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chroma

Create and query embedding collections with metadata filters for semantic search.

3|Updated Feb 21, 2026
Search Tags:#semantic search#embeddings#vector database#RAG#AI applications#chroma
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill chroma-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/15-rag/chroma
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill chroma-ihatesea69

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 AI applications to efficiently search and retrieve information based on semantic similarity.

Core Features & Use Cases

  • Vector Storage: Store embeddings and associated metadata.
  • Semantic Search: Perform similarity searches over your data.
  • Metadata Filtering: Filter search results based on metadata criteria.
  • Use Case: Integrate Chroma into a RAG (Retrieval-Augmented Generation) pipeline to allow an LLM to access and reason over a large corpus of documents.

Quick Start

Use the chroma skill to create a new collection named 'my_documents' and add the provided text documents with their corresponding IDs.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I set up a vector database for semantic search in a RAG pipeline?▼

To build a RAG pipeline with semantic search, use Chroma to create collections, add documents with embeddings and metadata, and perform similarity searches. It runs locally for development or in production clusters.

What is an open-source embedding database for AI applications?▼

An open-source embedding database like Chroma stores vector embeddings and associated metadata, enabling AI applications to efficiently retrieve information based on semantic similarity rather than exact keyword matches.

Can I filter semantic search results by metadata in a vector database?▼

Yes, you can filter semantic search results by metadata criteria. Chroma supports adding documents with associated metadata and allows you to query via similarity while applying specific metadata filters.

Does the chroma vector database work with LangChain and LlamaIndex?▼

Yes, Chroma integrates with LangChain and LlamaIndex. This allows you to embed it directly into RAG pipelines so large language models can access and reason over your stored document corpus.

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

Yes, sentence-transformers is required to generate vector embeddings. Chroma relies on this dependency to transform text documents into embeddings for semantic similarity storage and retrieval.

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