🤖 Generative AI – Applied Projects and Demos

This repository is a comprehensive collection of Generative AI projects, covering LLM applications, RAG pipelines, fine-tuning, Hugging Face demos, AWS Bedrock, Google VertexAI, LangChain, LlamaIndex, and more.
It is designed for researchers, developers, and enthusiasts exploring the practical side of Generative AI with hands-on implementations.

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🚀 Features

• 🔎 RAG Pipelines – Context-aware retrieval with LangChain, LlamaIndex, Pinecone, ChromaDB, Weaviate.
• 🧠 LLM Applications – Chatbots, MySQL query assistant, interview bot, custom web assistants.
• 🎨 Generative Models – Text-to-Image (Stable Diffusion, DALL·E), Text-to-Speech (BARK), Summarization (Pegasus).
• ⚡ Fine-tuning – LLaMA 2, Falcon, GPT-3, Gemini, and VertexAI-based models.
• ☁️ Cloud Integrations – AWS Bedrock, Google VertexAI demos.
• 🛠️ MLOps & LLMOps – Deployment-ready workflows, CI/CD with Docker, and modular architecture.

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📂 Project Structure

The repository is organized by frameworks, models, and use cases:

• CHAT-MYSQL-GUI/ → LLM-powered SQL assistant with Chainlit
• ChatBot using ChainLit/ → Conversational AI chatbot
• Data Preprocessing and Embeddings/ → NLP preprocessing, embeddings, ML classification
• Finetuning_LLMs/ → LLaMA 2 fine-tuning, Gemini tutorials
• HuggingFace/ → Summarization, Text-to-Image, Text-to-Speech
• LLMOps/ → AWS Bedrock integrations, GenAI project with FAISS
• LLM_Application using VertexAI/ → Apps & RAG with Google VertexAI
• Langchain/ → Multi-agent, interview bot, custom chatbot with LangChain
• LlamaIndex/ → Stock analysis & retrieval demos with LlamaIndex
• MedBot/ → Medical assistant with Pinecone + Flask
• Open Source LLMs/ → Falcon 7B with LangChain & ChromaDB
• OpenAI/ → Whisper (speech-to-text), DALL·E, fine-tuning, chatbot demos
• RAG/ → End-to-end RAG pipelines with OpenAI, Gemini
• SourceCode Analyser/ → AI-powered code analysis assistant
• Vector Databases/ → ChromaDB, Pinecone, Weaviate demos
• GANs/ → Basic GANs, DCGAN for image generation

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🛠️ Tech Stack

• Languages: Python, SQL
• Frameworks & Libraries: LangChain, LlamaIndex, Hugging Face, Chainlit, Streamlit
• Vector Databases: Pinecone, ChromaDB, Weaviate, FAISS
• Models: OpenAI GPT, LLaMA 2, Falcon, Gemini, Pegasus, Whisper, Stable Diffusion, BARK
• Cloud Platforms: AWS Bedrock, Google VertexAI
• DevOps: Docker, GitHub Actions

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⚡ Getting Started

1. Clone the repository:

   git clone https://github.com/PRANAVBALAJIRS/Generative-AI.git
   cd Generative-AI
   

2. Create a virtual environment and install dependencies:

    python -m venv venv
    source venv/bin/activate  # (Linux/Mac)
    venv\Scripts\activate     # (Windows)
    pip install -r requirements.txt
   
   

3. Run individual projects (example):

   cd MedBot
   python app.py