Chroma - Open-Source AI Search

Description

Chroma is an open-source search infrastructure specifically designed for AI applications. It provides a robust solution for developers seeking to implement efficient and scalable search functionalities. Chroma supports various search types, including vector, full-text, regex, and metadata search, catering to diverse data retrieval needs.

Built on object storage, Chroma offers fast and scalable performance. It is engineered to handle billions of multi-tenant indexes, ensuring low-latency queries. The system is designed with automatic data tiering and caching, optimizing performance and cost-effectiveness. Chroma's architecture leverages object storage, making it up to 10x cheaper than legacy search systems. It also features zero-ops infrastructure, auto-scaling capabilities, and no manual tuning requirements.

Chroma's key features include sparse vector search, lexical search (BM25, SPLADE), semantic similarity search, full-text search, metadata search, and dataset versioning. It offers command-line tools for development and supports various programming languages like TypeScript, Python, and Rust. Chroma also provides enterprise-level features such as BYOC (Bring Your Own Cloud) in your VPC, multi-cloud/multi-region replication, and point-in-time recovery, ensuring a resilient and scalable search system.

Chroma is ideal for developers, data scientists, and AI engineers working on projects that require efficient and scalable search capabilities. It is particularly well-suited for applications involving vector embeddings, semantic search, and knowledge base querying. The open-source nature of Chroma allows for community contributions and customization, providing users with control and flexibility in building their search infrastructure.

Chroma's Core Features

• Sparse vector search

• Lexical search (BM25, SPLADE)

• Semantic similarity search

• Full-text search

• Metadata search

• Dataset versioning

• Command-line tools

• Automatic data tiering

• Zero-ops infrastructure

• Multi-tenant indexing

• BYOC (Bring Your Own Cloud)

• Multi-region replication

• Point-in-time recovery

How to use Chroma?

1. 1

   Get Started: Start free on Cloud or get started locally.

2. 2

   Configure: Configure the client and collection for sparse embeddings.

3. 3

   Add Documents: Add documents with sparse embeddings.

4. 4

   Query: Query with sparse vector.

5. 5

   Analyze Results: Review the search results, ranked by score.

Chroma's Use Cases

• Semantic Search
• Knowledge Base
• Vector Search
• Full-Text Search
• Metadata Filtering
• AI Agents
• Context Engineering

FAQ from Chroma

• What is Chroma?

  Chroma is open-source search infrastructure for AI, offering fast, scalable search capabilities.

• What types of search does Chroma support?

  Chroma supports vector, full-text, regex, and metadata search.

• What are the benefits of using Chroma?

  Benefits include fast queries, low latency, cost-effectiveness, and zero-ops infrastructure.

• How does Chroma scale?

  Chroma scales with your data and traffic, built on object storage with automatic data tiering.

• What programming languages does Chroma support?

  Chroma supports TypeScript, Python, and Rust.

• What is the license for Chroma?

  Chroma is open-source under the Apache 2.0 license.

• Who can benefit from using Chroma?

  Developers, data scientists, and AI engineers can benefit from Chroma.

• Does Chroma offer enterprise features?

  Yes, Chroma offers features like BYOC, multi-region replication, and point-in-time recovery for enterprises.

• How can I get support for Chroma?

  Support options include the open-source community on Discord, direct support from engineers, and customized SLAs for enterprise plans.

• Where can I find documentation for Chroma?

  You can find documentation on the Chroma website.