LangChain Projects Repository

A comprehensive collection of LangChain-based applications demonstrating various AI-powered use cases including chatbots, document Q&A, language translation, and more.

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📁 Repository Structure

This repository contains multiple independent projects, each showcasing different LangChain capabilities:

LangChain/
├── IntroSection/              # LangChain basics and middleware concepts
├── Building-chatBot/          # Basic chatbot implementation
├── E2E_QA_ChatBot/           # End-to-end Q&A chatbot with multiple models
├── LanguageTranslation/      # Translation API with LangServe
├── RAG_document_QA/          # RAG-based document question answering
└── Text-summarization/       # Web page content summarizer

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🚀 Projects Overview

1. IntroSection - LangChain Fundamentals

Introduction to LangChain concepts, basic chains, and middleware patterns.

Contents:

• LangchainIntro.ipynb - Getting started with LangChain
• Middlewares.ipynb - Understanding LangChain middlewares

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2. Building-chatBot - Basic ChatBot

Simple chatbot implementation demonstrating core LangChain chat functionality.

Contents:

• ChatBot.ipynb - Basic chatbot notebook

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3. E2E_QA_ChatBot - Multi-Model Q&A Chatbot

A fully-featured Streamlit chatbot supporting multiple AI models from Groq - all 100% FREE!

Features:

• ✅ 9 Different Models: OpenAI-compatible, LLaMA, Mixtral, Gemma
• ✅ Runtime API Key Input: No .env file needed
• ✅ Configurable Parameters: Temperature, max tokens
• ✅ Persistent Chat History: Session-based conversations

Quick Start:

cd E2E_QA_ChatBot
streamlit run app.py

Models Available:

• OpenAI GPT OSS 120B (OpenAI-compatible)
• Meta LLaMA 3.3 70B, 3.1 8B/70B, 3.2 1B/3B
• Mixtral 8x7B
• Google Gemma 2 9B, 7B

📖 Full Documentation

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4. LanguageTranslation - Translation API

A FastAPI-based translation service using LangChain and LangServe.

Features:

• 🌐 Multi-language Translation: English to any language
• ⚡ REST API: FastAPI with automatic Swagger docs
• 🔗 LangServe Integration: Easy-to-use API routes

Quick Start:

cd LanguageTranslation
python serve.py

API Endpoint:

• POST http://localhost:8000/chain/invoke

Example Request:

{
  "input": {
    "language": "French",
    "text": "Hello, how are you?"
  },
  "config": {},
  "kwargs": {}
}

Interactive Docs: http://localhost:8000/docs

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5. RAG_document_QA - Document Q&A with RAG

Retrieval-Augmented Generation application for asking questions about PDF documents.

Features:

• 📄 PDF Processing: Automatic document loading and chunking
• 🔍 Semantic Search: FAISS vector store for similarity search
• 🤖 AI-Powered Answers: Groq ChatGroq model with context
• 💾 Ollama Embeddings: Local embeddings generation
• 📊 Context Display: View source chunks used for answers

Quick Start:

cd RAG_document_QA
# Make sure Ollama is running: ollama serve
streamlit run app.py

Prerequisites:

• Ollama installed and running
• PDF documents in research_papers/ folder
• Groq API key

📖 Full Documentation

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6. Text-summarization - Web Page Content Summarizer

AI-powered content summarization tool that extracts and summarizes content from web pages.

Features:

• 🌐 Web Page Summarization: Summarizes articles, blogs, and documentation
• 🤖 ChatGroq AI: Uses LLaMA 3.3 70B (128K context) for intelligent summarization
• 🔑 Runtime API Key: Enter API key in UI - no .env file required
• 👁️ Content Preview: View original content before summary
• 📊 Metadata Display: Shows source information
• ⚡ Handles Long Content: Automatic chunking for large articles

Quick Start:

cd Text-summarization
streamlit run app.py
# Enter Groq API key in sidebar
# Paste any webpage URL

Example URLs:

• Web page: https://example.com/article
• News articles, blogs, technical docs

📖 Full Documentation

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🛠️ Setup & Installation

Prerequisites

• Python 3.8+ (3.10+ recommended)
• Ollama (for RAG_document_QA project)
• Groq API Key (get it FREE at console.groq.com)

Installation Steps

1. Clone or navigate to the repository:

   cd "C:\Users\LangChain"

2. Create and activate a virtual environment:

   python -m venv .myvenv
   .\.myvenv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   Create a .env file in the project root:

   GROG_API_KEY=your_groq_api_key_here
   GROQ_API_KEY=your_groq_api_key_here

   Note: Some projects use GROG_API_KEY and others use GROQ_API_KEY

5. Install Ollama (for RAG project):

   • Download from ollama.ai
   • Pull required model: ollama pull llama3.1

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📦 Dependencies

Core packages used across projects:

• LangChain - Framework for LLM applications
• LangServe - API deployment for LangChain
• LangChain-Groq - Groq model integration
• Streamlit - Web UI framework
• FastAPI - REST API framework
• FAISS - Vector similarity search
• Ollama - Local embeddings
• PyPDF - PDF processing

Install all dependencies:

pip install -r requirements.txt

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🎯 Quick Start Guide

For Chatbots (E2E_QA_ChatBot):

cd E2E_QA_ChatBot
streamlit run app.py
# Enter Groq API key in UI

For Translation API (LanguageTranslation):

cd LanguageTranslation
python serve.py
# Visit http://localhost:8000/docs

For Document Q&A (RAG_document_QA):

# Start Ollama first
ollama serve

# In another terminal
cd RAG_document_QA
streamlit run app.py

For Content Summarization (Text-summarization):

cd Text-summarization
streamlit run app.py
# Enter Groq API key in sidebar
# Paste webpage URL

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🔑 API Keys

All projects use Groq models, which are:

• ✅ 100% FREE - No credit card required
• ⚡ Lightning Fast - Extremely fast inference
• 🔄 Generous Limits - Suitable for development and production

Get your free API key: console.groq.com/keys

Two Ways to Use API Keys:

1. Runtime Input (Recommended): Enter in the UI sidebar when the app starts
2. Environment Variable (Optional): Set in .env file for convenience

Most projects support entering the API key at runtime through the UI!

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📖 Documentation

Each project has its own detailed README:

• E2E_QA_ChatBot Documentation
• RAG_document_QA Documentation
• Text-summarization Documentation

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🤝 Contributing

Feel free to explore, modify, and extend these projects for your own use cases.

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📝 Notes

• Projects are independent and can be run separately
• Shared dependencies are in the root requirements.txt
• Environment variables can be set globally in root .env
• Each project may have specific setup requirements (see individual READMEs)

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🐛 Troubleshooting

Common Issues:

1. Import errors: Make sure virtual environment is activated and dependencies are installed
2. API key errors: Check .env file exists with correct key names
3. Ollama errors: Ensure Ollama is running with ollama serve
4. Port conflicts: Change ports in respective app files if 8000/8501 are in use

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