knowledge-rag

pip install knowledge-rag   →   restart Claude Code   →   search_knowledge("your query")

⭐ Star History

_{Chart updated daily by GitHub Action}

🎯 Why knowledge-rag

Most RAG frameworks fall into one of three traps: (1) they require you to ship your data to a cloud API, (2) they hand you 300 building blocks and 0 opinionated defaults, or (3) they bundle RAG as a 5% feature of a much bigger platform you didn't ask for.

knowledge-rag does one thing well: it is the MCP-native local RAG server that Claude Code, Cursor, Windsurf, VS Code, Cline, Gemini CLI and Zed can search out of the box — with enterprise plumbing (bearer auth, Prometheus metrics, rate limiting, health probes, structured JSON logging, zero-downtime reindex) that no other RAG-focused OSS ships built-in.

📊 How knowledge-rag compares to other RAG frameworks

We audited 16 popular RAG frameworks and platforms (LlamaIndex, LangChain, ChromaDB, Weaviate, Qdrant, RAGFlow, LightRAG, DSPy, GraphRAG, Haystack, RAG-Anything, kotaemon, txtai, llmware, Dify, open-webui, FastGPT) so you can pick honestly.

Legend: ✅ built-in · 🟡 plugin / paid tier / partial · ❌ not available · ⚠️ license or default concern

The 5 dimensions where knowledge-rag is unique: health probes + JSON logging + Prometheus + rate limit + bearer auth simultaneously built-in on an OSS RAG-focused MCP server. Zero-downtime reindex + async background reindex + nightly chaos/soak/mutation are documented on nobody else's README.

🚀 Quick Start (3 minutes, from zero to your first query)

Pick your integration path — knowledge-rag ships the same server through every channel.

Path 1 — Claude Code, Cursor, Windsurf, Cline, VS Code, Gemini CLI, Zed (MCP)

pip install knowledge-rag
knowledge-rag init                    # scaffolds config.yaml + documents/

Drop your PDFs, markdown, code files into documents/. Restart your MCP client. Ask it:

search_knowledge("your query")

That's it. First query loads the ONNX embedding model (~200MB, one-off download). Subsequent queries are cached and hit sub-second latency.

Path 2 — HTTP / SSE server (multi-user, air-gapped, load-balanced)

# config.yaml
server:
  transport: "sse"                    # or "streamable-http"
  host: "0.0.0.0"
  port: 8179
  auth:
    bearer_token: "your-secret-token"
  rate_limit:
    enabled: true
    requests_per_minute: 60
  metrics:
    enabled: true
    port: 9179
  logging:
    format: "json"                    # ELK / Loki / Datadog / CloudWatch ready

knowledge-rag --transport sse

• Health probe: curl http://your-host:8179/health → 200 + JSON payload
• Prometheus scrape: http://your-host:9179/metrics
• MCP dispatcher: authenticated via Authorization: Bearer your-secret-token

Path 3 — Docker (models pre-downloaded, air-gapped ready)

docker pull ghcr.io/lyonzin/knowledge-rag:latest
docker run -v $(pwd)/documents:/app/documents -p 8179:8179 ghcr.io/lyonzin/knowledge-rag:latest

Full installation guide with all 5 methods, 8 MCP client configurations, and GPU setup: docs/INSTALLATION.md →

🤖 Ready-to-use skills for AI agents

Installing knowledge-rag gives your agent 13 MCP tools. It does not tell the agent when to use them. That is what the skills/ folder solves — drop-in behavioural skills for Claude Code, Cursor, Windsurf, Cline, Zed, VS Code Copilot that turn "AI with access to RAG" into "AI that actually uses RAG first".

10 skills, MIT licensed, organized by kind:

Install — pick the shortest path for your machine:

# Option 1 — Via skills.sh (needs Node — one command, zero clone)
npx skills add lyonzin/knowledge-rag

# Option 2 — Via our install.sh (no Node needed; works on Linux/macOS/WSL/Git Bash)
curl -fsSL https://raw.githubusercontent.com/lyonzin/knowledge-rag/master/skills/install.sh | bash

Both restart-Claude-Code and you are done. Option 2 supports --project, --only rag-check-first,rag-cite-sources, --dry-run, --help.

For Cursor, Windsurf, Cline and full manual instructions → skills/README.md · Full catalog with skill chains → skills/CATALOG.md

🛠️ The 13 MCP tools your agent gets

Once installed, your AI agent gets these 13 tools automatically:

Full API reference with parameter details, return schemas, examples: docs/API.md →

🏢 Enterprise Features (built-in, zero configuration)

Every RAG framework claims "production-ready." Here is what knowledge-rag ships in the OSS core, verified by regression tests, that competitors either paywall, plugin-ify, or simply don't have.

💼 Use Cases (real corpora, real teams)

Security Teams — Red / Blue / CTF

Preset: cybersecurity.yaml · 8 categories · 200+ routing keywords · 69 query expansions

Ingest MITRE ATT&CK, threat reports, exploit writeups, incident reports. Search from Claude Code with search_knowledge("privilege escalation windows") and get instant recall across your entire corpus. Air-gapped — nothing leaves the laptop.

Development Teams — Design Docs, Runbooks, Code

Preset: developer.yaml · 9 categories · 150+ routing keywords · 50+ expansions

Replace Confluence hunting. Ingest architecture docs, ADRs, runbooks, code, API specs. Devs ask their AI agent "how do we authenticate the payment service" and get the exact ADR + implementation file citation.

Research Labs — Papers, Notebooks, Datasets

Preset: research.yaml · 9 categories · 100+ routing keywords · 40+ expansions

Index arXiv papers, lab notebooks, dataset documentation. Semantic search finds papers by intent, not just keywords — cross-encoder reranking surfaces the actually-relevant one instead of five that share a term.

Enterprise Knowledge Base — Air-gapped, Auditable

Preset: general.yaml · blank slate, pure semantic search

Deploy via SSE on a single VM. 40+ users authenticated via bearer token, rate-limited, Prometheus-monitored, /health probes wired to your load balancer, JSON logs shipped to Datadog. No cloud calls. Meets LGPD, GDPR, HIPAA data-locality requirements by design.

Verified at scale: production reproduction on a 5 889-doc / 75 016-chunk corpus with concurrent queries during a nuclear rebuild — zero downtime, zero errors (see CHANGELOG v4.8.3).

🏗️ Architecture at a glance

End-to-end view of how MCP clients, the retrieval pipeline, storage, and enterprise plumbing connect. Every arrow is a real code path — nothing pictured here is aspirational.

flowchart TB
    subgraph CLIENTS["MCP Clients (any of these)"]
        C1[Claude Code]
        C2[Claude Desktop]
        C3[Cursor]
        C4[Windsurf]
        C5[VS Code · Cline · Gemini CLI · Zed]
    end

    subgraph TRANSPORT["Transport Layer"]
        T1[stdio<br/>1 process per client]
        T2[SSE / streamable-http<br/>1 server serves N clients]
    end

    subgraph MIDDLEWARE["ASGI Middleware Chain (HTTP mode)"]
        M1[HealthMiddleware<br/>/health · /healthz]
        M2[BearerAuthMiddleware<br/>constant-time compare]
        M3[Rate Limiter<br/>sliding window]
    end

    subgraph MCP["13 MCP Tools (frozen contract)"]
        MT1[search_knowledge]
        MT2[get_document · search_similar]
        MT3[add_document · add_from_url · update · remove]
        MT4[reindex_documents · get_reindex_status]
        MT5[list_documents · list_categories · get_index_stats · evaluate_retrieval]
    end

    subgraph SEARCH["Retrieval Pipeline"]
        R[Query Router<br/>lexical vs semantic]
        F[FTS5 Fast-Path<br/>opt-in · lt 10ms]
        BM[BM25 Inverted Index<br/>128x faster than baseline]
        SE[Semantic Search<br/>FastEmbed ONNX lazy-loaded]
        RRF[Reciprocal Rank Fusion]
        CE[Cross-Encoder Rerank<br/>MiniLM-L-6-v2]
        QC[Query Cache<br/>LRU + 5-min TTL]
    end

    subgraph STORAGE["Storage (100% local)"]
        CH[ChromaDB<br/>vectors + metadata<br/>WAL mode]
        FT[SQLite FTS5<br/>lexical index<br/>WAL + busy-timeout]
        MD[index_metadata.json<br/>durable state]
    end

    subgraph INGEST["Document Ingestion"]
        FS[documents/ folder]
        WD[Watchdog<br/>10s debounce]
        PA[20 Parsers<br/>MD · PDF · DOCX · code · IPYNB]
        CK[Chunker<br/>markdown-aware · code-aware]
        EM[FastEmbed ONNX<br/>384D bge-small-en-v1.5]
        DD[SHA256 Dedup]
        SW[Zero-downtime Staging Swap<br/>rollback on validation fail]
    end

    subgraph OBS["Enterprise Observability (opt-in)"]
        PM[Prometheus /metrics<br/>7 canonical + histograms]
        LG[Structured JSON logs<br/>ELK · Loki · Datadog · CloudWatch]
        HC[Health payload<br/>version · uptime · cache stats]
    end

    subgraph CFG["Configuration"]
        YM[config.yaml<br/>+ 5 domain presets]
    end

    C1 & C2 & C3 & C4 & C5 -->|MCP protocol| T1
    C1 & C2 & C3 & C4 & C5 -.->|remote deploy| T2
    T1 --> MCP
    T2 --> M1 --> M2 --> M3 --> MCP

    MT1 --> QC
    QC -->|cache miss| R
    R -->|lexical| F
    R -->|semantic| SE
    R -->|hybrid| BM
    F --> CH
    F --> FT
    BM --> CH
    SE --> CH
    BM --> RRF
    SE --> RRF
    RRF --> CE
    CE --> QC

    MT2 --> CH
    MT3 --> INGEST
    MT4 --> SW
    MT5 --> CH

    FS --> WD --> PA
    PA --> CK --> EM --> DD --> CH
    SW -.->|atomic swap| CH
    SW -.-> FT
    CH -.-> MD

    MCP -.->|instrumented| PM
    MCP -.->|logs| LG
    M1 --> HC

    YM -.-> SEARCH
    YM -.-> STORAGE
    YM -.-> OBS
    YM -.-> MIDDLEWARE

    classDef client fill:#3776AB,stroke:#1e5a8a,color:#fff
    classDef transport fill:#00A67E,stroke:#006e54,color:#fff
    classDef middleware fill:#6b46c1,stroke:#4c1d95,color:#fff
    classDef storage fill:#4b5563,stroke:#1f2937,color:#fff
    classDef obs fill:#dc2626,stroke:#7f1d1d,color:#fff
    classDef ingest fill:#f59e0b,stroke:#78350f,color:#fff

    class C1,C2,C3,C4,C5 client
    class T1,T2 transport
    class M1,M2,M3 middleware
    class CH,FT,MD storage
    class PM,LG,HC obs
    class FS,WD,PA,CK,EM,DD,SW ingest

Reading the diagram (top → bottom):

1. Any MCP client — Claude Code, Cursor, Windsurf, and 5 others — connects via the transport of your choice (stdio for personal use, SSE/streamable-http for teams).
2. HTTP mode chains 3 ASGI middlewares in order: health probes first (always answered), then bearer auth (fenced with WWW-Authenticate), then rate limiter (sliding window).
3. All 13 MCP tools are decorated with @rate_limited + @instrument — Prometheus counts every call, rate limiter enforces RPM+burst, both zero-cost when disabled.
4. search_knowledge checks the query cache first; cache miss routes through the Query Router (regex classifier) to either the FTS5 fast-path (lexical) or the hybrid pipeline (BM25 + semantic + RRF + cross-encoder rerank).
5. Storage is 100% local: ChromaDB (WAL mode) for vectors + metadata, SQLite FTS5 (WAL + busy-timeout) for lexical fast-path, index_metadata.json for durable state.
6. Document ingestion runs continuously: watchdog observes documents/, 20 parsers handle each format, chunker respects language boundaries, FastEmbed ONNX generates embeddings, SHA256 deduplicates, and a staging swap performs zero-downtime rebuilds with rollback-on-failure.
7. Enterprise observability (opt-in) — Prometheus /metrics, structured JSON logs, /health payload — attaches to the same instrumentation points, no code changes required.
8. config.yaml (with 5 domain presets) controls every subsystem — no environment variable spaghetti, no hardcoded paths.

Complete architecture — 4 detailed Mermaid diagrams (System Overview · Query Flow · Document Ingestion · hybrid_alpha effect): docs/ARCHITECTURE.md

📄 20 File Formats — parsed natively, no plugins needed

Every parser is chunk-aware — Markdown splits at ## headers, code splits at function/class boundaries, notebooks skip base64 outputs, PDFs use PyMuPDF, spreadsheets extract sheet-by-sheet. 18 formats are enabled by default; the 2 MetaTrader formats are opt-in (add to documents.supported_formats in config.yaml).

Enable an opt-in format — add the extension to documents.supported_formats in your config.yaml: yaml documents: supported_formats: [".md", ".pdf", ".mq4", ".mqh"]

Full parser reference with per-format notes: docs/CONFIGURATION.md

🔌 Choose your MCP integration

Complete client configuration guide with JSON schemas per client: docs/INSTALLATION.md#use-with-other-mcp-clients →

⚙️ Configuration in 30 seconds

# config.yaml — everything is optional; defaults just work

paths:
  documents_dir: "./documents"
  data_dir: "./data"

models:
  embedding:
    profile: "compact"                  # "compact" | "quality" | "multilingual" | "custom"
    gpu: "auto"                         # "auto" | "true" | "false"
  reranker:
    enabled: true                       # cross-encoder rerank

search:
  default_results: 5
  max_results: 100

server:                                 # optional — SSE / HTTP mode
  transport: "stdio"                    # or "sse" / "streamable-http"
  auth:
    bearer_token: ""                    # set a secret to enable auth
  rate_limit:
    enabled: false
  metrics:
    enabled: false
  logging:
    format: "text"                      # or "json"

Pre-built presets: cybersecurity.yaml · developer.yaml · research.yaml · general.yaml · multilingual.yaml

Complete configuration reference — every field, every default, tuning guide: docs/CONFIGURATION.md →

🔒 Security & Compliance

knowledge-rag is designed for teams that cannot let their documents leave the perimeter.

OpenSSF Best Practices badge: passing · project ID #13864

📈 Numbers that matter

• 26 000+ total downloads on PyPI · 250+ GitHub stars · 70+ enterprise teams (private + community)
• 700+ tests collected · 1.33:1 test-to-code ratio · codecov trend gate ±0.5pp
• 35+ status checks on every PR (9-cell OS×Python matrix · 7 quality pillars)
• 20 file formats parsed natively · 13 MCP tools frozen · 5 domain presets (cyber · dev · research · multilingual · general)
• BM25 128× faster than baseline · cross-encoder +1.88pp Recall@10 (p<0.001) · cache −40% p95 latency
• 1 800+ files / 39 K chunks indexed in < 3 min on a modern laptop (typical developer corpus)
• Verified in production on 5 889-doc / 75 016-chunk corpora

Public benchmark dashboard: https://lyonzin.github.io/knowledge-rag/

📚 Documentation

🤝 Community & Support

• Report a bug → Open an issue
• Ask a question → GitHub Discussions
• Report a vulnerability → Security advisory (private)
• Contribute → CONTRIBUTING.md

Response SLA (best-effort, community project): - Security reports: within 48 h - Bug reports with reproduction: within 5 business days - Feature requests: triaged on next release cycle

🗺️ Recent releases

• v4.8.5 (2026-08-13) — Enterprise observability: /health endpoint + opt-in JSON structured logging
• v4.8.4 (2026-08-13) — Patch: security + durability + defensive fixes
• v4.8.3 (2026-08-10) — Critical hotfix: nuclear-rebuild + smart-reindex hardening on 50k+ chunk corpora
• v4.8.2 (2026-08-10) — FTS5 lexical fast-path opt-in release
• v4.8.0 (2026-08-06) — Multilingual foundation + zero-downtime reindex

Full history: CHANGELOG.md →

📜 License

MIT License — LICENSE. Forever. No cloud upsell, no dual-licensing, no restrictive clauses. Fork it, sell derivatives, embed it in commercial products — the license does not care.

🙏 Acknowledgments

Built on the shoulders of amazing open-source projects:

• Anthropic MCP — Model Context Protocol spec + Python SDK
• ChromaDB — vector database that just works
• FastEmbed — ONNX embeddings, no PyTorch bloat
• HuggingFace — model hosting + Xenova/ms-marco-MiniLM-L-6-v2 cross-encoder
• BAAI — the bge-small-en-v1.5 embedding model

Community contributors: @Hohlas · @eeshsaxena · Sergey Khokhlov · and everyone who filed issues or PRs.