Chroma - Open-Source Embedding Database

The AI-native database for building LLM applications with memory.

When to use Chroma

Use Chroma when:

• Building RAG (retrieval-augmented generation) applications
• Need local/self-hosted vector database
• Want open-source solution (Apache 2.0)
• Prototyping in notebooks
• Semantic search over documents
• Storing embeddings with metadata

Metrics:

• 24,300+ GitHub stars
• 1,900+ forks
• v1.3.3 (stable, weekly releases)
• Apache 2.0 license

Use alternatives instead:

• Pinecone: Managed cloud, auto-scaling
• FAISS: Pure similarity search, no metadata
• Weaviate: Production ML-native database
• Qdrant: High performance, Rust-based

Quick start

Installation

# Python
pip install chromadb

# JavaScript/TypeScript
npm install chromadb @chroma-core/default-embed

Basic usage (Python)

import chromadb

# Create client
client = chromadb.Client()

# Create collection
collection = client.create_collection(name="my_collection")

# Add documents
collection.add(
    documents=["This is document 1", "This is document 2"],
    metadatas=[{"source": "doc1"}, {"source": "doc2"}],
    ids=["id1", "id2"]
)

# Query
results = collection.query(
    query_texts=["document about topic"],
    n_results=2
)

print(results)

Core operations

1. Create collection

# Simple collection
collection = client.create_collection("my_docs")

# With custom embedding function
from chromadb.utils import embedding_functions

openai_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="your-key",
    model_name="text-embedding-3-small"
)

collection = client.create_collection(
    name="my_docs",
    embedding_function=openai_ef
)

# Get existing collection
collection = client.get_collection("my_docs")

# Delete collection
client.delete_collection("my_docs")

2. Add documents

# Add with auto-generated IDs
collection.add(
    documents=["Doc 1", "Doc 2", "Doc 3"],
    metadatas=[
        {"source": "web", "category": "tutorial"},
        {"source": "pdf", "page": 5},
        {"source": "api", "timestamp": "2025-01-01"}
    ],
    ids=["id1", "id2", "id3"]
)

# Add with custom embeddings
collection.add(
    embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],
    documents=["Doc 1", "Doc 2"],
    ids=["id1", "id2"]
)

3. Query (similarity search)

# Basic query
results = collection.query(
    query_texts=["machine learning tutorial"],
    n_results=5
)

# Query with filters
results = collection.query(
    query_texts=["Python programming"],
    n_results=3,
    where={"source": "web"}
)

# Query with metadata filters
results = collection.query(
    query_texts=["advanced topics"],
    where={
        "$and": [
            {"category": "tutorial"},
            {"difficulty": {"$gte": 3}}
        ]
    }
)

# Access results
print(results["documents"])      # List of matching documents
print(results["metadatas"])      # Metadata for each doc
print(results["distances"])      # Similarity scores
print(results["ids"])            # Document IDs

4. Get documents

# Get by IDs
docs = collection.get(
    ids=["id1", "id2"]
)

# Get with filters
docs = collection.get(
    where={"category": "tutorial"},
    limit=10
)

# Get all documents
docs = collection.get()

5. Update documents

# Update document content
collection.update(
    ids=["id1"],
    documents=["Updated content"],
    metadatas=[{"source": "updated"}]
)

6. Delete documents

# Delete by IDs
collection.delete(ids=["id1", "id2"])

# Delete with filter
collection.delete(
    where={"source": "outdated"}
)

Persistent storage

# Persist to disk
client = chromadb.PersistentClient(path="./chroma_db")

collection = client.create_collection("my_docs")
collection.add(documents=["Doc 1"], ids=["id1"])

# Data persisted automatically
# Reload later with same path
client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_collection("my_docs")

Embedding functions

Default (Sente