I’m having trouble getting metadata to stick when uploading documents to Azure OpenAI’s vector storage system.
My current setup:
Using Azure OpenAI API (2025-04-01-preview version)
Python SDK for handling file operations
Trying to attach custom metadata like document titles and source links
The issue:
When I upload a document with the assistants purpose and then attempt to assign metadata through the vector store file creation method, the metadata doesn’t get saved. The response always shows the attributes field as empty.
Code sample:
# First upload the document
doc_upload = client.files.create(
file=document_data,
purpose="assistants" # This might be causing the problem
)
document_id = doc_upload.id
# Try to assign metadata - this part fails
vector_doc = client.vector_stores.files.create(
vector_store_id=store_id,
file_id=document_id,
attributes={"doc_title": "Sample Document", "origin_url": "https://test.com"}
)
Interestingly, when I use the REST API directly, I can update the metadata successfully. But the Python SDK seems to ignore the attributes parameter completely.
Has anyone run into this before? Is there something special about how metadata works with assistant-purpose files? Could this be a bug in the current SDK release?
yeah, this is a known bug with the python sdk. try adding chunked=true when you create the vector store file - it usually fixes metadata persistence issues. also double-check that your attributes dict converts to json correctly before the api call. the sdk sometimes mangles it.
Hit this exact problem a few months ago building a document processing pipeline. Python SDK’s metadata handling is buggy as hell.
It’s probably the SDK version not serializing the attributes parameter right. I’ve seen this where REST API works fine but the Python wrapper just silently drops metadata.
Instead of fighting Azure’s wonky behavior, here’s what I’d do:
Build an automation workflow for the whole document upload and metadata process. Create a flow that uploads docs, assigns metadata reliably, and handles retries when stuff breaks.
I’ve built similar workflows that grab documents from different sources, process them, and push to vector stores with metadata intact. The automation deals with all the API quirks and version differences automatically.
You can add monitoring to catch failed metadata assignments and auto-retry with different methods. Way more reliable than hoping the SDK works consistently.
This saved me tons of debugging time. Let automation handle Azure’s mess instead of banging your head against it.
Hit this exact issue last week. It’s not the SDK version - it’s how Azure processes file uploads. When you use purpose=“assistants”, Azure runs extra processing steps that mess with metadata attachment. Switch to purpose=“retrieval” before creating the vector store file. This completely fixed the metadata persistence for me. The assistants purpose triggers some validation that strips custom attributes. Once I switched, metadata showed up consistently in the attributes field. Simple fix that beats wrestling with SDK workarounds or timing issues.
This happens because Azure OpenAI processes vector store files asynchronously and drops metadata if you assign it too early. I ran into the same thing with large document collections last year. The fix isn’t updating your SDK - it’s about timing. You need to wait for the file to finish processing before creating the vector store. Poll the file status every few seconds until it shows “processed”, then call your vector store creation method. Also watch out for special characters in your metadata values. I’ve seen silent failures with URLs that have query parameters - Azure accepts them but strips the metadata. Try URL encoding your origin_url before passing it to the attributes parameter. The REST API works fine because it handles async processing differently. The Python SDK needs you to manage the timing yourself.