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README.md

LeapfrogAI API

Important

See the API package documentation for general pre-requisites, dependent components, and package deployment instructions

This document is only applicable for spinning up the API in a local Python development environment.

Local Development Setup

Important

Execute the following commands from this sub-directory

Running

Important

The following steps assume that you already have a deployed and accessible UDS Kubernetes cluster and LeapfrogAI. Please follow the steps within the DEVELOPMENT.md for details.

  1. Install dependencies

    make install

  2. Create a config.yaml using the config.example.yaml as a template.

  3. Run the FastAPI application

    make dev API_PORT=8080

  4. Create an API key with test user "leapfrogai@defenseunicorns.com" and test password "password", lasting 30 days from creation time

    # If the in-cluster API is up, and not testing the API workflow
make api-key API_BASE_URL=http://localhost:8080

    To create a new 30-day API key, use the following:

    # If the in-cluster API is up, and not testing the API workflow
make new-api-key API_BASE_URL=http://localhost:8080

    The newest API key will be printed to a .env file located within this directory.

  5. Make calls to the API Swagger endpoint at http://localhost:8080/docs using your API token as the HTTPBearer token.

    • Hit Authorize on the Swagger page to enter your API key

Access

See the "Access" section of the DEVELOPMENT.md for different ways to connect the API to a model backend or Supabase.

Tests

See the tests directory documentation for more details.

Reranking Configuration

The LeapfrogAI API includes a Retrieval Augmented Generation (RAG) pipeline for enhanced question answering. This section details how to configure its reranking options. All RAG configurations are managed through the /leapfrogai/v1/rag/configure API endpoint.

1. Enabling/Disabling Reranking

Reranking improves the accuracy and relevance of RAG responses. You can enable or disable it using the enable_reranking parameter:

  • Enable Reranking: Send a PATCH request to /leapfrogai/v1/rag/configure with the following JSON payload:
{
  "enable_reranking": true
}
  • Disable Reranking: Send a PATCH request with:
{
  "enable_reranking": false
}

2. Selecting a Reranking Model

Multiple reranking models are supported, each offering different performance characteristics. Choose your preferred model using the ranking_model parameter. Ensure you've installed any necessary Python dependencies for your chosen model (see the rerankers library documentation on dependencies).

  • Supported Models: The system supports several models, including (but not limited to) flashrank, rankllm, cross-encoder, and colbert. Refer to the rerankers library documentation for a complete list and details on their capabilities.

  • Model Selection: Use a PATCH request to /leapfrogai/v1/rag/configure with the desired model:

{
  "enable_reranking": true,  // Reranking must be enabled
  "ranking_model": "rankllm" // Or another supported model
}

3. Adjusting the Number of Results Before Reranking (rag_top_k_when_reranking)

This parameter sets the number of top results retrieved from the vector database before the reranking process begins. A higher value increases the diversity of candidates considered for reranking but also increases processing time. A lower value can lead to missing relevant results if not carefully chosen. This setting is only relevant when reranking is enabled.

  • Configuration: Use a PATCH request to /leapfrogai/v1/rag/configure to set this value:
{
  "enable_reranking": true,
  "ranking_model": "flashrank",
  "rag_top_k_when_reranking": 150 // Adjust this value as needed
}

4. Retrieving the Current RAG Configuration

To check the current RAG configuration (including reranking status, model, and rag_top_k_when_reranking), send a GET request to /leapfrogai/v1/rag/configure. The response will be a JSON object containing all the current settings.

5. Example Configuration Flow

  1. Initial Setup: Start with reranking enabled using the default flashrank model and a rag_top_k_when_reranking value of 100.

  2. Experiment with Models: Test different reranking models (rankllm, colbert, etc.) by changing the ranking_model parameter and observing the impact on response quality. Adjust rag_top_k_when_reranking as needed to find the optimal balance between diversity and performance.

  3. Fine-tuning: Once you identify a suitable model, fine-tune the rag_top_k_when_reranking parameter for optimal performance. Monitor response times and quality to determine the best setting.

  4. Disabling Reranking: If needed, disable reranking by setting "enable_reranking": false.

Remember to always consult the rerankers library documentation for information on supported models and their specific requirements. The API documentation provides further details on request formats and potential error responses.