Overview

Ragpi is an open-source AI assistant API that leverages LLMs to search and answer questions using technical sources. It builds knowledge bases from documentation websites, GitHub Issues, and repository README files, storing them in a vector database for efficient retrieval. Using an Agentic RAG approach, it dynamically retrieves relevant documents to answer queries through its REST API.

Features

• REST API - Simple API for interacting with the AI assistant and managing sources
• Agentic RAG System - Dynamic document retrieval based on queries
• Hybrid Search - Combines semantic and keyword search using Reciprocal Rank Fusion (RRF)
• Flexible Connectors - Support for docs, GitHub issues, and READMEs
• LLM Integration - Response generation using selected LLM providers
• Observability - Basic OpenTelemetry tracing support

Example Workflow

1. Set up a Source with a Connector:

   • Use the /sources endpoint to configure a source with your chosen connector.
   • Each connector type has its own configuration parameters.

   Example payload using the Sitemap connector:

   {
     "name": "example-docs",
     "description": "Documentation for example project. It contains information about configuration, usage, and deployment.",
     "connector": {
       "type": "sitemap",
       "sitemap_url": "https://docs.example.com/sitemap.xml"
     }
   }

2. Monitor Source Synchronization:

   • After adding a source, documents will be synced automatically. You can monitor the sync process through the /tasks endpoint.

3. Chat with the AI Assistant:

   • Use the /chat endpoint to query the AI assistant using the configured sources:

     {
       "sources": ["example-docs"],
       "messages": [
         { "role": "user", "content": "How do I deploy the example project?" }
       ]
     }
   • You can also interact with the AI assistant through the Discord or Slack integration.