knowledge-rag — Local Hybrid RAG for MCP

# knowledge-rag
[![PyPI](https://img.shields.io/pypi/v/knowledge-rag)](https://pypi.org/project/knowledge-rag/) [![NPM](https://img.shields.io/npm/v/knowledge-rag)](https://www.npmjs.com/package/knowledge-rag) [![PyPI Downloads](https://static.pepy.tech/personalized-badge/knowledge-rag?period=total&units=INTERNATIONAL_SYSTEM&left_color=BLACK&right_color=GREEN&left_text=downloads)](https://pepy.tech/projects/knowledge-rag) ![Python](https://img.shields.io/badge/python-3.11%2B-green.svg) ![License](https://img.shields.io/badge/license-MIT-yellow.svg) ![Platform](https://img.shields.io/badge/platform-Windows%20%7C%20Linux%20%7C%20macOS-lightgrey.svg) ![GPU](https://img.shields.io/badge/GPU-NVIDIA%20CUDA-76B900.svg?logo=nvidia) [![CI](https://github.com/lyonzin/knowledge-rag/actions/workflows/ci.yml/badge.svg)](https://github.com/lyonzin/knowledge-rag/actions/workflows/ci.yml) [![CodeQL](https://github.com/lyonzin/knowledge-rag/actions/workflows/security.yml/badge.svg)](https://github.com/lyonzin/knowledge-rag/actions/workflows/security.yml) [![Quality Gate](https://github.com/lyonzin/knowledge-rag/actions/workflows/quality-gate.yml/badge.svg)](https://github.com/lyonzin/knowledge-rag/actions/workflows/quality-gate.yml) [![Glama Score](https://glama.ai/mcp/servers/lyonzin/knowledge-rag/badges/score.svg)](https://glama.ai/mcp/servers/lyonzin/knowledge-rag) [![OpenSSF Best Practices](https://bestpractices.coreinfrastructure.org/projects/13864/badge)](https://bestpractices.coreinfrastructure.org/projects/13864) ### The MCP-first local RAG server for Claude Code, Cursor, and every AI agent. **Hybrid search · Cross-encoder reranking · 35 file formats · 100% local · Zero cloud · Enterprise-grade plumbing built-in.** ```bash pip install knowledge-rag → restart Claude Code → search_knowledge("your query") ``` [Quick Start](#quick-start-3-minutes-from-zero-to-your-first-query) · [Why knowledge-rag](#why-knowledge-rag) · [Compare](#how-knowledge-rag-compares-to-other-rag-frameworks) · [Enterprise Features](#enterprise-features-built-in-zero-configuration) · [Docs](#documentation)
--- ## ⭐ Star History
knowledge-rag star history chart — GitHub star growth over time 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 is an MCP-native local RAG server** for clients such as Claude Code, Cursor, Windsurf, VS Code, Cline, Gemini CLI and Zed. It includes optional bearer authentication, Prometheus metrics, rate limiting, health probes, structured JSON logging and staging-based rebuilds. Configure the transport and access controls for the intended deployment.
### 🔒 100% local, 0% cloud Embedding generation, indexing, and retrieval run locally. Model downloads and `add_from_url` use network access, and configured MCP clients receive retrieved content. Choose the corpus, client permissions, and network exposure to fit your deployment; this project does not certify regulatory compliance. ### 🚀 Zero-friction setup `pip install knowledge-rag` → restart your MCP client → done. **No Docker mandatory. No Ollama required. No separate embedding server.** Everything runs in-process via FastEmbed ONNX. **Works offline** after the first model download.
### 🛡️ Production-grade OSS 7-pillar quality workflow, a 9-cell OS×Python CI matrix (Linux + Windows + macOS × 3.11/3.12/3.13), and nightly chaos, bounded-memory soak and mutation tests. Inspect the checks on the revision being deployed; CI coverage is not a guarantee of zero defects. ### 💰 Zero ongoing cost No token bills. No SaaS tier. No paid features hidden behind a wall. **MIT license, forever.** Runs on the laptop you already have — GPU optional, CPU works fine with FastEmbed ONNX.
--- ## 📊 How knowledge-rag compares to other RAG frameworks Compare the features needed for your deployment rather than treating framework checklists as a benchmark. Knowledge-rag combines MCP tools, local embeddings, ChromaDB persistence, BM25, optional FTS5 and optional reranking in one process. | Decision | What to verify | | --- | --- | | Local or remote processing | Where models run, which client receives content, and which features need network access | | Retrieval quality | Expected-source queries on your corpus, including its languages and identifiers | | Performance | Cold initialization, indexing, uncached queries, memory peaks and concurrent use on comparable hardware | | Operations | Authentication, metrics exposure, backups, recovery and one-writer ownership | | Extensibility | Supported parsers, model profiles and the public MCP contract | The [audit report](docs/audit-2026-10-03.md) records a reproducible local workload and its limitations. Results against an older implementation of this project are not speed comparisons against other RAG frameworks. --- ## 🚀 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) ```bash 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) ```yaml # 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 ``` ```bash 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` ### Multi-client setup (Shared Server via Streamable HTTP) `stdio` is ideal when one MCP client owns one `knowledge-rag` process. If several clients (e.g. Claude, Cursor, LM Studio, or multiple terminal windows) should share one index and one running process, run `knowledge-rag` once with Streamable HTTP and point each HTTP-capable client at the same endpoint (prompted by [#217](https://github.com/lyonzin/knowledge-rag/pull/217) and issue [#223](https://github.com/lyonzin/knowledge-rag/issues/223)): ```bash knowledge-rag --transport streamable-http # MCP endpoint: http://127.0.0.1:8179/mcp ``` - **Local MCP clients (Cursor, LM Studio, etc.):** Point their remote-server URL directly at `http://127.0.0.1:8179/mcp`. - **Claude Custom Connectors (Remote MCP):** - **Network requirement:** Custom connector requests originate from Anthropic's cloud infrastructure across web, desktop, and mobile, so they cannot reach local `127.0.0.1`. You must expose your server via a secure, publicly accessible HTTPS endpoint (e.g. Cloudflare Tunnel, ngrok, or reverse proxy) at `https://your-public-domain/mcp`. Configure the proxy to send an upstream `Host` accepted by the HTTP app for `server.host` (for the default loopback configuration, use `127.0.0.1:8179`); forwarding the public hostname unchanged can produce `421 Invalid Host header`. Keep bearer authentication and HTTPS enabled. Local `claude_desktop_config.json` uses `command`/`args` for local stdio processes and does not support remote HTTP endpoints. - **Setup & Auth:** In Claude (Pro/Max plans: **Customize > Connectors > + Add > Add custom connector**; Team/Enterprise owners: **Organization settings > Connectors**), enter your public endpoint. In **Request headers**, set header name `Authorization` with value `Bearer ` matching `server.auth.bearer_token` in `config.yaml` (see [Anthropic Custom Connectors documentation](https://support.claude.com/en/articles/11175166-get-started-with-custom-connectors-using-remote-mcp)). | Transport | Clients | Shared index / process | RAM overhead | Process management | Startup & query latency | Best fit | |---|:---:|:---:|---|---|---|---| | `stdio` | 1 | No | 1 runtime per client; models load lazily in each process when queried | Client manages its own process; no separate server | Spawns per client; pays preflight and first-query model load | Simplest local setup (single client) | | `sse` | Many | Yes | Shared server process; single shared model load across clients | Operator manages one background server | Reuses warm server; first-query model load still applies | Legacy HTTP clients | | `streamable-http` | Many | Yes | Shared server process; single shared model load across clients | Operator manages one background server | Reuses warm server; first-query model load still applies | **Preferred** shared-server setup | When an HTTP transport is selected, `knowledge-rag` automatically enables its single-instance lock (`KNOWLEDGE_RAG_SINGLE_INSTANCE=1`) to prevent multiple servers from binding the same data directory or port. > **Security note:** The default host is `127.0.0.1`. If you bind to `0.0.0.0` or deploy across a network, configure `server.auth.bearer_token` in `config.yaml` and terminate TLS (HTTPS) via a reverse proxy (e.g. Caddy, Nginx) to protect bearer tokens in transit; otherwise the server warns that the HTTP endpoint is unauthenticated. ### Path 3 — Docker (models pre-downloaded, air-gapped ready) ```bash 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 →](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/`](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:** | # | Skill | What it does | |---|---|---| | 1 | [`rag-check-first`](skills/foundation/rag-check-first/SKILL.md) | Search the corpus **before** answering any technical claim | | 2 | [`rag-cite-sources`](skills/foundation/rag-cite-sources/SKILL.md) | Every claim ships with `path:line` citations | | 3 | [`rag-onboard-context`](skills/foundation/rag-onboard-context/SKILL.md) | First interaction of a session probes what is indexed | | 4 | [`rag-deep-dive`](skills/workflow/rag-deep-dive/SKILL.md) | 3-step drill: `search` → `fetch` → `find similar` | | 5 | [`rag-web-fallback`](skills/workflow/rag-web-fallback/SKILL.md) | Only hit the web when local RAG comes back empty | | 6 | [`rag-troubleshoot`](skills/workflow/rag-troubleshoot/SKILL.md) | Bug / error → RAG first for prior fixes | | 7 | [`rag-code-review`](skills/workflow/rag-code-review/SKILL.md) | Review consults ADRs / patterns before commenting | | 8 | [`rag-index-decisions`](skills/maintenance/rag-index-decisions/SKILL.md) | After a decision, index it back — close the feedback loop | | 9 | [`rag-security-first`](skills/domain/rag-security-first/SKILL.md) | Security tasks: MITRE / CVE / runbook first | | 10 | [`rag-evaluate-quality`](skills/maintenance/rag-evaluate-quality/SKILL.md) | Weekly checkup — MRR@5 · Recall@5 · Precision@5 | **Install — pick the shortest path for your machine:** ```bash # 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](skills/README.md) · Full catalog with skill chains → [skills/CATALOG.md](skills/CATALOG.md) --- ## 🛠️ The 13 MCP tools your agent gets Once installed, your AI agent gets these 13 tools automatically: | Tool | Purpose | |---|---| | `search_knowledge` | Hybrid semantic + BM25 with cross-encoder rerank | | `get_document` | Retrieve full content of one document | | `search_similar` | Find documents similar to a reference | | `evaluate_retrieval` | Measure MRR@5 · Recall@5 · Precision@5 | | `add_document` | Index a new document via MCP | | `update_document` | Re-index a changed document | | `remove_document` | Drop a document + all its chunks | | `add_from_url` | Fetch, sanitize, and index a URL | | `list_documents` | Enumerate indexed documents | | `list_categories` | Auto-tagged by folder path | | `get_index_stats` | Corpus size, cache hit rate, embedding dim | | `reindex_documents` | Smart incremental OR nuclear rebuild | | `get_reindex_status` | Live progress polling (async reindex) | **Full API reference with parameter details, return schemas, examples:** [docs/API.md →](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.**
### Security - **Bearer token auth** on SSE / HTTP transports — constant-time comparison (`hmac.compare_digest`), RFC 6750 challenge, 401 fenced with `WWW-Authenticate` header - **Path traversal + symlink escape defenses** — `validate_path_within` guarding 6 CRUD tools (CWE-22, CWE-59) - **External-content handling** — known-token neutralization and a stored provenance fence, with parser metadata propagation. These are limited mitigations; clients must still treat retrieved content as untrusted data. See [SECURITY.md](SECURITY.md). - **OpenSSF Best Practices** project [#13864](https://www.bestpractices.dev/en/projects/13864) · **CodeQL** weekly scan · **Bandit + Semgrep + Gitleaks + pip-audit** workflows. Review actual check results and exceptions for the release you deploy. - **PyPI Trusted Publishing** via OIDC (zero long-lived tokens in CI) ### Observability - **Prometheus `/metrics` endpoint** — custom histogram buckets tuned for RAG (p95 ≤ 10ms fast-path targets), 7 canonical metrics via `@instrument` decorator on all 13 tools - **Rate limiting** — thread-safe sliding-window counter, per-client RPM + burst, zero overhead when disabled - **Health probes** — `GET /health` and `/healthz` returning `{status, version, uptime_seconds, cache}` in front of the auth middleware (probes always succeed) - **Structured JSON logging** — opt-in via `server.logging.format: "json"`, one JSON object per record ready for ELK / Loki / Datadog / CloudWatch - **Public benchmark dashboard** on GitHub Pages
### Scale & performance - **SSE / streamable-http transport** — 1 server serves N MCP clients, ChromaDB WAL mode enabled automatically, shared embedding model + query cache - **BM25 inverted index** — keyword candidate retrieval without scoring every document for every query - **FTS5 SQLite fast path** — opt-in lexical retrieval; measure relevance and latency on your identifiers and corpus - **Cross-encoder reranking** — Xenova/ms-marco-MiniLM-L-6-v2; optional model scoring of a candidate pool - **NVIDIA CUDA embeddings** and experimental Windows AMD DirectML opt-in, with actual-session checks and CPU fallback; [setup and compatibility limits](docs/gpu-setup.md) - **Query cache** — LRU + 5-minute TTL, with mutation invalidation - **Staging rebuild** — populate and validate a replacement while the current collection remains available; handled publication failures roll back - **Async background reindex** with `get_reindex_status()` polling ### Reliability - **Nightly chaos injection** — HuggingFace Hub offline · ONNX zero-byte replay · watchdog crash recovery (3 scenarios in `tests/chaos/`) - **50,000-iteration soak test** — checks a fixed memory-growth budget with stub embeddings (`KNOWLEDGE_RAG_SOAK_ITERATIONS=50000`); this is not a one-hour real-model endurance test - **Mutation testing** (mutmut) on `instance_lock` + `preflight` — catches tests that are too weak - **Determinism check** — full test suite × 3, catches flakes - **Backwards compatibility** — 13 MCP tools, signature regression tests and legacy YAML fixtures; optional additions preserve existing calls - **API surface AST diff** — checks structural signature changes alongside behavioral tests - **9-cell CI matrix** — Linux + Windows + macOS × 3.11 + 3.12 + 3.13
--- ## 💼 Use Cases (real corpora, real teams) ### Security Teams — Red / Blue / CTF **Preset:** [`cybersecurity.yaml`](presets/cybersecurity.yaml) · 8 categories · 200+ routing keywords · 69 query expansions Ingest MITRE ATT&CK, threat reports, exploit writeups and incident reports. Search from an MCP client with `search_knowledge("privilege escalation windows")`. Offline operation requires cached models; the chosen client receives retrieved content and determines what happens to it afterwards. ### Development Teams — Design Docs, Runbooks, Code **Preset:** [`developer.yaml`](presets/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`](presets/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`](presets/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](CHANGELOG.md#v483-2026-08-10--critical-hotfix-nuclear-rebuild--smart-reindex-hardening)). --- ## 🏗️ 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. ```mermaid 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
1 process per client] T2[SSE / streamable-http
1 server serves N clients] end subgraph MIDDLEWARE["ASGI Middleware Chain (HTTP mode)"] M1[HealthMiddleware
/health · /healthz] M2[BearerAuthMiddleware
constant-time compare] M3[Rate Limiter
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
lexical vs semantic] F[FTS5 Fast-Path
opt-in lexical retrieval] BM[BM25 Inverted Index
keyword candidates] SE[Semantic Search
FastEmbed ONNX lazy-loaded] RRF[Reciprocal Rank Fusion] CE[Cross-Encoder Rerank
MiniLM-L-6-v2] QC[Query Cache
LRU + 5-min TTL] end subgraph STORAGE["Storage (100% local)"] CH[ChromaDB
vectors + metadata
WAL mode] FT[SQLite FTS5
lexical index
WAL + busy-timeout] MD[index_metadata.json
durable state] end subgraph INGEST["Document Ingestion"] FS[documents/ folder] WD[Watchdog
10s debounce] PA[Format parsers
MD · PDF · DOCX · code · IaC · IPYNB] CK[Chunker
markdown-aware · code-aware] EM[FastEmbed ONNX
384D bge-small-en-v1.5] DD[SHA256 Dedup] SW[Staging Rebuild
rollback on handled publication failure] end subgraph OBS["Enterprise Observability (opt-in)"] PM[Prometheus /metrics
7 canonical + histograms] LG[Structured JSON logs
ELK · Loki · Datadog · CloudWatch] HC[Health payload
version · uptime · cache stats] end subgraph CFG["Configuration"] YM[config.yaml
+ 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 -.->|validated publication| 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` — metrics record calls and the rate limiter enforces RPM+burst when enabled. Disabled features skip that work, while the lightweight wrapper calls remain. 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**: when enabled, watchdog observes `documents/`; format parsers create chunks, FastEmbed generates embeddings, and source metadata supports incremental updates. Full rebuilds use staging. See the operations guide for publication and recovery limits. 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. **Component ownership, query/ingestion diagrams, memory boundaries and hybrid weights:** [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) --- ## 📄 35 File Formats — parsed natively, no plugins needed Parsers use format-specific extraction: Markdown sections, code boundaries, notebook cells without base64 outputs, PDF text and spreadsheet rows. Several extensions share one parser. The runtime fallback list contains 33 formats; the documented template and presets can select a smaller list. Inspect your effective `documents.supported_formats`. The two MetaTrader extensions are opt-in. | # | Format | Extension | Parser | Default | Notes | |---|--------|-----------|--------|:-------:|-------| | 1 | Markdown | `.md` | Section-aware (splits at `##`) | Yes | Headers preserved as chunk boundaries | | 2 | Plain Text | `.txt` | Fixed-size chunking | Yes | 1000 chars + 200 overlap | | 3 | PDF | `.pdf` | PyMuPDF extraction or explicit page-marked UTF-8 text | Yes | No OCR; extracted-text recognition is limited to 4 MiB | | 4 | Word | `.docx` | python-docx | Yes | Headings preserved as markdown | | 5 | Excel | `.xlsx` | openpyxl | Yes | Sheet-by-sheet extraction | | 6 | PowerPoint | `.pptx` | python-pptx | Yes | Slide-by-slide extraction | | 7 | Jupyter Notebook | `.ipynb` | Cell-aware parser | Yes | Markdown + code cells only; skips outputs/base64 | | 8 | JSON | `.json` | Structure-aware | Yes | Flattened key-value extraction | | 9 | CSV | `.csv` | Row-based parser | Yes | Headers + rows as text | | 10 | XML | `.xml` | XML parser | Yes | Root element + namespace metadata | | 11 | Python | `.py` | Code-aware parser | Yes | Functions/classes as chunks | | 12 | C Source | `.c` | Code-aware parser | Yes | Functions / structs / includes extracted | | 13 | C/C++ Header | `.h` | Code-aware parser | Yes | Function declarations + structs extracted | | 14 | C++ Source | `.cpp` | Code-aware parser | Yes | Classes / structs / includes extracted | | 15 | JavaScript | `.js` | Code-aware parser | Yes | Functions / classes / imports (ESM + CJS) | | 16 | React JSX | `.jsx` | Code-aware parser | Yes | Same as JS parser | | 17 | TypeScript | `.ts` | Code-aware parser | Yes | Functions / classes / interfaces / enums / imports | | 18 | React TSX | `.tsx` | Code-aware parser | Yes | Same as TS parser | | 19 | Go | `.go` | Code-aware parser | Yes | Functions / structs / imports extracted | | 20 | Rust | `.rs` | Code-aware parser | Yes | Functions / structs / enums / traits / `use` imports | | 21 | Kotlin | `.kt` | Code-aware parser | Yes | Functions (incl. class members) / classes extracted | | 22 | YAML | `.yaml` | YAML parser | Yes | Kubernetes kind / apiVersion / name extracted | | 23 | YAML | `.yml` | YAML parser | Yes | Same as YAML parser | | 24 | HuJSON | `.hujson` | HuJSON parser | Yes | JSON with comments + trailing commas (e.g. Tailscale ACLs) | | 25 | CUE | `.cue` | Code-aware parser | Yes | Imports / package extracted | | 26 | Protocol Buffers | `.proto` | Proto parser | Yes | Services / messages / RPCs extracted | | 27 | Rego | `.rego` | Code-aware parser | Yes | OPA policies — imports / package extracted | | 28 | SQL | `.sql` | SQL parser | Yes | Table names + statement types extracted | | 29 | Shell | `.sh` | Shell parser | Yes | Function names extracted | | 30 | jq | `.jq` | Shell parser | Yes | Indexed as shell-style script | | 31 | Dockerfile | `Dockerfile` | Text parser | Yes | Matched by exact filename (no extension) | | 32 | Makefile | `Makefile` | Text parser | Yes | Matched by exact filename (no extension) | | 33 | Tiltfile | `Tiltfile` | Code-aware parser | Yes | Starlark — `def` functions / `load()` extracted | | 34 | MQL4 Source | `.mq4` | Code parser | **No** | MetaTrader — opt-in via `documents.supported_formats` | | 35 | MQL4 Header | `.mqh` | Code parser | **No** | MetaTrader — opt-in via `documents.supported_formats` | Files with a `.pdf` extension can also contain an explicit text extraction: the first line must be `[Page N]` with a positive page number, followed by nonempty UTF-8 text. An optional UTF-8 BOM and tab/CR/LF whitespace are accepted. This recognition is limited to 4 MiB, rejects other C0/C1 controls and DEL, and never reinterprets a file containing PDF magic in the first 1 KiB as text. Accepted extractions retain their source path and are marked `content_format=extracted_text` and `page_count_source=markers`; the marker count is not proof of the original PDF's total page count. Binary PDFs keep the native parser and its error diagnostics. Invalid or oversized textual lookalikes remain errors. > **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](docs/CONFIGURATION.md) --- ## 🔌 Choose your MCP integration
**Claude Code**
`~/.claude.json` 		**Claude Desktop**
`claude_desktop_config.json` 		**Cursor**
`~/.cursor/mcp.json` 		**Windsurf**
`~/.codeium/windsurf/mcp_config.json` 		**VS Code**
Copilot Chat `mcp.json` 		**Cline · Gemini CLI · Zed**
Native MCP
**Complete client configuration guide with JSON schemas per client:** [docs/INSTALLATION.md#use-with-other-mcp-clients →](docs/INSTALLATION.md#use-with-other-mcp-clients) > ⚠️ **Operating model — read once.** In `stdio` mode, your MCP client (Claude Desktop, Code, Cursor, etc.) **spawns and owns the knowledge-rag server process**. Do **NOT** also run a second `knowledge-rag` writer in a terminal while that stdio client is open — two writer processes against the same `data_dir` corrupt the ChromaDB HNSW segment on Windows (see [issue #216](https://github.com/lyonzin/knowledge-rag/issues/216)). For HTTP transports, manually running one server and connecting HTTP clients to it is the supported shared-server workflow; do not mix it with another writer using the same `data_dir`. Recommended baseline for stdio clients: add `"KNOWLEDGE_RAG_SINGLE_INSTANCE": "1"` to the `env` block of your MCP config as a safety net. Full explanation + how to run CLI commands safely: [docs/single-instance.md](docs/single-instance.md). > ⚠️ **Do not install under cloud-sync folders.** Keep `paths.data_dir` on a local path. OneDrive (incl. `OneDrive - `), iCloud Drive, Dropbox, Google Drive, Box, pCloud, MEGAsync, Nextcloud/ownCloud and Sync all open file handles on the live index to upload it, racing with ChromaDB's HNSW mmap and SQLite WAL writes and triggering native access violations (`0xC0000005` on Windows, `SIGBUS` on *nix). Preflight auto-repair then moves the active index to `data/backups/auto-repair-*` and you lose the corpus. Since v4.11.0, preflight aborts with `EX_CONFIG` (78) when `data_dir` resolves under one of these providers. Point `data_dir` at a local path (e.g. `%LOCALAPPDATA%\knowledge-rag\data` on Windows, `~/.local/share/knowledge-rag/data` on Linux/macOS). If you really need to run under cloud sync, set `paths.allow_cloud_sync: true` to acknowledge the risk and continue. --- ## ⚙️ Configuration in 30 seconds ```yaml # 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" | "directml" device_id: null # DirectML: explicit DXGI adapter index 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`](presets/cybersecurity.yaml) · [`developer.yaml`](presets/developer.yaml) · [`research.yaml`](presets/research.yaml) · [`general.yaml`](presets/general.yaml) · [`multilingual.yaml`](presets/multilingual.yaml) **Complete configuration reference — every field, every default, tuning guide:** [docs/CONFIGURATION.md →](docs/CONFIGURATION.md) --- ## 🔒 Security & Compliance knowledge-rag is designed for teams that cannot let their documents leave the perimeter. | Requirement | How knowledge-rag delivers | |---|---| | **Data locality (LGPD / GDPR / HIPAA)** | 100% on-premise, zero egress network calls after initial model download | | **Air-gapped deployment** | ONNX models pre-cached; set `HF_HUB_OFFLINE=1` to enforce zero-network | | **CVE monitoring** | Dependabot (weekly) + pip-audit + Socket + CodeQL | | **Supply chain security** | PyPI Trusted Publishing via OIDC (no long-lived tokens) | | **Vulnerability disclosure** | Private security advisory via [SECURITY.md](SECURITY.md) | | **Signed release attestations** | GitHub release attestations on every published version | | **Dependency management** | Version ranges in `requirements.txt` / `pyproject.toml`; resolve and record a tested environment for reproducibility | | **Authenticated access** | Bearer token middleware on SSE / HTTP transports (constant-time compare, RFC 6750) | | **Rate limiting** | Sliding-window per-client RPM + burst (opt-in, zero-cost when disabled) | | **Audit-ready logging** | Opt-in structured JSON logs → ship to your SIEM | | **Path traversal defenses** | CWE-22 / CWE-59 guards on 6 CRUD tools | | **External-content mitigation** | Known-token neutralization and provenance fencing on `add_from_url`; not a general prompt-injection prevention guarantee | **OpenSSF Best Practices** badge: passing · project ID [#13864](https://bestpractices.coreinfrastructure.org/projects/13864) --- ## 📈 Numbers that matter - **13 MCP tools**, configurable format selection and five domain presets. - **Nine OS/Python CI combinations** configured for Linux, macOS and Windows with Python 3.11–3.13. - **Measured CPU/CUDA indexing and retrieval, AMD validation and memory regressions:** [audit results and reproduction](docs/audit-2026-10-03.md). - **Historical implementation benchmarks:** [v4.7.1 baseline](docs/perf-baseline-v4.7.1.md). Historical ratios apply to their recorded workloads, not every corpus or competing product. The [public benchmark dashboard](https://lyonzin.github.io/knowledge-rag/) tracks CI workloads. Mocked storage/model workloads and real-model measurements answer different questions; inspect the methodology and units before comparing them. --- ## 📚 Documentation | Doc | What's inside | |---|---| | [**Installation guide**](docs/INSTALLATION.md) | 5 install methods · 8 MCP client integrations · GPU setup | | [**API reference**](docs/API.md) | Complete reference for all 13 MCP tools | | [**Configuration reference**](docs/CONFIGURATION.md) | Every `config.yaml` field · presets · tuning | | [**Architecture**](docs/ARCHITECTURE.md) | Components · query/ingestion flow · memory/concurrency boundaries · hybrid weights | | [**Troubleshooting**](docs/TROUBLESHOOTING.md) | 11 common issues + solutions | | [**FTS5 fast-path guide**](docs/features/fts5_fast_path.md) | Opt-in lexical fast-path — when and how | | [**Reindex operations**](docs/reindex-operations.md) | Zero-downtime rebuild · resume · checkpoint | | [**GPU setup**](docs/gpu-setup.md) | CUDA runtime compatibility · CPU fallback · AMD/macOS GPU limitations | | [**Migration to v4.8.0**](docs/migration-v4.8.0.md) | Embedding profile · multilingual · zero-downtime | | [**Security policy**](SECURITY.md) | Threat model · disclosure channel | | [**Contributing**](CONTRIBUTING.md) | Development · testing · PR process | | [**Changelog**](CHANGELOG.md) | All release notes since v1.0.0 | | [**Memory/GPU audit**](docs/audit-2026-10-03.md) | Reproducible workloads · measured results · validation limits · open PR/issue review | --- ## 🤝 Community & Support - **Report a bug** → [Open an issue](https://github.com/lyonzin/knowledge-rag/issues/new/choose) - **Ask a question** → [GitHub Discussions](https://github.com/lyonzin/knowledge-rag/discussions) - **Report a vulnerability** → [Security advisory](https://github.com/lyonzin/knowledge-rag/security/advisories/new) (private) - **Contribute** → [CONTRIBUTING.md](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 →](CHANGELOG.md) --- ## 📜 License **MIT License** — [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**](https://modelcontextprotocol.io/) — Model Context Protocol spec + Python SDK - [**ChromaDB**](https://www.trychroma.com/) — vector database that just works - [**FastEmbed**](https://github.com/qdrant/fastembed) — ONNX embeddings, no PyTorch bloat - [**HuggingFace**](https://huggingface.co/) — model hosting + `Xenova/ms-marco-MiniLM-L-6-v2` cross-encoder - [**BAAI**](https://huggingface.co/BAAI) — the `bge-small-en-v1.5` embedding model **Community contributors:** [@Hohlas](https://github.com/Hohlas) · [@eeshsaxena](https://github.com/eeshsaxena) · Sergey Khokhlov · and everyone who filed issues or PRs. ---
**Built by [Ailton Rocha (Lyon.)](https://github.com/lyonzin)** · Star ⭐ if this saves you time · [Report an issue](https://github.com/lyonzin/knowledge-rag/issues/new/choose) · [Contribute](CONTRIBUTING.md) *knowledge-rag — the MCP-first local RAG server for Claude Code, Cursor, Windsurf, and every AI agent.*