from fastapi import APIRouter, Depends
from sqlalchemy import or_, text
from app.db.session import get_db
from app.pipeline.enricher import Enricher

router = APIRouter(prefix="/api")
enricher = Enricher()

@router.get("/search")
def search(q: str = "", category: str = None, person: str = None,
           date: str = None, page: int = None, job_id: int = None, semantic: bool = False, db=Depends(get_db)):
    from app.db.models import Article
    query = db.query(Article)
    if category: query = query.filter(Article.category == category)
    if date: query = query.filter(Article.date == date)
    if page: query = query.filter(Article.page == page)
    if job_id: query = query.filter(Article.upload_job_id == job_id)
    if person: query = query.filter(Article.persons.any(person))
    if q and not semantic:
        query = query.filter(or_(Article.headline.ilike(f"%{q}%"),
                                 Article.text.ilike(f"%{q}%")))
        return query.limit(50).all()
    if semantic and q:
        vec = enricher.embed(q)
        # pgvector cosine distance
        return db.execute(text("""
            SELECT * FROM articles ORDER BY embedding <=> :v LIMIT 30
        """), {"v": str(vec)}).fetchall()
    return query.limit(50).all()