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Custom Prompt Management

Connect LiteLLM to your prompt management system with custom hooks.

Overview​

How it works​

Quick Start​

1. Create Your Custom Prompt Manager​

Create a class that inherits from CustomPromptManagement to handle prompt retrieval and formatting:

Example Implementation

Create a new file called custom_prompt.py and add this code. The key method here is get_chat_completion_prompt you can implement custom logic to retrieve and format prompts based on the prompt_id and prompt_variables.

from typing import List, Tuple, Optional
from litellm.integrations.custom_prompt_management import CustomPromptManagement
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import StandardCallbackDynamicParams

class MyCustomPromptManagement(CustomPromptManagement):
    def get_chat_completion_prompt(
        self,
        model: str,
        messages: List[AllMessageValues],
        non_default_params: dict,
        prompt_id: str,
        prompt_variables: Optional[dict],
        dynamic_callback_params: StandardCallbackDynamicParams,
    ) -> Tuple[str, List[AllMessageValues], dict]:
        """
        Retrieve and format prompts based on prompt_id.
        
        Returns:
            - model: The model to use
            - messages: The formatted messages
            - non_default_params: Optional parameters like temperature
        """
        # Example matching the diagram: Add system message for prompt_id "1234"
        if prompt_id == "1234":
            # Prepend system message while preserving existing messages
            new_messages = [
                {"role": "system", "content": "Be a good Bot!"},
            ] + messages
            return model, new_messages, non_default_params
        
        # Default: Return original messages if no prompt_id match
        return model, messages, non_default_params

prompt_management = MyCustomPromptManagement()

2. Configure Your Prompt Manager in LiteLLM config.yaml​

model_list:
  - model_name: gpt-4
    litellm_params:
      model: openai/gpt-4
      api_key: os.environ/OPENAI_API_KEY

litellm_settings:
  callbacks: custom_prompt.prompt_management  # sets litellm.callbacks = [prompt_management]

3. Start LiteLLM Gateway​

Mount your custom_logger.py on the LiteLLM Docker container.

docker run -d \
  -p 4000:4000 \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  --name my-app \
  -v $(pwd)/my_config.yaml:/app/config.yaml \
  -v $(pwd)/custom_logger.py:/app/custom_logger.py \
  my-app:latest \
  --config /app/config.yaml \
  --port 4000 \
  --detailed_debug \

4. Test Your Custom Prompt Manager​

When you pass prompt_id="1234", the custom prompt manager will add a system message "Be a good Bot!" to your conversation:

from openai import OpenAI

client = OpenAI(
    api_key="sk-1234",
    base_url="http://0.0.0.0:4000"
)

response = client.chat.completions.create(
    model="gemini-1.5-pro",
    messages=[{"role": "user", "content": "hi"}],
    prompt_id="1234"
)

print(response.choices[0].message.content)

The request will be transformed from:

{
    "model": "gemini-1.5-pro",
    "messages": [{"role": "user", "content": "hi"}],
    "prompt_id": "1234"
}

To:

{
    "model": "gemini-1.5-pro",
    "messages": [
        {"role": "system", "content": "Be a good Bot!"},
        {"role": "user", "content": "hi"}
    ]
}