"""Vergunningzoeker agent — agentic search over permits.

Receives questions, generates search queries, searches the permit
database, and synthesizes answers. Each step is logged for transparency.
"""

import json
from druppie_sdk import DruppieClient
from app.models import Permit

druppie = DruppieClient()


def run_agent(question: str, history: list[dict], db) -> dict:
    steps = []

    # Build conversation context
    context_str = ""
    if history:
        recent = history[-4:]
        context_str = "\n".join(
            f"{'Gebruiker' if m['role'] == 'user' else 'Assistent'}: {m['content'][:200]}"
            for m in recent
        )

    # Step 1: Generate search queries
    full_prompt = question
    if context_str:
        full_prompt = f"Gesprekscontext:\n{context_str}\n\nNieuwe vraag: {question}"

    query_result = druppie.call("llm", "chat", {
        "prompt": (
            "De gebruiker stelt een vraag over vergunningen in een database. "
            "Genereer 1-3 zoektermen (komma-gescheiden) waarmee relevante "
            "vergunningen gevonden kunnen worden. Antwoord ALLEEN met de zoektermen.\n\n"
            f"Vraag: {full_prompt}"
        ),
        "system": "Je genereert zoektermen voor een vergunningendatabase. Antwoord alleen met komma-gescheiden zoektermen.",
    })
    search_terms = [t.strip() for t in query_result.get("answer", question).split(",") if t.strip()]

    steps.append({
        "type": "search",
        "label": f"Zoektermen: {', '.join(search_terms)}",
        "detail": search_terms,
    })

    # Step 2: Search database with each term
    found = {}
    search_details = []
    for term in search_terms[:3]:
        like = f"%{term}%"
        results = db.query(Permit).filter(
            Permit.permit_number.ilike(like)
            | Permit.applicant_name.ilike(like)
            | Permit.permit_holder_name.ilike(like)
            | Permit.location.ilike(like)
            | Permit.permit_type.ilike(like)
            | Permit.applicable_law.ilike(like)
            | Permit.work_type.ilike(like)
            | Permit.extracted_text.ilike(like)
        ).limit(10).all()
        new_ids = [p.id for p in results if p.id not in found]
        for p in results:
            found[p.id] = p
        search_details.append(f'"{term}" → {len(results)} resultaten ({len(new_ids)} nieuw)')

    steps.append({
        "type": "query",
        "label": f"{len(found)} vergunningen gevonden",
        "detail": search_details,
    })

    # Step 3: Build context from found permits
    if found:
        context_parts = []
        for p in found.values():
            parts = [f"Vergunning #{p.id}: {p.permit_number or 'geen nummer'}"]
            if p.applicant_name: parts.append(f"  Aanvrager: {p.applicant_name}")
            if p.permit_holder_name: parts.append(f"  Houder: {p.permit_holder_name}")
            if p.permit_type: parts.append(f"  Type: {p.permit_type}")
            if p.location: parts.append(f"  Locatie: {p.location}")
            if p.issue_date: parts.append(f"  Datum: {p.issue_date}")
            if p.archive_status: parts.append(f"  Archiefstatus: {p.archive_status}")
            if p.applicable_law: parts.append(f"  Wet: {p.applicable_law}")
            if p.work_type: parts.append(f"  Type werk: {p.work_type}")
            if p.extracted_text:
                parts.append(f"  Tekst (fragment): {p.extracted_text[:400]}")
            context_parts.append("\n".join(parts))
        context = "\n\n".join(context_parts)
    else:
        context = "Geen vergunningen gevonden in de database."

    # Step 4: LLM synthesizes answer
    steps.append({"type": "reasoning", "label": "Antwoord genereren", "detail": None})

    answer_result = druppie.call("llm", "chat", {
        "prompt": (
            f"Beantwoord de volgende vraag op basis van de vergunningendata.\n\n"
            f"Vraag: {full_prompt}\n\n"
            f"Gevonden vergunningen ({len(found)} resultaten):\n\n{context}"
        ),
        "system": (
            "Je bent een assistent voor waterschap-medewerkers. Beantwoord vragen "
            "over vergunningen op basis van de aangeleverde data. Wees specifiek "
            "en verwijs naar vergunningnummers. Als de data onvoldoende is, zeg dat eerlijk."
        ),
    })

    return {
        "answer": answer_result.get("answer", "Geen antwoord ontvangen."),
        "steps": steps,
    }