So after I upload a file and create a vector store I link the file to the vector store and try and use it. Sometimes however, if not usually, whatever I’m doing with the file (or vector store) fails because the file(s) has/have not been indexed.
Subsequent tries work, as do tries after a static delay.
I noticed the status field has been deprecated according to the API documentation but it never worked for this purpose anyway. Quite often I’d see ‘processed’ in the status and it would still fail to use the file(s). Reference to status attribute for files.
I thought about caching a time stamp and delay if the current time isn’t more than X seconds after the timestamp for decentralized systems.
This isn’t optimal:
What if it takes longer than the delay?
– Same problem as before.
What if it takes less time than the delay?
– I’ve just wasted time waiting for nothing.
The other thing I thought of is to just retry until it works but that’s even more of a kludge and by the time that fails it might be ready anyway.
Is there really no way to test (something we can poll) if a file has been indexed and is ready for use? How do people handle file uploads and ad-hoc operations immediately afterwards?
Pseudo code:
Upload, create, link...
while True:
poll the file's status
if status is good:
break
sleep 2
Do stuff with the file...
During create time, you can send a list of files. These are all processed as one job that gives a status update.
The vector store object gives you a counter with different dispositions of individual files you can poll.
Real code:
print("Polling the vector store for completion status...")
while True:
vs = client.beta.vector_stores.retrieve(vector_store_id=vector_store_id)
in_progress = vs.file_counts.in_progress
completed = vs.file_counts.completed
failed = vs.file_counts.failed
cancelled = vs.file_counts.cancelled
total = vs.file_counts.total
# Print a one-line status update
print(
f" Vector Store {vector_store_id} status: {vs.status} "
f"({completed}/{total} completed, {in_progress} in progress, "
f"{failed} failed, {cancelled} cancelled)"
)
# Check for failures or cancellations
if failed > 0:
raise RuntimeError(
f"Vector Store {vector_store_id} encountered failed file(s). Aborting!"
)
if cancelled > 0:
raise RuntimeError(
f"Vector Store {vector_store_id} has cancelled file(s). Aborting!"
)
# Break when all files are completed
if completed == total:
print("All files in the vector store are completed.\n")
break
time.sleep(2)
The AI line-inflation version
(Note: the AI’s exponential backoff is NOT the best algorithm. You could predict how long your files might take based on byte count and type, and the past performance of the API, for polling more intensely when expected)
The current polling implementation is straightforward and functional, but it can be improved significantly by incorporating best practices for robustness, efficiency, and maintainability. Specifically, consider these enhancements:
Recommended Improvements:
Exponential Backoff:
Instead of a fixed sleep interval, use exponential backoff to reduce unnecessary API calls and handle potential rate limits gracefully.
Timeout Handling:
Implement a maximum timeout to avoid infinite loops if the vector store never reaches completion.
Encapsulate Polling Logic:
Wrap the polling logic into a reusable function or method, improving readability and maintainability.
Structured Logging:
Replace print statements with Python’s built-in logging module for better control over verbosity and log management.
Exception Handling and Retry Logic:
Add retry logic for transient API errors, ensuring robustness against temporary network or API issues.
Refactored Example with Best Practices:
Here’s a refined, production-grade polling implementation:
import logging
import time
from typing import Optional
from openai import OpenAIError
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
def poll_vector_store_completion(
client,
vector_store_id: str,
initial_delay: float = 2.0,
max_delay: float = 30.0,
timeout: float = 600.0,
) -> None:
"""
Polls the vector store until all files are indexed or an error occurs.
Args:
client: OpenAI API client instance.
vector_store_id: ID of the vector store to poll.
initial_delay: Initial delay between polling attempts (seconds).
max_delay: Maximum delay between polling attempts (seconds).
timeout: Maximum total polling duration before aborting (seconds).
Raises:
RuntimeError: If indexing fails, is cancelled, or timeout is reached.
"""
start_time = time.monotonic()
delay = initial_delay
while True:
try:
vs = client.beta.vector_stores.retrieve(vector_store_id=vector_store_id)
except OpenAIError as e:
logger.warning(f"API error encountered: {e}. Retrying in {delay:.1f}s...")
time.sleep(delay)
delay = min(delay * 2, max_delay)
continue # Retry on transient API errors
counts = vs.file_counts
logger.info(
f"Vector Store {vector_store_id} status: {vs.status} "
f"({counts.completed}/{counts.total} completed, "
f"{counts.in_progress} in progress, {counts.failed} failed, "
f"{counts.cancelled} cancelled)"
)
if counts.failed > 0:
raise RuntimeError(
f"Vector Store {vector_store_id} encountered failed file(s). Aborting!"
)
if counts.cancelled > 0:
raise RuntimeError(
f"Vector Store {vector_store_id} has cancelled file(s). Aborting!"
)
if counts.completed == counts.total:
logger.info("All files in the vector store are completed.")
break
elapsed = time.monotonic() - start_time
if elapsed > timeout:
raise RuntimeError(
f"Polling timed out after {timeout:.1f}s for Vector Store {vector_store_id}."
)
time.sleep(delay)
delay = min(delay * 1.5, max_delay) # Gradually increase delay
# Usage example:
poll_vector_store_completion(client, vector_store_id)
Why This is Better:
Exponential Backoff: Reduces API load and gracefully handles transient errors.
Timeout: Prevents indefinite waiting.
Structured Logging: Provides clear, timestamped, and configurable output.
Encapsulation: Makes the polling logic reusable and maintainable.
Robustness: Handles transient API errors gracefully, improving reliability.
This approach demonstrates best algorithmic practices clearly and concisely, making it suitable as a coding example for experienced developers.
Thanks a ton for your really detailed response and code. I’ve implemented the code and done thorough testing but, alas, it doesn’t mean the indexing is actually done.
I’ve implemented a sleep before the break as a hack…
...
if counts.completed == counts.total:
logger.info("All files in the vector store are completed.")
logger.info("Sleeping for 10 seconds to allow indexing to complete.")
# HACK: Wait for indexing to complete should be handled by this polling function but
# doesn't seem to work.
# For this reason we need to sleep for 10 seconds.
time.sleep(10)
break
...
I’d really like it if this was worked out though. Perhaps OpenAI staff can address this?