ChromaDB is an open-source, AI-native embedding database designed to make it easy to build LLM applications with embeddings. With over 27,000 GitHub stars, it provides a simple API for storing, querying, and managing vector embeddings with metadata filtering, making it the go-to choice for rapid prototyping and production RAG applications.

• Simple API – NumPy-like simplicity for adding, querying, and managing embeddings
• AI-Native Design – Purpose-built for embeddings and RAG workflows
• Pluggable Embedding Functions – Built-in support for OpenAI, Sentence Transformers, Cohere, Gemini, Jina AI, Ollama, and custom functions
• Hybrid Search – Vector similarity combined with metadata filtering, regex, and full-text search
• Multiple Deployment Modes – In-memory, persistent (embedded), and client-server
• Framework Integration – Native support for LangChain, LlamaIndex, and other RAG frameworks
• Metadata Arrays – Support for string, number, and boolean arrays in metadata filtering (added 2026)

ChromaDB uses a collection-based structure separating vector storage, metadata storage, and embedding generation:

• Collection Layer – Logical groupings of embeddings, documents, IDs, and metadata
• Embedding Functions – Modular, pluggable components that generate vectors from text or images
• Vector Store – HNSW indexing for approximate nearest neighbor search with configurable distance metrics
• Metadata Store – Separate storage for structured metadata enabling efficient filtering
• Storage Backends – DuckDB+Parquet (default persistent), SQLite, or PostgreSQL

ChromaDB supports three deployment modes for different use cases:

ChromaDB combines vector similarity with exact metadata filters using where clauses:

• Equality matching: { “source”: “wiki” }
• Range queries: { “score”: { “$gt”: 0.8 } }
• Set membership: { “topic”: { “$in”: [“ai”, “ml”] } }
• Array metadata (2026): Complex multi-value filters on array fields

import chromadb
from chromadb.utils import embedding_functions
 
# Initialize persistent client
client = chromadb.PersistentClient(path="./chroma_db")
 
# Configure embedding function
ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="your-api-key",
    model_name="text-embedding-3-small"
)
 
# Create or get collection
collection = client.get_or_create_collection(
    name="documents",
    embedding_function=ef,
    metadata={"hnsw:space": "cosine"}
)
 
# Add documents with metadata
collection.add(
    documents=[
        "RAG combines retrieval with generation for grounded answers",
        "Vector databases store high-dimensional embeddings",
        "Knowledge graphs capture entity relationships"
    ],
    metadatas=[
        {"source": "tutorial", "topic": "rag"},
        {"source": "docs", "topic": "database"},
        {"source": "paper", "topic": "knowledge_graph"}
    ],
    ids=["doc1", "doc2", "doc3"]
)
 
# Query with metadata filter
results = collection.query(
    query_texts=["How does retrieval augmented generation work?"],
    n_results=3,
    where={"topic": {"$in": ["rag", "database"]}},
    include=["documents", "metadatas", "distances"]
)
 
for doc, meta, dist in zip(
    results["documents"][0],
    results["metadatas"][0],
    results["distances"][0]
):
    print(f"[{dist:.4f}] ({meta['source']}) {doc[:60]}...")

• ChromaDB GitHub Repository
• ChromaDB Official Website
• ChromaDB Documentation

• Qdrant – High-performance Rust vector database
• Milvus – Cloud-native vector database at scale
• Mem0 – Memory layer supporting ChromaDB as backend
• RAGFlow – RAG engine for document understanding
• LightRAG – Knowledge graph RAG framework