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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

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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

Open In Colab

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 
      "parent_observation_id": "obs-id9"            # set langfuse Parent Observation 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
      "debug_langfuse": True,                                      # Will log the exact metadata sent to litellm for the trace/generation as `metadata_passed_to_litellm` 
  },
)

print(response)

You can also pass metadata as part of the request header with a langfuse_* prefix:

curl --location --request POST 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'langfuse_trace_id: trace-id2' \
    --header 'langfuse_trace_user_id: user-id2' \
    --header 'langfuse_trace_metadata: {"key":"value"}' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
}'

Trace & Generation Parameters​

Trace Specific Parameters​

  • trace_id - Identifier for the trace, must use existing_trace_id instead of 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
  • parent_observation_id - Identifier for the parent observation, defaults to None
  • 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​

Redact Messages and Responses from all 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.

Redact Messages and Responses from specific Langfuse Logging​

In the metadata typically passed for text completion or embedding calls you can set specific keys to mask the messages and responses for this call.

Setting mask_input to True will mask the input from being logged for this call

Setting mask_output to True will make the output from being logged for this call.

Be aware that if you are continuing an existing trace, and you set update_trace_keys to include either input or output and you set the corresponding mask_input or mask_output, then that trace will have its existing input and/or output replaced with a redacted message.

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​