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.github/workflows/interpret_load_test.py vendored
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@ -77,6 +77,9 @@ if __name__ == "__main__":
new_release_body = (
existing_release_body
+ "\n\n"
+ "### Don't want to maintain your internal proxy? get in touch 🎉"
+ "\nHosted Proxy Alpha: https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat"
+ "\n\n"
+ "## Load Test LiteLLM Proxy Results"
+ "\n\n"
+ markdown_table

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.gitignore vendored
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@ -50,3 +50,5 @@ kub.yaml
loadtest_kub.yaml
litellm/proxy/_new_secret_config.yaml
litellm/proxy/_new_secret_config.yaml
litellm/proxy/_super_secret_config.yaml
litellm/proxy/_super_secret_config.yaml

7
README.md
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@ -5,7 +5,7 @@
<p align="center">Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, etc.]
<br>
</p>
<h4 align="center"><a href="https://docs.litellm.ai/docs/simple_proxy" target="_blank">OpenAI Proxy Server</a> | <a href="https://docs.litellm.ai/docs/enterprise"target="_blank">Enterprise Tier</a></h4>
<h4 align="center"><a href="https://docs.litellm.ai/docs/simple_proxy" target="_blank">OpenAI Proxy Server</a> | <a href="https://docs.litellm.ai/docs/hosted" target="_blank"> Hosted Proxy (Preview)</a> | <a href="https://docs.litellm.ai/docs/enterprise"target="_blank">Enterprise Tier</a></h4>
<h4 align="center">
<a href="https://pypi.org/project/litellm/" target="_blank">
<img src="https://img.shields.io/pypi/v/litellm.svg" alt="PyPI Version">
@ -128,7 +128,9 @@ response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content
# OpenAI Proxy - ([Docs](https://docs.litellm.ai/docs/simple_proxy))
Set Budgets & Rate limits across multiple projects
Track spend + Load Balance across multiple projects
[Hosted Proxy (Preview)](https://docs.litellm.ai/docs/hosted)
The proxy provides:
@ -225,6 +227,7 @@ curl 'http://0.0.0.0:4000/key/generate' \
| [perplexity-ai](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | ✅ |
| [Groq AI](https://docs.litellm.ai/docs/providers/groq) | ✅ | ✅ | ✅ | ✅ |
| [anyscale](https://docs.litellm.ai/docs/providers/anyscale) | ✅ | ✅ | ✅ | ✅ |
| [IBM - watsonx.ai](https://docs.litellm.ai/docs/providers/watsonx) | ✅ | ✅ | ✅ | ✅ | ✅
| [voyage ai](https://docs.litellm.ai/docs/providers/voyage) | | | | | ✅ |
| [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) | | | | | ✅ |

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docs/my-website/docs/debugging/local_debugging.md
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@ -23,6 +23,14 @@ response = completion(model="gpt-3.5-turbo", messages=messages)
response = completion("command-nightly", messages)
```
## JSON Logs
If you need to store the logs as JSON, just set the `litellm.json_logs = True`.
We currently just log the raw POST request from litellm as a JSON - [**See Code**].
[Share feedback here](https://github.com/BerriAI/litellm/issues)
## Logger Function
But sometimes all you care about is seeing exactly what's getting sent to your api call and what's being returned - e.g. if the api call is failing, why is that happening? what are the exact params being set?

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docs/my-website/docs/enterprise.md
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@ -8,12 +8,13 @@ For companies that need SSO, user management and professional support for LiteLL
:::
This covers:
- ✅ **Features under the [LiteLLM Commercial License](https://docs.litellm.ai/docs/proxy/enterprise):**
- ✅ **Features under the [LiteLLM Commercial License (Content Mod, Custom Tags, etc.)](https://docs.litellm.ai/docs/proxy/enterprise)**
- ✅ **Feature Prioritization**
- ✅ **Custom Integrations**
- ✅ **Professional Support - Dedicated discord + slack**
- ✅ **Custom SLAs**
- ✅ **Secure access with Single Sign-On**
- ✅ [**Secure UI access with Single Sign-On**](../docs/proxy/ui.md#setup-ssoauth-for-ui)
- ✅ [**JWT-Auth**](../docs/proxy/token_auth.md)
## Frequently Asked Questions

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@ -0,0 +1,49 @@
import Image from '@theme/IdealImage';
# Hosted LiteLLM Proxy
LiteLLM maintains the proxy, so you can focus on your core products.
## [**Get Onboarded**](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
This is in alpha. Schedule a call with us, and we'll give you a hosted proxy within 30 minutes.
[**🚨 Schedule Call**](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
### **Status**: Alpha
Our proxy is already used in production by customers.
See our status page for [**live reliability**](https://status.litellm.ai/)
### **Benefits**
- **No Maintenance, No Infra**: We'll maintain the proxy, and spin up any additional infrastructure (e.g.: separate server for spend logs) to make sure you can load balance + track spend across multiple LLM projects.
- **Reliable**: Our hosted proxy is tested on 1k requests per second, making it reliable for high load.
- **Secure**: LiteLLM is currently undergoing SOC-2 compliance, to make sure your data is as secure as possible.
### Pricing
Pricing is based on usage. We can figure out a price that works for your team, on the call.
[**🚨 Schedule Call**](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
## **Screenshots**
### 1. Create keys
<Image img={require('../img/litellm_hosted_ui_create_key.png')} />
### 2. Add Models
<Image img={require('../img/litellm_hosted_ui_add_models.png')}/>
### 3. Track spend
<Image img={require('../img/litellm_hosted_usage_dashboard.png')} />
### 4. Configure load balancing
<Image img={require('../img/litellm_hosted_ui_router.png')} />
#### [**🚨 Schedule Call**](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)

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docs/my-website/docs/load_test.md
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@ -213,3 +213,349 @@ asyncio.run(loadtest_fn())
```
## Multi-Instance TPM/RPM Load Test (Router)
Test if your defined tpm/rpm limits are respected across multiple instances of the Router object.
In our test:
- Max RPM per deployment is = 100 requests per minute
- Max Throughput / min on router = 200 requests per minute (2 deployments)
- Load we'll send through router = 600 requests per minute
:::info
If you don't want to call a real LLM API endpoint, you can setup a fake openai server. [See code](#extra---setup-fake-openai-server)
:::
### Code
Let's hit the router with 600 requests per minute.
Copy this script 👇. Save it as `test_loadtest_router.py` AND run it with `python3 test_loadtest_router.py`
```python
from litellm import Router
import litellm
litellm.suppress_debug_info = True
litellm.set_verbose = False
import logging
logging.basicConfig(level=logging.CRITICAL)
import os, random, uuid, time, asyncio
# Model list for OpenAI and Anthropic models
model_list = [
{
"model_name": "fake-openai-endpoint",
"litellm_params": {
"model": "gpt-3.5-turbo",
"api_key": "my-fake-key",
"api_base": "http://0.0.0.0:8080",
"rpm": 100
},
},
{
"model_name": "fake-openai-endpoint",
"litellm_params": {
"model": "gpt-3.5-turbo",
"api_key": "my-fake-key",
"api_base": "http://0.0.0.0:8081",
"rpm": 100
},
},
]
router_1 = Router(model_list=model_list, num_retries=0, enable_pre_call_checks=True, routing_strategy="usage-based-routing-v2", redis_host=os.getenv("REDIS_HOST"), redis_port=os.getenv("REDIS_PORT"), redis_password=os.getenv("REDIS_PASSWORD"))
router_2 = Router(model_list=model_list, num_retries=0, routing_strategy="usage-based-routing-v2", enable_pre_call_checks=True, redis_host=os.getenv("REDIS_HOST"), redis_port=os.getenv("REDIS_PORT"), redis_password=os.getenv("REDIS_PASSWORD"))
async def router_completion_non_streaming():
try:
client: Router = random.sample([router_1, router_2], 1)[0] # randomly pick b/w clients
# print(f"client={client}")
response = await client.acompletion(
model="fake-openai-endpoint", # [CHANGE THIS] (if you call it something else on your proxy)
messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
)
return response
except Exception as e:
# print(e)
return None
async def loadtest_fn():
start = time.time()
n = 600 # Number of concurrent tasks
tasks = [router_completion_non_streaming() for _ in range(n)]
chat_completions = await asyncio.gather(*tasks)
successful_completions = [c for c in chat_completions if c is not None]
print(n, time.time() - start, len(successful_completions))
def get_utc_datetime():
import datetime as dt
from datetime import datetime
if hasattr(dt, "UTC"):
return datetime.now(dt.UTC) # type: ignore
else:
return datetime.utcnow() # type: ignore
# Run the event loop to execute the async function
async def parent_fn():
for _ in range(10):
dt = get_utc_datetime()
current_minute = dt.strftime("%H-%M")
print(f"triggered new batch - {current_minute}")
await loadtest_fn()
await asyncio.sleep(10)
asyncio.run(parent_fn())
```
## Multi-Instance TPM/RPM Load Test (Proxy)
Test if your defined tpm/rpm limits are respected across multiple instances.
The quickest way to do this is by testing the [proxy](./proxy/quick_start.md). The proxy uses the [router](./routing.md) under the hood, so if you're using either of them, this test should work for you.
In our test:
- Max RPM per deployment is = 100 requests per minute
- Max Throughput / min on proxy = 200 requests per minute (2 deployments)
- Load we'll send to proxy = 600 requests per minute
So we'll send 600 requests per minute, but expect only 200 requests per minute to succeed.
:::info
If you don't want to call a real LLM API endpoint, you can setup a fake openai server. [See code](#extra---setup-fake-openai-server)
:::
### 1. Setup config
```yaml
model_list:
- litellm_params:
api_base: http://0.0.0.0:8080
api_key: my-fake-key
model: openai/my-fake-model
rpm: 100
model_name: fake-openai-endpoint
- litellm_params:
api_base: http://0.0.0.0:8081
api_key: my-fake-key
model: openai/my-fake-model-2
rpm: 100
model_name: fake-openai-endpoint
router_settings:
num_retries: 0
enable_pre_call_checks: true
redis_host: os.environ/REDIS_HOST ## 👈 IMPORTANT! Setup the proxy w/ redis
redis_password: os.environ/REDIS_PASSWORD
redis_port: os.environ/REDIS_PORT
routing_strategy: usage-based-routing-v2
```
### 2. Start proxy 2 instances
**Instance 1**
```bash
litellm --config /path/to/config.yaml --port 4000
## RUNNING on http://0.0.0.0:4000
```
**Instance 2**
```bash
litellm --config /path/to/config.yaml --port 4001
## RUNNING on http://0.0.0.0:4001
```
### 3. Run Test
Let's hit the proxy with 600 requests per minute.
Copy this script 👇. Save it as `test_loadtest_proxy.py` AND run it with `python3 test_loadtest_proxy.py`
```python
from openai import AsyncOpenAI, AsyncAzureOpenAI
import random, uuid
import time, asyncio, litellm
# import logging
# logging.basicConfig(level=logging.DEBUG)
#### LITELLM PROXY ####
litellm_client = AsyncOpenAI(
api_key="sk-1234", # [CHANGE THIS]
base_url="http://0.0.0.0:4000"
)
litellm_client_2 = AsyncOpenAI(
api_key="sk-1234", # [CHANGE THIS]
base_url="http://0.0.0.0:4001"
)
async def proxy_completion_non_streaming():
try:
client = random.sample([litellm_client, litellm_client_2], 1)[0] # randomly pick b/w clients
# print(f"client={client}")
response = await client.chat.completions.create(
model="fake-openai-endpoint", # [CHANGE THIS] (if you call it something else on your proxy)
messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
)
return response
except Exception as e:
# print(e)
return None
async def loadtest_fn():
start = time.time()
n = 600 # Number of concurrent tasks
tasks = [proxy_completion_non_streaming() for _ in range(n)]
chat_completions = await asyncio.gather(*tasks)
successful_completions = [c for c in chat_completions if c is not None]
print(n, time.time() - start, len(successful_completions))
def get_utc_datetime():
import datetime as dt
from datetime import datetime
if hasattr(dt, "UTC"):
return datetime.now(dt.UTC) # type: ignore
else:
return datetime.utcnow() # type: ignore
# Run the event loop to execute the async function
async def parent_fn():
for _ in range(10):
dt = get_utc_datetime()
current_minute = dt.strftime("%H-%M")
print(f"triggered new batch - {current_minute}")
await loadtest_fn()
await asyncio.sleep(10)
asyncio.run(parent_fn())
```
### Extra - Setup Fake OpenAI Server
Let's setup a fake openai server with a RPM limit of 100.
Let's call our file `fake_openai_server.py`.
```
# import sys, os
# sys.path.insert(
# 0, os.path.abspath("../")
# ) # Adds the parent directory to the system path
from fastapi import FastAPI, Request, status, HTTPException, Depends
from fastapi.responses import StreamingResponse
from fastapi.security import OAuth2PasswordBearer
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from fastapi import FastAPI, Request, HTTPException, UploadFile, File
import httpx, os, json
from openai import AsyncOpenAI
from typing import Optional
from slowapi import Limiter
from slowapi.util import get_remote_address
from slowapi.errors import RateLimitExceeded
from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import PlainTextResponse
class ProxyException(Exception):
# NOTE: DO NOT MODIFY THIS
# This is used to map exactly to OPENAI Exceptions
def __init__(
self,
message: str,
type: str,
param: Optional[str],
code: Optional[int],
):
self.message = message
self.type = type
self.param = param
self.code = code
def to_dict(self) -> dict:
"""Converts the ProxyException instance to a dictionary."""
return {
"message": self.message,
"type": self.type,
"param": self.param,
"code": self.code,
}
limiter = Limiter(key_func=get_remote_address)
app = FastAPI()
app.state.limiter = limiter
@app.exception_handler(RateLimitExceeded)
async def _rate_limit_exceeded_handler(request: Request, exc: RateLimitExceeded):
return JSONResponse(status_code=429,
content={"detail": "Rate Limited!"})
app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# for completion
@app.post("/chat/completions")
@app.post("/v1/chat/completions")
@limiter.limit("100/minute")
async def completion(request: Request):
# raise HTTPException(status_code=429, detail="Rate Limited!")
return {
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"model": None,
"system_fingerprint": "fp_44709d6fcb",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "\n\nHello there, how may I assist you today?",
},
"logprobs": None,
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21
}
}
if __name__ == "__main__":
import socket
import uvicorn
port = 8080
while True:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
result = sock.connect_ex(('0.0.0.0', port))
if result != 0:
print(f"Port {port} is available, starting server...")
break
else:
port += 1
uvicorn.run(app, host="0.0.0.0", port=port)
```
```bash
python3 fake_openai_server.py
```

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@ -331,49 +331,25 @@ response = litellm.completion(model="gpt-3.5-turbo", messages=messages, metadata
## Examples
### Custom Callback to track costs for Streaming + Non-Streaming
By default, the response cost is accessible in the logging object via `kwargs["response_cost"]` on success (sync + async)
```python
# Step 1. Write your custom callback function
def track_cost_callback(
kwargs, # kwargs to completion
completion_response, # response from completion
start_time, end_time # start/end time
):
try:
# init logging config
logging.basicConfig(
filename='cost.log',
level=logging.INFO,
format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S'
)
# check if it has collected an entire stream response
if "complete_streaming_response" in kwargs:
# for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost
completion_response=kwargs["complete_streaming_response"]
input_text = kwargs["messages"]
output_text = completion_response["choices"][0]["message"]["content"]
response_cost = litellm.completion_cost(
model = kwargs["model"],
messages = input_text,
completion=output_text
)
print("streaming response_cost", response_cost)
logging.info(f"Model {kwargs['model']} Cost: ${response_cost:.8f}")
# for non streaming responses
else:
# we pass the completion_response obj
if kwargs["stream"] != True:
response_cost = litellm.completion_cost(completion_response=completion_response)
print("regular response_cost", response_cost)
logging.info(f"Model {completion_response.model} Cost: ${response_cost:.8f}")
response_cost = kwargs["response_cost"] # litellm calculates response cost for you
print("regular response_cost", response_cost)
except:
pass
# Assign the custom callback function
# Step 2. Assign the custom callback function
litellm.success_callback = [track_cost_callback]
# Step 3. Make litellm.completion call
response = completion(
model="gpt-3.5-turbo",
messages=[

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@ -0,0 +1,68 @@
# Greenscale Tutorial
[Greenscale](https://greenscale.ai/) is a production monitoring platform for your LLM-powered app that provides you granular key insights into your GenAI spending and responsible usage. Greenscale only captures metadata to minimize the exposure risk of personally identifiable information (PII).
## Getting Started
Use Greenscale to log requests across all LLM Providers
liteLLM provides `callbacks`, making it easy for you to log data depending on the status of your responses.
## Using Callbacks
First, email `hello@greenscale.ai` to get an API_KEY.
Use just 1 line of code, to instantly log your responses **across all providers** with Greenscale:
```python
litellm.success_callback = ["greenscale"]
```
### Complete code
```python
from litellm import completion
## set env variables
os.environ['GREENSCALE_API_KEY'] = 'your-greenscale-api-key'
os.environ['GREENSCALE_ENDPOINT'] = 'greenscale-endpoint'
os.environ["OPENAI_API_KEY"]= ""
# set callback
litellm.success_callback = ["greenscale"]
#openai call
response = completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]
metadata={
"greenscale_project": "acme-project",
"greenscale_application": "acme-application"
}
)
```
## Additional information in metadata
You can send any additional information to Greenscale by using the `metadata` field in completion and `greenscale_` prefix. This can be useful for sending metadata about the request, such as the project and application name, customer_id, enviornment, or any other information you want to track usage. `greenscale_project` and `greenscale_application` are required fields.
```python
#openai call with additional metadata
response = completion(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": "Hi 👋 - i'm openai"}
],
metadata={
"greenscale_project": "acme-project",
"greenscale_application": "acme-application",
"greenscale_customer_id": "customer-123"
}
)
```
## Support & Talk with Greenscale Team
- [Schedule Demo 👋](https://calendly.com/nandesh/greenscale)
- [Website 💻](https://greenscale.ai)
- Our email ✉️ `hello@greenscale.ai`

7
docs/my-website/docs/observability/langfuse_integration.md
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@ -121,10 +121,12 @@ response = completion(
metadata={
"generation_name": "ishaan-test-generation", # set langfuse Generation Name
"generation_id": "gen-id22", # set langfuse Generation ID
"trace_id": "trace-id22", # set langfuse Trace ID
"trace_user_id": "user-id2", # set langfuse Trace User ID
"session_id": "session-1", # set langfuse Session ID
"tags": ["tag1", "tag2"] # set langfuse Tags
"trace_id": "trace-id22", # set langfuse Trace ID
### OR ###
"existing_trace_id": "trace-id22", # if generation is continuation of past trace. This prevents default behaviour of setting a trace name
},
)
@ -167,6 +169,9 @@ messages = [
chat(messages)
```
## Redacting Messages, Response Content from Langfuse Logging
Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to langfuse, but request metadata will still be logged.
## Troubleshooting & Errors
### Data not getting logged to Langfuse ?

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docs/my-website/docs/observability/langsmith_integration.md
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@ -57,7 +57,7 @@ os.environ["LANGSMITH_API_KEY"] = ""
os.environ['OPENAI_API_KEY']=""
# set langfuse as a callback, litellm will send the data to langfuse
litellm.success_callback = ["langfuse"]
litellm.success_callback = ["langsmith"]
response = litellm.completion(
model="gpt-3.5-turbo",

4
docs/my-website/docs/observability/sentry.md
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@ -40,5 +40,9 @@ response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content
print(response)
```
## Redacting Messages, Response Content from Sentry Logging
Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to sentry, but request metadata will still be logged.
[Let us know](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+) if you need any additional options from Sentry.

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@ -224,6 +224,91 @@ assert isinstance(
```
### Parallel Function Calling
Here's how to pass the result of a function call back to an anthropic model:
```python
from litellm import completion
import os
os.environ["ANTHROPIC_API_KEY"] = "sk-ant.."
litellm.set_verbose = True
### 1ST FUNCTION CALL ###
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 in Fahrenheit?",
}
]
try:
# test without max tokens
response = completion(
model="anthropic/claude-3-opus-20240229",
messages=messages,
tools=tools,
tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
response.choices[0].message.tool_calls[0].function.arguments, str
)
messages.append(
response.choices[0].message.model_dump()
) # Add assistant tool invokes
tool_result = (
'{"location": "Boston", "temperature": "72", "unit": "fahrenheit"}'
)
# Add user submitted tool results in the OpenAI format
messages.append(
{
"tool_call_id": response.choices[0].message.tool_calls[0].id,
"role": "tool",
"name": response.choices[0].message.tool_calls[0].function.name,
"content": tool_result,
}
)
### 2ND FUNCTION CALL ###
# In the second response, Claude should deduce answer from tool results
second_response = completion(
model="anthropic/claude-3-opus-20240229",
messages=messages,
tools=tools,
tool_choice="auto",
)
print(second_response)
except Exception as e:
print(f"An error occurred - {str(e)}")
```
s/o @[Shekhar Patnaik](https://www.linkedin.com/in/patnaikshekhar) for requesting this!
## Usage - Vision
```python

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@ -23,7 +23,7 @@ In certain use-cases you may need to make calls to the models and pass [safety s
```python
response = completion(
model="gemini/gemini-pro",
messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]
messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}],
safety_settings=[
{
"category": "HARM_CATEGORY_HARASSMENT",

2
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@ -48,6 +48,8 @@ We support ALL Groq models, just set `groq/` as a prefix when sending completion
| Model Name | Function Call |
|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| llama3-8b-8192 | `completion(model="groq/llama3-8b-8192", messages)` |
| llama3-70b-8192 | `completion(model="groq/llama3-70b-8192", messages)` |
| llama2-70b-4096 | `completion(model="groq/llama2-70b-4096", messages)` |
| mixtral-8x7b-32768 | `completion(model="groq/mixtral-8x7b-32768", messages)` |
| gemma-7b-it | `completion(model="groq/gemma-7b-it", messages)` |

44
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View file

@ -53,6 +53,50 @@ All models listed here https://docs.mistral.ai/platform/endpoints are supported.
| open-mixtral-8x22b | `completion(model="mistral/open-mixtral-8x22b", messages)` |
## Function Calling
```python
from litellm import completion
# set env
os.environ["MISTRAL_API_KEY"] = "your-api-key"
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?"}]
response = completion(
model="mistral/mistral-large-latest",
messages=messages,
tools=tools,
tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
response.choices[0].message.tool_calls[0].function.arguments, str
)
```
## Sample Usage - Embedding
```python
from litellm import embedding

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@ -1,7 +1,16 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Replicate
LiteLLM supports all models on Replicate
## Usage
<Tabs>
<TabItem value="sdk" label="SDK">
### API KEYS
```python
import os
@ -16,14 +25,175 @@ import os
## set ENV variables
os.environ["REPLICATE_API_KEY"] = "replicate key"
# replicate llama-2 call
# replicate llama-3 call
response = completion(
model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
model="replicate/meta/meta-llama-3-8b-instruct",
messages = [{ "content": "Hello, how are you?","role": "user"}]
)
```
### Example - Calling Replicate Deployments
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Add models to your config.yaml
```yaml
model_list:
- model_name: llama-3
litellm_params:
model: replicate/meta/meta-llama-3-8b-instruct
api_key: os.environ/REPLICATE_API_KEY
```
2. Start the proxy
```bash
$ litellm --config /path/to/config.yaml --debug
```
3. Send Request to LiteLLM Proxy Server
<Tabs>
<TabItem value="openai" label="OpenAI Python v1.0.0+">
```python
import openai
client = openai.OpenAI(
api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys
base_url="http://0.0.0.0:4000" # litellm-proxy-base url
)
response = client.chat.completions.create(
model="llama-3",
messages = [
{
"role": "system",
"content": "Be a good human!"
},
{
"role": "user",
"content": "What do you know about earth?"
}
]
)
print(response)
```
</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": "llama-3",
"messages": [
{
"role": "system",
"content": "Be a good human!"
},
{
"role": "user",
"content": "What do you know about earth?"
}
],
}'
```
</TabItem>
</Tabs>
### Expected Replicate Call
This is the call litellm will make to replicate, from the above example:
```bash
POST Request Sent from LiteLLM:
curl -X POST \
https://api.replicate.com/v1/models/meta/meta-llama-3-8b-instruct \
-H 'Authorization: Token your-api-key' -H 'Content-Type: application/json' \
-d '{'version': 'meta/meta-llama-3-8b-instruct', 'input': {'prompt': '<|start_header_id|>system<|end_header_id|>\n\nBe a good human!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat do you know about earth?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'}}'
```
</TabItem>
</Tabs>
## Advanced Usage - Prompt Formatting
LiteLLM has prompt template mappings for all `meta-llama` llama3 instruct models. [**See Code**](https://github.com/BerriAI/litellm/blob/4f46b4c3975cd0f72b8c5acb2cb429d23580c18a/litellm/llms/prompt_templates/factory.py#L1360)
To apply a custom prompt template:
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import litellm
import os
os.environ["REPLICATE_API_KEY"] = ""
# Create your own custom prompt template
litellm.register_prompt_template(
model="togethercomputer/LLaMA-2-7B-32K",
initial_prompt_value="You are a good assistant" # [OPTIONAL]
roles={
"system": {
"pre_message": "[INST] <<SYS>>\n", # [OPTIONAL]
"post_message": "\n<</SYS>>\n [/INST]\n" # [OPTIONAL]
},
"user": {
"pre_message": "[INST] ", # [OPTIONAL]
"post_message": " [/INST]" # [OPTIONAL]
},
"assistant": {
"pre_message": "\n" # [OPTIONAL]
"post_message": "\n" # [OPTIONAL]
}
}
final_prompt_value="Now answer as best you can:" # [OPTIONAL]
)
def test_replicate_custom_model():
model = "replicate/togethercomputer/LLaMA-2-7B-32K"
response = completion(model=model, messages=messages)
print(response['choices'][0]['message']['content'])
return response
test_replicate_custom_model()
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```yaml
# Model-specific parameters
model_list:
- model_name: mistral-7b # model alias
litellm_params: # actual params for litellm.completion()
model: "replicate/mistralai/Mistral-7B-Instruct-v0.1"
api_key: os.environ/REPLICATE_API_KEY
initial_prompt_value: "\n"
roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}}
final_prompt_value: "\n"
bos_token: "<s>"
eos_token: "</s>"
max_tokens: 4096
```
</TabItem>
</Tabs>
## Advanced Usage - Calling Replicate Deployments
Calling a [deployed replicate LLM](https://replicate.com/deployments)
Add the `replicate/deployments/` prefix to your model, so litellm will call the `deployments` endpoint. This will call `ishaan-jaff/ishaan-mistral` deployment on replicate
@ -40,7 +210,7 @@ Replicate responses can take 3-5 mins due to replicate cold boots, if you're try
:::
### Replicate Models
## Replicate Models
liteLLM supports all replicate LLMs
For replicate models ensure to add a `replicate/` prefix to the `model` arg. liteLLM detects it using this arg.
@ -49,15 +219,15 @@ Below are examples on how to call replicate LLMs using liteLLM
Model Name | Function Call | Required OS Variables |
-----------------------------|----------------------------------------------------------------|--------------------------------------|
replicate/llama-2-70b-chat | `completion(model='replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf', messages, supports_system_prompt=True)` | `os.environ['REPLICATE_API_KEY']` |
a16z-infra/llama-2-13b-chat| `completion(model='replicate/a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52', messages, supports_system_prompt=True)`| `os.environ['REPLICATE_API_KEY']` |
replicate/llama-2-70b-chat | `completion(model='replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf', messages)` | `os.environ['REPLICATE_API_KEY']` |
a16z-infra/llama-2-13b-chat| `completion(model='replicate/a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52', messages)`| `os.environ['REPLICATE_API_KEY']` |
replicate/vicuna-13b | `completion(model='replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b', messages)` | `os.environ['REPLICATE_API_KEY']` |
daanelson/flan-t5-large | `completion(model='replicate/daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f', messages)` | `os.environ['REPLICATE_API_KEY']` |
custom-llm | `completion(model='replicate/custom-llm-version-id', messages)` | `os.environ['REPLICATE_API_KEY']` |
replicate deployment | `completion(model='replicate/deployments/ishaan-jaff/ishaan-mistral', messages)` | `os.environ['REPLICATE_API_KEY']` |
### Passing additional params - max_tokens, temperature
## Passing additional params - max_tokens, temperature
See all litellm.completion supported params [here](https://docs.litellm.ai/docs/completion/input)
```python
@ -73,11 +243,22 @@ response = completion(
messages = [{ "content": "Hello, how are you?","role": "user"}],
max_tokens=20,
temperature=0.5
)
```
### Passings Replicate specific params
**proxy**
```yaml
model_list:
- model_name: llama-3
litellm_params:
model: replicate/meta/meta-llama-3-8b-instruct
api_key: os.environ/REPLICATE_API_KEY
max_tokens: 20
temperature: 0.5
```
## Passings Replicate specific params
Send params [not supported by `litellm.completion()`](https://docs.litellm.ai/docs/completion/input) but supported by Replicate by passing them to `litellm.completion`
Example `seed`, `min_tokens` are Replicate specific param
@ -98,3 +279,15 @@ response = completion(
top_k=20,
)
```
**proxy**
```yaml
model_list:
- model_name: llama-3
litellm_params:
model: replicate/meta/meta-llama-3-8b-instruct
api_key: os.environ/REPLICATE_API_KEY
min_tokens: 2
top_k: 20
```

7
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@ -4,6 +4,13 @@ LiteLLM supports all models on VLLM.
🚀[Code Tutorial](https://github.com/BerriAI/litellm/blob/main/cookbook/VLLM_Model_Testing.ipynb)
:::info
To call a HOSTED VLLM Endpoint use [these docs](./openai_compatible.md)
:::
### Quick Start
```
pip install litellm vllm

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@ -0,0 +1,284 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# IBM watsonx.ai
LiteLLM supports all IBM [watsonx.ai](https://watsonx.ai/) foundational models and embeddings.
## Environment Variables
```python
os.environ["WATSONX_URL"] = "" # (required) Base URL of your WatsonX instance
# (required) either one of the following:
os.environ["WATSONX_APIKEY"] = "" # IBM cloud API key
os.environ["WATSONX_TOKEN"] = "" # IAM auth token
# optional - can also be passed as params to completion() or embedding()
os.environ["WATSONX_PROJECT_ID"] = "" # Project ID of your WatsonX instance
os.environ["WATSONX_DEPLOYMENT_SPACE_ID"] = "" # ID of your deployment space to use deployed models
```
See [here](https://cloud.ibm.com/apidocs/watsonx-ai#api-authentication) for more information on how to get an access token to authenticate to watsonx.ai.
## Usage
<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_IBM_Watsonx.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
```python
import os
from litellm import completion
os.environ["WATSONX_URL"] = ""
os.environ["WATSONX_APIKEY"] = ""
response = completion(
model="watsonx/ibm/granite-13b-chat-v2",
messages=[{ "content": "what is your favorite colour?","role": "user"}],
project_id="<my-project-id>" # or pass with os.environ["WATSONX_PROJECT_ID"]
)
response = completion(
model="watsonx/meta-llama/llama-3-8b-instruct",
messages=[{ "content": "what is your favorite colour?","role": "user"}],
project_id="<my-project-id>"
)
```
## Usage - Streaming
```python
import os
from litellm import completion
os.environ["WATSONX_URL"] = ""
os.environ["WATSONX_APIKEY"] = ""
os.environ["WATSONX_PROJECT_ID"] = ""
response = completion(
model="watsonx/ibm/granite-13b-chat-v2",
messages=[{ "content": "what is your favorite colour?","role": "user"}],
stream=True
)
for chunk in response:
print(chunk)
```
#### Example Streaming Output Chunk
```json
{
"choices": [
{
"finish_reason": null,
"index": 0,
"delta": {
"content": "I don't have a favorite color, but I do like the color blue. What's your favorite color?"
}
}
],
"created": null,
"model": "watsonx/ibm/granite-13b-chat-v2",
"usage": {
"prompt_tokens": null,
"completion_tokens": null,
"total_tokens": null
}
}
```
## Usage - Models in deployment spaces
Models that have been deployed to a deployment space (e.g.: tuned models) can be called using the `deployment/<deployment_id>` format (where `<deployment_id>` is the ID of the deployed model in your deployment space).
The ID of your deployment space must also be set in the environment variable `WATSONX_DEPLOYMENT_SPACE_ID` or passed to the function as `space_id=<deployment_space_id>`.
```python
import litellm
response = litellm.completion(
model="watsonx/deployment/<deployment_id>",
messages=[{"content": "Hello, how are you?", "role": "user"}],
space_id="<deployment_space_id>"
)
```
## Usage - Embeddings
LiteLLM also supports making requests to IBM watsonx.ai embedding models. The credential needed for this is the same as for completion.
```python
from litellm import embedding
response = embedding(
model="watsonx/ibm/slate-30m-english-rtrvr",
input=["What is the capital of France?"],
project_id="<my-project-id>"
)
print(response)
# EmbeddingResponse(model='ibm/slate-30m-english-rtrvr', data=[{'object': 'embedding', 'index': 0, 'embedding': [-0.037463713, -0.02141933, -0.02851813, 0.015519324, ..., -0.0021367231, -0.01704561, -0.001425816, 0.0035238306]}], object='list', usage=Usage(prompt_tokens=8, total_tokens=8))
```
## OpenAI Proxy Usage
Here's how to call IBM watsonx.ai with the LiteLLM Proxy Server
### 1. Save keys in your environment
```bash
export WATSONX_URL=""
export WATSONX_APIKEY=""
export WATSONX_PROJECT_ID=""
```
### 2. Start the proxy
<Tabs>
<TabItem value="cli" label="CLI">
```bash
$ litellm --model watsonx/meta-llama/llama-3-8b-instruct
# Server running on http://0.0.0.0:4000
```
</TabItem>
<TabItem value="config" label="config.yaml">
```yaml
model_list:
- model_name: llama-3-8b
litellm_params:
# all params accepted by litellm.completion()
model: watsonx/meta-llama/llama-3-8b-instruct
api_key: "os.environ/WATSONX_API_KEY" # does os.getenv("WATSONX_API_KEY")
```
</TabItem>
</Tabs>
### 3. Test it
<Tabs>
<TabItem value="Curl" label="Curl Request">
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "llama-3-8b",
"messages": [
{
"role": "user",
"content": "what is your favorite colour?"
}
]
}
'
```
</TabItem>
<TabItem value="openai" label="OpenAI v1.0.0+">
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="llama-3-8b", messages=[
{
"role": "user",
"content": "what is your favorite colour?"
}
])
print(response)
```
</TabItem>
<TabItem value="langchain" label="Langchain">
```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
model = "llama-3-8b",
temperature=0.1
)
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>
## Authentication
### Passing credentials as parameters
You can also pass the credentials as parameters to the completion and embedding functions.
```python
import os
from litellm import completion
response = completion(
model="watsonx/ibm/granite-13b-chat-v2",
messages=[{ "content": "What is your favorite color?","role": "user"}],
url="",
api_key="",
project_id=""
)
```
## Supported IBM watsonx.ai Models
Here are some examples of models available in IBM watsonx.ai that you can use with LiteLLM:
| Mode Name | Command |
| ---------- | --------- |
| Flan T5 XXL | `completion(model=watsonx/google/flan-t5-xxl, messages=messages)` |
| Flan Ul2 | `completion(model=watsonx/google/flan-ul2, messages=messages)` |
| Mt0 XXL | `completion(model=watsonx/bigscience/mt0-xxl, messages=messages)` |
| Gpt Neox | `completion(model=watsonx/eleutherai/gpt-neox-20b, messages=messages)` |
| Mpt 7B Instruct2 | `completion(model=watsonx/ibm/mpt-7b-instruct2, messages=messages)` |
| Starcoder | `completion(model=watsonx/bigcode/starcoder, messages=messages)` |
| Llama 2 70B Chat | `completion(model=watsonx/meta-llama/llama-2-70b-chat, messages=messages)` |
| Llama 2 13B Chat | `completion(model=watsonx/meta-llama/llama-2-13b-chat, messages=messages)` |
| Granite 13B Instruct | `completion(model=watsonx/ibm/granite-13b-instruct-v1, messages=messages)` |
| Granite 13B Chat | `completion(model=watsonx/ibm/granite-13b-chat-v1, messages=messages)` |
| Flan T5 XL | `completion(model=watsonx/google/flan-t5-xl, messages=messages)` |
| Granite 13B Chat V2 | `completion(model=watsonx/ibm/granite-13b-chat-v2, messages=messages)` |
| Granite 13B Instruct V2 | `completion(model=watsonx/ibm/granite-13b-instruct-v2, messages=messages)` |
| Elyza Japanese Llama 2 7B Instruct | `completion(model=watsonx/elyza/elyza-japanese-llama-2-7b-instruct, messages=messages)` |
| Mixtral 8X7B Instruct V01 Q | `completion(model=watsonx/ibm-mistralai/mixtral-8x7b-instruct-v01-q, messages=messages)` |
For a list of all available models in watsonx.ai, see [here](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx&locale=en&audience=wdp).
## Supported IBM watsonx.ai Embedding Models
| Model Name | Function Call |
|----------------------|---------------------------------------------|
| Slate 30m | `embedding(model="watsonx/ibm/slate-30m-english-rtrvr", input=input)` |
| Slate 125m | `embedding(model="watsonx/ibm/slate-125m-english-rtrvr", input=input)` |
For a list of all available embedding models in watsonx.ai, see [here](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx).

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@ -1,13 +1,13 @@
# Slack Alerting
# 🚨 Alerting
Get alerts for:
- hanging LLM api calls
- failed LLM api calls
- slow LLM api calls
- budget Tracking per key/user:
- Hanging LLM api calls
- Failed LLM api calls
- Slow LLM api calls
- Budget Tracking per key/user:
- When a User/Key crosses their Budget
- When a User/Key is 15% away from crossing their Budget
- failed db read/writes
- Failed db read/writes
## Quick Start

16
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@ -61,6 +61,22 @@ litellm_settings:
ttl: 600 # will be cached on redis for 600s
```
## SSL
just set `REDIS_SSL="True"` in your .env, and LiteLLM will pick this up.
```env
REDIS_SSL="True"
```
For quick testing, you can also use REDIS_URL, eg.:
```
REDIS_URL="rediss://.."
```
but we **don't** recommend using REDIS_URL in prod. We've noticed a performance difference between using it vs. redis_host, port, etc.
#### Step 2: Add Redis Credentials to .env
Set either `REDIS_URL` or the `REDIS_HOST` in your os environment, to enable caching.

3
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@ -62,9 +62,11 @@ model_list:
litellm_settings: # module level litellm settings - https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py
drop_params: True
success_callback: ["langfuse"] # OPTIONAL - if you want to start sending LLM Logs to Langfuse. Make sure to set `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` in your env
general_settings:
master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234)
alerting: ["slack"] # [OPTIONAL] If you want Slack Alerts for Hanging LLM requests, Slow llm responses, Budget Alerts. Make sure to set `SLACK_WEBHOOK_URL` in your env
```
:::info
@ -600,6 +602,7 @@ general_settings:
"general_settings": {
"completion_model": "string",
"disable_spend_logs": "boolean", # turn off writing each transaction to the db
"disable_master_key_return": "boolean", # turn off returning master key on UI (checked on '/user/info' endpoint)
"disable_reset_budget": "boolean", # turn off reset budget scheduled task
"enable_jwt_auth": "boolean", # allow proxy admin to auth in via jwt tokens with 'litellm_proxy_admin' in claims
"enforce_user_param": "boolean", # requires all openai endpoint requests to have a 'user' param

84
docs/my-website/docs/proxy/deploy.md
View file

@ -231,13 +231,16 @@ Your OpenAI proxy server is now running on `http://127.0.0.1:4000`.
| Docs | When to Use |
| --- | --- |
| [Quick Start](#quick-start) | call 100+ LLMs + Load Balancing |
| [Deploy with Database](#deploy-with-database) | + use Virtual Keys + Track Spend |
| [Deploy with Database](#deploy-with-database) | + use Virtual Keys + Track Spend (Note: When deploying with a database providing a `DATABASE_URL` and `LITELLM_MASTER_KEY` are required in your env ) |
| [LiteLLM container + Redis](#litellm-container--redis) | + load balance across multiple litellm containers |
| [LiteLLM Database container + PostgresDB + Redis](#litellm-database-container--postgresdb--redis) | + use Virtual Keys + Track Spend + load balance across multiple litellm containers |
## Deploy with Database
### Docker, Kubernetes, Helm Chart
Requirements:
- Need a postgres database (e.g. [Supabase](https://supabase.com/), [Neon](https://neon.tech/), etc) Set `DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname>` in your env
- Set a `LITELLM_MASTER_KEY`, this is your Proxy Admin key - you can use this to create other keys (🚨 must start with `sk-`)
<Tabs>
@ -252,6 +255,8 @@ docker pull ghcr.io/berriai/litellm-database:main-latest
```shell
docker run \
-v $(pwd)/litellm_config.yaml:/app/config.yaml \
-e LITELLM_MASTER_KEY=sk-1234 \
-e DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname> \
-e AZURE_API_KEY=d6*********** \
-e AZURE_API_BASE=https://openai-***********/ \
-p 4000:4000 \
@ -267,26 +272,63 @@ Your OpenAI proxy server is now running on `http://0.0.0.0:4000`.
#### Step 1. Create deployment.yaml
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: litellm-deployment
spec:
replicas: 1
selector:
matchLabels:
app: litellm
template:
metadata:
labels:
app: litellm
spec:
containers:
- name: litellm-container
image: ghcr.io/berriai/litellm-database:main-latest
env:
- name: DATABASE_URL
value: postgresql://<user>:<password>@<host>:<port>/<dbname>
apiVersion: apps/v1
kind: Deployment
metadata:
name: litellm-deployment
spec:
replicas: 3
selector:
matchLabels:
app: litellm
template:
metadata:
labels:
app: litellm
spec:
containers:
- name: litellm-container
image: ghcr.io/berriai/litellm:main-latest
imagePullPolicy: Always
env:
- name: AZURE_API_KEY
value: "d6******"
- name: AZURE_API_BASE
value: "https://ope******"
- name: LITELLM_MASTER_KEY
value: "sk-1234"
- name: DATABASE_URL
value: "po**********"
args:
- "--config"
- "/app/proxy_config.yaml" # Update the path to mount the config file
volumeMounts: # Define volume mount for proxy_config.yaml
- name: config-volume
mountPath: /app
readOnly: true
livenessProbe:
httpGet:
path: /health/liveliness
port: 4000
initialDelaySeconds: 120
periodSeconds: 15
successThreshold: 1
failureThreshold: 3
timeoutSeconds: 10
readinessProbe:
httpGet:
path: /health/readiness
port: 4000
initialDelaySeconds: 120
periodSeconds: 15
successThreshold: 1
failureThreshold: 3
timeoutSeconds: 10
volumes: # Define volume to mount proxy_config.yaml
- name: config-volume
configMap:
name: litellm-config
```
```bash

26
docs/my-website/docs/proxy/logging.md
View file

@ -401,7 +401,7 @@ litellm_settings:
Start the LiteLLM Proxy and make a test request to verify the logs reached your callback API
## Logging Proxy Input/Output - Langfuse
We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successfull LLM calls to langfuse
We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successfull LLM calls to langfuse. Make sure to set `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` in your environment
**Step 1** Install langfuse
@ -419,7 +419,13 @@ litellm_settings:
success_callback: ["langfuse"]
```
**Step 3**: Start the proxy, make a test request
**Step 3**: Set required env variables for logging to langfuse
```shell
export LANGFUSE_PUBLIC_KEY="pk_kk"
export LANGFUSE_SECRET_KEY="sk_ss
```
**Step 4**: Start the proxy, make a test request
Start proxy
```shell
@ -569,6 +575,22 @@ curl -X POST 'http://0.0.0.0:4000/key/generate' \
All requests made with these keys will log data to their team-specific logging.
### Redacting Messages, Response Content from Langfuse Logging
Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to langfuse, but request metadata will still be logged.
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
success_callback: ["langfuse"]
turn_off_message_logging: True
```
## Logging Proxy Input/Output - DataDog
We will use the `--config` to set `litellm.success_callback = ["datadog"]` this will log all successfull LLM calls to DataDog

50
docs/my-website/docs/proxy/prod.md
View file

@ -16,7 +16,7 @@ Expected Performance in Production
| `/chat/completions` Requests/hour | `126K` |
## 1. Switch of Debug Logging
## 1. Switch off Debug Logging
Remove `set_verbose: True` from your config.yaml
```yaml
@ -40,7 +40,7 @@ Use this Docker `CMD`. This will start the proxy with 1 Uvicorn Async Worker
CMD ["--port", "4000", "--config", "./proxy_server_config.yaml"]
```
## 2. Batch write spend updates every 60s
## 3. Batch write spend updates every 60s
The default proxy batch write is 10s. This is to make it easy to see spend when debugging locally.
@ -49,11 +49,35 @@ In production, we recommend using a longer interval period of 60s. This reduces
```yaml
general_settings:
master_key: sk-1234
proxy_batch_write_at: 5 # 👈 Frequency of batch writing logs to server (in seconds)
proxy_batch_write_at: 60 # 👈 Frequency of batch writing logs to server (in seconds)
```
## 4. use Redis 'port','host', 'password'. NOT 'redis_url'
## 3. Move spend logs to separate server
When connecting to Redis use redis port, host, and password params. Not 'redis_url'. We've seen a 80 RPS difference between these 2 approaches when using the async redis client.
This is still something we're investigating. Keep track of it [here](https://github.com/BerriAI/litellm/issues/3188)
Recommended to do this for prod:
```yaml
router_settings:
routing_strategy: usage-based-routing-v2
# redis_url: "os.environ/REDIS_URL"
redis_host: os.environ/REDIS_HOST
redis_port: os.environ/REDIS_PORT
redis_password: os.environ/REDIS_PASSWORD
```
## 5. Switch off resetting budgets
Add this to your config.yaml. (Only spend per Key, User and Team will be tracked - spend per API Call will not be written to the LiteLLM Database)
```yaml
general_settings:
disable_reset_budget: true
```
## 6. Move spend logs to separate server (BETA)
Writing each spend log to the db can slow down your proxy. In testing we saw a 70% improvement in median response time, by moving writing spend logs to a separate server.
@ -141,24 +165,6 @@ A t2.micro should be sufficient to handle 1k logs / minute on this server.
This consumes at max 120MB, and <0.1 vCPU.
## 4. Switch off resetting budgets
Add this to your config.yaml. (Only spend per Key, User and Team will be tracked - spend per API Call will not be written to the LiteLLM Database)
```yaml
general_settings:
disable_spend_logs: true
disable_reset_budget: true
```
## 5. Switch of `litellm.telemetry`
Switch of all telemetry tracking done by litellm
```yaml
litellm_settings:
telemetry: False
```
## Machine Specifications to Deploy LiteLLM
| Service | Spec | CPUs | Memory | Architecture | Version|

9
docs/my-website/docs/proxy/grafana_metrics.md → docs/my-website/docs/proxy/prometheus.md
View file

@ -14,6 +14,7 @@ model_list:
model: gpt-3.5-turbo
litellm_settings:
success_callback: ["prometheus"]
failure_callback: ["prometheus"]
```
Start the proxy
@ -48,9 +49,10 @@ http://localhost:4000/metrics
| Metric Name | Description |
|----------------------|--------------------------------------|
| `litellm_requests_metric` | Number of requests made, per `"user", "key", "model"` |
| `litellm_spend_metric` | Total Spend, per `"user", "key", "model"` |
| `litellm_total_tokens` | input + output tokens per `"user", "key", "model"` |
| `litellm_requests_metric` | Number of requests made, per `"user", "key", "model", "team", "end-user"` |
| `litellm_spend_metric` | Total Spend, per `"user", "key", "model", "team", "end-user"` |
| `litellm_total_tokens` | input + output tokens per `"user", "key", "model", "team", "end-user"` |
| `litellm_llm_api_failed_requests_metric` | Number of failed LLM API requests per `"user", "key", "model", "team", "end-user"` |
## Monitor System Health
@ -69,3 +71,4 @@ litellm_settings:
|----------------------|--------------------------------------|
| `litellm_redis_latency` | histogram latency for redis calls |
| `litellm_redis_fails` | Number of failed redis calls |
| `litellm_self_latency` | Histogram latency for successful litellm api call |

23
docs/my-website/docs/proxy/quick_start.md
View file

@ -348,6 +348,29 @@ query_result = embeddings.embed_query(text)
print(f"TITAN EMBEDDINGS")
print(query_result[:5])
```
</TabItem>
<TabItem value="litellm" label="LiteLLM SDK">
This is **not recommended**. There is duplicate logic as the proxy also uses the sdk, which might lead to unexpected errors.
```python
from litellm import completion
response = completion(
model="openai/gpt-3.5-turbo",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
api_key="anything",
base_url="http://0.0.0.0:4000"
)
print(response)
```
</TabItem>
</Tabs>

15
docs/my-website/docs/proxy/reliability.md
View file

@ -136,6 +136,21 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
'
```
### Test it!
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data-raw '{
"model": "zephyr-beta", # 👈 MODEL NAME to fallback from
"messages": [
{"role": "user", "content": "what color is red"}
],
"mock_testing_fallbacks": true
}'
```
## Advanced - Context Window Fallbacks
**Before call is made** check if a call is within model context window with **`enable_pre_call_checks: true`**.

3
docs/my-website/docs/proxy/user_keys.md
View file

@ -121,6 +121,9 @@ from langchain.prompts.chat import (
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
import os
os.environ["OPENAI_API_KEY"] = "anything"
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000",

33
docs/my-website/docs/routing.md
View file

@ -95,7 +95,7 @@ print(response)
- `router.image_generation()` - completion calls in OpenAI `/v1/images/generations` endpoint format
- `router.aimage_generation()` - async image generation calls
### Advanced - Routing Strategies
## Advanced - Routing Strategies
#### Routing Strategies - Weighted Pick, Rate Limit Aware, Least Busy, Latency Based
Router provides 4 strategies for routing your calls across multiple deployments:
@ -279,7 +279,7 @@ router_settings:
```
</TabItem>
<TabItem value="simple-shuffle" label="(Default) Weighted Pick">
<TabItem value="simple-shuffle" label="(Default) Weighted Pick (Async)">
**Default** Picks a deployment based on the provided **Requests per minute (rpm) or Tokens per minute (tpm)**
@ -443,6 +443,35 @@ asyncio.run(router_acompletion())
## Basic Reliability
### Max Parallel Requests (ASYNC)
Used in semaphore for async requests on router. Limit the max concurrent calls made to a deployment. Useful in high-traffic scenarios.
If tpm/rpm is set, and no max parallel request limit given, we use the RPM or calculated RPM (tpm/1000/6) as the max parallel request limit.
```python
from litellm import Router
model_list = [{
"model_name": "gpt-4",
"litellm_params": {
"model": "azure/gpt-4",
...
"max_parallel_requests": 10 # 👈 SET PER DEPLOYMENT
}
}]
### OR ###
router = Router(model_list=model_list, default_max_parallel_requests=20) # 👈 SET DEFAULT MAX PARALLEL REQUESTS
# deployment max parallel requests > default max parallel requests
```
[**See Code**](https://github.com/BerriAI/litellm/blob/a978f2d8813c04dad34802cb95e0a0e35a3324bc/litellm/utils.py#L5605)
### Timeouts
The timeout set in router is for the entire length of the call, and is passed down to the completion() call level as well.

21
docs/my-website/docs/set_keys.md
View file

@ -5,6 +5,9 @@ LiteLLM allows you to specify the following:
* API Base
* API Version
* API Type
* Project
* Location
* Token
Useful Helper functions:
* [`check_valid_key()`](#check_valid_key)
@ -43,6 +46,24 @@ os.environ['AZURE_API_TYPE'] = "azure" # [OPTIONAL]
os.environ['OPENAI_API_BASE'] = "https://openai-gpt-4-test2-v-12.openai.azure.com/"
```
### Setting Project, Location, Token
For cloud providers:
- Azure
- Bedrock
- GCP
- Watson AI
you might need to set additional parameters. LiteLLM provides a common set of params, that we map across all providers.
| | LiteLLM param | Watson | Vertex AI | Azure | Bedrock |
|------|--------------|--------------|--------------|--------------|--------------|
| Project | project | watsonx_project | vertex_project | n/a | n/a |
| Region | region_name | watsonx_region_name | vertex_location | n/a | aws_region_name |
| Token | token | watsonx_token or token | n/a | azure_ad_token | n/a |
If you want, you can call them by their provider-specific params as well.
## litellm variables
### litellm.api_key

6
docs/my-website/docusaurus.config.js
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@ -105,6 +105,12 @@ const config = {
label: 'Enterprise',
to: "docs/enterprise"
},
{
sidebarId: 'tutorialSidebar',
position: 'left',
label: '🚀 Hosted',
to: "docs/hosted"
},
{
href: 'https://github.com/BerriAI/litellm',
label: 'GitHub',

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15
docs/my-website/sidebars.js
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@ -43,6 +43,12 @@ const sidebars = {
"proxy/user_keys",
"proxy/enterprise",
"proxy/virtual_keys",
"proxy/alerting",
{
type: "category",
label: "Logging",
items: ["proxy/logging", "proxy/streaming_logging"],
},
"proxy/team_based_routing",
"proxy/ui",
"proxy/cost_tracking",
@ -58,12 +64,7 @@ const sidebars = {
"proxy/pii_masking",
"proxy/prompt_injection",
"proxy/caching",
{
type: "category",
label: "Logging, Alerting",
items: ["proxy/logging", "proxy/alerting", "proxy/streaming_logging"],
},
"proxy/grafana_metrics",
"proxy/prometheus",
"proxy/call_hooks",
"proxy/rules",
"proxy/cli",
@ -148,6 +149,7 @@ const sidebars = {
"providers/openrouter",
"providers/custom_openai_proxy",
"providers/petals",
"providers/watsonx",
],
},
"proxy/custom_pricing",
@ -173,6 +175,7 @@ const sidebars = {
"observability/langsmith_integration",
"observability/slack_integration",
"observability/traceloop_integration",
"observability/athina_integration",
"observability/lunary_integration",
"observability/athina_integration",
"observability/helicone_integration",

2
docs/my-website/src/pages/token_usage.md
View file

@ -16,7 +16,7 @@ However, we also expose 3 public helper functions to calculate token usage acros
```python
from litellm import token_counter
messages = [{"user": "role", "content": "Hey, how's it going"}]
messages = [{"role": "user", "content": "Hey, how's it going"}]
print(token_counter(model="gpt-3.5-turbo", messages=messages))
```

8
litellm-js/spend-logs/package-lock.json generated
View file

@ -6,7 +6,7 @@
"": {
"dependencies": {
"@hono/node-server": "^1.9.0",
"hono": "^4.1.5"
"hono": "^4.2.7"
},
"devDependencies": {
"@types/node": "^20.11.17",
@ -463,9 +463,9 @@
}
},
"node_modules/hono": {
"version": "4.1.5",
"resolved": "https://registry.npmjs.org/hono/-/hono-4.1.5.tgz",
"integrity": "sha512-3ChJiIoeCxvkt6vnkxJagplrt1YZg3NyNob7ssVeK2PUqEINp4q1F94HzFnvY9QE8asVmbW5kkTDlyWylfg2vg==",
"version": "4.2.7",
"resolved": "https://registry.npmjs.org/hono/-/hono-4.2.7.tgz",
"integrity": "sha512-k1xHi86tJnRIVvqhFMBDGFKJ8r5O+bEsT4P59ZK59r0F300Xd910/r237inVfuT/VmE86RQQffX4OYNda6dLXw==",
"engines": {
"node": ">=16.0.0"
}

2
litellm-js/spend-logs/package.json
View file

@ -4,7 +4,7 @@
},
"dependencies": {
"@hono/node-server": "^1.9.0",
"hono": "^4.1.5"
"hono": "^4.2.7"
},
"devDependencies": {
"@types/node": "^20.11.17",

33
litellm/__init__.py
View file

@ -2,7 +2,7 @@
import threading, requests, os
from typing import Callable, List, Optional, Dict, Union, Any, Literal
from litellm.caching import Cache
from litellm._logging import set_verbose, _turn_on_debug, verbose_logger
from litellm._logging import set_verbose, _turn_on_debug, verbose_logger, json_logs
from litellm.proxy._types import (
KeyManagementSystem,
KeyManagementSettings,
@ -16,11 +16,24 @@ dotenv.load_dotenv()
if set_verbose == True:
_turn_on_debug()
#############################################
### Callbacks /Logging / Success / Failure Handlers ###
input_callback: List[Union[str, Callable]] = []
success_callback: List[Union[str, Callable]] = []
failure_callback: List[Union[str, Callable]] = []
service_callback: List[Union[str, Callable]] = []
callbacks: List[Callable] = []
_langfuse_default_tags: Optional[
List[
Literal[
"user_api_key_alias",
"user_api_key_user_id",
"user_api_key_user_email",
"user_api_key_team_alias",
"semantic-similarity",
"proxy_base_url",
]
]
] = None
_async_input_callback: List[Callable] = (
[]
) # internal variable - async custom callbacks are routed here.
@ -32,6 +45,9 @@ _async_failure_callback: List[Callable] = (
) # internal variable - async custom callbacks are routed here.
pre_call_rules: List[Callable] = []
post_call_rules: List[Callable] = []
turn_off_message_logging: Optional[bool] = False
## end of callbacks #############
email: Optional[str] = (
None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
)
@ -43,6 +59,7 @@ max_tokens = 256 # OpenAI Defaults
drop_params = False
modify_params = False
retry = True
### AUTH ###
api_key: Optional[str] = None
openai_key: Optional[str] = None
azure_key: Optional[str] = None
@ -52,6 +69,7 @@ cohere_key: Optional[str] = None
clarifai_key: Optional[str] = None
maritalk_key: Optional[str] = None
ai21_key: Optional[str] = None
ollama_key: Optional[str] = None
openrouter_key: Optional[str] = None
huggingface_key: Optional[str] = None
vertex_project: Optional[str] = None
@ -61,7 +79,12 @@ cloudflare_api_key: Optional[str] = None
baseten_key: Optional[str] = None
aleph_alpha_key: Optional[str] = None
nlp_cloud_key: Optional[str] = None
common_cloud_provider_auth_params: dict = {
"params": ["project", "region_name", "token"],
"providers": ["vertex_ai", "bedrock", "watsonx", "azure"],
}
use_client: bool = False
ssl_verify: bool = True
disable_streaming_logging: bool = False
### GUARDRAILS ###
llamaguard_model_name: Optional[str] = None
@ -283,6 +306,7 @@ aleph_alpha_models: List = []
bedrock_models: List = []
deepinfra_models: List = []
perplexity_models: List = []
watsonx_models: List = []
for key, value in model_cost.items():
if value.get("litellm_provider") == "openai":
open_ai_chat_completion_models.append(key)
@ -327,6 +351,8 @@ for key, value in model_cost.items():
deepinfra_models.append(key)
elif value.get("litellm_provider") == "perplexity":
perplexity_models.append(key)
elif value.get("litellm_provider") == "watsonx":
watsonx_models.append(key)
# known openai compatible endpoints - we'll eventually move this list to the model_prices_and_context_window.json dictionary
openai_compatible_endpoints: List = [
@ -530,6 +556,7 @@ model_list = (
+ perplexity_models
+ maritalk_models
+ vertex_language_models
+ watsonx_models
)
provider_list: List = [
@ -569,6 +596,7 @@ provider_list: List = [
"cloudflare",
"xinference",
"fireworks_ai",
"watsonx",
"custom", # custom apis
]
@ -590,6 +618,7 @@ models_by_provider: dict = {
"deepinfra": deepinfra_models,
"perplexity": perplexity_models,
"maritalk": maritalk_models,
"watsonx": watsonx_models,
}
# mapping for those models which have larger equivalents
@ -701,9 +730,11 @@ from .llms.bedrock import (
AmazonLlamaConfig,
AmazonStabilityConfig,
AmazonMistralConfig,
AmazonBedrockGlobalConfig,
)
from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig
from .llms.azure import AzureOpenAIConfig, AzureOpenAIError
from .llms.watsonx import IBMWatsonXAIConfig
from .main import * # type: ignore
from .integrations import *
from .exceptions import (

2
litellm/_logging.py
View file

@ -1,7 +1,7 @@
import logging
set_verbose = False
json_logs = False
# Create a handler for the logger (you may need to adapt this based on your needs)
handler = logging.StreamHandler()
handler.setLevel(logging.DEBUG)

54
litellm/_redis.py
View file

@ -32,6 +32,25 @@ def _get_redis_kwargs():
return available_args
def _get_redis_url_kwargs(client=None):
if client is None:
client = redis.Redis.from_url
arg_spec = inspect.getfullargspec(redis.Redis.from_url)
# Only allow primitive arguments
exclude_args = {
"self",
"connection_pool",
"retry",
}
include_args = ["url"]
available_args = [x for x in arg_spec.args if x not in exclude_args] + include_args
return available_args
def _get_redis_env_kwarg_mapping():
PREFIX = "REDIS_"
@ -91,27 +110,39 @@ def _get_redis_client_logic(**env_overrides):
redis_kwargs.pop("password", None)
elif "host" not in redis_kwargs or redis_kwargs["host"] is None:
raise ValueError("Either 'host' or 'url' must be specified for redis.")
litellm.print_verbose(f"redis_kwargs: {redis_kwargs}")
# litellm.print_verbose(f"redis_kwargs: {redis_kwargs}")
return redis_kwargs
def get_redis_client(**env_overrides):
redis_kwargs = _get_redis_client_logic(**env_overrides)
if "url" in redis_kwargs and redis_kwargs["url"] is not None:
redis_kwargs.pop(
"connection_pool", None
) # redis.from_url doesn't support setting your own connection pool
return redis.Redis.from_url(**redis_kwargs)
args = _get_redis_url_kwargs()
url_kwargs = {}
for arg in redis_kwargs:
if arg in args:
url_kwargs[arg] = redis_kwargs[arg]
return redis.Redis.from_url(**url_kwargs)
return redis.Redis(**redis_kwargs)
def get_redis_async_client(**env_overrides):
redis_kwargs = _get_redis_client_logic(**env_overrides)
if "url" in redis_kwargs and redis_kwargs["url"] is not None:
redis_kwargs.pop(
"connection_pool", None
) # redis.from_url doesn't support setting your own connection pool
return async_redis.Redis.from_url(**redis_kwargs)
args = _get_redis_url_kwargs(client=async_redis.Redis.from_url)
url_kwargs = {}
for arg in redis_kwargs:
if arg in args:
url_kwargs[arg] = redis_kwargs[arg]
else:
litellm.print_verbose(
"REDIS: ignoring argument: {}. Not an allowed async_redis.Redis.from_url arg.".format(
arg
)
)
return async_redis.Redis.from_url(**url_kwargs)
return async_redis.Redis(
socket_timeout=5,
**redis_kwargs,
@ -124,4 +155,9 @@ def get_redis_connection_pool(**env_overrides):
return async_redis.BlockingConnectionPool.from_url(
timeout=5, url=redis_kwargs["url"]
)
connection_class = async_redis.Connection
if "ssl" in redis_kwargs and redis_kwargs["ssl"] is not None:
connection_class = async_redis.SSLConnection
redis_kwargs.pop("ssl", None)
redis_kwargs["connection_class"] = connection_class
return async_redis.BlockingConnectionPool(timeout=5, **redis_kwargs)

77
litellm/_service_logger.py
View file

@ -1,9 +1,13 @@
import litellm
import litellm, traceback
from litellm.proxy._types import UserAPIKeyAuth
from .types.services import ServiceTypes, ServiceLoggerPayload
from .integrations.prometheus_services import PrometheusServicesLogger
from .integrations.custom_logger import CustomLogger
from datetime import timedelta
from typing import Union
class ServiceLogging:
class ServiceLogging(CustomLogger):
"""
Separate class used for monitoring health of litellm-adjacent services (redis/postgres).
"""
@ -14,11 +18,12 @@ class ServiceLogging:
self.mock_testing_async_success_hook = 0
self.mock_testing_sync_failure_hook = 0
self.mock_testing_async_failure_hook = 0
if "prometheus_system" in litellm.service_callback:
self.prometheusServicesLogger = PrometheusServicesLogger()
def service_success_hook(self, service: ServiceTypes, duration: float):
def service_success_hook(
self, service: ServiceTypes, duration: float, call_type: str
):
"""
[TODO] Not implemented for sync calls yet. V0 is focused on async monitoring (used by proxy).
"""
@ -26,7 +31,7 @@ class ServiceLogging:
self.mock_testing_sync_success_hook += 1
def service_failure_hook(
self, service: ServiceTypes, duration: float, error: Exception
self, service: ServiceTypes, duration: float, error: Exception, call_type: str
):
"""
[TODO] Not implemented for sync calls yet. V0 is focused on async monitoring (used by proxy).
@ -34,7 +39,9 @@ class ServiceLogging:
if self.mock_testing:
self.mock_testing_sync_failure_hook += 1
async def async_service_success_hook(self, service: ServiceTypes, duration: float):
async def async_service_success_hook(
self, service: ServiceTypes, duration: float, call_type: str
):
"""
- For counting if the redis, postgres call is successful
"""
@ -42,7 +49,11 @@ class ServiceLogging:
self.mock_testing_async_success_hook += 1
payload = ServiceLoggerPayload(
is_error=False, error=None, service=service, duration=duration
is_error=False,
error=None,
service=service,
duration=duration,
call_type=call_type,
)
for callback in litellm.service_callback:
if callback == "prometheus_system":
@ -51,7 +62,11 @@ class ServiceLogging:
)
async def async_service_failure_hook(
self, service: ServiceTypes, duration: float, error: Exception
self,
service: ServiceTypes,
duration: float,
error: Union[str, Exception],
call_type: str,
):
"""
- For counting if the redis, postgres call is unsuccessful
@ -59,8 +74,18 @@ class ServiceLogging:
if self.mock_testing:
self.mock_testing_async_failure_hook += 1
error_message = ""
if isinstance(error, Exception):
error_message = str(error)
elif isinstance(error, str):
error_message = error
payload = ServiceLoggerPayload(
is_error=True, error=str(error), service=service, duration=duration
is_error=True,
error=error_message,
service=service,
duration=duration,
call_type=call_type,
)
for callback in litellm.service_callback:
if callback == "prometheus_system":
@ -69,3 +94,37 @@ class ServiceLogging:
await self.prometheusServicesLogger.async_service_failure_hook(
payload=payload
)
async def async_post_call_failure_hook(
self, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth
):
"""
Hook to track failed litellm-service calls
"""
return await super().async_post_call_failure_hook(
original_exception, user_api_key_dict
)
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
"""
Hook to track latency for litellm proxy llm api calls
"""
try:
_duration = end_time - start_time
if isinstance(_duration, timedelta):
_duration = _duration.total_seconds()
elif isinstance(_duration, float):
pass
else:
raise Exception(
"Duration={} is not a float or timedelta object. type={}".format(
_duration, type(_duration)
)
) # invalid _duration value
await self.async_service_success_hook(
service=ServiceTypes.LITELLM,
duration=_duration,
call_type=kwargs["call_type"],
)
except Exception as e:
raise e

236
litellm/caching.py
View file

@ -13,7 +13,6 @@ import json, traceback, ast, hashlib
from typing import Optional, Literal, List, Union, Any, BinaryIO
from openai._models import BaseModel as OpenAIObject
from litellm._logging import verbose_logger
from litellm._service_logger import ServiceLogging
from litellm.types.services import ServiceLoggerPayload, ServiceTypes
import traceback
@ -90,6 +89,13 @@ class InMemoryCache(BaseCache):
return_val.append(val)
return return_val
def increment_cache(self, key, value: int, **kwargs) -> int:
# get the value
init_value = self.get_cache(key=key) or 0
value = init_value + value
self.set_cache(key, value, **kwargs)
return value
async def async_get_cache(self, key, **kwargs):
return self.get_cache(key=key, **kwargs)
@ -132,6 +138,7 @@ class RedisCache(BaseCache):
**kwargs,
):
from ._redis import get_redis_client, get_redis_connection_pool
from litellm._service_logger import ServiceLogging
import redis
redis_kwargs = {}
@ -142,18 +149,19 @@ class RedisCache(BaseCache):
if password is not None:
redis_kwargs["password"] = password
### HEALTH MONITORING OBJECT ###
if kwargs.get("service_logger_obj", None) is not None and isinstance(
kwargs["service_logger_obj"], ServiceLogging
):
self.service_logger_obj = kwargs.pop("service_logger_obj")
else:
self.service_logger_obj = ServiceLogging()
redis_kwargs.update(kwargs)
self.redis_client = get_redis_client(**redis_kwargs)
self.redis_kwargs = redis_kwargs
self.async_redis_conn_pool = get_redis_connection_pool(**redis_kwargs)
if "url" in redis_kwargs and redis_kwargs["url"] is not None:
parsed_kwargs = redis.connection.parse_url(redis_kwargs["url"])
redis_kwargs.update(parsed_kwargs)
self.redis_kwargs.update(parsed_kwargs)
# pop url
self.redis_kwargs.pop("url")
# redis namespaces
self.namespace = namespace
# for high traffic, we store the redis results in memory and then batch write to redis
@ -165,8 +173,15 @@ class RedisCache(BaseCache):
except Exception as e:
pass
### HEALTH MONITORING OBJECT ###
self.service_logger_obj = ServiceLogging()
### ASYNC HEALTH PING ###
try:
# asyncio.get_running_loop().create_task(self.ping())
result = asyncio.get_running_loop().create_task(self.ping())
except Exception:
pass
### SYNC HEALTH PING ###
self.redis_client.ping()
def init_async_client(self):
from ._redis import get_redis_async_client
@ -198,6 +213,42 @@ class RedisCache(BaseCache):
f"LiteLLM Caching: set() - Got exception from REDIS : {str(e)}"
)
def increment_cache(self, key, value: int, **kwargs) -> int:
_redis_client = self.redis_client
start_time = time.time()
try:
result = _redis_client.incr(name=key, amount=value)
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="increment_cache",
)
)
return result
except Exception as e:
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="increment_cache",
)
)
verbose_logger.error(
"LiteLLM Redis Caching: increment_cache() - Got exception from REDIS %s, Writing value=%s",
str(e),
value,
)
traceback.print_exc()
raise e
async def async_scan_iter(self, pattern: str, count: int = 100) -> list:
start_time = time.time()
try:
@ -216,7 +267,9 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS, duration=_duration
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_scan_iter",
)
) # DO NOT SLOW DOWN CALL B/C OF THIS
return keys
@ -227,7 +280,10 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS, duration=_duration, error=e
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_scan_iter",
)
)
raise e
@ -267,7 +323,9 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS, duration=_duration
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_set_cache",
)
)
except Exception as e:
@ -275,7 +333,10 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS, duration=_duration, error=e
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_set_cache",
)
)
# NON blocking - notify users Redis is throwing an exception
@ -292,6 +353,10 @@ class RedisCache(BaseCache):
"""
_redis_client = self.init_async_client()
start_time = time.time()
print_verbose(
f"Set Async Redis Cache: key list: {cache_list}\nttl={ttl}, redis_version={self.redis_version}"
)
try:
async with _redis_client as redis_client:
async with redis_client.pipeline(transaction=True) as pipe:
@ -316,7 +381,9 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS, duration=_duration
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_set_cache_pipeline",
)
)
return results
@ -326,7 +393,10 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS, duration=_duration, error=e
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_set_cache_pipeline",
)
)
@ -359,6 +429,7 @@ class RedisCache(BaseCache):
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_increment",
)
)
return result
@ -368,7 +439,10 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS, duration=_duration, error=e
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_increment",
)
)
verbose_logger.error(
@ -459,7 +533,9 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS, duration=_duration
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_get_cache",
)
)
return response
@ -469,7 +545,10 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS, duration=_duration, error=e
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_get_cache",
)
)
# NON blocking - notify users Redis is throwing an exception
@ -497,7 +576,9 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS, duration=_duration
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_batch_get_cache",
)
)
@ -519,21 +600,81 @@ class RedisCache(BaseCache):
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS, duration=_duration, error=e
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_batch_get_cache",
)
)
print_verbose(f"Error occurred in pipeline read - {str(e)}")
return key_value_dict
async def ping(self):
def sync_ping(self) -> bool:
"""
Tests if the sync redis client is correctly setup.
"""
print_verbose(f"Pinging Sync Redis Cache")
start_time = time.time()
try:
response = self.redis_client.ping()
print_verbose(f"Redis Cache PING: {response}")
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
self.service_logger_obj.service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="sync_ping",
)
return response
except Exception as e:
# NON blocking - notify users Redis is throwing an exception
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
self.service_logger_obj.service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="sync_ping",
)
print_verbose(
f"LiteLLM Redis Cache PING: - Got exception from REDIS : {str(e)}"
)
traceback.print_exc()
raise e
async def ping(self) -> bool:
_redis_client = self.init_async_client()
start_time = time.time()
async with _redis_client as redis_client:
print_verbose(f"Pinging Async Redis Cache")
try:
response = await redis_client.ping()
print_verbose(f"Redis Cache PING: {response}")
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.REDIS,
duration=_duration,
call_type="async_ping",
)
)
return response
except Exception as e:
# NON blocking - notify users Redis is throwing an exception
## LOGGING ##
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_failure_hook(
service=ServiceTypes.REDIS,
duration=_duration,
error=e,
call_type="async_ping",
)
)
print_verbose(
f"LiteLLM Redis Cache PING: - Got exception from REDIS : {str(e)}"
)
@ -1064,6 +1205,30 @@ class DualCache(BaseCache):
except Exception as e:
print_verbose(e)
def increment_cache(
self, key, value: int, local_only: bool = False, **kwargs
) -> int:
"""
Key - the key in cache
Value - int - the value you want to increment by
Returns - int - the incremented value
"""
try:
result: int = value
if self.in_memory_cache is not None:
result = self.in_memory_cache.increment_cache(key, value, **kwargs)
if self.redis_cache is not None and local_only == False:
result = self.redis_cache.increment_cache(key, value, **kwargs)
return result
except Exception as e:
print_verbose(f"LiteLLM Cache: Excepton async add_cache: {str(e)}")
traceback.print_exc()
raise e
def get_cache(self, key, local_only: bool = False, **kwargs):
# Try to fetch from in-memory cache first
try:
@ -1116,7 +1281,7 @@ class DualCache(BaseCache):
self.in_memory_cache.set_cache(key, redis_result[key], **kwargs)
for key, value in redis_result.items():
result[sublist_keys.index(key)] = value
result[keys.index(key)] = value
print_verbose(f"async batch get cache: cache result: {result}")
return result
@ -1166,10 +1331,8 @@ class DualCache(BaseCache):
keys, **kwargs
)
print_verbose(f"in_memory_result: {in_memory_result}")
if in_memory_result is not None:
result = in_memory_result
if None in result and self.redis_cache is not None and local_only == False:
"""
- for the none values in the result
@ -1185,22 +1348,23 @@ class DualCache(BaseCache):
if redis_result is not None:
# Update in-memory cache with the value from Redis
for key in redis_result:
await self.in_memory_cache.async_set_cache(
key, redis_result[key], **kwargs
)
for key, value in redis_result.items():
if value is not None:
await self.in_memory_cache.async_set_cache(
key, redis_result[key], **kwargs
)
for key, value in redis_result.items():
index = keys.index(key)
result[index] = value
sublist_dict = dict(zip(sublist_keys, redis_result))
for key, value in sublist_dict.items():
result[sublist_keys.index(key)] = value
print_verbose(f"async batch get cache: cache result: {result}")
return result
except Exception as e:
traceback.print_exc()
async def async_set_cache(self, key, value, local_only: bool = False, **kwargs):
print_verbose(
f"async set cache: cache key: {key}; local_only: {local_only}; value: {value}"
)
try:
if self.in_memory_cache is not None:
await self.in_memory_cache.async_set_cache(key, value, **kwargs)

12
litellm/exceptions.py
View file

@ -82,14 +82,18 @@ class UnprocessableEntityError(UnprocessableEntityError): # type: ignore
class Timeout(APITimeoutError): # type: ignore
def __init__(self, message, model, llm_provider):
self.status_code = 408
self.message = message
self.model = model
self.llm_provider = llm_provider
request = httpx.Request(method="POST", url="https://api.openai.com/v1")
super().__init__(
request=request
) # Call the base class constructor with the parameters it needs
self.status_code = 408
self.message = message
self.model = model
self.llm_provider = llm_provider
# custom function to convert to str
def __str__(self):
return str(self.message)
class PermissionDeniedError(PermissionDeniedError): # type:ignore

13
litellm/integrations/custom_logger.py
View file

@ -6,7 +6,7 @@ import requests
from litellm.proxy._types import UserAPIKeyAuth
from litellm.caching import DualCache
from typing import Literal, Union
from typing import Literal, Union, Optional
dotenv.load_dotenv() # Loading env variables using dotenv
import traceback
@ -46,6 +46,17 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
pass
#### PRE-CALL CHECKS - router/proxy only ####
"""
Allows usage-based-routing-v2 to run pre-call rpm checks within the picked deployment's semaphore (concurrency-safe tpm/rpm checks).
"""
async def async_pre_call_check(self, deployment: dict) -> Optional[dict]:
pass
def pre_call_check(self, deployment: dict) -> Optional[dict]:
pass
#### CALL HOOKS - proxy only ####
"""
Control the modify incoming / outgoung data before calling the model

51
litellm/integrations/greenscale.py Normal file
View file

@ -0,0 +1,51 @@
import requests
import json
import traceback
from datetime import datetime, timezone
class GreenscaleLogger:
def __init__(self):
import os
self.greenscale_api_key = os.getenv("GREENSCALE_API_KEY")
self.headers = {
"api-key": self.greenscale_api_key,
"Content-Type": "application/json"
}
self.greenscale_logging_url = os.getenv("GREENSCALE_ENDPOINT")
def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
try:
response_json = response_obj.model_dump() if response_obj else {}
data = {
"modelId": kwargs.get("model"),
"inputTokenCount": response_json.get("usage", {}).get("prompt_tokens"),
"outputTokenCount": response_json.get("usage", {}).get("completion_tokens"),
}
data["timestamp"] = datetime.now(timezone.utc).strftime('%Y-%m-%dT%H:%M:%SZ')
if type(end_time) == datetime and type(start_time) == datetime:
data["invocationLatency"] = int((end_time - start_time).total_seconds() * 1000)
# Add additional metadata keys to tags
tags = []
metadata = kwargs.get("litellm_params", {}).get("metadata", {})
for key, value in metadata.items():
if key.startswith("greenscale"):
if key == "greenscale_project":
data["project"] = value
elif key == "greenscale_application":
data["application"] = value
else:
tags.append({"key": key.replace("greenscale_", ""), "value": str(value)})
data["tags"] = tags
response = requests.post(self.greenscale_logging_url, headers=self.headers, data=json.dumps(data, default=str))
if response.status_code != 200:
print_verbose(f"Greenscale Logger Error - {response.text}, {response.status_code}")
else:
print_verbose(f"Greenscale Logger Succeeded - {response.text}")
except Exception as e:
print_verbose(f"Greenscale Logger Error - {e}, Stack trace: {traceback.format_exc()}")
pass

84
litellm/integrations/langfuse.py
View file

@ -12,7 +12,9 @@ import litellm
class LangFuseLogger:
# Class variables or attributes
def __init__(self, langfuse_public_key=None, langfuse_secret=None):
def __init__(
self, langfuse_public_key=None, langfuse_secret=None, flush_interval=1
):
try:
from langfuse import Langfuse
except Exception as e:
@ -31,9 +33,17 @@ class LangFuseLogger:
host=self.langfuse_host,
release=self.langfuse_release,
debug=self.langfuse_debug,
flush_interval=1, # flush interval in seconds
flush_interval=flush_interval, # flush interval in seconds
)
# set the current langfuse project id in the environ
# this is used by Alerting to link to the correct project
try:
project_id = self.Langfuse.client.projects.get().data[0].id
os.environ["LANGFUSE_PROJECT_ID"] = project_id
except:
project_id = None
if os.getenv("UPSTREAM_LANGFUSE_SECRET_KEY") is not None:
self.upstream_langfuse_secret_key = os.getenv(
"UPSTREAM_LANGFUSE_SECRET_KEY"
@ -76,6 +86,7 @@ class LangFuseLogger:
print_verbose(
f"Langfuse Logging - Enters logging function for model {kwargs}"
)
litellm_params = kwargs.get("litellm_params", {})
metadata = (
litellm_params.get("metadata", {}) or {}
@ -133,6 +144,7 @@ class LangFuseLogger:
self._log_langfuse_v2(
user_id,
metadata,
litellm_params,
output,
start_time,
end_time,
@ -224,6 +236,7 @@ class LangFuseLogger:
self,
user_id,
metadata,
litellm_params,
output,
start_time,
end_time,
@ -252,15 +265,18 @@ class LangFuseLogger:
tags = metadata_tags
trace_name = metadata.get("trace_name", None)
if trace_name is None:
trace_id = metadata.get("trace_id", None)
existing_trace_id = metadata.get("existing_trace_id", None)
if trace_name is None and existing_trace_id is None:
# just log `litellm-{call_type}` as the trace name
## DO NOT SET TRACE_NAME if trace-id set. this can lead to overwriting of past traces.
trace_name = f"litellm-{kwargs.get('call_type', 'completion')}"
trace_params = {
"name": trace_name,
"input": input,
"user_id": metadata.get("trace_user_id", user_id),
"id": metadata.get("trace_id", None),
"id": trace_id or existing_trace_id,
"session_id": metadata.get("session_id", None),
}
@ -278,13 +294,13 @@ class LangFuseLogger:
clean_metadata = {}
if isinstance(metadata, dict):
for key, value in metadata.items():
# generate langfuse tags
if key in [
"user_api_key",
"user_api_key_user_id",
"user_api_key_team_id",
"semantic-similarity",
]:
# generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy
if (
litellm._langfuse_default_tags is not None
and isinstance(litellm._langfuse_default_tags, list)
and key in litellm._langfuse_default_tags
):
tags.append(f"{key}:{value}")
# clean litellm metadata before logging
@ -298,13 +314,53 @@ class LangFuseLogger:
else:
clean_metadata[key] = value
if (
litellm._langfuse_default_tags is not None
and isinstance(litellm._langfuse_default_tags, list)
and "proxy_base_url" in litellm._langfuse_default_tags
):
proxy_base_url = os.environ.get("PROXY_BASE_URL", None)
if proxy_base_url is not None:
tags.append(f"proxy_base_url:{proxy_base_url}")
api_base = litellm_params.get("api_base", None)
if api_base:
clean_metadata["api_base"] = api_base
vertex_location = kwargs.get("vertex_location", None)
if vertex_location:
clean_metadata["vertex_location"] = vertex_location
aws_region_name = kwargs.get("aws_region_name", None)
if aws_region_name:
clean_metadata["aws_region_name"] = aws_region_name
if supports_tags:
if "cache_hit" in kwargs:
if kwargs["cache_hit"] is None:
kwargs["cache_hit"] = False
tags.append(f"cache_hit:{kwargs['cache_hit']}")
clean_metadata["cache_hit"] = kwargs["cache_hit"]
trace_params.update({"tags": tags})
proxy_server_request = litellm_params.get("proxy_server_request", None)
if proxy_server_request:
method = proxy_server_request.get("method", None)
url = proxy_server_request.get("url", None)
headers = proxy_server_request.get("headers", None)
clean_headers = {}
if headers:
for key, value in headers.items():
# these headers can leak our API keys and/or JWT tokens
if key.lower() not in ["authorization", "cookie", "referer"]:
clean_headers[key] = value
clean_metadata["request"] = {
"method": method,
"url": url,
"headers": clean_headers,
}
print_verbose(f"trace_params: {trace_params}")
trace = self.Langfuse.trace(**trace_params)
@ -323,7 +379,11 @@ class LangFuseLogger:
# just log `litellm-{call_type}` as the generation name
generation_name = f"litellm-{kwargs.get('call_type', 'completion')}"
system_fingerprint = response_obj.get("system_fingerprint", None)
if response_obj is not None and "system_fingerprint" in response_obj:
system_fingerprint = response_obj.get("system_fingerprint", None)
else:
system_fingerprint = None
if system_fingerprint is not None:
optional_params["system_fingerprint"] = system_fingerprint

57
litellm/integrations/langsmith.py
View file

@ -7,6 +7,19 @@ from datetime import datetime
dotenv.load_dotenv() # Loading env variables using dotenv
import traceback
import asyncio
import types
from pydantic import BaseModel
def is_serializable(value):
non_serializable_types = (
types.CoroutineType,
types.FunctionType,
types.GeneratorType,
BaseModel,
)
return not isinstance(value, non_serializable_types)
class LangsmithLogger:
@ -21,7 +34,9 @@ class LangsmithLogger:
def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
# Method definition
# inspired by Langsmith http api here: https://github.com/langchain-ai/langsmith-cookbook/blob/main/tracing-examples/rest/rest.ipynb
metadata = kwargs.get('litellm_params', {}).get("metadata", {}) or {} # if metadata is None
metadata = (
kwargs.get("litellm_params", {}).get("metadata", {}) or {}
) # if metadata is None
# set project name and run_name for langsmith logging
# users can pass project_name and run name to litellm.completion()
@ -51,26 +66,42 @@ class LangsmithLogger:
new_kwargs = {}
for key in kwargs:
value = kwargs[key]
if key == "start_time" or key == "end_time":
if key == "start_time" or key == "end_time" or value is None:
pass
elif type(value) == datetime.datetime:
new_kwargs[key] = value.isoformat()
elif type(value) != dict:
elif type(value) != dict and is_serializable(value=value):
new_kwargs[key] = value
requests.post(
if isinstance(response_obj, BaseModel):
try:
response_obj = response_obj.model_dump()
except:
response_obj = response_obj.dict() # type: ignore
print(f"response_obj: {response_obj}")
data = {
"name": run_name,
"run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain"
"inputs": new_kwargs,
"outputs": response_obj,
"session_name": project_name,
"start_time": start_time,
"end_time": end_time,
}
print(f"data: {data}")
response = requests.post(
"https://api.smith.langchain.com/runs",
json={
"name": run_name,
"run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain"
"inputs": {**new_kwargs},
"outputs": response_obj.json(),
"session_name": project_name,
"start_time": start_time,
"end_time": end_time,
},
json=data,
headers={"x-api-key": self.langsmith_api_key},
)
if response.status_code >= 300:
print_verbose(f"Error: {response.status_code}")
else:
print_verbose("Run successfully created")
print_verbose(
f"Langsmith Layer Logging - final response object: {response_obj}"
)

36
litellm/integrations/prometheus.py
View file

@ -19,27 +19,33 @@ class PrometheusLogger:
**kwargs,
):
try:
verbose_logger.debug(f"in init prometheus metrics")
print(f"in init prometheus metrics")
from prometheus_client import Counter
self.litellm_llm_api_failed_requests_metric = Counter(
name="litellm_llm_api_failed_requests_metric",
documentation="Total number of failed LLM API calls via litellm",
labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
)
self.litellm_requests_metric = Counter(
name="litellm_requests_metric",
documentation="Total number of LLM calls to litellm",
labelnames=["end_user", "hashed_api_key", "model", "team"],
labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
)
# Counter for spend
self.litellm_spend_metric = Counter(
"litellm_spend_metric",
"Total spend on LLM requests",
labelnames=["end_user", "hashed_api_key", "model", "team"],
labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
)
# Counter for total_output_tokens
self.litellm_tokens_metric = Counter(
"litellm_total_tokens",
"Total number of input + output tokens from LLM requests",
labelnames=["end_user", "hashed_api_key", "model", "team"],
labelnames=["end_user", "hashed_api_key", "model", "team", "user"],
)
except Exception as e:
print_verbose(f"Got exception on init prometheus client {str(e)}")
@ -61,15 +67,21 @@ class PrometheusLogger:
# unpack kwargs
model = kwargs.get("model", "")
response_cost = kwargs.get("response_cost", 0.0)
response_cost = kwargs.get("response_cost", 0.0) or 0
litellm_params = kwargs.get("litellm_params", {}) or {}
proxy_server_request = litellm_params.get("proxy_server_request") or {}
end_user_id = proxy_server_request.get("body", {}).get("user", None)
user_id = litellm_params.get("metadata", {}).get(
"user_api_key_user_id", None
)
user_api_key = litellm_params.get("metadata", {}).get("user_api_key", None)
user_api_team = litellm_params.get("metadata", {}).get(
"user_api_key_team_id", None
)
tokens_used = response_obj.get("usage", {}).get("total_tokens", 0)
if response_obj is not None:
tokens_used = response_obj.get("usage", {}).get("total_tokens", 0)
else:
tokens_used = 0
print_verbose(
f"inside track_prometheus_metrics, model {model}, response_cost {response_cost}, tokens_used {tokens_used}, end_user_id {end_user_id}, user_api_key {user_api_key}"
@ -85,14 +97,20 @@ class PrometheusLogger:
user_api_key = hash_token(user_api_key)
self.litellm_requests_metric.labels(
end_user_id, user_api_key, model, user_api_team
end_user_id, user_api_key, model, user_api_team, user_id
).inc()
self.litellm_spend_metric.labels(
end_user_id, user_api_key, model, user_api_team
end_user_id, user_api_key, model, user_api_team, user_id
).inc(response_cost)
self.litellm_tokens_metric.labels(
end_user_id, user_api_key, model, user_api_team
end_user_id, user_api_key, model, user_api_team, user_id
).inc(tokens_used)
### FAILURE INCREMENT ###
if "exception" in kwargs:
self.litellm_llm_api_failed_requests_metric.labels(
end_user_id, user_api_key, model, user_api_team, user_id
).inc()
except Exception as e:
traceback.print_exc()
verbose_logger.debug(

45
litellm/integrations/prometheus_services.py
View file

@ -44,9 +44,18 @@ class PrometheusServicesLogger:
) # store the prometheus histogram/counter we need to call for each field in payload
for service in self.services:
histogram = self.create_histogram(service)
counter = self.create_counter(service)
self.payload_to_prometheus_map[service] = [histogram, counter]
histogram = self.create_histogram(service, type_of_request="latency")
counter_failed_request = self.create_counter(
service, type_of_request="failed_requests"
)
counter_total_requests = self.create_counter(
service, type_of_request="total_requests"
)
self.payload_to_prometheus_map[service] = [
histogram,
counter_failed_request,
counter_total_requests,
]
self.prometheus_to_amount_map: dict = (
{}
@ -74,26 +83,26 @@ class PrometheusServicesLogger:
return metric
return None
def create_histogram(self, label: str):
metric_name = "litellm_{}_latency".format(label)
def create_histogram(self, service: str, type_of_request: str):
metric_name = "litellm_{}_{}".format(service, type_of_request)
is_registered = self.is_metric_registered(metric_name)
if is_registered:
return self.get_metric(metric_name)
return self.Histogram(
metric_name,
"Latency for {} service".format(label),
labelnames=[label],
"Latency for {} service".format(service),
labelnames=[service],
)
def create_counter(self, label: str):
metric_name = "litellm_{}_failed_requests".format(label)
def create_counter(self, service: str, type_of_request: str):
metric_name = "litellm_{}_{}".format(service, type_of_request)
is_registered = self.is_metric_registered(metric_name)
if is_registered:
return self.get_metric(metric_name)
return self.Counter(
metric_name,
"Total failed requests for {} service".format(label),
labelnames=[label],
"Total {} for {} service".format(type_of_request, service),
labelnames=[service],
)
def observe_histogram(
@ -129,6 +138,12 @@ class PrometheusServicesLogger:
labels=payload.service.value,
amount=payload.duration,
)
elif isinstance(obj, self.Counter) and "total_requests" in obj._name:
self.increment_counter(
counter=obj,
labels=payload.service.value,
amount=1, # LOG TOTAL REQUESTS TO PROMETHEUS
)
def service_failure_hook(self, payload: ServiceLoggerPayload):
if self.mock_testing:
@ -141,7 +156,7 @@ class PrometheusServicesLogger:
self.increment_counter(
counter=obj,
labels=payload.service.value,
amount=1, # LOG ERROR COUNT TO PROMETHEUS
amount=1, # LOG ERROR COUNT / TOTAL REQUESTS TO PROMETHEUS
)
async def async_service_success_hook(self, payload: ServiceLoggerPayload):
@ -160,6 +175,12 @@ class PrometheusServicesLogger:
labels=payload.service.value,
amount=payload.duration,
)
elif isinstance(obj, self.Counter) and "total_requests" in obj._name:
self.increment_counter(
counter=obj,
labels=payload.service.value,
amount=1, # LOG TOTAL REQUESTS TO PROMETHEUS
)
async def async_service_failure_hook(self, payload: ServiceLoggerPayload):
print(f"received error payload: {payload.error}")

541
litellm/integrations/slack_alerting.py Normal file
View file

@ -0,0 +1,541 @@
#### What this does ####
# Class for sending Slack Alerts #
import dotenv, os
dotenv.load_dotenv() # Loading env variables using dotenv
import copy
import traceback
from litellm._logging import verbose_logger, verbose_proxy_logger
import litellm
from typing import List, Literal, Any, Union, Optional, Dict
from litellm.caching import DualCache
import asyncio
import aiohttp
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
import datetime
class SlackAlerting:
# Class variables or attributes
def __init__(
self,
alerting_threshold: float = 300,
alerting: Optional[List] = [],
alert_types: Optional[
List[
Literal[
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
]
]
] = [
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
],
alert_to_webhook_url: Optional[
Dict
] = None, # if user wants to separate alerts to diff channels
):
self.alerting_threshold = alerting_threshold
self.alerting = alerting
self.alert_types = alert_types
self.internal_usage_cache = DualCache()
self.async_http_handler = AsyncHTTPHandler()
self.alert_to_webhook_url = alert_to_webhook_url
self.langfuse_logger = None
try:
from litellm.integrations.langfuse import LangFuseLogger
self.langfuse_logger = LangFuseLogger(
os.getenv("LANGFUSE_PUBLIC_KEY"),
os.getenv("LANGFUSE_SECRET_KEY"),
flush_interval=1,
)
except:
pass
pass
def update_values(
self,
alerting: Optional[List] = None,
alerting_threshold: Optional[float] = None,
alert_types: Optional[List] = None,
alert_to_webhook_url: Optional[Dict] = None,
):
if alerting is not None:
self.alerting = alerting
if alerting_threshold is not None:
self.alerting_threshold = alerting_threshold
if alert_types is not None:
self.alert_types = alert_types
if alert_to_webhook_url is not None:
# update the dict
if self.alert_to_webhook_url is None:
self.alert_to_webhook_url = alert_to_webhook_url
else:
self.alert_to_webhook_url.update(alert_to_webhook_url)
async def deployment_in_cooldown(self):
pass
async def deployment_removed_from_cooldown(self):
pass
def _all_possible_alert_types(self):
# used by the UI to show all supported alert types
# Note: This is not the alerts the user has configured, instead it's all possible alert types a user can select
return [
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
]
def _add_langfuse_trace_id_to_alert(
self,
request_info: str,
request_data: Optional[dict] = None,
kwargs: Optional[dict] = None,
type: Literal["hanging_request", "slow_response"] = "hanging_request",
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
):
import uuid
# For now: do nothing as we're debugging why this is not working as expected
if request_data is not None:
trace_id = request_data.get("metadata", {}).get(
"trace_id", None
) # get langfuse trace id
if trace_id is None:
trace_id = "litellm-alert-trace-" + str(uuid.uuid4())
request_data["metadata"]["trace_id"] = trace_id
elif kwargs is not None:
_litellm_params = kwargs.get("litellm_params", {})
trace_id = _litellm_params.get("metadata", {}).get(
"trace_id", None
) # get langfuse trace id
if trace_id is None:
trace_id = "litellm-alert-trace-" + str(uuid.uuid4())
_litellm_params["metadata"]["trace_id"] = trace_id
# Log hanging request as an error on langfuse
if type == "hanging_request":
if self.langfuse_logger is not None:
_logging_kwargs = copy.deepcopy(request_data)
if _logging_kwargs is None:
_logging_kwargs = {}
_logging_kwargs["litellm_params"] = {}
request_data = request_data or {}
_logging_kwargs["litellm_params"]["metadata"] = request_data.get(
"metadata", {}
)
# log to langfuse in a separate thread
import threading
threading.Thread(
target=self.langfuse_logger.log_event,
args=(
_logging_kwargs,
None,
start_time,
end_time,
None,
print,
"ERROR",
"Requests is hanging",
),
).start()
_langfuse_host = os.environ.get("LANGFUSE_HOST", "https://cloud.langfuse.com")
_langfuse_project_id = os.environ.get("LANGFUSE_PROJECT_ID")
# langfuse urls look like: https://us.cloud.langfuse.com/project/************/traces/litellm-alert-trace-ididi9dk-09292-************
_langfuse_url = (
f"{_langfuse_host}/project/{_langfuse_project_id}/traces/{trace_id}"
)
request_info += f"\n🪢 Langfuse Trace: {_langfuse_url}"
return request_info
def _response_taking_too_long_callback(
self,
kwargs, # kwargs to completion
start_time,
end_time, # start/end time
):
try:
time_difference = end_time - start_time
# Convert the timedelta to float (in seconds)
time_difference_float = time_difference.total_seconds()
litellm_params = kwargs.get("litellm_params", {})
model = kwargs.get("model", "")
api_base = litellm.get_api_base(model=model, optional_params=litellm_params)
messages = kwargs.get("messages", None)
# if messages does not exist fallback to "input"
if messages is None:
messages = kwargs.get("input", None)
# only use first 100 chars for alerting
_messages = str(messages)[:100]
return time_difference_float, model, api_base, _messages
except Exception as e:
raise e
def _get_deployment_latencies_to_alert(self, metadata=None):
if metadata is None:
return None
if "_latency_per_deployment" in metadata:
# Translate model_id to -> api_base
# _latency_per_deployment is a dictionary that looks like this:
"""
_latency_per_deployment: {
api_base: 0.01336697916666667
}
"""
_message_to_send = ""
_deployment_latencies = metadata["_latency_per_deployment"]
if len(_deployment_latencies) == 0:
return None
try:
# try sorting deployments by latency
_deployment_latencies = sorted(
_deployment_latencies.items(), key=lambda x: x[1]
)
_deployment_latencies = dict(_deployment_latencies)
except:
pass
for api_base, latency in _deployment_latencies.items():
_message_to_send += f"\n{api_base}: {round(latency,2)}s"
_message_to_send = "```" + _message_to_send + "```"
return _message_to_send
async def response_taking_too_long_callback(
self,
kwargs, # kwargs to completion
completion_response, # response from completion
start_time,
end_time, # start/end time
):
if self.alerting is None or self.alert_types is None:
return
time_difference_float, model, api_base, messages = (
self._response_taking_too_long_callback(
kwargs=kwargs,
start_time=start_time,
end_time=end_time,
)
)
request_info = f"\nRequest Model: `{model}`\nAPI Base: `{api_base}`\nMessages: `{messages}`"
slow_message = f"`Responses are slow - {round(time_difference_float,2)}s response time > Alerting threshold: {self.alerting_threshold}s`"
if time_difference_float > self.alerting_threshold:
if "langfuse" in litellm.success_callback:
request_info = self._add_langfuse_trace_id_to_alert(
request_info=request_info, kwargs=kwargs, type="slow_response"
)
# add deployment latencies to alert
if (
kwargs is not None
and "litellm_params" in kwargs
and "metadata" in kwargs["litellm_params"]
):
_metadata = kwargs["litellm_params"]["metadata"]
_deployment_latency_map = self._get_deployment_latencies_to_alert(
metadata=_metadata
)
if _deployment_latency_map is not None:
request_info += (
f"\nAvailable Deployment Latencies\n{_deployment_latency_map}"
)
await self.send_alert(
message=slow_message + request_info,
level="Low",
alert_type="llm_too_slow",
)
async def log_failure_event(self, original_exception: Exception):
pass
async def response_taking_too_long(
self,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
type: Literal["hanging_request", "slow_response"] = "hanging_request",
request_data: Optional[dict] = None,
):
if self.alerting is None or self.alert_types is None:
return
if request_data is not None:
model = request_data.get("model", "")
messages = request_data.get("messages", None)
if messages is None:
# if messages does not exist fallback to "input"
messages = request_data.get("input", None)
# try casting messages to str and get the first 100 characters, else mark as None
try:
messages = str(messages)
messages = messages[:100]
except:
messages = ""
request_info = f"\nRequest Model: `{model}`\nMessages: `{messages}`"
else:
request_info = ""
if type == "hanging_request":
await asyncio.sleep(
self.alerting_threshold
) # Set it to 5 minutes - i'd imagine this might be different for streaming, non-streaming, non-completion (embedding + img) requests
if (
request_data is not None
and request_data.get("litellm_status", "") != "success"
and request_data.get("litellm_status", "") != "fail"
):
if request_data.get("deployment", None) is not None and isinstance(
request_data["deployment"], dict
):
_api_base = litellm.get_api_base(
model=model,
optional_params=request_data["deployment"].get(
"litellm_params", {}
),
)
if _api_base is None:
_api_base = ""
request_info += f"\nAPI Base: {_api_base}"
elif request_data.get("metadata", None) is not None and isinstance(
request_data["metadata"], dict
):
# In hanging requests sometime it has not made it to the point where the deployment is passed to the `request_data``
# in that case we fallback to the api base set in the request metadata
_metadata = request_data["metadata"]
_api_base = _metadata.get("api_base", "")
if _api_base is None:
_api_base = ""
request_info += f"\nAPI Base: `{_api_base}`"
# only alert hanging responses if they have not been marked as success
alerting_message = (
f"`Requests are hanging - {self.alerting_threshold}s+ request time`"
)
if "langfuse" in litellm.success_callback:
request_info = self._add_langfuse_trace_id_to_alert(
request_info=request_info,
request_data=request_data,
type="hanging_request",
start_time=start_time,
end_time=end_time,
)
# add deployment latencies to alert
_deployment_latency_map = self._get_deployment_latencies_to_alert(
metadata=request_data.get("metadata", {})
)
if _deployment_latency_map is not None:
request_info += f"\nDeployment Latencies\n{_deployment_latency_map}"
await self.send_alert(
message=alerting_message + request_info,
level="Medium",
alert_type="llm_requests_hanging",
)
async def budget_alerts(
self,
type: Literal[
"token_budget",
"user_budget",
"user_and_proxy_budget",
"failed_budgets",
"failed_tracking",
"projected_limit_exceeded",
],
user_max_budget: float,
user_current_spend: float,
user_info=None,
error_message="",
):
if self.alerting is None or self.alert_types is None:
# do nothing if alerting is not switched on
return
if "budget_alerts" not in self.alert_types:
return
_id: str = "default_id" # used for caching
if type == "user_and_proxy_budget":
user_info = dict(user_info)
user_id = user_info["user_id"]
_id = user_id
max_budget = user_info["max_budget"]
spend = user_info["spend"]
user_email = user_info["user_email"]
user_info = f"""\nUser ID: {user_id}\nMax Budget: ${max_budget}\nSpend: ${spend}\nUser Email: {user_email}"""
elif type == "token_budget":
token_info = dict(user_info)
token = token_info["token"]
_id = token
spend = token_info["spend"]
max_budget = token_info["max_budget"]
user_id = token_info["user_id"]
user_info = f"""\nToken: {token}\nSpend: ${spend}\nMax Budget: ${max_budget}\nUser ID: {user_id}"""
elif type == "failed_tracking":
user_id = str(user_info)
_id = user_id
user_info = f"\nUser ID: {user_id}\n Error {error_message}"
message = "Failed Tracking Cost for" + user_info
await self.send_alert(
message=message, level="High", alert_type="budget_alerts"
)
return
elif type == "projected_limit_exceeded" and user_info is not None:
"""
Input variables:
user_info = {
"key_alias": key_alias,
"projected_spend": projected_spend,
"projected_exceeded_date": projected_exceeded_date,
}
user_max_budget=soft_limit,
user_current_spend=new_spend
"""
message = f"""\n🚨 `ProjectedLimitExceededError` 💸\n\n`Key Alias:` {user_info["key_alias"]} \n`Expected Day of Error`: {user_info["projected_exceeded_date"]} \n`Current Spend`: {user_current_spend} \n`Projected Spend at end of month`: {user_info["projected_spend"]} \n`Soft Limit`: {user_max_budget}"""
await self.send_alert(
message=message, level="High", alert_type="budget_alerts"
)
return
else:
user_info = str(user_info)
# percent of max_budget left to spend
if user_max_budget > 0:
percent_left = (user_max_budget - user_current_spend) / user_max_budget
else:
percent_left = 0
verbose_proxy_logger.debug(
f"Budget Alerts: Percent left: {percent_left} for {user_info}"
)
## PREVENTITIVE ALERTING ## - https://github.com/BerriAI/litellm/issues/2727
# - Alert once within 28d period
# - Cache this information
# - Don't re-alert, if alert already sent
_cache: DualCache = self.internal_usage_cache
# check if crossed budget
if user_current_spend >= user_max_budget:
verbose_proxy_logger.debug("Budget Crossed for %s", user_info)
message = "Budget Crossed for" + user_info
result = await _cache.async_get_cache(key=message)
if result is None:
await self.send_alert(
message=message, level="High", alert_type="budget_alerts"
)
await _cache.async_set_cache(key=message, value="SENT", ttl=2419200)
return
# check if 5% of max budget is left
if percent_left <= 0.05:
message = "5% budget left for" + user_info
cache_key = "alerting:{}".format(_id)
result = await _cache.async_get_cache(key=cache_key)
if result is None:
await self.send_alert(
message=message, level="Medium", alert_type="budget_alerts"
)
await _cache.async_set_cache(key=cache_key, value="SENT", ttl=2419200)
return
# check if 15% of max budget is left
if percent_left <= 0.15:
message = "15% budget left for" + user_info
result = await _cache.async_get_cache(key=message)
if result is None:
await self.send_alert(
message=message, level="Low", alert_type="budget_alerts"
)
await _cache.async_set_cache(key=message, value="SENT", ttl=2419200)
return
return
async def send_alert(
self,
message: str,
level: Literal["Low", "Medium", "High"],
alert_type: Literal[
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
],
):
"""
Alerting based on thresholds: - https://github.com/BerriAI/litellm/issues/1298
- Responses taking too long
- Requests are hanging
- Calls are failing
- DB Read/Writes are failing
- Proxy Close to max budget
- Key Close to max budget
Parameters:
level: str - Low|Medium|High - if calls might fail (Medium) or are failing (High); Currently, no alerts would be 'Low'.
message: str - what is the alert about
"""
if self.alerting is None:
return
from datetime import datetime
import json
# Get the current timestamp
current_time = datetime.now().strftime("%H:%M:%S")
_proxy_base_url = os.getenv("PROXY_BASE_URL", None)
formatted_message = (
f"Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}"
)
if _proxy_base_url is not None:
formatted_message += f"\n\nProxy URL: `{_proxy_base_url}`"
# check if we find the slack webhook url in self.alert_to_webhook_url
if (
self.alert_to_webhook_url is not None
and alert_type in self.alert_to_webhook_url
):
slack_webhook_url = self.alert_to_webhook_url[alert_type]
else:
slack_webhook_url = os.getenv("SLACK_WEBHOOK_URL", None)
if slack_webhook_url is None:
raise Exception("Missing SLACK_WEBHOOK_URL from environment")
payload = {"text": formatted_message}
headers = {"Content-type": "application/json"}
response = await self.async_http_handler.post(
url=slack_webhook_url,
headers=headers,
data=json.dumps(payload),
)
if response.status_code == 200:
pass
else:
print("Error sending slack alert. Error=", response.text) # noqa

2
litellm/llms/aleph_alpha.py
View file

@ -298,7 +298,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

3
litellm/llms/anthropic.py
View file

@ -258,8 +258,9 @@ class AnthropicChatCompletion(BaseLLM):
self.async_handler = AsyncHTTPHandler(
timeout=httpx.Timeout(timeout=600.0, connect=5.0)
)
data["stream"] = True
response = await self.async_handler.post(
api_base, headers=headers, data=json.dumps(data)
api_base, headers=headers, data=json.dumps(data), stream=True
)
if response.status_code != 200:

3
litellm/llms/anthropic_text.py
View file

@ -137,7 +137,8 @@ class AnthropicTextCompletion(BaseLLM):
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

9
litellm/llms/azure.py
View file

@ -96,6 +96,15 @@ class AzureOpenAIConfig(OpenAIConfig):
top_p,
)
def get_mapped_special_auth_params(self) -> dict:
return {"token": "azure_ad_token"}
def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
for param, value in non_default_params.items():
if param == "token":
optional_params["azure_ad_token"] = value
return optional_params
def select_azure_base_url_or_endpoint(azure_client_params: dict):
# azure_client_params = {

43
litellm/llms/baseten.py
View file

@ -55,9 +55,11 @@ def completion(
"inputs": prompt,
"prompt": prompt,
"parameters": optional_params,
"stream": True
if "stream" in optional_params and optional_params["stream"] == True
else False,
"stream": (
True
if "stream" in optional_params and optional_params["stream"] == True
else False
),
}
## LOGGING
@ -71,9 +73,11 @@ def completion(
completion_url_fragment_1 + model + completion_url_fragment_2,
headers=headers,
data=json.dumps(data),
stream=True
if "stream" in optional_params and optional_params["stream"] == True
else False,
stream=(
True
if "stream" in optional_params and optional_params["stream"] == True
else False
),
)
if "text/event-stream" in response.headers["Content-Type"] or (
"stream" in optional_params and optional_params["stream"] == True
@ -102,28 +106,28 @@ def completion(
and "data" in completion_response["model_output"]
and isinstance(completion_response["model_output"]["data"], list)
):
model_response["choices"][0]["message"][
"content"
] = completion_response["model_output"]["data"][0]
model_response["choices"][0]["message"]["content"] = (
completion_response["model_output"]["data"][0]
)
elif isinstance(completion_response["model_output"], str):
model_response["choices"][0]["message"][
"content"
] = completion_response["model_output"]
model_response["choices"][0]["message"]["content"] = (
completion_response["model_output"]
)
elif "completion" in completion_response and isinstance(
completion_response["completion"], str
):
model_response["choices"][0]["message"][
"content"
] = completion_response["completion"]
model_response["choices"][0]["message"]["content"] = (
completion_response["completion"]
)
elif isinstance(completion_response, list) and len(completion_response) > 0:
if "generated_text" not in completion_response:
raise BasetenError(
message=f"Unable to parse response. Original response: {response.text}",
status_code=response.status_code,
)
model_response["choices"][0]["message"][
"content"
] = completion_response[0]["generated_text"]
model_response["choices"][0]["message"]["content"] = (
completion_response[0]["generated_text"]
)
## GETTING LOGPROBS
if (
"details" in completion_response[0]
@ -155,7 +159,8 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

32
litellm/llms/bedrock.py
View file

@ -29,6 +29,24 @@ class BedrockError(Exception):
) # Call the base class constructor with the parameters it needs
class AmazonBedrockGlobalConfig:
def __init__(self):
pass
def get_mapped_special_auth_params(self) -> dict:
"""
Mapping of common auth params across bedrock/vertex/azure/watsonx
"""
return {"region_name": "aws_region_name"}
def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
mapped_params = self.get_mapped_special_auth_params()
for param, value in non_default_params.items():
if param in mapped_params:
optional_params[mapped_params[param]] = value
return optional_params
class AmazonTitanConfig:
"""
Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-text-express-v1
@ -653,6 +671,10 @@ def convert_messages_to_prompt(model, messages, provider, custom_prompt_dict):
prompt = prompt_factory(
model=model, messages=messages, custom_llm_provider="bedrock"
)
elif provider == "meta":
prompt = prompt_factory(
model=model, messages=messages, custom_llm_provider="bedrock"
)
else:
prompt = ""
for message in messages:
@ -1028,7 +1050,7 @@ def completion(
total_tokens=response_body["usage"]["input_tokens"]
+ response_body["usage"]["output_tokens"],
)
model_response.usage = _usage
setattr(model_response, "usage", _usage)
else:
outputText = response_body["completion"]
model_response["finish_reason"] = response_body["stop_reason"]
@ -1071,8 +1093,10 @@ def completion(
status_code=response_metadata.get("HTTPStatusCode", 500),
)
## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
if getattr(model_response.usage, "total_tokens", None) is None:
## CALCULATING USAGE - bedrock charges on time, not tokens - have some mapping of cost here.
if not hasattr(model_response, "usage"):
setattr(model_response, "usage", Usage())
if getattr(model_response.usage, "total_tokens", None) is None: # type: ignore
prompt_tokens = response_metadata.get(
"x-amzn-bedrock-input-token-count", len(encoding.encode(prompt))
)
@ -1089,7 +1113,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
model_response["created"] = int(time.time())
model_response["model"] = model

2
litellm/llms/cloudflare.py
View file

@ -167,7 +167,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

2
litellm/llms/cohere.py
View file

@ -237,7 +237,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

5
litellm/llms/cohere_chat.py
View file

@ -43,6 +43,7 @@ class CohereChatConfig:
presence_penalty (float, optional): Used to reduce repetitiveness of generated tokens.
tools (List[Dict[str, str]], optional): A list of available tools (functions) that the model may suggest invoking.
tool_results (List[Dict[str, Any]], optional): A list of results from invoking tools.
seed (int, optional): A seed to assist reproducibility of the model's response.
"""
preamble: Optional[str] = None
@ -62,6 +63,7 @@ class CohereChatConfig:
presence_penalty: Optional[int] = None
tools: Optional[list] = None
tool_results: Optional[list] = None
seed: Optional[int] = None
def __init__(
self,
@ -82,6 +84,7 @@ class CohereChatConfig:
presence_penalty: Optional[int] = None,
tools: Optional[list] = None,
tool_results: Optional[list] = None,
seed: Optional[int] = None,
) -> None:
locals_ = locals()
for key, value in locals_.items():
@ -302,5 +305,5 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

9
litellm/llms/custom_httpx/http_handler.py
View file

@ -41,13 +41,12 @@ class AsyncHTTPHandler:
data: Optional[Union[dict, str]] = None, # type: ignore
params: Optional[dict] = None,
headers: Optional[dict] = None,
stream: bool = False,
):
response = await self.client.post(
url,
data=data, # type: ignore
params=params,
headers=headers,
req = self.client.build_request(
"POST", url, data=data, params=params, headers=headers # type: ignore
)
response = await self.client.send(req, stream=stream)
return response
def __del__(self) -> None:

2
litellm/llms/gemini.py
View file

@ -311,7 +311,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

8
litellm/llms/maritalk.py
View file

@ -152,9 +152,9 @@ def completion(
else:
try:
if len(completion_response["answer"]) > 0:
model_response["choices"][0]["message"][
"content"
] = completion_response["answer"]
model_response["choices"][0]["message"]["content"] = (
completion_response["answer"]
)
except Exception as e:
raise MaritalkError(
message=response.text, status_code=response.status_code
@ -174,7 +174,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

8
litellm/llms/nlp_cloud.py
View file

@ -185,9 +185,9 @@ def completion(
else:
try:
if len(completion_response["generated_text"]) > 0:
model_response["choices"][0]["message"][
"content"
] = completion_response["generated_text"]
model_response["choices"][0]["message"]["content"] = (
completion_response["generated_text"]
)
except:
raise NLPCloudError(
message=json.dumps(completion_response),
@ -205,7 +205,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

4
litellm/llms/ollama.py
View file

@ -228,7 +228,7 @@ def get_ollama_response(
model_response["choices"][0]["message"]["content"] = response_json["response"]
model_response["created"] = int(time.time())
model_response["model"] = "ollama/" + model
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt))) # type: ignore
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt, disallowed_special=()))) # type: ignore
completion_tokens = response_json.get("eval_count", len(response_json.get("message",dict()).get("content", "")))
model_response["usage"] = litellm.Usage(
prompt_tokens=prompt_tokens,
@ -330,7 +330,7 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
]
model_response["created"] = int(time.time())
model_response["model"] = "ollama/" + data["model"]
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"]))) # type: ignore
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"], disallowed_special=()))) # type: ignore
completion_tokens = response_json.get("eval_count", len(response_json.get("message",dict()).get("content", "")))
model_response["usage"] = litellm.Usage(
prompt_tokens=prompt_tokens,

66
litellm/llms/ollama_chat.py
View file

@ -148,7 +148,7 @@ class OllamaChatConfig:
if param == "top_p":
optional_params["top_p"] = value
if param == "frequency_penalty":
optional_params["repeat_penalty"] = param
optional_params["repeat_penalty"] = value
if param == "stop":
optional_params["stop"] = value
if param == "response_format" and value["type"] == "json_object":
@ -184,6 +184,7 @@ class OllamaChatConfig:
# ollama implementation
def get_ollama_response(
api_base="http://localhost:11434",
api_key: Optional[str] = None,
model="llama2",
messages=None,
optional_params=None,
@ -236,6 +237,7 @@ def get_ollama_response(
if stream == True:
response = ollama_async_streaming(
url=url,
api_key=api_key,
data=data,
model_response=model_response,
encoding=encoding,
@ -244,6 +246,7 @@ def get_ollama_response(
else:
response = ollama_acompletion(
url=url,
api_key=api_key,
data=data,
model_response=model_response,
encoding=encoding,
@ -252,12 +255,17 @@ def get_ollama_response(
)
return response
elif stream == True:
return ollama_completion_stream(url=url, data=data, logging_obj=logging_obj)
return ollama_completion_stream(
url=url, api_key=api_key, data=data, logging_obj=logging_obj
)
response = requests.post(
url=f"{url}",
json=data,
)
_request = {
"url": f"{url}",
"json": data,
}
if api_key is not None:
_request["headers"] = "Bearer {}".format(api_key)
response = requests.post(**_request) # type: ignore
if response.status_code != 200:
raise OllamaError(status_code=response.status_code, message=response.text)
@ -307,10 +315,16 @@ def get_ollama_response(
return model_response
def ollama_completion_stream(url, data, logging_obj):
with httpx.stream(
url=url, json=data, method="POST", timeout=litellm.request_timeout
) as response:
def ollama_completion_stream(url, api_key, data, logging_obj):
_request = {
"url": f"{url}",
"json": data,
"method": "POST",
"timeout": litellm.request_timeout,
}
if api_key is not None:
_request["headers"] = "Bearer {}".format(api_key)
with httpx.stream(**_request) as response:
try:
if response.status_code != 200:
raise OllamaError(
@ -329,12 +343,20 @@ def ollama_completion_stream(url, data, logging_obj):
raise e
async def ollama_async_streaming(url, data, model_response, encoding, logging_obj):
async def ollama_async_streaming(
url, api_key, data, model_response, encoding, logging_obj
):
try:
client = httpx.AsyncClient()
async with client.stream(
url=f"{url}", json=data, method="POST", timeout=litellm.request_timeout
) as response:
_request = {
"url": f"{url}",
"json": data,
"method": "POST",
"timeout": litellm.request_timeout,
}
if api_key is not None:
_request["headers"] = "Bearer {}".format(api_key)
async with client.stream(**_request) as response:
if response.status_code != 200:
raise OllamaError(
status_code=response.status_code, message=response.text
@ -353,13 +375,25 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
async def ollama_acompletion(
url, data, model_response, encoding, logging_obj, function_name
url,
api_key: Optional[str],
data,
model_response,
encoding,
logging_obj,
function_name,
):
data["stream"] = False
try:
timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) # 10 minutes
async with aiohttp.ClientSession(timeout=timeout) as session:
resp = await session.post(url, json=data)
_request = {
"url": f"{url}",
"json": data,
}
if api_key is not None:
_request["headers"] = "Bearer {}".format(api_key)
resp = await session.post(**_request)
if resp.status != 200:
text = await resp.text()

8
litellm/llms/oobabooga.py
View file

@ -99,9 +99,9 @@ def completion(
)
else:
try:
model_response["choices"][0]["message"][
"content"
] = completion_response["choices"][0]["message"]["content"]
model_response["choices"][0]["message"]["content"] = (
completion_response["choices"][0]["message"]["content"]
)
except:
raise OobaboogaError(
message=json.dumps(completion_response),
@ -115,7 +115,7 @@ def completion(
completion_tokens=completion_response["usage"]["completion_tokens"],
total_tokens=completion_response["usage"]["total_tokens"],
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

3
litellm/llms/openai.py
View file

@ -223,7 +223,7 @@ class OpenAITextCompletionConfig:
model_response_object.choices = choice_list
if "usage" in response_object:
model_response_object.usage = response_object["usage"]
setattr(model_response_object, "usage", response_object["usage"])
if "id" in response_object:
model_response_object.id = response_object["id"]
@ -447,6 +447,7 @@ class OpenAIChatCompletion(BaseLLM):
)
else:
openai_aclient = client
## LOGGING
logging_obj.pre_call(
input=data["messages"],

2
litellm/llms/palm.py
View file

@ -191,7 +191,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

8
litellm/llms/petals.py
View file

@ -41,9 +41,9 @@ class PetalsConfig:
"""
max_length: Optional[int] = None
max_new_tokens: Optional[
int
] = litellm.max_tokens # petals requires max tokens to be set
max_new_tokens: Optional[int] = (
litellm.max_tokens
) # petals requires max tokens to be set
do_sample: Optional[bool] = None
temperature: Optional[float] = None
top_k: Optional[int] = None
@ -203,7 +203,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

209
litellm/llms/prompt_templates/factory.py
View file

@ -3,8 +3,14 @@ import requests, traceback
import json, re, xml.etree.ElementTree as ET
from jinja2 import Template, exceptions, meta, BaseLoader
from jinja2.sandbox import ImmutableSandboxedEnvironment
from typing import Optional, Any
from typing import List
from typing import (
Any,
List,
Mapping,
MutableMapping,
Optional,
Sequence,
)
import litellm
@ -145,6 +151,12 @@ def mistral_api_pt(messages):
elif isinstance(m["content"], str):
texts = m["content"]
new_m = {"role": m["role"], "content": texts}
if new_m["role"] == "tool" and m.get("name"):
new_m["name"] = m["name"]
if m.get("tool_calls"):
new_m["tool_calls"] = m["tool_calls"]
new_messages.append(new_m)
return new_messages
@ -218,6 +230,26 @@ def phind_codellama_pt(messages):
return prompt
known_tokenizer_config = {
"mistralai/Mistral-7B-Instruct-v0.1": {
"tokenizer": {
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
"bos_token": "<s>",
"eos_token": "</s>",
},
"status": "success",
},
"meta-llama/Meta-Llama-3-8B-Instruct": {
"tokenizer": {
"chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
"bos_token": "<|begin_of_text|>",
"eos_token": "",
},
"status": "success",
},
}
def hf_chat_template(model: str, messages: list, chat_template: Optional[Any] = None):
# Define Jinja2 environment
env = ImmutableSandboxedEnvironment()
@ -246,20 +278,23 @@ def hf_chat_template(model: str, messages: list, chat_template: Optional[Any] =
else:
return {"status": "failure"}
tokenizer_config = _get_tokenizer_config(model)
if model in known_tokenizer_config:
tokenizer_config = known_tokenizer_config[model]
else:
tokenizer_config = _get_tokenizer_config(model)
if (
tokenizer_config["status"] == "failure"
or "chat_template" not in tokenizer_config["tokenizer"]
):
raise Exception("No chat template found")
## read the bos token, eos token and chat template from the json
tokenizer_config = tokenizer_config["tokenizer"]
bos_token = tokenizer_config["bos_token"]
eos_token = tokenizer_config["eos_token"]
chat_template = tokenizer_config["chat_template"]
tokenizer_config = tokenizer_config["tokenizer"] # type: ignore
bos_token = tokenizer_config["bos_token"] # type: ignore
eos_token = tokenizer_config["eos_token"] # type: ignore
chat_template = tokenizer_config["chat_template"] # type: ignore
try:
template = env.from_string(chat_template)
template = env.from_string(chat_template) # type: ignore
except Exception as e:
raise e
@ -402,6 +437,35 @@ def format_prompt_togetherai(messages, prompt_format, chat_template):
return prompt
### IBM Granite
def ibm_granite_pt(messages: list):
"""
IBM's Granite models uses the template:
<|system|> {system_message} <|user|> {user_message} <|assistant|> {assistant_message}
See: https://www.ibm.com/docs/en/watsonx-as-a-service?topic=solutions-supported-foundation-models
"""
return custom_prompt(
messages=messages,
role_dict={
"system": {
"pre_message": "<|system|>\n",
"post_message": "\n",
},
"user": {
"pre_message": "<|user|>\n",
"post_message": "\n",
},
"assistant": {
"pre_message": "<|assistant|>\n",
"post_message": "\n",
},
},
).strip()
### ANTHROPIC ###
@ -466,10 +530,11 @@ def construct_tool_use_system_prompt(
): # from https://github.com/anthropics/anthropic-cookbook/blob/main/function_calling/function_calling.ipynb
tool_str_list = []
for tool in tools:
tool_function = get_attribute_or_key(tool, "function")
tool_str = construct_format_tool_for_claude_prompt(
tool["function"]["name"],
tool["function"].get("description", ""),
tool["function"].get("parameters", {}),
get_attribute_or_key(tool_function, "name"),
get_attribute_or_key(tool_function, "description", ""),
get_attribute_or_key(tool_function, "parameters", {}),
)
tool_str_list.append(tool_str)
tool_use_system_prompt = (
@ -593,7 +658,8 @@ def convert_to_anthropic_tool_result_xml(message: dict) -> str:
</function_results>
"""
name = message.get("name")
content = message.get("content")
content = message.get("content", "")
content = content.replace("<", "&lt;").replace(">", "&gt;").replace("&", "&amp;")
# We can't determine from openai message format whether it's a successful or
# error call result so default to the successful result template
@ -614,13 +680,15 @@ def convert_to_anthropic_tool_result_xml(message: dict) -> str:
def convert_to_anthropic_tool_invoke_xml(tool_calls: list) -> str:
invokes = ""
for tool in tool_calls:
if tool["type"] != "function":
if get_attribute_or_key(tool, "type") != "function":
continue
tool_name = tool["function"]["name"]
tool_function = get_attribute_or_key(tool, "function")
tool_name = get_attribute_or_key(tool_function, "name")
tool_arguments = get_attribute_or_key(tool_function, "arguments")
parameters = "".join(
f"<{param}>{val}</{param}>\n"
for param, val in json.loads(tool["function"]["arguments"]).items()
for param, val in json.loads(tool_arguments).items()
)
invokes += (
"<invoke>\n"
@ -674,7 +742,7 @@ def anthropic_messages_pt_xml(messages: list):
{
"type": "text",
"text": (
convert_to_anthropic_tool_result(messages[msg_i])
convert_to_anthropic_tool_result_xml(messages[msg_i])
if messages[msg_i]["role"] == "tool"
else messages[msg_i]["content"]
),
@ -695,7 +763,7 @@ def anthropic_messages_pt_xml(messages: list):
if messages[msg_i].get(
"tool_calls", []
): # support assistant tool invoke convertion
assistant_text += convert_to_anthropic_tool_invoke( # type: ignore
assistant_text += convert_to_anthropic_tool_invoke_xml( # type: ignore
messages[msg_i]["tool_calls"]
)
@ -807,12 +875,18 @@ def convert_to_anthropic_tool_invoke(tool_calls: list) -> list:
anthropic_tool_invoke = [
{
"type": "tool_use",
"id": tool["id"],
"name": tool["function"]["name"],
"input": json.loads(tool["function"]["arguments"]),
"id": get_attribute_or_key(tool, "id"),
"name": get_attribute_or_key(
get_attribute_or_key(tool, "function"), "name"
),
"input": json.loads(
get_attribute_or_key(
get_attribute_or_key(tool, "function"), "arguments"
)
),
}
for tool in tool_calls
if tool["type"] == "function"
if get_attribute_or_key(tool, "type") == "function"
]
return anthropic_tool_invoke
@ -978,6 +1052,30 @@ def get_system_prompt(messages):
return system_prompt, messages
def convert_to_documents(
observations: Any,
) -> List[MutableMapping]:
"""Converts observations into a 'document' dict"""
documents: List[MutableMapping] = []
if isinstance(observations, str):
# strings are turned into a key/value pair and a key of 'output' is added.
observations = [{"output": observations}]
elif isinstance(observations, Mapping):
# single mappings are transformed into a list to simplify the rest of the code.
observations = [observations]
elif not isinstance(observations, Sequence):
# all other types are turned into a key/value pair within a list
observations = [{"output": observations}]
for doc in observations:
if not isinstance(doc, Mapping):
# types that aren't Mapping are turned into a key/value pair.
doc = {"output": doc}
documents.append(doc)
return documents
def convert_openai_message_to_cohere_tool_result(message):
"""
OpenAI message with a tool result looks like:
@ -1019,7 +1117,7 @@ def convert_openai_message_to_cohere_tool_result(message):
"parameters": {"location": "San Francisco, CA"},
"generation_id": tool_call_id,
},
"outputs": [content],
"outputs": convert_to_documents(content),
}
return cohere_tool_result
@ -1032,8 +1130,9 @@ def cohere_message_pt(messages: list):
if message["role"] == "tool":
tool_result = convert_openai_message_to_cohere_tool_result(message)
tool_results.append(tool_result)
else:
prompt += message["content"]
elif message.get("content"):
prompt += message["content"] + "\n\n"
prompt = prompt.rstrip()
return prompt, tool_results
@ -1107,12 +1206,6 @@ def _gemini_vision_convert_messages(messages: list):
Returns:
tuple: A tuple containing the prompt (a string) and the processed images (a list of objects representing the images).
"""
try:
from PIL import Image
except:
raise Exception(
"gemini image conversion failed please run `pip install Pillow`"
)
try:
# given messages for gpt-4 vision, convert them for gemini
@ -1139,6 +1232,12 @@ def _gemini_vision_convert_messages(messages: list):
image = _load_image_from_url(img)
processed_images.append(image)
else:
try:
from PIL import Image
except:
raise Exception(
"gemini image conversion failed please run `pip install Pillow`"
)
# Case 2: Image filepath (e.g. temp.jpeg) given
image = Image.open(img)
processed_images.append(image)
@ -1306,18 +1405,62 @@ def prompt_factory(
return anthropic_pt(messages=messages)
elif "mistral." in model:
return mistral_instruct_pt(messages=messages)
elif "llama2" in model and "chat" in model:
return llama_2_chat_pt(messages=messages)
elif "llama3" in model and "instruct" in model:
return hf_chat_template(
model="meta-llama/Meta-Llama-3-8B-Instruct",
messages=messages,
)
elif custom_llm_provider == "clarifai":
if "claude" in model:
return anthropic_pt(messages=messages)
elif custom_llm_provider == "perplexity":
for message in messages:
message.pop("name", None)
return messages
elif custom_llm_provider == "azure_text":
return azure_text_pt(messages=messages)
elif custom_llm_provider == "watsonx":
if "granite" in model and "chat" in model:
# granite-13b-chat-v1 and granite-13b-chat-v2 use a specific prompt template
return ibm_granite_pt(messages=messages)
elif "ibm-mistral" in model and "instruct" in model:
# models like ibm-mistral/mixtral-8x7b-instruct-v01-q use the mistral instruct prompt template
return mistral_instruct_pt(messages=messages)
elif "meta-llama/llama-3" in model and "instruct" in model:
# https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/
return custom_prompt(
role_dict={
"system": {
"pre_message": "<|start_header_id|>system<|end_header_id|>\n",
"post_message": "<|eot_id|>",
},
"user": {
"pre_message": "<|start_header_id|>user<|end_header_id|>\n",
"post_message": "<|eot_id|>",
},
"assistant": {
"pre_message": "<|start_header_id|>assistant<|end_header_id|>\n",
"post_message": "<|eot_id|>",
},
},
messages=messages,
initial_prompt_value="<|begin_of_text|>",
final_prompt_value="<|start_header_id|>assistant<|end_header_id|>\n",
)
try:
if "meta-llama/llama-2" in model and "chat" in model:
return llama_2_chat_pt(messages=messages)
elif (
"meta-llama/llama-3" in model or "meta-llama-3" in model
) and "instruct" in model:
return hf_chat_template(
model="meta-llama/Meta-Llama-3-8B-Instruct",
messages=messages,
)
elif (
"tiiuae/falcon" in model
): # Note: for the instruct models, it's best to use a User: .., Assistant:.. approach in your prompt template.
@ -1358,3 +1501,9 @@ def prompt_factory(
return default_pt(
messages=messages
) # default that covers Bloom, T-5, any non-chat tuned model (e.g. base Llama2)
def get_attribute_or_key(tool_or_function, attribute, default=None):
if hasattr(tool_or_function, attribute):
return getattr(tool_or_function, attribute)
return tool_or_function.get(attribute, default)

14
litellm/llms/replicate.py
View file

@ -112,10 +112,16 @@ def start_prediction(
}
initial_prediction_data = {
"version": version_id,
"input": input_data,
}
if ":" in version_id and len(version_id) > 64:
model_parts = version_id.split(":")
if (
len(model_parts) > 1 and len(model_parts[1]) == 64
): ## checks if model name has a 64 digit code - e.g. "meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3"
initial_prediction_data["version"] = model_parts[1]
## LOGGING
logging_obj.pre_call(
input=input_data["prompt"],
@ -307,9 +313,7 @@ def completion(
result, logs = handle_prediction_response(
prediction_url, api_key, print_verbose
)
model_response["ended"] = (
time.time()
) # for pricing this must remain right after calling api
## LOGGING
logging_obj.post_call(
input=prompt,
@ -345,7 +349,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

4
litellm/llms/sagemaker.py
View file

@ -399,7 +399,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response
@ -617,7 +617,7 @@ async def async_completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

3
litellm/llms/together_ai.py
View file

@ -2,6 +2,7 @@
Deprecated. We now do together ai calls via the openai client.
Reference: https://docs.together.ai/docs/openai-api-compatibility
"""
import os, types
import json
from enum import Enum
@ -225,7 +226,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

216
litellm/llms/vertex_ai.py
View file

@ -22,6 +22,35 @@ class VertexAIError(Exception):
) # Call the base class constructor with the parameters it needs
class ExtendedGenerationConfig(dict):
"""Extended parameters for the generation."""
def __init__(
self,
*,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
candidate_count: Optional[int] = None,
max_output_tokens: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
response_mime_type: Optional[str] = None,
frequency_penalty: Optional[float] = None,
presence_penalty: Optional[float] = None,
):
super().__init__(
temperature=temperature,
top_p=top_p,
top_k=top_k,
candidate_count=candidate_count,
max_output_tokens=max_output_tokens,
stop_sequences=stop_sequences,
response_mime_type=response_mime_type,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
)
class VertexAIConfig:
"""
Reference: https://cloud.google.com/vertex-ai/docs/generative-ai/chat/test-chat-prompts
@ -43,6 +72,10 @@ class VertexAIConfig:
- `stop_sequences` (List[str]): The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
- `frequency_penalty` (float): This parameter is used to penalize the model from repeating the same output. The default value is 0.0.
- `presence_penalty` (float): This parameter is used to penalize the model from generating the same output as the input. The default value is 0.0.
Note: Please make sure to modify the default parameters as required for your use case.
"""
@ -53,6 +86,8 @@ class VertexAIConfig:
response_mime_type: Optional[str] = None
candidate_count: Optional[int] = None
stop_sequences: Optional[list] = None
frequency_penalty: Optional[float] = None
presence_penalty: Optional[float] = None
def __init__(
self,
@ -63,6 +98,8 @@ class VertexAIConfig:
response_mime_type: Optional[str] = None,
candidate_count: Optional[int] = None,
stop_sequences: Optional[list] = None,
frequency_penalty: Optional[float] = None,
presence_penalty: Optional[float] = None,
) -> None:
locals_ = locals()
for key, value in locals_.items():
@ -106,7 +143,9 @@ class VertexAIConfig:
optional_params["temperature"] = value
if param == "top_p":
optional_params["top_p"] = value
if param == "stream":
if (
param == "stream" and value == True
): # sending stream = False, can cause it to get passed unchecked and raise issues
optional_params["stream"] = value
if param == "n":
optional_params["candidate_count"] = value
@ -119,6 +158,10 @@ class VertexAIConfig:
optional_params["max_output_tokens"] = value
if param == "response_format" and value["type"] == "json_object":
optional_params["response_mime_type"] = "application/json"
if param == "frequency_penalty":
optional_params["frequency_penalty"] = value
if param == "presence_penalty":
optional_params["presence_penalty"] = value
if param == "tools" and isinstance(value, list):
from vertexai.preview import generative_models
@ -141,6 +184,20 @@ class VertexAIConfig:
pass
return optional_params
def get_mapped_special_auth_params(self) -> dict:
"""
Common auth params across bedrock/vertex_ai/azure/watsonx
"""
return {"project": "vertex_project", "region_name": "vertex_location"}
def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
mapped_params = self.get_mapped_special_auth_params()
for param, value in non_default_params.items():
if param in mapped_params:
optional_params[mapped_params[param]] = value
return optional_params
import asyncio
@ -184,8 +241,7 @@ def _get_image_bytes_from_url(image_url: str) -> bytes:
image_bytes = response.content
return image_bytes
except requests.exceptions.RequestException as e:
# Handle any request exceptions (e.g., connection error, timeout)
return b"" # Return an empty bytes object or handle the error as needed
raise Exception(f"An exception occurs with this image - {str(e)}")
def _load_image_from_url(image_url: str):
@ -206,7 +262,8 @@ def _load_image_from_url(image_url: str):
)
image_bytes = _get_image_bytes_from_url(image_url)
return Image.from_bytes(image_bytes)
return Image.from_bytes(data=image_bytes)
def _gemini_vision_convert_messages(messages: list):
@ -363,42 +420,6 @@ def completion(
from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types # type: ignore
import google.auth # type: ignore
class ExtendedGenerationConfig(GenerationConfig):
"""Extended parameters for the generation."""
def __init__(
self,
*,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
candidate_count: Optional[int] = None,
max_output_tokens: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
response_mime_type: Optional[str] = None,
):
args_spec = inspect.getfullargspec(gapic_content_types.GenerationConfig)
if "response_mime_type" in args_spec.args:
self._raw_generation_config = gapic_content_types.GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
candidate_count=candidate_count,
max_output_tokens=max_output_tokens,
stop_sequences=stop_sequences,
response_mime_type=response_mime_type,
)
else:
self._raw_generation_config = gapic_content_types.GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
candidate_count=candidate_count,
max_output_tokens=max_output_tokens,
stop_sequences=stop_sequences,
)
## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744
print_verbose(
f"VERTEX AI: vertex_project={vertex_project}; vertex_location={vertex_location}"
@ -522,6 +543,7 @@ def completion(
"instances": instances,
"vertex_location": vertex_location,
"vertex_project": vertex_project,
"safety_settings": safety_settings,
**optional_params,
}
if optional_params.get("stream", False) is True:
@ -536,8 +558,9 @@ def completion(
tools = optional_params.pop("tools", None)
prompt, images = _gemini_vision_convert_messages(messages=messages)
content = [prompt] + images
if "stream" in optional_params and optional_params["stream"] == True:
stream = optional_params.pop("stream")
stream = optional_params.pop("stream", False)
if stream == True:
request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), safety_settings={safety_settings}, stream={stream})\n"
logging_obj.pre_call(
input=prompt,
@ -550,12 +573,12 @@ def completion(
model_response = llm_model.generate_content(
contents=content,
generation_config=ExtendedGenerationConfig(**optional_params),
generation_config=optional_params,
safety_settings=safety_settings,
stream=True,
tools=tools,
)
optional_params["stream"] = True
return model_response
request_str += f"response = llm_model.generate_content({content})\n"
@ -572,7 +595,7 @@ def completion(
## LLM Call
response = llm_model.generate_content(
contents=content,
generation_config=ExtendedGenerationConfig(**optional_params),
generation_config=optional_params,
safety_settings=safety_settings,
tools=tools,
)
@ -627,7 +650,7 @@ def completion(
},
)
model_response = chat.send_message_streaming(prompt, **optional_params)
optional_params["stream"] = True
return model_response
request_str += f"chat.send_message({prompt}, **{optional_params}).text\n"
@ -659,7 +682,7 @@ def completion(
},
)
model_response = llm_model.predict_streaming(prompt, **optional_params)
optional_params["stream"] = True
return model_response
request_str += f"llm_model.predict({prompt}, **{optional_params}).text\n"
@ -784,7 +807,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response
except Exception as e:
raise VertexAIError(status_code=500, message=str(e))
@ -805,55 +828,18 @@ async def async_completion(
instances=None,
vertex_project=None,
vertex_location=None,
safety_settings=None,
**optional_params,
):
"""
Add support for acompletion calls for gemini-pro
"""
try:
from vertexai.preview.generative_models import GenerationConfig
from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types # type: ignore
class ExtendedGenerationConfig(GenerationConfig):
"""Extended parameters for the generation."""
def __init__(
self,
*,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
candidate_count: Optional[int] = None,
max_output_tokens: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
response_mime_type: Optional[str] = None,
):
args_spec = inspect.getfullargspec(gapic_content_types.GenerationConfig)
if "response_mime_type" in args_spec.args:
self._raw_generation_config = gapic_content_types.GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
candidate_count=candidate_count,
max_output_tokens=max_output_tokens,
stop_sequences=stop_sequences,
response_mime_type=response_mime_type,
)
else:
self._raw_generation_config = gapic_content_types.GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
candidate_count=candidate_count,
max_output_tokens=max_output_tokens,
stop_sequences=stop_sequences,
)
if mode == "vision":
print_verbose("\nMaking VertexAI Gemini Pro Vision Call")
print_verbose("\nMaking VertexAI Gemini Pro/Vision Call")
print_verbose(f"\nProcessing input messages = {messages}")
tools = optional_params.pop("tools", None)
stream = optional_params.pop("stream", False)
prompt, images = _gemini_vision_convert_messages(messages=messages)
content = [prompt] + images
@ -870,9 +856,11 @@ async def async_completion(
)
## LLM Call
# print(f"final content: {content}")
response = await llm_model._generate_content_async(
contents=content,
generation_config=ExtendedGenerationConfig(**optional_params),
generation_config=optional_params,
safety_settings=safety_settings,
tools=tools,
)
@ -1030,7 +1018,7 @@ async def async_completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response
except Exception as e:
raise VertexAIError(status_code=500, message=str(e))
@ -1051,50 +1039,12 @@ async def async_streaming(
instances=None,
vertex_project=None,
vertex_location=None,
safety_settings=None,
**optional_params,
):
"""
Add support for async streaming calls for gemini-pro
"""
from vertexai.preview.generative_models import GenerationConfig
from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types # type: ignore
class ExtendedGenerationConfig(GenerationConfig):
"""Extended parameters for the generation."""
def __init__(
self,
*,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
candidate_count: Optional[int] = None,
max_output_tokens: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
response_mime_type: Optional[str] = None,
):
args_spec = inspect.getfullargspec(gapic_content_types.GenerationConfig)
if "response_mime_type" in args_spec.args:
self._raw_generation_config = gapic_content_types.GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
candidate_count=candidate_count,
max_output_tokens=max_output_tokens,
stop_sequences=stop_sequences,
response_mime_type=response_mime_type,
)
else:
self._raw_generation_config = gapic_content_types.GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
candidate_count=candidate_count,
max_output_tokens=max_output_tokens,
stop_sequences=stop_sequences,
)
if mode == "vision":
stream = optional_params.pop("stream")
tools = optional_params.pop("tools", None)
@ -1115,11 +1065,11 @@ async def async_streaming(
response = await llm_model._generate_content_streaming_async(
contents=content,
generation_config=ExtendedGenerationConfig(**optional_params),
generation_config=optional_params,
safety_settings=safety_settings,
tools=tools,
)
optional_params["stream"] = True
optional_params["tools"] = tools
elif mode == "chat":
chat = llm_model.start_chat()
optional_params.pop(
@ -1138,7 +1088,7 @@ async def async_streaming(
},
)
response = chat.send_message_streaming_async(prompt, **optional_params)
optional_params["stream"] = True
elif mode == "text":
optional_params.pop(
"stream", None

4
litellm/llms/vertex_ai_anthropic.py
View file

@ -349,7 +349,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response
except Exception as e:
raise VertexAIError(status_code=500, message=str(e))
@ -422,7 +422,7 @@ async def async_completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response

4
litellm/llms/vllm.py
View file

@ -104,7 +104,7 @@ def completion(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
return model_response
@ -186,7 +186,7 @@ def batch_completions(
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
setattr(model_response, "usage", usage)
final_outputs.append(model_response)
return final_outputs

609
litellm/llms/watsonx.py Normal file
View file

@ -0,0 +1,609 @@
from enum import Enum
import json, types, time # noqa: E401
from contextlib import contextmanager
from typing import Callable, Dict, Optional, Any, Union, List
import httpx
import requests
import litellm
from litellm.utils import ModelResponse, get_secret, Usage
from .base import BaseLLM
from .prompt_templates import factory as ptf
class WatsonXAIError(Exception):
def __init__(self, status_code, message, url: Optional[str] = None):
self.status_code = status_code
self.message = message
url = url or "https://https://us-south.ml.cloud.ibm.com"
self.request = httpx.Request(method="POST", url=url)
self.response = httpx.Response(status_code=status_code, request=self.request)
super().__init__(
self.message
) # Call the base class constructor with the parameters it needs
class IBMWatsonXAIConfig:
"""
Reference: https://cloud.ibm.com/apidocs/watsonx-ai#text-generation
(See ibm_watsonx_ai.metanames.GenTextParamsMetaNames for a list of all available params)
Supported params for all available watsonx.ai foundational models.
- `decoding_method` (str): One of "greedy" or "sample"
- `temperature` (float): Sets the model temperature for sampling - not available when decoding_method='greedy'.
- `max_new_tokens` (integer): Maximum length of the generated tokens.
- `min_new_tokens` (integer): Maximum length of input tokens. Any more than this will be truncated.
- `length_penalty` (dict): A dictionary with keys "decay_factor" and "start_index".
- `stop_sequences` (string[]): list of strings to use as stop sequences.
- `top_k` (integer): top k for sampling - not available when decoding_method='greedy'.
- `top_p` (integer): top p for sampling - not available when decoding_method='greedy'.
- `repetition_penalty` (float): token repetition penalty during text generation.
- `truncate_input_tokens` (integer): Truncate input tokens to this length.
- `include_stop_sequences` (bool): If True, the stop sequence will be included at the end of the generated text in the case of a match.
- `return_options` (dict): A dictionary of options to return. Options include "input_text", "generated_tokens", "input_tokens", "token_ranks". Values are boolean.
- `random_seed` (integer): Random seed for text generation.
- `moderations` (dict): Dictionary of properties that control the moderations, for usages such as Hate and profanity (HAP) and PII filtering.
- `stream` (bool): If True, the model will return a stream of responses.
"""
decoding_method: Optional[str] = "sample"
temperature: Optional[float] = None
max_new_tokens: Optional[int] = None # litellm.max_tokens
min_new_tokens: Optional[int] = None
length_penalty: Optional[dict] = None # e.g {"decay_factor": 2.5, "start_index": 5}
stop_sequences: Optional[List[str]] = None # e.g ["}", ")", "."]
top_k: Optional[int] = None
top_p: Optional[float] = None
repetition_penalty: Optional[float] = None
truncate_input_tokens: Optional[int] = None
include_stop_sequences: Optional[bool] = False
return_options: Optional[Dict[str, bool]] = None
random_seed: Optional[int] = None # e.g 42
moderations: Optional[dict] = None
stream: Optional[bool] = False
def __init__(
self,
decoding_method: Optional[str] = None,
temperature: Optional[float] = None,
max_new_tokens: Optional[int] = None,
min_new_tokens: Optional[int] = None,
length_penalty: Optional[dict] = None,
stop_sequences: Optional[List[str]] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
repetition_penalty: Optional[float] = None,
truncate_input_tokens: Optional[int] = None,
include_stop_sequences: Optional[bool] = None,
return_options: Optional[dict] = None,
random_seed: Optional[int] = None,
moderations: Optional[dict] = None,
stream: Optional[bool] = None,
**kwargs,
) -> None:
locals_ = locals()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return {
k: v
for k, v in cls.__dict__.items()
if not k.startswith("__")
and not isinstance(
v,
(
types.FunctionType,
types.BuiltinFunctionType,
classmethod,
staticmethod,
),
)
and v is not None
}
def get_supported_openai_params(self):
return [
"temperature", # equivalent to temperature
"max_tokens", # equivalent to max_new_tokens
"top_p", # equivalent to top_p
"frequency_penalty", # equivalent to repetition_penalty
"stop", # equivalent to stop_sequences
"seed", # equivalent to random_seed
"stream", # equivalent to stream
]
def get_mapped_special_auth_params(self) -> dict:
"""
Common auth params across bedrock/vertex_ai/azure/watsonx
"""
return {
"project": "watsonx_project",
"region_name": "watsonx_region_name",
"token": "watsonx_token",
}
def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
mapped_params = self.get_mapped_special_auth_params()
for param, value in non_default_params.items():
if param in mapped_params:
optional_params[mapped_params[param]] = value
return optional_params
def convert_messages_to_prompt(model, messages, provider, custom_prompt_dict):
# handle anthropic prompts and amazon titan prompts
if model in custom_prompt_dict:
# check if the model has a registered custom prompt
model_prompt_dict = custom_prompt_dict[model]
prompt = ptf.custom_prompt(
messages=messages,
role_dict=model_prompt_dict.get(
"role_dict", model_prompt_dict.get("roles")
),
initial_prompt_value=model_prompt_dict.get("initial_prompt_value", ""),
final_prompt_value=model_prompt_dict.get("final_prompt_value", ""),
bos_token=model_prompt_dict.get("bos_token", ""),
eos_token=model_prompt_dict.get("eos_token", ""),
)
return prompt
elif provider == "ibm":
prompt = ptf.prompt_factory(
model=model, messages=messages, custom_llm_provider="watsonx"
)
elif provider == "ibm-mistralai":
prompt = ptf.mistral_instruct_pt(messages=messages)
else:
prompt = ptf.prompt_factory(
model=model, messages=messages, custom_llm_provider="watsonx"
)
return prompt
class WatsonXAIEndpoint(str, Enum):
TEXT_GENERATION = "/ml/v1/text/generation"
TEXT_GENERATION_STREAM = "/ml/v1/text/generation_stream"
DEPLOYMENT_TEXT_GENERATION = "/ml/v1/deployments/{deployment_id}/text/generation"
DEPLOYMENT_TEXT_GENERATION_STREAM = (
"/ml/v1/deployments/{deployment_id}/text/generation_stream"
)
EMBEDDINGS = "/ml/v1/text/embeddings"
PROMPTS = "/ml/v1/prompts"
class IBMWatsonXAI(BaseLLM):
"""
Class to interface with IBM Watsonx.ai API for text generation and embeddings.
Reference: https://cloud.ibm.com/apidocs/watsonx-ai
"""
api_version = "2024-03-13"
def __init__(self) -> None:
super().__init__()
def _prepare_text_generation_req(
self,
model_id: str,
prompt: str,
stream: bool,
optional_params: dict,
print_verbose: Optional[Callable] = None,
) -> dict:
"""
Get the request parameters for text generation.
"""
api_params = self._get_api_params(optional_params, print_verbose=print_verbose)
# build auth headers
api_token = api_params.get("token")
headers = {
"Authorization": f"Bearer {api_token}",
"Content-Type": "application/json",
"Accept": "application/json",
}
extra_body_params = optional_params.pop("extra_body", {})
optional_params.update(extra_body_params)
# init the payload to the text generation call
payload = {
"input": prompt,
"moderations": optional_params.pop("moderations", {}),
"parameters": optional_params,
}
request_params = dict(version=api_params["api_version"])
# text generation endpoint deployment or model / stream or not
if model_id.startswith("deployment/"):
# deployment models are passed in as 'deployment/<deployment_id>'
if api_params.get("space_id") is None:
raise WatsonXAIError(
status_code=401,
url=api_params["url"],
message="Error: space_id is required for models called using the 'deployment/' endpoint. Pass in the space_id as a parameter or set it in the WX_SPACE_ID environment variable.",
)
deployment_id = "/".join(model_id.split("/")[1:])
endpoint = (
WatsonXAIEndpoint.DEPLOYMENT_TEXT_GENERATION_STREAM.value
if stream
else WatsonXAIEndpoint.DEPLOYMENT_TEXT_GENERATION.value
)
endpoint = endpoint.format(deployment_id=deployment_id)
else:
payload["model_id"] = model_id
payload["project_id"] = api_params["project_id"]
endpoint = (
WatsonXAIEndpoint.TEXT_GENERATION_STREAM
if stream
else WatsonXAIEndpoint.TEXT_GENERATION
)
url = api_params["url"].rstrip("/") + endpoint
return dict(
method="POST", url=url, headers=headers, json=payload, params=request_params
)
def _get_api_params(
self, params: dict, print_verbose: Optional[Callable] = None
) -> dict:
"""
Find watsonx.ai credentials in the params or environment variables and return the headers for authentication.
"""
# Load auth variables from params
url = params.pop("url", params.pop("api_base", params.pop("base_url", None)))
api_key = params.pop("apikey", None)
token = params.pop("token", None)
project_id = params.pop(
"project_id", params.pop("watsonx_project", None)
) # watsonx.ai project_id - allow 'watsonx_project' to be consistent with how vertex project implementation works -> reduce provider-specific params
space_id = params.pop("space_id", None) # watsonx.ai deployment space_id
region_name = params.pop("region_name", params.pop("region", None))
if region_name is None:
region_name = params.pop(
"watsonx_region_name", params.pop("watsonx_region", None)
) # consistent with how vertex ai + aws regions are accepted
wx_credentials = params.pop(
"wx_credentials",
params.pop(
"watsonx_credentials", None
), # follow {provider}_credentials, same as vertex ai
)
api_version = params.pop("api_version", IBMWatsonXAI.api_version)
# Load auth variables from environment variables
if url is None:
url = (
get_secret("WATSONX_API_BASE") # consistent with 'AZURE_API_BASE'
or get_secret("WATSONX_URL")
or get_secret("WX_URL")
or get_secret("WML_URL")
)
if api_key is None:
api_key = (
get_secret("WATSONX_APIKEY")
or get_secret("WATSONX_API_KEY")
or get_secret("WX_API_KEY")
)
if token is None:
token = get_secret("WATSONX_TOKEN") or get_secret("WX_TOKEN")
if project_id is None:
project_id = (
get_secret("WATSONX_PROJECT_ID")
or get_secret("WX_PROJECT_ID")
or get_secret("PROJECT_ID")
)
if region_name is None:
region_name = (
get_secret("WATSONX_REGION")
or get_secret("WX_REGION")
or get_secret("REGION")
)
if space_id is None:
space_id = (
get_secret("WATSONX_DEPLOYMENT_SPACE_ID")
or get_secret("WATSONX_SPACE_ID")
or get_secret("WX_SPACE_ID")
or get_secret("SPACE_ID")
)
# credentials parsing
if wx_credentials is not None:
url = wx_credentials.get("url", url)
api_key = wx_credentials.get(
"apikey", wx_credentials.get("api_key", api_key)
)
token = wx_credentials.get(
"token",
wx_credentials.get(
"watsonx_token", token
), # follow format of {provider}_token, same as azure - e.g. 'azure_ad_token=..'
)
# verify that all required credentials are present
if url is None:
raise WatsonXAIError(
status_code=401,
message="Error: Watsonx URL not set. Set WX_URL in environment variables or pass in as a parameter.",
)
if token is None and api_key is not None:
# generate the auth token
if print_verbose:
print_verbose("Generating IAM token for Watsonx.ai")
token = self.generate_iam_token(api_key)
elif token is None and api_key is None:
raise WatsonXAIError(
status_code=401,
url=url,
message="Error: API key or token not found. Set WX_API_KEY or WX_TOKEN in environment variables or pass in as a parameter.",
)
if project_id is None:
raise WatsonXAIError(
status_code=401,
url=url,
message="Error: Watsonx project_id not set. Set WX_PROJECT_ID in environment variables or pass in as a parameter.",
)
return {
"url": url,
"api_key": api_key,
"token": token,
"project_id": project_id,
"space_id": space_id,
"region_name": region_name,
"api_version": api_version,
}
def completion(
self,
model: str,
messages: list,
custom_prompt_dict: dict,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
logging_obj,
optional_params: dict,
litellm_params: Optional[dict] = None,
logger_fn=None,
timeout: Optional[float] = None,
):
"""
Send a text generation request to the IBM Watsonx.ai API.
Reference: https://cloud.ibm.com/apidocs/watsonx-ai#text-generation
"""
stream = optional_params.pop("stream", False)
# Load default configs
config = IBMWatsonXAIConfig.get_config()
for k, v in config.items():
if k not in optional_params:
optional_params[k] = v
# Make prompt to send to model
provider = model.split("/")[0]
# model_name = "/".join(model.split("/")[1:])
prompt = convert_messages_to_prompt(
model, messages, provider, custom_prompt_dict
)
def process_text_request(request_params: dict) -> ModelResponse:
with self._manage_response(
request_params, logging_obj=logging_obj, input=prompt, timeout=timeout
) as resp:
json_resp = resp.json()
generated_text = json_resp["results"][0]["generated_text"]
prompt_tokens = json_resp["results"][0]["input_token_count"]
completion_tokens = json_resp["results"][0]["generated_token_count"]
model_response["choices"][0]["message"]["content"] = generated_text
model_response["finish_reason"] = json_resp["results"][0]["stop_reason"]
model_response["created"] = int(time.time())
model_response["model"] = model
setattr(
model_response,
"usage",
Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
),
)
return model_response
def process_stream_request(
request_params: dict,
) -> litellm.CustomStreamWrapper:
# stream the response - generated chunks will be handled
# by litellm.utils.CustomStreamWrapper.handle_watsonx_stream
with self._manage_response(
request_params,
logging_obj=logging_obj,
stream=True,
input=prompt,
timeout=timeout,
) as resp:
response = litellm.CustomStreamWrapper(
resp.iter_lines(),
model=model,
custom_llm_provider="watsonx",
logging_obj=logging_obj,
)
return response
try:
## Get the response from the model
req_params = self._prepare_text_generation_req(
model_id=model,
prompt=prompt,
stream=stream,
optional_params=optional_params,
print_verbose=print_verbose,
)
if stream:
return process_stream_request(req_params)
else:
return process_text_request(req_params)
except WatsonXAIError as e:
raise e
except Exception as e:
raise WatsonXAIError(status_code=500, message=str(e))
def embedding(
self,
model: str,
input: Union[list, str],
api_key: Optional[str] = None,
logging_obj=None,
model_response=None,
optional_params=None,
encoding=None,
):
"""
Send a text embedding request to the IBM Watsonx.ai API.
"""
if optional_params is None:
optional_params = {}
# Load default configs
config = IBMWatsonXAIConfig.get_config()
for k, v in config.items():
if k not in optional_params:
optional_params[k] = v
# Load auth variables from environment variables
if isinstance(input, str):
input = [input]
if api_key is not None:
optional_params["api_key"] = api_key
api_params = self._get_api_params(optional_params)
# build auth headers
api_token = api_params.get("token")
headers = {
"Authorization": f"Bearer {api_token}",
"Content-Type": "application/json",
"Accept": "application/json",
}
# init the payload to the text generation call
payload = {
"inputs": input,
"model_id": model,
"project_id": api_params["project_id"],
"parameters": optional_params,
}
request_params = dict(version=api_params["api_version"])
url = api_params["url"].rstrip("/") + WatsonXAIEndpoint.EMBEDDINGS
# request = httpx.Request(
# "POST", url, headers=headers, json=payload, params=request_params
# )
req_params = {
"method": "POST",
"url": url,
"headers": headers,
"json": payload,
"params": request_params,
}
with self._manage_response(
req_params, logging_obj=logging_obj, input=input
) as resp:
json_resp = resp.json()
results = json_resp.get("results", [])
embedding_response = []
for idx, result in enumerate(results):
embedding_response.append(
{"object": "embedding", "index": idx, "embedding": result["embedding"]}
)
model_response["object"] = "list"
model_response["data"] = embedding_response
model_response["model"] = model
input_tokens = json_resp.get("input_token_count", 0)
model_response.usage = Usage(
prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
)
return model_response
def generate_iam_token(self, api_key=None, **params):
headers = {}
headers["Content-Type"] = "application/x-www-form-urlencoded"
if api_key is None:
api_key = get_secret("WX_API_KEY") or get_secret("WATSONX_API_KEY")
if api_key is None:
raise ValueError("API key is required")
headers["Accept"] = "application/json"
data = {
"grant_type": "urn:ibm:params:oauth:grant-type:apikey",
"apikey": api_key,
}
response = httpx.post(
"https://iam.cloud.ibm.com/identity/token", data=data, headers=headers
)
response.raise_for_status()
json_data = response.json()
iam_access_token = json_data["access_token"]
self.token = iam_access_token
return iam_access_token
@contextmanager
def _manage_response(
self,
request_params: dict,
logging_obj: Any,
stream: bool = False,
input: Optional[Any] = None,
timeout: Optional[float] = None,
):
request_str = (
f"response = {request_params['method']}(\n"
f"\turl={request_params['url']},\n"
f"\tjson={request_params['json']},\n"
f")"
)
logging_obj.pre_call(
input=input,
api_key=request_params["headers"].get("Authorization"),
additional_args={
"complete_input_dict": request_params["json"],
"request_str": request_str,
},
)
if timeout:
request_params["timeout"] = timeout
try:
if stream:
resp = requests.request(
**request_params,
stream=True,
)
resp.raise_for_status()
yield resp
else:
resp = requests.request(**request_params)
resp.raise_for_status()
yield resp
except Exception as e:
raise WatsonXAIError(status_code=500, message=str(e))
if not stream:
logging_obj.post_call(
input=input,
api_key=request_params["headers"].get("Authorization"),
original_response=json.dumps(resp.json()),
additional_args={
"status_code": resp.status_code,
"complete_input_dict": request_params["json"],
},
)

83
litellm/main.py
View file

@ -12,7 +12,6 @@ from typing import Any, Literal, Union, BinaryIO
from functools import partial
import dotenv, traceback, random, asyncio, time, contextvars
from copy import deepcopy
import httpx
import litellm
from ._logging import verbose_logger
@ -64,6 +63,7 @@ from .llms import (
vertex_ai,
vertex_ai_anthropic,
maritalk,
watsonx,
)
from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion
from .llms.azure import AzureChatCompletion
@ -343,6 +343,7 @@ async def acompletion(
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=completion_kwargs,
extra_kwargs=kwargs,
)
@ -408,8 +409,10 @@ def mock_completion(
model_response["created"] = int(time.time())
model_response["model"] = model
model_response.usage = Usage(
prompt_tokens=10, completion_tokens=20, total_tokens=30
setattr(
model_response,
"usage",
Usage(prompt_tokens=10, completion_tokens=20, total_tokens=30),
)
try:
@ -609,6 +612,7 @@ def completion(
"client",
"rpm",
"tpm",
"max_parallel_requests",
"input_cost_per_token",
"output_cost_per_token",
"input_cost_per_second",
@ -652,6 +656,7 @@ def completion(
model
] # update the model to the actual value if an alias has been passed in
model_response = ModelResponse()
setattr(model_response, "usage", litellm.Usage())
if (
kwargs.get("azure", False) == True
): # don't remove flag check, to remain backwards compatible for repos like Codium
@ -1732,13 +1737,14 @@ def completion(
or optional_params.pop("vertex_ai_credentials", None)
or get_secret("VERTEXAI_CREDENTIALS")
)
new_params = deepcopy(optional_params)
if "claude-3" in model:
model_response = vertex_ai_anthropic.completion(
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
optional_params=new_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding,
@ -1754,7 +1760,7 @@ def completion(
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
optional_params=new_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding,
@ -1907,6 +1913,43 @@ def completion(
## RESPONSE OBJECT
response = response
elif custom_llm_provider == "watsonx":
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
response = watsonx.IBMWatsonXAI().completion(
model=model,
messages=messages,
custom_prompt_dict=custom_prompt_dict,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params, # type: ignore
logger_fn=logger_fn,
encoding=encoding,
logging_obj=logging,
timeout=timeout,
)
if (
"stream" in optional_params
and optional_params["stream"] == True
and not isinstance(response, CustomStreamWrapper)
):
# don't try to access stream object,
response = CustomStreamWrapper(
iter(response),
model,
custom_llm_provider="watsonx",
logging_obj=logging,
)
if optional_params.get("stream", False):
## LOGGING
logging.post_call(
input=messages,
api_key=None,
original_response=response,
)
## RESPONSE OBJECT
response = response
elif custom_llm_provider == "vllm":
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
model_response = vllm.completion(
@ -1990,9 +2033,16 @@ def completion(
or "http://localhost:11434"
)
api_key = (
api_key
or litellm.ollama_key
or os.environ.get("OLLAMA_API_KEY")
or litellm.api_key
)
## LOGGING
generator = ollama_chat.get_ollama_response(
api_base,
api_key,
model,
messages,
optional_params,
@ -2188,6 +2238,7 @@ def completion(
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@ -2549,6 +2600,7 @@ async def aembedding(*args, **kwargs):
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@ -2600,6 +2652,7 @@ def embedding(
client = kwargs.pop("client", None)
rpm = kwargs.pop("rpm", None)
tpm = kwargs.pop("tpm", None)
max_parallel_requests = kwargs.pop("max_parallel_requests", None)
model_info = kwargs.get("model_info", None)
metadata = kwargs.get("metadata", None)
encoding_format = kwargs.get("encoding_format", None)
@ -2657,6 +2710,7 @@ def embedding(
"client",
"rpm",
"tpm",
"max_parallel_requests",
"input_cost_per_token",
"output_cost_per_token",
"input_cost_per_second",
@ -2975,6 +3029,15 @@ def embedding(
client=client,
aembedding=aembedding,
)
elif custom_llm_provider == "watsonx":
response = watsonx.IBMWatsonXAI().embedding(
model=model,
input=input,
encoding=encoding,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
)
else:
args = locals()
raise ValueError(f"No valid embedding model args passed in - {args}")
@ -2990,7 +3053,10 @@ def embedding(
)
## Map to OpenAI Exception
raise exception_type(
model=model, original_exception=e, custom_llm_provider=custom_llm_provider
model=model,
original_exception=e,
custom_llm_provider=custom_llm_provider,
extra_kwargs=kwargs,
)
@ -3084,6 +3150,7 @@ async def atext_completion(*args, **kwargs):
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@ -3421,6 +3488,7 @@ async def aimage_generation(*args, **kwargs):
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@ -3511,6 +3579,7 @@ def image_generation(
"client",
"rpm",
"tpm",
"max_parallel_requests",
"input_cost_per_token",
"output_cost_per_token",
"hf_model_name",
@ -3620,6 +3689,7 @@ def image_generation(
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=locals(),
extra_kwargs=kwargs,
)
@ -3669,6 +3739,7 @@ async def atranscription(*args, **kwargs):
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)

176
litellm/model_prices_and_context_window_backup.json
View file

@ -650,6 +650,7 @@
"input_cost_per_token": 0.000002,
"output_cost_per_token": 0.000006,
"litellm_provider": "mistral",
"supports_function_calling": true,
"mode": "chat"
},
"mistral/mistral-small-latest": {
@ -659,6 +660,7 @@
"input_cost_per_token": 0.000002,
"output_cost_per_token": 0.000006,
"litellm_provider": "mistral",
"supports_function_calling": true,
"mode": "chat"
},
"mistral/mistral-medium": {
@ -735,6 +737,26 @@
"mode": "chat",
"supports_function_calling": true
},
"groq/llama3-8b-8192": {
"max_tokens": 8192,
"max_input_tokens": 8192,
"max_output_tokens": 8192,
"input_cost_per_token": 0.00000010,
"output_cost_per_token": 0.00000010,
"litellm_provider": "groq",
"mode": "chat",
"supports_function_calling": true
},
"groq/llama3-70b-8192": {
"max_tokens": 8192,
"max_input_tokens": 8192,
"max_output_tokens": 8192,
"input_cost_per_token": 0.00000064,
"output_cost_per_token": 0.00000080,
"litellm_provider": "groq",
"mode": "chat",
"supports_function_calling": true
},
"groq/mixtral-8x7b-32768": {
"max_tokens": 32768,
"max_input_tokens": 32768,
@ -789,7 +811,9 @@
"input_cost_per_token": 0.00000025,
"output_cost_per_token": 0.00000125,
"litellm_provider": "anthropic",
"mode": "chat"
"mode": "chat",
"supports_function_calling": true,
"tool_use_system_prompt_tokens": 264
},
"claude-3-opus-20240229": {
"max_tokens": 4096,
@ -798,7 +822,9 @@
"input_cost_per_token": 0.000015,
"output_cost_per_token": 0.000075,
"litellm_provider": "anthropic",
"mode": "chat"
"mode": "chat",
"supports_function_calling": true,
"tool_use_system_prompt_tokens": 395
},
"claude-3-sonnet-20240229": {
"max_tokens": 4096,
@ -807,7 +833,9 @@
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000015,
"litellm_provider": "anthropic",
"mode": "chat"
"mode": "chat",
"supports_function_calling": true,
"tool_use_system_prompt_tokens": 159
},
"text-bison": {
"max_tokens": 1024,
@ -1113,7 +1141,8 @@
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000015,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat"
"mode": "chat",
"supports_function_calling": true
},
"vertex_ai/claude-3-haiku@20240307": {
"max_tokens": 4096,
@ -1122,7 +1151,8 @@
"input_cost_per_token": 0.00000025,
"output_cost_per_token": 0.00000125,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat"
"mode": "chat",
"supports_function_calling": true
},
"vertex_ai/claude-3-opus@20240229": {
"max_tokens": 4096,
@ -1131,7 +1161,8 @@
"input_cost_per_token": 0.0000015,
"output_cost_per_token": 0.0000075,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat"
"mode": "chat",
"supports_function_calling": true
},
"textembedding-gecko": {
"max_tokens": 3072,
@ -1387,6 +1418,123 @@
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-2-13b": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.0000001,
"output_cost_per_token": 0.0000005,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-2-13b-chat": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.0000001,
"output_cost_per_token": 0.0000005,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-2-70b": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000065,
"output_cost_per_token": 0.00000275,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-2-70b-chat": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000065,
"output_cost_per_token": 0.00000275,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-2-7b": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000005,
"output_cost_per_token": 0.00000025,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-2-7b-chat": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000005,
"output_cost_per_token": 0.00000025,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-3-70b": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000065,
"output_cost_per_token": 0.00000275,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-3-70b-instruct": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000065,
"output_cost_per_token": 0.00000275,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-3-8b": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000005,
"output_cost_per_token": 0.00000025,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/meta/llama-3-8b-instruct": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000005,
"output_cost_per_token": 0.00000025,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/mistralai/mistral-7b-v0.1": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000005,
"output_cost_per_token": 0.00000025,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/mistralai/mistral-7b-instruct-v0.2": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000005,
"output_cost_per_token": 0.00000025,
"litellm_provider": "replicate",
"mode": "chat"
},
"replicate/mistralai/mixtral-8x7b-instruct-v0.1": {
"max_tokens": 4096,
"max_input_tokens": 4096,
"max_output_tokens": 4096,
"input_cost_per_token": 0.0000003,
"output_cost_per_token": 0.000001,
"litellm_provider": "replicate",
"mode": "chat"
},
"openrouter/openai/gpt-3.5-turbo": {
"max_tokens": 4095,
"input_cost_per_token": 0.0000015,
@ -1515,6 +1663,13 @@
"litellm_provider": "openrouter",
"mode": "chat"
},
"openrouter/meta-llama/llama-3-70b-instruct": {
"max_tokens": 8192,
"input_cost_per_token": 0.0000008,
"output_cost_per_token": 0.0000008,
"litellm_provider": "openrouter",
"mode": "chat"
},
"j2-ultra": {
"max_tokens": 8192,
"max_input_tokens": 8192,
@ -1762,7 +1917,8 @@
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000015,
"litellm_provider": "bedrock",
"mode": "chat"
"mode": "chat",
"supports_function_calling": true
},
"anthropic.claude-3-haiku-20240307-v1:0": {
"max_tokens": 4096,
@ -1771,7 +1927,8 @@
"input_cost_per_token": 0.00000025,
"output_cost_per_token": 0.00000125,
"litellm_provider": "bedrock",
"mode": "chat"
"mode": "chat",
"supports_function_calling": true
},
"anthropic.claude-3-opus-20240229-v1:0": {
"max_tokens": 4096,
@ -1780,7 +1937,8 @@
"input_cost_per_token": 0.000015,
"output_cost_per_token": 0.000075,
"litellm_provider": "bedrock",
"mode": "chat"
"mode": "chat",
"supports_function_calling": true
},
"anthropic.claude-v1": {
"max_tokens": 8191,

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litellm/proxy/_experimental/out/index.html
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4
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103
litellm/proxy/_new_secret_config.yaml
View file

@ -1,52 +1,61 @@
model_list:
- model_name: fake-openai-endpoint
litellm_params:
model: openai/my-fake-model
api_key: my-fake-key
# api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
api_base: http://0.0.0.0:8080
stream_timeout: 0.001
rpm: 10
- litellm_params:
model: azure/chatgpt-v-2
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
stream_timeout: 0.001
model_name: azure-gpt-3.5
# - model_name: text-embedding-ada-002
# litellm_params:
# model: text-embedding-ada-002
# api_key: os.environ/OPENAI_API_KEY
- model_name: gpt-instruct
litellm_params:
model: text-completion-openai/gpt-3.5-turbo-instruct
# api_key: my-fake-key
# api_base: https://exampleopenaiendpoint-production.up.railway.app/
environment_variables:
LANGFUSE_PUBLIC_KEY: Q6K8MQN6L7sPYSJiFKM9eNrETOx6V/FxVPup4FqdKsZK1hyR4gyanlQ2KHLg5D5afng99uIt0JCEQ2jiKF9UxFvtnb4BbJ4qpeceH+iK8v/bdg==
LANGFUSE_SECRET_KEY: 5xQ7KMa6YMLsm+H/Pf1VmlqWq1NON5IoCxABhkUBeSck7ftsj2CmpkL2ZwrxwrktgiTUBH+3gJYBX+XBk7lqOOUpvmiLjol/E5lCqq0M1CqLWA==
SLACK_WEBHOOK_URL: RJjhS0Hhz0/s07sCIf1OTXmTGodpK9L2K9p953Z+fOX0l2SkPFT6mB9+yIrLufmlwEaku5NNEBKy//+AG01yOd+7wV1GhK65vfj3B/gTN8t5cuVnR4vFxKY5Rx4eSGLtzyAs+aIBTp4GoNXDIjroCqfCjPkItEZWCg==
general_settings:
alerting:
- slack
alerting_threshold: 300
database_connection_pool_limit: 100
database_connection_timeout: 60
disable_master_key_return: true
health_check_interval: 300
proxy_batch_write_at: 60
ui_access_mode: all
# master_key: sk-1234
litellm_settings:
success_callback: ["prometheus"]
service_callback: ["prometheus_system"]
upperbound_key_generate_params:
max_budget: os.environ/LITELLM_UPPERBOUND_KEYS_MAX_BUDGET
allowed_fails: 3
failure_callback:
- prometheus
num_retries: 3
service_callback:
- prometheus_system
success_callback:
- langfuse
- prometheus
- langsmith
model_list:
- litellm_params:
model: gpt-3.5-turbo
model_name: gpt-3.5-turbo
- litellm_params:
api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
api_key: my-fake-key
model: openai/my-fake-model
stream_timeout: 0.001
model_name: fake-openai-endpoint
- litellm_params:
api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
api_key: my-fake-key
model: openai/my-fake-model-2
stream_timeout: 0.001
model_name: fake-openai-endpoint
- litellm_params:
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: 2023-07-01-preview
model: azure/chatgpt-v-2
stream_timeout: 0.001
model_name: azure-gpt-3.5
- litellm_params:
api_key: os.environ/OPENAI_API_KEY
model: text-embedding-ada-002
model_name: text-embedding-ada-002
- litellm_params:
model: text-completion-openai/gpt-3.5-turbo-instruct
model_name: gpt-instruct
router_settings:
routing_strategy: usage-based-routing-v2
enable_pre_call_checks: true
redis_host: os.environ/REDIS_HOST
redis_password: os.environ/REDIS_PASSWORD
redis_port: os.environ/REDIS_PORT
enable_pre_call_checks: True
general_settings:
master_key: sk-1234
allow_user_auth: true
alerting: ["slack"]
store_model_in_db: True // set via environment variable - os.environ["STORE_MODEL_IN_DB"] = "True"
proxy_batch_write_at: 5 # 👈 Frequency of batch writing logs to server (in seconds)
enable_jwt_auth: True
alerting: ["slack"]
litellm_jwtauth:
admin_jwt_scope: "litellm_proxy_admin"
public_key_ttl: os.environ/LITELLM_PUBLIC_KEY_TTL
user_id_jwt_field: "sub"
org_id_jwt_field: "azp"

19
litellm/proxy/_super_secret_config.yaml Normal file
View file

@ -0,0 +1,19 @@
model_list:
- litellm_params:
api_base: http://0.0.0.0:8080
api_key: my-fake-key
model: openai/my-fake-model
rpm: 100
model_name: fake-openai-endpoint
- litellm_params:
api_base: http://0.0.0.0:8081
api_key: my-fake-key
model: openai/my-fake-model-2
rpm: 100
model_name: fake-openai-endpoint
router_settings:
num_retries: 0
enable_pre_call_checks: true
redis_host: os.environ/REDIS_HOST
redis_password: os.environ/REDIS_PASSWORD
redis_port: os.environ/REDIS_PORT

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