Build a Coding Assistant with Weaviate MCP: RAG over Code & Docs

The text discusses the implementation of a coding assistant using Weaviate's Model Context Protocol (MCP) server to efficiently manage and retrieve data from a codebase and its documentation. The challenge of working without retrieval is highlighted, as it can lead to inefficiencies when agents guess context or unnecessarily use up tokens. Retrieval-Augmented Generation (RAG) typically solves this by indexing the codebase in a vector database, allowing for precise data retrieval. Weaviate simplifies this process by integrating the MCP server directly within the database, reducing operational complexity. The document outlines steps to set up this system, including enabling MCP, designing schemas for code and documentation chunks, ingesting data, and connecting LLM clients like Claude Code, Cursor, and VS Code. The use of hybrid search, which combines BM25 and vector search, is emphasized for balancing identifier and semantic intent retrieval, thus enhancing the coding assistant's capability to understand and interact with the codebase. This setup allows agents to efficiently query and retrieve relevant code or documentation segments, making the assistant more knowledgeable and effective in providing real-time, contextually accurate responses.

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