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chroma

Store embeddings and metadata in Chroma for vector search and RAG applications.

1|Updated Mar 5, 2026
Search Tags:#semantic search#embeddings#rag#vector database#chroma#metadata filtering#chromadb
One-click install
npx skills add https://github.com/Clay-HHK/claude-skills --skill chroma-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/Clay-HHK/claude-skills/tree/main/chroma
Command: npx skills add https://github.com/Clay-HHK/claude-skills --skill chroma-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Building semantic search or RAG applications requires a database that stores embeddings alongside metadata and supports similarity queries, which is complex to implement from scratch. ## Core Features & Use Cases - Vector Storage & Search: Create collections, add documents with embeddings and metadata, and run similarity queries with metadata filters using operators like $and, $gt, and $in. - Flexible Embedding Functions: Use the default Sentence Transformers model, OpenAI, HuggingFace, or custom embedding functions. - Framework Integration: Connect with LangChain and LlamaIndex, or run Chroma in server mode for multi-user production deployments. - Use Case: Build a document Q&A system by chunking documents, storing them in a persistent Chroma collection, and retrieving relevant passages to augment LLM prompts. ## Quick Start Use the chroma skill to create a persistent collection, add my documents with metadata, and run a filtered similarity search.

Frequently Asked Questions about chroma

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

FAQPage Schema
How do I store and search embeddings with Chroma in Python?▼

Install chromadb, create a client and collection, then call collection.add with documents, metadatas, and ids. Use collection.query with query_texts and n_results to run similarity search and retrieve matching documents, distances, and metadata.

Chroma vs Pinecone vs FAISS for vector search?▼

Chroma is open-source and self-hosted, suited for local development and RAG prototypes with metadata filtering. Pinecone is a managed cloud service with auto-scaling, while FAISS provides pure similarity search without metadata storage.

Does Chroma support metadata filtering in queries?▼

Yes, Chroma supports metadata filtering using the where parameter with exact matches, comparison operators like $gt and $lte, logical operators $and and $or, and $in for list membership. Filters combine with vector similarity in a single query.

Can I use Chroma with LangChain or LlamaIndex?▼

Yes, Chroma integrates with LangChain via langchain_chroma.Chroma for vector stores and retrievers, and with LlamaIndex via ChromaVectorStore for building query engines over indexed documents.

How do I persist Chroma data between sessions?▼

Use chromadb.PersistentClient with a path argument such as ./chroma_db instead of the default in-memory client. Data is saved automatically and can be reloaded later by connecting with the same path.

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