📅 2025-04-08 — Session: Optimized Asynchronous Data Extraction Pipeline
🕒 18:35–18:55
🏷️ Labels: Async, AI, Data Extraction, Python, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary aim of this session was to enhance the asynchronous data extraction pipeline using OpenAI’s API, focusing on improving efficiency and error handling.
Key Activities
- Implemented an asynchronous AI call to extract data from text snippets, saving results to a CSV file.
- Enhanced file parsing using Pandas to handle whitespace and unexpected characters.
- Integrated a reusable function
get_recent_files()into the file processing pipeline to streamline file retrieval and parsing. - Addressed error handling in asynchronous data extraction, fixing issues with undefined variables and ensuring a smooth execution flow.
- Optimized the data extraction process by detailing the function structure and providing recommendations for workflow stabilization.
Achievements
- Successfully defined and executed an asynchronous AI call for data extraction.
- Improved data parsing techniques in Python, specifically using Pandas.
- Established a robust file processing pipeline with effective error handling mechanisms.
Pending Tasks
- Further testing and validation of the optimized pipeline to ensure stability across different datasets.