{"repo":"dannwaneri/vectorize-mcp-worker","free":true,"listed":false,"github":"https://github.com/dannwaneri/vectorize-mcp-worker","clone":"git clone https://github.com/dannwaneri/vectorize-mcp-worker.git","description":"Hybrid RAG Worker on Cloudflare Edge — Vector + BM25 search, metadata filtering, multimodal vision, MCP server, and intelligent query routing. ~400ms p99.","language":"TypeScript","stars":26,"topics":["bm25","cloudflare-ai","cloudflare-workers","d1","embeddings","hybrid-search","knowledge-base","llm","mcp","rag"],"license":"MIT","category":"ai-agents","readme_excerpt":"Vectorize MCP Worker Production-grade RAG on Cloudflare Workers. $5/month. No servers. No Pinecone bill. Hybrid search, knowledge reflection, multimodal ingestion, metadata filtering, multi-tenancy, rate limiting, and a native MCP server — all in one deployable Worker. --- Why This Exists Andrej Karpathy made a compelling argument recently: LLMs are becoming the new Wikipedia. Ask the model, get the answer. For general knowledge questions, he's right — if you're asking \"what is gradient descent,\" you don't need a retrieval pipeline. But that framing has a blind spot: Your data isn't in any LLM's training set. It never will be. Your internal docs, customer contracts, support tickets, financial reports, product changelog — none of it lives in any model. The moment you need answers grounded in your specific knowledge base, you need retrieval. The moment you need results that are cited, up-to-date, and not hallucinated, you need RAG. This project is for that use case. It's not trying to beat LLMs at general knowledge. It's making them useful on your data. The second reason this exists: most RAG tutorials hand you a 50-line Python script that calls OpenAI and Pinecone and calls it a day. That's fine for a weekend demo. It falls apart the moment you need multi-tenancy, metadata filtering, sub-second cached responses, a proper MCP server, and something you'd actually trust in production. This is what happens when you build the thing properly from the start. --- Key Features Hybrid S","default_branch":null,"files":null,"tree":[],"storefront":"/r/dannwaneri","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/dannwaneri/vectorize-mcp-worker/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}