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vectorise-mcp

vectorise-mcp

by jameslovespancakes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

vectorise-mcp

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

Related MCP server: local-document-rag-agent

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.

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