Database API Key

ChromaDB REST API

Open-source AI-native embedding database for LLM apps

ChromaDB is an open-source vector database designed for AI applications that need to store and retrieve embeddings efficiently. It provides semantic search capabilities, integrates seamlessly with LLMs, and offers a simple API for managing collections of embeddings with metadata filtering. Developers use ChromaDB to build RAG systems, semantic search engines, and AI-powered applications that require fast similarity search over high-dimensional vectors.

Base URL https://api.chromadb.com/v1

API Endpoints

Method	Endpoint	Description
GET	`/heartbeat`	Check if the ChromaDB server is running and responsive
POST	`/collections`	Create a new collection for storing embeddings with specified metadata
GET	`/collections`	List all collections in the database with their configurations
GET	`/collections/{collection_id}`	Get details about a specific collection including count and metadata
DELETE	`/collections/{collection_id}`	Delete a collection and all its embeddings permanently
POST	`/collections/{collection_id}/add`	Add embeddings, documents, and metadata to a collection
POST	`/collections/{collection_id}/update`	Update existing embeddings, documents, or metadata in a collection
POST	`/collections/{collection_id}/upsert`	Insert or update embeddings based on IDs in a collection
POST	`/collections/{collection_id}/query`	Perform similarity search using query embeddings with filters
POST	`/collections/{collection_id}/get`	Retrieve embeddings by IDs or filter criteria from a collection
POST	`/collections/{collection_id}/delete`	Delete specific embeddings from a collection by IDs or filters
POST	`/collections/{collection_id}/count`	Get the count of embeddings in a collection matching filters
GET	`/version`	Get the ChromaDB server version information
POST	`/tenants`	Create a new tenant for multi-tenancy support
GET	`/tenants`	List all tenants in the ChromaDB instance

Code Examples

# Create a collection
curl -X POST https://api.chromadb.com/v1/collections \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "name": "document_embeddings",
    "metadata": {"description": "Product documentation embeddings"},
    "embedding_function": "default"
  }'

# Add embeddings to collection
curl -X POST https://api.chromadb.com/v1/collections/doc_collection_id/add \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "embeddings": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
    "documents": ["First document text", "Second document text"],
    "metadatas": [{"source": "docs"}, {"source": "blog"}],
    "ids": ["doc1", "doc2"]
  }'

# Query for similar embeddings
curl -X POST https://api.chromadb.com/v1/collections/doc_collection_id/query \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "query_embeddings": [[0.15, 0.25, 0.35]],
    "n_results": 5,
    "where": {"source": "docs"}
  }'

// Create a collection
const createCollection = async () => {
  const response = await fetch('https://api.chromadb.com/v1/collections', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      name: 'document_embeddings',
      metadata: { description: 'Product documentation embeddings' },
      embedding_function: 'default'
    })
  });
  return await response.json();
};

// Add embeddings
const addEmbeddings = async (collectionId) => {
  const response = await fetch(`https://api.chromadb.com/v1/collections/${collectionId}/add`, {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      embeddings: [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
      documents: ['First document text', 'Second document text'],
      metadatas: [{ source: 'docs' }, { source: 'blog' }],
      ids: ['doc1', 'doc2']
    })
  });
  return await response.json();
};

// Query similar embeddings
const querySimilar = async (collectionId) => {
  const response = await fetch(`https://api.chromadb.com/v1/collections/${collectionId}/query`, {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      query_embeddings: [[0.15, 0.25, 0.35]],
      n_results: 5,
      where: { source: 'docs' }
    })
  });
  return await response.json();
};

import requests
import json

API_KEY = 'YOUR_API_KEY'
BASE_URL = 'https://api.chromadb.com/v1'
HEADERS = {
    'Authorization': f'Bearer {API_KEY}',
    'Content-Type': 'application/json'
}

# Create a collection
def create_collection():
    response = requests.post(
        f'{BASE_URL}/collections',
        headers=HEADERS,
        json={
            'name': 'document_embeddings',
            'metadata': {'description': 'Product documentation embeddings'},
            'embedding_function': 'default'
        }
    )
    return response.json()

# Add embeddings to collection
def add_embeddings(collection_id):
    response = requests.post(
        f'{BASE_URL}/collections/{collection_id}/add',
        headers=HEADERS,
        json={
            'embeddings': [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
            'documents': ['First document text', 'Second document text'],
            'metadatas': [{'source': 'docs'}, {'source': 'blog'}],
            'ids': ['doc1', 'doc2']
        }
    )
    return response.json()

# Query for similar embeddings
def query_similar(collection_id):
    response = requests.post(
        f'{BASE_URL}/collections/{collection_id}/query',
        headers=HEADERS,
        json={
            'query_embeddings': [[0.15, 0.25, 0.35]],
            'n_results': 5,
            'where': {'source': 'docs'}
        }
    )
    return response.json()

# Get collection details
def get_collection(collection_id):
    response = requests.get(
        f'{BASE_URL}/collections/{collection_id}',
        headers=HEADERS
    )
    return response.json()

Connect ChromaDB to AI

Deploy a ChromaDB MCP server on IOX Cloud and connect it to Claude, ChatGPT, Cursor, or any AI client. Your AI assistant gets direct access to ChromaDB through these tools:

chromadb_create_collection Create a new ChromaDB collection with specified name and metadata for organizing embeddings

chromadb_add_embeddings Add document embeddings with metadata to a collection for semantic search and retrieval

chromadb_semantic_search Perform similarity search across embeddings using query vectors with optional metadata filters

chromadb_retrieve_documents Retrieve documents and embeddings by IDs or metadata filters from a collection

chromadb_delete_embeddings Delete specific embeddings from a collection by IDs or matching metadata criteria

Deploy in 60 seconds

Describe what you need, AI generates the code, and IOX deploys it globally.

Deploy ChromaDB MCP Server →

ChromaDB REST API

API Endpoints

Code Examples

Connect ChromaDB to AI

Deploy in 60 seconds

Related APIs