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Chensihakniroth
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chroma

Store embeddings and query documents with vector similarity and metadata filtering.

Updated Jun 7, 2026
Search Tags:#semantic search#embeddings#langchain#rag#vector database#Chroma#metadata filtering
One-click install
npx skills add https://github.com/Chensihakniroth/ANAKOT-AGENT --skill chroma-chensihakniroth
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chroma
Source: https://github.com/Chensihakniroth/ANAKOT-AGENT/tree/main/optional-skills/mlops/chroma
Command: npx skills add https://github.com/Chensihakniroth/ANAKOT-AGENT --skill chroma-chensihakniroth

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Building RAG and semantic search applications requires a vector database to store embeddings and retrieve relevant documents, and Chroma provides an open-source, self-hosted solution that runs locally without managed cloud infrastructure. ## Core Features & Use Cases - Vector Storage & Search: Store documents with embeddings and metadata, then query by semantic similarity with a simple 4-function API. - Metadata Filtering: Combine similarity search with filters using comparison and logical operators to narrow results. - Framework Integration: Works with LangChain and LlamaIndex for building retrieval pipelines, plus persistent storage and server mode for production. - Use Case: Build a document Q&A system by chunking your knowledge base, storing embeddings in a persistent Chroma collection, and retrieving the top matching passages to feed into an LLM prompt. ## Quick Start Install chromadb with pip and ask the agent to create a persistent collection, add your documents with metadata, and run a similarity query against them.

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 retrieve the most similar documents by embedding distance.

Chroma vs Pinecone vs FAISS for vector search?▼

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

Does Chroma support persistent storage across restarts?▼

Yes, use chromadb.PersistentClient with a path argument to persist data to disk. Data is saved automatically and can be reloaded later by connecting with the same path.

Can I use Chroma with LangChain or LlamaIndex?▼

Yes, Chroma integrates with both frameworks. Use langchain_chroma.Chroma to create a vector store and retriever, or ChromaVectorStore in LlamaIndex backed by an existing Chroma collection.

How do I filter Chroma query results by metadata?▼

Pass a where clause to collection.query with exact matches, comparison operators like $gt and $lte, or logical operators $and and $or. This narrows the search space before similarity ranking.

When should I not use Chroma for a vector database?▼

Avoid Chroma when you need managed auto-scaling in the cloud, where Pinecone fits better, or when you need high-performance Rust-based serving, where Qdrant is more appropriate.

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