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chroma

Store and retrieve vector embeddings for semantic search and document retrieval.

Updated Jun 25, 2026
Search Tags:#semantic search#embeddings#rag#vector database#chroma#document retrieval#metadata filtering
One-click install
npx skills add https://github.com/davpatel605-beep/hermusagent --skill chroma-davpatel605-beep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/davpatel605-beep/hermusagent/tree/main/backend/vendor/hermes/optional-skills/mlops/chroma
Command: npx skills add https://github.com/davpatel605-beep/hermusagent --skill chroma-davpatel605-beep

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the challenge of storing, retrieving, and searching AI embeddings efficiently by providing a simple vector database workflow for semantic search and retrieval-augmented generation applications.

Core Features & Use Cases

  • Vector Storage and Retrieval: Store embeddings, documents, and metadata, then perform similarity searches across AI application data.
  • Metadata Filtering and Integrations: Filter results by metadata and connect Chroma with frameworks such as LangChain and LlamaIndex for RAG workflows.
  • Use Case: Build a document question-answering system that retrieves relevant passages from a local knowledge base before generating responses.

Quick Start

Use the chroma skill to create a local vector database for my documents and enable semantic search over the stored 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 retrieve vector embeddings for a local knowledge base?▼

You can store and retrieve vector embeddings for a local knowledge base by using a local vector database to save document embeddings and metadata, then querying for similarity matches.

How do I filter document retrieval results by metadata in a RAG system?▼

You can filter document retrieval results by metadata in a RAG system by applying metadata filtering capabilities during similarity searches across your stored vector embeddings.

Does this semantic search workflow integrate with LangChain and LlamaIndex?▼

Yes, this semantic search workflow integrates with frameworks such as LangChain and LlamaIndex to connect stored vector embeddings and metadata into retrieval-augmented generation pipelines.

What is the best way to build a document question-answering system using semantic search?▼

The best way to build a document question-answering system using semantic search is to retrieve relevant passages from a local vector database before generating AI responses.

Can I use a local vector database for scalable retrieval pipelines without external services?▼

Yes, you can use a local vector database for scalable retrieval pipelines without external services by leveraging persistence capabilities to store and query embeddings and metadata locally.

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