The TextWrapper class provides functionality for wrapping long pieces of text into multiple shorter lines while preserving the initial and subsequent indents.
- Shows how an individual can use Python for speech recognition (SR), Push-to-- Talk (PTT) systems, and large action models to create their own AI assistant.
- Describes the process of creating a "la Rabbit prototype" with Python code on Raspberry Pi hardware.
- Emphasizes how these components can be combined for various tasks using different APIs like OpenAI or LLaMA (Large Language Model from Meta).
- Describes the process of creating a "la Rabbit prototype" with Python code on Raspberry Pi hardware.
- Emphasizes how these components can be combined for various tasks using different APIs like OpenAI or LLaMA (Large Language Model from Meta).
A prototype tool powered by Large Language Models to make querying your databases as easy as saying the word.
- Introduction to QueryGPT, a tool using Large Language Models (LLMs) for natural language database queries
- Focus on implementing a basic iteration of the system, with potential for significant enhancements
- Aim is to provide the LLM with the database schema and have it answer questions based on that context
- Discussion on prompt engineering, which is designing inputs for generative AI tools to produce optimal results
- Introduction to QueryGPT, a tool using Large Language Models (LLMs) for natural language database queries
- Focus on implementing a basic iteration of the system, with potential for significant enhancements
- Aim is to provide the LLM with the database schema and have it answer questions based on that context
- Discussion on prompt engineering, which is designing inputs for generative AI tools to produce optimal results
Quick reference for the significant changes introduced with each new version of Python. It covers changes to Python syntax and the standard library, as well as valuable tools, links, and utilities that can aid with upgrading code bases.
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). It provides a simple yet robust interface using llama-cpp-python, allowing users to chat with LLM models, execute structured function calls and get structured output.
how to write memory-efficient classes in Python, focusing on three techniques: using slots, optimizing the use of class attributes, and minimizing object creation within loops.
