GEN-AI 🚀

An end-to-end framework for building intelligent, modular, and production-ready AI systems using LangChain, LangGraph, and Retrieval-Augmented Generation (RAG).

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🧠 Overview

GEN-AI is a hands-on project focused on RAG pipelines, LangChain workflows, and AI agent orchestration.
It demonstrates how to build scalable and interpretable AI systems with structured logic, modular design, and custom retrievers.

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⚙️ Key Features

• Retrieval-Augmented Generation (RAG) — Ingestion, chunking, embedding, and hybrid vector search.
• LangChain & LangGraph Workflows — Sequential, parallel, and conditional chain execution.
• AI Agents — Custom logic agents and ReAct-style reasoning loops.
• Vector Databases — Integrations with Chroma, FAISS, and OpenSearch.
• Structured Outputs — Pydantic-based output formatting and validation.
• Interactive Notebooks — Ready-to-run Jupyter notebooks for experiments.

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🧩 Project Structure

GEN-AI: 0-Data Ingestion and Parsing: - data_parsing_pdf.ipynb - Hybrid Retriver.ipynb - chroma-db/ Langchain: - simple_chain.py - parallel_chains.py - conditional_chains.py - chatbot.py LangGraph: - Simple_LangGraph.ipynb - LLM_Powered_LangGraph.ipynb Projects: - AI_Powered_LangGraph.ipynb Vector Embedding and Databases: - Embeddings.ipynb

• main.py
• pyproject.toml
• requirements.txt
• README.md

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🛠️ Tech Stack

• Python 3.13
• LangChain
• LangGraph
• Chroma / FAISS / OpenSearch
• Pydantic
• Jupyter Notebooks
• OpenAI / Hugging Face APIs

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🚀 Setup Instructions

# Clone the repository
git clone https://github.com/HammadAli08/GEN-AI.git
cd GEN-AI

# (Optional) create virtual environment
python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run main script
python main.py