This repository, "GenAI-Tutorial," by harsh1504660, provides a collection of tutorials and examples focused on Generative AI, with a particular emphasis on the LangChain library. It is designed to help you explore various components and functionalities of LangChain through interactive code examples.
This tutorial covers a wide range of LangChain functionalities, organized into dedicated modules:
- LangChain Structured Output: Learn how to handle structured outputs with LangChain.
- Langchain Chains: Understand and implement different types of chains in LangChain for complex workflows.
- Langchain Document Loader: Explore various methods for loading documents into LangChain.
- Langchain Models: Dive into different language models and their integration with LangChain.
- Langchain Output Parser: Discover how to parse and format outputs from language models.
- Langchain Prompts: Learn to craft effective prompts for various Generative AI tasks.
- Langchain Runnables: Understand the concept and implementation of runnables in LangChain.
- Langchain Text Splitters: Explore techniques for splitting text into manageable chunks for processing.
- Langchain Tool Calling: Learn how to enable language models to use external tools.
- Langchain Tools: Discover and utilize various tools available within LangChain.
- Langchain VectorStores: Understand how to use vector stores for efficient data retrieval.
- Retrieval Augmented Generation: Explore the powerful concept of RAG for enhanced text generation.
- Jupyter Notebook: The tutorials are primarily presented as interactive Jupyter Notebooks, allowing for easy execution and experimentation.
- Python: All code examples are written in Python, leveraging the LangChain library.
To get started with these tutorials, clone the repository to your local machine:
git clone [https://github.com/harsh1504660/GenAi-Tutorial.git](https://github.com/harsh1504660/GenAi-Tutorial.git)