Pinecone
Managed vector database for AI applications
Chroma
Embedding database for building LLM apps with memory
Chroma is an embedding database that enables Python and JavaScript developers to add semantic search and memory to LLM applications with a simple 4-function API.
Overview
The AI-Native Database for LLM Applications
Chroma is an embedding database designed to make building LLM applications with memory straightforward and scalable. Whether you're prototyping in a notebook or deploying to production, Chroma provides the same simple API for storing documents, generating embeddings, and retrieving semantically similar content.
Built for Developer Productivity
With just four core functions—create, add, query, and get—developers can implement semantic search, retrieval-augmented generation (RAG), and "chat your data" workflows in minutes. Chroma handles tokenization, embedding generation, and indexing automatically, though you can bring your own embeddings and models when needed. The database integrates seamlessly with LangChain, LlamaIndex, and popular embedding providers including OpenAI, Cohere, and Sentence Transformers.
From Prototype to Production
Chroma runs in-memory for rapid prototyping, supports persistent storage for development, and scales to client-server deployments for production workloads. The project is fully typed, tested, and documented, with Apache 2.0 licensing. A managed Chroma Cloud service offers serverless vector and full-text search for teams seeking a hosted solution. Built primarily in Rust with Python and TypeScript clients, Chroma delivers performance without sacrificing developer experience.
Highlights
4-function API for adding documents, querying by similarity, and filtering results
Automatic embedding generation with support for custom models and providers
Seamless scaling from in-memory prototypes to client-server production deployments
Native integrations with LangChain, LlamaIndex, and major embedding services
Pros
- Extremely simple API reduces time-to-first-query for LLM applications
- Flexible deployment options from local development to managed cloud service
- Comprehensive documentation and active community support via Discord
- Apache 2.0 license enables commercial use without restrictions
Considerations
- Rust-based core may require compilation for certain deployment scenarios
- Limited to nearest-neighbor search paradigms versus traditional SQL queries
- Relatively new project with evolving feature set and API surface
- Performance characteristics at extreme scale not extensively documented
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Fit guide
Great for
- Developers building RAG pipelines and semantic search for LLM applications
- Teams prototyping conversational AI with document retrieval requirements
- Python and JavaScript projects needing drop-in vector search capabilities
- Organizations wanting self-hosted embedding databases with cloud migration paths
Not ideal when
- Applications requiring complex relational queries or ACID transactions
- Teams needing battle-tested vector databases for mission-critical workloads at petabyte scale
- Projects constrained to languages outside Python and JavaScript ecosystems
- Use cases demanding real-time streaming ingestion of high-velocity data
How teams use it
Retrieval-Augmented Generation (RAG)
Query relevant documents from your knowledge base and inject them into LLM context windows for grounded, factual responses
Semantic Document Search
Enable natural language queries across internal documentation, returning conceptually similar content rather than keyword matches
Conversational Memory for Chatbots
Store conversation history as embeddings to retrieve relevant context and maintain coherent multi-turn dialogues
Content Recommendation Systems
Match user queries or preferences to similar articles, products, or media using embedding similarity
Tech snapshot
Rust59%
Python20%
TypeScript9%
Go7%
Jupyter Notebook4%
JavaScript1%
Frequently asked questions
What are embeddings and why do I need a vector database?
Embeddings convert text, images, or audio into numerical vectors that capture semantic meaning. Vector databases like Chroma store these embeddings and enable fast similarity search, allowing you to find conceptually related content rather than exact keyword matches.
Can I use my own embedding models with Chroma?
Yes. While Chroma uses Sentence Transformers by default, you can provide your own embeddings from OpenAI, Cohere, or custom models. You can also pass embeddings directly when adding documents.
How does Chroma scale from development to production?
Chroma runs in-memory for prototyping, supports persistent local storage for development, and offers client-server mode for production deployments. The managed Chroma Cloud service provides serverless scaling without infrastructure management.
Does Chroma integrate with LangChain and LlamaIndex?
Yes. Chroma has native integrations with both LangChain (Python and JavaScript) and LlamaIndex, making it straightforward to use as the retrieval layer in LLM orchestration frameworks.
What license does Chroma use?
Chroma is licensed under Apache 2.0, which permits commercial use, modification, and distribution with minimal restrictions.
Project at a glance
Active- Stars
- 25,596
- Watchers
- 25,596
- Forks
- 2,006
LicenseApache-2.0
Repo age3 years old
Last commit3 days ago
Self-hostingSupported
Primary languageRust
Last synced 2 days ago