chroma

Store and query embeddings with metadata filtering for semantic search and RAG.

Updated Mar 16, 2026

Search Tags:#database #rag #local #chroma #embedding #semantic-search #vector-store

Ready to run

npx skills add https://github.com/arsity/scholar-tools --skill chroma-arsity

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/15-rag/chroma
Command: npx skills add https://github.com/arsity/scholar-tools --skill chroma-arsity

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Chroma provides a local, open-source vector store to manage embeddings and metadata for AI applications, enabling fast retrieval, memory, and scalable search without relying on external services.

Core Features & Use Cases

Persistent local vector store with metadata filtering and server mode.
Integration with LangChain, LlamaIndex, and other tools.
Supports multiple embedding providers and scalable memory.
Use cases: semantic search over documents, RAG pipelines, and memory for chatbots.

Quick Start

Install chromadb, initialize a local collection, and index your documents to start semantic search.

Frequently Asked Questions about chroma

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I store and query embeddings locally for a RAG pipeline?▼

You can store and query embeddings locally for a RAG pipeline using an open-source vector store like Chroma. It provides a persistent local collection to manage embeddings and metadata, enabling fast semantic search and document retrieval without relying on external services.

What is the best way to add memory to my AI chatbot without external services?▼

The best way to add memory to an AI chatbot without external services is using a local vector database like Chroma. It stores embeddings locally to provide scalable memory, allowing your chatbot to retrieve conversational context and past interactions efficiently.

Can I use LangChain and LlamaIndex with a local vector store for semantic search?▼

Yes, you can use LangChain and LlamaIndex with a local vector store for semantic search. Chroma offers direct integrations with both frameworks, allowing you to index documents and retrieve relevant context seamlessly within your existing AI pipelines.

How do I filter documents by metadata during a semantic search?▼

To filter documents by metadata during a semantic search, you need a vector store that supports metadata filtering. Chroma allows you to attach metadata to your embeddings and apply filters during retrieval, ensuring your queries return only highly specific documents.

Does an open-source vector database support server mode for production pipelines?▼

Yes, an open-source vector database can support server mode for production pipelines. Chroma includes a server mode feature, allowing you to scale your embedding storage and semantic search capabilities from local notebook experiments to full production environments.

When should I not use a local persistent store for embeddings?▼

You should not use a local persistent store for embeddings if your application requires distributed querying across multiple machines or managed cloud scalability. Local stores like Chroma are optimized for local development and single-node production pipelines rather than massive distributed clusters.