Knowledge RAG

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

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

Tip: The parser dispatch is extensible. Any format mapped in _parsers can be enabled via supported_formats in config.yaml.

Features

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.

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

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

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.

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.

Returns: JSON with document content, metadata, keywords, and chunk count.

reindex_documents

Index or reindex all documents in the knowledge base.

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.

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.

update_document

Update an existing document. Removes old chunks from the index and re-indexes with new content.

remove_document

Remove a document from the knowledge base index. Optionally deletes 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.

search_similar

Find documents similar to a given document using embedding similarity.

evaluate_retrieval

Evaluate retrieval quality with test queries. Useful for tuning hybrid_alpha, testing query expansion effectiveness, or validating after reindexing.

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                      # 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:

Creating your own preset: Copy config.example.yaml, fill in your categories/keywords/expansions, save to presets/your-domain.yaml.

Configuration Reference

Paths

Relative paths resolve from the project root. Absolute paths work too.

Documents

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

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

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

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:

# 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:

knowledge-rag
knowledge-rag-guarded

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. 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 (~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.

Changelog

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 (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

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 file for details.

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

Author

Lyon.

Security Researcher | Developer