The chats.py module, part of the google.genai library, is designed to facilitate and manage multi-turn conversations (chat sessions) with a Generative AI model, such as Google's Gemini. It abstracts away the complexities of managing conversation history, sending messages, and processing responses, providing a higher-level interface for building conversational AI applications.
This document outlines how to create and manage conversational sessions using the google.genai library in Python. It covers initializing chat sessions, sending messages, overriding parameters for single requests, and importantly, how to permanently update parameters like temperature and maxoutputtokens while preserving system_instruction and existing history.
For an extensible chat application where users can choose different AI models (e.g., Gemini, OpenAI, and more in the future), a structured approach using Abstract Base Classes (ABCs) and the Factory Pattern is highly effective. This allows you to define a common interface for all AI providers and easily plug in new ones without modifying existing core logic.
Python follows the PEP 8 style guide for naming conventions. This guide helps improve code readability and maintainability across files, classes, methods, and variables. Below are best practices for naming in Python:
The most common use case for the OpenAI API in Python is generating chat completions. This involves sending a list of messages to a specified model and receiving a generated response. The API supports both synchronous (blocking) and asynchronous (non-blocking) calls, and responses can be received as a single object or streamed as chunks.