ovos-chromadb-embeddings-plugin 0.3.0a4

A chromadb embeddings plugin for OVOS

ovos-chromadb-embeddings-plugin

ChromaDB-backed EmbeddingsDB vector store plugin for OpenVoiceOS.

Install

pip install ovos-chromadb-embeddings-plugin

What is an EmbeddingsDB?

EmbeddingsDB is the abstract base class from ovos-plugin-manager for vector stores. Plugins implementing it are discovered automatically by OPM under the entry-point group opm.embeddings. This plugin registers as:

opm.embeddings → ovos-chromadb-embeddings-plugin → ChromaEmbeddingsDB

OVOS subsystems call OVOSPluginFactory.get_plugin("opm.embeddings") to obtain a configured store without coupling to a specific backend, so any EmbeddingsDB plugin (ChromaDB here, or e.g. qdrant) is a drop-in swap.

Where this fits in OVOS

This plugin is the vector store half of the stack — it stores and searches vectors but does not produce them. Pair it with an embedding producer such as ovos-gguf-embeddings-plugin (text → vectors), or the face / voice embedders.

Concrete consumers that can be backed by this store:

Consumer	Uses the store for
ovos-persona-server	RAG: the OpenAI-compatible Files / Vector-Stores / /search endpoints
ovos-memory-plugins	long-term semantic memory for a persona
face / voice recognition	nearest-neighbour identity lookup over enrolment vectors

It is local-first: in persistent mode it runs fully offline on a CPU with no server.

Quickstart

import tempfile, numpy as np
from ovos_chromadb_embeddings import ChromaEmbeddingsDB

with tempfile.TemporaryDirectory() as tmp:
    db = ChromaEmbeddingsDB(config={"path": tmp})

    # Store a few 4-d vectors
    db.add_embeddings("apple",  np.array([0.9, 0.1, 0.0, 0.0]))
    db.add_embeddings("banana", np.array([0.0, 0.9, 0.1, 0.0]))
    db.add_embeddings("cherry", np.array([0.0, 0.0, 0.9, 0.1]))

    # Nearest-neighbour query
    query = np.array([0.85, 0.15, 0.0, 0.0])
    results = db.query(query, top_k=2)
    # → [("apple", 0.003...), ("banana", 0.45...)]
    print(results[0][0])  # "apple"

query returns (id, distance) tuples ordered nearest-first. The score is a distance, not a similarity — lower is closer for the default cosine metric (and for l2). Change the metric with hnsw:space (see Configuration). The query vector must have the same dimensionality as the stored vectors.

Configuration

Pass a config dict to ChromaEmbeddingsDB(config=...) or set it in your OVOS configuration under the plugin key.

Key	Type	Default	Description
path	str	"./chromadb_storage"	Local persistence directory (PersistentClient mode).
host	str	—	Remote ChromaDB server host. When set, uses HttpClient instead of PersistentClient.
port	int	8000	Port for the remote ChromaDB server (HttpClient mode only).
default_collection_name	str	"embeddings"	Name of the collection created/used on init.
hnsw:space	str	"cosine"	Distance metric for HNSW index. Accepted: "cosine", "l2", "ip". Set via collection metadata.

Local (persistent) mode

db = ChromaEmbeddingsDB(config={"path": "/var/lib/ovos/chromadb"})

Remote server mode

db = ChromaEmbeddingsDB(config={"host": "192.168.1.10", "port": 8000})

API overview

Method	Description
add_embeddings(key, embedding, metadata, collection_name)	Upsert a single vector.
add_embeddings_batch(keys, embeddings, metadata, collection_name)	Upsert a list of vectors.
get_embeddings(key, collection_name, return_metadata)	Retrieve a vector by key.
get_embeddings_batch(keys, collection_name, return_metadata)	Retrieve multiple vectors.
delete_embeddings(key, collection_name)	Delete a vector by key.
delete_embeddings_batch(keys, collection_name)	Delete multiple vectors.
query(embedding, top_k, return_metadata, collection_name)	ANN search; returns [(id, distance)].
create_collection(name, metadata)	Create (or get) a named collection.
get_collection(name)	Retrieve a collection handle (raises ValueError if absent).
delete_collection(name)	Drop a collection.
list_collections()	List all collections.
count_embeddings_in_collection(collection_name)	Count stored vectors.

Documentation

• docs/configuration.md — full config reference (local vs remote, distance metrics)
• docs/usage.md — collections, CRUD, batch ops, metadata, numpy in/out

Examples

• examples/quickstart.py — add vectors + query
• examples/collections.py — multi-collection workflow
• examples/remote_server.py — HttpClient usage

Testing

pip install -e ".[test]"
pytest test/ -v

The test suite uses a temporary PersistentClient with no network access. test/test_e2e.py runs a real end-to-end flow (add → query → verify nearest neighbour) using a small deterministic local embedder so it passes in CI without model downloads.

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Credits

Originally developed by TigreGótico for OpenVoiceOS, sponsored by VisioLab. Modernized under the NGI0 Commons Fund / NLnet.

This work was sponsored by VisioLab, part of Royal Dutch Visio, is the test, education, and research center in the field of (innovative) assistive technology for blind and visually impaired people and professionals. We explore (new) technological developments such as Voice, VR and AI and make the knowledge and expertise we gain available to everyone.

This project was funded through the NGI0 Commons Fund, a fund established by NLnet with financial support from the European Commission's Next Generation Internet programme, under the aegis of DG Communications Networks, Content and Technology under grant agreement No 101135429.