#rag

Local-first markdown context engine for AI coding agents. Indexes a repo's docs into SQLite (FTS5 + local vectors) and exposes CLI, HTTP, and MCP over one service core. Ships a docs registry, skills/agents sync, and code-to-doc governance.

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Upstash Vector Database PHP Client, Upsert, Query and Range vectors

Local-first docs retrieval for AI agents: mirror any docs corpus to Markdown, index with SQLite FTS5 (pure Go), search with optional embedding/LLM rerank, and measure quality with reproducible evals.

Give any MCP-capable agent persistent memory: remember/recall over a tiered store with hybrid vector + keyword retrieval. Single Go binary, SQLite or Postgres, embedded admin UI.

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Lightweight QMD alternative: fully local hybrid search engine (BM25 + vector + rerank) and MCP server in a single ~15MB Go binary. No Node.js. Runs on Raspberry Pi.

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A robust, strictly-typed Node.js and Browser library for parsing office files into a rich Abstract Syntax Tree (AST) and generating high-fidelity output in multiple formats. Parses: docx · pptx · xlsx · odt · odp · ods · pdf · rtf · csv · md · html. Generates: Markdown · HTML · CSV · RTF · PDF · Plain Text · RAG Chunks

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