AI App with LangChain and Streamlit

Overview

This lecture explains how to build a custom AI application using the LangChain framework with Streamlit and OpenAI's GPT models, focusing on creating a YouTube script and title generator with memory and Wikipedia integration.

Introduction to Large Language Models and LangChain

• Large language models (LLMs) are widely adopted in software products for AI-powered features.
• LangChain is a Python framework that simplifies building applications using LLMs and tools.
• LangChain consists of six modules: models, prompts, indexes, memory, chains, and agents.

Setting Up the Environment

• Create two files: apikey.py for storing the OpenAI API key and app.py for the main application.
• Set API key as an environment variable.
• Install dependencies: Streamlit, LangChain, OpenAI, Wikipedia, ChromaDB, tiktoken.

Building a Basic LLM Application with Streamlit

• Import necessary modules (Streamlit, OpenAI via LangChain).
• Create a basic Streamlit app UI with a title and text input for prompts.
• Set up and run the OpenAI LLM; display the generated response in the app.

Enhancing with Prompt Templates and Chains

• Use LangChain’s PromptTemplate to format user input for consistent LLM prompts.
• Use LLMChain to chain prompt templates and LLMs.
• Switch from direct LLM calls to using chains for more organized outputs.

Sequential Chains for Multi-Step Outputs

• Use SimpleSequentialChain to sequentially generate a title, then a script.
• Note: SimpleSequentialChain only returns the final output (the script).
• Replace with SequentialChain for accessing multiple outputs (title and script).

Adding Memory to the Application

• Import and use ConversationBufferMemory for storing chat history.
• Attach memory to chains to preserve conversation history and context.
• Display memory in the Streamlit app using expanders for better UX.

Integrating Tools: Wikipedia API

• Import Wikipedia API wrapper from LangChain utilities.
• Modify prompt templates to include Wikipedia research in script generation.
• Separate memory buffers for title and script allow independent tracking.

Final App Structure and Functionality

• The app generates both a YouTube title and script, incorporates Wikipedia research, and maintains history.
• Outputs are displayed separately for title, script, Wikipedia research, and history.

Key Terms & Definitions

• LLM (Large Language Model) — A machine learning model trained on large text datasets to generate human-like language.
• LangChain — A Python framework for building applications powered by LLMs and tools.
• PromptTemplate — A structured template for formatting prompts passed to LLMs.
• LLMChain — A combination of a prompt template and an LLM for sequential processing.
• SequentialChain — Chains multiple LLMChains to process stepwise outputs.
• ConversationBufferMemory — Stores conversation history for context in LLM-powered apps.

Action Items / Next Steps

• Review and experiment with LangChain modules and Streamlit app structure.
• Try adding more tools or functionalities (e.g., indexes) as mentioned in the lecture.
• For further learning, explore the LangChain documentation or related machine learning courses.