Implemented PDF Processing Pipeline and Fixes

2 min read

📅 2025-11-19 — Session: Implemented PDF Processing Pipeline and Fixes

🕒 03:50–05:20
🏷️ Labels: Pdf Processing, Python, Chroma Db, Automation
📂 Project: Dev

Session Goal

The session aimed to implement and refine a PDF processing pipeline using Python, focusing on enhancing functionality, error handling, and integration with Chroma DB for data embedding.

Key Activities

  • Developed a structured plan for PDF processing, including API contracts and CLI sequences for data embedding in Chroma DB.
  • Refactored the pdf_ingestor.py script to support multiple input formats and recursive processing.
  • Patched the script to fix TEI filename generation, preventing collisions.
  • Created a CLI runbook for end-to-end PDF ingestion and embedding, including safety checks.
  • Corrected the chunks_to_records function in the TEI parser to align with the pipeline’s calling convention.
  • Managed Chroma DB collections, including setup and backend configuration.
  • Diagnosed and resolved Python import and Chroma collection errors.

Achievements

  • Successfully implemented a robust PDF processing pipeline with enhanced functionality and error handling.
  • Established clear mappings between PDFs and TEI files, ensuring data integrity.
  • Improved CLI workflows for efficient data processing and embedding.

Pending Tasks

  • Further testing of the pipeline to ensure stability and performance under different scenarios.
  • Continuous monitoring for potential errors or areas of improvement in the workflow.

Graph View

Backlinks