📅 2025-08-17 — Session: Enhanced GitHub Ingestion with Async Support
🕒 20:00–21:25
🏷️ Labels: Github, Async, Python, Jupyter, Data Ingestion
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance the GitHub data ingestion pipeline by integrating asynchronous processing, improving error handling, and ensuring compatibility with Jupyter notebooks.
Key Activities
- Integrated GitHub repositories into the existing data ingestion pipeline using a custom Python script.
- Conducted a smoke test of the GitHub repo ingestion process using an SQLite database.
- Refactored the ingestion process to separate file conversion to
TextNodes from embedding and upserting. - Addressed challenges of using asyncio in Jupyter notebooks, providing solutions for async ingestion pipelines.
- Improved GitHub API token handling and error resilience, specifically for 401 Unauthorized errors.
- Diagnosed and patched issues in the GitHub repository ingestion process, including commit SHA handling and checkpoint file filtering.
- Integrated a real embedder and Chroma upsert functionality into the ingestion process.
- Debugged async functions and GitHub API calls in Jupyter notebooks, using strategies like
nest_asyncio.
Achievements
- Successfully integrated async support for GitHub ingestion in Jupyter environments.
- Enhanced error handling and resilience in the ingestion pipeline.
- Improved metadata handling and ensured compatibility with both Jupyter and CLI environments.
Pending Tasks
- Further testing of the integrated async ingestion process in diverse environments.
- Optimization of the filtering logic for checkpoint files and directories.