422, # status_code=422,
message="Unprocessable response object - {}".format(response.text), # message="Unprocessable response object - {}".format(response.text),
) # )
def process_response( def process_response(
self, self,
@ -484,21 +500,19 @@ class AnthropicChatCompletion(BaseLLM):
headers={}, headers={},
) -> Union[ModelResponse, CustomStreamWrapper]: ) -> Union[ModelResponse, CustomStreamWrapper]:
async_handler = _get_async_httpx_client() async_handler = _get_async_httpx_client()
try:
response = await async_handler.post(api_base, headers=headers, json=data) response = await async_handler.post(api_base, headers=headers, json=data)
if stream and _is_function_call: except Exception as e:
return self.process_streaming_response( ## LOGGING
model=model, logging_obj.post_call(
response=response, input=messages,
model_response=model_response,
stream=stream,
logging_obj=logging_obj,
api_key=api_key, api_key=api_key,
data=data, original_response=str(e),
messages=messages, additional_args={"complete_input_dict": data},
print_verbose=print_verbose,
optional_params=optional_params,
encoding=encoding,
) )
raise e
return self.process_response( return self.process_response(
model=model, model=model,
response=response, response=response,
@ -588,13 +602,16 @@ class AnthropicChatCompletion(BaseLLM):
optional_params["tools"] = anthropic_tools optional_params["tools"] = anthropic_tools
stream = optional_params.pop("stream", None) stream = optional_params.pop("stream", None)
is_vertex_request: bool = optional_params.pop("is_vertex_request", False)
data = { data = {
"model": model,
"messages": messages, "messages": messages,
**optional_params, **optional_params,
} }
if is_vertex_request is False:
data["model"] = model
## LOGGING ## LOGGING
logging_obj.pre_call( logging_obj.pre_call(
input=messages, input=messages,
@ -608,7 +625,7 @@ class AnthropicChatCompletion(BaseLLM):
print_verbose(f"_is_function_call: {_is_function_call}") print_verbose(f"_is_function_call: {_is_function_call}")
if acompletion == True: if acompletion == True:
if ( if (
stream and not _is_function_call stream is True
): # if function call - fake the streaming (need complete blocks for output parsing in openai format) ): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
print_verbose("makes async anthropic streaming POST request") print_verbose("makes async anthropic streaming POST request")
data["stream"] = stream data["stream"] = stream
@ -652,7 +669,7 @@ class AnthropicChatCompletion(BaseLLM):
else: else:
## COMPLETION CALL ## COMPLETION CALL
if ( if (
stream and not _is_function_call stream is True
): # if function call - fake the streaming (need complete blocks for output parsing in openai format) ): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
print_verbose("makes anthropic streaming POST request") print_verbose("makes anthropic streaming POST request")
data["stream"] = stream data["stream"] = stream
@ -668,7 +685,9 @@ class AnthropicChatCompletion(BaseLLM):
status_code=response.status_code, message=response.text status_code=response.status_code, message=response.text
) )
completion_stream = response.iter_lines() completion_stream = ModelResponseIterator(
streaming_response=response.iter_lines(), sync_stream=True
)
streaming_response = CustomStreamWrapper( streaming_response = CustomStreamWrapper(
completion_stream=completion_stream, completion_stream=completion_stream,
model=model, model=model,
@ -686,20 +705,6 @@ class AnthropicChatCompletion(BaseLLM):
status_code=response.status_code, message=response.text status_code=response.status_code, message=response.text
) )
if stream and _is_function_call:
return self.process_streaming_response(
model=model,
response=response,
model_response=model_response,
stream=stream,
logging_obj=logging_obj,
api_key=api_key,
data=data,
messages=messages,
print_verbose=print_verbose,
optional_params=optional_params,
encoding=encoding,
)
return self.process_response( return self.process_response(
model=model, model=model,
response=response, response=response,
@ -720,26 +725,206 @@ class AnthropicChatCompletion(BaseLLM):
class ModelResponseIterator: class ModelResponseIterator:
def __init__(self, model_response): def __init__(self, streaming_response, sync_stream: bool):
self.model_response = model_response self.streaming_response = streaming_response
self.is_done = False self.response_iterator = self.streaming_response
def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
try:
verbose_logger.debug(f"\n\nRaw chunk:\n{chunk}\n")
type_chunk = chunk.get("type", "") or ""
text = ""
tool_use: Optional[ChatCompletionToolCallChunk] = None
is_finished = False
finish_reason = ""
usage: Optional[ChatCompletionUsageBlock] = None
index = int(chunk.get("index", 0))
if type_chunk == "content_block_delta":
"""
Anthropic content chunk
chunk = {'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': 'Hello'}}
"""
content_block = ContentBlockDelta(**chunk) # type: ignore
if "text" in content_block["delta"]:
text = content_block["delta"]["text"]
elif "partial_json" in content_block["delta"]:
tool_use = {
"id": None,
"type": "function",
"function": {
"name": None,
"arguments": content_block["delta"]["partial_json"],
},
"index": content_block["index"],
}
elif type_chunk == "content_block_start":
"""
event: content_block_start
data: {"type":"content_block_start","index":1,"content_block":{"type":"tool_use","id":"toolu_01T1x1fJ34qAmk2tNTrN7Up6","name":"get_weather","input":{}}}
"""
content_block_start = ContentBlockStart(**chunk) # type: ignore
if content_block_start["content_block"]["type"] == "text":
text = content_block_start["content_block"]["text"]
elif content_block_start["content_block"]["type"] == "tool_use":
tool_use = {
"id": content_block_start["content_block"]["id"],
"type": "function",
"function": {
"name": content_block_start["content_block"]["name"],
"arguments": "",
},
"index": content_block_start["index"],
}
elif type_chunk == "message_delta":
"""
Anthropic
chunk = {'type': 'message_delta', 'delta': {'stop_reason': 'max_tokens', 'stop_sequence': None}, 'usage': {'output_tokens': 10}}
"""
# TODO - get usage from this chunk, set in response
message_delta = MessageBlockDelta(**chunk) # type: ignore
finish_reason = map_finish_reason(
finish_reason=message_delta["delta"].get("stop_reason", "stop")
or "stop"
)
usage = ChatCompletionUsageBlock(
prompt_tokens=message_delta["usage"].get("input_tokens", 0),
completion_tokens=message_delta["usage"].get("output_tokens", 0),
total_tokens=message_delta["usage"].get("input_tokens", 0)
+ message_delta["usage"].get("output_tokens", 0),
)
is_finished = True
elif type_chunk == "message_start":
"""
Anthropic
chunk = {
"type": "message_start",
"message": {
"id": "msg_vrtx_011PqREFEMzd3REdCoUFAmdG",
"type": "message",
"role": "assistant",
"model": "claude-3-sonnet-20240229",
"content": [],
"stop_reason": null,
"stop_sequence": null,
"usage": {
"input_tokens": 270,
"output_tokens": 1
}
}
}
"""
message_start_block = MessageStartBlock(**chunk) # type: ignore
usage = ChatCompletionUsageBlock(
prompt_tokens=message_start_block["message"]
.get("usage", {})
.get("input_tokens", 0),
completion_tokens=message_start_block["message"]
.get("usage", {})
.get("output_tokens", 0),
total_tokens=message_start_block["message"]
.get("usage", {})
.get("input_tokens", 0)
+ message_start_block["message"]
.get("usage", {})
.get("output_tokens", 0),
)
elif type_chunk == "error":
"""
{"type":"error","error":{"details":null,"type":"api_error","message":"Internal server error"} }
"""
_error_dict = chunk.get("error", {}) or {}
message = _error_dict.get("message", None) or str(chunk)
raise AnthropicError(
message=message,
status_code=500, # it looks like Anthropic API does not return a status code in the chunk error - default to 500
)
returned_chunk = GenericStreamingChunk(
text=text,
tool_use=tool_use,
is_finished=is_finished,
finish_reason=finish_reason,
usage=usage,
index=index,
)
return returned_chunk
except json.JSONDecodeError:
raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
# Sync iterator # Sync iterator
def __iter__(self): def __iter__(self):
return self return self
def __next__(self): def __next__(self):
if self.is_done: try:
chunk = self.response_iterator.__next__()
except StopIteration:
raise StopIteration raise StopIteration
self.is_done = True except ValueError as e:
return self.model_response raise RuntimeError(f"Error receiving chunk from stream: {e}")
try:
str_line = chunk
if isinstance(chunk, bytes): # Handle binary data
str_line = chunk.decode("utf-8") # Convert bytes to string
index = str_line.find("data:")
if index != -1:
str_line = str_line[index:]
if str_line.startswith("data:"):
data_json = json.loads(str_line[5:])
return self.chunk_parser(chunk=data_json)
else:
return GenericStreamingChunk(
text="",
is_finished=False,
finish_reason="",
usage=None,
index=0,
tool_use=None,
)
except StopIteration:
raise StopIteration
except ValueError as e:
raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
# Async iterator # Async iterator
def __aiter__(self): def __aiter__(self):
self.async_response_iterator = self.streaming_response.__aiter__()
return self return self
async def __anext__(self): async def __anext__(self):
if self.is_done: try:
chunk = await self.async_response_iterator.__anext__()
except StopAsyncIteration:
raise StopAsyncIteration raise StopAsyncIteration
self.is_done = True except ValueError as e:
return self.model_response raise RuntimeError(f"Error receiving chunk from stream: {e}")
try:
str_line = chunk
if isinstance(chunk, bytes): # Handle binary data
str_line = chunk.decode("utf-8") # Convert bytes to string
index = str_line.find("data:")
if index != -1:
str_line = str_line[index:]
if str_line.startswith("data:"):
data_json = json.loads(str_line[5:])
return self.chunk_parser(chunk=data_json)
else:
return GenericStreamingChunk(
text="",
is_finished=False,
finish_reason="",
usage=None,
index=0,
tool_use=None,
)
except StopAsyncIteration:
raise StopAsyncIteration
except ValueError as e:
raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")

20
litellm/llms/azure.py
View file

@ -1149,7 +1149,13 @@ class AzureChatCompletion(BaseLLM):
error_data = response.json() error_data = response.json()
raise AzureOpenAIError(status_code=400, message=json.dumps(error_data)) raise AzureOpenAIError(status_code=400, message=json.dumps(error_data))
return response result = response.json()["result"]
return httpx.Response(
status_code=200,
headers=response.headers,
content=json.dumps(result).encode("utf-8"),
request=httpx.Request(method="POST", url="https://api.openai.com/v1"),
)
return await async_handler.post( return await async_handler.post(
url=api_base, url=api_base,
json=data, json=data,
@ -1248,7 +1254,13 @@ class AzureChatCompletion(BaseLLM):
error_data = response.json() error_data = response.json()
raise AzureOpenAIError(status_code=400, message=json.dumps(error_data)) raise AzureOpenAIError(status_code=400, message=json.dumps(error_data))
return response result = response.json()["result"]
return httpx.Response(
status_code=200,
headers=response.headers,
content=json.dumps(result).encode("utf-8"),
request=httpx.Request(method="POST", url="https://api.openai.com/v1"),
)
return sync_handler.post( return sync_handler.post(
url=api_base, url=api_base,
json=data, json=data,
@ -1323,7 +1335,7 @@ class AzureChatCompletion(BaseLLM):
api_key=api_key, api_key=api_key,
data=data, data=data,
) )
response = httpx_response.json()["result"] response = httpx_response.json()
stringified_response = response stringified_response = response
## LOGGING ## LOGGING
@ -1430,7 +1442,7 @@ class AzureChatCompletion(BaseLLM):
api_key=api_key or "", api_key=api_key or "",
data=data, data=data,
) )
response = httpx_response.json()["result"] response = httpx_response.json()
## LOGGING ## LOGGING
logging_obj.post_call( logging_obj.post_call(

5
litellm/llms/bedrock_httpx.py
View file

@ -1394,7 +1394,7 @@ class BedrockConverseLLM(BaseLLM):
content_str = "" content_str = ""
tools: List[ChatCompletionToolCallChunk] = [] tools: List[ChatCompletionToolCallChunk] = []
if message is not None: if message is not None:
for content in message["content"]: for idx, content in enumerate(message["content"]):
""" """
- Content is either a tool response or text - Content is either a tool response or text
""" """
@ -1409,6 +1409,7 @@ class BedrockConverseLLM(BaseLLM):
id=content["toolUse"]["toolUseId"], id=content["toolUse"]["toolUseId"],
type="function", type="function",
function=_function_chunk, function=_function_chunk,
index=idx,
) )
tools.append(_tool_response_chunk) tools.append(_tool_response_chunk)
chat_completion_message["content"] = content_str chat_completion_message["content"] = content_str
@ -2001,6 +2002,7 @@ class AWSEventStreamDecoder:
"name": start_obj["toolUse"]["name"], "name": start_obj["toolUse"]["name"],
"arguments": "", "arguments": "",
}, },
"index": index,
} }
elif "delta" in chunk_data: elif "delta" in chunk_data:
delta_obj = ContentBlockDeltaEvent(**chunk_data["delta"]) delta_obj = ContentBlockDeltaEvent(**chunk_data["delta"])
@ -2014,6 +2016,7 @@ class AWSEventStreamDecoder:
"name": None, "name": None,
"arguments": delta_obj["toolUse"]["input"], "arguments": delta_obj["toolUse"]["input"],
}, },
"index": index,
} }
elif "stopReason" in chunk_data: elif "stopReason" in chunk_data:
finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop")) finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop"))

34
litellm/llms/cohere_chat.py
View file

@ -1,13 +1,19 @@
import os, types
import json import json
import os
import time
import traceback
import types
from enum import Enum from enum import Enum
import requests # type: ignore
import time, traceback
from typing import Callable, Optional from typing import Callable, Optional
from litellm.utils import ModelResponse, Choices, Message, Usage
import litellm
import httpx # type: ignore import httpx # type: ignore
from .prompt_templates.factory import cohere_message_pt import requests # type: ignore
import litellm
from litellm.types.llms.cohere import ToolResultObject
from litellm.utils import Choices, Message, ModelResponse, Usage
from .prompt_templates.factory import cohere_message_pt, cohere_messages_pt_v2
class CohereError(Exception): class CohereError(Exception):
@ -196,17 +202,17 @@ def completion(
api_base: str, api_base: str,
model_response: ModelResponse, model_response: ModelResponse,
print_verbose: Callable, print_verbose: Callable,
optional_params: dict,
encoding, encoding,
api_key, api_key,
logging_obj, logging_obj,
optional_params=None,
litellm_params=None, litellm_params=None,
logger_fn=None, logger_fn=None,
): ):
headers = validate_environment(api_key) headers = validate_environment(api_key)
completion_url = api_base completion_url = api_base
model = model model = model
prompt, tool_results = cohere_message_pt(messages=messages) most_recent_message, chat_history = cohere_messages_pt_v2(messages=messages)
## Load Config ## Load Config
config = litellm.CohereConfig.get_config() config = litellm.CohereConfig.get_config()
@ -221,18 +227,18 @@ def completion(
_is_function_call = True _is_function_call = True
cohere_tools = construct_cohere_tool(tools=optional_params["tools"]) cohere_tools = construct_cohere_tool(tools=optional_params["tools"])
optional_params["tools"] = cohere_tools optional_params["tools"] = cohere_tools
if len(tool_results) > 0: if isinstance(most_recent_message, dict):
optional_params["tool_results"] = tool_results optional_params["tool_results"] = [most_recent_message]
elif isinstance(most_recent_message, str):
optional_params["message"] = most_recent_message
data = { data = {
"model": model, "model": model,
"message": prompt,
**optional_params, **optional_params,
} }
## LOGGING ## LOGGING
logging_obj.pre_call( logging_obj.pre_call(
input=prompt, input=most_recent_message,
api_key=api_key, api_key=api_key,
additional_args={ additional_args={
"complete_input_dict": data, "complete_input_dict": data,
@ -256,7 +262,7 @@ def completion(
else: else:
## LOGGING ## LOGGING
logging_obj.post_call( logging_obj.post_call(
input=prompt, input=most_recent_message,
api_key=api_key, api_key=api_key,
original_response=response.text, original_response=response.text,
additional_args={"complete_input_dict": data}, additional_args={"complete_input_dict": data},

65
litellm/llms/nvidia_nim.py
View file

@ -58,7 +58,33 @@ class NvidiaNimConfig:
and v is not None and v is not None
} }
def get_supported_openai_params(self): def get_supported_openai_params(self, model: str) -> list:
"""
Get the supported OpenAI params for the given model
Updated on July 5th, 2024 - based on https://docs.api.nvidia.com/nim/reference
"""
if model in [
"google/recurrentgemma-2b",
"google/gemma-2-27b-it",
"google/gemma-2-9b-it",
"gemma-2-9b-it",
]:
return ["stream", "temperature", "top_p", "max_tokens", "stop", "seed"]
elif model == "nvidia/nemotron-4-340b-instruct":
return [
"stream",
"temperature",
"top_p",
"max_tokens",
]
elif model == "nvidia/nemotron-4-340b-reward":
return [
"stream",
]
elif model in ["google/codegemma-1.1-7b"]:
# most params - but no 'seed' :(
return [ return [
"stream", "stream",
"temperature", "temperature",
@ -68,11 +94,44 @@ class NvidiaNimConfig:
"max_tokens", "max_tokens",
"stop", "stop",
] ]
else:
# DEFAULT Case - The vast majority of Nvidia NIM Models lie here
# "upstage/solar-10.7b-instruct",
# "snowflake/arctic",
# "seallms/seallm-7b-v2.5",
# "nvidia/llama3-chatqa-1.5-8b",
# "nvidia/llama3-chatqa-1.5-70b",
# "mistralai/mistral-large",
# "mistralai/mixtral-8x22b-instruct-v0.1",
# "mistralai/mixtral-8x7b-instruct-v0.1",
# "mistralai/mistral-7b-instruct-v0.3",
# "mistralai/mistral-7b-instruct-v0.2",
# "mistralai/codestral-22b-instruct-v0.1",
# "microsoft/phi-3-small-8k-instruct",
# "microsoft/phi-3-small-128k-instruct",
# "microsoft/phi-3-mini-4k-instruct",
# "microsoft/phi-3-mini-128k-instruct",
# "microsoft/phi-3-medium-4k-instruct",
# "microsoft/phi-3-medium-128k-instruct",
# "meta/llama3-70b-instruct",
# "meta/llama3-8b-instruct",
# "meta/llama2-70b",
# "meta/codellama-70b",
return [
"stream",
"temperature",
"top_p",
"frequency_penalty",
"presence_penalty",
"max_tokens",
"stop",
"seed",
]
def map_openai_params( def map_openai_params(
self, non_default_params: dict, optional_params: dict self, model: str, non_default_params: dict, optional_params: dict
) -> dict: ) -> dict:
supported_openai_params = self.get_supported_openai_params() supported_openai_params = self.get_supported_openai_params(model=model)
for param, value in non_default_params.items(): for param, value in non_default_params.items():
if param in supported_openai_params: if param in supported_openai_params:
optional_params[param] = value optional_params[param] = value

6
litellm/llms/ollama_chat.py
View file

@ -501,8 +501,10 @@ async def ollama_acompletion(
{ {
"id": f"call_{str(uuid.uuid4())}", "id": f"call_{str(uuid.uuid4())}",
"function": { "function": {
"name": function_call["name"], "name": function_call.get("name", function_name),
"arguments": json.dumps(function_call["arguments"]), "arguments": json.dumps(
function_call.get("arguments", function_call)
),
}, },
"type": "function", "type": "function",
} }

323
litellm/llms/prompt_templates/factory.py
View file

@ -547,10 +547,13 @@ def ibm_granite_pt(messages: list):
}, },
"user": { "user": {
"pre_message": "<|user|>\n", "pre_message": "<|user|>\n",
"post_message": "\n", # Assistant tag is needed in the prompt after the user message
# to avoid the model completing the users sentence before it answers
# https://www.ibm.com/docs/en/watsonx/w-and-w/2.0.x?topic=models-granite-13b-chat-v2-prompting-tips#chat
"post_message": "\n<|assistant|>\n",
}, },
"assistant": { "assistant": {
"pre_message": "<|assistant|>\n", "pre_message": "",
"post_message": "\n", "post_message": "\n",
}, },
}, },
@ -1022,16 +1025,17 @@ def convert_to_gemini_tool_call_invoke(
def convert_to_gemini_tool_call_result( def convert_to_gemini_tool_call_result(
message: dict, message: dict,
last_message_with_tool_calls: Optional[dict],
) -> litellm.types.llms.vertex_ai.PartType: ) -> litellm.types.llms.vertex_ai.PartType:
""" """
OpenAI message with a tool result looks like: OpenAI message with a tool result looks like:
{ {
"tool_call_id": "tool_1", "tool_call_id": "tool_1",
"role": "tool", "role": "tool",
"name": "get_current_weather",
"content": "function result goes here", "content": "function result goes here",
}, },
# NOTE: Function messages have been deprecated
OpenAI message with a function call result looks like: OpenAI message with a function call result looks like:
{ {
"role": "function", "role": "function",
@ -1040,7 +1044,23 @@ def convert_to_gemini_tool_call_result(
} }
""" """
content = message.get("content", "") content = message.get("content", "")
name = message.get("name", "") name = ""
# Recover name from last message with tool calls
if last_message_with_tool_calls:
tools = last_message_with_tool_calls.get("tool_calls", [])
msg_tool_call_id = message.get("tool_call_id", None)
for tool in tools:
prev_tool_call_id = tool.get("id", None)
if (
msg_tool_call_id
and prev_tool_call_id
and msg_tool_call_id == prev_tool_call_id
):
name = tool.get("function", {}).get("name", "")
if not name:
raise Exception("Missing corresponding tool call for tool response message")
# We can't determine from openai message format whether it's a successful or # We can't determine from openai message format whether it's a successful or
# error call result so default to the successful result template # error call result so default to the successful result template
@ -1279,7 +1299,9 @@ def anthropic_messages_pt(messages: list):
) )
else: else:
raise Exception( raise Exception(
"Invalid first message. Should always start with 'role'='user' for Anthropic. System prompt is sent separately for Anthropic. set 'litellm.modify_params = True' or 'litellm_settings:modify_params = True' on proxy, to insert a placeholder user message - '.' as the first message, " "Invalid first message={}. Should always start with 'role'='user' for Anthropic. System prompt is sent separately for Anthropic. set 'litellm.modify_params = True' or 'litellm_settings:modify_params = True' on proxy, to insert a placeholder user message - '.' as the first message, ".format(
new_messages
)
) )
if new_messages[-1]["role"] == "assistant": if new_messages[-1]["role"] == "assistant":
@ -1393,16 +1415,37 @@ def convert_to_documents(
return documents return documents
def convert_openai_message_to_cohere_tool_result(message): from litellm.types.llms.cohere import (
CallObject,
ChatHistory,
ChatHistoryChatBot,
ChatHistorySystem,
ChatHistoryToolResult,
ChatHistoryUser,
ToolCallObject,
ToolResultObject,
)
def convert_openai_message_to_cohere_tool_result(
message, tool_calls: List
) -> ToolResultObject:
""" """
OpenAI message with a tool result looks like: OpenAI message with a tool result looks like:
{ {
"tool_call_id": "tool_1", "tool_call_id": "tool_1",
"role": "tool", "role": "tool",
"name": "get_current_weather",
"content": {"location": "San Francisco, CA", "unit": "fahrenheit", "temperature": "72"}, "content": {"location": "San Francisco, CA", "unit": "fahrenheit", "temperature": "72"},
}, },
""" """
"""
OpenAI message with a function call looks like:
{
"role": "function",
"name": "get_current_weather",
"content": "function result goes here",
}
"""
""" """
Cohere tool_results look like: Cohere tool_results look like:
@ -1412,7 +1455,6 @@ def convert_openai_message_to_cohere_tool_result(message):
"parameters": { "parameters": {
"day": "2023-09-29" "day": "2023-09-29"
}, },
"generation_id": "4807c924-9003-4d6b-8069-eda03962c465"
}, },
"outputs": [ "outputs": [
{ {
@ -1422,30 +1464,255 @@ def convert_openai_message_to_cohere_tool_result(message):
] ]
}, },
""" """
content_str: str = message.get("content", "")
if len(content_str) > 0:
try:
content = json.loads(content_str)
except json.JSONDecodeError:
content = {"result": content_str}
else:
content = {}
name = ""
arguments = {}
# Recover name from last message with tool calls
if len(tool_calls) > 0:
tools = tool_calls
msg_tool_call_id = message.get("tool_call_id", None)
for tool in tools:
prev_tool_call_id = tool.get("id", None)
if (
msg_tool_call_id
and prev_tool_call_id
and msg_tool_call_id == prev_tool_call_id
):
name = tool.get("function", {}).get("name", "")
arguments_str = tool.get("function", {}).get("arguments", "")
if arguments_str is not None and len(arguments_str) > 0:
arguments = json.loads(arguments_str)
tool_call_id = message.get("tool_call_id") if message["role"] == "function":
name = message.get("name") name = message.get("name")
content = message.get("content") cohere_tool_result: ToolResultObject = {
"call": CallObject(name=name, parameters=arguments),
"outputs": [content],
}
return cohere_tool_result
else:
# We can't determine from openai message format whether it's a successful or
# error call result so default to the successful result template
# Create the Cohere tool_result dictionary
cohere_tool_result = { cohere_tool_result = {
"call": { "call": CallObject(name=name, parameters=arguments),
"name": name, "outputs": [content],
"parameters": {"location": "San Francisco, CA"},
"generation_id": tool_call_id,
},
"outputs": convert_to_documents(content),
} }
return cohere_tool_result return cohere_tool_result
def get_all_tool_calls(messages: List) -> List:
"""
Returns extracted list of `tool_calls`.
Done to handle openai no longer returning tool call 'name' in tool results.
"""
tool_calls: List = []
for m in messages:
if m.get("tool_calls", None) is not None:
if isinstance(m["tool_calls"], list):
tool_calls.extend(m["tool_calls"])
return tool_calls
def convert_to_cohere_tool_invoke(tool_calls: list) -> List[ToolCallObject]:
"""
OpenAI tool invokes:
{
"role": "assistant",
"content": null,
"tool_calls": [
{
"id": "call_abc123",
"type": "function",
"function": {
"name": "get_current_weather",
"arguments": "{\n\"location\": \"Boston, MA\"\n}"
}
}
]
},
"""
"""
Cohere tool invokes:
{
"role": "CHATBOT",
"tool_calls": [{"name": "get_weather", "parameters": {"location": "San Francisco, CA"}}]
}
"""
cohere_tool_invoke: List[ToolCallObject] = [
{
"name": get_attribute_or_key(
get_attribute_or_key(tool, "function"), "name"
),
"parameters": json.loads(
get_attribute_or_key(
get_attribute_or_key(tool, "function"), "arguments"
)
),
}
for tool in tool_calls
if get_attribute_or_key(tool, "type") == "function"
]
return cohere_tool_invoke
def cohere_messages_pt_v2(
messages: List,
) -> Tuple[Union[str, ToolResultObject], ChatHistory]:
"""
Returns a tuple(Union[tool_result, message], chat_history)
- if last message is tool result -> return 'tool_result'
- if last message is text -> return message (str)
- return preceding messages as 'chat_history'
Note:
- cannot specify message if the last entry in chat history contains tool results
- message must be at least 1 token long or tool results must be specified.
"""
tool_calls: List = get_all_tool_calls(messages=messages)
## GET MOST RECENT MESSAGE
most_recent_message = messages.pop(-1)
returned_message: Union[ToolResultObject, str] = ""
if (
most_recent_message.get("role", "") is not None
and most_recent_message["role"] == "tool"
):
# tool result
returned_message = convert_openai_message_to_cohere_tool_result(
most_recent_message, tool_calls
)
else:
content: Union[str, List] = most_recent_message.get("content")
if isinstance(content, str):
returned_message = content
else:
for chunk in content:
if chunk.get("type") == "text":
returned_message += chunk.get("text")
## CREATE CHAT HISTORY
user_message_types = {"user"}
tool_message_types = {"tool", "function"}
# reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them.
new_messages: ChatHistory = []
msg_i = 0
while msg_i < len(messages):
user_content: str = ""
init_msg_i = msg_i
## MERGE CONSECUTIVE USER CONTENT ##
while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types:
if isinstance(messages[msg_i]["content"], list):
for m in messages[msg_i]["content"]:
if m.get("type", "") == "text":
user_content += m["text"]
else:
user_content += messages[msg_i]["content"]
msg_i += 1
if len(user_content) > 0:
new_messages.append(ChatHistoryUser(role="USER", message=user_content))
system_content: str = ""
## MERGE CONSECUTIVE SYSTEM CONTENT ##
while msg_i < len(messages) and messages[msg_i]["role"] == "system":
if isinstance(messages[msg_i]["content"], list):
for m in messages[msg_i]["content"]:
if m.get("type", "") == "text":
system_content += m["text"]
else:
system_content += messages[msg_i]["content"]
msg_i += 1
if len(system_content) > 0:
new_messages.append(
ChatHistorySystem(role="SYSTEM", message=system_content)
)
assistant_content: str = ""
assistant_tool_calls: List[ToolCallObject] = []
## MERGE CONSECUTIVE ASSISTANT CONTENT ##
while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
assistant_text = (
messages[msg_i].get("content") or ""
) # either string or none
if assistant_text:
assistant_content += assistant_text
if messages[msg_i].get(
"tool_calls", []
): # support assistant tool invoke conversion
assistant_tool_calls.extend(
convert_to_cohere_tool_invoke(messages[msg_i]["tool_calls"])
)
if messages[msg_i].get("function_call"):
assistant_tool_calls.extend(
convert_to_cohere_tool_invoke(messages[msg_i]["function_call"])
)
msg_i += 1
if len(assistant_content) > 0:
new_messages.append(
ChatHistoryChatBot(
role="CHATBOT",
message=assistant_content,
tool_calls=assistant_tool_calls,
)
)
## MERGE CONSECUTIVE TOOL RESULTS
tool_results: List[ToolResultObject] = []
while msg_i < len(messages) and messages[msg_i]["role"] in tool_message_types:
tool_results.append(
convert_openai_message_to_cohere_tool_result(
messages[msg_i], tool_calls
)
)
msg_i += 1
if len(tool_results) > 0:
new_messages.append(
ChatHistoryToolResult(role="TOOL", tool_results=tool_results)
)
if msg_i == init_msg_i: # prevent infinite loops
raise Exception(
"Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
messages[msg_i]
)
)
return returned_message, new_messages
def cohere_message_pt(messages: list): def cohere_message_pt(messages: list):
tool_calls: List = get_all_tool_calls(messages=messages)
prompt = "" prompt = ""
tool_results = [] tool_results = []
for message in messages: for message in messages:
# check if this is a tool_call result # check if this is a tool_call result
if message["role"] == "tool": if message["role"] == "tool":
tool_result = convert_openai_message_to_cohere_tool_result(message) tool_result = convert_openai_message_to_cohere_tool_result(
message, tool_calls=tool_calls
)
tool_results.append(tool_result) tool_results.append(tool_result)
elif message.get("content"): elif message.get("content"):
prompt += message["content"] + "\n\n" prompt += message["content"] + "\n\n"
@ -1636,6 +1903,26 @@ def azure_text_pt(messages: list):
return prompt return prompt
###### AZURE AI #######
def stringify_json_tool_call_content(messages: List) -> List:
"""
- Check 'content' in tool role -> convert to dict (if not) -> stringify
Done for azure_ai/cohere calls to handle results of a tool call
"""
for m in messages:
if m["role"] == "tool" and isinstance(m["content"], str):
# check if content is a valid json object
try:
json.loads(m["content"])
except json.JSONDecodeError:
m["content"] = json.dumps({"result": m["content"]})
return messages
###### AMAZON BEDROCK ####### ###### AMAZON BEDROCK #######
from litellm.types.llms.bedrock import ContentBlock as BedrockContentBlock from litellm.types.llms.bedrock import ContentBlock as BedrockContentBlock

8
litellm/llms/replicate.py
View file

@ -295,7 +295,15 @@ def handle_prediction_response_streaming(prediction_url, api_token, print_verbos
response_data = response.json() response_data = response.json()
status = response_data["status"] status = response_data["status"]
if "output" in response_data: if "output" in response_data:
try:
output_string = "".join(response_data["output"]) output_string = "".join(response_data["output"])
except Exception as e:
raise ReplicateError(
status_code=422,
message="Unable to parse response. Got={}".format(
response_data["output"]
),
)
new_output = output_string[len(previous_output) :] new_output = output_string[len(previous_output) :]
print_verbose(f"New chunk: {new_output}") print_verbose(f"New chunk: {new_output}")
yield {"output": new_output, "status": status} yield {"output": new_output, "status": status}

17
litellm/llms/sagemaker.py
View file

@ -9,6 +9,7 @@ from litellm.utils import ModelResponse, EmbeddingResponse, get_secret, Usage
import sys import sys
from copy import deepcopy from copy import deepcopy
import httpx # type: ignore import httpx # type: ignore
import io
from .prompt_templates.factory import prompt_factory, custom_prompt from .prompt_templates.factory import prompt_factory, custom_prompt
@ -25,10 +26,6 @@ class SagemakerError(Exception):
) # Call the base class constructor with the parameters it needs ) # Call the base class constructor with the parameters it needs
import io
import json
class TokenIterator: class TokenIterator:
def __init__(self, stream, acompletion: bool = False): def __init__(self, stream, acompletion: bool = False):
if acompletion == False: if acompletion == False:
@ -185,7 +182,8 @@ def completion(
# I assume majority of users use .env for auth # I assume majority of users use .env for auth
region_name = ( region_name = (
get_secret("AWS_REGION_NAME") get_secret("AWS_REGION_NAME")
or "us-west-2" # default to us-west-2 if user not specified or aws_region_name # get region from config file if specified
or "us-west-2" # default to us-west-2 if region not specified
) )
client = boto3.client( client = boto3.client(
service_name="sagemaker-runtime", service_name="sagemaker-runtime",
@ -439,7 +437,8 @@ async def async_streaming(
# I assume majority of users use .env for auth # I assume majority of users use .env for auth
region_name = ( region_name = (
get_secret("AWS_REGION_NAME") get_secret("AWS_REGION_NAME")
or "us-west-2" # default to us-west-2 if user not specified or aws_region_name # get region from config file if specified
or "us-west-2" # default to us-west-2 if region not specified
) )
_client = session.client( _client = session.client(
service_name="sagemaker-runtime", service_name="sagemaker-runtime",
@ -506,7 +505,8 @@ async def async_completion(
# I assume majority of users use .env for auth # I assume majority of users use .env for auth
region_name = ( region_name = (
get_secret("AWS_REGION_NAME") get_secret("AWS_REGION_NAME")
or "us-west-2" # default to us-west-2 if user not specified or aws_region_name # get region from config file if specified
or "us-west-2" # default to us-west-2 if region not specified
) )
_client = session.client( _client = session.client(
service_name="sagemaker-runtime", service_name="sagemaker-runtime",
@ -661,7 +661,8 @@ def embedding(
# I assume majority of users use .env for auth # I assume majority of users use .env for auth
region_name = ( region_name = (
get_secret("AWS_REGION_NAME") get_secret("AWS_REGION_NAME")
or "us-west-2" # default to us-west-2 if user not specified or aws_region_name # get region from config file if specified
or "us-west-2" # default to us-west-2 if region not specified
) )
client = boto3.client( client = boto3.client(
service_name="sagemaker-runtime", service_name="sagemaker-runtime",

8
litellm/llms/vertex_ai.py
View file

@ -155,6 +155,7 @@ class VertexAIConfig:
"response_format", "response_format",
"n", "n",
"stop", "stop",
"extra_headers",
] ]
def map_openai_params(self, non_default_params: dict, optional_params: dict): def map_openai_params(self, non_default_params: dict, optional_params: dict):
@ -328,6 +329,8 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
user_message_types = {"user", "system"} user_message_types = {"user", "system"}
contents: List[ContentType] = [] contents: List[ContentType] = []
last_message_with_tool_calls = None
msg_i = 0 msg_i = 0
try: try:
while msg_i < len(messages): while msg_i < len(messages):
@ -383,6 +386,7 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
messages[msg_i]["tool_calls"] messages[msg_i]["tool_calls"]
) )
) )
last_message_with_tool_calls = messages[msg_i]
else: else:
assistant_text = ( assistant_text = (
messages[msg_i].get("content") or "" messages[msg_i].get("content") or ""
@ -397,7 +401,9 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
## APPEND TOOL CALL MESSAGES ## ## APPEND TOOL CALL MESSAGES ##
if msg_i < len(messages) and messages[msg_i]["role"] == "tool": if msg_i < len(messages) and messages[msg_i]["role"] == "tool":
_part = convert_to_gemini_tool_call_result(messages[msg_i]) _part = convert_to_gemini_tool_call_result(
messages[msg_i], last_message_with_tool_calls
)
contents.append(ContentType(parts=[_part])) # type: ignore contents.append(ContentType(parts=[_part])) # type: ignore
msg_i += 1 msg_i += 1
if msg_i == init_msg_i: # prevent infinite loops if msg_i == init_msg_i: # prevent infinite loops

240
litellm/llms/vertex_ai_anthropic.py
View file

@ -15,6 +15,7 @@ import requests # type: ignore
import litellm import litellm
from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.litellm_core_utils.core_helpers import map_finish_reason
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.types.llms.anthropic import AnthropicMessagesToolChoice
from litellm.types.utils import ResponseFormatChunk from litellm.types.utils import ResponseFormatChunk
from litellm.utils import CustomStreamWrapper, ModelResponse, Usage from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
@ -121,6 +122,17 @@ class VertexAIAnthropicConfig:
optional_params["max_tokens"] = value optional_params["max_tokens"] = value
if param == "tools": if param == "tools":
optional_params["tools"] = value optional_params["tools"] = value
if param == "tool_choice":
_tool_choice: Optional[AnthropicMessagesToolChoice] = None
if value == "auto":
_tool_choice = {"type": "auto"}
elif value == "required":
_tool_choice = {"type": "any"}
elif isinstance(value, dict):
_tool_choice = {"type": "tool", "name": value["function"]["name"]}
if _tool_choice is not None:
optional_params["tool_choice"] = _tool_choice
if param == "stream": if param == "stream":
optional_params["stream"] = value optional_params["stream"] = value
if param == "stop": if param == "stop":
@ -177,17 +189,29 @@ def get_vertex_client(
_credentials, cred_project_id = VertexLLM().load_auth( _credentials, cred_project_id = VertexLLM().load_auth(
credentials=vertex_credentials, project_id=vertex_project credentials=vertex_credentials, project_id=vertex_project
) )
vertex_ai_client = AnthropicVertex( vertex_ai_client = AnthropicVertex(
project_id=vertex_project or cred_project_id, project_id=vertex_project or cred_project_id,
region=vertex_location or "us-central1", region=vertex_location or "us-central1",
access_token=_credentials.token, access_token=_credentials.token,
) )
access_token = _credentials.token
else: else:
vertex_ai_client = client vertex_ai_client = client
access_token = client.access_token
return vertex_ai_client, access_token return vertex_ai_client, access_token
def create_vertex_anthropic_url(
vertex_location: str, vertex_project: str, model: str, stream: bool
) -> str:
if stream is True:
return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/anthropic/models/{model}:streamRawPredict"
else:
return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/anthropic/models/{model}:rawPredict"
def completion( def completion(
model: str, model: str,
messages: list, messages: list,
@ -196,6 +220,8 @@ def completion(
encoding, encoding,
logging_obj, logging_obj,
optional_params: dict, optional_params: dict,
custom_prompt_dict: dict,
headers: Optional[dict],
vertex_project=None, vertex_project=None,
vertex_location=None, vertex_location=None,
vertex_credentials=None, vertex_credentials=None,
@ -207,6 +233,9 @@ def completion(
try: try:
import vertexai import vertexai
from anthropic import AnthropicVertex from anthropic import AnthropicVertex
from litellm.llms.anthropic import AnthropicChatCompletion
from litellm.llms.vertex_httpx import VertexLLM
except: except:
raise VertexAIError( raise VertexAIError(
status_code=400, status_code=400,
@ -222,203 +251,58 @@ def completion(
) )
try: try:
vertex_ai_client, access_token = get_vertex_client( vertex_httpx_logic = VertexLLM()
client=client,
vertex_project=vertex_project, access_token, project_id = vertex_httpx_logic._ensure_access_token(
vertex_location=vertex_location, credentials=vertex_credentials, project_id=vertex_project
vertex_credentials=vertex_credentials,
) )
anthropic_chat_completions = AnthropicChatCompletion()
## Load Config ## Load Config
config = litellm.VertexAIAnthropicConfig.get_config() config = litellm.VertexAIAnthropicConfig.get_config()
for k, v in config.items(): for k, v in config.items():
if k not in optional_params: if k not in optional_params:
optional_params[k] = v optional_params[k] = v
## Format Prompt ## CONSTRUCT API BASE
_is_function_call = False stream = optional_params.get("stream", False)
_is_json_schema = False
messages = copy.deepcopy(messages) api_base = create_vertex_anthropic_url(
optional_params = copy.deepcopy(optional_params) vertex_location=vertex_location or "us-central1",
# Separate system prompt from rest of message vertex_project=vertex_project or project_id,
system_prompt_indices = []
system_prompt = ""
for idx, message in enumerate(messages):
if message["role"] == "system":
system_prompt += message["content"]
system_prompt_indices.append(idx)
if len(system_prompt_indices) > 0:
for idx in reversed(system_prompt_indices):
messages.pop(idx)
if len(system_prompt) > 0:
optional_params["system"] = system_prompt
# Checks for 'response_schema' support - if passed in
if "response_format" in optional_params:
response_format_chunk = ResponseFormatChunk(
**optional_params["response_format"] # type: ignore
)
supports_response_schema = litellm.supports_response_schema(
model=model, custom_llm_provider="vertex_ai"
)
if (
supports_response_schema is False
and response_format_chunk["type"] == "json_object"
and "response_schema" in response_format_chunk
):
_is_json_schema = True
user_response_schema_message = response_schema_prompt(
model=model, model=model,
response_schema=response_format_chunk["response_schema"], stream=stream,
)
messages.append(
{"role": "user", "content": user_response_schema_message}
)
messages.append({"role": "assistant", "content": "{"})
optional_params.pop("response_format")
# Format rest of message according to anthropic guidelines
try:
messages = prompt_factory(
model=model, messages=messages, custom_llm_provider="anthropic_xml"
)
except Exception as e:
raise VertexAIError(status_code=400, message=str(e))
## Handle Tool Calling
if "tools" in optional_params:
_is_function_call = True
tool_calling_system_prompt = construct_tool_use_system_prompt(
tools=optional_params["tools"]
)
optional_params["system"] = (
optional_params.get("system", "\n") + tool_calling_system_prompt
) # add the anthropic tool calling prompt to the system prompt
optional_params.pop("tools")
stream = optional_params.pop("stream", None)
data = {
"model": model,
"messages": messages,
**optional_params,
}
print_verbose(f"_is_function_call: {_is_function_call}")
## Completion Call
print_verbose(
f"VERTEX AI: vertex_project={vertex_project}; vertex_location={vertex_location}; vertex_credentials={vertex_credentials}"
) )
if acompletion == True: if headers is not None:
""" vertex_headers = headers
- async streaming else:
- async completion vertex_headers = {}
"""
if stream is not None and stream == True: vertex_headers.update({"Authorization": "Bearer {}".format(access_token)})
return async_streaming(
optional_params.update(
{"anthropic_version": "vertex-2023-10-16", "is_vertex_request": True}
)
return anthropic_chat_completions.completion(
model=model, model=model,
messages=messages, messages=messages,
data=data, api_base=api_base,
print_verbose=print_verbose, custom_prompt_dict=custom_prompt_dict,
model_response=model_response, model_response=model_response,
logging_obj=logging_obj,
vertex_project=vertex_project,
vertex_location=vertex_location,
optional_params=optional_params,
client=client,
access_token=access_token,
)
else:
return async_completion(
model=model,
messages=messages,
data=data,
print_verbose=print_verbose, print_verbose=print_verbose,
model_response=model_response, encoding=encoding,
api_key=access_token,
logging_obj=logging_obj, logging_obj=logging_obj,
vertex_project=vertex_project,
vertex_location=vertex_location,
optional_params=optional_params, optional_params=optional_params,
client=client, acompletion=acompletion,
access_token=access_token, litellm_params=litellm_params,
) logger_fn=logger_fn,
if stream is not None and stream == True: headers=vertex_headers,
## LOGGING
logging_obj.pre_call(
input=messages,
api_key=None,
additional_args={
"complete_input_dict": optional_params,
},
)
response = vertex_ai_client.messages.create(**data, stream=True) # type: ignore
return response
## LOGGING
logging_obj.pre_call(
input=messages,
api_key=None,
additional_args={
"complete_input_dict": optional_params,
},
) )
message = vertex_ai_client.messages.create(**data) # type: ignore
## LOGGING
logging_obj.post_call(
input=messages,
api_key="",
original_response=message,
additional_args={"complete_input_dict": data},
)
text_content: str = message.content[0].text
## TOOL CALLING - OUTPUT PARSE
if text_content is not None and contains_tag("invoke", text_content):
function_name = extract_between_tags("tool_name", text_content)[0]
function_arguments_str = extract_between_tags("invoke", text_content)[
0
].strip()
function_arguments_str = f"<invoke>{function_arguments_str}</invoke>"
function_arguments = parse_xml_params(function_arguments_str)
_message = litellm.Message(
tool_calls=[
{
"id": f"call_{uuid.uuid4()}",
"type": "function",
"function": {
"name": function_name,
"arguments": json.dumps(function_arguments),
},
}
],
content=None,
)
model_response.choices[0].message = _message # type: ignore
else:
if (
_is_json_schema
): # follows https://github.com/anthropics/anthropic-cookbook/blob/main/misc/how_to_enable_json_mode.ipynb
json_response = "{" + text_content[: text_content.rfind("}") + 1]
model_response.choices[0].message.content = json_response # type: ignore
else:
model_response.choices[0].message.content = text_content # type: ignore
model_response.choices[0].finish_reason = map_finish_reason(message.stop_reason)
## CALCULATING USAGE
prompt_tokens = message.usage.input_tokens
completion_tokens = message.usage.output_tokens
model_response["created"] = int(time.time())
model_response["model"] = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
setattr(model_response, "usage", usage)
return model_response
except Exception as e: except Exception as e:
raise VertexAIError(status_code=500, message=str(e)) raise VertexAIError(status_code=500, message=str(e))

72
litellm/llms/vertex_httpx.py
View file

@ -603,15 +603,15 @@ class VertexLLM(BaseLLM):
## GET USAGE ## ## GET USAGE ##
usage = litellm.Usage( usage = litellm.Usage(
prompt_tokens=completion_response["usageMetadata"][ prompt_tokens=completion_response["usageMetadata"].get(
"promptTokenCount" "promptTokenCount", 0
], ),
completion_tokens=completion_response["usageMetadata"].get( completion_tokens=completion_response["usageMetadata"].get(
"candidatesTokenCount", 0 "candidatesTokenCount", 0
), ),
total_tokens=completion_response["usageMetadata"][ total_tokens=completion_response["usageMetadata"].get(
"totalTokenCount" "totalTokenCount", 0
], ),
) )
setattr(model_response, "usage", usage) setattr(model_response, "usage", usage)
@ -647,15 +647,15 @@ class VertexLLM(BaseLLM):
## GET USAGE ## ## GET USAGE ##
usage = litellm.Usage( usage = litellm.Usage(
prompt_tokens=completion_response["usageMetadata"][ prompt_tokens=completion_response["usageMetadata"].get(
"promptTokenCount" "promptTokenCount", 0
], ),
completion_tokens=completion_response["usageMetadata"].get( completion_tokens=completion_response["usageMetadata"].get(
"candidatesTokenCount", 0 "candidatesTokenCount", 0
), ),
total_tokens=completion_response["usageMetadata"][ total_tokens=completion_response["usageMetadata"].get(
"totalTokenCount" "totalTokenCount", 0
], ),
) )
setattr(model_response, "usage", usage) setattr(model_response, "usage", usage)
@ -687,6 +687,7 @@ class VertexLLM(BaseLLM):
id=f"call_{str(uuid.uuid4())}", id=f"call_{str(uuid.uuid4())}",
type="function", type="function",
function=_function_chunk, function=_function_chunk,
index=candidate.get("index", idx),
) )
tools.append(_tool_response_chunk) tools.append(_tool_response_chunk)
@ -705,11 +706,15 @@ class VertexLLM(BaseLLM):
## GET USAGE ## ## GET USAGE ##
usage = litellm.Usage( usage = litellm.Usage(
prompt_tokens=completion_response["usageMetadata"]["promptTokenCount"], prompt_tokens=completion_response["usageMetadata"].get(
"promptTokenCount", 0
),
completion_tokens=completion_response["usageMetadata"].get( completion_tokens=completion_response["usageMetadata"].get(
"candidatesTokenCount", 0 "candidatesTokenCount", 0
), ),
total_tokens=completion_response["usageMetadata"]["totalTokenCount"], total_tokens=completion_response["usageMetadata"].get(
"totalTokenCount", 0
),
) )
setattr(model_response, "usage", usage) setattr(model_response, "usage", usage)
@ -748,10 +753,12 @@ class VertexLLM(BaseLLM):
if project_id is None: if project_id is None:
project_id = creds.project_id project_id = creds.project_id
else: else:
creds, project_id = google_auth.default( creds, creds_project_id = google_auth.default(
quota_project_id=project_id, quota_project_id=project_id,
scopes=["https://www.googleapis.com/auth/cloud-platform"], scopes=["https://www.googleapis.com/auth/cloud-platform"],
) )
if project_id is None:
project_id = creds_project_id
creds.refresh(Request()) creds.refresh(Request())
@ -1035,9 +1042,7 @@ class VertexLLM(BaseLLM):
safety_settings: Optional[List[SafetSettingsConfig]] = optional_params.pop( safety_settings: Optional[List[SafetSettingsConfig]] = optional_params.pop(
"safety_settings", None "safety_settings", None
) # type: ignore ) # type: ignore
cached_content: Optional[str] = optional_params.pop( cached_content: Optional[str] = optional_params.pop("cached_content", None)
"cached_content", None
)
generation_config: Optional[GenerationConfig] = GenerationConfig( generation_config: Optional[GenerationConfig] = GenerationConfig(
**optional_params **optional_params
) )
@ -1325,26 +1330,43 @@ class ModelResponseIterator:
gemini_chunk = processed_chunk["candidates"][0] gemini_chunk = processed_chunk["candidates"][0]
if ( if "content" in gemini_chunk:
"content" in gemini_chunk if "text" in gemini_chunk["content"]["parts"][0]:
and "text" in gemini_chunk["content"]["parts"][0]
):
text = gemini_chunk["content"]["parts"][0]["text"] text = gemini_chunk["content"]["parts"][0]["text"]
elif "functionCall" in gemini_chunk["content"]["parts"][0]:
function_call = ChatCompletionToolCallFunctionChunk(
name=gemini_chunk["content"]["parts"][0]["functionCall"][
"name"
],
arguments=json.dumps(
gemini_chunk["content"]["parts"][0]["functionCall"]["args"]
),
)
tool_use = ChatCompletionToolCallChunk(
id=str(uuid.uuid4()),
type="function",
function=function_call,
index=0,
)
if "finishReason" in gemini_chunk: if "finishReason" in gemini_chunk:
finish_reason = map_finish_reason( finish_reason = map_finish_reason(
finish_reason=gemini_chunk["finishReason"] finish_reason=gemini_chunk["finishReason"]
) )
## DO NOT SET 'finish_reason' = True ## DO NOT SET 'is_finished' = True
## GEMINI SETS FINISHREASON ON EVERY CHUNK! ## GEMINI SETS FINISHREASON ON EVERY CHUNK!
if "usageMetadata" in processed_chunk: if "usageMetadata" in processed_chunk:
usage = ChatCompletionUsageBlock( usage = ChatCompletionUsageBlock(
prompt_tokens=processed_chunk["usageMetadata"]["promptTokenCount"], prompt_tokens=processed_chunk["usageMetadata"].get(
"promptTokenCount", 0
),
completion_tokens=processed_chunk["usageMetadata"].get( completion_tokens=processed_chunk["usageMetadata"].get(
"candidatesTokenCount", 0 "candidatesTokenCount", 0
), ),
total_tokens=processed_chunk["usageMetadata"]["totalTokenCount"], total_tokens=processed_chunk["usageMetadata"].get(
"totalTokenCount", 0
),
) )
returned_chunk = GenericStreamingChunk( returned_chunk = GenericStreamingChunk(

87
litellm/main.py
View file

@ -113,6 +113,7 @@ from .llms.prompt_templates.factory import (
function_call_prompt, function_call_prompt,
map_system_message_pt, map_system_message_pt,
prompt_factory, prompt_factory,
stringify_json_tool_call_content,
) )
from .llms.text_completion_codestral import CodestralTextCompletion from .llms.text_completion_codestral import CodestralTextCompletion
from .llms.triton import TritonChatCompletion from .llms.triton import TritonChatCompletion
@ -984,6 +985,7 @@ def completion(
mock_delay=kwargs.get("mock_delay", None), mock_delay=kwargs.get("mock_delay", None),
custom_llm_provider=custom_llm_provider, custom_llm_provider=custom_llm_provider,
) )
if custom_llm_provider == "azure": if custom_llm_provider == "azure":
# azure configs # azure configs
api_type = get_secret("AZURE_API_TYPE") or "azure" api_type = get_secret("AZURE_API_TYPE") or "azure"
@ -1114,6 +1116,73 @@ def completion(
"api_base": api_base, "api_base": api_base,
}, },
) )
elif custom_llm_provider == "azure_ai":
api_base = (
api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
or litellm.api_base
or get_secret("AZURE_AI_API_BASE")
)
# set API KEY
api_key = (
api_key
or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
or get_secret("AZURE_AI_API_KEY")
)
headers = headers or litellm.headers
## LOAD CONFIG - if set
config = litellm.OpenAIConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
## FOR COHERE
if "command-r" in model: # make sure tool call in messages are str
messages = stringify_json_tool_call_content(messages=messages)
## COMPLETION CALL
try:
response = openai_chat_completions.completion(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
print_verbose=print_verbose,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
timeout=timeout, # type: ignore
custom_prompt_dict=custom_prompt_dict,
client=client, # pass AsyncOpenAI, OpenAI client
organization=organization,
custom_llm_provider=custom_llm_provider,
)
except Exception as e:
## LOGGING - log the original exception returned
logging.post_call(
input=messages,
api_key=api_key,
original_response=str(e),
additional_args={"headers": headers},
)
raise e
if optional_params.get("stream", False):
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={"headers": headers},
)
elif ( elif (
custom_llm_provider == "text-completion-openai" custom_llm_provider == "text-completion-openai"
or "ft:babbage-002" in model or "ft:babbage-002" in model
@ -2008,6 +2077,8 @@ def completion(
vertex_credentials=vertex_credentials, vertex_credentials=vertex_credentials,
logging_obj=logging, logging_obj=logging,
acompletion=acompletion, acompletion=acompletion,
headers=headers,
custom_prompt_dict=custom_prompt_dict,
) )
else: else:
model_response = vertex_ai.completion( model_response = vertex_ai.completion(
@ -4297,6 +4368,8 @@ def transcription(
model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore 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
if dynamic_api_key is not None:
api_key = dynamic_api_key
optional_params = { optional_params = {
"language": language, "language": language,
"prompt": prompt, "prompt": prompt,
@ -4338,7 +4411,7 @@ def transcription(
azure_ad_token=azure_ad_token, azure_ad_token=azure_ad_token,
max_retries=max_retries, max_retries=max_retries,
) )
elif custom_llm_provider == "openai": elif custom_llm_provider == "openai" or custom_llm_provider == "groq":
api_base = ( api_base = (
api_base api_base
or litellm.api_base or litellm.api_base
@ -4944,14 +5017,22 @@ def stream_chunk_builder(
else: else:
completion_output = "" completion_output = ""
# # Update usage information if needed # # Update usage information if needed
prompt_tokens = 0
completion_tokens = 0
for chunk in chunks:
if "usage" in chunk:
if "prompt_tokens" in chunk["usage"]:
prompt_tokens = chunk["usage"].get("prompt_tokens", 0) or 0
if "completion_tokens" in chunk["usage"]:
completion_tokens = chunk["usage"].get("completion_tokens", 0) or 0
try: try:
response["usage"]["prompt_tokens"] = token_counter( response["usage"]["prompt_tokens"] = prompt_tokens or token_counter(
model=model, messages=messages model=model, messages=messages
) )
except: # don't allow this failing to block a complete streaming response from being returned except: # don't allow this failing to block a complete streaming response from being returned
print_verbose(f"token_counter failed, assuming prompt tokens is 0") print_verbose(f"token_counter failed, assuming prompt tokens is 0")
response["usage"]["prompt_tokens"] = 0 response["usage"]["prompt_tokens"] = 0
response["usage"]["completion_tokens"] = token_counter( response["usage"]["completion_tokens"] = completion_tokens or token_counter(
model=model, model=model,
text=completion_output, text=completion_output,
count_response_tokens=True, # count_response_tokens is a Flag to tell token counter this is a response, No need to add extra tokens we do for input messages count_response_tokens=True, # count_response_tokens is a Flag to tell token counter this is a response, No need to add extra tokens we do for input messages

40
litellm/model_prices_and_context_window_backup.json
View file

@ -398,6 +398,26 @@
"output_cost_per_second": 0.0001, "output_cost_per_second": 0.0001,
"litellm_provider": "openai" "litellm_provider": "openai"
}, },
"tts-1": {
"mode": "audio_speech",
"input_cost_per_character": 0.000015,
"litellm_provider": "openai"
},
"tts-1-hd": {
"mode": "audio_speech",
"input_cost_per_character": 0.000030,
"litellm_provider": "openai"
},
"azure/tts-1": {
"mode": "audio_speech",
"input_cost_per_character": 0.000015,
"litellm_provider": "azure"
},
"azure/tts-1-hd": {
"mode": "audio_speech",
"input_cost_per_character": 0.000030,
"litellm_provider": "azure"
},
"azure/whisper-1": { "azure/whisper-1": {
"mode": "audio_transcription", "mode": "audio_transcription",
"input_cost_per_second": 0, "input_cost_per_second": 0,
@ -905,7 +925,7 @@
}, },
"deepseek-coder": { "deepseek-coder": {
"max_tokens": 4096, "max_tokens": 4096,
"max_input_tokens": 32000, "max_input_tokens": 128000,
"max_output_tokens": 4096, "max_output_tokens": 4096,
"input_cost_per_token": 0.00000014, "input_cost_per_token": 0.00000014,
"output_cost_per_token": 0.00000028, "output_cost_per_token": 0.00000028,
@ -2002,10 +2022,10 @@
"max_tokens": 8192, "max_tokens": 8192,
"max_input_tokens": 2097152, "max_input_tokens": 2097152,
"max_output_tokens": 8192, "max_output_tokens": 8192,
"input_cost_per_token": 0.00000035, "input_cost_per_token": 0.0000035,
"input_cost_per_token_above_128k_tokens": 0.0000007, "input_cost_per_token_above_128k_tokens": 0.000007,
"output_cost_per_token": 0.00000105, "output_cost_per_token": 0.0000105,
"output_cost_per_token_above_128k_tokens": 0.0000021, "output_cost_per_token_above_128k_tokens": 0.000021,
"litellm_provider": "gemini", "litellm_provider": "gemini",
"mode": "chat", "mode": "chat",
"supports_system_messages": true, "supports_system_messages": true,
@ -2013,16 +2033,16 @@
"supports_vision": true, "supports_vision": true,
"supports_tool_choice": true, "supports_tool_choice": true,
"supports_response_schema": true, "supports_response_schema": true,
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" "source": "https://ai.google.dev/pricing"
}, },
"gemini/gemini-1.5-pro-latest": { "gemini/gemini-1.5-pro-latest": {
"max_tokens": 8192, "max_tokens": 8192,
"max_input_tokens": 1048576, "max_input_tokens": 1048576,
"max_output_tokens": 8192, "max_output_tokens": 8192,
"input_cost_per_token": 0.00000035, "input_cost_per_token": 0.0000035,
"input_cost_per_token_above_128k_tokens": 0.0000007, "input_cost_per_token_above_128k_tokens": 0.000007,
"output_cost_per_token": 0.00000105, "output_cost_per_token": 0.00000105,
"output_cost_per_token_above_128k_tokens": 0.0000021, "output_cost_per_token_above_128k_tokens": 0.000021,
"litellm_provider": "gemini", "litellm_provider": "gemini",
"mode": "chat", "mode": "chat",
"supports_system_messages": true, "supports_system_messages": true,
@ -2030,7 +2050,7 @@
"supports_vision": true, "supports_vision": true,
"supports_tool_choice": true, "supports_tool_choice": true,
"supports_response_schema": true, "supports_response_schema": true,
"source": "https://ai.google.dev/models/gemini" "source": "https://ai.google.dev/pricing"
}, },
"gemini/gemini-pro-vision": { "gemini/gemini-pro-vision": {
"max_tokens": 2048, "max_tokens": 2048,

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litellm/proxy/_experimental/out/onboarding.txt
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6
litellm/proxy/_new_secret_config.yaml
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@ -1,8 +1,10 @@
model_list: model_list:
- model_name: claude-3-5-sonnet # all requests where model not in your config go to this deployment - model_name: tts
litellm_params: litellm_params:
model: "openai/*" model: "openai/*"
mock_response: "Hello world!" - model_name: gemini-1.5-flash
litellm_params:
model: gemini/gemini-1.5-flash
general_settings: general_settings:
alerting: ["slack"] alerting: ["slack"]

19
litellm/proxy/_super_secret_config.yaml
View file

@ -1,24 +1,24 @@
model_list: model_list:
- model_name: claude-3-5-sonnet - model_name: claude-3-5-sonnet
litellm_params: litellm_params:
model: anthropic/claude-3-5-sonnet model: claude-3-haiku-20240307
- model_name: gemini-1.5-flash-gemini # - model_name: gemini-1.5-flash-gemini
litellm_params: # litellm_params:
model: vertex_ai_beta/gemini-1.5-flash # model: vertex_ai_beta/gemini-1.5-flash
api_base: https://gateway.ai.cloudflare.com/v1/fa4cdcab1f32b95ca3b53fd36043d691/test/google-vertex-ai/v1/projects/adroit-crow-413218/locations/us-central1/publishers/google/models/gemini-1.5-flash # api_base: https://gateway.ai.cloudflare.com/v1/fa4cdcab1f32b95ca3b53fd36043d691/test/google-vertex-ai/v1/projects/adroit-crow-413218/locations/us-central1/publishers/google/models/gemini-1.5-flash
- litellm_params: - litellm_params:
api_base: http://0.0.0.0:8080 api_base: http://0.0.0.0:8080
api_key: '' api_key: ''
model: openai/my-fake-model model: gpt-4o
rpm: 800 rpm: 800
model_name: gpt-3.5-turbo-fake-model input_cost_per_token: 300
model_name: gpt-4o
- model_name: llama3-70b-8192 - model_name: llama3-70b-8192
litellm_params: litellm_params:
model: groq/llama3-70b-8192 model: groq/llama3-70b-8192
- model_name: fake-openai-endpoint - model_name: fake-openai-endpoint
litellm_params: litellm_params:
model: predibase/llama-3-8b-instruct model: predibase/llama-3-8b-instruct
api_base: "http://0.0.0.0:8081"
api_key: os.environ/PREDIBASE_API_KEY api_key: os.environ/PREDIBASE_API_KEY
tenant_id: os.environ/PREDIBASE_TENANT_ID tenant_id: os.environ/PREDIBASE_TENANT_ID
max_new_tokens: 256 max_new_tokens: 256
@ -38,6 +38,9 @@ model_list:
- litellm_params: - litellm_params:
model: anthropic.claude-3-sonnet-20240229-v1:0 model: anthropic.claude-3-sonnet-20240229-v1:0
model_name: bedrock-anthropic-claude-3 model_name: bedrock-anthropic-claude-3
- litellm_params:
model: claude-3-haiku-20240307
model_name: anthropic-claude-3
- litellm_params: - litellm_params:
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_key: os.environ/AZURE_API_KEY api_key: os.environ/AZURE_API_KEY

5
litellm/proxy/_types.py
View file

@ -218,6 +218,7 @@ class LiteLLMRoutes(enum.Enum):
"/v2/model/info", "/v2/model/info",
"/v2/key/info", "/v2/key/info",
"/model_group/info", "/model_group/info",
"/health",
] ]
# NOTE: ROUTES ONLY FOR MASTER KEY - only the Master Key should be able to Reset Spend # NOTE: ROUTES ONLY FOR MASTER KEY - only the Master Key should be able to Reset Spend
@ -670,6 +671,10 @@ class UpdateUserRequest(GenerateRequestBase):
return values return values
class DeleteUserRequest(LiteLLMBase):
user_ids: List[str] # required
class NewCustomerRequest(LiteLLMBase): class NewCustomerRequest(LiteLLMBase):
""" """
Create a new customer, allocate a budget to them Create a new customer, allocate a budget to them

20
litellm/proxy/auth/litellm_license.py
View file

@ -3,6 +3,7 @@
import base64 import base64
import json import json
import os import os
import traceback
from datetime import datetime from datetime import datetime
from litellm._logging import verbose_proxy_logger from litellm._logging import verbose_proxy_logger
@ -54,9 +55,13 @@ class LicenseCheck:
premium = response_json["verify"] premium = response_json["verify"]
assert isinstance(premium, bool) assert isinstance(premium, bool)
return premium return premium
except Exception as e: except Exception as e:
verbose_proxy_logger.error(
"litellm.proxy.auth.litellm_license.py::_verify - Unable to verify License via api. - {}".format(
str(e)
)
)
return False return False
def is_premium(self) -> bool: def is_premium(self) -> bool:
@ -67,11 +72,14 @@ class LicenseCheck:
try: try:
if self.license_str is None: if self.license_str is None:
return False return False
elif self.verify_license_without_api_request( elif (
self.verify_license_without_api_request(
public_key=self.public_key, license_key=self.license_str public_key=self.public_key, license_key=self.license_str
)
is True
): ):
return True return True
elif self._verify(license_str=self.license_str): elif self._verify(license_str=self.license_str) is True:
return True return True
return False return False
except Exception as e: except Exception as e:
@ -113,5 +121,9 @@ class LicenseCheck:
return True return True
except Exception as e: except Exception as e:
verbose_proxy_logger.error(str(e)) verbose_proxy_logger.debug(
"litellm.proxy.auth.litellm_license.py::verify_license_without_api_request - Unable to verify License locally. - {}".format(
str(e)
)
)
return False return False

167
litellm/proxy/common_utils/admin_ui_utils.py Normal file
View file

@ -0,0 +1,167 @@
import os
def show_missing_vars_in_env():
from fastapi.responses import HTMLResponse
from litellm.proxy.proxy_server import master_key, prisma_client
if prisma_client is None and master_key is None:
return HTMLResponse(
content=missing_keys_form(
missing_key_names="DATABASE_URL, LITELLM_MASTER_KEY"
),
status_code=200,
)
if prisma_client is None:
return HTMLResponse(
content=missing_keys_form(missing_key_names="DATABASE_URL"), status_code=200
)
if master_key is None:
return HTMLResponse(
content=missing_keys_form(missing_key_names="LITELLM_MASTER_KEY"),
status_code=200,
)
return None
# LiteLLM Admin UI - Non SSO Login
url_to_redirect_to = os.getenv("PROXY_BASE_URL", "")
url_to_redirect_to += "/login"
html_form = f"""
<!DOCTYPE html>
<html>
<head>
<title>LiteLLM Login</title>
<style>
body {{
font-family: Arial, sans-serif;
background-color: #f4f4f4;
margin: 0;
padding: 0;
display: flex;
justify-content: center;
align-items: center;
height: 100vh;
}}
form {{
background-color: #fff;
padding: 20px;
border-radius: 8px;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}}
label {{
display: block;
margin-bottom: 8px;
}}
input {{
width: 100%;
padding: 8px;
margin-bottom: 16px;
box-sizing: border-box;
border: 1px solid #ccc;
border-radius: 4px;
}}
input[type="submit"] {{
background-color: #4caf50;
color: #fff;
cursor: pointer;
}}
input[type="submit"]:hover {{
background-color: #45a049;
}}
</style>
</head>
<body>
<form action="{url_to_redirect_to}" method="post">
<h2>LiteLLM Login</h2>
<p>By default Username is "admin" and Password is your set LiteLLM Proxy `MASTER_KEY`</p>
<p>If you need to set UI credentials / SSO docs here: <a href="https://docs.litellm.ai/docs/proxy/ui" target="_blank">https://docs.litellm.ai/docs/proxy/ui</a></p>
<br>
<label for="username">Username:</label>
<input type="text" id="username" name="username" required>
<label for="password">Password:</label>
<input type="password" id="password" name="password" required>
<input type="submit" value="Submit">
</form>
"""
def missing_keys_form(missing_key_names: str):
missing_keys_html_form = """
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
body {{
font-family: Arial, sans-serif;
background-color: #f4f4f9;
color: #333;
margin: 20px;
line-height: 1.6;
}}
.container {{
max-width: 800px;
margin: auto;
padding: 20px;
background: #fff;
border: 1px solid #ddd;
border-radius: 5px;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}}
h1 {{
font-size: 24px;
margin-bottom: 20px;
}}
pre {{
background: #f8f8f8;
padding: 1px;
border: 1px solid #ccc;
border-radius: 4px;
overflow-x: auto;
font-size: 14px;
}}
.env-var {{
font-weight: normal;
}}
.comment {{
font-weight: normal;
color: #777;
}}
</style>
<title>Environment Setup Instructions</title>
</head>
<body>
<div class="container">
<h1>Environment Setup Instructions</h1>
<p>Please add the following variables to your environment variables:</p>
<pre>
<span class="env-var">LITELLM_MASTER_KEY="sk-1234"</span> <span class="comment"># Your master key for the proxy server. Can use this to send /chat/completion requests etc</span>
<span class="env-var">LITELLM_SALT_KEY="sk-XXXXXXXX"</span> <span class="comment"># Can NOT CHANGE THIS ONCE SET - It is used to encrypt/decrypt credentials stored in DB. If value of 'LITELLM_SALT_KEY' changes your models cannot be retrieved from DB</span>
<span class="env-var">DATABASE_URL="postgres://..."</span> <span class="comment"># Need a postgres database? (Check out Supabase, Neon, etc)</span>
<span class="comment">## OPTIONAL ##</span>
<span class="env-var">PORT=4000</span> <span class="comment"># DO THIS FOR RENDER/RAILWAY</span>
<span class="env-var">STORE_MODEL_IN_DB="True"</span> <span class="comment"># Allow storing models in db</span>
</pre>
<h1>Missing Environment Variables</h1>
<p>{missing_keys}</p>
</div>
<div class="container">
<h1>Need Help? Support</h1>
<p>Discord: <a href="https://discord.com/invite/wuPM9dRgDw" target="_blank">https://discord.com/invite/wuPM9dRgDw</a></p>
<p>Docs: <a href="https://docs.litellm.ai/docs/" target="_blank">https://docs.litellm.ai/docs/</a></p>
</div>
</body>
</html>
"""
return missing_keys_html_form.format(missing_keys=missing_key_names)

89
litellm/proxy/common_utils/encrypt_decrypt_utils.py Normal file
View file

@ -0,0 +1,89 @@
import base64
import os
from litellm._logging import verbose_proxy_logger
LITELLM_SALT_KEY = os.getenv("LITELLM_SALT_KEY", None)
verbose_proxy_logger.debug(
"LITELLM_SALT_KEY is None using master_key to encrypt/decrypt secrets stored in DB"
)
def encrypt_value_helper(value: str):
from litellm.proxy.proxy_server import master_key
signing_key = LITELLM_SALT_KEY
if LITELLM_SALT_KEY is None:
signing_key = master_key
try:
if isinstance(value, str):
encrypted_value = encrypt_value(value=value, signing_key=signing_key) # type: ignore
encrypted_value = base64.b64encode(encrypted_value).decode("utf-8")
return encrypted_value
raise ValueError(
f"Invalid value type passed to encrypt_value: {type(value)} for Value: {value}\n Value must be a string"
)
except Exception as e:
raise e
def decrypt_value_helper(value: str):
from litellm.proxy.proxy_server import master_key
signing_key = LITELLM_SALT_KEY
if LITELLM_SALT_KEY is None:
signing_key = master_key
try:
if isinstance(value, str):
decoded_b64 = base64.b64decode(value)
value = decrypt_value(value=decoded_b64, signing_key=signing_key) # type: ignore
return value
except Exception as e:
verbose_proxy_logger.error(f"Error decrypting value: {value}\nError: {str(e)}")
# [Non-Blocking Exception. - this should not block decrypting other values]
pass
def encrypt_value(value: str, signing_key: str):
import hashlib
import nacl.secret
import nacl.utils
# get 32 byte master key #
hash_object = hashlib.sha256(signing_key.encode())
hash_bytes = hash_object.digest()
# initialize secret box #
box = nacl.secret.SecretBox(hash_bytes)
# encode message #
value_bytes = value.encode("utf-8")
encrypted = box.encrypt(value_bytes)
return encrypted
def decrypt_value(value: bytes, signing_key: str) -> str:
import hashlib
import nacl.secret
import nacl.utils
# get 32 byte master key #
hash_object = hashlib.sha256(signing_key.encode())
hash_bytes = hash_object.digest()
# initialize secret box #
box = nacl.secret.SecretBox(hash_bytes)
# Convert the bytes object to a string
plaintext = box.decrypt(value)
plaintext = plaintext.decode("utf-8") # type: ignore
return plaintext # type: ignore

219
litellm/proxy/common_utils/init_callbacks.py Normal file
View file

@ -0,0 +1,219 @@
from typing import Any, List, Optional, get_args
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.proxy._types import CommonProxyErrors, LiteLLMPromptInjectionParams
from litellm.proxy.utils import get_instance_fn
blue_color_code = "\033[94m"
reset_color_code = "\033[0m"
def initialize_callbacks_on_proxy(
value: Any,
premium_user: bool,
config_file_path: str,
litellm_settings: dict,
):
from litellm.proxy.proxy_server import prisma_client
verbose_proxy_logger.debug(
f"{blue_color_code}initializing callbacks={value} on proxy{reset_color_code}"
)
if isinstance(value, list):
imported_list: List[Any] = []
known_compatible_callbacks = list(
get_args(litellm._custom_logger_compatible_callbacks_literal)
)
for callback in value: # ["presidio", <my-custom-callback>]
if isinstance(callback, str) and callback in known_compatible_callbacks:
imported_list.append(callback)
elif isinstance(callback, str) and callback == "otel":
from litellm.integrations.opentelemetry import OpenTelemetry
from litellm.proxy import proxy_server
open_telemetry_logger = OpenTelemetry()
imported_list.append(open_telemetry_logger)
setattr(proxy_server, "open_telemetry_logger", open_telemetry_logger)
elif isinstance(callback, str) and callback == "presidio":
from litellm.proxy.hooks.presidio_pii_masking import (
_OPTIONAL_PresidioPIIMasking,
)
pii_masking_object = _OPTIONAL_PresidioPIIMasking()
imported_list.append(pii_masking_object)
elif isinstance(callback, str) and callback == "llamaguard_moderations":
from enterprise.enterprise_hooks.llama_guard import (
_ENTERPRISE_LlamaGuard,
)
if premium_user != True:
raise Exception(
"Trying to use Llama Guard"
+ CommonProxyErrors.not_premium_user.value
)
llama_guard_object = _ENTERPRISE_LlamaGuard()
imported_list.append(llama_guard_object)
elif isinstance(callback, str) and callback == "hide_secrets":
from enterprise.enterprise_hooks.secret_detection import (
_ENTERPRISE_SecretDetection,
)
if premium_user != True:
raise Exception(
"Trying to use secret hiding"
+ CommonProxyErrors.not_premium_user.value
)
_secret_detection_object = _ENTERPRISE_SecretDetection()
imported_list.append(_secret_detection_object)
elif isinstance(callback, str) and callback == "openai_moderations":
from enterprise.enterprise_hooks.openai_moderation import (
_ENTERPRISE_OpenAI_Moderation,
)
if premium_user != True:
raise Exception(
"Trying to use OpenAI Moderations Check"
+ CommonProxyErrors.not_premium_user.value
)
openai_moderations_object = _ENTERPRISE_OpenAI_Moderation()
imported_list.append(openai_moderations_object)
elif isinstance(callback, str) and callback == "lakera_prompt_injection":
from enterprise.enterprise_hooks.lakera_ai import (
_ENTERPRISE_lakeraAI_Moderation,
)
if premium_user != True:
raise Exception(
"Trying to use LakeraAI Prompt Injection"
+ CommonProxyErrors.not_premium_user.value
)
lakera_moderations_object = _ENTERPRISE_lakeraAI_Moderation()
imported_list.append(lakera_moderations_object)
elif isinstance(callback, str) and callback == "google_text_moderation":
from enterprise.enterprise_hooks.google_text_moderation import (
_ENTERPRISE_GoogleTextModeration,
)
if premium_user != True:
raise Exception(
"Trying to use Google Text Moderation"
+ CommonProxyErrors.not_premium_user.value
)
google_text_moderation_obj = _ENTERPRISE_GoogleTextModeration()
imported_list.append(google_text_moderation_obj)
elif isinstance(callback, str) and callback == "llmguard_moderations":
from enterprise.enterprise_hooks.llm_guard import _ENTERPRISE_LLMGuard
if premium_user != True:
raise Exception(
"Trying to use Llm Guard"
+ CommonProxyErrors.not_premium_user.value
)
llm_guard_moderation_obj = _ENTERPRISE_LLMGuard()
imported_list.append(llm_guard_moderation_obj)
elif isinstance(callback, str) and callback == "blocked_user_check":
from enterprise.enterprise_hooks.blocked_user_list import (
_ENTERPRISE_BlockedUserList,
)
if premium_user != True:
raise Exception(
"Trying to use ENTERPRISE BlockedUser"
+ CommonProxyErrors.not_premium_user.value
)
blocked_user_list = _ENTERPRISE_BlockedUserList(
prisma_client=prisma_client
)
imported_list.append(blocked_user_list)
elif isinstance(callback, str) and callback == "banned_keywords":
from enterprise.enterprise_hooks.banned_keywords import (
_ENTERPRISE_BannedKeywords,
)
if premium_user != True:
raise Exception(
"Trying to use ENTERPRISE BannedKeyword"
+ CommonProxyErrors.not_premium_user.value
)
banned_keywords_obj = _ENTERPRISE_BannedKeywords()
imported_list.append(banned_keywords_obj)
elif isinstance(callback, str) and callback == "detect_prompt_injection":
from litellm.proxy.hooks.prompt_injection_detection import (
_OPTIONAL_PromptInjectionDetection,
)
prompt_injection_params = None
if "prompt_injection_params" in litellm_settings:
prompt_injection_params_in_config = litellm_settings[
"prompt_injection_params"
]
prompt_injection_params = LiteLLMPromptInjectionParams(
**prompt_injection_params_in_config
)
prompt_injection_detection_obj = _OPTIONAL_PromptInjectionDetection(
prompt_injection_params=prompt_injection_params,
)
imported_list.append(prompt_injection_detection_obj)
elif isinstance(callback, str) and callback == "batch_redis_requests":
from litellm.proxy.hooks.batch_redis_get import (
_PROXY_BatchRedisRequests,
)
batch_redis_obj = _PROXY_BatchRedisRequests()
imported_list.append(batch_redis_obj)
elif isinstance(callback, str) and callback == "azure_content_safety":
from litellm.proxy.hooks.azure_content_safety import (
_PROXY_AzureContentSafety,
)
azure_content_safety_params = litellm_settings[
"azure_content_safety_params"
]
for k, v in azure_content_safety_params.items():
if (
v is not None
and isinstance(v, str)
and v.startswith("os.environ/")
):
azure_content_safety_params[k] = litellm.get_secret(v)
azure_content_safety_obj = _PROXY_AzureContentSafety(
**azure_content_safety_params,
)
imported_list.append(azure_content_safety_obj)
else:
verbose_proxy_logger.debug(
f"{blue_color_code} attempting to import custom calback={callback} {reset_color_code}"
)
imported_list.append(
get_instance_fn(
value=callback,
config_file_path=config_file_path,
)
)
if isinstance(litellm.callbacks, list):
litellm.callbacks.extend(imported_list)
else:
litellm.callbacks = imported_list # type: ignore
else:
litellm.callbacks = [
get_instance_fn(
value=value,
config_file_path=config_file_path,
)
]
verbose_proxy_logger.debug(
f"{blue_color_code} Initialized Callbacks - {litellm.callbacks} {reset_color_code}"
)

21
litellm/proxy/common_utils/openai_endpoint_utils.py Normal file
View file

@ -0,0 +1,21 @@
"""
Contains utils used by OpenAI compatible endpoints
"""
def remove_sensitive_info_from_deployment(deployment_dict: dict) -> dict:
"""
Removes sensitive information from a deployment dictionary.
Args:
deployment_dict (dict): The deployment dictionary to remove sensitive information from.
Returns:
dict: The modified deployment dictionary with sensitive information removed.
"""
deployment_dict["litellm_params"].pop("api_key", None)
deployment_dict["litellm_params"].pop("vertex_credentials", None)
deployment_dict["litellm_params"].pop("aws_access_key_id", None)
deployment_dict["litellm_params"].pop("aws_secret_access_key", None)
return deployment_dict

91
litellm/proxy/guardrails/guardrail_helpers.py Normal file
View file

@ -0,0 +1,91 @@
from litellm._logging import verbose_proxy_logger
from litellm.proxy.guardrails.init_guardrails import guardrail_name_config_map
from litellm.proxy.proxy_server import UserAPIKeyAuth
from litellm.types.guardrails import *
async def should_proceed_based_on_metadata(data: dict, guardrail_name: str) -> bool:
"""
checks if this guardrail should be applied to this call
"""
if "metadata" in data and isinstance(data["metadata"], dict):
if "guardrails" in data["metadata"]:
# expect users to pass
# guardrails: { prompt_injection: true, rail_2: false }
request_guardrails = data["metadata"]["guardrails"]
verbose_proxy_logger.debug(
"Guardrails %s passed in request - checking which to apply",
request_guardrails,
)
requested_callback_names = []
# get guardrail configs from `init_guardrails.py`
# for all requested guardrails -> get their associated callbacks
for _guardrail_name, should_run in request_guardrails.items():
if should_run is False:
verbose_proxy_logger.debug(
"Guardrail %s skipped because request set to False",
_guardrail_name,
)
continue
# lookup the guardrail in guardrail_name_config_map
guardrail_item: GuardrailItem = guardrail_name_config_map[
_guardrail_name
]
guardrail_callbacks = guardrail_item.callbacks
requested_callback_names.extend(guardrail_callbacks)
verbose_proxy_logger.debug(
"requested_callback_names %s", requested_callback_names
)
if guardrail_name in requested_callback_names:
return True
# Do no proceeed if - "metadata": { "guardrails": { "lakera_prompt_injection": false } }
return False
return True
async def should_proceed_based_on_api_key(
user_api_key_dict: UserAPIKeyAuth, guardrail_name: str
) -> bool:
"""
checks if this guardrail should be applied to this call
"""
if user_api_key_dict.permissions is not None:
# { prompt_injection: true, rail_2: false }
verbose_proxy_logger.debug(
"Guardrails valid for API Key= %s - checking which to apply",
user_api_key_dict.permissions,
)
if not isinstance(user_api_key_dict.permissions, dict):
verbose_proxy_logger.error(
"API Key permissions must be a dict - %s running guardrail %s",
user_api_key_dict,
guardrail_name,
)
return True
for _guardrail_name, should_run in user_api_key_dict.permissions.items():
if should_run is False:
verbose_proxy_logger.debug(
"Guardrail %s skipped because request set to False",
_guardrail_name,
)
continue
# lookup the guardrail in guardrail_name_config_map
guardrail_item: GuardrailItem = guardrail_name_config_map[_guardrail_name]
guardrail_callbacks = guardrail_item.callbacks
if guardrail_name in guardrail_callbacks:
return True
# Do not proceeed if - "metadata": { "guardrails": { "lakera_prompt_injection": false } }
return False
return True

61
litellm/proxy/guardrails/init_guardrails.py Normal file
View file

@ -0,0 +1,61 @@
import traceback
from typing import Dict, List
from pydantic import BaseModel, RootModel
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.proxy.common_utils.init_callbacks import initialize_callbacks_on_proxy
from litellm.types.guardrails import GuardrailItem
all_guardrails: List[GuardrailItem] = []
guardrail_name_config_map: Dict[str, GuardrailItem] = {}
def initialize_guardrails(
guardrails_config: list,
premium_user: bool,
config_file_path: str,
litellm_settings: dict,
):
try:
verbose_proxy_logger.debug(f"validating guardrails passed {guardrails_config}")
global all_guardrails
for item in guardrails_config:
"""
one item looks like this:
{'prompt_injection': {'callbacks': ['lakera_prompt_injection', 'prompt_injection_api_2'], 'default_on': True}}
"""
for k, v in item.items():
guardrail_item = GuardrailItem(**v, guardrail_name=k)
all_guardrails.append(guardrail_item)
guardrail_name_config_map[k] = guardrail_item
# set appropriate callbacks if they are default on
default_on_callbacks = set()
for guardrail in all_guardrails:
verbose_proxy_logger.debug(guardrail.guardrail_name)
verbose_proxy_logger.debug(guardrail.default_on)
if guardrail.default_on is True:
# add these to litellm callbacks if they don't exist
for callback in guardrail.callbacks:
if callback not in litellm.callbacks:
default_on_callbacks.add(callback)
default_on_callbacks_list = list(default_on_callbacks)
if len(default_on_callbacks_list) > 0:
initialize_callbacks_on_proxy(
value=default_on_callbacks_list,
premium_user=premium_user,
config_file_path=config_file_path,
litellm_settings=litellm_settings,
)
except Exception as e:
verbose_proxy_logger.error(f"error initializing guardrails {str(e)}")
traceback.print_exc()
raise e

132
litellm/proxy/hooks/dynamic_rate_limiter.py
View file

@ -3,6 +3,7 @@
## Tracks num active projects per minute ## Tracks num active projects per minute
import asyncio import asyncio
import os
import sys import sys
import traceback import traceback
from datetime import datetime from datetime import datetime
@ -81,28 +82,61 @@ class _PROXY_DynamicRateLimitHandler(CustomLogger):
def update_variables(self, llm_router: Router): def update_variables(self, llm_router: Router):
self.llm_router = llm_router self.llm_router = llm_router
async def check_available_tpm( async def check_available_usage(
self, model: str self, model: str, priority: Optional[str] = None
) -> Tuple[Optional[int], Optional[int], Optional[int]]: ) -> Tuple[
Optional[int], Optional[int], Optional[int], Optional[int], Optional[int]
]:
""" """
For a given model, get its available tpm For a given model, get its available tpm
Params:
- model: str, the name of the model in the router model_list
- priority: Optional[str], the priority for the request.
Returns Returns
- Tuple[available_tpm, model_tpm, active_projects] - Tuple[available_tpm, available_tpm, model_tpm, model_rpm, active_projects]
- available_tpm: int or null - always 0 or positive.
- available_tpm: int or null - always 0 or positive. - available_tpm: int or null - always 0 or positive.
- remaining_model_tpm: int or null. If available tpm is int, then this will be too. - remaining_model_tpm: int or null. If available tpm is int, then this will be too.
- remaining_model_rpm: int or null. If available rpm is int, then this will be too.
- active_projects: int or null - active_projects: int or null
""" """
active_projects = await self.internal_usage_cache.async_get_cache(model=model) try:
current_model_tpm: Optional[int] = await self.llm_router.get_model_group_usage( weight: float = 1
model_group=model if (
litellm.priority_reservation is None
or priority not in litellm.priority_reservation
):
verbose_proxy_logger.error(
"Priority Reservation not set. priority={}, but litellm.priority_reservation is {}.".format(
priority, litellm.priority_reservation
)
)
elif priority is not None and litellm.priority_reservation is not None:
if os.getenv("LITELLM_LICENSE", None) is None:
verbose_proxy_logger.error(
"PREMIUM FEATURE: Reserving tpm/rpm by priority is a premium feature. Please add a 'LITELLM_LICENSE' to your .env to enable this.\nGet a license: https://docs.litellm.ai/docs/proxy/enterprise."
)
else:
weight = litellm.priority_reservation[priority]
active_projects = await self.internal_usage_cache.async_get_cache(
model=model
)
current_model_tpm, current_model_rpm = (
await self.llm_router.get_model_group_usage(model_group=model)
) )
model_group_info: Optional[ModelGroupInfo] = ( model_group_info: Optional[ModelGroupInfo] = (
self.llm_router.get_model_group_info(model_group=model) self.llm_router.get_model_group_info(model_group=model)
) )
total_model_tpm: Optional[int] = None total_model_tpm: Optional[int] = None
if model_group_info is not None and model_group_info.tpm is not None: total_model_rpm: Optional[int] = None
if model_group_info is not None:
if model_group_info.tpm is not None:
total_model_tpm = model_group_info.tpm total_model_tpm = model_group_info.tpm
if model_group_info.rpm is not None:
total_model_rpm = model_group_info.rpm
remaining_model_tpm: Optional[int] = None remaining_model_tpm: Optional[int] = None
if total_model_tpm is not None and current_model_tpm is not None: if total_model_tpm is not None and current_model_tpm is not None:
@ -110,17 +144,47 @@ class _PROXY_DynamicRateLimitHandler(CustomLogger):
elif total_model_tpm is not None: elif total_model_tpm is not None:
remaining_model_tpm = total_model_tpm remaining_model_tpm = total_model_tpm
remaining_model_rpm: Optional[int] = None
if total_model_rpm is not None and current_model_rpm is not None:
remaining_model_rpm = total_model_rpm - current_model_rpm
elif total_model_rpm is not None:
remaining_model_rpm = total_model_rpm
available_tpm: Optional[int] = None available_tpm: Optional[int] = None
if remaining_model_tpm is not None: if remaining_model_tpm is not None:
if active_projects is not None: if active_projects is not None:
available_tpm = int(remaining_model_tpm / active_projects) available_tpm = int(remaining_model_tpm * weight / active_projects)
else: else:
available_tpm = remaining_model_tpm available_tpm = int(remaining_model_tpm * weight)
if available_tpm is not None and available_tpm < 0: if available_tpm is not None and available_tpm < 0:
available_tpm = 0 available_tpm = 0
return available_tpm, remaining_model_tpm, active_projects
available_rpm: Optional[int] = None
if remaining_model_rpm is not None:
if active_projects is not None:
available_rpm = int(remaining_model_rpm * weight / active_projects)
else:
available_rpm = int(remaining_model_rpm * weight)
if available_rpm is not None and available_rpm < 0:
available_rpm = 0
return (
available_tpm,
available_rpm,
remaining_model_tpm,
remaining_model_rpm,
active_projects,
)
except Exception as e:
verbose_proxy_logger.error(
"litellm.proxy.hooks.dynamic_rate_limiter.py::check_available_usage: Exception occurred - {}\n{}".format(
str(e), traceback.format_exc()
)
)
return None, None, None, None, None
async def async_pre_call_hook( async def async_pre_call_hook(
self, self,
@ -140,13 +204,19 @@ class _PROXY_DynamicRateLimitHandler(CustomLogger):
]: # raise exception if invalid, return a str for the user to receive - if rejected, or return a modified dictionary for passing into litellm ]: # raise exception if invalid, return a str for the user to receive - if rejected, or return a modified dictionary for passing into litellm
""" """
- For a model group - For a model group
- Check if tpm available - Check if tpm/rpm available
- Raise RateLimitError if no tpm available - Raise RateLimitError if no tpm/rpm available
""" """
if "model" in data: if "model" in data:
available_tpm, model_tpm, active_projects = await self.check_available_tpm( key_priority: Optional[str] = user_api_key_dict.metadata.get(
model=data["model"] "priority", None
) )
available_tpm, available_rpm, model_tpm, model_rpm, active_projects = (
await self.check_available_usage(
model=data["model"], priority=key_priority
)
)
### CHECK TPM ###
if available_tpm is not None and available_tpm == 0: if available_tpm is not None and available_tpm == 0:
raise HTTPException( raise HTTPException(
status_code=429, status_code=429,
@ -159,7 +229,20 @@ class _PROXY_DynamicRateLimitHandler(CustomLogger):
) )
}, },
) )
elif available_tpm is not None: ### CHECK RPM ###
elif available_rpm is not None and available_rpm == 0:
raise HTTPException(
status_code=429,
detail={
"error": "Key={} over available RPM={}. Model RPM={}, Active keys={}".format(
user_api_key_dict.api_key,
available_rpm,
model_rpm,
active_projects,
)
},
)
elif available_rpm is not None or available_tpm is not None:
## UPDATE CACHE WITH ACTIVE PROJECT ## UPDATE CACHE WITH ACTIVE PROJECT
asyncio.create_task( asyncio.create_task(
self.internal_usage_cache.async_set_cache_sadd( # this is a set self.internal_usage_cache.async_set_cache_sadd( # this is a set
@ -182,15 +265,24 @@ class _PROXY_DynamicRateLimitHandler(CustomLogger):
), "Model info for model with id={} is None".format( ), "Model info for model with id={} is None".format(
response._hidden_params["model_id"] response._hidden_params["model_id"]
) )
available_tpm, remaining_model_tpm, active_projects = ( key_priority: Optional[str] = user_api_key_dict.metadata.get(
await self.check_available_tpm(model=model_info["model_name"]) "priority", None
) )
response._hidden_params["additional_headers"] = { available_tpm, available_rpm, model_tpm, model_rpm, active_projects = (
await self.check_available_usage(
model=model_info["model_name"], priority=key_priority
)
)
response._hidden_params["additional_headers"] = (
{ # Add additional response headers - easier debugging
"x-litellm-model_group": model_info["model_name"], "x-litellm-model_group": model_info["model_name"],
"x-ratelimit-remaining-litellm-project-tokens": available_tpm, "x-ratelimit-remaining-litellm-project-tokens": available_tpm,
"x-ratelimit-remaining-model-tokens": remaining_model_tpm, "x-ratelimit-remaining-litellm-project-requests": available_rpm,
"x-ratelimit-remaining-model-tokens": model_tpm,
"x-ratelimit-remaining-model-requests": model_rpm,
"x-ratelimit-current-active-projects": active_projects, "x-ratelimit-current-active-projects": active_projects,
} }
)
return response return response
return await super().async_post_call_success_hook( return await super().async_post_call_success_hook(

50
litellm/proxy/hooks/presidio_pii_masking.py
View file

@ -8,21 +8,26 @@
# Tell us how we can improve! - Krrish & Ishaan # Tell us how we can improve! - Krrish & Ishaan
import asyncio
import json
import traceback
import uuid
from typing import Optional, Union from typing import Optional, Union
import litellm, traceback, uuid, json # noqa: E401
from litellm.caching import DualCache import aiohttp
from litellm.proxy._types import UserAPIKeyAuth
from litellm.integrations.custom_logger import CustomLogger
from fastapi import HTTPException from fastapi import HTTPException
import litellm # noqa: E401
from litellm._logging import verbose_proxy_logger from litellm._logging import verbose_proxy_logger
from litellm.caching import DualCache
from litellm.integrations.custom_logger import CustomLogger
from litellm.proxy._types import UserAPIKeyAuth
from litellm.utils import ( from litellm.utils import (
ModelResponse,
EmbeddingResponse, EmbeddingResponse,
ImageResponse, ImageResponse,
ModelResponse,
StreamingChoices, StreamingChoices,
) )
import aiohttp
import asyncio
class _OPTIONAL_PresidioPIIMasking(CustomLogger): class _OPTIONAL_PresidioPIIMasking(CustomLogger):
@ -57,22 +62,41 @@ class _OPTIONAL_PresidioPIIMasking(CustomLogger):
f"An error occurred: {str(e)}, file_path={ad_hoc_recognizers}" f"An error occurred: {str(e)}, file_path={ad_hoc_recognizers}"
) )
self.presidio_analyzer_api_base = litellm.get_secret( self.validate_environment()
def validate_environment(self):
self.presidio_analyzer_api_base: Optional[str] = litellm.get_secret(
"PRESIDIO_ANALYZER_API_BASE", None "PRESIDIO_ANALYZER_API_BASE", None
) ) # type: ignore
self.presidio_anonymizer_api_base = litellm.get_secret( self.presidio_anonymizer_api_base: Optional[str] = litellm.get_secret(
"PRESIDIO_ANONYMIZER_API_BASE", None "PRESIDIO_ANONYMIZER_API_BASE", None
) ) # type: ignore
if self.presidio_analyzer_api_base is None: if self.presidio_analyzer_api_base is None:
raise Exception("Missing `PRESIDIO_ANALYZER_API_BASE` from environment") raise Exception("Missing `PRESIDIO_ANALYZER_API_BASE` from environment")
elif not self.presidio_analyzer_api_base.endswith("/"): if not self.presidio_analyzer_api_base.endswith("/"):
self.presidio_analyzer_api_base += "/" self.presidio_analyzer_api_base += "/"
if not (
self.presidio_analyzer_api_base.startswith("http://")
or self.presidio_analyzer_api_base.startswith("https://")
):
# add http:// if unset, assume communicating over private network - e.g. render
self.presidio_analyzer_api_base = (
"http://" + self.presidio_analyzer_api_base
)
if self.presidio_anonymizer_api_base is None: if self.presidio_anonymizer_api_base is None:
raise Exception("Missing `PRESIDIO_ANONYMIZER_API_BASE` from environment") raise Exception("Missing `PRESIDIO_ANONYMIZER_API_BASE` from environment")
elif not self.presidio_anonymizer_api_base.endswith("/"): if not self.presidio_anonymizer_api_base.endswith("/"):
self.presidio_anonymizer_api_base += "/" self.presidio_anonymizer_api_base += "/"
if not (
self.presidio_anonymizer_api_base.startswith("http://")
or self.presidio_anonymizer_api_base.startswith("https://")
):
# add http:// if unset, assume communicating over private network - e.g. render
self.presidio_anonymizer_api_base = (
"http://" + self.presidio_anonymizer_api_base
)
def print_verbose(self, print_statement): def print_verbose(self, print_statement):
try: try:

1
litellm/proxy/litellm_pre_call_utils.py
View file

@ -176,6 +176,7 @@ async def add_litellm_data_to_request(
def _add_otel_traceparent_to_data(data: dict, request: Request): def _add_otel_traceparent_to_data(data: dict, request: Request):
from litellm.proxy.proxy_server import open_telemetry_logger from litellm.proxy.proxy_server import open_telemetry_logger
if data is None: if data is None:
return return
if open_telemetry_logger is None: if open_telemetry_logger is None:

118
litellm/proxy/management_endpoints/internal_user_endpoints.py
View file

@ -9,25 +9,26 @@ These are members of a Team on LiteLLM
/user/delete /user/delete
""" """
import asyncio
import copy import copy
import json import json
import uuid
import re import re
import traceback
import asyncio
import secrets import secrets
from typing import Optional, List import traceback
import fastapi import uuid
from fastapi import Depends, Request, APIRouter, Header, status
from fastapi import HTTPException
import litellm
from datetime import datetime, timedelta, timezone from datetime import datetime, timedelta, timezone
from typing import List, Optional
import fastapi
from fastapi import APIRouter, Depends, Header, HTTPException, Request, status
import litellm
from litellm._logging import verbose_proxy_logger from litellm._logging import verbose_proxy_logger
from litellm.proxy._types import *
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
from litellm.proxy.management_endpoints.key_management_endpoints import ( from litellm.proxy.management_endpoints.key_management_endpoints import (
generate_key_helper_fn, generate_key_helper_fn,
) )
from litellm.proxy._types import *
router = APIRouter() router = APIRouter()
@ -55,6 +56,7 @@ async def new_user(data: NewUserRequest):
- send_invite_email: Optional[bool] - Specify if an invite email should be sent. - send_invite_email: Optional[bool] - Specify if an invite email should be sent.
- user_role: Optional[str] - Specify a user role - "proxy_admin", "proxy_admin_viewer", "internal_user", "internal_user_viewer", "team", "customer". Info about each role here: `https://github.com/BerriAI/litellm/litellm/proxy/_types.py#L20` - user_role: Optional[str] - Specify a user role - "proxy_admin", "proxy_admin_viewer", "internal_user", "internal_user_viewer", "team", "customer". Info about each role here: `https://github.com/BerriAI/litellm/litellm/proxy/_types.py#L20`
- max_budget: Optional[float] - Specify max budget for a given user. - max_budget: Optional[float] - Specify max budget for a given user.
- budget_duration: Optional[str] - Budget is reset at the end of specified duration. If not set, budget is never reset. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
- models: Optional[list] - Model_name's a user is allowed to call. (if empty, key is allowed to call all models) - models: Optional[list] - Model_name's a user is allowed to call. (if empty, key is allowed to call all models)
- tpm_limit: Optional[int] - Specify tpm limit for a given user (Tokens per minute) - tpm_limit: Optional[int] - Specify tpm limit for a given user (Tokens per minute)
- rpm_limit: Optional[int] - Specify rpm limit for a given user (Requests per minute) - rpm_limit: Optional[int] - Specify rpm limit for a given user (Requests per minute)
@ -280,9 +282,9 @@ async def user_info(
``` ```
""" """
from litellm.proxy.proxy_server import ( from litellm.proxy.proxy_server import (
prisma_client,
general_settings, general_settings,
litellm_master_key_hash, litellm_master_key_hash,
prisma_client,
) )
try: try:
@ -674,3 +676,99 @@ async def get_users(
) )
return all_users return all_users
@router.post(
"/user/delete",
tags=["Internal User management"],
dependencies=[Depends(user_api_key_auth)],
)
async def delete_user(
data: DeleteUserRequest,
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
litellm_changed_by: Optional[str] = Header(
None,
description="The litellm-changed-by header enables tracking of actions performed by authorized users on behalf of other users, providing an audit trail for accountability",
),
):
"""
delete user and associated user keys
```
curl --location 'http://0.0.0.0:8000/team/delete' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data-raw '{
"user_ids": ["45e3e396-ee08-4a61-a88e-16b3ce7e0849"]
}'
```
Parameters:
- user_ids: List[str] - The list of user id's to be deleted.
"""
from litellm.proxy.proxy_server import (
_duration_in_seconds,
create_audit_log_for_update,
litellm_proxy_admin_name,
prisma_client,
user_api_key_cache,
)
if prisma_client is None:
raise HTTPException(status_code=500, detail={"error": "No db connected"})
if data.user_ids is None:
raise HTTPException(status_code=400, detail={"error": "No user id passed in"})
# check that all teams passed exist
for user_id in data.user_ids:
user_row = await prisma_client.db.litellm_usertable.find_unique(
where={"user_id": user_id}
)
if user_row is None:
raise HTTPException(
status_code=404,
detail={"error": f"User not found, passed user_id={user_id}"},
)
else:
# Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
# we do this after the first for loop, since first for loop is for validation. we only want this inserted after validation passes
if litellm.store_audit_logs is True:
# make an audit log for each team deleted
_user_row = user_row.json(exclude_none=True)
asyncio.create_task(
create_audit_log_for_update(
request_data=LiteLLM_AuditLogs(
id=str(uuid.uuid4()),
updated_at=datetime.now(timezone.utc),
changed_by=litellm_changed_by
or user_api_key_dict.user_id
or litellm_proxy_admin_name,
changed_by_api_key=user_api_key_dict.api_key,
table_name=LitellmTableNames.USER_TABLE_NAME,
object_id=user_id,
action="deleted",
updated_values="{}",
before_value=_user_row,
)
)
)
# End of Audit logging
## DELETE ASSOCIATED KEYS
await prisma_client.db.litellm_verificationtoken.delete_many(
where={"user_id": {"in": data.user_ids}}
)
## DELETE USERS
deleted_users = await prisma_client.db.litellm_usertable.delete_many(
where={"user_id": {"in": data.user_ids}}
)
return deleted_users

1
litellm/proxy/management_endpoints/key_management_endpoints.py
View file

@ -61,6 +61,7 @@ async def generate_key_fn(
- spend: Optional[int] - Amount spent by key. Default is 0. Will be updated by proxy whenever key is used. https://docs.litellm.ai/docs/proxy/virtual_keys#managing-auth---tracking-spend - spend: Optional[int] - Amount spent by key. Default is 0. Will be updated by proxy whenever key is used. https://docs.litellm.ai/docs/proxy/virtual_keys#managing-auth---tracking-spend
- send_invite_email: Optional[bool] - Whether to send an invite email to the user_id, with the generate key - send_invite_email: Optional[bool] - Whether to send an invite email to the user_id, with the generate key
- max_budget: Optional[float] - Specify max budget for a given key. - max_budget: Optional[float] - Specify max budget for a given key.
- budget_duration: Optional[str] - Budget is reset at the end of specified duration. If not set, budget is never reset. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
- max_parallel_requests: Optional[int] - Rate limit a user based on the number of parallel requests. Raises 429 error, if user's parallel requests > x. - max_parallel_requests: Optional[int] - Rate limit a user based on the number of parallel requests. Raises 429 error, if user's parallel requests > x.
- metadata: Optional[dict] - Metadata for key, store information for key. Example metadata = {"team": "core-infra", "app": "app2", "email": "ishaan@berri.ai" } - metadata: Optional[dict] - Metadata for key, store information for key. Example metadata = {"team": "core-infra", "app": "app2", "email": "ishaan@berri.ai" }
- permissions: Optional[dict] - key-specific permissions. Currently just used for turning off pii masking (if connected). Example - {"pii": false} - permissions: Optional[dict] - key-specific permissions. Currently just used for turning off pii masking (if connected). Example - {"pii": false}

20
litellm/proxy/proxy_config.yaml
View file

@ -19,7 +19,6 @@ model_list:
model: mistral/mistral-embed model: mistral/mistral-embed
general_settings: general_settings:
master_key: sk-1234
pass_through_endpoints: pass_through_endpoints:
- path: "/v1/rerank" - path: "/v1/rerank"
target: "https://api.cohere.com/v1/rerank" target: "https://api.cohere.com/v1/rerank"
@ -36,15 +35,14 @@ general_settings:
LANGFUSE_SECRET_KEY: "os.environ/LANGFUSE_DEV_SK_KEY" LANGFUSE_SECRET_KEY: "os.environ/LANGFUSE_DEV_SK_KEY"
litellm_settings: litellm_settings:
return_response_headers: true callbacks: ["otel"]
success_callback: ["prometheus"] guardrails:
callbacks: ["otel", "hide_secrets"] - prompt_injection:
failure_callback: ["prometheus"] callbacks: [lakera_prompt_injection, hide_secrets]
store_audit_logs: true default_on: true
redact_messages_in_exceptions: True - hide_secrets:
enforced_params: callbacks: [hide_secrets]
- user default_on: true
- metadata
- metadata.generation_name

455
litellm/proxy/proxy_server.py
View file

@ -140,8 +140,21 @@ from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
## Import All Misc routes here ## ## Import All Misc routes here ##
from litellm.proxy.caching_routes import router as caching_router from litellm.proxy.caching_routes import router as caching_router
from litellm.proxy.common_utils.admin_ui_utils import (
html_form,
show_missing_vars_in_env,
)
from litellm.proxy.common_utils.debug_utils import router as debugging_endpoints_router from litellm.proxy.common_utils.debug_utils import router as debugging_endpoints_router
from litellm.proxy.common_utils.encrypt_decrypt_utils import (
decrypt_value_helper,
encrypt_value_helper,
)
from litellm.proxy.common_utils.http_parsing_utils import _read_request_body from litellm.proxy.common_utils.http_parsing_utils import _read_request_body
from litellm.proxy.common_utils.init_callbacks import initialize_callbacks_on_proxy
from litellm.proxy.common_utils.openai_endpoint_utils import (
remove_sensitive_info_from_deployment,
)
from litellm.proxy.guardrails.init_guardrails import initialize_guardrails
from litellm.proxy.health_check import perform_health_check from litellm.proxy.health_check import perform_health_check
from litellm.proxy.health_endpoints._health_endpoints import router as health_router from litellm.proxy.health_endpoints._health_endpoints import router as health_router
from litellm.proxy.hooks.prompt_injection_detection import ( from litellm.proxy.hooks.prompt_injection_detection import (
@ -181,13 +194,9 @@ from litellm.proxy.utils import (
_get_projected_spend_over_limit, _get_projected_spend_over_limit,
_is_projected_spend_over_limit, _is_projected_spend_over_limit,
_is_valid_team_configs, _is_valid_team_configs,
decrypt_value,
encrypt_value,
get_error_message_str, get_error_message_str,
get_instance_fn, get_instance_fn,
hash_token, hash_token,
html_form,
missing_keys_html_form,
reset_budget, reset_budget,
send_email, send_email,
update_spend, update_spend,
@ -202,6 +211,7 @@ from litellm.router import ModelInfo as RouterModelInfo
from litellm.router import updateDeployment from litellm.router import updateDeployment
from litellm.scheduler import DefaultPriorities, FlowItem, Scheduler from litellm.scheduler import DefaultPriorities, FlowItem, Scheduler
from litellm.types.llms.openai import HttpxBinaryResponseContent from litellm.types.llms.openai import HttpxBinaryResponseContent
from litellm.types.router import RouterGeneralSettings
try: try:
from litellm._version import version from litellm._version import version
@ -1237,6 +1247,7 @@ class ProxyConfig:
## DB ## DB
if prisma_client is not None and ( if prisma_client is not None and (
general_settings.get("store_model_in_db", False) == True general_settings.get("store_model_in_db", False) == True
or store_model_in_db is True
): ):
_tasks = [] _tasks = []
keys = [ keys = [
@ -1443,248 +1454,28 @@ class ProxyConfig:
) )
elif key == "cache" and value == False: elif key == "cache" and value == False:
pass pass
elif key == "guardrails":
if premium_user is not True:
raise ValueError(
"Trying to use `guardrails` on config.yaml "
+ CommonProxyErrors.not_premium_user.value
)
initialize_guardrails(
guardrails_config=value,
premium_user=premium_user,
config_file_path=config_file_path,
litellm_settings=litellm_settings,
)
elif key == "callbacks": elif key == "callbacks":
if isinstance(value, list):
imported_list: List[Any] = []
known_compatible_callbacks = list(
get_args(
litellm._custom_logger_compatible_callbacks_literal
)
)
for callback in value: # ["presidio", <my-custom-callback>]
if (
isinstance(callback, str)
and callback in known_compatible_callbacks
):
imported_list.append(callback)
elif isinstance(callback, str) and callback == "otel":
from litellm.integrations.opentelemetry import (
OpenTelemetry,
)
open_telemetry_logger = OpenTelemetry() initialize_callbacks_on_proxy(
imported_list.append(open_telemetry_logger)
elif isinstance(callback, str) and callback == "presidio":
from litellm.proxy.hooks.presidio_pii_masking import (
_OPTIONAL_PresidioPIIMasking,
)
pii_masking_object = _OPTIONAL_PresidioPIIMasking()
imported_list.append(pii_masking_object)
elif (
isinstance(callback, str)
and callback == "llamaguard_moderations"
):
from enterprise.enterprise_hooks.llama_guard import (
_ENTERPRISE_LlamaGuard,
)
if premium_user != True:
raise Exception(
"Trying to use Llama Guard"
+ CommonProxyErrors.not_premium_user.value
)
llama_guard_object = _ENTERPRISE_LlamaGuard()
imported_list.append(llama_guard_object)
elif (
isinstance(callback, str) and callback == "hide_secrets"
):
from enterprise.enterprise_hooks.secret_detection import (
_ENTERPRISE_SecretDetection,
)
if premium_user != True:
raise Exception(
"Trying to use secret hiding"
+ CommonProxyErrors.not_premium_user.value
)
_secret_detection_object = _ENTERPRISE_SecretDetection()
imported_list.append(_secret_detection_object)
elif (
isinstance(callback, str)
and callback == "openai_moderations"
):
from enterprise.enterprise_hooks.openai_moderation import (
_ENTERPRISE_OpenAI_Moderation,
)
if premium_user != True:
raise Exception(
"Trying to use OpenAI Moderations Check"
+ CommonProxyErrors.not_premium_user.value
)
openai_moderations_object = (
_ENTERPRISE_OpenAI_Moderation()
)
imported_list.append(openai_moderations_object)
elif (
isinstance(callback, str)
and callback == "lakera_prompt_injection"
):
from enterprise.enterprise_hooks.lakera_ai import (
_ENTERPRISE_lakeraAI_Moderation,
)
if premium_user != True:
raise Exception(
"Trying to use LakeraAI Prompt Injection"
+ CommonProxyErrors.not_premium_user.value
)
lakera_moderations_object = (
_ENTERPRISE_lakeraAI_Moderation()
)
imported_list.append(lakera_moderations_object)
elif (
isinstance(callback, str)
and callback == "google_text_moderation"
):
from enterprise.enterprise_hooks.google_text_moderation import (
_ENTERPRISE_GoogleTextModeration,
)
if premium_user != True:
raise Exception(
"Trying to use Google Text Moderation"
+ CommonProxyErrors.not_premium_user.value
)
google_text_moderation_obj = (
_ENTERPRISE_GoogleTextModeration()
)
imported_list.append(google_text_moderation_obj)
elif (
isinstance(callback, str)
and callback == "llmguard_moderations"
):
from enterprise.enterprise_hooks.llm_guard import (
_ENTERPRISE_LLMGuard,
)
if premium_user != True:
raise Exception(
"Trying to use Llm Guard"
+ CommonProxyErrors.not_premium_user.value
)
llm_guard_moderation_obj = _ENTERPRISE_LLMGuard()
imported_list.append(llm_guard_moderation_obj)
elif (
isinstance(callback, str)
and callback == "blocked_user_check"
):
from enterprise.enterprise_hooks.blocked_user_list import (
_ENTERPRISE_BlockedUserList,
)
if premium_user != True:
raise Exception(
"Trying to use ENTERPRISE BlockedUser"
+ CommonProxyErrors.not_premium_user.value
)
blocked_user_list = _ENTERPRISE_BlockedUserList(
prisma_client=prisma_client
)
imported_list.append(blocked_user_list)
elif (
isinstance(callback, str)
and callback == "banned_keywords"
):
from enterprise.enterprise_hooks.banned_keywords import (
_ENTERPRISE_BannedKeywords,
)
if premium_user != True:
raise Exception(
"Trying to use ENTERPRISE BannedKeyword"
+ CommonProxyErrors.not_premium_user.value
)
banned_keywords_obj = _ENTERPRISE_BannedKeywords()
imported_list.append(banned_keywords_obj)
elif (
isinstance(callback, str)
and callback == "detect_prompt_injection"
):
from litellm.proxy.hooks.prompt_injection_detection import (
_OPTIONAL_PromptInjectionDetection,
)
prompt_injection_params = None
if "prompt_injection_params" in litellm_settings:
prompt_injection_params_in_config = (
litellm_settings["prompt_injection_params"]
)
prompt_injection_params = (
LiteLLMPromptInjectionParams(
**prompt_injection_params_in_config
)
)
prompt_injection_detection_obj = (
_OPTIONAL_PromptInjectionDetection(
prompt_injection_params=prompt_injection_params,
)
)
imported_list.append(prompt_injection_detection_obj)
elif (
isinstance(callback, str)
and callback == "batch_redis_requests"
):
from litellm.proxy.hooks.batch_redis_get import (
_PROXY_BatchRedisRequests,
)
batch_redis_obj = _PROXY_BatchRedisRequests()
imported_list.append(batch_redis_obj)
elif (
isinstance(callback, str)
and callback == "azure_content_safety"
):
from litellm.proxy.hooks.azure_content_safety import (
_PROXY_AzureContentSafety,
)
azure_content_safety_params = litellm_settings[
"azure_content_safety_params"
]
for k, v in azure_content_safety_params.items():
if (
v is not None
and isinstance(v, str)
and v.startswith("os.environ/")
):
azure_content_safety_params[k] = (
litellm.get_secret(v)
)
azure_content_safety_obj = _PROXY_AzureContentSafety(
**azure_content_safety_params,
)
imported_list.append(azure_content_safety_obj)
else:
imported_list.append(
get_instance_fn(
value=callback,
config_file_path=config_file_path,
)
)
litellm.callbacks = imported_list # type: ignore
else:
litellm.callbacks = [
get_instance_fn(
value=value, value=value,
premium_user=premium_user,
config_file_path=config_file_path, config_file_path=config_file_path,
litellm_settings=litellm_settings,
) )
]
verbose_proxy_logger.debug(
f"{blue_color_code} Initialized Callbacks - {litellm.callbacks} {reset_color_code}"
)
elif key == "post_call_rules": elif key == "post_call_rules":
litellm.post_call_rules = [ litellm.post_call_rules = [
get_instance_fn(value=value, config_file_path=config_file_path) get_instance_fn(value=value, config_file_path=config_file_path)
@ -1980,7 +1771,11 @@ class ProxyConfig:
if k in available_args: if k in available_args:
router_params[k] = v router_params[k] = v
router = litellm.Router( router = litellm.Router(
**router_params, assistants_config=assistants_config **router_params,
assistants_config=assistants_config,
router_general_settings=RouterGeneralSettings(
async_only_mode=True # only init async clients
),
) # type:ignore ) # type:ignore
return router, router.get_model_list(), general_settings return router, router.get_model_list(), general_settings
@ -2095,16 +1890,8 @@ class ProxyConfig:
# decrypt values # decrypt values
for k, v in _litellm_params.items(): for k, v in _litellm_params.items():
if isinstance(v, str): if isinstance(v, str):
# decode base64
try:
decoded_b64 = base64.b64decode(v)
except Exception as e:
verbose_proxy_logger.error(
"Error decoding value - {}".format(v)
)
continue
# decrypt value # decrypt value
_value = decrypt_value(value=decoded_b64, master_key=master_key) _value = decrypt_value_helper(value=v)
# sanity check if string > size 0 # sanity check if string > size 0
if len(_value) > 0: if len(_value) > 0:
_litellm_params[k] = _value _litellm_params[k] = _value
@ -2148,13 +1935,8 @@ class ProxyConfig:
if isinstance(_litellm_params, dict): if isinstance(_litellm_params, dict):
# decrypt values # decrypt values
for k, v in _litellm_params.items(): for k, v in _litellm_params.items():
if isinstance(v, str): decrypted_value = decrypt_value_helper(value=v)
# decode base64 _litellm_params[k] = decrypted_value
decoded_b64 = base64.b64decode(v)
# decrypt value
_litellm_params[k] = decrypt_value(
value=decoded_b64, master_key=master_key # type: ignore
)
_litellm_params = LiteLLM_Params(**_litellm_params) _litellm_params = LiteLLM_Params(**_litellm_params)
else: else:
verbose_proxy_logger.error( verbose_proxy_logger.error(
@ -2172,7 +1954,12 @@ class ProxyConfig:
) )
if len(_model_list) > 0: if len(_model_list) > 0:
verbose_proxy_logger.debug(f"_model_list: {_model_list}") verbose_proxy_logger.debug(f"_model_list: {_model_list}")
llm_router = litellm.Router(model_list=_model_list) llm_router = litellm.Router(
model_list=_model_list,
router_general_settings=RouterGeneralSettings(
async_only_mode=True # only init async clients
),
)
verbose_proxy_logger.debug(f"updated llm_router: {llm_router}") verbose_proxy_logger.debug(f"updated llm_router: {llm_router}")
else: else:
verbose_proxy_logger.debug(f"len new_models: {len(new_models)}") verbose_proxy_logger.debug(f"len new_models: {len(new_models)}")
@ -2210,10 +1997,8 @@ class ProxyConfig:
environment_variables = config_data.get("environment_variables", {}) environment_variables = config_data.get("environment_variables", {})
for k, v in environment_variables.items(): for k, v in environment_variables.items():
try: try:
if v is not None: decrypted_value = decrypt_value_helper(value=v)
decoded_b64 = base64.b64decode(v) os.environ[k] = decrypted_value
value = decrypt_value(value=decoded_b64, master_key=master_key) # type: ignore
os.environ[k] = value
except Exception as e: except Exception as e:
verbose_proxy_logger.error( verbose_proxy_logger.error(
"Error setting env variable: %s - %s", k, str(e) "Error setting env variable: %s - %s", k, str(e)
@ -2935,6 +2720,10 @@ async def chat_completion(
except: except:
data = json.loads(body_str) data = json.loads(body_str)
verbose_proxy_logger.debug(
"Request received by LiteLLM:\n{}".format(json.dumps(data, indent=4)),
)
data = await add_litellm_data_to_request( data = await add_litellm_data_to_request(
data=data, data=data,
request=request, request=request,
@ -2974,6 +2763,7 @@ async def chat_completion(
) )
## LOGGING OBJECT ## - initialize logging object for logging success/failure events for call ## LOGGING OBJECT ## - initialize logging object for logging success/failure events for call
## IMPORTANT Note: - initialize this before running pre-call checks. Ensures we log rejected requests to langfuse.
data["litellm_call_id"] = str(uuid.uuid4()) data["litellm_call_id"] = str(uuid.uuid4())
logging_obj, data = litellm.utils.function_setup( logging_obj, data = litellm.utils.function_setup(
original_function="acompletion", original_function="acompletion",
@ -3586,8 +3376,9 @@ async def embeddings(
) )
verbose_proxy_logger.debug(traceback.format_exc()) verbose_proxy_logger.debug(traceback.format_exc())
if isinstance(e, HTTPException): if isinstance(e, HTTPException):
message = get_error_message_str(e)
raise ProxyException( raise ProxyException(
message=getattr(e, "message", str(e)), message=message,
type=getattr(e, "type", "None"), type=getattr(e, "type", "None"),
param=getattr(e, "param", "None"), param=getattr(e, "param", "None"),
code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST), code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
@ -6144,11 +5935,8 @@ async def add_new_model(
_litellm_params_dict = model_params.litellm_params.dict(exclude_none=True) _litellm_params_dict = model_params.litellm_params.dict(exclude_none=True)
_orignal_litellm_model_name = model_params.litellm_params.model _orignal_litellm_model_name = model_params.litellm_params.model
for k, v in _litellm_params_dict.items(): for k, v in _litellm_params_dict.items():
if isinstance(v, str): encrypted_value = encrypt_value_helper(value=v)
encrypted_value = encrypt_value(value=v, master_key=master_key) # type: ignore model_params.litellm_params[k] = encrypted_value
model_params.litellm_params[k] = base64.b64encode(
encrypted_value
).decode("utf-8")
_data: dict = { _data: dict = {
"model_id": model_params.model_info.id, "model_id": model_params.model_info.id,
"model_name": model_params.model_name, "model_name": model_params.model_name,
@ -6279,11 +6067,8 @@ async def update_model(
### ENCRYPT PARAMS ### ### ENCRYPT PARAMS ###
for k, v in _new_litellm_params_dict.items(): for k, v in _new_litellm_params_dict.items():
if isinstance(v, str): encrypted_value = encrypt_value_helper(value=v)
encrypted_value = encrypt_value(value=v, master_key=master_key) # type: ignore model_params.litellm_params[k] = encrypted_value
model_params.litellm_params[k] = base64.b64encode(
encrypted_value
).decode("utf-8")
### MERGE WITH EXISTING DATA ### ### MERGE WITH EXISTING DATA ###
merged_dictionary = {} merged_dictionary = {}
@ -6863,26 +6648,81 @@ async def model_metrics_exceptions(
@router.get( @router.get(
"/model/info", "/model/info",
description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)",
tags=["model management"], tags=["model management"],
dependencies=[Depends(user_api_key_auth)], dependencies=[Depends(user_api_key_auth)],
) )
@router.get( @router.get(
"/v1/model/info", "/v1/model/info",
description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)",
tags=["model management"], tags=["model management"],
dependencies=[Depends(user_api_key_auth)], dependencies=[Depends(user_api_key_auth)],
) )
async def model_info_v1( async def model_info_v1(
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
litellm_model_id: Optional[str] = None,
): ):
global llm_model_list, general_settings, user_config_file_path, proxy_config """
Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)
Parameters:
litellm_model_id: Optional[str] = None (this is the value of `x-litellm-model-id` returned in response headers)
- When litellm_model_id is passed, it will return the info for that specific model
- When litellm_model_id is not passed, it will return the info for all models
Returns:
Returns a dictionary containing information about each model.
Example Response:
```json
{
"data": [
{
"model_name": "fake-openai-endpoint",
"litellm_params": {
"api_base": "https://exampleopenaiendpoint-production.up.railway.app/",
"model": "openai/fake"
},
"model_info": {
"id": "112f74fab24a7a5245d2ced3536dd8f5f9192c57ee6e332af0f0512e08bed5af",
"db_model": false
}
}
]
}
```
"""
global llm_model_list, general_settings, user_config_file_path, proxy_config, llm_router
if llm_model_list is None: if llm_model_list is None:
raise HTTPException( raise HTTPException(
status_code=500, detail={"error": "LLM Model List not loaded in"} status_code=500, detail={"error": "LLM Model List not loaded in"}
) )
if llm_router is None:
raise HTTPException(
status_code=500,
detail={
"error": "LLM Router is not loaded in. Make sure you passed models in your config.yaml or on the LiteLLM Admin UI."
},
)
if litellm_model_id is not None:
# user is trying to get specific model from litellm router
deployment_info = llm_router.get_deployment(model_id=litellm_model_id)
if deployment_info is None:
raise HTTPException(
status_code=404,
detail={
"error": f"Model id = {litellm_model_id} not found on litellm proxy"
},
)
_deployment_info_dict = deployment_info.model_dump()
_deployment_info_dict = remove_sensitive_info_from_deployment(
deployment_dict=_deployment_info_dict
)
return {"data": _deployment_info_dict}
all_models: List[dict] = [] all_models: List[dict] = []
## CHECK IF MODEL RESTRICTIONS ARE SET AT KEY/TEAM LEVEL ## ## CHECK IF MODEL RESTRICTIONS ARE SET AT KEY/TEAM LEVEL ##
if llm_model_list is None: if llm_model_list is None:
@ -6944,10 +6784,7 @@ async def model_info_v1(
model_info[k] = v model_info[k] = v
model["model_info"] = model_info model["model_info"] = model_info
# don't return the llm credentials # don't return the llm credentials
model["litellm_params"].pop("api_key", None) model = remove_sensitive_info_from_deployment(deployment_dict=model)
model["litellm_params"].pop("vertex_credentials", None)
model["litellm_params"].pop("aws_access_key_id", None)
model["litellm_params"].pop("aws_secret_access_key", None)
verbose_proxy_logger.debug("all_models: %s", all_models) verbose_proxy_logger.debug("all_models: %s", all_models)
return {"data": all_models} return {"data": all_models}
@ -7349,10 +7186,9 @@ async def google_login(request: Request):
) )
####### Detect DB + MASTER KEY in .env ####### ####### Detect DB + MASTER KEY in .env #######
if prisma_client is None or master_key is None: missing_env_vars = show_missing_vars_in_env()
from fastapi.responses import HTMLResponse if missing_env_vars is not None:
return missing_env_vars
return HTMLResponse(content=missing_keys_html_form, status_code=200)
# get url from request # get url from request
redirect_url = os.getenv("PROXY_BASE_URL", str(request.base_url)) redirect_url = os.getenv("PROXY_BASE_URL", str(request.base_url))
@ -7867,22 +7703,12 @@ async def claim_onboarding_link(data: InvitationClaim):
) )
#### CHECK IF CLAIMED #### CHECK IF CLAIMED
##### if claimed - check if within valid session (within 10 minutes of being claimed) ##### if claimed - accept
##### if unclaimed - reject ##### if unclaimed - reject
current_time = litellm.utils.get_utc_datetime() if invite_obj.is_accepted is True:
# this is a valid invite that was accepted
if invite_obj.is_accepted == True: pass
time_difference = current_time - invite_obj.updated_at
# Check if the difference is within 10 minutes
if time_difference > timedelta(minutes=10):
raise HTTPException(
status_code=401,
detail={
"error": "The invitation link has already been claimed. Please ask your admin for a new invite link."
},
)
else: else:
raise HTTPException( raise HTTPException(
status_code=401, status_code=401,
@ -8565,11 +8391,8 @@ async def update_config(config_info: ConfigYAML):
# encrypt updated_environment_variables # # encrypt updated_environment_variables #
for k, v in _updated_environment_variables.items(): for k, v in _updated_environment_variables.items():
if isinstance(v, str): encrypted_value = encrypt_value_helper(value=v)
encrypted_value = encrypt_value(value=v, master_key=master_key) # type: ignore _updated_environment_variables[k] = encrypted_value
_updated_environment_variables[k] = base64.b64encode(
encrypted_value
).decode("utf-8")
_existing_env_variables = config["environment_variables"] _existing_env_variables = config["environment_variables"]
@ -8986,11 +8809,8 @@ async def get_config():
env_vars_dict[_var] = None env_vars_dict[_var] = None
else: else:
# decode + decrypt the value # decode + decrypt the value
decoded_b64 = base64.b64decode(env_variable) decrypted_value = decrypt_value_helper(value=env_variable)
_decrypted_value = decrypt_value( env_vars_dict[_var] = decrypted_value
value=decoded_b64, master_key=master_key
)
env_vars_dict[_var] = _decrypted_value
_data_to_return.append({"name": _callback, "variables": env_vars_dict}) _data_to_return.append({"name": _callback, "variables": env_vars_dict})
elif _callback == "langfuse": elif _callback == "langfuse":
@ -9006,11 +8826,8 @@ async def get_config():
_langfuse_env_vars[_var] = None _langfuse_env_vars[_var] = None
else: else:
# decode + decrypt the value # decode + decrypt the value
decoded_b64 = base64.b64decode(env_variable) decrypted_value = decrypt_value_helper(value=env_variable)
_decrypted_value = decrypt_value( _langfuse_env_vars[_var] = decrypted_value
value=decoded_b64, master_key=master_key
)
_langfuse_env_vars[_var] = _decrypted_value
_data_to_return.append( _data_to_return.append(
{"name": _callback, "variables": _langfuse_env_vars} {"name": _callback, "variables": _langfuse_env_vars}
@ -9031,10 +8848,7 @@ async def get_config():
_slack_env_vars[_var] = _value _slack_env_vars[_var] = _value
else: else:
# decode + decrypt the value # decode + decrypt the value
decoded_b64 = base64.b64decode(env_variable) _decrypted_value = decrypt_value_helper(value=env_variable)
_decrypted_value = decrypt_value(
value=decoded_b64, master_key=master_key
)
_slack_env_vars[_var] = _decrypted_value _slack_env_vars[_var] = _decrypted_value
_alerting_types = proxy_logging_obj.slack_alerting_instance.alert_types _alerting_types = proxy_logging_obj.slack_alerting_instance.alert_types
@ -9070,10 +8884,7 @@ async def get_config():
_email_env_vars[_var] = None _email_env_vars[_var] = None
else: else:
# decode + decrypt the value # decode + decrypt the value
decoded_b64 = base64.b64decode(env_variable) _decrypted_value = decrypt_value_helper(value=env_variable)
_decrypted_value = decrypt_value(
value=decoded_b64, master_key=master_key
)
_email_env_vars[_var] = _decrypted_value _email_env_vars[_var] = _decrypted_value
alerting_data.append( alerting_data.append(

8
litellm/proxy/secret_managers/aws_secret_manager.py
View file

@ -79,7 +79,13 @@ class AWSKeyManagementService_V2:
raise ValueError("Missing required environment variable - AWS_REGION_NAME") raise ValueError("Missing required environment variable - AWS_REGION_NAME")
## CHECK IF LICENSE IN ENV ## - premium feature ## CHECK IF LICENSE IN ENV ## - premium feature
if os.getenv("LITELLM_LICENSE", None) is None: is_litellm_license_in_env: bool = False
if os.getenv("LITELLM_LICENSE", None) is not None:
is_litellm_license_in_env = True
elif os.getenv("LITELLM_SECRET_AWS_KMS_LITELLM_LICENSE", None) is not None:
is_litellm_license_in_env = True
if is_litellm_license_in_env is False:
raise ValueError( raise ValueError(
"AWSKeyManagementService V2 is an Enterprise Feature. Please add a valid LITELLM_LICENSE to your envionment." "AWSKeyManagementService V2 is an Enterprise Feature. Please add a valid LITELLM_LICENSE to your envionment."
) )

98
litellm/proxy/spend_tracking/spend_management_endpoints.py
View file

@ -821,6 +821,14 @@ async def get_global_spend_report(
default="team", default="team",
description="Group spend by internal team or customer or api_key", description="Group spend by internal team or customer or api_key",
), ),
api_key: Optional[str] = fastapi.Query(
default=None,
description="View spend for a specific api_key. Example api_key='sk-1234",
),
internal_user_id: Optional[str] = fastapi.Query(
default=None,
description="View spend for a specific internal_user_id. Example internal_user_id='1234",
),
): ):
""" """
Get Daily Spend per Team, based on specific startTime and endTime. Per team, view usage by each key, model Get Daily Spend per Team, based on specific startTime and endTime. Per team, view usage by each key, model
@ -873,6 +881,96 @@ async def get_global_spend_report(
raise ValueError( raise ValueError(
"/spend/report endpoint " + CommonProxyErrors.not_premium_user.value "/spend/report endpoint " + CommonProxyErrors.not_premium_user.value
) )
if api_key is not None:
verbose_proxy_logger.debug("Getting /spend for api_key: %s", api_key)
if api_key.startswith("sk-"):
api_key = hash_token(token=api_key)
sql_query = """
WITH SpendByModelApiKey AS (
SELECT
sl.api_key,
sl.model,
SUM(sl.spend) AS model_cost,
SUM(sl.prompt_tokens) AS model_input_tokens,
SUM(sl.completion_tokens) AS model_output_tokens
FROM
"LiteLLM_SpendLogs" sl
WHERE
sl."startTime" BETWEEN $1::date AND $2::date AND sl.api_key = $3
GROUP BY
sl.api_key,
sl.model
)
SELECT
api_key,
SUM(model_cost) AS total_cost,
SUM(model_input_tokens) AS total_input_tokens,
SUM(model_output_tokens) AS total_output_tokens,
jsonb_agg(jsonb_build_object(
'model', model,
'total_cost', model_cost,
'total_input_tokens', model_input_tokens,
'total_output_tokens', model_output_tokens
)) AS model_details
FROM
SpendByModelApiKey
GROUP BY
api_key
ORDER BY
total_cost DESC;
"""
db_response = await prisma_client.db.query_raw(
sql_query, start_date_obj, end_date_obj, api_key
)
if db_response is None:
return []
return db_response
elif internal_user_id is not None:
verbose_proxy_logger.debug(
"Getting /spend for internal_user_id: %s", internal_user_id
)
sql_query = """
WITH SpendByModelApiKey AS (
SELECT
sl.api_key,
sl.model,
SUM(sl.spend) AS model_cost,
SUM(sl.prompt_tokens) AS model_input_tokens,
SUM(sl.completion_tokens) AS model_output_tokens
FROM
"LiteLLM_SpendLogs" sl
WHERE
sl."startTime" BETWEEN $1::date AND $2::date AND sl.user = $3
GROUP BY
sl.api_key,
sl.model
)
SELECT
api_key,
SUM(model_cost) AS total_cost,
SUM(model_input_tokens) AS total_input_tokens,
SUM(model_output_tokens) AS total_output_tokens,
jsonb_agg(jsonb_build_object(
'model', model,
'total_cost', model_cost,
'total_input_tokens', model_input_tokens,
'total_output_tokens', model_output_tokens
)) AS model_details
FROM
SpendByModelApiKey
GROUP BY
api_key
ORDER BY
total_cost DESC;
"""
db_response = await prisma_client.db.query_raw(
sql_query, start_date_obj, end_date_obj, internal_user_id
)
if db_response is None:
return []
return db_response
if group_by == "team": if group_by == "team":
# first get data from spend logs -> SpendByModelApiKey # first get data from spend logs -> SpendByModelApiKey

247
litellm/proxy/utils.py
View file

@ -7,6 +7,7 @@ import os
import re import re
import smtplib import smtplib
import subprocess import subprocess
import threading
import time import time
import traceback import traceback
from datetime import datetime, timedelta from datetime import datetime, timedelta
@ -31,6 +32,7 @@ from litellm.caching import DualCache, RedisCache
from litellm.exceptions import RejectedRequestError from litellm.exceptions import RejectedRequestError
from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.custom_logger import CustomLogger
from litellm.integrations.slack_alerting import SlackAlerting from litellm.integrations.slack_alerting import SlackAlerting
from litellm.litellm_core_utils.litellm_logging import Logging
from litellm.llms.custom_httpx.httpx_handler import HTTPHandler from litellm.llms.custom_httpx.httpx_handler import HTTPHandler
from litellm.proxy._types import ( from litellm.proxy._types import (
AlertType, AlertType,
@ -48,6 +50,7 @@ from litellm.proxy.hooks.max_budget_limiter import _PROXY_MaxBudgetLimiter
from litellm.proxy.hooks.parallel_request_limiter import ( from litellm.proxy.hooks.parallel_request_limiter import (
_PROXY_MaxParallelRequestsHandler, _PROXY_MaxParallelRequestsHandler,
) )
from litellm.types.utils import CallTypes
if TYPE_CHECKING: if TYPE_CHECKING:
from opentelemetry.trace import Span as _Span from opentelemetry.trace import Span as _Span
@ -350,38 +353,9 @@ class ProxyLogging:
raise HTTPException( raise HTTPException(
status_code=400, detail={"error": response} status_code=400, detail={"error": response}
) )
print_verbose(f"final data being sent to {call_type} call: {data}")
return data return data
except Exception as e: except Exception as e:
if "litellm_logging_obj" in data:
logging_obj: litellm.litellm_core_utils.litellm_logging.Logging = data[
"litellm_logging_obj"
]
## ASYNC FAILURE HANDLER ##
error_message = ""
if isinstance(e, HTTPException):
if isinstance(e.detail, str):
error_message = e.detail
elif isinstance(e.detail, dict):
error_message = json.dumps(e.detail)
else:
error_message = str(e)
else:
error_message = str(e)
error_raised = Exception(f"{error_message}")
await logging_obj.async_failure_handler(
exception=error_raised,
traceback_exception=traceback.format_exc(),
)
## SYNC FAILURE HANDLER ##
try:
logging_obj.failure_handler(
error_raised, traceback.format_exc()
) # DO NOT MAKE THREADED - router retry fallback relies on this!
except Exception as error_val:
pass
raise e raise e
async def during_call_hook( async def during_call_hook(
@ -595,6 +569,41 @@ class ProxyLogging:
) )
) )
### LOGGING ###
if isinstance(original_exception, HTTPException):
litellm_logging_obj: Optional[Logging] = request_data.get(
"litellm_logging_obj", None
)
if litellm_logging_obj is None:
import uuid
request_data["litellm_call_id"] = str(uuid.uuid4())
litellm_logging_obj, data = litellm.utils.function_setup(
original_function="IGNORE_THIS",
rules_obj=litellm.utils.Rules(),
start_time=datetime.now(),
**request_data,
)
if litellm_logging_obj is not None:
# log the custom exception
await litellm_logging_obj.async_failure_handler(
exception=original_exception,
traceback_exception=traceback.format_exc(),
start_time=time.time(),
end_time=time.time(),
)
threading.Thread(
target=litellm_logging_obj.failure_handler,
args=(
original_exception,
traceback.format_exc(),
time.time(),
time.time(),
),
).start()
for callback in litellm.callbacks: for callback in litellm.callbacks:
try: try:
_callback: Optional[CustomLogger] = None _callback: Optional[CustomLogger] = None
@ -611,6 +620,7 @@ class ProxyLogging:
) )
except Exception as e: except Exception as e:
raise e raise e
return return
async def post_call_success_hook( async def post_call_success_hook(
@ -2695,178 +2705,6 @@ def _is_valid_team_configs(team_id=None, team_config=None, request_data=None):
return return
def encrypt_value(value: str, master_key: str):
import hashlib
import nacl.secret
import nacl.utils
# get 32 byte master key #
hash_object = hashlib.sha256(master_key.encode())
hash_bytes = hash_object.digest()
# initialize secret box #
box = nacl.secret.SecretBox(hash_bytes)
# encode message #
value_bytes = value.encode("utf-8")
encrypted = box.encrypt(value_bytes)
return encrypted
def decrypt_value(value: bytes, master_key: str) -> str:
import hashlib
import nacl.secret
import nacl.utils
# get 32 byte master key #
hash_object = hashlib.sha256(master_key.encode())
hash_bytes = hash_object.digest()
# initialize secret box #
box = nacl.secret.SecretBox(hash_bytes)
# Convert the bytes object to a string
plaintext = box.decrypt(value)
plaintext = plaintext.decode("utf-8") # type: ignore
return plaintext # type: ignore
# LiteLLM Admin UI - Non SSO Login
url_to_redirect_to = os.getenv("PROXY_BASE_URL", "")
url_to_redirect_to += "/login"
html_form = f"""
<!DOCTYPE html>
<html>
<head>
<title>LiteLLM Login</title>
<style>
body {{
font-family: Arial, sans-serif;
background-color: #f4f4f4;
margin: 0;
padding: 0;
display: flex;
justify-content: center;
align-items: center;
height: 100vh;
}}
form {{
background-color: #fff;
padding: 20px;
border-radius: 8px;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}}
label {{
display: block;
margin-bottom: 8px;
}}
input {{
width: 100%;
padding: 8px;
margin-bottom: 16px;
box-sizing: border-box;
border: 1px solid #ccc;
border-radius: 4px;
}}
input[type="submit"] {{
background-color: #4caf50;
color: #fff;
cursor: pointer;
}}
input[type="submit"]:hover {{
background-color: #45a049;
}}
</style>
</head>
<body>
<form action="{url_to_redirect_to}" method="post">
<h2>LiteLLM Login</h2>
<p>By default Username is "admin" and Password is your set LiteLLM Proxy `MASTER_KEY`</p>
<p>If you need to set UI credentials / SSO docs here: <a href="https://docs.litellm.ai/docs/proxy/ui" target="_blank">https://docs.litellm.ai/docs/proxy/ui</a></p>
<br>
<label for="username">Username:</label>
<input type="text" id="username" name="username" required>
<label for="password">Password:</label>
<input type="password" id="password" name="password" required>
<input type="submit" value="Submit">
</form>
"""
missing_keys_html_form = """
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
body {
font-family: Arial, sans-serif;
background-color: #f4f4f9;
color: #333;
margin: 20px;
line-height: 1.6;
}
.container {
max-width: 600px;
margin: auto;
padding: 20px;
background: #fff;
border: 1px solid #ddd;
border-radius: 5px;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}
h1 {
font-size: 24px;
margin-bottom: 20px;
}
pre {
background: #f8f8f8;
padding: 10px;
border: 1px solid #ccc;
border-radius: 4px;
overflow-x: auto;
font-size: 14px;
}
.env-var {
font-weight: normal;
}
.comment {
font-weight: normal;
color: #777;
}
</style>
<title>Environment Setup Instructions</title>
</head>
<body>
<div class="container">
<h1>Environment Setup Instructions</h1>
<p>Please add the following configurations to your environment variables:</p>
<pre>
<span class="env-var">LITELLM_MASTER_KEY="sk-1234"</span> <span class="comment"># make this unique. must start with `sk-`.</span>
<span class="env-var">DATABASE_URL="postgres://..."</span> <span class="comment"># Need a postgres database? (Check out Supabase, Neon, etc)</span>
<span class="comment">## OPTIONAL ##</span>
<span class="env-var">PORT=4000</span> <span class="comment"># DO THIS FOR RENDER/RAILWAY</span>
<span class="env-var">STORE_MODEL_IN_DB="True"</span> <span class="comment"># Allow storing models in db</span>
</pre>
</div>
</body>
</html>
"""
def _to_ns(dt): def _to_ns(dt):
return int(dt.timestamp() * 1e9) return int(dt.timestamp() * 1e9)
@ -2878,6 +2716,11 @@ def get_error_message_str(e: Exception) -> str:
error_message = e.detail error_message = e.detail
elif isinstance(e.detail, dict): elif isinstance(e.detail, dict):
error_message = json.dumps(e.detail) error_message = json.dumps(e.detail)
elif hasattr(e, "message"):
if isinstance(e.message, "str"):
error_message = e.message
elif isinstance(e.message, dict):
error_message = json.dumps(e.message)
else: else:
error_message = str(e) error_message = str(e)
else: else:

506
litellm/router.py
View file

@ -51,6 +51,10 @@ from litellm.router_strategy.lowest_cost import LowestCostLoggingHandler
from litellm.router_strategy.lowest_latency import LowestLatencyLoggingHandler from litellm.router_strategy.lowest_latency import LowestLatencyLoggingHandler
from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler
from litellm.router_strategy.lowest_tpm_rpm_v2 import LowestTPMLoggingHandler_v2 from litellm.router_strategy.lowest_tpm_rpm_v2 import LowestTPMLoggingHandler_v2
from litellm.router_utils.client_initalization_utils import (
set_client,
should_initialize_sync_client,
)
from litellm.router_utils.handle_error import send_llm_exception_alert from litellm.router_utils.handle_error import send_llm_exception_alert
from litellm.scheduler import FlowItem, Scheduler from litellm.scheduler import FlowItem, Scheduler
from litellm.types.llms.openai import ( from litellm.types.llms.openai import (
@ -63,6 +67,7 @@ from litellm.types.llms.openai import (
Thread, Thread,
) )
from litellm.types.router import ( from litellm.types.router import (
SPECIAL_MODEL_INFO_PARAMS,
AlertingConfig, AlertingConfig,
AllowedFailsPolicy, AllowedFailsPolicy,
AssistantsTypedDict, AssistantsTypedDict,
@ -74,6 +79,7 @@ from litellm.types.router import (
ModelInfo, ModelInfo,
RetryPolicy, RetryPolicy,
RouterErrors, RouterErrors,
RouterGeneralSettings,
updateDeployment, updateDeployment,
updateLiteLLMParams, updateLiteLLMParams,
) )
@ -165,6 +171,7 @@ class Router:
routing_strategy_args: dict = {}, # just for latency-based routing routing_strategy_args: dict = {}, # just for latency-based routing
semaphore: Optional[asyncio.Semaphore] = None, semaphore: Optional[asyncio.Semaphore] = None,
alerting_config: Optional[AlertingConfig] = None, alerting_config: Optional[AlertingConfig] = None,
router_general_settings: Optional[RouterGeneralSettings] = None,
) -> None: ) -> None:
""" """
Initialize the Router class with the given parameters for caching, reliability, and routing strategy. Initialize the Router class with the given parameters for caching, reliability, and routing strategy.
@ -242,6 +249,9 @@ class Router:
verbose_router_logger.setLevel(logging.INFO) verbose_router_logger.setLevel(logging.INFO)
elif debug_level == "DEBUG": elif debug_level == "DEBUG":
verbose_router_logger.setLevel(logging.DEBUG) verbose_router_logger.setLevel(logging.DEBUG)
self.router_general_settings: Optional[RouterGeneralSettings] = (
router_general_settings
)
self.assistants_config = assistants_config self.assistants_config = assistants_config
self.deployment_names: List = ( self.deployment_names: List = (
@ -3243,450 +3253,6 @@ class Router:
except Exception as e: except Exception as e:
raise e raise e
def set_client(self, model: dict):
"""
- Initializes Azure/OpenAI clients. Stores them in cache, b/c of this - https://github.com/BerriAI/litellm/issues/1278
- Initializes Semaphore for client w/ rpm. Stores them in cache. b/c of this - https://github.com/BerriAI/litellm/issues/2994
"""
client_ttl = self.client_ttl
litellm_params = model.get("litellm_params", {})
model_name = litellm_params.get("model")
model_id = model["model_info"]["id"]
# ### IF RPM SET - initialize a semaphore ###
rpm = litellm_params.get("rpm", None)
tpm = litellm_params.get("tpm", None)
max_parallel_requests = litellm_params.get("max_parallel_requests", None)
calculated_max_parallel_requests = calculate_max_parallel_requests(
rpm=rpm,
max_parallel_requests=max_parallel_requests,
tpm=tpm,
default_max_parallel_requests=self.default_max_parallel_requests,
)
if calculated_max_parallel_requests:
semaphore = asyncio.Semaphore(calculated_max_parallel_requests)
cache_key = f"{model_id}_max_parallel_requests_client"
self.cache.set_cache(
key=cache_key,
value=semaphore,
local_only=True,
)
#### for OpenAI / Azure we need to initalize the Client for High Traffic ########
custom_llm_provider = litellm_params.get("custom_llm_provider")
custom_llm_provider = custom_llm_provider or model_name.split("/", 1)[0] or ""
default_api_base = None
default_api_key = None
if custom_llm_provider in litellm.openai_compatible_providers:
_, custom_llm_provider, api_key, api_base = litellm.get_llm_provider(
model=model_name
)
default_api_base = api_base
default_api_key = api_key
if (
model_name in litellm.open_ai_chat_completion_models
or custom_llm_provider in litellm.openai_compatible_providers
or custom_llm_provider == "azure"
or custom_llm_provider == "azure_text"
or custom_llm_provider == "custom_openai"
or custom_llm_provider == "openai"
or custom_llm_provider == "text-completion-openai"
or "ft:gpt-3.5-turbo" in model_name
or model_name in litellm.open_ai_embedding_models
):
is_azure_ai_studio_model: bool = False
if custom_llm_provider == "azure":
if litellm.utils._is_non_openai_azure_model(model_name):
is_azure_ai_studio_model = True
custom_llm_provider = "openai"
# remove azure prefx from model_name
model_name = model_name.replace("azure/", "")
# glorified / complicated reading of configs
# user can pass vars directly or they can pas os.environ/AZURE_API_KEY, in which case we will read the env
# we do this here because we init clients for Azure, OpenAI and we need to set the right key
api_key = litellm_params.get("api_key") or default_api_key
if (
api_key
and isinstance(api_key, str)
and api_key.startswith("os.environ/")
):
api_key_env_name = api_key.replace("os.environ/", "")
api_key = litellm.get_secret(api_key_env_name)
litellm_params["api_key"] = api_key
api_base = litellm_params.get("api_base")
base_url = litellm_params.get("base_url")
api_base = (
api_base or base_url or default_api_base
) # allow users to pass in `api_base` or `base_url` for azure
if api_base and api_base.startswith("os.environ/"):
api_base_env_name = api_base.replace("os.environ/", "")
api_base = litellm.get_secret(api_base_env_name)
litellm_params["api_base"] = api_base
## AZURE AI STUDIO MISTRAL CHECK ##
"""
Make sure api base ends in /v1/
if not, add it - https://github.com/BerriAI/litellm/issues/2279
"""
if (
is_azure_ai_studio_model is True
and api_base is not None
and isinstance(api_base, str)
and not api_base.endswith("/v1/")
):
# check if it ends with a trailing slash
if api_base.endswith("/"):
api_base += "v1/"
elif api_base.endswith("/v1"):
api_base += "/"
else:
api_base += "/v1/"
api_version = litellm_params.get("api_version")
if api_version and api_version.startswith("os.environ/"):
api_version_env_name = api_version.replace("os.environ/", "")
api_version = litellm.get_secret(api_version_env_name)
litellm_params["api_version"] = api_version
timeout = litellm_params.pop("timeout", None) or litellm.request_timeout
if isinstance(timeout, str) and timeout.startswith("os.environ/"):
timeout_env_name = timeout.replace("os.environ/", "")
timeout = litellm.get_secret(timeout_env_name)
litellm_params["timeout"] = timeout
stream_timeout = litellm_params.pop(
"stream_timeout", timeout
) # if no stream_timeout is set, default to timeout
if isinstance(stream_timeout, str) and stream_timeout.startswith(
"os.environ/"
):
stream_timeout_env_name = stream_timeout.replace("os.environ/", "")
stream_timeout = litellm.get_secret(stream_timeout_env_name)
litellm_params["stream_timeout"] = stream_timeout
max_retries = litellm_params.pop(
"max_retries", 0
) # router handles retry logic
if isinstance(max_retries, str) and max_retries.startswith("os.environ/"):
max_retries_env_name = max_retries.replace("os.environ/", "")
max_retries = litellm.get_secret(max_retries_env_name)
litellm_params["max_retries"] = max_retries
# proxy support
organization = litellm_params.get("organization", None)
if isinstance(organization, str) and organization.startswith("os.environ/"):
organization_env_name = organization.replace("os.environ/", "")
organization = litellm.get_secret(organization_env_name)
litellm_params["organization"] = organization
if custom_llm_provider == "azure" or custom_llm_provider == "azure_text":
if api_base is None or not isinstance(api_base, str):
filtered_litellm_params = {
k: v
for k, v in model["litellm_params"].items()
if k != "api_key"
}
_filtered_model = {
"model_name": model["model_name"],
"litellm_params": filtered_litellm_params,
}
raise ValueError(
f"api_base is required for Azure OpenAI. Set it on your config. Model - {_filtered_model}"
)
azure_ad_token = litellm_params.get("azure_ad_token")
if azure_ad_token is not None:
if azure_ad_token.startswith("oidc/"):
azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
if api_version is None:
api_version = "2023-07-01-preview"
if "gateway.ai.cloudflare.com" in api_base:
if not api_base.endswith("/"):
api_base += "/"
azure_model = model_name.replace("azure/", "")
api_base += f"{azure_model}"
cache_key = f"{model_id}_async_client"
_client = openai.AsyncAzureOpenAI(
api_key=api_key,
azure_ad_token=azure_ad_token,
base_url=api_base,
api_version=api_version,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
cache_key = f"{model_id}_client"
_client = openai.AzureOpenAI( # type: ignore
api_key=api_key,
azure_ad_token=azure_ad_token,
base_url=api_base,
api_version=api_version,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.Client(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
# streaming clients can have diff timeouts
cache_key = f"{model_id}_stream_async_client"
_client = openai.AsyncAzureOpenAI( # type: ignore
api_key=api_key,
azure_ad_token=azure_ad_token,
base_url=api_base,
api_version=api_version,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
cache_key = f"{model_id}_stream_client"
_client = openai.AzureOpenAI( # type: ignore
api_key=api_key,
azure_ad_token=azure_ad_token,
base_url=api_base,
api_version=api_version,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.Client(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
else:
_api_key = api_key
if _api_key is not None and isinstance(_api_key, str):
# only show first 5 chars of api_key
_api_key = _api_key[:8] + "*" * 15
verbose_router_logger.debug(
f"Initializing Azure OpenAI Client for {model_name}, Api Base: {str(api_base)}, Api Key:{_api_key}"
)
azure_client_params = {
"api_key": api_key,
"azure_endpoint": api_base,
"api_version": api_version,
"azure_ad_token": azure_ad_token,
}
from litellm.llms.azure import select_azure_base_url_or_endpoint
# this decides if we should set azure_endpoint or base_url on Azure OpenAI Client
# required to support GPT-4 vision enhancements, since base_url needs to be set on Azure OpenAI Client
azure_client_params = select_azure_base_url_or_endpoint(
azure_client_params
)
cache_key = f"{model_id}_async_client"
_client = openai.AsyncAzureOpenAI( # type: ignore
**azure_client_params,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
cache_key = f"{model_id}_client"
_client = openai.AzureOpenAI( # type: ignore
**azure_client_params,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.Client(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
# streaming clients should have diff timeouts
cache_key = f"{model_id}_stream_async_client"
_client = openai.AsyncAzureOpenAI( # type: ignore
**azure_client_params,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
cache_key = f"{model_id}_stream_client"
_client = openai.AzureOpenAI( # type: ignore
**azure_client_params,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.Client(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
else:
_api_key = api_key # type: ignore
if _api_key is not None and isinstance(_api_key, str):
# only show first 5 chars of api_key
_api_key = _api_key[:8] + "*" * 15
verbose_router_logger.debug(
f"Initializing OpenAI Client for {model_name}, Api Base:{str(api_base)}, Api Key:{_api_key}"
)
cache_key = f"{model_id}_async_client"
_client = openai.AsyncOpenAI( # type: ignore
api_key=api_key,
base_url=api_base,
timeout=timeout,
max_retries=max_retries,
organization=organization,
http_client=httpx.AsyncClient(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
cache_key = f"{model_id}_client"
_client = openai.OpenAI( # type: ignore
api_key=api_key,
base_url=api_base,
timeout=timeout,
max_retries=max_retries,
organization=organization,
http_client=httpx.Client(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
# streaming clients should have diff timeouts
cache_key = f"{model_id}_stream_async_client"
_client = openai.AsyncOpenAI( # type: ignore
api_key=api_key,
base_url=api_base,
timeout=stream_timeout,
max_retries=max_retries,
organization=organization,
http_client=httpx.AsyncClient(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
# streaming clients should have diff timeouts
cache_key = f"{model_id}_stream_client"
_client = openai.OpenAI( # type: ignore
api_key=api_key,
base_url=api_base,
timeout=stream_timeout,
max_retries=max_retries,
organization=organization,
http_client=httpx.Client(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
def _generate_model_id(self, model_group: str, litellm_params: dict): def _generate_model_id(self, model_group: str, litellm_params: dict):
""" """
Helper function to consistently generate the same id for a deployment Helper function to consistently generate the same id for a deployment
@ -3721,7 +3287,7 @@ class Router:
deployment = Deployment( deployment = Deployment(
**model, **model,
model_name=_model_name, model_name=_model_name,
litellm_params=_litellm_params, # type: ignore litellm_params=LiteLLM_Params(**_litellm_params),
model_info=_model_info, model_info=_model_info,
) )
@ -3830,7 +3396,9 @@ class Router:
raise Exception(f"Unsupported provider - {custom_llm_provider}") raise Exception(f"Unsupported provider - {custom_llm_provider}")
# init OpenAI, Azure clients # init OpenAI, Azure clients
self.set_client(model=deployment.to_json(exclude_none=True)) set_client(
litellm_router_instance=self, model=deployment.to_json(exclude_none=True)
)
# set region (if azure model) ## PREVIEW FEATURE ## # set region (if azure model) ## PREVIEW FEATURE ##
if litellm.enable_preview_features == True: if litellm.enable_preview_features == True:
@ -4183,25 +3751,42 @@ class Router:
return model_group_info return model_group_info
async def get_model_group_usage(self, model_group: str) -> Optional[int]: async def get_model_group_usage(
self, model_group: str
) -> Tuple[Optional[int], Optional[int]]:
""" """
Returns remaining tpm quota for model group Returns remaining tpm/rpm quota for model group
Returns:
- usage: Tuple[tpm, rpm]
""" """
dt = get_utc_datetime() dt = get_utc_datetime()
current_minute = dt.strftime( current_minute = dt.strftime(
"%H-%M" "%H-%M"
) # use the same timezone regardless of system clock ) # use the same timezone regardless of system clock
tpm_keys: List[str] = [] tpm_keys: List[str] = []
rpm_keys: List[str] = []
for model in self.model_list: for model in self.model_list:
if "model_name" in model and model["model_name"] == model_group: if "model_name" in model and model["model_name"] == model_group:
tpm_keys.append( tpm_keys.append(
f"global_router:{model['model_info']['id']}:tpm:{current_minute}" f"global_router:{model['model_info']['id']}:tpm:{current_minute}"
) )
rpm_keys.append(
f"global_router:{model['model_info']['id']}:rpm:{current_minute}"
)
combined_tpm_rpm_keys = tpm_keys + rpm_keys
combined_tpm_rpm_values = await self.cache.async_batch_get_cache(
keys=combined_tpm_rpm_keys
)
if combined_tpm_rpm_values is None:
return None, None
tpm_usage_list: Optional[List] = combined_tpm_rpm_values[: len(tpm_keys)]
rpm_usage_list: Optional[List] = combined_tpm_rpm_values[len(tpm_keys) :]
## TPM ## TPM
tpm_usage_list: Optional[List] = await self.cache.async_batch_get_cache(
keys=tpm_keys
)
tpm_usage: Optional[int] = None tpm_usage: Optional[int] = None
if tpm_usage_list is not None: if tpm_usage_list is not None:
for t in tpm_usage_list: for t in tpm_usage_list:
@ -4209,8 +3794,15 @@ class Router:
if tpm_usage is None: if tpm_usage is None:
tpm_usage = 0 tpm_usage = 0
tpm_usage += t tpm_usage += t
## RPM
return tpm_usage rpm_usage: Optional[int] = None
if rpm_usage_list is not None:
for t in rpm_usage_list:
if isinstance(t, int):
if rpm_usage is None:
rpm_usage = 0
rpm_usage += t
return tpm_usage, rpm_usage
def get_model_ids(self) -> List[str]: def get_model_ids(self) -> List[str]:
""" """
@ -4334,7 +3926,7 @@ class Router:
""" """
Re-initialize the client Re-initialize the client
""" """
self.set_client(model=deployment) set_client(litellm_router_instance=self, model=deployment)
client = self.cache.get_cache(key=cache_key, local_only=True) client = self.cache.get_cache(key=cache_key, local_only=True)
return client return client
else: else:
@ -4344,7 +3936,7 @@ class Router:
""" """
Re-initialize the client Re-initialize the client
""" """
self.set_client(model=deployment) set_client(litellm_router_instance=self, model=deployment)
client = self.cache.get_cache(key=cache_key, local_only=True) client = self.cache.get_cache(key=cache_key, local_only=True)
return client return client
else: else:
@ -4355,7 +3947,7 @@ class Router:
""" """
Re-initialize the client Re-initialize the client
""" """
self.set_client(model=deployment) set_client(litellm_router_instance=self, model=deployment)
client = self.cache.get_cache(key=cache_key) client = self.cache.get_cache(key=cache_key)
return client return client
else: else:
@ -4365,7 +3957,7 @@ class Router:
""" """
Re-initialize the client Re-initialize the client
""" """
self.set_client(model=deployment) set_client(litellm_router_instance=self, model=deployment)
client = self.cache.get_cache(key=cache_key) client = self.cache.get_cache(key=cache_key)
return client return client

566
litellm/router_utils/client_initalization_utils.py Normal file
View file

@ -0,0 +1,566 @@
import asyncio
import traceback
from typing import TYPE_CHECKING, Any
import openai
import litellm
from litellm._logging import verbose_router_logger
from litellm.llms.azure import get_azure_ad_token_from_oidc
from litellm.llms.custom_httpx.azure_dall_e_2 import (
AsyncCustomHTTPTransport,
CustomHTTPTransport,
)
from litellm.utils import calculate_max_parallel_requests
if TYPE_CHECKING:
from litellm.router import Router as _Router
LitellmRouter = _Router
else:
LitellmRouter = Any
def should_initialize_sync_client(
litellm_router_instance: LitellmRouter,
) -> bool:
"""
Returns if Sync OpenAI, Azure Clients should be initialized.
Do not init sync clients when router.router_general_settings.async_only_mode is True
"""
if litellm_router_instance is None:
return False
if litellm_router_instance.router_general_settings is not None:
if (
hasattr(litellm_router_instance, "router_general_settings")
and hasattr(
litellm_router_instance.router_general_settings, "async_only_mode"
)
and litellm_router_instance.router_general_settings.async_only_mode is True
):
return False
return True
def set_client(litellm_router_instance: LitellmRouter, model: dict):
"""
- Initializes Azure/OpenAI clients. Stores them in cache, b/c of this - https://github.com/BerriAI/litellm/issues/1278
- Initializes Semaphore for client w/ rpm. Stores them in cache. b/c of this - https://github.com/BerriAI/litellm/issues/2994
"""
client_ttl = litellm_router_instance.client_ttl
litellm_params = model.get("litellm_params", {})
model_name = litellm_params.get("model")
model_id = model["model_info"]["id"]
# ### IF RPM SET - initialize a semaphore ###
rpm = litellm_params.get("rpm", None)
tpm = litellm_params.get("tpm", None)
max_parallel_requests = litellm_params.get("max_parallel_requests", None)
calculated_max_parallel_requests = calculate_max_parallel_requests(
rpm=rpm,
max_parallel_requests=max_parallel_requests,
tpm=tpm,
default_max_parallel_requests=litellm_router_instance.default_max_parallel_requests,
)
if calculated_max_parallel_requests:
semaphore = asyncio.Semaphore(calculated_max_parallel_requests)
cache_key = f"{model_id}_max_parallel_requests_client"
litellm_router_instance.cache.set_cache(
key=cache_key,
value=semaphore,
local_only=True,
)
#### for OpenAI / Azure we need to initalize the Client for High Traffic ########
custom_llm_provider = litellm_params.get("custom_llm_provider")
custom_llm_provider = custom_llm_provider or model_name.split("/", 1)[0] or ""
default_api_base = None
default_api_key = None
if custom_llm_provider in litellm.openai_compatible_providers:
_, custom_llm_provider, api_key, api_base = litellm.get_llm_provider(
model=model_name
)
default_api_base = api_base
default_api_key = api_key
if (
model_name in litellm.open_ai_chat_completion_models
or custom_llm_provider in litellm.openai_compatible_providers
or custom_llm_provider == "azure"
or custom_llm_provider == "azure_text"
or custom_llm_provider == "custom_openai"
or custom_llm_provider == "openai"
or custom_llm_provider == "text-completion-openai"
or "ft:gpt-3.5-turbo" in model_name
or model_name in litellm.open_ai_embedding_models
):
is_azure_ai_studio_model: bool = False
if custom_llm_provider == "azure":
if litellm.utils._is_non_openai_azure_model(model_name):
is_azure_ai_studio_model = True
custom_llm_provider = "openai"
# remove azure prefx from model_name
model_name = model_name.replace("azure/", "")
# glorified / complicated reading of configs
# user can pass vars directly or they can pas os.environ/AZURE_API_KEY, in which case we will read the env
# we do this here because we init clients for Azure, OpenAI and we need to set the right key
api_key = litellm_params.get("api_key") or default_api_key
if api_key and isinstance(api_key, str) and api_key.startswith("os.environ/"):
api_key_env_name = api_key.replace("os.environ/", "")
api_key = litellm.get_secret(api_key_env_name)
litellm_params["api_key"] = api_key
api_base = litellm_params.get("api_base")
base_url = litellm_params.get("base_url")
api_base = (
api_base or base_url or default_api_base
) # allow users to pass in `api_base` or `base_url` for azure
if api_base and api_base.startswith("os.environ/"):
api_base_env_name = api_base.replace("os.environ/", "")
api_base = litellm.get_secret(api_base_env_name)
litellm_params["api_base"] = api_base
## AZURE AI STUDIO MISTRAL CHECK ##
"""
Make sure api base ends in /v1/
if not, add it - https://github.com/BerriAI/litellm/issues/2279
"""
if (
is_azure_ai_studio_model is True
and api_base is not None
and isinstance(api_base, str)
and not api_base.endswith("/v1/")
):
# check if it ends with a trailing slash
if api_base.endswith("/"):
api_base += "v1/"
elif api_base.endswith("/v1"):
api_base += "/"
else:
api_base += "/v1/"
api_version = litellm_params.get("api_version")
if api_version and api_version.startswith("os.environ/"):
api_version_env_name = api_version.replace("os.environ/", "")
api_version = litellm.get_secret(api_version_env_name)
litellm_params["api_version"] = api_version
timeout = litellm_params.pop("timeout", None) or litellm.request_timeout
if isinstance(timeout, str) and timeout.startswith("os.environ/"):
timeout_env_name = timeout.replace("os.environ/", "")
timeout = litellm.get_secret(timeout_env_name)
litellm_params["timeout"] = timeout
stream_timeout = litellm_params.pop(
"stream_timeout", timeout
) # if no stream_timeout is set, default to timeout
if isinstance(stream_timeout, str) and stream_timeout.startswith("os.environ/"):
stream_timeout_env_name = stream_timeout.replace("os.environ/", "")
stream_timeout = litellm.get_secret(stream_timeout_env_name)
litellm_params["stream_timeout"] = stream_timeout
max_retries = litellm_params.pop("max_retries", 0) # router handles retry logic
if isinstance(max_retries, str) and max_retries.startswith("os.environ/"):
max_retries_env_name = max_retries.replace("os.environ/", "")
max_retries = litellm.get_secret(max_retries_env_name)
litellm_params["max_retries"] = max_retries
# proxy support
import os
import httpx
# Check if the HTTP_PROXY and HTTPS_PROXY environment variables are set and use them accordingly.
http_proxy = os.getenv("HTTP_PROXY", None)
https_proxy = os.getenv("HTTPS_PROXY", None)
no_proxy = os.getenv("NO_PROXY", None)
# Create the proxies dictionary only if the environment variables are set.
sync_proxy_mounts = None
async_proxy_mounts = None
if http_proxy is not None and https_proxy is not None:
sync_proxy_mounts = {
"http://": httpx.HTTPTransport(proxy=httpx.Proxy(url=http_proxy)),
"https://": httpx.HTTPTransport(proxy=httpx.Proxy(url=https_proxy)),
}
async_proxy_mounts = {
"http://": httpx.AsyncHTTPTransport(proxy=httpx.Proxy(url=http_proxy)),
"https://": httpx.AsyncHTTPTransport(
proxy=httpx.Proxy(url=https_proxy)
),
}
# assume no_proxy is a list of comma separated urls
if no_proxy is not None and isinstance(no_proxy, str):
no_proxy_urls = no_proxy.split(",")
for url in no_proxy_urls: # set no-proxy support for specific urls
sync_proxy_mounts[url] = None # type: ignore
async_proxy_mounts[url] = None # type: ignore
organization = litellm_params.get("organization", None)
if isinstance(organization, str) and organization.startswith("os.environ/"):
organization_env_name = organization.replace("os.environ/", "")
organization = litellm.get_secret(organization_env_name)
litellm_params["organization"] = organization
if custom_llm_provider == "azure" or custom_llm_provider == "azure_text":
if api_base is None or not isinstance(api_base, str):
filtered_litellm_params = {
k: v for k, v in model["litellm_params"].items() if k != "api_key"
}
_filtered_model = {
"model_name": model["model_name"],
"litellm_params": filtered_litellm_params,
}
raise ValueError(
f"api_base is required for Azure OpenAI. Set it on your config. Model - {_filtered_model}"
)
azure_ad_token = litellm_params.get("azure_ad_token")
if azure_ad_token is not None:
if azure_ad_token.startswith("oidc/"):
azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
if api_version is None:
api_version = litellm.AZURE_DEFAULT_API_VERSION
if "gateway.ai.cloudflare.com" in api_base:
if not api_base.endswith("/"):
api_base += "/"
azure_model = model_name.replace("azure/", "")
api_base += f"{azure_model}"
cache_key = f"{model_id}_async_client"
_client = openai.AsyncAzureOpenAI(
api_key=api_key,
azure_ad_token=azure_ad_token,
base_url=api_base,
api_version=api_version,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=async_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
if should_initialize_sync_client(
litellm_router_instance=litellm_router_instance
):
cache_key = f"{model_id}_client"
_client = openai.AzureOpenAI( # type: ignore
api_key=api_key,
azure_ad_token=azure_ad_token,
base_url=api_base,
api_version=api_version,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.Client(
transport=CustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=sync_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
# streaming clients can have diff timeouts
cache_key = f"{model_id}_stream_async_client"
_client = openai.AsyncAzureOpenAI( # type: ignore
api_key=api_key,
azure_ad_token=azure_ad_token,
base_url=api_base,
api_version=api_version,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=async_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
if should_initialize_sync_client(
litellm_router_instance=litellm_router_instance
):
cache_key = f"{model_id}_stream_client"
_client = openai.AzureOpenAI( # type: ignore
api_key=api_key,
azure_ad_token=azure_ad_token,
base_url=api_base,
api_version=api_version,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.Client(
transport=CustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=sync_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
else:
_api_key = api_key
if _api_key is not None and isinstance(_api_key, str):
# only show first 5 chars of api_key
_api_key = _api_key[:8] + "*" * 15
verbose_router_logger.debug(
f"Initializing Azure OpenAI Client for {model_name}, Api Base: {str(api_base)}, Api Key:{_api_key}"
)
azure_client_params = {
"api_key": api_key,
"azure_endpoint": api_base,
"api_version": api_version,
"azure_ad_token": azure_ad_token,
}
from litellm.llms.azure import select_azure_base_url_or_endpoint
# this decides if we should set azure_endpoint or base_url on Azure OpenAI Client
# required to support GPT-4 vision enhancements, since base_url needs to be set on Azure OpenAI Client
azure_client_params = select_azure_base_url_or_endpoint(
azure_client_params
)
cache_key = f"{model_id}_async_client"
_client = openai.AsyncAzureOpenAI( # type: ignore
**azure_client_params,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=async_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
if should_initialize_sync_client(
litellm_router_instance=litellm_router_instance
):
cache_key = f"{model_id}_client"
_client = openai.AzureOpenAI( # type: ignore
**azure_client_params,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.Client(
transport=CustomHTTPTransport(
verify=litellm.ssl_verify,
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
),
mounts=sync_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
# streaming clients should have diff timeouts
cache_key = f"{model_id}_stream_async_client"
_client = openai.AsyncAzureOpenAI( # type: ignore
**azure_client_params,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=async_proxy_mounts,
),
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
if should_initialize_sync_client(
litellm_router_instance=litellm_router_instance
):
cache_key = f"{model_id}_stream_client"
_client = openai.AzureOpenAI( # type: ignore
**azure_client_params,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.Client(
transport=CustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=sync_proxy_mounts,
),
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
else:
_api_key = api_key # type: ignore
if _api_key is not None and isinstance(_api_key, str):
# only show first 5 chars of api_key
_api_key = _api_key[:8] + "*" * 15
verbose_router_logger.debug(
f"Initializing OpenAI Client for {model_name}, Api Base:{str(api_base)}, Api Key:{_api_key}"
)
cache_key = f"{model_id}_async_client"
_client = openai.AsyncOpenAI( # type: ignore
api_key=api_key,
base_url=api_base,
timeout=timeout,
max_retries=max_retries,
organization=organization,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=async_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
if should_initialize_sync_client(
litellm_router_instance=litellm_router_instance
):
cache_key = f"{model_id}_client"
_client = openai.OpenAI( # type: ignore
api_key=api_key,
base_url=api_base,
timeout=timeout,
max_retries=max_retries,
organization=organization,
http_client=httpx.Client(
transport=CustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=sync_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
# streaming clients should have diff timeouts
cache_key = f"{model_id}_stream_async_client"
_client = openai.AsyncOpenAI( # type: ignore
api_key=api_key,
base_url=api_base,
timeout=stream_timeout,
max_retries=max_retries,
organization=organization,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=async_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
if should_initialize_sync_client(
litellm_router_instance=litellm_router_instance
):
# streaming clients should have diff timeouts
cache_key = f"{model_id}_stream_client"
_client = openai.OpenAI( # type: ignore
api_key=api_key,
base_url=api_base,
timeout=stream_timeout,
max_retries=max_retries,
organization=organization,
http_client=httpx.Client(
transport=CustomHTTPTransport(
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
verify=litellm.ssl_verify,
),
mounts=sync_proxy_mounts,
), # type: ignore
)
litellm_router_instance.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr

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73
litellm/tests/test_amazing_vertex_completion.py
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@ -203,7 +203,7 @@ def test_vertex_ai_anthropic():
# ) # )
def test_vertex_ai_anthropic_streaming(): def test_vertex_ai_anthropic_streaming():
try: try:
# load_vertex_ai_credentials() load_vertex_ai_credentials()
# litellm.set_verbose = True # litellm.set_verbose = True
@ -223,8 +223,9 @@ def test_vertex_ai_anthropic_streaming():
stream=True, stream=True,
) )
# print("\nModel Response", response) # print("\nModel Response", response)
for chunk in response: for idx, chunk in enumerate(response):
print(f"chunk: {chunk}") print(f"chunk: {chunk}")
streaming_format_tests(idx=idx, chunk=chunk)
# raise Exception("it worked!") # raise Exception("it worked!")
except litellm.RateLimitError as e: except litellm.RateLimitError as e:
@ -294,8 +295,10 @@ async def test_vertex_ai_anthropic_async_streaming():
stream=True, stream=True,
) )
idx = 0
async for chunk in response: async for chunk in response:
print(f"chunk: {chunk}") streaming_format_tests(idx=idx, chunk=chunk)
idx += 1
except litellm.RateLimitError as e: except litellm.RateLimitError as e:
pass pass
except Exception as e: except Exception as e:
@ -637,11 +640,13 @@ def test_gemini_pro_vision_base64():
pytest.fail(f"An exception occurred - {str(e)}") pytest.fail(f"An exception occurred - {str(e)}")
@pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call") # @pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call")
@pytest.mark.parametrize("provider", ["vertex_ai_beta"]) # "vertex_ai", @pytest.mark.parametrize(
"model", ["vertex_ai_beta/gemini-1.5-pro", "vertex_ai/claude-3-sonnet@20240229"]
) # "vertex_ai",
@pytest.mark.parametrize("sync_mode", [True]) # "vertex_ai", @pytest.mark.parametrize("sync_mode", [True]) # "vertex_ai",
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_gemini_pro_function_calling_httpx(provider, sync_mode): async def test_gemini_pro_function_calling_httpx(model, sync_mode):
try: try:
load_vertex_ai_credentials() load_vertex_ai_credentials()
litellm.set_verbose = True litellm.set_verbose = True
@ -679,7 +684,7 @@ async def test_gemini_pro_function_calling_httpx(provider, sync_mode):
] ]
data = { data = {
"model": "{}/gemini-1.5-pro".format(provider), "model": model,
"messages": messages, "messages": messages,
"tools": tools, "tools": tools,
"tool_choice": "required", "tool_choice": "required",
@ -1108,7 +1113,7 @@ async def test_gemini_pro_httpx_custom_api_base(provider):
extra_headers={"hello": "world"}, extra_headers={"hello": "world"},
) )
except Exception as e: except Exception as e:
pass print("Receives error - {}\n{}".format(str(e), traceback.format_exc()))
mock_call.assert_called_once() mock_call.assert_called_once()
@ -1116,7 +1121,7 @@ async def test_gemini_pro_httpx_custom_api_base(provider):
assert "hello" in mock_call.call_args.kwargs["headers"] assert "hello" in mock_call.call_args.kwargs["headers"]
@pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call") # @pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call")
@pytest.mark.parametrize("sync_mode", [True]) @pytest.mark.parametrize("sync_mode", [True])
@pytest.mark.parametrize("provider", ["vertex_ai"]) @pytest.mark.parametrize("provider", ["vertex_ai"])
@pytest.mark.asyncio @pytest.mark.asyncio
@ -1155,7 +1160,6 @@ async def test_gemini_pro_function_calling(provider, sync_mode):
{ {
"role": "tool", "role": "tool",
"tool_call_id": "call_123", "tool_call_id": "call_123",
"name": "get_weather",
"content": "27 degrees celsius and clear in San Francisco, CA", "content": "27 degrees celsius and clear in San Francisco, CA",
}, },
# Now the assistant can reply with the result of the tool call. # Now the assistant can reply with the result of the tool call.
@ -1378,6 +1382,54 @@ async def test_vertexai_aembedding():
pytest.fail(f"Error occurred: {e}") pytest.fail(f"Error occurred: {e}")
@pytest.mark.asyncio
def test_tool_name_conversion():
messages = [
{
"role": "system",
"content": "Your name is Litellm Bot, you are a helpful assistant",
},
# User asks for their name and weather in San Francisco
{
"role": "user",
"content": "Hello, what is your name and can you tell me the weather?",
},
# Assistant replies with a tool call
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_123",
"type": "function",
"index": 0,
"function": {
"name": "get_weather",
"arguments": '{"location":"San Francisco, CA"}',
},
}
],
},
# The result of the tool call is added to the history
{
"role": "tool",
"tool_call_id": "call_123",
"content": "27 degrees celsius and clear in San Francisco, CA",
},
# Now the assistant can reply with the result of the tool call.
]
translated_messages = _gemini_convert_messages_with_history(messages=messages)
print(f"\n\ntranslated_messages: {translated_messages}\ntranslated_messages")
# assert that the last tool response has the corresponding tool name
assert (
translated_messages[-1]["parts"][0]["function_response"]["name"]
== "get_weather"
)
# Extra gemini Vision tests for completion + stream, async, async + stream # Extra gemini Vision tests for completion + stream, async, async + stream
# if we run into issues with gemini, we will also add these to our ci/cd pipeline # if we run into issues with gemini, we will also add these to our ci/cd pipeline
# def test_gemini_pro_vision_stream(): # def test_gemini_pro_vision_stream():
@ -1526,7 +1578,6 @@ def test_prompt_factory():
{ {
"role": "tool", "role": "tool",
"tool_call_id": "call_123", "tool_call_id": "call_123",
"name": "get_weather",
"content": "27 degrees celsius and clear in San Francisco, CA", "content": "27 degrees celsius and clear in San Francisco, CA",
}, },
# Now the assistant can reply with the result of the tool call. # Now the assistant can reply with the result of the tool call.

64
litellm/tests/test_caching.py
View file

@ -1,6 +1,9 @@
import sys, os, uuid import os
import sys
import time import time
import traceback import traceback
import uuid
from dotenv import load_dotenv from dotenv import load_dotenv
load_dotenv() load_dotenv()
@ -9,12 +12,15 @@ import os
sys.path.insert( sys.path.insert(
0, os.path.abspath("../..") 0, os.path.abspath("../..")
) # Adds the parent directory to the system path ) # Adds the parent directory to the system path
import pytest import asyncio
import litellm import hashlib
from litellm import embedding, completion, aembedding
from litellm.caching import Cache
import random import random
import hashlib, asyncio
import pytest
import litellm
from litellm import aembedding, completion, embedding
from litellm.caching import Cache
# litellm.set_verbose=True # litellm.set_verbose=True
@ -656,6 +662,7 @@ def test_redis_cache_completion():
assert response1.created == response2.created assert response1.created == response2.created
assert response1.choices[0].message.content == response2.choices[0].message.content assert response1.choices[0].message.content == response2.choices[0].message.content
# test_redis_cache_completion() # test_redis_cache_completion()
@ -877,6 +884,7 @@ async def test_redis_cache_acompletion_stream_bedrock():
print(e) print(e)
raise e raise e
def test_disk_cache_completion(): def test_disk_cache_completion():
litellm.set_verbose = False litellm.set_verbose = False
@ -1569,3 +1577,47 @@ async def test_redis_semantic_cache_acompletion():
) )
print(f"response2: {response2}") print(f"response2: {response2}")
assert response1.id == response2.id assert response1.id == response2.id
def test_caching_redis_simple(caplog):
"""
Relevant issue - https://github.com/BerriAI/litellm/issues/4511
"""
litellm.cache = Cache(
type="redis", url=os.getenv("REDIS_SSL_URL")
) # passing `supported_call_types = ["completion"]` has no effect
s = time.time()
x = completion(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello, how are you? Wink"}],
stream=True,
)
for m in x:
print(m)
print(time.time() - s)
s2 = time.time()
x = completion(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello, how are you? Wink"}],
stream=True,
)
for m in x:
print(m)
print(time.time() - s2)
redis_async_caching_error = False
redis_service_logging_error = False
captured_logs = [rec.message for rec in caplog.records]
print(f"captured_logs: {captured_logs}")
for item in captured_logs:
if "Error connecting to Async Redis client" in item:
redis_async_caching_error = True
if "ServiceLogging.async_service_success_hook" in item:
redis_service_logging_error = True
assert redis_async_caching_error is False
assert redis_service_logging_error is False

99
litellm/tests/test_completion.py
View file

@ -408,6 +408,103 @@ def test_completion_claude_3_function_call(model):
pytest.fail(f"Error occurred: {e}") pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize("sync_mode", [True])
@pytest.mark.parametrize(
"model, api_key, api_base",
[
("gpt-3.5-turbo", None, None),
("claude-3-opus-20240229", None, None),
("command-r", None, None),
("anthropic.claude-3-sonnet-20240229-v1:0", None, None),
(
"azure_ai/command-r-plus",
os.getenv("AZURE_COHERE_API_KEY"),
os.getenv("AZURE_COHERE_API_BASE"),
),
],
)
@pytest.mark.asyncio
async def test_model_function_invoke(model, sync_mode, api_key, api_base):
try:
litellm.set_verbose = True
messages = [
{
"role": "system",
"content": "Your name is Litellm Bot, you are a helpful assistant",
},
# User asks for their name and weather in San Francisco
{
"role": "user",
"content": "Hello, what is your name and can you tell me the weather?",
},
# Assistant replies with a tool call
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_123",
"type": "function",
"index": 0,
"function": {
"name": "get_weather",
"arguments": '{"location": "San Francisco, CA"}',
},
}
],
},
# The result of the tool call is added to the history
{
"role": "tool",
"tool_call_id": "call_123",
"content": "27 degrees celsius and clear in San Francisco, CA",
},
# Now the assistant can reply with the result of the tool call.
]
tools = [
{
"type": "function",
"function": {
"name": "get_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",
}
},
"required": ["location"],
},
},
}
]
data = {
"model": model,
"messages": messages,
"tools": tools,
"api_key": api_key,
"api_base": api_base,
}
if sync_mode:
response = litellm.completion(**data)
else:
response = await litellm.acompletion(**data)
print(f"response: {response}")
except litellm.RateLimitError as e:
pass
except Exception as e:
if "429 Quota exceeded" in str(e):
pass
else:
pytest.fail("An unexpected exception occurred - {}".format(str(e)))
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_anthropic_no_content_error(): async def test_anthropic_no_content_error():
""" """
@ -3505,6 +3602,8 @@ def test_completion_nvidia_nim():
"content": "What's the weather like in Boston today in Fahrenheit?", "content": "What's the weather like in Boston today in Fahrenheit?",
} }
], ],
presence_penalty=0.5,
frequency_penalty=0.1,
) )
# Add any assertions here to check the response # Add any assertions here to check the response
print(response) print(response)

15
litellm/tests/test_completion_cost.py
View file

@ -712,7 +712,6 @@ def test_vertex_ai_claude_completion_cost():
assert cost == predicted_cost assert cost == predicted_cost
@pytest.mark.parametrize("sync_mode", [True, False]) @pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_completion_cost_hidden_params(sync_mode): async def test_completion_cost_hidden_params(sync_mode):
@ -732,6 +731,7 @@ async def test_completion_cost_hidden_params(sync_mode):
assert "response_cost" in response._hidden_params assert "response_cost" in response._hidden_params
assert isinstance(response._hidden_params["response_cost"], float) assert isinstance(response._hidden_params["response_cost"], float)
def test_vertex_ai_gemini_predict_cost(): def test_vertex_ai_gemini_predict_cost():
model = "gemini-1.5-flash" model = "gemini-1.5-flash"
messages = [{"role": "user", "content": "Hey, hows it going???"}] messages = [{"role": "user", "content": "Hey, hows it going???"}]
@ -739,3 +739,16 @@ def test_vertex_ai_gemini_predict_cost():
assert predictive_cost > 0 assert predictive_cost > 0
@pytest.mark.parametrize("model", ["openai/tts-1", "azure/tts-1"])
def test_completion_cost_tts(model):
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
cost = completion_cost(
model=model,
prompt="the quick brown fox jumped over the lazy dogs",
call_type="speech",
)
assert cost > 0

22
litellm/tests/test_config.py
View file

@ -2,23 +2,30 @@
## Unit tests for ProxyConfig class ## Unit tests for ProxyConfig class
import sys, os import os
import sys
import traceback import traceback
from dotenv import load_dotenv from dotenv import load_dotenv
load_dotenv() load_dotenv()
import os, io import io
import os
sys.path.insert( sys.path.insert(
0, os.path.abspath("../..") 0, os.path.abspath("../..")
) # Adds the parent directory to the system path ) # Adds the parent directory to the system path
import pytest, litellm
from pydantic import BaseModel, ConfigDict
from litellm.proxy.proxy_server import ProxyConfig
from litellm.proxy.utils import encrypt_value, ProxyLogging, DualCache
from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo
from typing import Literal from typing import Literal
import pytest
from pydantic import BaseModel, ConfigDict
import litellm
from litellm.proxy.common_utils.encrypt_decrypt_utils import encrypt_value
from litellm.proxy.proxy_server import ProxyConfig
from litellm.proxy.utils import DualCache, ProxyLogging
from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo
class DBModel(BaseModel): class DBModel(BaseModel):
model_id: str model_id: str
@ -28,6 +35,7 @@ class DBModel(BaseModel):
model_config = ConfigDict(protected_namespaces=()) model_config = ConfigDict(protected_namespaces=())
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_delete_deployment(): async def test_delete_deployment():
""" """

32
litellm/tests/test_configs/test_guardrails_config.yaml Normal file
View file

@ -0,0 +1,32 @@
model_list:
- litellm_params:
api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
api_key: os.environ/AZURE_EUROPE_API_KEY
model: azure/gpt-35-turbo
model_name: azure-model
- litellm_params:
api_base: https://my-endpoint-canada-berri992.openai.azure.com
api_key: os.environ/AZURE_CANADA_API_KEY
model: azure/gpt-35-turbo
model_name: azure-model
- litellm_params:
api_base: https://openai-france-1234.openai.azure.com
api_key: os.environ/AZURE_FRANCE_API_KEY
model: azure/gpt-turbo
model_name: azure-model
litellm_settings:
guardrails:
- prompt_injection:
callbacks: [lakera_prompt_injection, detect_prompt_injection]
default_on: true
- hide_secrets:
callbacks: [hide_secrets]
default_on: true
- moderations:
callbacks: [openai_moderations]
default_on: false

155
litellm/tests/test_dynamic_rate_limit_handler.py
View file

@ -109,17 +109,56 @@ async def test_available_tpm(num_projects, dynamic_rate_limit_handler):
## CHECK AVAILABLE TPM PER PROJECT ## CHECK AVAILABLE TPM PER PROJECT
availability, _, _ = await dynamic_rate_limit_handler.check_available_tpm( resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
model=model
) availability = resp[0]
expected_availability = int(model_tpm / num_projects) expected_availability = int(model_tpm / num_projects)
assert availability == expected_availability assert availability == expected_availability
@pytest.mark.parametrize("num_projects", [1, 2, 100])
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_rate_limit_raised(dynamic_rate_limit_handler, user_api_key_auth): async def test_available_rpm(num_projects, dynamic_rate_limit_handler):
model = "my-fake-model"
## SET CACHE W/ ACTIVE PROJECTS
projects = [str(uuid.uuid4()) for _ in range(num_projects)]
await dynamic_rate_limit_handler.internal_usage_cache.async_set_cache_sadd(
model=model, value=projects
)
model_rpm = 100
llm_router = Router(
model_list=[
{
"model_name": model,
"litellm_params": {
"model": "gpt-3.5-turbo",
"api_key": "my-key",
"api_base": "my-base",
"rpm": model_rpm,
},
}
]
)
dynamic_rate_limit_handler.update_variables(llm_router=llm_router)
## CHECK AVAILABLE rpm PER PROJECT
resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
availability = resp[1]
expected_availability = int(model_rpm / num_projects)
assert availability == expected_availability
@pytest.mark.parametrize("usage", ["rpm", "tpm"])
@pytest.mark.asyncio
async def test_rate_limit_raised(dynamic_rate_limit_handler, user_api_key_auth, usage):
""" """
Unit test. Tests if rate limit error raised when quota exhausted. Unit test. Tests if rate limit error raised when quota exhausted.
""" """
@ -133,7 +172,7 @@ async def test_rate_limit_raised(dynamic_rate_limit_handler, user_api_key_auth):
model=model, value=projects model=model, value=projects
) )
model_tpm = 0 model_usage = 0
llm_router = Router( llm_router = Router(
model_list=[ model_list=[
{ {
@ -142,7 +181,7 @@ async def test_rate_limit_raised(dynamic_rate_limit_handler, user_api_key_auth):
"model": "gpt-3.5-turbo", "model": "gpt-3.5-turbo",
"api_key": "my-key", "api_key": "my-key",
"api_base": "my-base", "api_base": "my-base",
"tpm": model_tpm, usage: model_usage,
}, },
} }
] ]
@ -151,11 +190,14 @@ async def test_rate_limit_raised(dynamic_rate_limit_handler, user_api_key_auth):
## CHECK AVAILABLE TPM PER PROJECT ## CHECK AVAILABLE TPM PER PROJECT
availability, _, _ = await dynamic_rate_limit_handler.check_available_tpm( resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
model=model
)
expected_availability = int(model_tpm / 1) if usage == "tpm":
availability = resp[0]
else:
availability = resp[1]
expected_availability = 0
assert availability == expected_availability assert availability == expected_availability
@ -217,9 +259,9 @@ async def test_base_case(dynamic_rate_limit_handler, mock_response):
for _ in range(2): for _ in range(2):
try: try:
# check availability # check availability
availability, _, _ = await dynamic_rate_limit_handler.check_available_tpm( resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
model=model
) availability = resp[0]
print( print(
"prev_availability={}, availability={}".format( "prev_availability={}, availability={}".format(
@ -273,9 +315,9 @@ async def test_update_cache(
dynamic_rate_limit_handler.update_variables(llm_router=llm_router) dynamic_rate_limit_handler.update_variables(llm_router=llm_router)
## INITIAL ACTIVE PROJECTS - ASSERT NONE ## INITIAL ACTIVE PROJECTS - ASSERT NONE
_, _, active_projects = await dynamic_rate_limit_handler.check_available_tpm( resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
model=model
) active_projects = resp[-1]
assert active_projects is None assert active_projects is None
@ -289,9 +331,9 @@ async def test_update_cache(
await asyncio.sleep(2) await asyncio.sleep(2)
## INITIAL ACTIVE PROJECTS - ASSERT 1 ## INITIAL ACTIVE PROJECTS - ASSERT 1
_, _, active_projects = await dynamic_rate_limit_handler.check_available_tpm( resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
model=model
) active_projects = resp[-1]
assert active_projects == 1 assert active_projects == 1
@ -357,9 +399,9 @@ async def test_multiple_projects(
for i in range(expected_runs + 1): for i in range(expected_runs + 1):
# check availability # check availability
availability, _, _ = await dynamic_rate_limit_handler.check_available_tpm( resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
model=model
) availability = resp[0]
## assert availability updated ## assert availability updated
if prev_availability is not None and availability is not None: if prev_availability is not None and availability is not None:
@ -389,12 +431,63 @@ async def test_multiple_projects(
await asyncio.sleep(3) await asyncio.sleep(3)
# check availability # check availability
availability, _, _ = await dynamic_rate_limit_handler.check_available_tpm( resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
model=model
) availability = resp[0]
assert availability == 0 assert availability == 0
@pytest.mark.parametrize("num_projects", [1, 2, 100])
@pytest.mark.asyncio
async def test_priority_reservation(num_projects, dynamic_rate_limit_handler):
"""
If reservation is set + `mock_testing_reservation` passed in
assert correct rpm is reserved
"""
model = "my-fake-model"
## SET CACHE W/ ACTIVE PROJECTS
projects = [str(uuid.uuid4()) for _ in range(num_projects)]
await dynamic_rate_limit_handler.internal_usage_cache.async_set_cache_sadd(
model=model, value=projects
)
litellm.priority_reservation = {"dev": 0.1, "prod": 0.9}
model_usage = 100
llm_router = Router(
model_list=[
{
"model_name": model,
"litellm_params": {
"model": "gpt-3.5-turbo",
"api_key": "my-key",
"api_base": "my-base",
"rpm": model_usage,
},
}
]
)
dynamic_rate_limit_handler.update_variables(llm_router=llm_router)
## CHECK AVAILABLE TPM PER PROJECT
resp = await dynamic_rate_limit_handler.check_available_usage(
model=model, priority="prod"
)
availability = resp[1]
expected_availability = int(
model_usage * litellm.priority_reservation["prod"] / num_projects
)
assert availability == expected_availability
@pytest.mark.skip( @pytest.mark.skip(
reason="Unstable on ci/cd due to curr minute changes. Refactor to handle minute changing" reason="Unstable on ci/cd due to curr minute changes. Refactor to handle minute changing"
) )
@ -456,9 +549,9 @@ async def test_multiple_projects_e2e(
print("expected_runs: {}".format(expected_runs)) print("expected_runs: {}".format(expected_runs))
for i in range(expected_runs + 1): for i in range(expected_runs + 1):
# check availability # check availability
availability, _, _ = await dynamic_rate_limit_handler.check_available_tpm( resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
model=model
) availability = resp[0]
## assert availability updated ## assert availability updated
if prev_availability is not None and availability is not None: if prev_availability is not None and availability is not None:
@ -488,7 +581,7 @@ async def test_multiple_projects_e2e(
await asyncio.sleep(3) await asyncio.sleep(3)
# check availability # check availability
availability, _, _ = await dynamic_rate_limit_handler.check_available_tpm( resp = await dynamic_rate_limit_handler.check_available_usage(model=model)
model=model
) availability = resp[0]
assert availability == 0 assert availability == 0

4
litellm/tests/test_image_generation.py
View file

@ -44,7 +44,9 @@ def test_image_generation_openai():
@pytest.mark.parametrize( @pytest.mark.parametrize(
"sync_mode", "sync_mode",
[True, False], [
True,
], # False
) # ) #
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_image_generation_azure(sync_mode): async def test_image_generation_azure(sync_mode):

43
litellm/tests/test_presidio_masking.py
View file

@ -1,8 +1,13 @@
# What is this? # What is this?
## Unit test for presidio pii masking ## Unit test for presidio pii masking
import sys, os, asyncio, time, random import asyncio
from datetime import datetime import os
import random
import sys
import time
import traceback import traceback
from datetime import datetime
from dotenv import load_dotenv from dotenv import load_dotenv
load_dotenv() load_dotenv()
@ -12,12 +17,40 @@ sys.path.insert(
0, os.path.abspath("../..") 0, os.path.abspath("../..")
) # Adds the parent directory to the system path ) # Adds the parent directory to the system path
import pytest import pytest
import litellm import litellm
from litellm.proxy.hooks.presidio_pii_masking import _OPTIONAL_PresidioPIIMasking
from litellm import Router, mock_completion from litellm import Router, mock_completion
from litellm.proxy.utils import ProxyLogging
from litellm.proxy._types import UserAPIKeyAuth
from litellm.caching import DualCache from litellm.caching import DualCache
from litellm.proxy._types import UserAPIKeyAuth
from litellm.proxy.hooks.presidio_pii_masking import _OPTIONAL_PresidioPIIMasking
from litellm.proxy.utils import ProxyLogging
@pytest.mark.parametrize(
"base_url",
[
"presidio-analyzer-s3pa:10000",
"https://presidio-analyzer-s3pa:10000",
"http://presidio-analyzer-s3pa:10000",
],
)
def test_validate_environment_missing_http(base_url):
pii_masking = _OPTIONAL_PresidioPIIMasking(mock_testing=True)
os.environ["PRESIDIO_ANALYZER_API_BASE"] = f"{base_url}/analyze"
os.environ["PRESIDIO_ANONYMIZER_API_BASE"] = f"{base_url}/anonymize"
pii_masking.validate_environment()
expected_url = base_url
if not (base_url.startswith("https://") or base_url.startswith("http://")):
expected_url = "http://" + base_url
assert (
pii_masking.presidio_anonymizer_api_base == f"{expected_url}/anonymize/"
), "Got={}, Expected={}".format(
pii_masking.presidio_anonymizer_api_base, f"{expected_url}/anonymize/"
)
assert pii_masking.presidio_analyzer_api_base == f"{expected_url}/analyze/"
@pytest.mark.asyncio @pytest.mark.asyncio

2
litellm/tests/test_prompt_factory.py
View file

@ -127,7 +127,7 @@ def test_anthropic_messages_pt():
messages = [] messages = []
with pytest.raises(Exception) as err: with pytest.raises(Exception) as err:
anthropic_messages_pt(messages) anthropic_messages_pt(messages)
assert "Invalid first message." in str(err.value) assert "Invalid first message" in str(err.value)
# codellama_prompt_format() # codellama_prompt_format()

100
litellm/tests/test_provider_specific_config.py
View file

@ -512,6 +512,106 @@ def sagemaker_test_completion():
# sagemaker_test_completion() # sagemaker_test_completion()
def test_sagemaker_default_region(mocker):
"""
If no regions are specified in config or in environment, the default region is us-west-2
"""
mock_client = mocker.patch("boto3.client")
try:
response = litellm.completion(
model="sagemaker/mock-endpoint",
messages=[
{
"content": "Hello, world!",
"role": "user"
}
]
)
except Exception:
pass # expected serialization exception because AWS client was replaced with a Mock
assert mock_client.call_args.kwargs["region_name"] == "us-west-2"
# test_sagemaker_default_region()
def test_sagemaker_environment_region(mocker):
"""
If a region is specified in the environment, use that region instead of us-west-2
"""
expected_region = "us-east-1"
os.environ["AWS_REGION_NAME"] = expected_region
mock_client = mocker.patch("boto3.client")
try:
response = litellm.completion(
model="sagemaker/mock-endpoint",
messages=[
{
"content": "Hello, world!",
"role": "user"
}
]
)
except Exception:
pass # expected serialization exception because AWS client was replaced with a Mock
del os.environ["AWS_REGION_NAME"] # cleanup
assert mock_client.call_args.kwargs["region_name"] == expected_region
# test_sagemaker_environment_region()
def test_sagemaker_config_region(mocker):
"""
If a region is specified as part of the optional parameters of the completion, including as
part of the config file, then use that region instead of us-west-2
"""
expected_region = "us-east-1"
mock_client = mocker.patch("boto3.client")
try:
response = litellm.completion(
model="sagemaker/mock-endpoint",
messages=[
{
"content": "Hello, world!",
"role": "user"
}
],
aws_region_name=expected_region,
)
except Exception:
pass # expected serialization exception because AWS client was replaced with a Mock
assert mock_client.call_args.kwargs["region_name"] == expected_region
# test_sagemaker_config_region()
def test_sagemaker_config_and_environment_region(mocker):
"""
If both the environment and config file specify a region, the environment region is expected
"""
expected_region = "us-east-1"
unexpected_region = "us-east-2"
os.environ["AWS_REGION_NAME"] = expected_region
mock_client = mocker.patch("boto3.client")
try:
response = litellm.completion(
model="sagemaker/mock-endpoint",
messages=[
{
"content": "Hello, world!",
"role": "user"
}
],
aws_region_name=unexpected_region,
)
except Exception:
pass # expected serialization exception because AWS client was replaced with a Mock
del os.environ["AWS_REGION_NAME"] # cleanup
assert mock_client.call_args.kwargs["region_name"] == expected_region
# test_sagemaker_config_and_environment_region()
# Bedrock # Bedrock

190
litellm/tests/test_proxy_reject_logging.py Normal file
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@ -0,0 +1,190 @@
# What is this?
## Unit test that rejected requests are also logged as failures
# What is this?
## This tests the llm guard integration
import asyncio
import os
import random
# What is this?
## Unit test for presidio pii masking
import sys
import time
import traceback
from datetime import datetime
from dotenv import load_dotenv
load_dotenv()
import os
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
from typing import Literal
import pytest
from fastapi import Request, Response
from starlette.datastructures import URL
import litellm
from litellm import Router, mock_completion
from litellm.caching import DualCache
from litellm.integrations.custom_logger import CustomLogger
from litellm.proxy._types import UserAPIKeyAuth
from litellm.proxy.enterprise.enterprise_hooks.secret_detection import (
_ENTERPRISE_SecretDetection,
)
from litellm.proxy.proxy_server import (
Depends,
HTTPException,
chat_completion,
completion,
embeddings,
)
from litellm.proxy.utils import ProxyLogging, hash_token
from litellm.router import Router
class testLogger(CustomLogger):
def __init__(self):
self.reaches_sync_failure_event = False
self.reaches_async_failure_event = False
async def async_pre_call_hook(
self,
user_api_key_dict: UserAPIKeyAuth,
cache: DualCache,
data: dict,
call_type: Literal[
"completion",
"text_completion",
"embeddings",
"image_generation",
"moderation",
"audio_transcription",
],
):
raise HTTPException(
status_code=429, detail={"error": "Max parallel request limit reached"}
)
async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
self.reaches_async_failure_event = True
def log_failure_event(self, kwargs, response_obj, start_time, end_time):
self.reaches_sync_failure_event = True
router = Router(
model_list=[
{
"model_name": "fake-model",
"litellm_params": {
"model": "openai/fake",
"api_base": "https://exampleopenaiendpoint-production.up.railway.app/",
"api_key": "sk-12345",
},
}
]
)
@pytest.mark.parametrize(
"route, body",
[
(
"/v1/chat/completions",
{
"model": "fake-model",
"messages": [
{
"role": "user",
"content": "Hello here is my OPENAI_API_KEY = sk-12345",
}
],
},
),
("/v1/completions", {"model": "fake-model", "prompt": "ping"}),
(
"/v1/embeddings",
{
"input": "The food was delicious and the waiter...",
"model": "text-embedding-ada-002",
"encoding_format": "float",
},
),
],
)
@pytest.mark.asyncio
async def test_chat_completion_request_with_redaction(route, body):
"""
IMPORTANT Enterprise Test - Do not delete it:
Makes a /chat/completions request on LiteLLM Proxy
Ensures that the secret is redacted EVEN on the callback
"""
from litellm.proxy import proxy_server
setattr(proxy_server, "llm_router", router)
_test_logger = testLogger()
litellm.callbacks = [_test_logger]
litellm.set_verbose = True
# Prepare the query string
query_params = "param1=value1&param2=value2"
# Create the Request object with query parameters
request = Request(
scope={
"type": "http",
"method": "POST",
"headers": [(b"content-type", b"application/json")],
"query_string": query_params.encode(),
}
)
request._url = URL(url=route)
async def return_body():
import json
return json.dumps(body).encode()
request.body = return_body
try:
if route == "/v1/chat/completions":
response = await chat_completion(
request=request,
user_api_key_dict=UserAPIKeyAuth(
api_key="sk-12345", token="hashed_sk-12345", rpm_limit=0
),
fastapi_response=Response(),
)
elif route == "/v1/completions":
response = await completion(
request=request,
user_api_key_dict=UserAPIKeyAuth(
api_key="sk-12345", token="hashed_sk-12345", rpm_limit=0
),
fastapi_response=Response(),
)
elif route == "/v1/embeddings":
response = await embeddings(
request=request,
user_api_key_dict=UserAPIKeyAuth(
api_key="sk-12345", token="hashed_sk-12345", rpm_limit=0
),
fastapi_response=Response(),
)
except:
pass
await asyncio.sleep(3)
assert _test_logger.reaches_async_failure_event is True
assert _test_logger.reaches_sync_failure_event is True

69
litellm/tests/test_proxy_setting_guardrails.py Normal file
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@ -0,0 +1,69 @@
import json
import os
import sys
from unittest import mock
from dotenv import load_dotenv
load_dotenv()
import asyncio
import io
import os
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import openai
import pytest
from fastapi import Response
from fastapi.testclient import TestClient
import litellm
from litellm.proxy.proxy_server import ( # Replace with the actual module where your FastAPI router is defined
initialize,
router,
save_worker_config,
)
@pytest.fixture
def client():
filepath = os.path.dirname(os.path.abspath(__file__))
config_fp = f"{filepath}/test_configs/test_guardrails_config.yaml"
asyncio.run(initialize(config=config_fp))
from litellm.proxy.proxy_server import app
return TestClient(app)
# raise openai.AuthenticationError
def test_active_callbacks(client):
response = client.get("/active/callbacks")
print("response", response)
print("response.text", response.text)
print("response.status_code", response.status_code)
json_response = response.json()
_active_callbacks = json_response["litellm.callbacks"]
expected_callback_names = [
"_ENTERPRISE_lakeraAI_Moderation",
"_OPTIONAL_PromptInjectionDetectio",
"_ENTERPRISE_SecretDetection",
]
for callback_name in expected_callback_names:
# check if any of the callbacks have callback_name as a substring
found_match = False
for callback in _active_callbacks:
if callback_name in callback:
found_match = True
break
assert (
found_match is True
), f"{callback_name} not found in _active_callbacks={_active_callbacks}"
assert not any(
"_ENTERPRISE_OpenAI_Moderation" in callback for callback in _active_callbacks
), f"_ENTERPRISE_OpenAI_Moderation should not be in _active_callbacks={_active_callbacks}"

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