Turbovec MCP Server: A local Retrieval-Augmented Generation (RAG) tool that allows AI to seamlessly read, chunk, remember, and semantically search through large project codebases.
Turbovec MCP (Long-Term Memory RAG for AI)
Turbovec MCP Server is a Model Context Protocol (MCP) implementation that acts as a persistent, long-term memory (Semantic RAG) for AI coding assistants like Zoo Code, Claude Desktop, and Cursor.
By running this local server, your AI assistant gains the ability to "read", "remember", and "semantically search" through vast amounts of code and documentation across different chat sessions, completely bypassing token limitations.
1. Context Window Limits: When working on large projects, pasting hundreds of files into the AI chat will exceed token limits or cause the AI to hallucinate. 2. AI Amnesia (Stateless Chats): Whenever you start a new chat tab, the AI forgets everything you discussed in the previous session (e.g., project architecture, specific coding guidelines). 3. Literal Search vs. Semantic Search: Standard file search (CTRL+F) requires exact keyword matches. This server allows the AI to search by meaning (e.g., searching for "user authentication" will find loginhandler).
- Persistent Local Memory: Data is safely saved to your local disk (metadata.json and index.bin). It never expires and survives across system restarts. - Intelligent Text Chunking: Automatically breaks down large documents into overlapping semantic chunks (1000 chars) before embedding, ensuring context is never lost. - Flawless MCP Stdio Communication: Strictly intercepts and suppresses rogue C-level progress bars (like tqdm from sentence-transformers) that normally corrupt JSON-RPC streams, ensuring a stable connection. - 100% Local Privacy: Runs entir
From the project README.
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Turbovec MCP Server: A local Retrieval-Augmented Generation (RAG) tool that allows AI to seamlessly read, chunk, remember, and semantically search through large project codebases.
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