📅 2025-05-02 — Session: Debugged LLM Tool Call and FastAPI Issues
🕒 02:40–04:05
🏷️ Labels: LLM, Fastapi, Debugging, Error Handling, Ai Kernel
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to debug various issues related to LLM tool calls and FastAPI endpoints to enhance system reliability and performance.
Key Activities
- Debugging LLM Tool Call Failures: Addressed timeout errors and NoneType result crashes by implementing better error handling.
- Diagnosing Local AI Kernel Issues: Conducted a step-by-step diagnostic process to troubleshoot local AI kernel issues using FastAPI and Uvicorn.
- Debugging FastAPI Queries: Created a checklist for debugging FastAPI query and POST request issues, including endpoint binding and OpenAI API call diagnostics.
- Resolving FastAPI Route Conflicts: Identified and resolved conflicting route definitions in FastAPI applications.
- Fixing LLMToolAgent Class: Corrected the LLMToolAgent class to ensure proper argument parsing and error handling.
- Reflection and Action Guide: Summarized accomplishments and outlined next steps for continued improvements.
Achievements
- Enhanced error handling for LLM tool calls.
- Improved diagnostic processes for FastAPI and local AI kernel issues.
- Corrected and optimized the LLMToolAgent class functionality.
Pending Tasks
- Implement the outlined next steps for automation projects, focusing on the Email Agent.
- Continue refining the Upwork funnel for efficiency improvements.
Outcomes
The session successfully addressed multiple debugging issues, improving the robustness of the LLM tool call architecture and FastAPI applications.