litellm/docs/my-website/docs/observability/langfuse_integration.md

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Langfuse - Logging LLM Input/Output

LangFuse is open Source Observability & Analytics for LLM Apps Detailed production traces and a granular view on quality, cost and latency

<Image img={require('../../img/langfuse.png')} />

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Pre-Requisites

Ensure you have run pip install langfuse for this integration

pip install langfuse>=2.0.0 litellm

Quick Start

Use just 2 lines of code, to instantly log your responses across all providers with Langfuse

Get your Langfuse API Keys from https://cloud.langfuse.com/

litellm.success_callback = ["langfuse"]
litellm.failure_callback = ["langfuse"] # logs errors to langfuse

# pip install langfuse 
import litellm
import os

# from https://cloud.langfuse.com/
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
# Optional, defaults to https://cloud.langfuse.com
os.environ["LANGFUSE_HOST"] # optional

# LLM API Keys
os.environ['OPENAI_API_KEY']=""

# set langfuse as a callback, litellm will send the data to langfuse
litellm.success_callback = ["langfuse"] 
 
# openai call
response = litellm.completion(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ]
)

Advanced

Set Custom Generation names, pass metadata

Pass generation_name in metadata

import litellm
from litellm import completion
import os

# from https://cloud.langfuse.com/
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""


# OpenAI and Cohere keys 
# You can use any of the litellm supported providers: https://docs.litellm.ai/docs/providers
os.environ['OPENAI_API_KEY']=""

# set langfuse as a callback, litellm will send the data to langfuse
litellm.success_callback = ["langfuse"] 
 
# openai call
response = completion(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ],
  metadata = {
    "generation_name": "litellm-ishaan-gen", # set langfuse generation name
    # custom metadata fields
    "project": "litellm-proxy" 
  }
)
 
print(response)

Set Custom Trace ID, Trace User ID, Trace Metadata, Trace Version, Trace Release and Tags

Pass trace_id, trace_user_id, trace_metadata, trace_version, trace_release, tags in metadata

import litellm
from litellm import completion
import os

# from https://cloud.langfuse.com/
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""

os.environ['OPENAI_API_KEY']=""

# set langfuse as a callback, litellm will send the data to langfuse
litellm.success_callback = ["langfuse"] 

# set custom langfuse trace params and generation params
response = completion(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ],
  metadata={
      "generation_name": "ishaan-test-generation",  # set langfuse Generation Name
      "generation_id": "gen-id22",                  # set langfuse Generation ID 
      "version":  "test-generation-version"         # set langfuse Generation Version
      "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
      "trace_metadata": {"key": "value"},           # set langfuse Trace Metadata
      "trace_version": "test-trace-version",        # set langfuse Trace Version (if not set, defaults to Generation Version)
      "trace_release": "test-trace-release",        # set langfuse Trace Release
      ### OR ### 
      "existing_trace_id": "trace-id22",            # if generation is continuation of past trace. This prevents default behaviour of setting a trace name
      ### OR enforce that certain fields are trace overwritten in the trace during the continuation ###
      "existing_trace_id": "trace-id22",
      "trace_metadata": {"key": "updated_trace_value"},            # The new value to use for the langfuse Trace Metadata
      "update_trace_keys": ["input", "output", "trace_metadata"],  # Updates the trace input & output to be this generations input & output also updates the Trace Metadata to match the passed in value
  },
)

print(response)

Trace & Generation Parameters

Trace Specific Parameters

• trace_id - Identifier for the trace, must use existing_trace_id instead or in conjunction with trace_id if this is an existing trace, auto-generated by default
• trace_name - Name of the trace, auto-generated by default
• session_id - Session identifier for the trace, defaults to None
• trace_version - Version for the trace, defaults to value for version
• trace_release - Release for the trace, defaults to None
• trace_metadata - Metadata for the trace, defaults to None
• trace_user_id - User identifier for the trace, defaults to completion argument user
• tags - Tags for the trace, defeaults to None

Updatable Parameters on Continuation

The following parameters can be updated on a continuation of a trace by passing in the following values into the update_trace_keys in the metadata of the completion.

• input - Will set the traces input to be the input of this latest generation
• output - Will set the traces output to be the output of this generation
• trace_version - Will set the trace version to be the provided value (To use the latest generations version instead, use version)
• trace_release - Will set the trace release to be the provided value
• trace_metadata - Will set the trace metadata to the provided value
• trace_user_id - Will set the trace user id to the provided value

Generation Specific Parameters

• generation_id - Identifier for the generation, auto-generated by default
• generation_name - Identifier for the generation, auto-generated by default
• prompt - Langfuse prompt object used for the generation, defaults to None

Any other key value pairs passed into the metadata not listed in the above spec for a litellm completion will be added as a metadata key value pair for the generation.

Use LangChain ChatLiteLLM + Langfuse

Pass trace_user_id, session_id in model_kwargs

import os
from langchain.chat_models import ChatLiteLLM
from langchain.schema import HumanMessage
import litellm

# from https://cloud.langfuse.com/
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""

os.environ['OPENAI_API_KEY']=""

# set langfuse as a callback, litellm will send the data to langfuse
litellm.success_callback = ["langfuse"] 

chat = ChatLiteLLM(
  model="gpt-3.5-turbo"
  model_kwargs={
      "metadata": {
        "trace_user_id": "user-id2", # set langfuse Trace User ID
        "session_id": "session-1" ,  # set langfuse Session ID
        "tags": ["tag1", "tag2"] 
      }
    }
  )
messages = [
    HumanMessage(
        content="what model are you"
    )
]
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 ?

• Ensure you're on the latest version of langfuse pip install langfuse -U. The latest version allows litellm to log JSON input/outputs to langfuse

Support & Talk to Founders

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