How do you use the OpenAI embedding function with ChromaDB?

ChromaDB has a built-in OpenAIEmbeddingFunction that calls the OpenAI Embeddings API. This gives higher-quality embeddings than the default local model, at the cost of API latency and usage fees. Use text-embedding-3-small for a balance of quality and cost, or text-embedding-3-large for maximum quality.

import chromadb
from chromadb.utils import embedding_functions
import os

client = chromadb.PersistentClient(path="./chroma_openai")

# Built-in OpenAI embedding function
ef_openai = embedding_functions.OpenAIEmbeddingFunction(
    api_key=os.environ["OPENAI_API_KEY"],
    model_name="text-embedding-3-small",  # 1536-dim, fast and cheap
    # model_name="text-embedding-3-large",  # 3072-dim, highest quality
    # model_name="text-embedding-ada-002",  # legacy, 1536-dim
)

collection = client.get_or_create_collection(
    name="openai_docs",
    embedding_function=ef_openai,
    metadata={"hnsw:space": "cosine"},
)

# Usage is identical to the default embedding function
collection.add(
    documents=[
        "FastAPI is a modern Python web framework for building APIs.",
        "ChromaDB stores embeddings for semantic search.",
    ],
    ids=["d1", "d2"],
)

# ChromaDB calls OpenAI API automatically on add() and query()
results = collection.query(
    query_texts=["vector database for retrieval"],
    n_results=1,
)
print(results["documents"])  # [["ChromaDB stores embeddings..."]]

Important consistency rule: you must use the exact same embedding model for both storing and querying. If you embed documents with text-embedding-3-small, all queries must also use text-embedding-3-small. Mixing models produces meaningless similarity scores.