Sample notebooks are available that you can use as a guide as you create notebooks of your own to do common tasks such as inferencing or tuning a foundation model.
To find available notebooks, search the Resource hub. You can add notebooks that you open from the Resource hub to your project, and then
run them.
You can install the ibm-watsonx-ai Python library in your integrated development environment by using the following command:
pip install ibm-watsonx-ai
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If you already have the library installed, include the -U parameter to pick up any updates and work with the latest version of the library.
pip install -U ibm-watsonx-ai
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Working with LangChain from a notebook
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LangChain is a framework that developers can use to create applications that incorporate large language models. LangChain can be useful when you want to link two or more functions together. For example, you can use LangChain as part of a retrieval-augmented
generation (RAG) task.
LlamaIndex is a framework for building large language model applications. You can leverage functions available from LlamaIndex, such as text-to-SQL or Pandas DataFrames capabilities in applications that you build with watsonx.ai foundation models.