114 lines
4.3 KiB
Python
114 lines
4.3 KiB
Python
"""Vergunningzoeker agent — agentic search over permits.
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Receives questions, generates search queries, searches the permit
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database, and synthesizes answers. Each step is logged for transparency.
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"""
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import json
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from druppie_sdk import DruppieClient
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from app.models import Permit
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druppie = DruppieClient()
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def run_agent(question: str, history: list[dict], db) -> dict:
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steps = []
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# Build conversation context
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context_str = ""
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if history:
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recent = history[-4:]
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context_str = "\n".join(
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f"{'Gebruiker' if m['role'] == 'user' else 'Assistent'}: {m['content'][:200]}"
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for m in recent
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)
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# Step 1: Generate search queries
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full_prompt = question
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if context_str:
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full_prompt = f"Gesprekscontext:\n{context_str}\n\nNieuwe vraag: {question}"
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query_result = druppie.call("llm", "chat", {
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"prompt": (
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"De gebruiker stelt een vraag over vergunningen in een database. "
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"Genereer 1-3 zoektermen (komma-gescheiden) waarmee relevante "
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"vergunningen gevonden kunnen worden. Antwoord ALLEEN met de zoektermen.\n\n"
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f"Vraag: {full_prompt}"
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),
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"system": "Je genereert zoektermen voor een vergunningendatabase. Antwoord alleen met komma-gescheiden zoektermen.",
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})
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search_terms = [t.strip() for t in query_result.get("answer", question).split(",") if t.strip()]
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steps.append({
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"type": "search",
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"label": f"Zoektermen: {', '.join(search_terms)}",
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"detail": search_terms,
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})
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# Step 2: Search database with each term
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found = {}
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search_details = []
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for term in search_terms[:3]:
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like = f"%{term}%"
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results = db.query(Permit).filter(
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Permit.permit_number.ilike(like)
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| Permit.applicant_name.ilike(like)
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| Permit.permit_holder_name.ilike(like)
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| Permit.location.ilike(like)
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| Permit.permit_type.ilike(like)
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| Permit.applicable_law.ilike(like)
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| Permit.work_type.ilike(like)
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| Permit.extracted_text.ilike(like)
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).limit(10).all()
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new_ids = [p.id for p in results if p.id not in found]
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for p in results:
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found[p.id] = p
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search_details.append(f'"{term}" → {len(results)} resultaten ({len(new_ids)} nieuw)')
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steps.append({
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"type": "query",
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"label": f"{len(found)} vergunningen gevonden",
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"detail": search_details,
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})
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# Step 3: Build context from found permits
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if found:
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context_parts = []
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for p in found.values():
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parts = [f"Vergunning #{p.id}: {p.permit_number or 'geen nummer'}"]
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if p.applicant_name: parts.append(f" Aanvrager: {p.applicant_name}")
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if p.permit_holder_name: parts.append(f" Houder: {p.permit_holder_name}")
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if p.permit_type: parts.append(f" Type: {p.permit_type}")
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if p.location: parts.append(f" Locatie: {p.location}")
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if p.issue_date: parts.append(f" Datum: {p.issue_date}")
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if p.archive_status: parts.append(f" Archiefstatus: {p.archive_status}")
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if p.applicable_law: parts.append(f" Wet: {p.applicable_law}")
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if p.work_type: parts.append(f" Type werk: {p.work_type}")
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if p.extracted_text:
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parts.append(f" Tekst (fragment): {p.extracted_text[:400]}")
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context_parts.append("\n".join(parts))
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context = "\n\n".join(context_parts)
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else:
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context = "Geen vergunningen gevonden in de database."
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# Step 4: LLM synthesizes answer
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steps.append({"type": "reasoning", "label": "Antwoord genereren", "detail": None})
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answer_result = druppie.call("llm", "chat", {
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"prompt": (
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f"Beantwoord de volgende vraag op basis van de vergunningendata.\n\n"
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f"Vraag: {full_prompt}\n\n"
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f"Gevonden vergunningen ({len(found)} resultaten):\n\n{context}"
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),
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"system": (
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"Je bent een assistent voor waterschap-medewerkers. Beantwoord vragen "
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"over vergunningen op basis van de aangeleverde data. Wees specifiek "
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"en verwijs naar vergunningnummers. Als de data onvoldoende is, zeg dat eerlijk."
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),
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})
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return {
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"answer": answer_result.get("answer", "Geen antwoord ontvangen."),
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"steps": steps,
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}
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