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Gen AI Projects

A comprehensive collection of AI-powered applications built with LangChain, Streamlit, and various free LLM APIs. This repository showcases practical implementations of chatbots, RAG systems, SQL agents, search engines, and text summarization tools.

🚀 Projects

1. Q&A Chatbot

Location: Q&A_chatbot/

A simple question-answering chatbot with a clean Streamlit interface.

Features:

  • Direct conversational interface
  • Powered by free OpenRouter models (Google Gemini Flash, Mistral)
  • Clean and intuitive UI
  • Configurable response parameters
  • Multiple model implementations (OpenRouter, Ollama)

Files:

  • app.py - Main Streamlit application
  • test_api.py - API connection testing
  • find_free_models.py - Discover available free models
  • ollamaapp.py - Ollama integration variant
  • test_free_models.py - Test free model availability
  • test_params.py - Parameter configuration testing

Run:

cd Q&A_chatbot
streamlit run app.py

2. RAG Q&A Chatbot

Location: rag_q&a_chatbot/

A Retrieval-Augmented Generation (RAG) chatbot that answers questions from uploaded PDF documents.

Features:

  • PDF document upload and processing
  • FAISS vector store for efficient retrieval
  • Custom embeddings implementation
  • Context-aware responses from documents
  • Question answering with source context

Files:

  • ragapp.py - Main RAG application
  • pdfs/ - Directory for PDF documents

Run:

cd rag_q&a_chatbot
streamlit run ragapp.py

3. Conversational Q&A Chatbot with PDF

Location: conversational_q&a_chatbot/

An advanced conversational chatbot with PDF support and persistent chat history.

Features:

  • Upload and query PDF documents
  • Session-based chat history
  • Conversational memory (remembers previous messages)
  • RAG-based responses with context
  • Streamlit Cloud ready
  • Free AI models via OpenRouter

Files:

  • app.py - Main application
  • requirements.txt - Python dependencies
  • README.md - Project-specific documentation

Run:

cd conversational_q&a_chatbot
streamlit run app.py

4. Chat with SQL Database

Location: chat_with_sqldb/

A natural language interface for SQL databases powered by LangChain SQL agents and Groq's LLaMA model.

Features:

  • 🗣️ Natural language to SQL query conversion
  • 🗄️ Multiple database support (SQLite and MySQL)
  • 🤖 AI-powered SQL agent using Groq LLaMA 3.3 70B
  • 💬 Interactive chat interface with message history
  • 🔍 Automatic query generation and execution

Files:

  • app.py - Main Streamlit application
  • sqlite.py - SQLite database utilities
  • requirements.txt - Dependencies
  • README.md - Detailed documentation
  • studentdb.db - Sample SQLite database

Run:

cd chat_with_sqldb
streamlit run app.py

Usage:

  1. Choose between SQLite (default) or MySQL
  2. Enter your Groq API key
  3. Ask questions about your database in plain English
  4. View SQL queries and results

5. AI-Powered Search Engine

Location: search_engine/

An intelligent search application that uses LangChain agents to search across multiple sources (web, academic papers, and Wikipedia).

Features:

  • 🔍 Multi-source search (DuckDuckGo, ArXiv, Wikipedia)
  • 🤖 Intelligent agent-based query routing
  • 🚀 Powered by Llama3-8B via Groq API
  • 💬 Streaming responses for better UX
  • 🎯 Context-aware tool selection
  • 📚 Academic paper search via ArXiv

Files:

  • app.py - Main Streamlit application
  • requirements.txt - Dependencies
  • README.md - Documentation
  • START_HERE.txt - Quick start guide
  • tools_agents.ipynb - Jupyter notebook with examples

Run:

cd search_engine
streamlit run app.py

Tools Available:

  • DuckDuckGo: General web search
  • ArXiv: Academic research papers
  • Wikipedia: Encyclopedia knowledge

6. Text Summarization

Location: summerize_text/

A Jupyter notebook demonstrating text summarization capabilities using Groq's LLaMA model.

Features:

  • Speech/text summarization
  • Powered by Groq LLaMA 3.3 70B
  • Expert system prompts for concise summaries
  • Interactive Jupyter notebook environment

Files:

  • text_summerization.ipynb - Main notebook with examples

Run:

cd summerize_text
jupyter notebook text_summerization.ipynb

🛠️ Tech Stack

Core Technologies:

  • Python 3.13+ - Programming language
  • Streamlit - Web UI framework
  • LangChain - LLM framework and chains
  • FAISS - Vector database for embeddings
  • PyPDF - PDF document processing
  • NumPy - Numerical operations for embeddings
  • python-dotenv - Environment variable management

AI Models & APIs:

  • OpenRouter API - Free AI models (Gemini, Mistral)
  • Groq API - Fast LLaMA model inference
  • SQLAlchemy - Database connections and ORM

Additional Tools:

  • ArXiv API - Academic paper search
  • Wikipedia API - Knowledge base queries
  • DuckDuckGo Search - Web search
  • Jupyter Notebook - Interactive development

📋 Prerequisites

  • Python 3.8+ (Python 3.13 recommended)
  • API Keys:
  • Virtual environment (recommended)

⚙️ Setup

1. Clone the Repository

git clone https://github.com/itsmeayan45/gen-ai.git
cd Gen-ai-projects

2. Create Virtual Environment

# Create virtual environment
python -m venv genvenv

# Activate (Windows)
genvenv\Scripts\activate

# Activate (Linux/Mac)
source genvenv/bin/activate

3. Install Dependencies

# Install all dependencies
pip install -r requirements.txt

4. Configure API Keys

Create a .env file in the project root:

OPENROUTER_API_KEY=your_openrouter_api_key_here
GROQ_API_KEY=your_groq_api_key_here
LANGCHAIN_API_KEY=your_langchain_api_key_here  # Optional

5. Run Any Project

Navigate to the project folder and run:

# Example: Run Q&A Chatbot
cd Q&A_chatbot
streamlit run app.py

# Example: Run Search Engine
cd search_engine
streamlit run app.py

# Example: Run SQL Chat
cd chat_with_sqldb
streamlit run app.py

🌐 Free AI Models Used

This repository uses completely free AI models:

OpenRouter (No Cost):

  • google/gemini-2.0-flash-exp:free
  • mistralai/mistral-7b-instruct:free
  • tngtech/tng-r1t-chimera:free

Groq (Free Tier):

  • llama-3.3-70b-versatile
  • llama3-8b-8192

No API costs required for basic usage! 🎉

📁 Project Structure

Gen-ai-projects/
├── Q&A_chatbot/                    # Basic Q&A chatbot
│   ├── app.py                      # Main application
│   ├── ollamaapp.py               # Ollama variant
│   ├── test_api.py                # API testing
│   └── find_free_models.py        # Model discovery
│
├── rag_q&a_chatbot/               # RAG chatbot with PDF
│   ├── ragapp.py                  # Main application
│   └── pdfs/                      # PDF storage
│
├── conversational_q&a_chatbot/    # Advanced conversational chatbot
│   ├── app.py                     # Main application
│   ├── requirements.txt           # Dependencies
│   └── README.md                  # Documentation
│
├── chat_with_sqldb/               # SQL database chatbot
│   ├── app.py                     # Main application
│   ├── sqlite.py                  # SQLite utilities
│   ├── studentdb.db              # Sample database
│   └── README.md                  # Documentation
│
├── search_engine/                 # AI-powered search
│   ├── app.py                     # Main application
│   ├── tools_agents.ipynb        # Examples notebook
│   └── README.md                  # Documentation
│
├── summerize_text/                # Text summarization
│   └── text_summerization.ipynb  # Jupyter notebook
│
├── genvenv/                       # Virtual environment
├── app.py                         # Root chatbot app
├── requirements.txt               # Global dependencies
├── .env                           # API keys (not in git)
├── .gitignore                     # Git exclusions
├── LICENSE                        # MIT License
└── README.md                      # This file

🚢 Deployment

Streamlit Cloud Deployment

Most projects are ready for deployment on Streamlit Cloud:

  1. Push to GitHub
  2. Connect to share.streamlit.io
  3. Add API keys in Streamlit secrets: 
    OPENROUTER_API_KEY = "your_key_here"
GROQ_API_KEY = "your_key_here"
  4. Deploy!

For detailed deployment instructions, see individual project README files.

💡 Use Cases

  • Q&A Chatbots: Customer support, FAQs, general inquiries
  • RAG Systems: Document analysis, knowledge base queries, research assistance
  • SQL Chat: Natural language database queries, data analysis
  • Search Engine: Academic research, web search, knowledge discovery
  • Text Summarization: Document summarization, content compression

🤝 Contributing

Contributions are welcome! Feel free to:

  • Fork this repository
  • Create a feature branch
  • Submit pull requests
  • Report issues
  • Suggest improvements

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Ayan

🙏 Acknowledgments

  • LangChain - Amazing LLM framework
  • OpenRouter - Free AI model access
  • Groq - Fast LLM inference
  • Streamlit - Easy-to-use web framework
  • Open Source Community - For all the amazing tools and libraries

⭐ Star This Repository

If you find this project helpful, please give it a star! It helps others discover these resources.

Happy Coding! 🚀

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