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| title: Student Support Chatbot | |
| emoji: 🎓 | |
| colorFrom: purple | |
| colorTo: indigo | |
| sdk: docker | |
| pinned: false | |
| # 🎓 Student Support Chatbot | |
| A fully deployed AI-powered RAG (Retrieval-Augmented Generation) chatbot that answers student queries about admissions, fees, courses, campus life, and more. Built with LangChain, Google Gemini, Pinecone, and Flask. | |
| --- | |
| ## 🏗️ Architecture | |
| ``` | |
| Student Question | |
| ↓ | |
| HuggingFace Embeddings (all-MiniLM-L6-v2) | |
| ↓ | |
| Pinecone Vector DB (semantic search → top 3 results) | |
| ↓ | |
| Google Gemini LLM (generates final answer) | |
| ↓ | |
| Flask Web App (chat interface) | |
| ``` | |
| **Dataset**: [`bot-remains/student-assistance-chatbot`](https://huggingface.co/datasets/bot-remains/student-assistance-chatbot) — 217 student Q&A pairs covering admissions, eligibility, fees, academics, and campus life. | |
| --- | |
| ## 🛠️ Tech Stack | |
| | Layer | Technology | | |
| | --------------- | ------------------------------------ | | |
| | LLM | Google Gemini 2.5 Flash | | |
| | Embeddings | HuggingFace `all-MiniLM-L6-v2` | | |
| | Vector Database | Pinecone | | |
| | Framework | LangChain | | |
| | Web App | Flask | | |
| | Deployment | AWS App Runner + Amazon ECR / Docker | | |
| --- | |
| ## 🚀 How to Run Locally | |
| ### Step 1: Clone the repository | |
| ```bash | |
| git clone https://github.com/aak007/Build-a-Complete-Medical-Chatbot-with-LLMs-LangChain-Pinecone-Flask-AWS.git | |
| cd Build-a-Complete-Medical-Chatbot-with-LLMs-LangChain-Pinecone-Flask-AWS | |
| ``` | |
| ### Step 2: Create and activate conda environment | |
| ```bash | |
| conda create -n medibot python=3.10 -y | |
| conda activate medibot | |
| ``` | |
| ### Step 3: Install dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### Step 4: Set up environment variables | |
| Create a `.env` file in the root directory: | |
| ```ini | |
| PINECONE_API_KEY=your_pinecone_api_key | |
| GOOGLE_API_KEY=your_gemini_api_key | |
| # Optional: choose your index name | |
| PINECONE_INDEX_NAME=student-chatbot | |
| # Dataset source options: hf | local | pdf | |
| DATA_SOURCE=hf | |
| # If DATA_SOURCE=hf | |
| HF_DATASET_NAME=bot-remains/student-assistance-chatbot | |
| # If DATA_SOURCE=local (csv/json/jsonl/txt/md) | |
| LOCAL_DATASET_PATH=data/my_dataset.csv | |
| TEXT_COLUMNS=question,answer | |
| # If DATA_SOURCE=pdf (single file or folder) | |
| # LOCAL_DATASET_PATH=data/student_buddy_qa.pdf | |
| ``` | |
| ### Step 5: Index the dataset into Pinecone _(Run only once)_ | |
| ```bash | |
| python store_index.py | |
| ``` | |
| This downloads the student Q&A dataset from HuggingFace and stores it as vector embeddings in your Pinecone `student-chatbot` index. | |
| ### Use Your Own Dataset | |
| You can replace the default dataset with your own data and re-index it. | |
| 1. Put your file inside the project, for example `data/my_dataset.csv`. | |
| 2. Update `.env`: | |
| ```ini | |
| DATA_SOURCE=local | |
| LOCAL_DATASET_PATH=data/my_dataset.csv | |
| TEXT_COLUMNS=question,answer | |
| PINECONE_INDEX_NAME=my-student-bot-index | |
| ``` | |
| 3. Rebuild vectors: | |
| ```bash | |
| python store_index.py | |
| ``` | |
| 4. Start app: | |
| ```bash | |
| python app.py | |
| ``` | |
| Supported local file formats: | |
| - `csv` (recommended) | |
| - `json` (array of objects) | |
| - `jsonl` (one JSON object per line) | |
| - `txt` / `md` (split by blank lines) | |
| For PDF knowledge bases: | |
| ```ini | |
| DATA_SOURCE=pdf | |
| LOCAL_DATASET_PATH=data/student_buddy_qa.pdf | |
| PINECONE_INDEX_NAME=student-buddy-index | |
| ``` | |
| Then run: | |
| ```bash | |
| python store_index.py | |
| python app.py | |
| ``` | |
| Example CSV schema: | |
| ```csv | |
| question,answer,category | |
| What is eligibility for B.Tech?,Candidates must pass 10+2 with PCM and required cutoff,admissions | |
| What are hostel fees?,Hostel fees vary by room type and campus policy,fees | |
| ``` | |
| ### Step 6: Start the app | |
| ```bash | |
| python app.py | |
| ``` | |
| ### Step 7: Open in browser | |
| ``` | |
| http://localhost:7860 | |
| ``` | |
| --- | |
| ## ☁️ Deployment | |
| ### AWS App Runner + ECR (Recommended) | |
| ```bash | |
| # 1. Authenticate Docker to AWS ECR | |
| aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <account-id>.dkr.ecr.us-east-1.amazonaws.com | |
| # 2. Build Docker image | |
| docker build --no-cache -t medical-bot . | |
| # 3. Tag image | |
| docker tag medical-bot:latest <account-id>.dkr.ecr.us-east-1.amazonaws.com/medical-bot:latest | |
| # 4. Push to ECR | |
| docker push <account-id>.dkr.ecr.us-east-1.amazonaws.com/medical-bot:latest | |
| ``` | |
| Then deploy via AWS App Runner, set **port 7860**, and add your `PINECONE_API_KEY` and `GOOGLE_API_KEY` as environment variables. | |
| --- | |
| ## 📁 Project Structure | |
| ``` | |
| ├── app.py # Flask web server | |
| ├── store_index.py # Downloads dataset & indexes into Pinecone | |
| ├── Dockerfile # Docker config for deployment | |
| ├── requirements.txt # Python dependencies | |
| ├── setup.py | |
| ├── src/ | |
| │ ├── helper.py # load_hf_dataset(), text_split(), embeddings | |
| │ └── prompt.py # System prompt for student support | |
| ├── static/ | |
| │ └── style.css # Premium student-themed UI | |
| └── templates/ | |
| └── chat.html # Chat interface | |
| ``` | |
| --- | |
| ## 🔑 Required API Keys | |
| | Key | Where to Get | | |
| | ------------------ | ------------------------------------------------------------- | | |
| | `PINECONE_API_KEY` | [app.pinecone.io](https://app.pinecone.io/) | | |
| | `GOOGLE_API_KEY` | [aistudio.google.com](https://aistudio.google.com/app/apikey) | | |