Chroma vs LanceDB

Chroma - the open-source data infrastructure for AI.

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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.

License

Apache 2.0

Demo: Multimodal Search by Keyword, Vector or with SQL

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Key Features:

• Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
• Comprehensive Search: Support for vector similarity search, full-text search and SQL.
• Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
• Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.

Products:

• LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
• LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, complete data sovereignty and security.

Ecosystem:

• Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
• Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
• Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.

How to Install:

Follow the Quickstart doc to set up LanceDB locally.

API & SDK: We also support Python, Typescript and Rust SDKs

Join Us and Contribute

We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.

If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.

Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.

Contributors

Stay in Touch With Us