Gemini RAG

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The Gemini RAG server serves as a powerful bridge, integrating Retrieval-Augmented Generation (RAG) directly into AI applications through Google's Gemini API File Search. It enables users to effortlessly construct dynamic knowledge bases by uploading various documents and text content, facilitating efficient querying and retrieval of highly relevant information. Fully compliant with the Model Context Protocol, this production-ready server boasts features like configurable Gemini models, robust TypeScript support, and dual transport options, streamlining the development of intelligent AI applications.

Características Principales

• File Search RAG for knowledge base creation and management
• Upload documents and text content to build searchable knowledge bases
• Query knowledge bases to retrieve relevant information efficiently
• Configurable Gemini models for queries via environment variables
• Full compatibility with the Model Context Protocol (MCP)
• 2 GitHub stars

Casos de Uso

• Enabling intelligent information retrieval for chatbots and virtual assistants
• Building AI applications requiring RAG-enhanced conversational abilities
• Creating and managing custom knowledge bases from proprietary documents