Introduction This post will walk you through building a text inference application using Meta’s Llama-3.1 model. We will use the data framework LlamaIndex python package in our app. I’m running this demo on WSL2 (Windows Subsystem for Linux) on Windows 11. Python 🐍 is run in an ipynb file on Visual Studio Code for Linux. […]
See Tutorial-01 to get rolling. Then open directory 02-tools-in-more-detail and select file tutorial-02.ipynb. Execute the notebook to make sure all is running before modifying. Meet the @tool decorator The @tool decorator applied to a function automatically generates the necessary schema and metadata an agent needs to understand and use the function. Add more import lines […]
Read More… from LangChain (Python) Tutorial-02: Tools in more detail
Introduction Welcome to LangChain, an open source framework for building agents and LLM applications! Agents work with language models and tools to create systems that reason about tasks, decide what provided tools to use, and come up with solutions to the problems (tasks). LangChain provides agent architectures out of the box. One of the objectives […]
Read More… from LangChain (Python) Tutorial-01: Your first agent