RAG Server

A FastMCP-based Retrieval-Augmented Generation server for dynamically ingesting public documents and querying them on-the-fly. This server implements the Model Context Protocol (MCP) to enable seamless integration between AI models and external data sources.

Features

• Document ingestion from public URLs (PDF, DOCX, DOC)
• Hybrid vector search using both OpenAI and Google Gemini embeddings
• Session-based context management via MCP
• Automatic fallback and retry mechanisms for embedding generation
• Support for chunking and overlapping text segments

Installation

uv pip install -e .

Tools

The server exposes the following MCP tools defined in src/rag_server/server.py:

ingest_urls

Description: Ingest a list of public URLs (PDF, DOCX, DOC) into an ephemeral session. Returns a session_id for querying. You can pass an existing session_id to ingest into a specific session.

Signature: ingest_urls(urls: list[str], session_id: Optional[str] = None) -> str

• urls: List of public document URLs to ingest.
• session_id (optional): Existing session identifier.

query_knowledge

Description: Query the ingested documents in the given session using RAG. Returns a generated answer.

Signature: query_knowledge(session_id: str, question: str) -> str

• session_id: Session identifier where documents were ingested.
• question: The question to query against ingested documents.