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:
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models_basics.ipynb: This notebook introduces the fundamental concepts of models in Langchain, detailing their structure and functionality.
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models_prompts_parsers.ipynb: This notebook delves into the basics of models in Langchain, with a focus on prompts and parsers.
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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.
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memory.ipynb: This notebook explores the memory aspects of Langchain, explaining how data is stored and retrieved.
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indexes.ipynb: This notebook covers the concept of indexes in Langchain, focusing on their creation, usage, and maintenance.
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agents.ipynb: This notebook explains the concept of agents in Langchain, covering how they interact and communicate within the system.
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chatgpt_plugins.ipynb: This notebook provides an overview of how to utilize plugins with ChatGPT in Langchain for enhanced functionality.
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evaluation.ipynb: This notebook discusses the methods and strategies for evaluating performance in Langchain.
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functions.ipynb: This notebook discusses the new 'Function Calling' functionality of OpenAI.
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open_source_chain.ipynb: This notebook contains code with Langchain which uses a Falcon 7B Model.
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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.
