Chroma — Agents-OSS

Overview

The lowest-friction way to get retrieval working: no server to stand up, no cluster to size. That makes it excellent for prototypes and small production loads, and the point at which you outgrow it is a real consideration to plan for rather than discover.

What can you do with Chroma?

Get retrieval running in one file — chromadb.Client() creates an in-memory store and collection.add() handles tokenization, embedding and indexing, so no embedding model is wired up by hand.
Filter alongside the vector search — collection.query() takes where for metadata equality and where_document with $contains for substring matching in the same call as the similarity search.
Move to client-server mode — chroma run --path /chroma_db_path starts a server against the same four-function API once a single embedded process is no longer enough.
Query from Python or JavaScript — The Python client comes from pip install chromadb and the JavaScript one from npm install chromadb; pointed at the same chroma run server, both work on one set of collections, so the job that writes and the service that queries need not share a language.

Documentation

Reproduced from the chroma-core/chroma README, published under Apache-2.0. Read the original ↗

Chroma Chroma

pip install chromadb # python client
# for javascript, npm install chromadb!
# for client-server mode, chroma run --path /chroma_db_path

Chroma Cloud

Our hosted service, Chroma Cloud, powers serverless vector, hybrid, and full-text search. It’s extremely fast, cost-effective, scalable and painless. Create a DB and try it out in under 30 seconds with $5 of free credits.

Get started with Chroma Cloud

API

The core API is only 4 functions (run our 💡 Google Colab):

import chromadb
# setup Chroma in-memory, for easy prototyping. Can add persistence easily!
client = chromadb.Client()

# Create collection. get_collection, get_or_create_collection, delete_collection also available!
collection = client.create_collection("all-my-documents")

# Add docs to the collection. Can also update and delete. Row-based API coming soon!
collection.add(
    documents=["This is document1", "This is document2"], # we handle tokenization, embedding, and indexing automatically. You can skip that and add your own embeddings as well
    metadatas=[{"source": "notion"}, {"source": "google-docs"}], # filter on these!
    ids=["doc1", "doc2"], # unique for each doc
)

# Query/search 2 most similar results. You can also .get by id
results = collection.query(
    query_texts=["This is a query document"],
    n_results=2,
    # where={"metadata_field": "is_equal_to_this"}, # optional filter
    # where_document={"$contains":"search_string"}  # optional filter
)

Learn about all features on our Docs

Get involved

Chroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.

Join the conversation on Discord - #contributing channel
Review the 🛣️ Roadmap and contribute your ideas
Grab an issue and open a PR - Good first issue tag
Read our contributing guide

Release Cadence We currently release new tagged versions of the pypi and npm packages on Mondays. Hotfixes go out at any time during the week.