Vectorise

Local MCP server that turns folders of documents into a hybrid vector + keyword index that Claude Desktop can search. Stays offline after first model download.

PyPI

Stack

• MCP: mcp (FastMCP), stdio transport
• Embeddings: BAAI/bge-small-en-v1.5 (384-dim)
• Reranker: BAAI/bge-reranker-base cross-encoder
• Vector DB: sqlite-vec
• Keyword DB: SQLite FTS5 (BM25)
• Fusion: Reciprocal Rank Fusion → cross-encoder rerank

Install

pip install vectorise-mcp                 # core
pip install "vectorise-mcp[ocr]"          # + OCR for scanned PDFs / images
pip install "vectorise-mcp[notify]"       # + desktop toast on job completion
pip install "vectorise-mcp[ocr,notify]"   # everything

vectorise-mcp setup                       # pre-download models (~250MB)

Python ≥ 3.10.

Wire into Claude Desktop

claude_desktop_config.json:

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

Config file location:

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

Restart Claude Desktop.

File support

Format	Notes
.pdf	text + OCR fallback for scanned pages
.docx, .pptx, .xlsx, .xlsm, .xls	full content + tables
.txt, .md, .markdown	UTF-8
.png, .jpg, .jpeg, .tiff, .bmp, .webp	OCR (requires [ocr])
.doc, .ppt	detected, skipped, reported

Tools exposed to Claude

Tool	What it does
vectorise_list_projects	list all indexed projects
vectorise_index_project(folder, project, mode)	start indexing job, returns job_id instantly
vectorise_reindex_project(project)	SHA1-incremental rescan of all sources
vectorise_index_status(job_id)	instant job snapshot incl. progress + ETA
vectorise_await_index(job_id, timeout_sec)	optional blocking wait
vectorise_list_jobs(active_only)	jobs from current server session
vectorise_search(project, query, k, candidate_pool, file_glob, subdirectory, page_min, page_max, min_similarity)	hybrid + reranked search
vectorise_delete_project(project)	delete project's .db

mode for vectorise_index_project: auto (default — incremental if path already indexed, error on conflict) / replace / append / fail.

Architecture

Indexing job runs in a daemon thread with its own asyncio loop. The MCP server's main loop stays free to serve index_status / search calls regardless of how heavy the embedding/OCR work is. Status calls are instant; search works on the partial index while a job is running.

folder
  ↓  parsers.parse                        (.pdf .docx .pptx .xlsx ...)
chunks (sentence-aware, 384 tok / 96 overlap, single-sentence hard-split)
  ↓  embedder.embed_passages              (BGE-small)
sqlite-vec   +   FTS5 (BM25)              ← per-file SHA1 dedup, basename collision auto-rename
  ↓  search                               (vector top-N + BM25 top-N)
RRF fusion → cross-encoder rerank → top-K

Project DBs live in ~/.vectorise-mcp/<name>.db. Self-contained — source folder can be deleted after indexing.

Config (env vars)

Var	Default	Purpose
VECTORISE_MCP_EMBED_MODEL	BAAI/bge-small-en-v1.5	must be 384-dim
VECTORISE_MCP_RERANKER_MODEL	BAAI/bge-reranker-base
VECTORISE_MCP_EMBED_BATCH	32
VECTORISE_MCP_RERANKER_BATCH	16
VECTORISE_MCP_OCR_MIN_CONFIDENCE	0.5	drop OCR lines below
VECTORISE_MCP_OCR_WORKERS	4	parallel page OCR threads
VECTORISE_MCP_OCR_DPI	200	PDF rasterisation DPI
VECTORISE_MCP_OCR_MAX_DIM	4000	downscale huge images before OCR
VECTORISE_MCP_NOTIFY	1	desktop toast on/off

Performance

	CPU	GPU
Indexing throughput	~80 chunks/sec	5–10× faster
Search latency (k=5, ≤500K chunks)	~150ms	similar
Disk per chunk	~2 KB
Cold start	~5s (lazy model load)

Local dev

git clone https://github.com/jameslovespancakes/Vectorised-Embedding-MCP
cd Vectorised-Embedding-MCP
pip install -e ".[ocr,notify]"

# tests bypass MCP transport, drive indexer + tools directly
python tests/smoke_test.py
python tests/smoke_test_projects.py
python tests/smoke_test_jobs.py
python tests/smoke_test_filters.py
python tests/smoke_test_office.py
python tests/smoke_test_chunking.py
python tests/smoke_test_legacy_skip.py

License

MIT.