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chroma

Manage an open-source embedding database for vector and full-text search.

Updated Aug 23, 2026
Search Tags:#semantic search#embeddings#vector database#RAG#AI applications#chroma
One-click install
npx skills add https://github.com/AXGZ21/hermes-agent-railway --skill chroma-axgz21
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/AXGZ21/hermes-agent-railway/tree/main/skills/mlops/chroma
Command: npx skills add https://github.com/AXGZ21/hermes-agent-railway --skill chroma-axgz21

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a robust, open-source solution for storing and querying vector embeddings, enabling efficient semantic search and retrieval for AI applications.

Core Features & Use Cases

  • Vector Storage: Store embeddings generated from text or other data.
  • Semantic Search: Perform similarity searches to find relevant information based on meaning.
  • RAG Applications: Power Retrieval Augmented Generation by providing context to LLMs.
  • Use Case: Integrate Chroma into a chatbot to allow it to recall past conversations or relevant documents for more informed responses.

Quick Start

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

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 vector embeddings for semantic search?▼

To perform semantic search, you store vector embeddings and metadata in a database, then query by similarity. This Skill manages an open-source embedding database to store and retrieve vectors for AI applications.

What is the best way to provide context to an LLM using RAG applications?▼

RAG applications require a vector database to retrieve relevant text based on semantic similarity. This Skill powers Retrieval Augmented Generation by storing document embeddings and filtering metadata to supply context to LLMs.

Can I use sentence-transformers with a vector database for AI applications?▼

Yes, this Skill uses sentence-transformers to generate embeddings and chromadb to manage the vector database. This combination allows you to store text data and perform similarity searches for AI applications.

Does this vector database work with LangChain and LlamaIndex?▼

Yes, the Skill offers integrations with LangChain and LlamaIndex. This allows you to embed the vector database directly into existing AI workflows for document retrieval and chatbot memory.

How do I filter document retrieval results by metadata in a vector database?▼

You filter document retrieval results by attaching metadata to stored embeddings and applying filters during your search. This Skill supports metadata filtering capabilities alongside vector and full-text search.

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