cd packages/ai_web_feeds
uv run alembic init alembic

uv run alembic revision --autogenerate -m "initial_schema"
uv run alembic upgrade head

from ai_web_feeds.data_sync import DataSyncOrchestrator
from ai_web_feeds import DatabaseManager

db = DatabaseManager("sqlite:///../../data/aiwebfeeds.db")
sync = DataSyncOrchestrator(db)

# Load feeds.yaml and topics.yaml into database
results = sync.full_sync()
print(f"Loaded {results['feeds_loaded']} feeds and {results['topics_loaded']} topics")

from ai_web_feeds import DatabaseManager
from ai_web_feeds.analytics import FeedAnalytics

db = DatabaseManager("sqlite:///../../data/aiwebfeeds.db")

with db.get_session() as session:
    analytics = FeedAnalytics(session)

    # Overview statistics
    stats = analytics.get_overview_stats()
    print(f"Total feeds: {stats['total_feeds']}")

    # Quality metrics
    quality = analytics.get_quality_metrics()
    print(f"Average quality score: {quality['avg_quality_score']:.2f}")

    # Health report
    health = analytics.generate_health_report()
    print(f"Healthy feeds: {health['overall_stats']['healthy_count']}")

from ai_web_feeds.analytics.advanced import AdvancedFeedAnalytics

with db.get_session() as session:
    analytics = AdvancedFeedAnalytics(session)

    # Predict feed health
    prediction = analytics.predict_feed_health("feed_id_123", days_ahead=7)
    print(f"Predicted health: {prediction['predicted_health']:.2f}")

    # Cluster similar feeds
    clusters = analytics.cluster_feeds_by_similarity(similarity_threshold=0.6)
    print(f"Found {len(clusters)} clusters")

    # Generate ML insights
    insights = analytics.generate_ml_insights_report()
    print(f"Top pattern: {insights['patterns'][0]['pattern_type']}")

from ai_web_feeds.data_sync import DataSyncOrchestrator

sync = DataSyncOrchestrator(db)

# Full bidirectional sync
results = sync.full_sync()

# Export enriched data
export_results = sync.export_enriched_feeds("../../data/feeds.enriched.yaml")
print(f"Exported {export_results['feeds_exported']} feeds")

# Sync with progress callback
def on_progress(current, total, item_type):
    print(f"Progress: {current}/{total} {item_type}")

results = sync.full_sync(progress_callback=on_progress)

from ai_web_feeds.models_advanced import (
    FeedHealthMetric,
    DataQualityMetric,
    ContentEmbedding
)

with db.get_session() as session:
    # Record health metric
    health = FeedHealthMetric(
        feed_source_id="feed_123",
        overall_health_score=0.85,
        availability_score=0.95,
        freshness_score=0.80,
        content_quality_score=0.90
    )
    session.add(health)

    # Store content embedding
    embedding = ContentEmbedding(
        feed_item_id="item_456",
        embedding_vector=[0.1, 0.2, 0.3],  # Actual embeddings from model
        model_name="text-embedding-ada-002",
        dimension=1536
    )
    session.add(embedding)

    session.commit()

from ai_web_feeds import DatabaseManager
from ai_web_feeds.analytics import FeedAnalytics

# Initialize
db = DatabaseManager("sqlite:///data/aiwebfeeds.db")
db.create_db_and_tables()

# Run analytics
with db.get_session() as session:
    analytics = FeedAnalytics(session)

    # Overview stats
    stats = analytics.get_overview_stats()

    # Quality metrics
    quality = analytics.get_quality_metrics()

    # Feed health
    health = analytics.get_feed_health_report("feed_xyz")

    # Full report
    report = analytics.generate_full_report()

from ai_web_feeds.analytics.advanced import AdvancedFeedAnalytics

with db.get_session() as session:
    analytics = AdvancedFeedAnalytics(session)

    # Predict feed health 7 days ahead
    prediction = analytics.predict_feed_health("feed_xyz", days_ahead=7)

    # Detect content patterns
    patterns = analytics.detect_content_patterns("feed_xyz")

    # Find similar feeds
    similarity = analytics.compute_feed_similarity("feed_1", "feed_2")

    # Cluster feeds
    clusters = analytics.cluster_feeds_by_similarity(similarity_threshold=0.6)

    # ML insights report
    insights = analytics.generate_ml_insights_report()

from ai_web_feeds.data_sync import DataSyncOrchestrator, SyncConfig

# Configure sync
config = SyncConfig(
    feeds_yaml_path=Path("data/feeds.yaml"),
    topics_yaml_path=Path("data/topics.yaml"),
    batch_size=100,
    update_existing=True,
)

# Initialize sync
sync = DataSyncOrchestrator(db, config)

# Full bidirectional sync
results = sync.full_sync()

print(f"Topics synced: {results['topics']}")
print(f"Feeds synced: {results['feeds']}")
print(f"Export complete: {results['export']}")

from ai_web_feeds.data_sync import FeedDataLoader

loader = FeedDataLoader(db)

# With progress callback
def progress(current, total):
    print(f"Loading feeds: {current}/{total}")

stats = loader.load_feeds_from_yaml(progress_callback=progress)
print(f"Inserted: {stats['inserted']}, Updated: {stats['updated']}")

from ai_web_feeds.data_sync import DataExporter

exporter = DataExporter(db)
output_path = exporter.export_enriched_feeds()
print(f"Exported to: {output_path}")

from ai_web_feeds import DatabaseManager

db = DatabaseManager("sqlite:///../../data/aiwebfeeds.db")

with db.get_session() as session:
    from ai_web_feeds.models import FeedSource
    feed_count = session.query(FeedSource).count()
    print(f"Database contains {feed_count} feeds")

# Check current version
uv run alembic current

# Upgrade to latest
uv run alembic upgrade head

# Downgrade one version
uv run alembic downgrade -1

# Show migration history
uv run alembic history

# SQLite backup
cp data/aiwebfeeds.db data/aiwebfeeds.db.backup

# Or use SQLite backup command
sqlite3 data/aiwebfeeds.db ".backup data/aiwebfeeds.db.backup"

# 1. Create tables
cd packages/ai_web_feeds
uv run python -c "from ai_web_feeds import DatabaseManager; db = DatabaseManager(); db.create_db_and_tables()"

# 2. Initialize Alembic
uv run alembic init alembic

# 3. Create initial migration
uv run alembic revision --autogenerate -m "initial_schema"

# 4. Apply migration
uv run alembic upgrade head

# 5. Load data
uv run python -c "from ai_web_feeds.data_sync import DataSyncOrchestrator; from ai_web_feeds import DatabaseManager; sync = DataSyncOrchestrator(DatabaseManager()); sync.full_sync()"

# 1. Modify models in models.py or models_advanced.py

# 2. Generate migration
uv run alembic revision --autogenerate -m "add_new_field"

# 3. Review migration file in alembic/versions/

# 4. Apply migration
uv run alembic upgrade head

# Run all tests with coverage
cd tests
uv run pytest --cov=ai_web_feeds --cov-report=html

# Run specific test file
uv run pytest tests/packages/ai_web_feeds/test_data_sync.py -v

# Run with markers
uv run pytest -m "not slow" -v