Implemented PDF Processing Pipeline and Fixes

• Day: 2025-11-19
• Time: 03:50 to 05:20
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Pdf Processing, Python, Chroma Db, Automation

Description

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.

Evidence

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• event_ids: []