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AST-aware chunking, incremental indexing, millions of LOC. Reranks results using authorship, churn, bug-fix rates, and 19 other signals — not just embedding similarity. Built on Qdrant. Works with Ollama (local) or cloud providers (OpenAI, Cohere, Voyage).\n\n> 📖 **[Full documentation](https://artk0de.github.io/TeaRAGs-MCP/)** — 15-minute quickstart, agent workflows, architecture deep dives.\n\n## 🧬 Trajectory Enrichment\n\nStandard code RAG retrieves by similarity alone. **Trajectory enrichment** augments each chunk with signals about how code *evolves* — at the function level, not just file level.\n\n- 🔀 **Git trajectory** — churn, authorship, volatility, bug-fix rates, task traceability. **19 signals** feed composable rerank presets (`hotspots`, `ownership`, `techDebt`, `securityAudit`...)\n- 🕸️ **Topological trajectory** *(planned)* — symbol graphs, cross-file coupling, blast radius\n\nOpt-in via `CODE_ENABLE_GIT_METADATA=true`. Without it — standard semantic search with AST-aware chunking.\n\n> 💡 An agent can **find stable templates**, **avoid anti-patterns**, **match domain owner's style**, and **assess modification risk** — all backed by empirical data. [Read more →](https://artk0de.github.io/TeaRAGs-MCP/introduction/core-concepts)\n\n## 🚀 Quick Start\n\n```bash\ngit clone https://github.com/artk0de/TeaRAGs-MCP.git\ncd TeaRAGs-MCP\nnpm install && npm run build\n\n# Start Qdrant + Ollama\npodman compose up -d\npodman exec ollama ollama pull unclemusclez/jina-embeddings-v2-base-code:latest\n\n# Add to Claude Code\nclaude mcp add tea-rags -s user -- node /path/to/tea-rags-mcp/build/index.js \\\n  -e QDRANT_URL=http://localhost:6333 \\\n  -e EMBEDDING_BASE_URL=http://localhost:11434\n```\n\nThen ask your agent: *\"Index this codebase for semantic search\"*\n\n## 📚 Documentation\n\n**[artk0de.github.io/TeaRAGs-MCP](https://artk0de.github.io/TeaRAGs-MCP/)**\n\n| | Section | What's inside |\n|---|---------|---------------|\n| 🏁 | [Quickstart](https://artk0de.github.io/TeaRAGs-MCP/quickstart/installation) | Installation, first index & query |\n| ⚙️ | [Configuration](https://artk0de.github.io/TeaRAGs-MCP/usage/configuration) | Env vars, providers, tuning |\n| 🤖 | [Agent Integration](https://artk0de.github.io/TeaRAGs-MCP/agent-integration/search-strategies) | Prompt strategies, generation modes, deep analysis |\n| 🏗️ | [Architecture](https://artk0de.github.io/TeaRAGs-MCP/architecture/overview) | Pipeline, data model, reranker internals |\n\n## 🤝 Contributing\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for workflow and conventions.\n\n## 🙏 Acknowledgments\n\nBuilt on a fork of **[mhalder/qdrant-mcp-server](https://github.com/mhalder/qdrant-mcp-server)** — clean architecture, solid tests, open-source spirit. And its ancestor **[qdrant/mcp-server-qdrant](https://github.com/qdrant/mcp-server-qdrant)**. Code vectorization inspired by **[claude-context](https://github.com/zilliztech/claude-context)** (Zilliz).\n\n_Feel free to fork this fork. It's forks all the way down._ 🐢\n\n## ⚖️ License\n\nMIT — see [LICENSE](LICENSE). Brand policy in [BRAND.md](BRAND.md).\n","readmeFilename":"README.md"}