lyonzin/knowledge-rag
GitHub: lyonzin/knowledge-rag
一个 100% 本地运行的 RAG 工具，专为 Claude Code 设计，支持 20 种文件格式、混合搜索与交叉重排序，无需服务器或 API 密钥。
Stars: 90 | Forks: 17
# Knowledge RAG
search | get | add | update | remove
reindex | list | stats | url | similar | evaluate"] end subgraph SEARCH["HYBRID SEARCH ENGINE"] direction LR ROUTER["Keyword Router
(word boundaries)"] SEMANTIC["Semantic Search
(ChromaDB)"] BM25["BM25 Keyword
(rank-bm25 + expansion)"] RRF["Reciprocal Rank
Fusion (RRF)"] RERANK["Cross-Encoder
Reranker"] ROUTER --> SEMANTIC ROUTER --> BM25 SEMANTIC --> RRF BM25 --> RRF RRF --> RERANK end subgraph STORAGE["STORAGE LAYER"] direction LR CHROMA[("ChromaDB
Vector Database")] COLLECTIONS["Collections
security | ctf
logscale | development"] CHROMA --- COLLECTIONS end subgraph EMBED["EMBEDDINGS (In-Process)"] FASTEMBED["FastEmbed ONNX
BAAI/bge-small-en-v1.5
(384D, CPU or GPU)"] CROSSENC["Cross-Encoder
ms-marco-MiniLM-L-6-v2"] FASTEMBED --- CROSSENC end subgraph INGEST["DOCUMENT INGESTION"] PARSERS["20 Parsers
MD | PDF | TXT | PY | C | H | CPP | JS | JSX | TS | TSX | JSON | XML | CSV
DOCX | XLSX | PPTX | IPYNB | MQH | MQ4"] CHUNKER["Chunking
MD: section-aware
Other: 1000 chars + 200 overlap"] PARSERS --> CHUNKER end CLAUDE["Claude Code"] --> MCP MCP --> SEARCH SEARCH --> STORAGE STORAGE --> EMBED INGEST --> EMBED #### EMBED --> STORAGE ### Query Processing Flow ```mermaid flowchart TB QUERY["User Query
'mimikatz credential dump'"] --> EXPAND subgraph EXPANSION["Query Expansion"] EXPAND["Synonym Expansion
mimikatz -> mimikatz, sekurlsa, logonpasswords"] end EXPAND --> ROUTER subgraph ROUTING["Keyword Routing"] ROUTER["Keyword Router"] MATCH{"Word Boundary
Match?"} CATEGORY["Filter: redteam"] NOFILTER["No Filter"] ROUTER --> MATCH MATCH -->|Yes| CATEGORY MATCH -->|No| NOFILTER end subgraph HYBRID["Hybrid Search"] direction LR SEMANTIC["Semantic Search
(ChromaDB embeddings)
Conceptual similarity"] BM25["BM25 Search
(expanded query)
Exact term matching"] end subgraph FUSION["Result Fusion + Reranking"] RRF["Reciprocal Rank Fusion
score = alpha * 1/(k+rank_sem)
+ (1-alpha) * 1/(k+rank_bm25)"] RERANK["Cross-Encoder Reranker
Re-scores top 3x candidates
query+doc pair scoring"] SORT["Sort by Reranker Score
Normalize to 0-1"] RRF --> RERANK --> SORT end CATEGORY --> HYBRID NOFILTER --> HYBRID SEMANTIC --> RRF BM25 --> RRF #### SORT --> RESULTS["Results
search_method: hybrid|semantic|keyword
score + reranker_score + raw_rrf_score"] ### Document Ingestion Flow ```mermaid flowchart LR subgraph INPUT["Input"] FILES["documents/
├── security/
├── development/
├── ctf/
└── general/"] end subgraph PARSE["Parse (20 formats)"] MD["Markdown"] PDF["PDF
(PyMuPDF)"] OFFICE["DOCX | XLSX
PPTX | CSV"] CODE["PY | C | H | CPP | JS | JSX
TS | TSX | JSON | XML | IPYNB"] end subgraph CHUNK["Chunk"] MDSPLIT["MD: Section-Aware
Split at ## headers"] TXTSPLIT["Other: Fixed-Size
1000 chars + 200 overlap"] DEDUP["SHA256 Dedup
Skip duplicate content"] end subgraph EMBED["Embed"] FASTEMBED["FastEmbed ONNX
bge-small-en-v1.5
(384D, CPU or GPU)"] end subgraph STORE["Store"] CHROMADB[("ChromaDB")] BM25IDX["BM25 Index"] end FILES --> MD & PDF & OFFICE & CODE MD --> MDSPLIT PDF & OFFICE & CODE --> TXTSPLIT MDSPLIT --> DEDUP TXTSPLIT --> DEDUP DEDUP --> EMBED #### EMBED --> STORE ### hybrid_alpha Parameter Effect ```mermaid flowchart LR subgraph ALPHA["hybrid_alpha values"] A0["0.0
Pure BM25
Instant"] A3["0.3 (default)
Keyword-heavy
Fast"] A5["0.5
Balanced"] A7["0.7
Semantic-heavy"] A10["1.0
Pure Semantic"] end subgraph USE["Best For"] U0["CVEs, tool names
exact matches"] U3["Technical queries
specific terms"] U5["General queries"] U7["Conceptual queries
related topics"] U10["'How to...' questions
conceptual search"] end A0 --- U0 A3 --- U3 A5 --- U5 A7 --- U7 #### A10 --- U10 --- ## Installation ### Prerequisites - Python 3.11+ - Claude Code CLI - *…or any other MCP client (Claude Desktop, Cursor, VS Code, Antigravity, opencode, Windsurf) — see [Use with other MCP clients](#use-with-other-mcp-clients)* - ~200MB disk for model cache (auto-downloaded on first run) - *Optional:* NVIDIA GPU + CUDA for accelerated embeddings (`pip install knowledge-rag[gpu]` + `models.embedding.gpu: true` in config) ### Install Methods Pick one — all produce the same running server. #### Option A: NPX (fastest) Requires Node.js 16+. Handles Python venv, pip install, and version upgrades automatically. ```bash #### claude mcp add knowledge-rag -s user -- npx -y knowledge-rag That's it. On first run, `npx` creates a venv at `~/.knowledge-rag/`, installs the PyPI package, and starts the MCP server. Subsequent runs reuse the cached venv. #### Option B: One-line installer ```bash # Linux/macOS: curl -fsSL https://raw.githubusercontent.com/lyonzin/knowledge-rag/master/install.sh | bash # Windows (PowerShell): #### irm https://raw.githubusercontent.com/lyonzin/knowledge-rag/master/install.ps1 | iex Then configure Claude Code: ```bash #### claude mcp add knowledge-rag -s user -- ~/knowledge-rag/venv/bin/python -m mcp_server.server > **Windows**: `claude mcp add knowledge-rag -s user -- %USERPROFILE%\knowledge-rag\venv\Scripts\python.exe -m mcp_server.server` #### Option C: pip install ```bash mkdir ~/knowledge-rag && cd ~/knowledge-rag python3 -m venv venv && source venv/bin/activate pip install knowledge-rag #### knowledge-rag init # Exports config template, presets, creates documents/ Then configure Claude Code: ```bash #### claude mcp add knowledge-rag -s user -- ~/knowledge-rag/venv/bin/python -m mcp_server.server > **Windows users**: Use `python` instead of `python3`, `venv\Scripts\activate` instead of `source venv/bin/activate`. > **Windows path**: `claude mcp add knowledge-rag -s user -- %USERPROFILE%\knowledge-rag\venv\Scripts\python.exe -m mcp_server.server` #### Option D: Clone from source ```bash git clone https://github.com/lyonzin/knowledge-rag.git ~/knowledge-rag cd ~/knowledge-rag python3 -m venv venv && source venv/bin/activate #### pip install -r requirements.txt Then configure Claude Code: ```bash #### claude mcp add knowledge-rag -s user -- ~/knowledge-rag/venv/bin/python -m mcp_server.server #### Option E: Docker ```bash #### docker pull ghcr.io/lyonzin/knowledge-rag:latest ```bash claude mcp add knowledge-rag -s user -- \ docker run -i --rm \ -v ~/knowledge-rag/documents:/app/documents \ -v ~/knowledge-rag/data:/app/data \ #### ghcr.io/lyonzin/knowledge-rag:latest Models are pre-downloaded in the image — no first-run delay.
### Use with other MCP clients
`knowledge-rag` is a standard **stdio MCP server** — it works with any MCP-compatible client, not only Claude Code. The launch command is the same everywhere (the `python -m mcp_server.server` from whichever install method you picked); only the **config file location** and **JSON shape** differ per client.
#### Clients using the standard `mcpServers` format
For **Claude Desktop, Cursor, Antigravity, and Windsurf**, use the same block — only the file location changes:
```json
{
"mcpServers": {
"knowledge-rag": {
"command": "/home/YOUR_USER/knowledge-rag/venv/bin/python",
"args": ["-m", "mcp_server.server"]
}
}
#### }
> **Windows**: set `command` to the full path of `venv\Scripts\python.exe`.
| Client | Config file | Notes |
|---|---|---|
| **Claude Code** | use `claude mcp add …` (see install methods above) | The CLI writes `~/.claude.json` for you — manual edits to it aren't reliably picked up. |
| **Claude Desktop** | macOS: `~/Library/Application Support/Claude/claude_desktop_config.json` · Windows: `%APPDATA%\Claude\claude_desktop_config.json` | Easiest: **Settings → Developer → Edit Config** opens the correct file (avoids the Windows Store/MSIX path quirk). |
| **Cursor** | `~/.cursor/mcp.json` (global) or `.cursor/mcp.json` (per project) | — |
| **Antigravity** | macOS/Linux: `~/.gemini/antigravity/mcp_config.json` · Windows: `%USERPROFILE%\.gemini\antigravity\mcp_config.json` | Open via Agent panel → **"…" → Manage MCP Servers → View raw config**. |
| **Windsurf** | `~/.codeium/windsurf/mcp_config.json` (global only) | Easiest: Cascade panel → MCP → **View raw config**. |
#### VS Code — uses a `servers` key
VS Code (Copilot MCP) nests servers under **`servers`**, not `mcpServers`. Put this in `.vscode/mcp.json` (workspace) or the file opened by the **MCP: Open User Configuration** command:
```json
{
"servers": {
"knowledge-rag": {
"type": "stdio",
"command": "/home/YOUR_USER/knowledge-rag/venv/bin/python",
"args": ["-m", "mcp_server.server"]
}
}
#### }
#### opencode — uses an `mcp` key
opencode nests servers under **`mcp`**, takes `command` as a single **array**, and uses `environment` instead of `env`. Put this in `opencode.json` (project root) or `~/.config/opencode/opencode.json` (global):
```jsonc
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"knowledge-rag": {
"type": "local",
"command": ["/home/YOUR_USER/knowledge-rag/venv/bin/python", "-m", "mcp_server.server"],
"enabled": true
}
}
#### }
> **Any other MCP client**: point it at the same command + args (`…/venv/bin/python -m mcp_server.server`). If it speaks stdio MCP, knowledge-rag works — only the config file's location and key naming differ. Check your client's docs for the exact path.
### Verify
```bash
#### claude mcp list
On first start, the server will:
1. Download the embedding model (~50MB, cached in `models_cache/`)
2. Auto-index any documents in the `documents/` directory
## 3. Start watching for file changes (auto-reindex)
## Usage
### Adding Documents
#### Place your documents in the `documents/` directory, organized by category:
documents/
├── security/ # Pentest, exploit, vulnerability docs
├── development/ # Code, APIs, frameworks
├── ctf/ # CTF writeups and methodology
├── logscale/ # LogScale/LQL documentation
#### └── general/ # Everything else
Or add documents programmatically via MCP tools:
```python
# Add from content
add_document(
content="# My Document\n\nContent here...",
filepath="security/my-technique.md",
category="security"
)
# Add from URL
add_from_url(
url="https://example.com/article",
category="security",
title="Custom Title"
#### )
### Searching
Claude uses the RAG system automatically when configured. You can also control search behavior:
```python
# Pure keyword search — instant, no embedding needed
search_knowledge("gtfobins suid", hybrid_alpha=0.0)
# Keyword-heavy (default) — fast, slight semantic boost
search_knowledge("mimikatz", hybrid_alpha=0.3)
# Balanced hybrid — both engines equally weighted
search_knowledge("SQL injection techniques", hybrid_alpha=0.5)
# Semantic-heavy — better for conceptual queries
search_knowledge("how to escalate privileges", hybrid_alpha=0.7)
# Pure semantic — embedding similarity only
#### search_knowledge("lateral movement strategies", hybrid_alpha=1.0)
### Indexing
Documents are automatically indexed on first startup. To manage the index:
```python
# Incremental: only re-index changed files (fast)
reindex_documents()
# Smart reindex: detect changes + rebuild BM25
reindex_documents(force=True)
# Nuclear rebuild: delete everything, re-embed all (use after model change)
#### reindex_documents(full_rebuild=True)
### Evaluating Retrieval Quality
```python
evaluate_retrieval(test_cases='[
{"query": "sql injection", "expected_filepath": "security/sqli-guide.md"},
{"query": "privilege escalation", "expected_filepath": "security/privesc.md"}
]')
# Returns: MRR@5, Recall@5, per-query results
## ```
## API Reference
### Search & Query
#### `search_knowledge`
Hybrid search combining semantic search + BM25 keyword search with cross-encoder reranking.
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `query` | string | required | Search query text (1-3 keywords recommended) |
| `max_results` | int | 5 | Maximum results to return (1-20) |
| `category` | string | null | Filter by category |
| `hybrid_alpha` | float | 0.3 | Balance: 0.0 = keyword only, 1.0 = semantic only |
**Returns:**
```json
{
"status": "success",
"query": "mimikatz credential dump",
"hybrid_alpha": 0.5,
"result_count": 3,
"cache_hit_rate": "0.0%",
"results": [
{
"content": "Mimikatz can extract credentials from memory...",
"source": "documents/security/credential-attacks.md",
"filename": "credential-attacks.md",
"category": "security",
"score": 0.9823,
"raw_rrf_score": 0.016393,
"reranker_score": 0.987654,
"semantic_rank": 2,
"bm25_rank": 1,
"search_method": "hybrid",
"keywords": ["mimikatz", "credential", "lsass"],
"routed_by": "redteam"
}
]
#### }
**Search Method Values:**
- `hybrid`: Found by both semantic and BM25 search (highest confidence)
- `semantic`: Found only by semantic search
## - `keyword`: Found only by BM25 keyword search
#### `get_document`
Retrieve the full content of a specific document.
| Parameter | Type | Description |
|-----------|------|-------------|
| `filepath` | string | Path to the document file |
## **Returns:** JSON with document content, metadata, keywords, and chunk count.
#### `reindex_documents`
Index or reindex all documents in the knowledge base.
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `force` | bool | false | Smart reindex: detects changes, rebuilds BM25. Fast. |
| `full_rebuild` | bool | false | Nuclear rebuild: deletes everything, re-embeds all documents. Use after model change. |
## **Returns:** JSON with indexing statistics (indexed, updated, skipped, deleted, chunks_added, chunks_removed, dedup_skipped, elapsed_seconds).
#### `list_categories`
List all document categories with their document counts.
**Returns:**
```json
{
"status": "success",
"categories": {
"security": 52,
"development": 8,
"ctf": 12,
"general": 3
},
"total_documents": 75
#### }
---
#### `list_documents`
List all indexed documents, optionally filtered by category.
| Parameter | Type | Description |
|-----------|------|-------------|
| `category` | string | Optional category filter |
## **Returns:** JSON array of documents with id, source, category, format, chunks, and keywords.
#### `get_index_stats`
Get statistics about the knowledge base index.
**Returns:**
```json
{
"status": "success",
"stats": {
"total_documents": 75,
"total_chunks": 9256,
"unique_content_hashes": 9100,
"categories": {"security": 52, "development": 8},
"supported_formats": [".md", ".txt", ".pdf", ".py", ".json", ".docx", ".xlsx", ".pptx", ".csv", ".ipynb"],
"embedding_model": "BAAI/bge-small-en-v1.5",
"embedding_dim": 384,
"reranker_model": "Xenova/ms-marco-MiniLM-L-6-v2",
"chunk_size": 1000,
"chunk_overlap": 200,
"query_cache": {
"size": 12,
"max_size": 100,
"ttl_seconds": 300,
"hits": 45,
"misses": 23,
"hit_rate": "66.2%"
}
}
#### }
---
### Document Management
#### `add_document`
Add a new document to the knowledge base from raw content. Saves the file to the documents directory and indexes it immediately.
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `content` | string | required | Full text content of the document |
| `filepath` | string | required | Relative path within documents dir (e.g., `security/new-technique.md`) |
## | `category` | string | "general" | Document category |
#### `update_document`
Update an existing document. Removes old chunks from the index and re-indexes with new content.
| Parameter | Type | Description |
|-----------|------|-------------|
| `filepath` | string | Full path to the document file |
## | `content` | string | New content for the document |
#### `remove_document`
Remove a document from the knowledge base index. Optionally deletes the file from disk.
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `filepath` | string | required | Path to the document file |
## | `delete_file` | bool | false | If true, also delete the file from disk |
#### `add_from_url`
Fetch content from a URL, strip HTML (scripts, styles, nav, footer, header), convert to markdown, and add to the knowledge base.
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `url` | string | required | URL to fetch content from |
| `category` | string | "general" | Document category |
## | `title` | string | null | Custom title (auto-detected from `` tag if not provided) |
#### `search_similar`
Find documents similar to a given document using embedding similarity.
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `filepath` | string | required | Path to the reference document |
## | `max_results` | int | 5 | Number of similar documents to return (1-20) |
#### `evaluate_retrieval`
Evaluate retrieval quality with test queries. Useful for tuning `hybrid_alpha`, testing query expansion effectiveness, or validating after reindexing.
| Parameter | Type | Description |
|-----------|------|-------------|
| `test_cases` | string (JSON) | Array of test cases: `[{"query": "...", "expected_filepath": "..."}, ...]` |
**Metrics:**
- **MRR@5** (Mean Reciprocal Rank): Average of 1/rank for expected documents. 1.0 = always first result.
## - **Recall@5**: Fraction of expected documents found in top 5 results. 1.0 = all found.
## Configuration
Knowledge RAG is fully configurable via a `config.yaml` file in the project root. If no `config.yaml` exists, sensible defaults are used — the system works out of the box with zero configuration.
### Quick Start
```bash
# Option 1: Use a preset
cp presets/cybersecurity.yaml config.yaml # Offensive/defensive security, CTFs
cp presets/developer.yaml config.yaml # Software engineering, APIs, DevOps
cp presets/research.yaml config.yaml # Academic research, papers, studies
cp presets/general.yaml config.yaml # Blank slate, pure semantic search
# Option 2: Start from the documented template
cp config.example.yaml config.yaml
# Edit config.yaml to your needs
Restart Claude Code after changing `config.yaml`.
### config.yaml Structure
# Paths — where your documents live
paths:
documents_dir: "./documents" # Scanned recursively
data_dir: "./data" # Index storage
models_cache_dir: "./models_cache" # Persistent embedding model cache
# Documents — what gets indexed and how
documents:
supported_formats: # File types to index
- .md
- .txt
- .pdf
- .docx
- .ipynb
# - .py # Uncomment to index code
exclude_patterns: # Glob patterns to skip
- "node_modules"
- ".venv"
- "__pycache__"
chunking:
chunk_size: 1000 # Max chars per chunk
chunk_overlap: 200 # Shared chars between chunks
# Models — AI models for search (all run locally, no API keys)
models:
embedding:
model: "BAAI/bge-small-en-v1.5" # ONNX, ~33MB, auto-downloaded
dimensions: 384
gpu: false # Set true + pip install knowledge-rag[gpu]
reranker:
enabled: true # Falls back to RRF if model is unavailable
model: "Xenova/ms-marco-MiniLM-L-6-v2"
top_k_multiplier: 3 # Candidates fetched before reranking
# Search — result limits and collection name
search:
default_results: 5
max_results: 20
collection_name: "knowledge_base" # Change for separate knowledge bases
# Categories — auto-tag documents by folder path
# Set to {} to disable categorization entirely
category_mappings:
"security/redteam": "redteam"
"security/blueteam": "blueteam"
"notes": "notes"
# Keyword routing — prioritize categories based on query keywords
# Set to {} for pure semantic search with no routing bias
keyword_routes:
redteam:
- pentest
- exploit
- privilege escalation
# Query expansion — expand abbreviations for better BM25 recall
# Set to {} for no expansion (search terms used as-is)
query_expansions:
sqli:
- sql injection
- sqli
privesc:
- privilege escalation
#### - privesc
> See `config.example.yaml` for the fully documented template with explanations for every field.
### Presets
Pre-built configurations for common use cases:
| Preset | File | Categories | Keywords | Expansions | Best For |
|--------|------|-----------|----------|-----------|----------|
| **Cybersecurity** | `presets/cybersecurity.yaml` | 8 | 200+ | 69 | Red/Blue Team, CTFs, threat hunting, exploit dev |
| **Developer** | `presets/developer.yaml` | 9 | 150+ | 50+ | Full-stack dev, APIs, DevOps, cloud, databases |
| **Research** | `presets/research.yaml` | 9 | 100+ | 40+ | Academic papers, thesis, lab notebooks, datasets |
| **General** | `presets/general.yaml` | 0 | 0 | 0 | Blank slate — pure semantic search, no domain logic |
**Creating your own preset**: Copy `config.example.yaml`, fill in your categories/keywords/expansions, save to `presets/your-domain.yaml`.
### Configuration Reference
#### Paths
| Field | Default | Description |
|-------|---------|-------------|
| `paths.documents_dir` | `./documents` | Root folder scanned recursively for documents |
| `paths.data_dir` | `./data` | Internal storage for ChromaDB and index metadata |
| `paths.models_cache_dir` | `./models_cache` | Persistent cache for embedding models (~250MB). Survives reboots |
Relative paths resolve from the project root. Absolute paths work too.
#### Documents
| Field | Default | Description |
|-------|---------|-------------|
| `documents.supported_formats` | .md .txt .pdf .py .json .docx .xlsx .pptx .csv .ipynb | File extensions to index |
| `documents.exclude_patterns` | `[]` (empty) | Glob patterns for files/dirs to skip during indexing |
| `documents.chunking.chunk_size` | 1000 | Max characters per chunk |
| `documents.chunking.chunk_overlap` | 200 | Characters shared between consecutive chunks |
**Chunking guidelines**: Short notes → 500/100. General use → 1000/200. Long technical docs → 1500/300.
For `.md` files, chunking splits at `##` and `###` header boundaries first. Sections larger than `chunk_size` are sub-chunked with overlap. Non-markdown files use fixed-size chunking.
#### Models
| Field | Default | Description |
|-------|---------|-------------|
| `models.embedding.model` | `BAAI/bge-small-en-v1.5` | Embedding model (ONNX, runs locally) |
| `models.embedding.dimensions` | 384 | Vector dimensions (must match model) |
| `models.embedding.gpu` | false | Enable CUDA GPU acceleration. Requires `pip install knowledge-rag[gpu]` |
| `models.reranker.enabled` | true | Enable cross-encoder reranking |
| `models.reranker.model` | `Xenova/ms-marco-MiniLM-L-6-v2` | Reranker model |
| `models.reranker.top_k_multiplier` | 3 | Fetch N*multiplier candidates for reranking |
If the reranker model is not available locally and the machine cannot download it, search now falls back to the RRF order from hybrid semantic+BM25 retrieval. This keeps `search_knowledge` available offline, but result ordering may be less precise for ambiguous queries until the reranker model is cached.
**Embedding model options** (fastest → most accurate):
- `BAAI/bge-small-en-v1.5` — 384D, ~33MB (default)
- `BAAI/bge-base-en-v1.5` — 768D, ~130MB
- `BAAI/bge-large-en-v1.5` — 1024D, ~335MB
- `intfloat/multilingual-e5-small` — 384D, 100+ languages
> **Warning**: Changing the embedding model after indexing requires `reindex_documents(full_rebuild=True)`.
#### Search
| Field | Default | Description |
|-------|---------|-------------|
| `search.default_results` | 5 | Results returned when no limit specified |
| `search.max_results` | 20 | Hard cap even if client requests more |
| `search.collection_name` | `knowledge_base` | ChromaDB collection — change for separate KBs |
#### Categories
Map folder paths to category names. Documents in matching folders get auto-tagged, enabling filtered searches.
```yaml
category_mappings:
"security/redteam": "redteam"
#### "security": "security"
Set `category_mappings: {}` to disable — documents are still searchable, just without category filters.
#### Keyword Routing
Route queries to categories based on keywords. When a query contains listed keywords, results from that category are prioritized (not filtered — other categories still appear, ranked lower).
```yaml
keyword_routes:
redteam:
- pentest
- exploit
#### - sqli
Single-word keywords use regex word boundaries (`\b`) — "api" won't match "RAPID". Multi-word keywords use substring matching.
Set `keyword_routes: {}` for pure semantic search.
#### Query Expansion
Expand search terms with synonyms before BM25 search. Supports single tokens, bigrams, and full query matches.
```yaml
query_expansions:
sqli:
- sql injection
- sqli
k8s:
- kubernetes
#### - k8s
Set `query_expansions: {}` for no expansion.
### Hybrid Search Tuning
| hybrid_alpha | Behavior | Best For |
|--------------|----------|----------|
| 0.0 | Pure BM25 keyword | Exact terms, CVEs, tool names |
| 0.3 | Keyword-heavy **(default)** | Technical queries with specific terms |
| 0.5 | Balanced | General queries |
| 0.7 | Semantic-heavy | Conceptual queries, related topics |
## | 1.0 | Pure semantic | "How to..." questions, abstract concepts |
## Project Structure
knowledge-rag/
├── mcp_server/
│ ├── __init__.py # Stdout protection + version
│ ├── config.py # YAML config loader + defaults
│ ├── ingestion.py # 20 parsers, chunking, metadata extraction
│ └── server.py # MCP server, ChromaDB, BM25, reranker, 12 tools
├── config.example.yaml # Documented config template (copy to config.yaml)
├── config.yaml # Your active configuration (git-ignored)
├── presets/ # Ready-to-use domain configurations
│ ├── cybersecurity.yaml
│ ├── developer.yaml
│ ├── research.yaml
│ └── general.yaml
├── documents/ # Your documents (scanned recursively)
├── data/
│ ├── chroma_db/ # ChromaDB vector database
│ └── index_metadata.json # Incremental indexing state
├── models_cache/ # Persistent embedding model cache
├── tests/ # Test suite (82 tests)
├── install.sh # Linux/macOS installer
├── install.ps1 # Windows installer
├── venv/ # Python virtual environment
├── requirements.txt
├── pyproject.toml
├── LICENSE
#### └── README.md
## Troubleshooting
### Python version mismatch
Requires Python 3.11 or newer.
#### python --version # Must be 3.11+
### FastEmbed model download fails
On first run, FastEmbed downloads models to `models_cache/`. If the download fails:
```bash
# Clear cache and retry
# Windows:
rmdir /s /q models_cache
# Linux/macOS:
rm -rf models_cache
# Then restart the MCP server
### Reranker model download fails
The reranker is lazy-loaded on the first query. If the model is not cached and the machine is offline, search continues without reranking and uses the RRF order from hybrid retrieval. To keep reranking enabled offline, run one query while online or pre-populate `models_cache/` on the target machine.
You can still disable reranking explicitly in `config.yaml`:
models:
reranker:
#### enabled: false
Disabling reranking reduces memory use and avoids first-query model loading. The tradeoff is lower ranking precision, especially when several chunks match the same terms but only one is the best answer.
### ChromaDB index crashes on startup
Native ChromaDB failures can terminate Python before normal exception handling runs. Startup now probes ChromaDB in a child process before initializing the MCP server. If the probe crashes, the active `chroma_db/` and `index_metadata.json` are moved to `data/backups/auto-repair-*`, and the next startup can rebuild a clean index.
The same guarded behavior is available through either console script:
```bash
knowledge-rag
#### knowledge-rag-guarded
### Index is empty
```bash
# Check documents directory has files
ls documents/
# Force reindex via Claude Code:
# reindex_documents(force=True)
# Or nuclear rebuild if model changed:
# reindex_documents(full_rebuild=True)
### MCP server not loading
1. Check `~/.claude.json` exists and has valid JSON in the `mcpServers` section
2. Verify paths use double backslashes (`\\`) on Windows
3. Restart Claude Code completely
4. Run `claude mcp list` to check connection status
### "Failed to connect" error
The MCP server uses stdout for JSON-RPC communication. If a library prints to stdout during init, the stream gets corrupted. v3.4.3+ includes stdout protection that prevents this. If you're on an older version, upgrade:
#### pip install --upgrade knowledge-rag
### Slow first query
The cross-encoder reranker model is lazy-loaded on the first query. This adds a one-time ~2-3 second delay for model download and loading. Subsequent queries are fast. If the model cannot be loaded, search falls back to RRF ordering and does not retry loading the reranker until the server restarts.
### Memory usage
With ~200 documents, expect ~300-500MB RAM. The embedding model (~200MB ONNX runtime resident, lazy-loaded on first query since v3.8.0) and reranker (~25MB, lazy-loaded) are loaded into memory only when actually used. For very large knowledge bases (1000+ documents), consider enabling GPU acceleration and using exclude patterns to limit index scope.
### Multiple MCP clients spawn duplicate servers
MCP stdio is one process per client by protocol — multiple Claude Code windows, Claude Desktop + IDE, etc. each spawn their own `knowledge-rag` process. Since v3.8.0 idle processes are cheap (no embedding model loaded until first query). If you've measured and want a hard cap of one server per data directory, opt in:
```bash
#### export KNOWLEDGE_RAG_SINGLE_INSTANCE=1
## A second instance exits immediately with code 75. Default is OFF (multi-client friendly). Full guide: [docs/single-instance.md](docs/single-instance.md). Sample MCP config: [examples/mcp-config-single-instance.json](examples/mcp-config-single-instance.json).
## Changelog
### v3.9.0 (2026-05-10) — Quality Gate
**Major governance + CI hardening release. No runtime behavior change in `mcp_server/`. Public API surface unchanged from v3.8.1.**
- **NEW** Quality Gate workflow (`.github/workflows/quality-gate.yml`) enforcing the 7 pillars on every PR: Security, Stability, Memory Leak, Versatility, Scalability, Versioning, Quality. 35+ status checks total.
- **NEW** Nightly resilience workflow (`.github/workflows/nightly.yml`): chaos suite (failure injection), 1h soak test (50K-iteration loop), determinism check (full suite × 3), mutation testing (mutmut). Auto-opens GitHub issue on any nightly failure.
- **NEW** Performance benchmark suite under `bench/` (12 microbenchmarks, pytest-benchmark) with 10% regression gate on every PR.
- **NEW** Public performance dashboard via GitHub Pages (`.github/workflows/bench-pages.yml`) — chart of latency/throughput per commit. Dormant until repo Pages is enabled.
- **NEW** Property-based fuzzing of all parsers via Hypothesis (`tests/test_ingestion_property.py`) — 200 random examples per CI run.
- **NEW** Memory baseline regression tests (`tests/test_memory_baseline.py`, cross-platform via psutil) — RSS bounded under 1000 queries; nightly soak amplifies to 50K iterations.
- **NEW** Property/locale/format/preset matrices (`tests/test_presets.py`, `tests/test_locale.py`, `tests/test_format_smoke.py`).
- **NEW** Backwards-compatibility regression tests (`tests/test_backwards_compat.py`) — legacy YAML configs from v3.6.0 / v3.7.0 still parse; all 12 MCP tool parameter names frozen.
- **NEW** AST-based public API surface diff (`scripts/check_api_surface.py`) — any breaking change blocks merge, baseline at `.github/api-surface-baseline.json`.
- **NEW** CHANGELOG enforcement (`scripts/check_changelog.py`) — user-facing PRs must add a bullet under `## Unreleased`; bypass via `skip-changelog` label.
- **NEW** Test count anti-regression (`scripts/check_test_count.py`) — guards against silent test deletion.
- **NEW** Conventional commits required on every PR title (commitlint via `amannn/action-semantic-pull-request`).
- **NEW** mypy `--strict` rolling out per-module (currently `instance_lock.py` + `preflight.py` + `scripts/`); interrogate docstring coverage ≥ 80%; radon, vulture, PR-size guard report-only.
- **NEW** CI matrix expanded to 9 cells: Linux + Windows + **macOS** × 3.11 + 3.12 + **3.13** (all required at v3.9.0; macOS / 3.13 promoted from experimental after two clean cycles).
- **NEW** Governance docs: `CONTRIBUTING.md`, `CODE_OF_CONDUCT.md`, `SECURITY.md`, `.github/PULL_REQUEST_TEMPLATE.md`, 3 issue templates, expanded `CODEOWNERS`.
- **NEW** Pre-commit hooks: ruff, gitleaks, version-sync, conventional commits.
- **CHORE** `.github/codecov.yml` enforcing coverage trend gate (-0.5pp blocks; new code ≥ 70%).
### v3.8.1 (2026-05-10) — hotfix
- **FIX (critical)**: `FastEmbedEmbeddings.__call__` no longer returns vectors of zeros when the ONNX model fails to load or `embed()` raises. The previous behavior silently corrupted the index — ChromaDB stored zero embeddings, `count()` reported normal numbers, smart-reindex skipped the bad chunks, and queries returned garbage scores with no error visible. Now raises `EmbeddingModelLoadError` / `EmbeddingError`. (#36)
- **FIX**: Sticky `_load_failed` flag — after a load failure, subsequent calls re-raise immediately instead of looping through HuggingFace download attempts (was the "frozen query" UX in v3.8.0).
- **NEW**: Sanity checks in `__call__` — embed count and dim mismatches raise `EmbeddingError` instead of silently returning malformed vectors.
- **TEST**: 7 new regression cases in `tests/test_lazy_embeddings.py`, including `test_does_not_return_zero_vectors_silently` as a guard for the whole class of bug.
- **NOTE**: This is a pre-existing bug in master, not introduced by v3.8.0. v3.8.0 lazy-load expanded the impact (failures moved to query time). All v3.8.0 users should upgrade.
### v3.8.0 (2026-05-10)
- **NEW**: Lazy-load FastEmbed embedding model (~200MB ONNX runtime). Loads on first query instead of startup — idle `knowledge-rag` processes are now cheap, which matters when MCP stdio clients spawn parallel server processes (multiple Claude Code windows, Claude Desktop + IDE, etc.). Public API unchanged. (#32)
- **NEW**: Opt-in single-instance guard via `KNOWLEDGE_RAG_SINGLE_INSTANCE=1` env var. **OFF by default** — multi-client MCP usage continues to work unchanged. When enabled, a second server process for the same `data_dir` exits with code 75 (`EX_TEMPFAIL`). Includes stale-PID recovery and SIGINT/SIGTERM handlers. See [docs/single-instance.md](docs/single-instance.md). (#33, original concept by @Hohlas in #31)
- **NEW**: `examples/mcp-config-single-instance.json` — sample MCP client config for the opt-in guard.
- **DOCS**: New `docs/single-instance.md` — when to use, when NOT to use, troubleshooting, full activation reference.
- **DOCS**: README troubleshooting section for "Multiple MCP clients spawn duplicate servers" + memory-usage note for lazy embeddings.
- **CHORE**: Sync version across `pyproject.toml`, `mcp_server/__init__.py`, and `npm/package.json` (was drifting since v3.5.x).
- **CHORE**: pytest `tmp_path_retention_count=1` to avoid Windows atexit cleanup race in CI.
- **ROADMAP**: Tracked v4.0 shared-service architecture (one daemon, many thin MCP clients) as the long-term fix for multi-process resource duplication. (#34)
### Unreleased
- **FIX**: Startup preflight probes ChromaDB in a child process and moves crashing persistent indexes to `data/backups/auto-repair-*` before MCP initialization.
- **FIX**: Reranker load failures now fall back to RRF ordering instead of failing `search_knowledge` on offline machines.
- **FIX**: Virtualenv project-root detection now handles Python symlinks that resolve to the system interpreter.
- **NEW**: `knowledge-rag-guarded` console script kept as an explicit guarded startup alias.
### v3.6.2 (2026-04-23)
- **INFRA**: NPM provenance attestation (SLSA supply chain security), full README on npm page
- **DOCS**: Reorganize Installation section — add NPX and Docker install methods, update What's New to v3.6.0
### v3.6.0 (2026-04-23)
- **NEW**: Multi-language code parsing — C (`.c`), C++ (`.cpp`/`.h`), JavaScript (`.js`/`.jsx`), TypeScript (`.ts`/`.tsx`) with per-language function/class/import extraction
- **NEW**: XML parser (`.xml`) — root element and namespace metadata extraction
- **NEW**: All 8 new formats default enabled — no config change needed
- **NEW**: NPM wrapper (`npx knowledge-rag`) + Docker image (`ghcr.io/lyonzin/knowledge-rag`)
- **NEW**: Automated release pipeline — PyPI (Trusted Publishing), NPM, Docker GHCR
- **IMPROVED**: Code parser reports correct `language` metadata per file type (was hardcoded to `"python"` for all code files)
### v3.5.2 (2026-04-16)
- **NEW**: Auto-discovery of CUDA 12 DLLs from pip-installed NVIDIA packages — no manual PATH configuration needed
- **NEW**: Graceful GPU→CPU fallback with `[WARN]` log when CUDA init fails (missing drivers, wrong version, etc.)
- **FIX**: Explicit `CPUExecutionProvider` when `gpu: false` — eliminates noisy CUDA probe errors in logs
- **FIX**: BASE_DIR resolution now correctly prefers directories with `config.yaml` over those with only `config.example.yaml` (fixes editable installs)
### v3.5.1 (2026-04-16)
- **FIX**: Removed Python upper bound constraint (`<3.13` → `>=3.11`). Python 3.13 and 3.14 now supported — onnxruntime ships wheels for both.
### v3.5.0 (2026-04-16)
- **NEW**: Optional GPU acceleration for ONNX embeddings — `pip install knowledge-rag[gpu]` + `models.embedding.gpu: true` in config. 5-10x faster indexing on NVIDIA GPUs with automatic CPU fallback.
- **DOCS**: Supported formats table added to README (20 formats)
### v3.4.3 (2026-04-16)
- **FIX**: Correct stdout protection via save/restore pattern — `__init__.py` saves original stdout and redirects to stderr during init, `server.py main()` restores it before `mcp.run()`. v3.4.2's global redirect broke MCP JSON-RPC response channel.
### v3.4.1 (2026-04-16)
- **FIX**: `pip install knowledge-rag` now auto-detects project directory from venv location
- **NEW**: `install.sh` — Linux/macOS installer with pip and from-source modes
- **IMPROVED**: BASE_DIR resolution chain: env var → source dir → venv parent → CWD → fallback
### v3.4.0 (2026-04-16)
- **NEW**: `models_cache_dir` — persistent embedding model cache, prevents re-download after reboots
- **NEW**: `exclude_patterns` — glob-based file/directory exclusion during indexing
- **NEW**: Jupyter Notebook (.ipynb) parser — extracts markdown and code cell sources only
- **NEW**: MCP stdout protection — redirects stdout to stderr before server start
- **NEW**: File watcher resilience — graceful fallback when Linux inotify limits are reached
- **NEW**: MetaTrader (.mq4, .mqh) support — opt-in code parsing
- **NEW**: 23 new tests (exclude patterns, ipynb parser, stdout protection)
- Community credit: [@Hohlas](https://github.com/Hohlas) ([PR #18](https://github.com/lyonzin/knowledge-rag/pull/18))
### v3.3.x
- **v3.3.2**: Full type validation on YAML config, bounds checking, version sync
- **v3.3.1**: YAML null value crash fix, presets bundled in pip wheel, `knowledge-rag init` CLI
- **v3.3.0**: YAML configuration system, 4 domain presets, generic use support
### v3.2.x
- **v3.2.4**: Symlink support with circular loop protection
- **v3.2.3**: BASE_DIR smart detection for pip installs
- **v3.2.2**: Plug-and-play pip install, `KNOWLEDGE_RAG_DIR` env var
- **v3.2.1**: Auto-recovery from corrupted ChromaDB
- **v3.2.0**: Parallel BM25 + Semantic search, adjacent chunk retrieval
### v3.1.x
- **v3.1.1**: Code block protection in markdown chunker, AAR category, 14 CVE aliases
- **v3.1.0**: DOCX/XLSX/PPTX/CSV support, file watcher, MMR diversification, PyPI publish
### v3.0.0 (2026-03-19)
- Replaced Ollama with FastEmbed (ONNX in-process)
- Cross-encoder reranking, markdown-aware chunking, query expansion
- 6 new MCP tools (12 total), auto-migration from v2.x
<details>
<summary>v2.x and earlier</summary>
- **v2.2.0**: `hybrid_alpha=0` skips Ollama, default changed from 0.5 to 0.3
- **v2.1.0**: Mermaid architecture diagrams
- **v2.0.0**: Hybrid search, RRF fusion, `hybrid_alpha` parameter
- **v1.1.0**: Incremental indexing, query cache, chunk deduplication
- **v1.0.1**: Auto-cleanup orphan folders, removed hardcoded paths
- **v1.0.0**: Initial release
## </details>
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes
4. Push to the branch (`git push origin feature/amazing-feature`)
## 5. Open a Pull Request
## License
## This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- [ChromaDB](https://www.trychroma.com/) — Vector database
- [FastEmbed](https://qdrant.github.io/fastembed/) — ONNX Runtime embeddings
- [FastMCP](https://github.com/anthropics/mcp) — Model Context Protocol framework
- [PyMuPDF](https://pymupdf.readthedocs.io/) — PDF parsing
- [rank-bm25](https://github.com/dorianbrown/rank_bm25) — BM25 Okapi implementation
- [Watchdog](https://github.com/gorakhargosh/watchdog) — File system monitoring
- [python-docx](https://python-docx.readthedocs.io/) / [openpyxl](https://openpyxl.readthedocs.io/) / [python-pptx](https://python-pptx.readthedocs.io/) — Office document parsing
- [PyYAML](https://pyyaml.org/) — YAML configuration parsing
## - [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/) — HTML parsing for URL ingestion
## Author
**Lyon.**
## Security Researcher | Developer
<div align="center">
**[Back to Top](#knowledge-rag)**
</div></div><div><strong>标签：</strong>BM25, Claude Code, CNCF毕业项目, Markdown, MCP工具, NLP, ONNX, PDF解析, RAG, SOC Prime, 代码搜索, 信息检索, 向量搜索, 多格式支持, 开发工具, 开源, 文档解析, 本地搜索, 本地索引, 检索增强生成, 混合搜索, 知识管理, 离线, 网络安全, 逆向工具, 重排序, 隐私保护, 零配置</div></article></div>
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[](https://pypi.org/project/knowledge-rag/)
[](https://www.npmjs.com/package/knowledge-rag)
[](https://pepy.tech/projects/knowledge-rag)
[](https://github.com/lyonzin/knowledge-rag/actions/workflows/ci.yml)
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[](https://github.com/lyonzin/knowledge-rag/actions/workflows/quality-gate.yml)
[](https://glama.ai/mcp/servers/lyonzin/knowledge-rag)
### Your docs, your machine, zero cloud. Claude Code searches them natively.
Drop your PDFs, markdown, code, notebooks — **1800+ files, 39K chunks, indexed in under 3 minutes.**
Hybrid search (BM25 + semantic vectors + cross-encoder reranking) through 12 MCP tools.
#### Everything runs locally via ONNX. No Docker, no Ollama, no API keys, no data leaves your machine. #### pip install knowledge-rag → restart Claude Code → search_knowledge("your query") **12 MCP Tools** | **Hybrid Search + Reranking** | **20 File Formats** | **Optional NVIDIA GPU** | **100% Local** [What's New](#whats-new-in-v390) | [Supported Formats](#supported-formats) | [Installation](#installation) | [Configuration](#configuration) | [API Reference](#api-reference) | [Architecture](#architecture) ##
## What's New in v3.9.0
### Quality Gate — 7-Pillar PR Validation
Every PR (including dependabot bumps and one-line fixes) is now evaluated against **35+ automated checks** spread across 7 pillars before any human review:
| Pillar | What it enforces | Tools |
|---|---|---|
| **1 Security** | SAST, secrets, CVEs, supply chain | bandit, semgrep, gitleaks, pip-audit, dependency-review, Snyk, CodeQL, Socket |
| **2 Stability** | Flake detection, coverage trend, test count, deterministic runs | pytest-rerunfailures, codecov ±0.5pp, test-count guard |
| **3 Memory Leak** | RSS bounded under 1000-query load, no idle bloat | psutil-based baseline tests + nightly 50K-iteration soak |
| **4 Versatility** | 9 OS×Python combos, 14 format parsers, 4 config presets, locale tolerance, property-based fuzzing | matrix CI on Linux+Windows+macOS × 3.11+3.12+3.13, Hypothesis |
| **5 Scalability** | Performance regression > 10% blocks merge, public bench dashboard | pytest-benchmark, GH Pages chart |
| **6 Versioning** | Atomic version sync, API surface diff, conventional commits, CHANGELOG enforcement, backwards compat | griffe-style AST diff, custom guards |
| **7 Quality** | Type strictness, docstring coverage, complexity, dead code | mypy strict, interrogate ≥80%, radon, vulture |
Plus a **nightly resilience workflow** that runs chaos failure-injection (HF down, ChromaDB corruption, watchdog crash, ONNX zero-byte replay), determinism check (full suite × 3), and mutation testing on selected modules.
Read the full philosophy in [CONTRIBUTING.md](CONTRIBUTING.md). Report bugs via [SECURITY.md](SECURITY.md) or the [issue templates](.github/ISSUE_TEMPLATE/).
### Critical Hotfix — No More Silent Zero-Vector Corruption (v3.8.1)
`FastEmbedEmbeddings.__call__` no longer swallows exceptions and returns `[[0.0]*dim, ...]` when the ONNX model fails to load. That bug pre-existed in master but was silent: ChromaDB happily stored zero embeddings, `count()` reported normal numbers, smart-reindex skipped them as "already indexed", and queries returned garbage similarity with no error visible. Now raises `EmbeddingModelLoadError` / `EmbeddingError` loudly. **All v3.8.0 users should upgrade.** Full details in [Changelog](#v381-2026-05-10--hotfix).
### Lazy-Loaded Embeddings — Cheaper Idle Processes (v3.8.0)
The FastEmbed ONNX model (~200MB resident) now loads on the **first query**, not at startup. Idle `knowledge-rag` processes are now genuinely cheap. Why this matters: MCP stdio is one-process-per-client by protocol — multiple Claude Code windows, Claude Desktop + IDE simultaneously, or review/approval flows that open extra connections all spawn their own processes. Before v3.8.0, every one of them paid the full embedding-model cost up front. Now only processes that actually serve queries load the model. Public API is unchanged.
### Opt-In Single-Instance Guard (v3.8.0)
For users who measured their setup and want a hard cap of one server per `data_dir`:
#### export KNOWLEDGE_RAG_SINGLE_INSTANCE=1
A second instance exits immediately with code 75. **OFF by default** so multi-client MCP usage continues to work unchanged. Stale-PID recovery + SIGINT/SIGTERM cleanup wired correctly. Full guide in [docs/single-instance.md](docs/single-instance.md). Sample MCP config in [examples/mcp-config-single-instance.json](examples/mcp-config-single-instance.json).
### 5 Ways to Install
```bash
npx -y knowledge-rag # NPM — zero setup, auto-manages Python venv
pip install knowledge-rag # PyPI — classic Python install
curl -fsSL .../install.sh | bash # One-line installer (Linux/macOS/Windows)
docker pull ghcr.io/lyonzin/knowledge-rag # Docker — models pre-downloaded
#### git clone ... && pip install -r ... # From source
All methods produce the same MCP server. See [Installation](#installation) for full instructions.
### Recent Highlights
- **v3.9.0** — **Quality Gate** activated: 35+ automated PR checks across 7 pillars (Security, Stability, Memory Leak, Versatility, Scalability, Versioning, Quality) + nightly resilience suite (chaos, soak, determinism, mutation)
- **v3.8.1** — Critical hotfix: loud-fail embeddings (no more silent zero-vector corruption); Windows CI flake erradicated (HF_HUB_OFFLINE + shell:bash + atexit wrapper)
- **v3.8.0** — Lazy-load embeddings, opt-in single-instance guard, version sync across PyPI/NPM/Docker
- **v3.6.0** — Multi-language code parsing (C/C++/JS/TS/XML), NPM wrapper, Docker image, automated release pipeline
- **v3.5.2** — CUDA DLL auto-discovery from pip packages, graceful GPU→CPU fallback, explicit CPU provider (no CUDA noise when `gpu: false`), BASE_DIR resolution fix for editable installs
- **v3.5.1** — Remove Python `<3.13` upper bound — 3.13 and 3.14 now supported
- **v3.5.0** — Optional GPU acceleration, supported formats table, full README rewrite
- **v3.4.3** — MCP stdout save/restore fix (v3.4.2 broke JSON-RPC responses)
- **v3.4.0** — Persistent model cache, exclude patterns, Jupyter Notebook parser, inotify resilience, MetaTrader support
## See [Changelog](#changelog) for full history.
## Supported Formats
| Format | Extension | Parser | Default | Notes |
|--------|-----------|--------|---------|-------|
| Markdown | `.md` | Section-aware (splits at `##`) | Yes | Headers preserved as chunk boundaries |
| Plain Text | `.txt` | Fixed-size chunking | Yes | 1000 chars + 200 overlap |
| PDF | `.pdf` | PyMuPDF extraction | Yes | Text-based PDFs only (no OCR) |
| Python | `.py` | Code-aware parser | Yes | Functions/classes as chunks |
| JSON | `.json` | Structure-aware | Yes | Flattened key-value extraction |
| CSV | `.csv` | Row-based parser | Yes | Headers + rows as text |
| Word | `.docx` | python-docx | Yes | Headings preserved as markdown |
| Excel | `.xlsx` | openpyxl | Yes | Sheet-by-sheet extraction |
| PowerPoint | `.pptx` | python-pptx | Yes | Slide-by-slide extraction |
| Jupyter Notebook | `.ipynb` | Cell-aware parser | Yes | Markdown + code cells only, no outputs/base64 |
| C Source | `.c` | Code-aware parser | Yes | Functions/structs/includes extracted |
| C/C++ Header | `.h` | Code-aware parser | Yes | Function declarations/structs extracted |
| C++ Source | `.cpp` | Code-aware parser | Yes | Classes/structs/includes extracted |
| JavaScript | `.js` | Code-aware parser | Yes | Functions/classes/imports (ESM + CJS) |
| React JSX | `.jsx` | Code-aware parser | Yes | Same as JS parser |
| TypeScript | `.ts` | Code-aware parser | Yes | Functions/classes/interfaces/enums/imports |
| React TSX | `.tsx` | Code-aware parser | Yes | Same as TS parser |
| XML | `.xml` | XML parser | Yes | Root element and namespace extraction |
| MQL4 Header | `.mqh` | Code parser | No | MetaTrader — add to `supported_formats` to enable |
| MQL4 Source | `.mq4` | Code parser | No | MetaTrader — add to `supported_formats` to enable |
## > **Tip:** The parser dispatch is extensible. Any format mapped in `_parsers` can be enabled via `supported_formats` in config.yaml.
## Features
| Feature | Description |
|---------|-------------|
| **Hybrid Search** | Semantic + BM25 keyword search with Reciprocal Rank Fusion |
| **Cross-Encoder Reranker** | Xenova/ms-marco-MiniLM-L-6-v2 re-scores top candidates for precision |
| **GPU Acceleration** | Optional ONNX CUDA support for 5-10x faster indexing |
| **YAML Configuration** | Fully customizable via `config.yaml` with domain-specific presets |
| **Query Expansion** | Configurable synonym mappings (69 security-term defaults) |
| **Markdown-Aware Chunking** | `.md` files split by `##`/`###` sections instead of fixed windows |
| **In-Process Embeddings** | FastEmbed ONNX Runtime (BAAI/bge-small-en-v1.5, 384D) |
| **Keyword Routing** | Word-boundary aware routing for domain-specific queries |
| **20 Format Parsers** | MD, TXT, PDF, PY, C, H, CPP, JS, JSX, TS, TSX, JSON, XML, CSV, DOCX, XLSX, PPTX, IPYNB + opt-in MQH/MQ4 |
| **Category Organization** | Organize docs by folder, auto-tagged by path |
| **Incremental Indexing** | Change detection via mtime/size — only re-indexes modified files |
| **Chunk Deduplication** | SHA256 content hashing prevents duplicate chunks |
| **Query Cache** | LRU cache with 5-min TTL for instant repeat queries |
| **Document CRUD** | Add, update, remove documents via MCP tools |
| **URL Ingestion** | Fetch URLs, strip HTML, convert to markdown, index |
| **Similarity Search** | Find documents similar to a reference document |
| **Retrieval Evaluation** | Built-in MRR@5 and Recall@5 metrics |
| **File Watcher** | Auto-reindex on document changes via watchdog (5s debounce) |
| **Exclude Patterns** | Glob-based file/directory exclusion during indexing |
| **MMR Diversification** | Maximal Marginal Relevance reduces redundant results |
| **Persistent Model Cache** | Embedding models cached in `models_cache/` — survives reboots |
| **Auto-Migration** | Detects embedding dimension mismatch and rebuilds automatically |
## | **12 MCP Tools** | Full CRUD + search + evaluation via Claude Code |
## Architecture
### System Overview
```mermaid
flowchart TB
subgraph MCP["MCP SERVER (FastMCP)"]
direction TB
TOOLS["12 MCP ToolsHybrid search (BM25 + semantic vectors + cross-encoder reranking) through 12 MCP tools.
#### Everything runs locally via ONNX. No Docker, no Ollama, no API keys, no data leaves your machine. #### pip install knowledge-rag → restart Claude Code → search_knowledge("your query") **12 MCP Tools** | **Hybrid Search + Reranking** | **20 File Formats** | **Optional NVIDIA GPU** | **100% Local** [What's New](#whats-new-in-v390) | [Supported Formats](#supported-formats) | [Installation](#installation) | [Configuration](#configuration) | [API Reference](#api-reference) | [Architecture](#architecture) ##
search | get | add | update | remove
reindex | list | stats | url | similar | evaluate"] end subgraph SEARCH["HYBRID SEARCH ENGINE"] direction LR ROUTER["Keyword Router
(word boundaries)"] SEMANTIC["Semantic Search
(ChromaDB)"] BM25["BM25 Keyword
(rank-bm25 + expansion)"] RRF["Reciprocal Rank
Fusion (RRF)"] RERANK["Cross-Encoder
Reranker"] ROUTER --> SEMANTIC ROUTER --> BM25 SEMANTIC --> RRF BM25 --> RRF RRF --> RERANK end subgraph STORAGE["STORAGE LAYER"] direction LR CHROMA[("ChromaDB
Vector Database")] COLLECTIONS["Collections
security | ctf
logscale | development"] CHROMA --- COLLECTIONS end subgraph EMBED["EMBEDDINGS (In-Process)"] FASTEMBED["FastEmbed ONNX
BAAI/bge-small-en-v1.5
(384D, CPU or GPU)"] CROSSENC["Cross-Encoder
ms-marco-MiniLM-L-6-v2"] FASTEMBED --- CROSSENC end subgraph INGEST["DOCUMENT INGESTION"] PARSERS["20 Parsers
MD | PDF | TXT | PY | C | H | CPP | JS | JSX | TS | TSX | JSON | XML | CSV
DOCX | XLSX | PPTX | IPYNB | MQH | MQ4"] CHUNKER["Chunking
MD: section-aware
Other: 1000 chars + 200 overlap"] PARSERS --> CHUNKER end CLAUDE["Claude Code"] --> MCP MCP --> SEARCH SEARCH --> STORAGE STORAGE --> EMBED INGEST --> EMBED #### EMBED --> STORAGE ### Query Processing Flow ```mermaid flowchart TB QUERY["User Query
'mimikatz credential dump'"] --> EXPAND subgraph EXPANSION["Query Expansion"] EXPAND["Synonym Expansion
mimikatz -> mimikatz, sekurlsa, logonpasswords"] end EXPAND --> ROUTER subgraph ROUTING["Keyword Routing"] ROUTER["Keyword Router"] MATCH{"Word Boundary
Match?"} CATEGORY["Filter: redteam"] NOFILTER["No Filter"] ROUTER --> MATCH MATCH -->|Yes| CATEGORY MATCH -->|No| NOFILTER end subgraph HYBRID["Hybrid Search"] direction LR SEMANTIC["Semantic Search
(ChromaDB embeddings)
Conceptual similarity"] BM25["BM25 Search
(expanded query)
Exact term matching"] end subgraph FUSION["Result Fusion + Reranking"] RRF["Reciprocal Rank Fusion
score = alpha * 1/(k+rank_sem)
+ (1-alpha) * 1/(k+rank_bm25)"] RERANK["Cross-Encoder Reranker
Re-scores top 3x candidates
query+doc pair scoring"] SORT["Sort by Reranker Score
Normalize to 0-1"] RRF --> RERANK --> SORT end CATEGORY --> HYBRID NOFILTER --> HYBRID SEMANTIC --> RRF BM25 --> RRF #### SORT --> RESULTS["Results
search_method: hybrid|semantic|keyword
score + reranker_score + raw_rrf_score"] ### Document Ingestion Flow ```mermaid flowchart LR subgraph INPUT["Input"] FILES["documents/
├── security/
├── development/
├── ctf/
└── general/"] end subgraph PARSE["Parse (20 formats)"] MD["Markdown"] PDF["PDF
(PyMuPDF)"] OFFICE["DOCX | XLSX
PPTX | CSV"] CODE["PY | C | H | CPP | JS | JSX
TS | TSX | JSON | XML | IPYNB"] end subgraph CHUNK["Chunk"] MDSPLIT["MD: Section-Aware
Split at ## headers"] TXTSPLIT["Other: Fixed-Size
1000 chars + 200 overlap"] DEDUP["SHA256 Dedup
Skip duplicate content"] end subgraph EMBED["Embed"] FASTEMBED["FastEmbed ONNX
bge-small-en-v1.5
(384D, CPU or GPU)"] end subgraph STORE["Store"] CHROMADB[("ChromaDB")] BM25IDX["BM25 Index"] end FILES --> MD & PDF & OFFICE & CODE MD --> MDSPLIT PDF & OFFICE & CODE --> TXTSPLIT MDSPLIT --> DEDUP TXTSPLIT --> DEDUP DEDUP --> EMBED #### EMBED --> STORE ### hybrid_alpha Parameter Effect ```mermaid flowchart LR subgraph ALPHA["hybrid_alpha values"] A0["0.0
Pure BM25
Instant"] A3["0.3 (default)
Keyword-heavy
Fast"] A5["0.5
Balanced"] A7["0.7
Semantic-heavy"] A10["1.0
Pure Semantic"] end subgraph USE["Best For"] U0["CVEs, tool names
exact matches"] U3["Technical queries
specific terms"] U5["General queries"] U7["Conceptual queries
related topics"] U10["'How to...' questions
conceptual search"] end A0 --- U0 A3 --- U3 A5 --- U5 A7 --- U7 #### A10 --- U10 --- ## Installation ### Prerequisites - Python 3.11+ - Claude Code CLI - *…or any other MCP client (Claude Desktop, Cursor, VS Code, Antigravity, opencode, Windsurf) — see [Use with other MCP clients](#use-with-other-mcp-clients)* - ~200MB disk for model cache (auto-downloaded on first run) - *Optional:* NVIDIA GPU + CUDA for accelerated embeddings (`pip install knowledge-rag[gpu]` + `models.embedding.gpu: true` in config) ### Install Methods Pick one — all produce the same running server. #### Option A: NPX (fastest) Requires Node.js 16+. Handles Python venv, pip install, and version upgrades automatically. ```bash #### claude mcp add knowledge-rag -s user -- npx -y knowledge-rag That's it. On first run, `npx` creates a venv at `~/.knowledge-rag/`, installs the PyPI package, and starts the MCP server. Subsequent runs reuse the cached venv. #### Option B: One-line installer ```bash # Linux/macOS: curl -fsSL https://raw.githubusercontent.com/lyonzin/knowledge-rag/master/install.sh | bash # Windows (PowerShell): #### irm https://raw.githubusercontent.com/lyonzin/knowledge-rag/master/install.ps1 | iex Then configure Claude Code: ```bash #### claude mcp add knowledge-rag -s user -- ~/knowledge-rag/venv/bin/python -m mcp_server.server > **Windows**: `claude mcp add knowledge-rag -s user -- %USERPROFILE%\knowledge-rag\venv\Scripts\python.exe -m mcp_server.server` #### Option C: pip install ```bash mkdir ~/knowledge-rag && cd ~/knowledge-rag python3 -m venv venv && source venv/bin/activate pip install knowledge-rag #### knowledge-rag init # Exports config template, presets, creates documents/ Then configure Claude Code: ```bash #### claude mcp add knowledge-rag -s user -- ~/knowledge-rag/venv/bin/python -m mcp_server.server > **Windows users**: Use `python` instead of `python3`, `venv\Scripts\activate` instead of `source venv/bin/activate`. > **Windows path**: `claude mcp add knowledge-rag -s user -- %USERPROFILE%\knowledge-rag\venv\Scripts\python.exe -m mcp_server.server` #### Option D: Clone from source ```bash git clone https://github.com/lyonzin/knowledge-rag.git ~/knowledge-rag cd ~/knowledge-rag python3 -m venv venv && source venv/bin/activate #### pip install -r requirements.txt Then configure Claude Code: ```bash #### claude mcp add knowledge-rag -s user -- ~/knowledge-rag/venv/bin/python -m mcp_server.server #### Option E: Docker ```bash #### docker pull ghcr.io/lyonzin/knowledge-rag:latest ```bash claude mcp add knowledge-rag -s user -- \ docker run -i --rm \ -v ~/knowledge-rag/documents:/app/documents \ -v ~/knowledge-rag/data:/app/data \ #### ghcr.io/lyonzin/knowledge-rag:latest Models are pre-downloaded in the image — no first-run delay.