I want to share my experience in the cloud, AI, and agents development areas.
The blog post explores integrating ChatWatsonx with LangChain for function calls, using a weather example. It aims to understand AI agent tools and actions. The process includes defining tools functions, creating WatsonxChat instance, and implementing a structured ChatPromptTemplate. While not fully successful, it highlights the importance of the prompt.
The blog post demonstrates using the ChatWatsonx class of langchain_ibm for "function calls" with LangChain and IBM watsonx™ AI. It provides an example of a chat function call for weather information for various cities. The post also includes instructions to set up and run the example. Additional resources and examples are also provided.
This blog post focuses on the importance of Prompt Engineering in AI models, particularly Large Language Models (LLMs), for reducing manual effort and automating validation processes. It emphasizes the need for automation to handle increasing test data and variable combinations, and discusses the use of the Watsonx.ai Prompt Lab for manual and initial automation processes. The post also highlights the significance of integrating automation with version control for consistency and reproducibility.
This blog post delves into the initial fine-tuning process for large language models (LLMs) for multi-turn conversations and their deployment on Text Generation Inference (TGI) servers. It covers topics such as use cases, data formats, training data preparation, server setup, and evaluation frameworks. The goal is to guide readers through the process of fine-tuning and deploying LLMs.
This cheat sheet provides a two-step guide for adding users to your watsonx project in IBM Cloud.
This blog post contains information about a simple example implementation for a simple question-answering pipeline using an inside-search (IBM Cloud Watson Discovery) and a prompt (IBM Watsonx with prompt-lab) to create an answer.
Thomas Suedbroecker's Blog 