What is Chroma DB used for?

Chroma is an AI-native open-source database used to store and search through vector embeddings, which is essential for building RAG applications and semantic search tools.

How do I persist data in Chroma?

You can use the PersistentClient in Chroma to save your vector database to a specific local path, ensuring your data is stored on disk and persists across sessions.

Is Chroma free for commercial use?

Yes, Chroma is released under the Apache 2.0 license, which allows for free use in both open-source and commercial applications.

Can I use Chroma locally?

Yes, Chroma is optimized for local development and self-hosting, making it an excellent choice for prototyping and privacy-focused AI projects.

Does this skill support LangChain?

Yes, the skill includes detailed implementation patterns for integrating Chroma with popular orchestration frameworks like LangChain and LlamaIndex.

Chroma Vector DB

Name: Chroma Vector DB
Author: zechenzhangAGI

byzechenzhangAGI

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384

Database Management

Manages local and server-based vector embeddings for RAG and semantic search applications.

About

Chroma is an open-source embedding database designed to simplify the development of AI-native applications with long-term memory. This skill provides a comprehensive implementation guide for storing and retrieving high-dimensional vectors, enabling developers to build Retrieval-Augmented Generation (RAG) systems with advanced metadata filtering and persistent storage capabilities. It is particularly useful for local development, prototyping in notebooks, and building self-hosted semantic search engines that require a lightweight yet powerful vector store.

Key Features

Flexible storage options ranging from in-memory notebooks to production clusters
Built-in support for OpenAI, HuggingFace, and custom embedding functions
Native integrations with LangChain, LlamaIndex, and popular embedding models
Advanced metadata filtering for granular control over retrieval results
384 GitHub stars
Simple 4-function API for storing and querying document embeddings

Use Cases

Building Retrieval-Augmented Generation (RAG) pipelines for private knowledge bases
Adding persistent memory to AI agents and LLM-powered applications
Implementing semantic search features across large document repositories

About

Key Features

Flexible storage options ranging from in-memory notebooks to production clusters
Built-in support for OpenAI, HuggingFace, and custom embedding functions
Native integrations with LangChain, LlamaIndex, and popular embedding models
Advanced metadata filtering for granular control over retrieval results
384 GitHub stars
Simple 4-function API for storing and querying document embeddings

Use Cases

Building Retrieval-Augmented Generation (RAG) pipelines for private knowledge bases
Adding persistent memory to AI agents and LLM-powered applications
Implementing semantic search features across large document repositories