🤖 GenAI Q&A Chatbot System

This repository hosts an intelligent and scalable Question-Answering (QA) chatbot system that combines the strengths of transformer-based models and retrieval-augmented generation (RAG) to provide context-aware, accurate responses to natural language queries.

Designed for real-world applications in customer service, knowledge bases, education, and enterprise automation, this project seamlessly integrates deep learning with vector search to achieve high performance and reliability.

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🔍 Why This Project?

🧩 The Problem: Pretrained AI Isn’t Always Enough Pretrained language models like BERT and GPT are powerful, but they have a limitation:

They only "know" what they were trained on — and that training data has a cutoff.

In real-world use cases like customer support, academic help, or domain-specific knowledge (e.g., healthcare, legal, enterprise), answers must be accurate, up-to-date, and based on external documents. Out-of-the-box models can hallucinate, give vague answers, or ignore context completely.

💡 The Solution: Build a Smart Q&A System with Context

To solve this, I built a Retrieval-Augmented Generation (RAG)-based Question Answering System from scratch. Here's how I approached the solution:

✅ Step 1: Fine-Tuned a BERT Model

I started with BERT (a pre-trained transformer) and fine-tuned it on a custom Q&A dataset. This helped the model:

Understand my domain better (not just general English)

Improve the precision of answer extraction from context

✅ Step 2: Created a Semantic Search Engine with FAISS

I used sentence-transformers to turn every document/context into a vector — a mathematical representation of its meaning. Then I stored those in a FAISS index, so when a user asks a question, the system:

Finds the top matching contexts

Sends them to the QA model as supporting evidence

✅ Step 3: Integrated It All with FastAPI

To make it usable, I wrapped everything in a FastAPI backend. Now users can:

Ask questions via API or frontend

Get fast, accurate answers in real-time

✅ Step 4: Performance Optimization

To make it lightweight and production-ready:

I applied dynamic quantization to reduce the model size and speed up inference

Ensured it supports scalable batch processing

🔨 What I Did in This Project

This wasn’t just plug-and-play — I actually engineered each layer:

Component	What I Did
Fine-tuning	Curated dataset, encoded QA pairs, trained and saved BERT-based QA model
Semantic Search	Used LangChain + FAISS to create and load a fast vector database
Backend Inference Logic	Wrote custom logic to connect search → context → BERT answer
Optimization	Quantized model for lower latency and smaller memory footprint
API Integration	Built FastAPI app with endpoints for QA + optional Streamlit interface
Documentation & ReadMe	Structured the project, wrote clear instructions, and explained architecture

🧠 In Simple Terms:

"You ask a question → the system finds relevant documents → the AI model reads them → it gives you a precise answer — just like a smart assistant who knows where to look."

✨ Key Features

✅ Fine-tuned BERT for domain-specific QA
✅ Retrieval-Augmented Generation for grounded answers
✅ FAISS-based fast semantic context retrieval
✅ API-powered and optionally UI-enabled deployment
✅ Quantization-enabled model for faster inference
✅ Modular, well-documented codebase

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🧠 System Architecture

                   +------------------------+
                   |   User Asks Question   |
                   +-----------+------------+
                               |
                               v
           +------------------+-------------------+
           |  Semantic Search (FAISS + Embeddings) |
           +------------------+-------------------+
                               |
                      Retrieved Context
                               |
                               v
           +------------------+-------------------+
           |  BERT QA Model (Fine-tuned)          |
           +------------------+-------------------+
                               |
                          Generated Answer
                               |
                               v
                   +------------------------+
                   |   Delivered to User    |
                   +------------------------+

This pipeline ensures both speed and accuracy by blending retrieval-based context with transformer-powered reasoning.

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🛠️ Technologies Used

Tool/Library	Role
transformers	Pre-trained BERT model + QA fine-tuning
sentence-transformers	High-quality sentence embeddings
faiss	Approximate nearest neighbor search
langchain	Vector store management and retrieval
pytorch	Training and inference
FastAPI	Real-time backend API
Streamlit	Optional web interface
torch.quantization	Inference acceleration

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📁 Project Structure

GenAI_QA_System/
├── data/
│   └── qa_dataset.json             # Custom Q&A training data
│
├── model/
│   ├── fine_tune.py               # Fine-tune BERT script
|   ├── run_inference.py           # Main RAG-based inference
│
├── api/
│   └── app.py                    # FastAPI entrypoint
└── optimized_inference.py         # Faster inference with quantized model
|── create_faiss_index.py          # Build FAISS vector store
├── requirements.txt               # All dependencies
└── README.md                      # You’re reading it!

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🚀 Quick Start

✅ Installation

git clone https://github.com/ansarimzp/GenAI_QA_System.git
cd GenAI_QA_System
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

🎓 Model Training & Index Creation

# Fine-tune the model on your custom dataset
python src/fine_tune.py

# Build the FAISS index
python src/create_faiss_index.py

🚦 Run the Backend API

uvicorn app.main:app --reload

Access the API at http://localhost:8000

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📡 API Usage

POST /ask

{
  "question": "What does BERT stand for?"
}

Response:

{
  "answer": "BERT stands for Bidirectional Encoder Representations from Transformers.",
  "confidence": 0.94
}

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💬 Interactive Streamlit UI

streamlit run app/ui.py

Navigate to http://localhost:8501 in your browser and start asking questions with a simple interface.

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🔧 Performance Optimization

• ✅ Model Quantization using torch.quantization to reduce memory and boost speed
• ⚡ FAISS-based Retrieval for sub-second document search
• ♻️ Context Caching and batched tokenization to reduce compute time

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🧪 Customize with Your Data

You can fine-tune the system to your domain:

1. Prepare a JSON dataset:

[
  {
    "context": "Tesla was founded in 2003 by engineers...",
    "question": "When was Tesla founded?",
    "answer": "2003"
  }
]

2. Fine-tune:

python src/fine_tune.py --data_path ./data/your_dataset.json

3. Re-index:

python src/create_faiss_index.py --data_path ./data/your_dataset.json

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🌱 Future Enhancements

• ☑️ Support for multilingual QA
• 🔄 Conversational memory with history
• 📈 Real-time feedback loop for answer quality
• 🐳 Docker-based deployment for portability
• 🤝 Integration with vector-capable databases (e.g., Weaviate, Pinecone)

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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🙏 Acknowledgements

• 🤗 Hugging Face for providing transformer models
• 🧠 LangChain for chaining logic and vector store wrappers
• 🔍 Facebook Research for FAISS
• 🚀 The open-source community for continued innovation

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Ready to build your own domain-specific AI Q&A engine?
Fork this project, fine-tune with your knowledge base, and deploy it in minutes.

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Let me know if you'd like me to generate this as a downloadable `README.md` file or help create the `ui.py` Streamlit interface.