LangChain for LLM Application Development

Welcome to the LangChain for LLM Application Development repository! This repository provides a collection of Jupyter notebooks designed to help developers learn how to use LangChain, a framework that simplifies building applications powered by large language models (LLMs). Each notebook focuses on specific features of LangChain, demonstrating various use cases.

Notebooks

1. Basic_LangChain_Usage.ipynb

This notebook provides an introduction to LangChain, showcasing how to get started with the framework and perform basic tasks, such as running queries through language models.

2. Chains_and_Pipelines.ipynb

In this notebook, we explore how to create and manage chains of prompts and pipelines, enabling you to build more complex interactions using LangChain.

3. LLM_Integrations.ipynb

This notebook demonstrates how to integrate various LLMs into your application using LangChain. It covers connecting to multiple models and handling model-specific features.

4. Memory_and_State_Management.ipynb

Learn how to maintain context between interactions in this notebook, which covers memory and state management in LangChain. This is essential for applications that require persistent conversations or history-aware features.

5. LangChain_with_APIs.ipynb

This notebook walks through integrating external APIs with LangChain, enabling you to fetch real-time data, perform computations, or call other services during LLM interactions.

6. Custom_Prompts_and_Templates.ipynb

Explore how to build custom prompts and templates tailored to your application's needs. This notebook focuses on generating structured and reusable templates to streamline prompt engineering.

7. LangChain_Tools_and_Utilities.ipynb

This notebook provides an overview of useful tools and utilities that LangChain offers to enhance LLM-driven application development, such as debugging utilities, monitoring, and logging.