What is ChromaDB's default embedding function and how does it work?

When you create a collection without specifying an embedding function, ChromaDB uses the SentenceTransformerEmbeddingFunction backed by the all-MiniLM-L6-v2 model from the sentence-transformers library. This model is downloaded automatically on first use and cached locally.

import chromadb
from chromadb.utils import embedding_functions

# Default — uses all-MiniLM-L6-v2 automatically
client = chromadb.Client()
collection_default = client.create_collection("default_embeddings")

# Equivalent explicit usage
ef = embedding_functions.SentenceTransformerEmbeddingFunction(
    model_name="all-MiniLM-L6-v2",  # 384-dim, fast, good English quality
)

# Using a different Sentence Transformer model
ef_large = embedding_functions.SentenceTransformerEmbeddingFunction(
    model_name="all-mpnet-base-v2",  # 768-dim, higher quality, slower
)

collection_large = client.create_collection(
    name="large_model",
    embedding_function=ef_large,
    metadata={"hnsw:space": "cosine"},
)

# You can call embedding functions directly to inspect output
embed = embedding_functions.SentenceTransformerEmbeddingFunction()
vectors = embed(["Hello world", "ChromaDB is great"])
print(len(vectors))      # 2 — one vector per input
print(len(vectors[0]))   # 384 — dimensions