ChromaDB
AI-Native Embedding Database
The open-source vector database built for LLM applications. Simple API, automatic embeddings, and seamless integration with AI frameworks.
Core Capabilities
Embedding Storage
- •Store vectors with metadata
- •Automatic tokenization
- •Built-in embedding generation
- •Custom embedding support
- •Document-level indexing
Similarity Search
- •Cosine similarity
- •L2 Euclidean distance
- •Inner product
- •High recall accuracy
- •Low-latency queries
Hybrid Search
- •Vector + keyword (BM25)
- •Weighted score combining
- •Metadata filtering
- •Filter operators ($or, $and)
- •Range queries
Developer Experience
- •Simple Python API
- •JavaScript/TypeScript SDK
- •Minimal configuration
- •Local-first design
- •Quick prototyping
LLM Integration
- •LangChain compatible
- •LlamaIndex support
- •RAG pipelines
- •Semantic caching
- •Context retrieval
Deployment
- •In-memory mode
- •Persistent storage
- •Docker support
- •Client-server mode
- •Cloud hosting options
Why We Deploy ChromaDB
Simplicity First
Get started in minutes, not hours. ChromaDB's API is designed for developers who want to build AI apps without complex configuration.
Local Development
Perfect for prototyping and development. Run entirely locally with in-memory storage, then scale to persistent storage for production.
Open Source
Fully open-source with Apache 2.0 license. No vendor lock-in, transparent codebase, and active community.
Cost Effective
Lightweight design means minimal deployment costs. Ideal for early-stage projects and small-to-medium workloads.
Get Started in Seconds
pip install chromadbimport chromadb
client = chromadb.Client()
collection = client.create_collection("docs")
collection.add(documents=["Hello world"], ids=["1"])results = collection.query(query_texts=["greeting"], n_results=5)Ready for Simple Vector Search?
We can help you integrate ChromaDB into your AI applications for fast prototyping and production deployments.