Check Existing Issues
Verify Feature Scope
Problem Description
Hi Open WebUI team and community,
I have successfully resolved the RAG content extraction limitations on Windows and complex PDF processing by creating a custom Tika-compatible proxy server that leverages Azure AI Foundry (Mistral OCR).
This setup fully addresses the HTTP PUT binary stream method and the specific JSON Dictionary format expected by Open WebUI's internal loader (avoiding the common AttributeError: 'list' object has no attribute 'get' crash).
I have posted the complete FastAPI proxy implementation script and the configuration guide here:
#19707 (comment)
(Note: Please replace the link above with your actual comment link!)
Hope this guide helps anyone looking to scale their RAG with high-precision enterprise OCR!
Proposed Solution
Proposed Implementation
We can implement a lightweight FastAPI proxy to intercept Open WebUI's file extraction pipeline and bridge it with Azure AI Foundry.
How it works:
- Handles
PUT method: Reads request.body() directly to capture the raw binary file stream sent by Open WebUI.
- Formats as a flat dictionary: Bypasses Tika's default list-of-dicts response format, returning a standard dictionary with
{"X-TIKA:content": extracted_text} to seamlessly integrate with Open WebUI's internal Loader.load() extraction method.
This enables high-precision OCR parsing (including markdown tables and checkboxes) for the RAG ingestion pipeline.
Alternatives Considered
No response
Additional Context
No response
Check Existing Issues
Verify Feature Scope
Problem Description
Hi Open WebUI team and community,
I have successfully resolved the RAG content extraction limitations on Windows and complex PDF processing by creating a custom Tika-compatible proxy server that leverages Azure AI Foundry (Mistral OCR).
This setup fully addresses the HTTP
PUTbinary stream method and the specific JSON Dictionary format expected by Open WebUI's internal loader (avoiding the commonAttributeError: 'list' object has no attribute 'get'crash).I have posted the complete FastAPI proxy implementation script and the configuration guide here:
#19707 (comment)
(Note: Please replace the link above with your actual comment link!)
Hope this guide helps anyone looking to scale their RAG with high-precision enterprise OCR!
Proposed Solution
Proposed Implementation
We can implement a lightweight FastAPI proxy to intercept Open WebUI's file extraction pipeline and bridge it with Azure AI Foundry.
How it works:
PUTmethod: Readsrequest.body()directly to capture the raw binary file stream sent by Open WebUI.{"X-TIKA:content": extracted_text}to seamlessly integrate with Open WebUI's internalLoader.load()extraction method.This enables high-precision OCR parsing (including markdown tables and checkboxes) for the RAG ingestion pipeline.
Alternatives Considered
No response
Additional Context
No response