How does ChromaDB's PersistentClient store data on disk, and what are its limitations?

The PersistentClient stores data in a directory you specify. Inside, ChromaDB uses SQLite for metadata (IDs, document text, metadata key-value pairs) and binary files for the HNSW vector index. All writes are flushed to disk automatically — there is no explicit save/commit step.

import chromadb
import os

# Create or open a persistent database
client = chromadb.PersistentClient(path="./my_vector_db")

# After this call, ./my_vector_db/ contains:
# - chroma.sqlite3         (metadata, documents, IDs)
# - <uuid>/               (one folder per collection)
#   - header.bin          (HNSW index configuration)
#   - data_level0.bin     (HNSW graph layer 0)
#   - length.bin          (element count)

col = client.get_or_create_collection("notes")
col.add(
    documents=["Remember to buy milk", "Meeting at 3pm tomorrow"],
    ids=["n1", "n2"],
)
# Data is persisted immediately — no commit needed

# Verify data survives restart:
del client, col  # simulate process exit
client2 = chromadb.PersistentClient(path="./my_vector_db")
col2 = client2.get_collection("notes")
print(col2.count())   # 2 — still there!
print(col2.get(ids=["n1"])["documents"])  # ["Remember to buy milk"]

# Check the files on disk
for root, dirs, files in os.walk("./my_vector_db"):
    for f in files:
        print(os.path.join(root, f))

For multi-process or production deployments, prefer running chroma run --path ./data as a server and connecting with HttpClient.