Chroma supports OpenAI’s embedding models out of the box, allowing you to use state-of-the-art embeddings for your vector search applications.
Installation pip install chromadb openai
Python usage Basic setup import chromadb from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction import os # Set your API key os.environ[ "OPENAI_API_KEY" ] = "your-api-key" # Create embedding function embedding_function = OpenAIEmbeddingFunction( api_key = os.environ[ "OPENAI_API_KEY" ], model_name = "text-embedding-3-small" ) # Create client and collection client = chromadb.Client() collection = client.create_collection( name = "openai_embeddings" , embedding_function = embedding_function ) # Add documents (embeddings generated automatically) collection.add( documents = [ "This is a document" , "This is another document" ], ids = [ "doc1" , "doc2" ] ) # Query results = collection.query( query_texts = [ "document query" ], n_results = 2 )
Available models Chroma supports all OpenAI embedding models:
Latest models
Legacy models
# text-embedding-3-small (recommended) # Dimensions: 1536 (default), configurable down to 512 # Cost-effective for most use cases embedding_function = OpenAIEmbeddingFunction( model_name = "text-embedding-3-small" ) # text-embedding-3-large # Dimensions: 3072 (default), configurable # Highest quality embeddings embedding_function = OpenAIEmbeddingFunction( model_name = "text-embedding-3-large" )
# text-embedding-ada-002 # Dimensions: 1536 (fixed) # Previous generation model embedding_function = OpenAIEmbeddingFunction( model_name = "text-embedding-ada-002" )
Custom dimensions # Use fewer dimensions for faster search and lower storage embedding_function = OpenAIEmbeddingFunction( api_key = os.environ[ "OPENAI_API_KEY" ], model_name = "text-embedding-3-small" , dimensions = 512 # Reduce from default 1536 )
API configuration from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction embedding_function = OpenAIEmbeddingFunction( api_key = "your-api-key" , model_name = "text-embedding-3-small" , api_base = "https://api.openai.com/v1" , # Custom endpoint api_type = "openai" , # or "azure" for Azure OpenAI api_version = "2023-05-15" , # For Azure deployment_id = "your-deployment" # For Azure )
JavaScript usage import { ChromaClient } from "chromadb" ; import { OpenAIEmbeddingFunction } from "chromadb" ; const embedder = new OpenAIEmbeddingFunction ({ openai_api_key: "your-api-key" , openai_model: "text-embedding-3-small" }); const client = new ChromaClient (); const collection = await client . createCollection ({ name: "openai_collection" , embeddingFunction: embedder }); // Add documents await collection . add ({ ids: [ "id1" , "id2" ], documents: [ "Document 1" , "Document 2" ] }); // Query const results = await collection . query ({ queryTexts: [ "query text" ], nResults: 2 });
Configuration model_name
string
default: "text-embedding-3-small"
OpenAI embedding model to use
Number of dimensions (text-embedding-3 models only)
Cost optimization Reduce costs : Use text-embedding-3-small with reduced dimensions (512 or 768) for most applications. This provides excellent quality at a fraction of the cost.
# Cost-effective configuration embedding_function = OpenAIEmbeddingFunction( model_name = "text-embedding-3-small" , dimensions = 512 # 67% reduction in storage and search time )
Azure OpenAI from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction # Azure OpenAI configuration embedding_function = OpenAIEmbeddingFunction( api_key = "your-azure-key" , api_base = "https://your-resource.openai.azure.com/" , api_type = "azure" , api_version = "2023-05-15" , deployment_id = "your-deployment-name" , model_name = "text-embedding-3-small" )
Error handling try : collection.add( documents = [ "document" ], ids = [ "id1" ] ) except Exception as e: if "rate_limit" in str (e).lower(): # Handle rate limit time.sleep( 1 ) # Retry elif "invalid_api_key" in str (e).lower(): # Handle authentication error print ( "Invalid API key" )
Best practices
Choose the right model Use text-embedding-3-small for most use cases. Only use text-embedding-3-large if you need the highest quality.
Batch requests Process documents in batches to avoid rate limits and improve throughput.
Cache embeddings Store embeddings to avoid re-computing them for the same content.
Monitor costs Track your API usage and consider using reduced dimensions to lower costs.
Resources Keep your OpenAI API key secure. Never commit it to version control. Use environment variables or secure key management.