chromadb

name	chromadb
description	Chroma — AI-native embedding database. In-process, lightweight vector store with automatic embedding, metadata filtering, and full-text search. Simplest path from prototype to production RAG.
tags	["chromadb","vector-database","embeddings","rag","semantic-search","python","zorai"]

Overview

Chroma is an AI-native embedding database optimized for RAG workflows. Lightweight, in-process, with automatic embedding via sentence-transformers, metadata filtering, and semantic search — no separate server required. Fastest path from prototype to production.

Installation

uv pip install chromadb

Basic Usage

import chromadb

client = chromadb.PersistentClient(path="./chroma_data")
collection = client.create_collection(name="documents")

# Add documents with metadata
collection.add(
    documents=["Paris is the capital of France.", "Berlin is the capital of Germany."],
    metadatas=[{"country": "France"}, {"country": "Germany"}],
    ids=["doc1", "doc2"],
)

# Query with filter
results = collection.query(
    query_texts=["What is the capital of France?"],
    n_results=3,
    where={"country": "France"},
)
print(results["documents"][0])

References

• Chroma docs
• Chroma GitHub