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vexicon

About

Knowledge store MCP Server.

Provides an RRF implementation using SQLite. Tunable via settings.

Introduces some opinionated defaults

Notes and reference material live in named spaces backed by Chroma collections with a SQLite FTS5 keyword index; searches fuse the vector and keyword rankings by reciprocal rank fusion.

Run the server over stdio with uv run vexicon.

Settings are read from VEXICON_* environment variables; see src/vexicon/settings.py. Data is stored under ~/.vexicon unless VEXICON_PERSISTENT_PATH and VEXICON_INDEX_DB_PATH say otherwise.

Settings

Variable Default Meaning
VEXICON_PERSISTENT_PATH ~/.vexicon/chroma Directory where Chroma stores its database.
VEXICON_INDEX_DB_PATH ~/.vexicon/hybrid.db SQLite file holding the keyword index.
VEXICON_VECTOR_WEIGHT 1.0 Weight of the vector ranking in fusion.
VEXICON_KEYWORD_WEIGHT 1.0 Weight of the keyword ranking in fusion.
VEXICON_RRF_RANK_OFFSET 60 Rank offset in reciprocal rank fusion.
VEXICON_DEVICE auto Device that runs embedding models: auto, cpu, or cuda.
VEXICON_IDLE_SECONDS 300 Seconds without activity before Chroma and embedding models are unloaded.

Compared with chroma-mcp

Compared against chroma-mcp 0.2.6 and chromadb 1.5.9.

Hybrid search without Chroma Cloud

  • vexicon combines vector and BM25 keyword rankings with reciprocal rank fusion, using a SQLite FTS5 index beside each local Chroma collection.

Token usage

  • vexicon's tool definitions take about 30% fewer tokens than chroma-mcp's.
  • search returns at most limit entries in total, while chroma-mcp returns n_results per query, so its response grows with every query a model adds.
  • Results always leave out embedding vectors, which add tokens without giving a model anything it can use.

Embedding models

  • Each space can use any sentence-transformers model, and tools let a model find and download one from Hugging Face.
  • Cached models load without network calls.
  • Models trained with separate query and document prompts get the matching prompt for searches and for stored entries.
  • Spaces always have their embedding model's max token size available in metadata for sizing entries before they are added.

Memory

  • Embedding models unload from memory after VEXICON_IDLE_SECONDS without use.

Defaults

  • Storage persists under ~/.vexicon by default.
  • Entry IDs are kebab-case mnemonics.
  • Duplicate entry IDs raise an error.
  • Spaces are published as MCP resources.
  • Chroma telemetry is off.

vexicon keeps its own fields in Chroma metadata:

  • Each entry's metadata holds created_at in epoch seconds for recency filters in where.
  • Each space's metadata holds its readme, embedding_repo_id, and embedding_max_tokens.
  • Callers cannot set those three keys through a space's metadata argument.
  • Tool results show these fields apart from the caller's own metadata.

Metadata

Release files for vexicon 0.5.0

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Source distribution for vexicon 0.5.0
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Table of built distributions (wheels) for vexicon 0.5.0
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vexicon-0.5.0-py3-none-any.whl Python 3 none any Details

Total release size: 51.7 kB

Release files / vexicon-0.5.0.tar.gz

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Release files / vexicon-0.5.0-py3-none-any.whl

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