🧠 AI Knowledge Assistant

A Retrieval-Augmented Generation (RAG) application built with Python, LangChain, Google Gemini, and vector embeddings. The assistant retrieves relevant information from a document collection and generates accurate, context-aware responses using a Large Language Model (LLM).

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🚀 Features

• Semantic document search using embeddings
• Retrieval-Augmented Generation (RAG)
• Google Gemini integration
• Modular project architecture
• Document chunking
• Vector-based retrieval
• Command-line interface
• Easily extendable for PDFs, local folders, and web applications

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🛠 Tech Stack

• Python
• LangChain
• Google Gemini API
• Vector Embeddings
• FAISS / Vector Store
• python-dotenv

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📂 Project Structure

AI-Knowledge-Assistant/
│
├── app.py
├── requirements.txt
├── README.md
├── .gitignore
│
├── data/
│   └── documents/
│       ├── docker.txt
│       ├── fastapi.txt
│       └── langchain.txt
│
├── utils/
│   ├── chunker.py
│   ├── embeddings.py
│   ├── load_documents.py
│   ├── prompt_builder.py
│   └── retriever.py
│
└── vector_store/
    └── vector_store.py

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⚙️ How It Works

1. Load documents
2. Split documents into chunks
3. Generate embeddings
4. Store embeddings in a vector store
5. Retrieve the most relevant chunks
6. Send retrieved context to Gemini
7. Generate an answer grounded in the retrieved documents

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📦 Installation

Clone the repository

git clone https://github.com/YOUR_USERNAME/AI-Knowledge-Assistant.git

Create a virtual environment

python -m venv venv

Activate it

Windows

venv\Scripts\activate

Install dependencies

pip install -r requirements.txt

Create a .env file

GEMINI_API_KEY=YOUR_API_KEY

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▶️ Run

python app.py

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💬 Example Questions

• What is LangChain?
• Explain Docker.
• What is FastAPI?
• Compare Docker and FastAPI.

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🎯 Skills Demonstrated

• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• LLM Integration
• Vector Embeddings
• Semantic Search
• Modular Python Development
• API Integration
• Git & GitHub

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🔮 Future Improvements

• PDF support
• Local folder indexing
• Chat memory
• FastAPI backend
• Streamlit web interface
• Multi-agent workflow
• MCP integration
• Persistent vector database

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👩‍💻 Author

Vasudha Pasumarthy