Prompt Management

Run experiments or change the specific model (e.g. from gpt-4o to gpt4o-mini finetune) from your prompt management tool (e.g. Langfuse) instead of making changes in the application.

Supported Integrations:

• Langfuse

Quick Start

SDK

import os 
import litellm

os.environ["LANGFUSE_PUBLIC_KEY"] = "public_key" # [OPTIONAL] set here or in `.completion`
os.environ["LANGFUSE_SECRET_KEY"] = "secret_key" # [OPTIONAL] set here or in `.completion`

litellm.set_verbose = True # see raw request to provider

resp = litellm.completion(
    model="langfuse/gpt-3.5-turbo",
    prompt_id="test-chat-prompt",
    prompt_variables={"user_message": "this is used"}, # [OPTIONAL]
    messages=[{"role": "user", "content": "<IGNORED>"}],
)

PROXY

1. Setup config.yaml

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: langfuse/gpt-3.5-turbo
      prompt_id: "<langfuse_prompt_id>"
      api_key: os.environ/OPENAI_API_KEY

2. Start the proxy

litellm --config config.yaml --detailed_debug

3. Test it!

CURL

curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
            "role": "user",
            "content": "THIS WILL BE IGNORED"
        }
    ],
    "prompt_variables": {
        "key": "this is used"
    }
}'

OpenAI Python SDK

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ],
    extra_body={
        "prompt_variables": { # [OPTIONAL]
            "key": "this is used"
        }
    }
)

print(response)

Expected Logs:

POST Request Sent from LiteLLM:
curl -X POST \
https://api.openai.com/v1/ \
-d '{'model': 'gpt-3.5-turbo', 'messages': <YOUR LANGFUSE PROMPT TEMPLATE>}'

How to set model

Set the model on LiteLLM

You can do langfuse/<litellm_model_name>

SDK

litellm.completion(
    model="langfuse/gpt-3.5-turbo", # or `langfuse/anthropic/claude-3-5-sonnet`
    ...
)

PROXY

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: langfuse/gpt-3.5-turbo # OR langfuse/anthropic/claude-3-5-sonnet
      prompt_id: <langfuse_prompt_id>
      api_key: os.environ/OPENAI_API_KEY

Set the model in Langfuse

If the model is specified in the Langfuse config, it will be used.

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/chatgpt-v-2
      api_key: os.environ/AZURE_API_KEY
      api_base: os.environ/AZURE_API_BASE

What is 'prompt_variables'?

• prompt_variables: A dictionary of variables that will be used to replace parts of the prompt.

What is 'prompt_id'?

• prompt_id: The ID of the prompt that will be used for the request.

What will the formatted prompt look like?

/chat/completions messages

The messages field sent in by the client is ignored.

The Langfuse prompt will replace the messages field.

To replace parts of the prompt, use the prompt_variables field. See how prompt variables are used

If the Langfuse prompt is a string, it will be sent as a user message (not all providers support system messages).

If the Langfuse prompt is a list, it will be sent as is (Langfuse chat prompts are OpenAI compatible).

Architectural Overview

API Reference

These are the params you can pass to the litellm.completion function in SDK and litellm_params in config.yaml

prompt_id: str # required
prompt_variables: Optional[dict] # optional
langfuse_public_key: Optional[str] # optional
langfuse_secret: Optional[str] # optional
langfuse_secret_key: Optional[str] # optional
langfuse_host: Optional[str] # optional