knowledge-rag 3.6.2

Local RAG System for Claude Code — Hybrid search + Cross-encoder Reranking + 12 MCP Tools + 20 Format Parsers. Zero external servers.

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 | Supported Formats | Installation | Configuration | API Reference | Architecture

---

What's New in v3.6.0

Multi-Language Code Parsing

Language-aware extraction for C, C++, JavaScript, TypeScript, and XML — functions, classes, structs, interfaces, imports, and namespaces are captured as searchable metadata. Total supported formats: 20.

5 Ways to Install

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 for full instructions.

Recent Highlights

• 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 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

flowchart TB
    subgraph MCP["MCP SERVER (FastMCP)"]
        direction TB
        TOOLS["12 MCP Tools<br/>search | get | add | update | remove<br/>reindex | list | stats | url | similar | evaluate"]
    end

    subgraph SEARCH["HYBRID SEARCH ENGINE"]
        direction LR
        ROUTER["Keyword Router<br/>(word boundaries)"]
        SEMANTIC["Semantic Search<br/>(ChromaDB)"]
        BM25["BM25 Keyword<br/>(rank-bm25 + expansion)"]
        RRF["Reciprocal Rank<br/>Fusion (RRF)"]
        RERANK["Cross-Encoder<br/>Reranker"]

        ROUTER --> SEMANTIC
        ROUTER --> BM25
        SEMANTIC --> RRF
        BM25 --> RRF
        RRF --> RERANK
    end

    subgraph STORAGE["STORAGE LAYER"]
        direction LR
        CHROMA[("ChromaDB<br/>Vector Database")]
        COLLECTIONS["Collections<br/>security | ctf<br/>logscale | development"]
        CHROMA --- COLLECTIONS
    end

    subgraph EMBED["EMBEDDINGS (In-Process)"]
        FASTEMBED["FastEmbed ONNX<br/>BAAI/bge-small-en-v1.5<br/>(384D, CPU or GPU)"]
        CROSSENC["Cross-Encoder<br/>ms-marco-MiniLM-L-6-v2"]
        FASTEMBED --- CROSSENC
    end

    subgraph INGEST["DOCUMENT INGESTION"]
        PARSERS["20 Parsers<br/>MD | PDF | TXT | PY | C | H | CPP | JS | JSX | TS | TSX | JSON | XML | CSV<br/>DOCX | XLSX | PPTX | IPYNB | MQH | MQ4"]
        CHUNKER["Chunking<br/>MD: section-aware<br/>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

flowchart TB
    QUERY["User Query<br/>'mimikatz credential dump'"] --> EXPAND

    subgraph EXPANSION["Query Expansion"]
        EXPAND["Synonym Expansion<br/>mimikatz -> mimikatz, sekurlsa, logonpasswords"]
    end

    EXPAND --> ROUTER

    subgraph ROUTING["Keyword Routing"]
        ROUTER["Keyword Router"]
        MATCH{"Word Boundary<br/>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<br/>(ChromaDB embeddings)<br/>Conceptual similarity"]
        BM25["BM25 Search<br/>(expanded query)<br/>Exact term matching"]
    end

    subgraph FUSION["Result Fusion + Reranking"]
        RRF["Reciprocal Rank Fusion<br/>score = alpha * 1/(k+rank_sem)<br/>+ (1-alpha) * 1/(k+rank_bm25)"]
        RERANK["Cross-Encoder Reranker<br/>Re-scores top 3x candidates<br/>query+doc pair scoring"]
        SORT["Sort by Reranker Score<br/>Normalize to 0-1"]

        RRF --> RERANK --> SORT
    end

    CATEGORY --> HYBRID
    NOFILTER --> HYBRID
    SEMANTIC --> RRF
    BM25 --> RRF

    SORT --> RESULTS["Results<br/>search_method: hybrid|semantic|keyword<br/>score + reranker_score + raw_rrf_score"]

Document Ingestion Flow

flowchart LR
    subgraph INPUT["Input"]
        FILES["documents/<br/>├── security/<br/>├── development/<br/>├── ctf/<br/>└── general/"]
    end

    subgraph PARSE["Parse (20 formats)"]
        MD["Markdown"]
        PDF["PDF<br/>(PyMuPDF)"]
        OFFICE["DOCX | XLSX<br/>PPTX | CSV"]
        CODE["PY | C | H | CPP | JS | JSX<br/>TS | TSX | JSON | XML | IPYNB"]
    end

    subgraph CHUNK["Chunk"]
        MDSPLIT["MD: Section-Aware<br/>Split at ## headers"]
        TXTSPLIT["Other: Fixed-Size<br/>1000 chars + 200 overlap"]
        DEDUP["SHA256 Dedup<br/>Skip duplicate content"]
    end

    subgraph EMBED["Embed"]
        FASTEMBED["FastEmbed ONNX<br/>bge-small-en-v1.5<br/>(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

flowchart LR
    subgraph ALPHA["hybrid_alpha values"]
        A0["0.0<br/>Pure BM25<br/>Instant"]
        A3["0.3 (default)<br/>Keyword-heavy<br/>Fast"]
        A5["0.5<br/>Balanced"]
        A7["0.7<br/>Semantic-heavy"]
        A10["1.0<br/>Pure Semantic"]
    end

    subgraph USE["Best For"]
        U0["CVEs, tool names<br/>exact matches"]
        U3["Technical queries<br/>specific terms"]
        U5["General queries"]
        U7["Conceptual queries<br/>related topics"]
        U10["'How to...' questions<br/>conceptual search"]
    end

    A0 --- U0
    A3 --- U3
    A5 --- U5
    A7 --- U7
    A10 --- U10

---

Installation

Prerequisites

• Python 3.11+
• Claude Code CLI
• ~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.

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

# 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:

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

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:

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

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:

claude mcp add knowledge-rag -s user -- ~/knowledge-rag/venv/bin/python -m mcp_server.server

Option E: Docker

docker pull ghcr.io/lyonzin/knowledge-rag:latest

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.

Alternative: manual JSON config

Add to ~/.claude.json:

Windows:

{
  "mcpServers": {
    "knowledge-rag": {
      "command": "C:\\Users\\YOUR_USER\\knowledge-rag\\venv\\Scripts\\python.exe",
      "args": ["-m", "mcp_server.server"]
    }
  }
}

Linux / macOS:

{
  "mcpServers": {
    "knowledge-rag": {
      "command": "/home/YOUR_USER/knowledge-rag/venv/bin/python",
      "args": ["-m", "mcp_server.server"]
    }
  }
}

Replace YOUR_USER with your username, or use the full path from echo $HOME.

Verify

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:

# 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:

# 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:

# 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

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:

{
  "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:

{
  "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:

{
  "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 <title> 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

# 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                      # Set false on low-resource machines
    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

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.

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).

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.

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

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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

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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:

# Clear cache and retry
# Windows:
rmdir /s /q models_cache

# Linux/macOS:
rm -rf models_cache

# Then restart the MCP server

Index is empty

# 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.

Memory usage

With ~200 documents, expect ~300-500MB RAM. The embedding model (~50MB) and reranker (~25MB) are loaded into memory. For very large knowledge bases (1000+ documents), consider enabling GPU acceleration and using exclude patterns to limit index scope.

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Changelog

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 (PR #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

v2.x and earlier

• 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

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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

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Acknowledgments

• ChromaDB — Vector database
• FastEmbed — ONNX Runtime embeddings
• FastMCP — Model Context Protocol framework
• PyMuPDF — PDF parsing
• rank-bm25 — BM25 Okapi implementation
• Watchdog — File system monitoring
• python-docx / openpyxl / python-pptx — Office document parsing
• PyYAML — YAML configuration parsing
• Beautiful Soup — HTML parsing for URL ingestion

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Author

Lyon.

Security Researcher | Developer

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