Code and examples for the YouTube series on LangChain.
This repository contains Jupyter notebooks, Python scripts, and other resources related to learning and experimenting with the LangChain library.
The repository is organized into the following main directories:
01-Models/: Explores different aspects of Language Models (LLMs) in LangChain, including:01-LLMs/: Basic usage of LLMs.02_Prompt_Templates/: Working with prompt templates.03_Output_Parsing/: Parsing the output from LLMs.04-Serialization/: Saving and loading LangChain objects.
02-Data-Connections/: Focuses on connecting LangChain to various data sources:data/: Sample data files used in the notebooks.chroma/,db/,faiss/: Directories related to vector stores and databases.- Notebooks covering data loaders, text splitters, embeddings, vector stores, retrievers, multi-vector retrievers, and context compression.
03-LCEL/: Demonstrates the LangChain Expression Language (LCEL) for building chains.04-Indexing/: Covers indexing strategies in LangChain.04-Memory/: Explores how to add memory to LangChain applications.LangChain_Apps/: Contains example applications built with LangChain:LangChain_Doc_Summerization/: An application for summarizing documents.
(To be added: Instructions on how to set up the environment, install dependencies, and run the examples.)
Each directory contains Jupyter notebooks (.ipynb) or Python scripts (.py) that demonstrate specific LangChain functionalities. You can explore these files to understand how to use different components of the library.
(To be added: Guidelines for contributing to this project, if applicable.)
This project is licensed under the MIT License. (Assuming MIT, please update if incorrect by checking the LICENSE file).