feat(server): add complete server for data scrapping

server/.env.example

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+# OpenAI API Configuration
+OPENAI_API_KEY=your_openai_api_key_here
+
+# PostgreSQL connection (used by DatabaseManager)
+DATABASE_URL=postgresql://postgres:postgres@localhost:5432/veille_technique
+
+# Optional: Override embedding model (OpenAI)
+EMBEDDING_MODEL=text-embedding-3-small
+
+# Optional: GitHub Token for higher rate limits
+GITHUB_TOKEN=your_github_token_here

server/ARCHITECTURE.md

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+# Watch Server - Complete Redesign
+
+## 📋 Summary of Changes
+
+The server has been redesigned with a modular architecture and two operating modes.
+
+## 🏗️ Architecture
+
+```
+server/
+├── main.py                 # Main server with WatchServer (orchestrator)
+├── database.py             # PostgreSQL + pgvector database manager
+├── embeddings.py           # Embeddings manager (vectors)
+├── config.py               # Centralized configuration
+├── examples.py             # Usage examples
+├── requirements.txt        # Python dependencies
+├── README.md               # Complete documentation
+└── scrapers/
+    ├── base.py             # Abstract BaseScraper interface
+    ├── arxiv_scraper.py     # Scraper for arXiv
+    ├── github_scraper.py    # Scraper for GitHub
+    ├── medium_scraper.py    # Scraper for Medium
+    ├── lemonde_scraper.py   # Scraper for Le Monde
+    └── huggingface_scraper.py # Scraper for Hugging Face
+```
+
+## 🎯 Operating Modes
+
+### 1️⃣ Backfill Mode (History)
+**When:** At startup (optional)
+
+**What:** Scrapes all available history from each source.
+
+**How:**
+```bash
+python main.py backfill --limit 100
+```
+
+**Flow:**
+1. Each scraper calls `scrape_all()`.
+2. Articles are saved (deduplicated by ID and hash).
+3. Embeddings are generated and stored.
+4. Sync history is recorded.
+
+### 2️⃣ Watch Mode (Monitoring)
+**When:** Continuous monitoring after (or without) backfill.
+
+**How:**
+```bash
+python main.py watch --interval 300
+```
+
+**Flow:**
+1. Infinite loop (default 5-minute interval).
+2. Each scraper calls `scrape_latest()`.
+3. New articles are saved; embeddings generated.
+4. Sync history is recorded.
+
+## 🔧 Main Components
+
+### BaseScraper (abstract interface)
+- `scrape_latest(limit)` → watch mode
+- `scrape_all(limit)` → backfill mode
+- `normalize_item()` → unified format
+
+### DatabaseManager
+- PostgreSQL persistence with pgvector
+- Tables: articles, embeddings (vector), sync_history
+- Automatic deduplication via `ON CONFLICT`
+- Vector-ready queries and batch operations
+
+### EmbeddingManager
+- Providers: Dummy, SentenceTransformers, OpenAI
+- Generates numpy vectors sized to the chosen model
+- Stores vectors directly in pgvector columns (no pickle)
+
+### WatchServer (orchestrator)
+- Initializes all scrapers
+- Manages both modes
+- Logging, statistics, and monitoring
+
+## 💾 Database Structure
+
+### Table `articles`
+```
+id (TEXT PRIMARY KEY)      # Unique identifier per source
+source_site (TEXT)         # arxiv, github, medium, le_monde, huggingface
+title (TEXT)               # Article title
+description (TEXT)         # Summary/content
+author_info (TEXT)         # Author(s)
+keywords (TEXT)            # Tags/categories
+content_url (TEXT)         # Link to source
+published_date (TIMESTAMPTZ) # Publication date
+item_type (TEXT)           # article, paper, repository, etc.
+created_at (TIMESTAMPTZ)   # When retrieved
+updated_at (TIMESTAMPTZ)   # Last update
+```
+
+### Table `embeddings`
+```
+id (SERIAL PRIMARY KEY)         # Unique embedding row
+article_id (TEXT UNIQUE)        # Link to articles.id
+embedding vector(1536)          # pgvector column (dimension tied to embedding model)
+embedding_model (TEXT)          # Which model generated the embedding
+created_at (TIMESTAMPTZ)        # When created
+```
+
+### Table `sync_history`
+```
+id (SERIAL PRIMARY KEY)         # Unique sync row
+source_site (TEXT)              # Which source
+sync_mode (TEXT)                # "watch" or "backfill"
+last_sync_time (TIMESTAMPTZ)    # When
+items_processed (INTEGER)       # How many articles
+created_at (TIMESTAMPTZ)        # When recorded
+```
+
+## 🚀 Usage
+
+### Simple startup
+```bash
+# 1. Fill DB with history
+python main.py backfill --limit 50
+
+# 2. Monitor continuously
+python main.py watch --interval 300
+
+# 3. Check stats
+python main.py stats
+```
+
+### With options
+```bash
+# Custom backfill
+python main.py backfill --limit 200 --db-url postgresql://user:pass@localhost:5432/veille_technique
+
+# Watch with 10-minute interval
+python main.py watch --interval 600
+
+# Stats on specific DB
+python main.py stats --db-url postgresql://user:pass@localhost:5432/veille_technique
+```
+
+## 📊 Complete Flow Example
+
+```
+Server startup
+│
+├─→ BACKFILL Mode (optional)
+│   ├─→ ArXiv.scrape_all(100)    → … articles → DB
+│   ├─→ GitHub.scrape_all(100)   → … articles → DB
+│   ├─→ Medium.scrape_all(100)   → … articles → DB
+│   ├─→ LeMonde.scrape_all(100)  → … articles → DB
+│   └─→ HF.scrape_all(100)       → … articles → DB
+│       ↓ All articles receive an embedding
+│
+└─→ WATCH Mode (infinite loop)
+    ├─→ Iteration 1 …
+    ├─→ [Wait interval]
+    └─→ Iteration 2 …
+```
+
+## 🔑 Key Design Points
+
+### ✓ Modularity
+- Independent scrapers; easy to add/remove
+- Interchangeable embedding providers
+
+### ✓ Robustness
+- Error isolation per scraper
+- Deduplication prevents duplicates
+
+### ✓ Scalability
+- Batch DB operations
+- Vector-ready schema
+- Structured logging
+
+### ✓ Maintainability
+- Clear code and docs
+- Centralized configuration
+- Usage examples included
+
+## 💻 How to View the Database
+
+Use PostgreSQL tooling (`psql`, `pgcli`, DBeaver, PgAdmin`) with `DATABASE_URL`.
+
+```bash
+# List tables
+psql "$DATABASE_URL" -c "\dt"
+
+# Check pgvector extension
+psql "$DATABASE_URL" -c "\dx vector"
+
+# Quick counts
+psql "$DATABASE_URL" -c "SELECT COUNT(*) FROM articles;"
+psql "$DATABASE_URL" -c "SELECT COUNT(*) FROM embeddings;"
+
+# Example vector query (top 5 nearest)
+psql "$DATABASE_URL" -c "SELECT article_id, embedding <-> '[0.1,0.2,...]' AS distance FROM embeddings ORDER BY embedding <-> '[0.1,0.2,...]' LIMIT 5;"
+
+# Last syncs
+psql "$DATABASE_URL" -c "SELECT * FROM sync_history ORDER BY created_at DESC LIMIT 5;"
+
+# Export (custom format)
+pg_dump --dbname="$DATABASE_URL" --format=c --file=veille_technique.dump
+```
+
+## 📝 Migration from Old Server
+
+Legacy code in `scrap/` remains for reference; the new server reuses scraping logic with the updated architecture.

server/config.py

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+"""Configuration for the watch server."""
+
+import os
+from dataclasses import dataclass
+from typing import Dict
+
+
+@dataclass
+class ScraperConfig:
+    """Configuration for a specific scraper."""
+    enabled: bool = True
+    limit_latest: int = 20
+
+    limit_all: int = 100
+
+
+@dataclass
+class ServerConfig:
+    """Global server configuration."""
+
+    db_url: str = os.getenv("DATABASE_URL", "postgresql://postgres:postgres@localhost:5432/veille_technique")
+
+    watch_interval_seconds: int = 300
+
+    log_level: str = "INFO"
+
+    scrapers: Dict[str, ScraperConfig] = None
+
+    def __post_init__(self):
+        """Initialize default scraper configuration."""
+        if self.scrapers is None:
+            self.scrapers = {
+                "arxiv": ScraperConfig(enabled=True, limit_latest=20, limit_all=100),
+                "github": ScraperConfig(enabled=True, limit_latest=20, limit_all=100),
+                "medium": ScraperConfig(enabled=True, limit_latest=20, limit_all=100),
+                "lemonde": ScraperConfig(enabled=True, limit_latest=20, limit_all=100),
+                "huggingface": ScraperConfig(enabled=True, limit_latest=20, limit_all=100),
+            }
+
+    @classmethod
+    def from_file(cls, filepath: str) -> "ServerConfig":
+        """Load configuration from JSON/YAML file."""
+        import json
+
+        try:
+            with open(filepath, 'r') as f:
+                data = json.load(f)
+
+            if "scrapers" in data:
+                data["scrapers"] = {
+                    name: ScraperConfig(**cfg)
+                    for name, cfg in data["scrapers"].items()
+                }
+
+            return cls(**data)
+        except FileNotFoundError:
+            print(f"Config file {filepath} not found, using default config")
+            return cls()
+
+
+DEFAULT_CONFIG = ServerConfig()
+
+DEV_CONFIG = ServerConfig(
+    db_url=os.getenv("DATABASE_URL", "postgresql://postgres:postgres@localhost:5432/veille_technique_dev"),
+    watch_interval_seconds=60,
+)
+
+PROD_CONFIG = ServerConfig(
+    db_url=os.getenv("DATABASE_URL", "postgresql://postgres:postgres@localhost:5432/veille_technique"),
+    watch_interval_seconds=600,
+    log_level="WARNING",
+)