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title: RAG Chatbot
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
---
# Physical AI RAG Backend
FastAPI backend for the Physical AI textbook RAG chatbot.
## Features
- **RAG Pipeline**: Retrieval-Augmented Generation using Cohere API
- **Vector Search**: Qdrant for semantic search
- **Conversation Storage**: Neon Postgres for chat history
- **Text Selection Context**: Support for querying with selected text
## Tech Stack
- FastAPI (Python 3.11+)
- Cohere API (embeddings + generation)
- Qdrant Cloud (vector database)
- Neon Serverless Postgres (conversation storage)
## Setup
### 1. Install Dependencies
```bash
cd backend
pip install -r requirements.txt
```
### 2. Configure Environment
Copy `.env.example` to `.env` and fill in your credentials:
```bash
cp .env.example .env
```
Required environment variables:
- `COHERE_API_KEY`: Your Cohere API key
- `QDRANT_URL`: Qdrant cluster URL
- `QDRANT_API_KEY`: Qdrant API key
- `NEON_DATABASE_URL`: Neon Postgres connection string
- `FRONTEND_URL`: Frontend URL for CORS
### 3. Setup Database
Run the schema on your Neon database:
```bash
psql $NEON_DATABASE_URL < app/db/schema.sql
```
### 4. Ingest Content
Parse MDX files and upload to Qdrant:
```bash
python scripts/ingest_content.py
```
This will:
- Parse all 11 chapters from `docs/chapters/`
- Create ~80-100 semantic chunks
- Generate embeddings via Cohere
- Upload to Qdrant
### 5. Run Server
```bash
uvicorn app.main:app --reload --port 8000
```
API will be available at `http://localhost:8000`
## API Endpoints
### Chat
**POST /api/chat/query**
```json
{
"query": "What is Physical AI?",
"conversation_id": "uuid-optional",
"filters": { "chapter": 1 }
}
```
**POST /api/chat/query-with-context**
```json
{
"query": "Explain this",
"selected_text": "Physical AI systems...",
"selection_metadata": {
"chapter_title": "Introduction",
"url": "/docs/chapters/physical-ai-intro"
}
}
```
**POST /api/chat/conversations**
Create a new conversation.
**GET /api/chat/conversations/{id}**
Get conversation with all messages.
### Health
**GET /api/health**
Basic health check.
**GET /api/health/detailed**
Detailed health check with database status.
## Deployment
### Railway (Recommended)
1. Create Railway project
2. Connect GitHub repo
3. Set environment variables
4. Deploy command: `uvicorn app.main:app --host 0.0.0.0 --port $PORT`
### Render
1. Create new Web Service
2. Connect GitHub repo
3. Build command: `pip install -r requirements.txt`
4. Start command: `uvicorn app.main:app --host 0.0.0.0 --port $PORT`
## Project Structure
```
backend/
├── app/
│ ├── main.py # FastAPI app
│ ├── config.py # Settings
│ ├── models/
│ │ ├── chat.py # Chat models
│ │ └── document.py # Document models
│ ├── services/
│ │ ├── embeddings.py # Cohere embeddings
│ │ ├── generation.py # Cohere generation
│ │ ├── retrieval.py # Qdrant search
│ │ └── rag_pipeline.py # Main RAG logic
│ ├── db/
│ │ ├── postgres.py # Neon client
│ │ ├── qdrant.py # Qdrant client
│ │ └── schema.sql # Database schema
│ └── api/
│ └── routes/
│ ├── chat.py # Chat endpoints
│ └── health.py # Health endpoints
├── scripts/
│ └── ingest_content.py # Content ingestion
└── requirements.txt
```
## Development
Run with auto-reload:
```bash
uvicorn app.main:app --reload
```
View API docs:
- Swagger UI: http://localhost:8000/docs
- ReDoc: http://localhost:8000/redoc
## Cost Estimate
- Cohere: ~$5-10/month (moderate usage)
- Qdrant Cloud: Free (1GB tier)
- Neon Postgres: Free tier
- Railway: Free (500 hours/month)
**Total: ~$5-10/month**
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