RAG

소개

RAG is an elaborate Python server designed to enhance retrieval-augmented generation (RAG) by offering multiple sophisticated search modalities for textual knowledge bases. It leverages PostgreSQL with `pgvector` for efficient storage and retrieval of text embeddings, aiming to provide nuanced search capabilities beyond simple keyword matching. Built on the `fastmcp` framework, it integrates seamlessly with other AI agents, enabling complex, interconnected AI workflows. Its design embraces complexity to deliver specialized search functions like semantic, question/answer, and style-based retrieval, making it suitable for users seeking highly customizable and powerful text search solutions.

주요 기능

• Highly extensible architecture, allowing for the addition of custom search modalities
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• Multiple search modalities: semantic, question/answer, and style-based search
• Persistent storage with PostgreSQL and `pgvector` extension for vector embeddings
• Integration with OpenAI-compatible embedding APIs for text vectorization
• Exposes search functionalities as Model Context Protocol (MCP) tools for AI agent communication

사용 사례

• Building advanced retrieval systems for large, dynamic text knowledge bases
• Integrating sophisticated text search and retrieval capabilities into AI assistants and agents
• Performing nuanced queries such as identifying textual style or conceptual similarity within content