LangChain Tutorials with Jupyter Notebooks
This repository contains a collection of Jupyter notebooks demonstrating various aspects of using the LangChain library for natural language processing (NLP) tasks. Whether you're a beginner with LangChain or looking for ways to extend your knowledge, these tutorials offer practical examples and insights.
What you'll find:
Diverse Tutorials: Covering a range of topics, from core concepts like embeddings and vector stores to advanced applications like conversational question-answering.
Interactive Notebooks: Designed for hands-on learning, allowing you to run the code and explore the results within your browser.
Clear Explanations: Each notebook provides detailed explanations of the code and results, helping you understand the underlying concepts.
Community & Support: Feel free to raise questions, share feedback, and contribute to the project through the GitHub discussions or issue tracker.
Getting Started:
Clone the repository: Use git clone https://github.com/atef-ataya/LangChain-Tutorial to download the files.
Install requirements: Open a terminal within the repository and run pip install -r requirements.txt to install necessary libraries.
Launch Jupyter Notebooks: Run jupyter notebook in the terminal to start the Jupyter server.
Explore the notebooks: Navigate to the notebooks directory and open any notebook to begin exploring the tutorials.
Notebooks you can try:
LangChain 101: Learn fundamental concepts like embeddings, vector stores, and retrieval.
Summarization with OpenAI: Explore text summarization using OpenAI models and LangChain pipelines.
Conversational Q&A with Pinecone: Build a conversational question-answering system using Pinecone and LangChain.
...and many more!
Feel free to:
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Fork this repository and contribute your own tutorials.
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Raise issues or share suggestions through the GitHub platform.
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Join the LangChain community for further discussions and support.
By diving into these LangChain tutorials, you'll gain practical skills and expand your understanding of this powerful NLP library. Let's explore the exciting world of LangChain together!
Additional Information:
LangChain Documentation: https://readthedocs.org/projects/langchain/
OpenAI Documentation: https://platform.openai.com/docs/introduction
Jupyter Notebook: https://jupyter.org/