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chroma

Manage an open-source vector database for storing embeddings and metadata.

228k|44.7k|Updated Jul 22, 2025
Search Tags:#semantic search#embeddings#vector database#RAG#AI#chroma
One-click install
npx skills add https://github.com/NousResearch/hermes-agent --skill chroma-nousresearch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/NousResearch/hermes-agent/tree/main/skills/mlops/chroma
Command: npx skills add https://github.com/NousResearch/hermes-agent --skill chroma-nousresearch

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a local, open-source solution for storing and querying vector embeddings, enabling AI applications to have memory and perform semantic searches.

Core Features & Use Cases

  • Vector Storage: Store embeddings and associated metadata efficiently.
  • Semantic Search: Perform similarity searches to find relevant information.
  • RAG Applications: Power Retrieval Augmented Generation by providing context to LLMs.
  • Use Case: Integrate Chroma into your RAG pipeline to allow your AI assistant to recall information from a knowledge base of documents, providing more accurate and context-aware responses.

Quick Start

Use the chroma skill to create a new collection named 'my_documents' and add the provided text content.

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 for semantic search locally?▼

To store and query embeddings for semantic search locally, you can use this open-source vector database to manage collections, add text content, and perform similarity searches to retrieve relevant information.

Can I use a vector database to provide context for RAG applications?▼

Yes, you can power Retrieval Augmented Generation (RAG) applications by storing document embeddings and metadata in this vector database, allowing LLMs to recall specific context for accurate responses.

Does Chroma work with LangChain and LlamaIndex frameworks?▼

Chroma supports integration with frameworks like LangChain and LlamaIndex, allowing you to embed the vector database directly into your existing AI pipeline for local development or self-hosting.

How do I filter vector search results using metadata?▼

You can perform metadata filtering alongside semantic search to retrieve precise results, allowing your AI application to query specific subsets of stored embeddings based on associated attributes.

Do I need sentence-transformers to generate embeddings for my vector database?▼

Yes, sentence-transformers is required as a dependency to generate vector embeddings from your text content before storing and querying them within the database for similarity searches.

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