Developed and Debugged PDF Processing Flask App

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

🕒 21:05–22:45
🏷️ Labels: Flask, Pdf Processing, Python, Debugging, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal: The primary objective of this session was to develop and debug a Flask application for processing PDF files. The app was intended to allow users to upload PDFs, extract and embed text, and perform queries using a vectorstore.

Key Activities:

  1. Development Plan: Initiated with a 30-minute rapid development plan for a PDF processing app, focusing on text extraction, embedding, and query processing.
  2. Flask Application Implementation: Developed a Flask application to handle PDF ingestion and query processing, integrating a vectorstore for document retrieval.
  3. Integration with Raptor Pipeline: Integrated the raptor_pipeline.py for document processing, including clustering, embedding, and summarization.
  4. Error Resolution: Addressed installation errors for langchain_chroma and Conda environment initialization issues. Debugged missing ‘cluster’ column in DataFrame and recursive function initialization errors in the app.
  5. Enhancements: Improved logging for better debugging and resolved HTTP 415 error in Flask endpoints.

Achievements:

  • Successfully developed a functional Flask app for PDF processing with integrated text embedding and querying capabilities.
  • Resolved critical errors related to module installations and environment setups.
  • Enhanced the app’s logging and error handling mechanisms.

Pending Tasks:

  • Further optimization of the ingestion and querying pipeline.
  • Continuous monitoring and updating of dependencies to prevent future errors.

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