LangChain for Generative AI: Using OpenAI LLMs in Python

This repository contains the code, resources, and examples for working with LangChain and OpenAI's Large Language Models (LLMs) in Python. The content focuses on integrating LangChain with various models, handling embeddings, managing chatbot memory, and building complex AI applications.

Repository Contents

Project Overview

This project demonstrates how to leverage LangChain in Python to build advanced AI applications. Below is a summary of the key areas covered:

1. Introduction to LangChain

   • Overview of LangChain and its components.

2. Understanding Large Language Models (LLMs)

   • Insights into LLMs, including their architecture and applications.

3. Setting Up the Environment

   • Installation guide and a "Hello World" example using Google Colab.

4. Working with LangChain Models

   • Examples of using different types of supported models, including Chat Models and Embeddings.

5. Prompting & Parsing

   • Best practices for prompting, formatting, and using LangChain's built-in prompt templates and output parsers.

6. Memory and Chaining in LangChain

   • Techniques for managing chatbot memory and building chains in LangChain.

7. LangChain Agents

   • Practical implementation of agents, including building an Arxiv Summarizer Agent.

Notebooks

Several Google Colab notebooks are provided to demonstrate the concepts discussed:

• LangChain Models
  Notebook for exploring different models.

• Prompt Templates & Output Parsers
  Notebook focusing on prompting techniques.

• LangChain Agents
  Example of building and deploying LangChain agents.

Key Features

• LangChain Integration: Learn how to connect LangChain to OpenAI's API suite.
• Embedding Techniques: Work with text embeddings and vector stores.
• Agent Development: Build and deploy AI agents using LangChain.