Chroma
Chroma is an open-source AI-native vector database that provides semantic, full-text, and regex search as a memory layer for LLM and RAG applications.
About this tool
Chroma
Website: https://github.com/chroma-core/chroma
Category: Curated Resource Lists / Tools for RAG & LLM apps
Type: Open-source vector database
Overview
Chroma is an open-source, AI-native vector database designed as a memory and retrieval layer for LLM and RAG (Retrieval-Augmented Generation) applications. It supports semantic, full-text, and regex search over stored data.
Features
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Vector database for AI applications
- Stores and indexes vector embeddings for use with LLMs and RAG pipelines.
- Acts as a dedicated memory layer for AI agents and applications.
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Search capabilities
- Semantic search over vector embeddings.
- Full-text search over stored documents or metadata.
- Regex search to match patterns in text fields.
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Open-source
- Source code available on GitHub under an open-source license.
- Actively developed in a public repository with multiple language components (e.g., Rust, Go directories present).
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Ecosystem and structure (from repo layout)
clientsdirectory indicating client libraries or SDKs.examplesandsample_appsfor reference implementations and example usage.docsfor documentation and guides.deploymentsandk8sfor deployment configurations (e.g., containerized or Kubernetes setups).- Multi-language support in implementation (
go,rustdirectories) and associated tooling/config files.
Typical Use Cases
- Building RAG systems that retrieve context for LLM prompts.
- Implementing semantic search over document corpora.
- Managing long-term memory for AI agents and chatbots.
Pricing
- Chroma is offered as open-source software via its GitHub repository.
- No paid pricing plans are described in the provided content.
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