Implemented async OpenAI API for batch processing

• Day: 2025-02-23
• Time: 20:20 to 21:25
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Openai, API, Async, Python, Error Handling, Course Design

Description

Session Goal:

The session aimed to explore and implement efficient batch processing using OpenAI’s API, focusing on asynchronous requests and error handling.

Key Activities:

• Explored two methods for batch API calls: asynchronous requests for parallel processing and OpenAI’s Batch API for large-scale jobs.
• Implemented an asynchronous translation process for a DataFrame’s ‘Snippet’ column using OpenAI’s API.
• Resolved a RuntimeError in Jupyter Notebooks using the nest_asyncio library to allow nested event loops.
• Debugged OpenAI API failures related to rate limits and invalid responses.
• Corrected asynchronous calls to OpenAI API, addressing TypeError issues with await and ChatCompletion.
• Refactored code for generating course session blueprints, focusing on function renaming, docstring updates, and system prompt enhancements.

Achievements:

• Successfully implemented and tested asynchronous API calls for batch processing.
• Improved error handling and debugging techniques for OpenAI API.
• Enhanced codebase for course session blueprint generation.

Pending Tasks:

• Further testing and optimization of batch processing methods.
• Integration of optimized course session planning prompts into the curriculum design process.

Evidence

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• event_ids: []