Lang Chain Crash Course and LLM Projects

Introduction

• Presenter: Krishn
• Platform: YouTube
• Topic: Lang Chain crash course and 6+ end-to-end LLM projects
• Models: OpenAI, Llama 2 by Meta, Google Gemini Pro, Hugging Face
• Tools: Hugging Face platform for deployment

Goals

• Happy New Year 2024 wishes
• Aim to help viewers achieve their goals and dreams
• Detailed Lang Chain crash course
• Development of 6+ end-to-end LLM projects
• Usage of different LLM models
• Frontend and backend development
• Deployment on Hugging Face platform

Projects and Tools

1. LLM Projects: Overview
   • Use models from OpenAI, Meta's Llama 2, Google Gemini Pro, and Hugging Face
   • Deploy on Hugging Face platform
2. Lang Chain Series
   • Practical orientation of Lang Chain
   • Increasing industry usage of Lang Chain
   • Series will cover all practical aspects and components of Lang Chain

Lang Chain Components

• LLMs: Large Language Models for various uses
• Prompt Templates: To structure input and guide responses from LLMs
• Output Parser: To format the output in a desired way
• Environment Setup: Set up VSCode, create virtual environments, and install necessary libraries

Example Project: Q&A Chatbot

• Tool: Streamlit for UI
• Uses Lang Chain and OpenAI models
• Example task: Asking about the capital of countries
• Components: LLM model, prompt template, chain, and response handling

Detailed Steps

1. Environment Setup
   • Use VSCode and create a new project folder
   • Create virtual environment and install required libraries (Lang Chain, OpenAI, Hugging Face Hub, Streamlit)
   • Write requirements.txt for easy library management
2. API Key Management
   • Securely store and access OpenAI API keys using environment variables

Creating an OpenAI LLM Model

• Create and configure LLM model with parameters like temperature
• Code Example: from langchain.llms import OpenAI import os openai_key = os.environ['OPENAI_API_KEY'] llm = OpenAI(api_key=openai_key, temperature=0.6)

Usage of Prompt Template

• Define input variables and structure
• Example: Define country name for querying the capital
• Code Example: from langchain.prompts import PromptTemplate prompt = PromptTemplate( input_variables=['country'], template='Tell me the capital of {country}' ) formatted_prompt = prompt.format(country='India') print(llm.predict(formatted_prompt))

Creating Chains

• Chain LLM model and prompt templates
• Use LLMChain to seamlessly combine them
• Code Example: from langchain.chains import LLMChain chain = LLMChain(llm=llm, prompt=prompt) response = chain.run('What is the capital of India?') print(response)

Combining Multiple Chains

• Use SequentialChain to handle multiple steps or transformations
• Define multiple prompt templates and LLM chains
• Aggregate outputs from multiple chains
• Code Example: from langchain.chains import SequentialChain combined_chain = SequentialChain( chains=[capital_chain, famous_chain], ... )

Chat Models

• Use Lang Chain's chat_openai for conversation modeling
• Define human, system, and AI messages
• Code Example: from langchain.chat_models import ChatOpenAI from langchain.schema import HumanMessage, SystemMessage, AIMessage chat_llm = ChatOpenAI(api_key=openai_key)

Prompt Template Integration with Output Parser

• Use prompt templates with output parser to format outputs
• Code Example: from langchain.schema import BaseOutputParser class CustomOutputParser(BaseOutputParser): def parse(self, text): return text.split(',') prompt = PromptTemplate( input_variables=['text'], template='Provide comma-separated values for: {text}', output_parser=CustomOutputParser() )

End-to-End Project Example

• Q&A Chatbot using Streamlit and Lang Chain
• Full project structure
• Initial setup with environment and API key
• Read and manage documents using Lang Chain
• Create text embeddings and store in vector database (e.g., Pinecone)
• Implement front-end interface using Streamlit

Example Code for Environment Setup & Execution

• Code Example: # app.py import streamlit as st from langchain import OpenAI from langchain.prompts import PromptTemplate prompt = PromptTemplate( input_variables=['question'], template='Answer the question: {question}' ) llm = OpenAI(api_key='your-openai-key') st.title('Q&A Chatbot') question = st.text_input('Ask a question') if st.button('Submit'): response = llm.predict(prompt.format(question=question)) st.write(response)

Final Thoughts

• Encourage practice and sharing of work
• Emphasize the importance of understanding the flow and components of Lang Chain projects