Langchain Course

This repository contains course materials for learning the Langchain concepts. Below are the Jupyter notebooks used in the course with a brief description of each:

• models_basics.ipynb: This notebook introduces the fundamental concepts of models in Langchain, detailing their structure and functionality.

• models_prompts_parsers.ipynb: This notebook delves into the basics of models in Langchain, with a focus on prompts and parsers.

• chains.ipynb: This notebook introduces chains in Langchain, elucidating their function and importance in the structure of the language model. We learn about the different types of chain and their use.

• memory.ipynb: This notebook explores the memory aspects of Langchain, explaining how data is stored and retrieved.

• indexes.ipynb: This notebook covers the concept of indexes in Langchain, focusing on their creation, usage, and maintenance.

• agents.ipynb: This notebook explains the concept of agents in Langchain, covering how they interact and communicate within the system.

• chatgpt_plugins.ipynb: This notebook provides an overview of how to utilize plugins with ChatGPT in Langchain for enhanced functionality.

• evaluation.ipynb: This notebook discusses the methods and strategies for evaluating performance in Langchain.

• functions.ipynb: This notebook discusses the new 'Function Calling' functionality of OpenAI.

• open_source_chain.ipynb: This notebook contains code with Langchain which uses a Falcon 7B Model.

• doctran.ipynb This notebook contains code that shows how to preprocess data with DocTran.

Note: The descriptions above are general and might not fully capture the content of each notebook. Please refer to the notebooks themselves for detailed information.