Developed and Debugged PDF Processing App

2 min read

📅 2025-01-23 — Session: Developed and Debugged PDF Processing App

🕒 21:00–22:40
🏷️ Labels: Pdf Processing, Flask, Python, Debugging, Langchain, Chroma
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to develop and debug a PDF processing application using Flask, Python, and various libraries like LangChain and Chroma.

Key Activities

  • Created a rapid development plan for a PDF processing app.
  • Implemented a Flask application for PDF ingestion and query processing.
  • Integrated the Raptor Pipeline with the Flask app for enhanced document processing.
  • Resolved installation errors for the langchain_chroma module.
  • Compared different PDF processing implementations for future improvements.
  • Fixed issues related to Conda environment initialization and module import errors.
  • Streamlined the text ingestion and querying pipeline.
  • Debugged issues related to missing columns in DataFrames during processing.
  • Prevented premature execution of recursive functions in the app.
  • Enhanced logging verbosity for better debugging and error handling.
  • Resolved HTTP 415 errors and form submission issues in Flask endpoints.
  • Verified and reinstalled Python modules as needed.

Achievements

  • Successfully developed a functional PDF processing application capable of text extraction, embedding, and querying.
  • Improved the robustness and error handling of the application.
  • Enhanced the logging and debugging capabilities of the system.

Pending Tasks

  • Further optimization of the PDF processing workflow.
  • Exploration of additional enhancements for the document processing pipeline.

Graph View

Backlinks