vectorise-mcp 0.8.2

Local stdio MCP server that turns folders of dense documents into a vector embedding DB Claude can search.

vectorise-mcp

Local stdio MCP server that turns folders of dense documents (PDFs, Word, text, markdown) into a hybrid retrieval database that Claude Desktop can semantically search mid-conversation. Built so Claude can effectively work with corpora far larger than its context window — point it at 100M+ tokens of reports and ask questions; it pulls only the relevant chunks.

Fully offline after first model download. No API keys. Free.

Why this is built for quality, not just for ticking a box

Cheap RAG implementations ("just embed with MiniLM and dot-product search") fail badly on dense documents. They miss rare terms, conflate similar sentences, and rank irrelevant chunks at the top. This server uses a stack designed for real retrieval quality:

Stage	What it does	Why
BGE-small-en-v1.5 embeddings	Dense semantic vectors (384-dim, normalized)	Top-tier MTEB scores at small size; far better than MiniLM on technical English.
SQLite FTS5 BM25	Keyword retrieval in parallel	Catches rare terms, names, IDs, acronyms that pure semantic search misses.
Reciprocal Rank Fusion	Merges vector + keyword candidates	Robust hybrid signal — neither side dominates.
bge-reranker-base cross-encoder	Re-scores top-50 jointly with the query	Massive precision boost; cross-encoders consistently rank 5-10 points higher than bi-encoders alone.
Sentence-aware chunking	384-tok chunks, 96-tok overlap, sentence-bounded	Preserves coherence; overlap stops boundary loss.
SHA1 incremental reindex	Only re-embeds changed files	Cheap to keep up to date as the folder evolves.

This is the same retrieval pattern used in production search systems (e.g., Anthropic's contextual retrieval, Vespa hybrid recipes).

Stack

Component	Library
MCP server	mcp SDK (FastMCP)
Embeddings	BAAI/bge-small-en-v1.5 (384-dim, ~130MB, CPU)
Reranker	BAAI/bge-reranker-base (~110MB, CPU)
Vector DB	sqlite-vec (single-file SQLite extension)
Keyword DB	SQLite FTS5 (BM25)
PDF	pypdf
DOCX	python-docx

Install

# core (text-based docs only)
pip install vectorise-mcp

# with OCR for scanned PDFs + images (.png .jpg .tiff .bmp .webp)
pip install "vectorise-mcp[ocr]"

vectorise-mcp setup       # downloads ~250MB models (+30MB OCR if installed)

Python ≥ 3.10. pip cannot reliably run post-install hooks (PEP 517), so models download on setup (or first serve boot if you skip setup). After that, fully offline.

OCR: uses rapidocr-onnxruntime (pure Python, ONNX, no system Tesseract install) and pypdfium2 (no Poppler). When installed, scanned PDF pages auto-fall-back to OCR; image files become first-class indexable docs.

Wire into Claude Desktop

Edit claude_desktop_config.json:

• Windows: %APPDATA%\Claude\claude_desktop_config.json
• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "vectorise": {
      "command": "vectorise-mcp",
      "args": ["serve"]
    }
  }
}

Ready-to-paste configs are also in the repo:

File	Use
claude_desktop_config.example.json	Minimal config — just this server.
examples/claude_desktop_config.windows.json	Windows-pathed variant with comment.
examples/claude_desktop_config.macos.json	macOS-pathed variant with comment.
examples/claude_desktop_config.linux.json	Linux-pathed variant with comment.
examples/claude_desktop_config.advanced.json	Pinned Python interpreter + env tuning for GPU.

If you already have other MCP servers in your config, merge the "vectorise" key into your existing mcpServers object — don't overwrite the whole file.

Restart Claude Desktop. Server eagerly loads both models on boot — first tool call is instant, no surprise pause.

Use it

In a Claude Desktop conversation:

"Index C:\Users\me\Documents\Q1Reports and tell me what the revenue projections were."

Claude will:

1. Call index_folder — streams progress notifications: "Indexing 47 files, ~2 min remaining…"
2. Call search — hybrid retrieval + cross-encoder rerank → top 5 chunks.
3. Synthesize answer, citing source file + page.

MCP tools exposed

Tool	What it does
index_folder(folder_path, collection?)	Walk folder, embed + index every supported doc. SHA1-safe to re-run.
reindex_folder(collection)	Re-scan source folder. Re-embed only changed files; drop deleted.
list_collections()	All indexed collections with size, doc count, indexed-at timestamp.
search(collection, query, k=5, candidate_pool=75, file_glob?, subdirectory?, page_min?, page_max?, min_similarity?)	Hybrid + reranked. Filters: filename glob, path substring, PDF page range, similarity floor. Claude is encouraged to raise k (10-20) and candidate_pool (150-300) for hard queries.
delete_collection(collection)	Drops the .db file. Returns freed MB.

All tools have docstrings — Claude reads them automatically.

Performance

Metric	Value
Indexing throughput (CPU)	~80 chunks/sec (bge-small)
100-page PDF index time	~3 sec
50 × 100-page PDFs	~3 min
100M-token corpus	~40 min one-time
Search latency (K=5, ≤500K chunks)	~150ms (vector + FTS + rerank)
Disk per chunk	~2 KB

GPU auto-detected by sentence-transformers if PyTorch sees CUDA/MPS — 5-10× faster.

Supported document types

• .pdf — page-aware extraction via pypdf. Pages with empty/sparse text auto-fall-back to OCR (if [ocr] extra installed).
• .docx — paragraphs + tables via python-docx
• .txt, .md, .markdown — UTF-8 text
• .png, .jpg, .jpeg, .tiff, .tif, .bmp, .webp — OCR via RapidOCR (requires [ocr] extra)

Unsupported files are skipped silently. Images without OCR installed are skipped with a warning.

Storage

All collections live under ~/.vectorise-mcp/. One .db file per collection, fully self-contained (vector + keyword + chunks + metadata). Portable — copy the file to share, back up the folder for safekeeping.

Configuration via env vars

Var	Default	Purpose
VECTORISE_MCP_EMBED_MODEL	BAAI/bge-small-en-v1.5	Override embedding model. Must produce 384-dim vectors.
VECTORISE_MCP_RERANKER_MODEL	BAAI/bge-reranker-base	Override cross-encoder reranker.
VECTORISE_MCP_EMBED_BATCH	32	Embedding batch size (lower if OOM).
VECTORISE_MCP_RERANKER_BATCH	16	Rerank batch size.

Troubleshooting

sqlite-vec extension fails to load The PyPI package ships prebuilt binaries; ensure your Python's sqlite has enable_load_extension (true on standard CPython).

Indexing very slow

• First file is slow — model loading (~10s).
• Install GPU PyTorch for big speedup. Check nvidia-smi / Activity Monitor.

Claude doesn't see the server

• Quit Claude Desktop fully; restart.
• Validate JSON (no trailing commas).
• Run vectorise-mcp serve in a terminal — should hang awaiting stdio. Errors there = install issue.

Search returns weak results

• Try larger candidate_pool (e.g. 100). Recall trades against latency.
• Check the chunk text in search results — if chunks are tiny, your source may be image-only PDFs (pypdf can't OCR; use a separate OCR step first).

License

MIT.