Feat/server route

.gitignore

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-.env
+.env
+target/
+*.lock
+.venv/
+__pycache__/

scrapper/START.md

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+# Scrapper Startup Guide
+
+This guide explains how to launch the technical watch scrapper system.
+
+## Prerequisites
+
+- **Python 3.9+**
+- **PostgreSQL** with **pgvector** extension
+- **OpenAI API Key** (for embeddings and entity extraction)
+- **(Optional)** GitHub Token for higher rate limits
+
+## Installation
+
+### 1. Create a Python Virtual Environment
+
+```bash
+cd scrapper
+python3 -m venv .venv
+source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+```
+
+### 2. Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Setup PostgreSQL with pgvector
+
+Install PostgreSQL and the pgvector extension:
+
+```bash
+# On Ubuntu/Debian
+sudo apt install postgresql postgresql-contrib
+sudo -u postgres psql -c "CREATE EXTENSION vector;"
+
+# Or using Docker
+docker run -d \
+  --name postgres-pgvector \
+  -e POSTGRES_PASSWORD=postgres \
+  -e POSTGRES_DB=veille_technique \
+  -p 5432:5432 \
+  pgvector/pgvector:pg16
+```
+
+### 4. Configure Environment Variables
+
+Copy the example environment file and configure it:
+
+```bash
+cp .env.example .env
+```
+
+Edit `.env` and set your credentials:
+
+```env
+OPENAI_API_KEY=your_openai_api_key_here
+DATABASE_URL=postgresql://postgres:postgres@localhost:5432/veille_technique
+EMBEDDING_MODEL=text-embedding-3-small
+GITHUB_TOKEN=your_github_token_here  # Optional
+```
+
+### 5. Initialize the Database
+
+The database schema will be created automatically on first run.
+
+## Running the Scrapper
+
+The scrapper has **3 modes**:
+
+### 1. Backfill Mode (Historical Data)
+
+Scrape entire available history from all sources:
+
+```bash
+python main.py backfill
+```
+
+Options:
+- `--limit N` - Maximum articles per source (default: 100)
+- `--db-url URL` - Override database URL
+- `--embedding-model MODEL` - Override embedding model
+- `--llm-model MODEL` - Override LLM model for entities
+
+Example with custom limit:
+```bash
+python main.py backfill --limit 200
+```
+
+### 2. Watch Mode (Continuous Monitoring)
+
+Scrape new articles continuously at regular intervals:
+
+```bash
+python main.py watch
+```
+
+Options:
+- `--interval SECONDS` - Scraping interval (default: 300s = 5 minutes)
+- `--db-url URL` - Override database URL
+- `--embedding-model MODEL` - Override embedding model
+- `--llm-model MODEL` - Override LLM model for entities
+
+Example with 10-minute interval:
+```bash
+python main.py watch --interval 600
+```
+
+Press `Ctrl+C` to stop the watch mode.
+
+### 3. Stats Mode (View Statistics)
+
+Display database statistics:
+
+```bash
+python main.py stats
+```
+
+## Available Scrapers
+
+The system includes scrapers for:
+
+- **ArXiv** - Scientific papers (cs.LG category by default)
+- **GitHub** - Trending repositories
+- **Medium** - Technical articles
+- **Le Monde** - News articles
+- **Hugging Face** - ML models and papers
+
+## Features
+
+Each scraped article is automatically:
+1. **Deduplicated** - By ID and content hash
+2. **Embedded** - Using OpenAI embeddings (for similarity search)
+3. **Analyzed** - Entities extracted via LLM (technologies, companies, people, etc.)
+
+## Configuration
+
+### Database Connection
+
+Set via environment variable or command-line:
+- Environment: `DATABASE_URL=postgresql://user:pass@host:port/dbname`
+- CLI: `--db-url postgresql://user:pass@host:port/dbname`
+
+### Embedding Model
+
+Configure the OpenAI embedding model:
+- Environment: `EMBEDDING_MODEL=text-embedding-3-small`
+- CLI: `--embedding-model text-embedding-3-small`
+
+Available models:
+- `text-embedding-3-small` (1536 dimensions, faster)
+- `text-embedding-3-large` (3072 dimensions, more accurate)
+
+### LLM Model
+
+Configure the LLM for entity extraction:
+- Environment: `LLM_MODEL=gpt-4o-mini`
+- CLI: `--llm-model gpt-4o-mini`
+
+## Troubleshooting
+
+### Missing OpenAI API Key
+
+```
+Error: OpenAI API key not found
+```
+
+**Solution**: Set `OPENAI_API_KEY` in your `.env` file.
+
+### PostgreSQL Connection Error
+
+```
+Error: could not connect to server
+```
+
+**Solution**: 
+1. Check PostgreSQL is running: `sudo systemctl status postgresql`
+2. Verify DATABASE_URL in `.env`
+3. Ensure pgvector extension is installed
+
+### Scraper Initialization Failed
+
+If a scraper fails to initialize, it will be skipped automatically. Check the logs for details.
+
+### Port Already in Use (PostgreSQL)
+
+If port 5432 is already used, either:
+1. Stop the conflicting service
+2. Use a different port in `DATABASE_URL`
+
+## Development Tips
+
+### Check Architecture
+
+See [ARCHITECTURE.md](ARCHITECTURE.md) for system design details.
+
+### Database Management
+
+View articles directly in PostgreSQL:
+```sql
+-- Connect to database
+psql $DATABASE_URL
+
+-- Count articles
+SELECT COUNT(*) FROM articles;
+
+-- View recent articles
+SELECT title, source, published_at FROM articles 
+ORDER BY published_at DESC LIMIT 10;
+
+-- Check embeddings
+SELECT COUNT(*) FROM embeddings;
+```
+
+## Recommended Workflow
+
+1. **Initial setup**: Run `backfill` mode once to populate historical data
+2. **Continuous monitoring**: Run `watch` mode to keep data up-to-date
+3. **Check progress**: Use `stats` mode to monitor collection
+
+Example:
+```bash
+# One-time: populate history
+python main.py backfill --limit 50
+
+# Continuous: monitor new content
+python main.py watch --interval 600
+
+# Anytime: check statistics
+python main.py stats
+```