What is Chroma?
Chroma is an open-source search infrastructure built specifically for AI applications. It lets developers store, search, and retrieve data using vectors, full-text, regex, and metadata—all in one system. Whether you're building a chatbot, recommendation engine, or AI agent, Chroma gives you fast, scalable search without the operational headaches.
Unlike traditional databases that struggle with AI workloads, Chroma is designed from the ground up for modern retrieval needs. It runs serverless in the cloud or on your own hardware, scales automatically, and uses cost-efficient object storage (like S3) so you don’t pay for idle memory. With over 15 million monthly downloads and 27k GitHub stars, it’s trusted by developers worldwide—and it’s completely free under the Apache 2.0 license.
What are the features of Chroma?
- Vector Search: Find semantically similar items using embeddings—ideal for AI-powered recommendations and RAG apps.
- Sparse Vector Search: Built-in support for BM25 and SPLADE, combining classic lexical relevance with modern neural retrieval.
- Full-Text & Regex Search: Search documents using keywords, phrases, or powerful regular expressions.
- Metadata Filtering: Narrow results by tags, categories, dates, or custom fields with faceted search.
- Zero-Ops Scaling: Automatically tiers data between hot cache, SSD, and object storage—no manual tuning needed.
- Collection Forking: Instantly duplicate datasets for A/B testing, versioning, or safe experimentation.
- Multi-Tenant Architecture: Supports millions of collections with strong isolation and low latency.
- Serverless Cloud Option: Start instantly with Chroma Cloud, including $5 in free credits.
What are the use cases of Chroma?
- Power Retrieval-Augmented Generation (RAG) systems by fetching relevant context for LLMs.
- Build intelligent AI agents that remember past interactions or reference documentation.
- Create semantic search for internal wikis, support docs, or knowledge bases.
- Enable personalized product recommendations based on user behavior or item similarity.
- Automate web content ingestion using Chroma Web Sync to crawl and embed pages.
- Index and query GitHub repositories automatically with Chroma Sync.
- Run large-scale evaluation of embedding models or chunking strategies.
- Support enterprise search with SOC 2 compliance, BYOC (Bring Your Own Cloud), and private networking.
How to use Chroma?
- Install Chroma via
pip install chromadb(Python) ornpm install chromadb(JavaScript/TypeScript). - Create a collection and add documents with optional metadata and embeddings.
- Query using vector similarity, keyword search, or combine both with hybrid ranking.
- Use the CLI or SDKs (Python, TypeScript, Rust) for local development or cloud deployment.
- Leverage Chroma Cloud for zero-infrastructure setup with automatic scaling.
- Monitor indexing status and tune consistency with read-level controls (available in newer versions).
