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chroma

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

Updated Mar 13, 2026
Search Tags:#semantic search#embeddings#vector database#RAG#AI applications#chroma
One-click install
npx skills add https://github.com/hochoa13/Asistente-Harlest --skill chroma-hochoa13
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/hochoa13/Asistente-Harlest/tree/main/skills/mlops/vector-databases/chroma
Command: npx skills add https://github.com/hochoa13/Asistente-Harlest --skill chroma-hochoa13

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 robust, open-source solution for storing and querying vector embeddings, enabling AI applications to efficiently retrieve relevant information based on semantic similarity.

Core Features & Use Cases

  • Vector Storage: Store embeddings and associated metadata.
  • Semantic Search: Perform similarity searches on text data.
  • RAG Applications: Power Retrieval-Augmented Generation systems.
  • Document Retrieval: Efficiently find relevant documents based on queries.
  • Use Case: Integrate Chroma into your RAG pipeline to store document embeddings, allowing your LLM to retrieve contextually relevant information for more accurate and informed responses.

Quick Start

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

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I store embeddings and metadata for semantic search in AI applications?▼

To store embeddings for semantic search, you can create a collection and add text documents alongside their corresponding IDs and metadata, enabling efficient vector and full-text retrieval for AI applications.

What is the best way to build a RAG pipeline with a vector database?▼

Building a RAG pipeline involves storing document embeddings in a vector database, which allows your LLM to retrieve contextually relevant information for more accurate, informed responses during generation.

Can I use Chroma with LangChain and LlamaIndex for document retrieval?▼

Yes, Chroma integrates seamlessly with LangChain and LlamaIndex, allowing you to implement efficient document retrieval workflows based on semantic similarity within these established framework environments.

Does this vector database support metadata filtering for document retrieval?▼

Yes, the vector database supports metadata filtering capabilities, allowing you to efficiently narrow down and find relevant documents based on specific metadata attributes during your retrieval process.

What dependencies do I need to run an open-source embedding database locally?▼

To run an open-source embedding database locally, you need the chromadb and sentence-transformers dependencies, which facilitate embedding storage and transform text into vector representations.

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