A Simple Postgres Logger for OpenAI Endpoints

Overview

Discover how to implement a simple Postgres logger for OpenAI endpoints, with demonstrations of local and remote database setups for tracking API calls, useful for evaluations and fine-tuning.

Syllabus

• Introduction to OpenAI Endpoints and Logging
Overview of OpenAI API functionality
Importance of logging API calls
Use cases: evaluation and fine-tuning
• Setting up the Development Environment
Required tools and software
Installing Python and necessary libraries
Setting up Postgres locally
• Designing the Postgres Database Schema
Understanding database tables and relationships
Creating a schema to log API requests and responses
Using pgAdmin or other tools for database management
• Implementing the Postgres Logger
Writing a simple Python script to interact with Postgres
Connecting to the local Postgres database
Inserting, updating, and retrieving API call logs
• Demonstrations of Local Database Logging
Setting up test API calls to OpenAI endpoints
Recording logs in the local Postgres database
Visualizing and analyzing log data
• Remote Database Setup for Logging
Configuring a remote Postgres server
Securing remote database connections
Connecting to remote Postgres from a Python script
• Deploying and Monitoring the Logger
Best practices for deploying the logger in production
Monitoring the health and performance of the logger
Addressing potential issues and troubleshooting
• Evaluations and Fine-tuning Using Logs
Analyzing log data for model evaluation
Using logs to inform fine-tuning strategies
Case studies or real-world examples of effective use of logs
• Conclusion and Further Resources
Recap of key learning outcomes
Additional resources and next steps for deeper learning
Q&A session and feedback mechanism

Subjects

Computer Science