Reviewing a simple LangChain setup in a Jupyter notebook

We are now ready to set up a complete pipeline that can later be lent to various NLP applications.

Refer to the Ch8_Setting_Up_LangChain_Configurations_and_Pipeline.ipynb notebook. This notebook implements the LangChain framework. We will walk through it step by step, explaining the different building blocks. We chose a simple use case here, as the main point of this code is to show how to set up a LangChain pipeline.

In this scenario, we are in the healthcare sector. We have many care givers; each has many patients they may see. The physician in chief made a request on behalf of all the physicians in the hospital to be able to use a smart search across their notes. They heard about the new emerging capabilities with LLMs, and they would like to have a tool where they can search within the medical reports they wrote.

For instance, one physician said the following:

“I often come across research that may be...