I want to share my experience in the cloud, AI, and agents development areas.
The blog post shows integrating watsonx Assistant and watsonx.ai to create a full-screen user interface for interacting with a large language model (LLM) using minimal coding. It outlines the motivation, architecture, setup process, and specific actions necessary to deploy the integration on IBM Cloud Code Engine.
This blog post explains the implementation of a custom travel assistant agent using the Bee Agent Framework. It covers creating a tool to suggest vacation locations and utilizing weather data, integrating with MLFlow for observability. The article emphasizes practical execution steps, system requirements, and the motivation behind combining location and weather insights for user queries.
IBM has launched the watsonx Developer Hub, consisting of four sections: Get Started, Capabilities, Guides, and Support. This Hub is a valuable resource for developers looking to learn about watsonx, emphasizing its significance in the development process.
This blog post explores the Bee Agent Framework integration with watsonx.ai, detailing the setup process for a weather agent example on MacOS. It discusses necessary installations, environment variable configurations, and code updates needed due to framework changes. The execution output illustrates how the agent retrieves current weather data for Las Vegas.
IBM Granite for Code models on Hugging Face are beneficial for developers, allowing seamless integration with VS Code. They support 116 programming languages and are available under an Apache 2.0 license.
This blog post describes the customization of the LangChain AI Agent example from IBM Developer using Watsonx in Python. It demonstrates the implementation of a weather query application with detailed steps. The post offers insight into model parameters, creating prompts, agent chains, tool definitions, and execution. Additionally, it provides links to additional resources for further exploration.
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.
The blog post introduces InstructLab, a project by IBM and Red Hat, outlining the fine-tuning process of the model "MODELS/MERLINITE-7B-LAB-Q4_K_M.GGUF." This involves data preparation, model training, testing, and conversion, finally serving the model to verify its accuracy, by using a personal musician example.
This short blog post is about where you can find great simple tutorials for "Watson Libraries for Embed".
Thomas Suedbroecker's Blog 