DevNexsler/RAG-In-A-Box

RAG In A Box

Drop your documents into a folder, run the indexer, and get a production-grade RAG pipeline with an MCP server — any MCP-compatible AI assistant (Claude Code, OpenClaw, Claude Desktop, Cursor, etc.) can search your documents with a single config entry.

No infrastructure to manage. No GPU required. Works with cloud APIs out of the box or fully self-hosted.

Use cases

• Personal knowledge base — Index your notes, PDFs, documents, images, audio, and video. Ask your AI assistant questions and get answers grounded in your own files.
• Company document search — Drop legal contracts, reports, SOPs into a folder. Employees search via any MCP-compatible assistant with metadata filters (by department, doc type, date, tags).
• Research assistant — Index papers, datasets, and notes. Search by meaning, not just keywords. LLM enrichment auto-extracts entities, topics, and key facts.
• Obsidian / Markdown vault — Works with any markdown source (Obsidian, HackMD, Notion exports, GitBook). Extracts YAML frontmatter for rich filtering.
• PDF-heavy workflows — Scanned PDFs get OCR automatically. Page-aware chunking keeps context intact. Metadata (author, dates, page count) extracted from PDF properties.
• Multi-agent tool — Expose your document collection as 16 MCP tools. Multiple agents can search, browse, filter, and manage taxonomy concurrently.
• Local Deep Research integration — Let LDR run agentic web/academic research while searching this RAG index through a LangChain retriever. See Local Deep Research Integration.

Why this over other RAG tools?

| Capability | RAG In A Box | Typical RAG | |---|---|---| | Search quality | 10-step hybrid pipeline (vector + BM25 + reranker + MMR) | Vector-only or basic hybrid | | Document understanding | LLM enrichment extracts summary, entities, topics, importance | Raw chunks, no enrichment | | Filtering | Pre-filter by tags, folder, doc