| |
|
| | --- |
| | title: "Odisha Disaster RAG Chatbot" |
| | emoji: "🌊" |
| | colorFrom: green |
| | colorTo: yellow |
| | sdk: docker |
| | app_file: app.py |
| | pinned: false |
| | license: mit |
| | --- |
| | # 🌀 Odisha Disaster Management RAG Chatbot |
| |
|
| | ## 📌 Overview |
| | Odisha faces recurring disasters every year such as **floods, cyclones, and droughts**. |
| | While the state has a strong disaster management authority (OSDMA), information is often scattered across reports, research papers, and government documents. |
| |
|
| | This project builds a **Retrieval-Augmented Generation (RAG) based chatbot** that provides citizens, researchers, and policymakers with **clear, reliable, and contextual answers** related to Odisha’s disaster management practices. |
| |
|
| | --- |
| |
|
| | ## ✨ Features |
| | - Handles **132 PDFs** and **12 text files** (OSDMA, IMD, NDMA, research papers). |
| | - **Preprocessing pipeline**: PDF/text extraction, cleaning, normalization, chunking. |
| | - **Embeddings** with `sentence-transformers/all-MiniLM-L6-v2`. |
| | - **FAISS Vector Database** for fast and efficient retrieval. |
| | - **RAG pipeline**: |
| | 1. User query → query structuring (handles poor English, spelling issues). |
| | 2. Retrieve relevant chunks from FAISS. |
| | 3. If no relevant results → no LLM call (saves cost). |
| | 4. If relevant → LLM generates structured, contextual answers. |
| | - **Prompt engineering** for better accuracy and reduced hallucinations. |
| | - Backend: **FastAPI**. |
| | - Frontend: **HTML, CSS, JS chatbot interface**. |
| |
|
| | --- |
| |
|
| | ## 🏗️ Architecture |
| |
|
| | **User Query → Query Structuring → FAISS Retriever → Relevant Chunks → LLM → Answer** |
| |
|
| | # 🛠️ Tech Stack |
| |
|
| | - **Python** (data handling & backend) |
| | - **PyPDF, TextLoader** → PDF/Text extraction |
| | - **FAISS** → Vector database |
| | - **HuggingFace Sentence Transformers** → Embeddings |
| | - **FastAPI** → Backend API |
| | - **HTML, CSS, JavaScript** → Frontend chatbot UI |
| | - **LLM (OpenAI / HuggingFace)** → Answer generation |
| |
|
| | --- |
| |
|
| | ## ⚙️ Installation |
| |
|
| | ### 1. Clone the repository |
| | ```bash |
| | git clone https://github.com/subhakanta156/odisha-disaster-knowledge-assistant.git |
| | ``` |
| | ### 2. Create virtual environment & install dependencies |
| | ```bash |
| | python -m venv venv |
| | source venv/bin/activate # Linux/Mac |
| | venv\Scripts\activate # Windows |
| | |
| | pip install -r requirements.txt |
| | ``` |
| | ### 3. Prepare the data |
| | - Place all PDFs/text files inside the data/ folder. |
| | - Run preprocessing & embedding script: |
| | ```bash |
| | python scripts/build_vector_store.py |
| | ``` |
| | ### 4. Run the FastAPI backend |
| | ```bash |
| | uvicorn app.main:app --reload |
| | ``` |
| | ### 5. Open the frontend |
| | - Open `frontend/index.html` in your browser. |
| | |
| | ## 🚀 Usage |
| |
|
| | Ask questions like: |
| |
|
| | - “How does Odisha’s disaster proneness compare with other Indian states?” |
| | - “Provide details of relief funds sanctioned for Odisha during the 1999 Super Cyclone.” |
| | - “Which Odisha agency is primarily responsible for issuing cyclone alerts?” |
| | - “Explain the key steps taken by the Odisha government if lives are lost in a disaster?” |
| |
|
| |
|
| | The system retrieves relevant chunks from reports and generates reliable, structured answers. |
| |
|
| | --- |
| |
|
| | ## 📊 Optimizations |
| |
|
| | - Added query filtering → No LLM call if retrieval fails (reduces cost). |
| | - Handled poor English queries via query restructuring. |
| | - Improved prompt engineering to minimize hallucinations. |
| |
|
| | --- |
| |
|
| | ## 📌 Future Improvements |
| |
|
| | - Add multilingual support (Odia/Hindi queries). |
| | - Deploy on cloud (AWS/GCP/Azure) with Docker. |
| | - Use advanced embeddings (e.g., `all-mpnet-base-v2`) for higher accuracy. |
| | - Add real-time updates (e.g., cyclone alerts). |
| |
|
| | --- |
| |
|
| | ## 👨💻 Author |
| |
|
| | **Subhakanta Rath** |
| |
|
| | MSc AI & ML @ IIIT Lucknow |
| |
|
| | Passionate about AI/ML, Data Engineering |
| |
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