chroma

Store and retrieve vector embeddings with metadata filtering for RAG applications.

Updated Jun 17, 2026

Search Tags:#semantic search #embeddings #ai #rag #vector database #chroma

Ready to run

npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill chroma-cxnaive

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/cxnaive/hermes-agent-llbot/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill chroma-cxnaive

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires chromadb, sentence-transformers, and includes references (resource) components.

What problem does it solve?

This skill addresses the complexity of managing unstructured data for LLM applications by providing a local, high-performance vector database for semantic search and retrieval-augmented generation.

Core Features & Use Cases

Vector Storage: Efficiently store and retrieve document embeddings with associated metadata.
Semantic Search: Perform similarity searches to find relevant context for LLM prompts.
Use Case: Build a local knowledge base for your agent by indexing thousands of documents, allowing it to answer questions based on your private data with high accuracy.

Quick Start

Use the chroma skill to initialize a persistent database at the path ./my_knowledge_base and add the provided documents to a new collection.

Frequently Asked Questions about chroma

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

FAQPage Schema

How do I build a local vector database for RAG applications?▼

To build a local vector database for RAG, you can use Chroma to initialize a persistent database at a specified path, store document embeddings with metadata, and retrieve them for semantic search. It supports local persistence for private knowledge bases.

What is semantic search and how does it work with document embeddings?▼

Semantic search with document embeddings works by converting unstructured text into vectors and storing them in a database like Chroma. It performs similarity searches to find the most relevant context for LLM prompts based on vector distance.

Can I use Chroma vector database with LangChain and LlamaIndex frameworks?▼

Yes, Chroma provides integration with major frameworks like LangChain and LlamaIndex. You can use it as the underlying vector store to manage embeddings and retrieve context within your existing LLM application workflows.

Does Chroma support metadata filtering for vector search results?▼

Yes, Chroma supports metadata filtering for vector search. Alongside storing document embeddings, it facilitates filtering by associated metadata, allowing you to narrow down retrieval results for more accurate RAG context.

What's the best way to store unstructured data for LLM applications locally?▼

The best way to store unstructured data for local LLM applications is using an open-source embedding database like Chroma. It manages high-performance vector storage and retrieval, simplifying the process of indexing private documents for AI agents.

Do I need sentence-transformers to generate embeddings for Chroma?▼

Yes, sentence-transformers is a required dependency for generating the vector embeddings stored in Chroma. It works alongside the chromadb package to process text into vectors for semantic search and retrieval.