📅 2025-05-06 — Session: Developed and Debugged SQLite Ingestion Pipeline
🕒 20:40–21:20
🏷️ Labels: Sqlite, Python, Database, Automation, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and debug a robust ingestion pipeline for SQLite databases that focuses on assistant messages.
Key Activities
- Implemented a Python function to insert only ‘assistant’ messages into an SQLite database, ensuring no duplicates and skipping non-assistant messages.
- Outlined steps to filter and reset the database, focusing on storing only assistant messages and addressing issues with existing user entries.
- Developed a Python script to reset the SQLite database by dropping the existing ‘messages’ table and creating a new one with only assistant messages from a JSON file.
- Proposed a modular ingestion pipeline structure with a controlled reset mechanism, message filters, and daily JSONL exports.
- Fixed a JSON vs JSONL parsing error by providing a robust loader function capable of handling both formats.
- Addressed a Jupyter runtime file error in the script, offering a quick fix and file type validation.
- Corrected the
extract_messages()function to ensure proper filtering and scope access for assistant messages.
Achievements
- Successfully created a modular and automated ingestion pipeline for SQLite databases.
- Resolved several errors related to JSON parsing and file handling, improving the robustness of the scripts.
Pending Tasks
- Further testing of the ingestion pipeline with various datasets to ensure reliability and performance.