Chroma
Open-source search and retrieval database built for AI applications, supporting vector, full-text, regex, and metadata search at any scale.
Community:
Product Overview
What is Chroma?
Chroma is an open-source embedding and vector database purpose-built for AI application development. It enables developers to store, manage, and query high-dimensional vector embeddings alongside metadata, making it straightforward to build retrieval-augmented generation (RAG) pipelines, semantic search engines, and memory layers for LLM-powered applications. Chroma supports local development and scales to petabytes via object storage on the cloud, with a fully managed serverless cloud offering available under the same API. Licensed under Apache 2.0 with over 21K GitHub stars and 5M+ monthly downloads, it has become one of the most widely adopted vector databases in the developer community.
Key Features
Multi-Mode Search
Supports vector similarity search, full-text search, regex matching, and metadata filtering in a unified interface, enabling rich and precise retrieval beyond simple nearest-neighbor lookup.
Seamless Embedding Integration
Built-in support for embedding models from OpenAI, HuggingFace, Google Cohere, and more — including a default Sentence Transformers model — so developers can get started without custom embedding pipelines.
Flexible Deployment Options
Runs in-memory for rapid prototyping, as a persistent local instance, or as a fully managed serverless cloud service on Chroma Cloud, all sharing the same developer API.
Framework & Language Compatibility
Native clients for Python, JavaScript, Ruby, PHP, Java and more, with deep integrations into LangChain, LlamaIndex, and other leading AI development frameworks.
Cloud-Native Scalability
Distributed, horizontally scalable architecture built on object storage with automatic data tiering, multi-tenancy, and SOC 2 Type I compliance for production workloads.
Use Cases
- RAG Applications : Developers building retrieval-augmented generation systems use Chroma to store document embeddings and retrieve the most relevant context to feed into LLMs at query time.
- Semantic Search : Teams embed and index large text corpora in Chroma to power semantic search engines that return results by meaning rather than keyword matching.
- LLM Memory & Context Management : Chroma serves as a persistent memory store for conversational agents and chatbots, allowing them to recall relevant past interactions or domain knowledge.
- Recommendation Systems : Product and content recommendation pipelines use Chroma to find items most similar to a user's preferences based on vector proximity.
- Multimodal Retrieval : Supports image and multimodal embeddings, enabling retrieval workflows that span text and visual data within the same database.
FAQs
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