GPT-4o Recommendation Engine (Basic Version) 💥 OpenAI Tool Calling 💥

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AI Summary

Summary of Video Transcript: Building a Basic Recommendation Engine with GPT-4 Mini

Overview

• The video demonstrates how to build a basic recommendation engine using GPT-4 Mini.
• It discusses the prevalence of recommendation engines in platforms like Amazon, Netflix, LinkedIn, Facebook, and Instagram.
• The engine is designed to suggest products like shoes, jackets, tops, and bottoms based on user input and inventory data.

Technical Details

• The code is written in a Google Colab notebook, which will be shared via YouTube description or GitHub.
• Users need to set their OpenAI API key and install necessary libraries (pip install openai).
• Libraries used include json, textwrap, and openai.
• The model GPT-4 Mini is chosen for cost efficiency, but other models can be used.
• The video uses pydantic for structured output without relying on the instruct library.
• A product search prompt is defined to guide the recommendation engine.
• The code defines product categories and subcategories, as well as search parameters using object-oriented programming concepts.

Implementation

• A function get_response is created to process user input and context, and to generate recommendations.
• The system’s role, user input, and context are defined and passed to the model.
• The pydantic function tool is used to simulate a product search in a database.
• The video shows how to print the tool call and the structured output, which includes categories and subcategories of recommended products.
• The output is designed to be connected to an internal RDBMS for actual product retrieval.

Example Outputs

• The engine suggests products based on user input, such as a warm blue jacket for a female with blue eyes, or rain jackets and boots for a male going to rainy Scotland.
• The structured output provides parameters that can be used to query a real database for product recommendations.

Final Thoughts

• The recommendation engine is a minimal implementation that can be improved by refining prompts and adding more product categories or cultural nuances.
• It avoids the cold start problem common in traditional recommendation engines by using LLMs to guess user preferences without prior ratings.
• The video suggests that LLM-based recommendation engines could be augmented with traditional engines for better performance.

Code Availability

• The code will be shared in the YouTube video description for viewers to experiment with.

(Note: No specific CLI commands, website URLs, or detailed instructions were provided in the transcript.)