How do you efficiently add large numbers of documents to ChromaDB using batching?

Adding tens of thousands of documents one at a time is slow because each call triggers embedding computation and index updates. The right approach is to batch documents into groups of 100–500 and add each batch with a single add() call — this amortises embedding overhead and index writes.

import chromadb
from chromadb.utils import embedding_functions
from typing import List

client = chromadb.PersistentClient(path="./bulk_db")
ef = embedding_functions.SentenceTransformerEmbeddingFunction(
    model_name="all-MiniLM-L6-v2"
)
collection = client.get_or_create_collection(
    "large_corpus", embedding_function=ef
)

# Simulate a large list of documents
documents = [f"Article about topic {i}" for i in range(10_000)]
ids       = [f"doc-{i}" for i in range(10_000)]
metadatas = [{"index": i, "batch": i // 500} for i in range(10_000)]

# Efficient batch insertion
BATCH_SIZE = 500

for start in range(0, len(documents), BATCH_SIZE):
    end = start + BATCH_SIZE
    collection.add(
        documents=documents[start:end],
        ids=ids[start:end],
        metadatas=metadatas[start:end],
    )
    print(f"Added batch {start // BATCH_SIZE + 1}, total: {collection.count()}")

print(f"Final count: {collection.count()}")  # 10000

# Alternative: provide pre-computed embeddings to skip re-embedding
# (useful when you already called the embedding API)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")

docs_batch = documents[:500]
vectors = model.encode(docs_batch, batch_size=64, show_progress_bar=True)
collection.add(
    embeddings=vectors.tolist(),
    documents=docs_batch,
    ids=ids[:500],
)