Gemini

In part one of this blog series , I explored the motivation behind developing a personal recommendation system. The main goals are to learn how recommendation systems work and to build a tool that helps me find interesting blog posts and articles from feeds where only 1 in 20 posts might match my content interests. If you are interested in the technical implementation, the complete codebase is available in this github repository . ...

Google Gemini now can be accessed via OpenAI python library: from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) ## rest of the code as you would use openai It support basic text generation, image input, function calling, structured output, and embeddings. More info and code examples can be found on Gemini docs .