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.
Unlock watsonx Capabilities: Where do you start finding implementation examples when you are an AI engineer or developer?
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.
An Example of how use the “Bee Agent Framework” (v0.0.33) with watsonx.ai
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 are available on Hugging Face and ready to be used locally with “watsonx Code Assistant”
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.
Implementing LangChain AI Agent with WatsonxLLM for a Weather Queries application
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.
Does it work to use ChatWatsonx from langchain_ibm to implement an agent that invokes functions?
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.
Integrating langchain_ibm with watsonx and LangChain for function calls: Example and Tutorial
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.
InstructLab and Taxonomy tree: LLM Foundation Model Fine-tuning Guide | Musician Example
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.
Find simple tutorials for `Watson Libraries for Embed`
This short blog post is about where you can find great simple tutorials for "Watson Libraries for Embed".
New Open-Source Multi-Cloud Asset to build SaaS
When software is provided as a managed service (SaaS), using a multi-tenant approach helps minimise costs for the deployments and operations of each tenant. In order to leverage these advantages, applications need to be designed so that they can be deployed to support multiple tenants, while maintaining isolation for security reasons. At the same time, common deployment and operation models are required so that new SaaS versions can be deployed to existing tenants, or to onboard new tenants, in a reliable and efficient way.
