🧠 Gen-AI: Exploring Generative AI with LangChain and OpenAI

Welcome to the Gen-AI repository! This project is a hands-on exploration of Generative AI, LLMs, and agentic architectures using tools like LangChain, OpenAI, and retrieval-augmented generation (RAG). The goal is to build intelligent, context-aware systems capable of conversational reasoning, knowledge extraction, and task execution.

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🚀 Project Objectives

• Build RAG pipelines using LangChain + FAISS
• Integrate OpenAI APIs for intelligent text generation
• Prototype multi-step agents with LangChain agents
• Explore prompt engineering and LLM evaluation
• Design domain-specific workflows for analytics and content tasks

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🧰 Tech Stack

• Python
• LangChain
• OpenAI GPT-3.5 / GPT-4 APIs
• FAISS (for vector search)
• Streamlit (for UI components)
• PyPDF2 / LangChain document loaders
• Jupyter Notebooks (for experimentation and EDA)

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📂 Repository Structure

Gen-AI/ │ ├── agents/ # Multi-agent and prompt engineering examples ├── app/ # Streamlit interfaces and demos ├── data/ # Example documents used for context ├── notebooks/ # Exploratory notebooks for LangChain, RAG, OpenAI ├── retriever/ # Vector DB and embedding logic ├── utils/ # Helper functions and wrappers └── requirements.txt # Python dependencies

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🧪 Sample Use Cases

• Conversational Q&A with uploaded PDFs
• Summarization of domain-specific documents
• Agent-based multi-step reasoning workflows
• Prompt optimization for business tasks

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🔧 Getting Started

1. Clone the repository:

git clone https://github.com/Shashwat1001/Gen-AI.git
cd Gen-AI

pip install -r requirements.txt


export OPENAI_API_KEY='your-api-key-here'


streamlit run app/main.py

Credits

Developed by Shashwat Shankar OpenAI, LangChain, and FAISS are acknowledged for powering the core components.

📫 Contact

For collaboration, suggestions, or questions, feel free to reach out: