GenerativeAI-Projects

Welcome to my Generative AI Projects repository! This repositoryy showcases end-to-end Generative AI and LLM-based applications, demonstrating how modern AI can automate reasoning, text understanding and decision-making tasks.

Each project focuses on practical, real-world applications of LangChain, LangGraph, CrewAI and RAG Pipelines, deployed with Streamlit for interactive use.

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Projects Overview

1. AI Ticket Traige System(LangGraph)

Automates the process of categorizing, summarizing, and routing healthcare support tickets using LangGraph and Groq's LLM.

The workflow intelligently classifies support issues, generated concise summaries, and assigns each ticket to the appropriate department - inproving efficiently and response time.

• Framework: LangGraph
• Model: Llama 3.1 8B (Groq API)
• Deployment: Streamlit
• Project Link

Key Features

• Classifies tickets into categories like Product Issue, Billing Issue, Login Issue, etc.
• Generates short summaries for each support ticket.
• Automatically routes isues to the correct department.
• Allows CSV upload and downloadable results.

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2. Business Consultant Analyst (CrewAI)

Business Consultant Analyst (CrewAI) is an AI-powered analytics system that reads datasets, performs Exploratory Data Analysis (EDA), generates visualizations, and produces business summaries with actionable insights.

• Framework: CrewAI
• Model: GPT-4o-mini
• Deployment: Local Python environment
• Project Link

Key Features

• Automatically reads and analyzes any CSV dataset.
• Performs statistical computations and correlation analysis.
• Generates charts using Matplotlib, Seaborn, or Plotly.
• Produces a professional business insights summary report.

Technologies Used

• Python 3.10
• Pandas, NumPy for data handling
• Matplotlib, Seaborn, Plotly for visualizations
• CrewAI for agent orchestration
• OpenAI LLM for generating insights

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3. QueryMyPDF

An intelligent RAG (Retrieval-Augmented Generation) system that allows users to upload PDFs and interactively chat with the document content.
The app uses embeddings, vector storage, and conversational memory for accurate context-based question answering.

• Frameworks: LangChain, Pinecone
• Models: Llama 3.1 (Groq), OpenAI Embeddings
• Deployment: Streamlit
• Project Link

Key Features

• Upload multiple PDFs and query them conversationally.
• Hybrid search using semantic (FAISS) and vector-based retrieval.
• Maintains conversation history with contextual responses.
• Built with LangChain, Pinecone Vector Store, and Groq API.

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Technologies & Libraries Used

• Programming Language: Python 3.10
• Frameworks & Tools:
  • LangChain, LangGraph, CrewAI – for building AI workflows
  • Groq API, OpenAI API – for LLM model inference
  • FAISS, Pinecone – for vector-based document retrieval
  • Streamlit – for web app deployment
  • pandas, numpy, matplotlib, seaborn, plotly – for data analysis and visualization
  • dotenv, tiktoken – for environment and token management