0150aba4f5
- Unify API: lib/api.ts uses /api/v1, inbox uses /api/inbox (rewrites) - Remove localhost refs: openapi, inbox page - Add rewrites: /api/inbox|tmc -> inbox-server, /api/v1 -> FastAPI - Add stub routes: knowledge/insights, recommendations, search, log-error - Transfer from PAPA: prompts (inspection, tmc), scripts, supabase, data/tmc-requests - Fix inbox-server: ORDER BY created_at, package.json - Remove redundant app/api/inbox/files route (rewrites handle it) - knowledge/ in gitignore (large PDFs) Co-authored-by: Cursor <cursoragent@cursor.com>
193 lines
8.8 KiB
Python
193 lines
8.8 KiB
Python
"""
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Оркестратор: запуск множества ИИ-агентов для анализа документа и подготовки по нормам законодательства.
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"""
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import json
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from typing import Any
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from sqlalchemy.orm import Session
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from .base import AgentResult
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from .llm_client import get_llm_client
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from .classifier import DocumentClassifierAgent
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from .norm_compliance import NormComplianceAgent
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from .cross_reference import CrossReferenceAgent
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from .comment_enrichment import CommentEnrichmentAgent
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from .judicial_practice import JudicialPracticeAgent
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from .formatting import FormattingAgent
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from app.models import LegalDocument, CrossReference, LegalComment, JudicialPractice, Jurisdiction
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class LegalAnalysisOrchestrator:
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"""Запускает цепочку агентов и при необходимости сохраняет перекрёстные ссылки в БД."""
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def __init__(self, db: Session | None = None, llm_client=None):
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self.db = db
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self.llm = llm_client or get_llm_client()
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self.agents = {
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"classifier": DocumentClassifierAgent(self.llm),
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"norm_compliance": NormComplianceAgent(self.llm),
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"cross_reference": CrossReferenceAgent(self.llm),
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"comment_enrichment": CommentEnrichmentAgent(self.llm),
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"judicial_practice": JudicialPracticeAgent(self.llm),
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"formatting": FormattingAgent(self.llm),
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}
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def _get_jurisdiction_code(self, jurisdiction_id: str | None) -> str:
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if not self.db or not jurisdiction_id:
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return "RU"
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j = self.db.get(Jurisdiction, jurisdiction_id)
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return (j.code if j else "RU")
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def _get_comment_candidates(self, jurisdiction_id: str | None, document_id: str | None, limit: int = 10) -> list[dict]:
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if not self.db:
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return []
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q = self.db.query(LegalComment).filter(LegalComment.jurisdiction_id == jurisdiction_id)
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if document_id:
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from sqlalchemy import or_
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q = q.filter(or_(LegalComment.document_id == document_id, LegalComment.document_id.is_(None)))
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rows = q.limit(limit).all()
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return [{"id": r.id, "title": r.title, "content": r.content, "article_ref": r.article_ref} for r in rows]
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def _get_practice_candidates(self, jurisdiction_id: str | None, document_id: str | None, limit: int = 15) -> list[dict]:
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if not self.db:
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return []
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q = self.db.query(JudicialPractice).filter(JudicialPractice.jurisdiction_id == jurisdiction_id)
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if document_id:
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from sqlalchemy import or_
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q = q.filter(or_(JudicialPractice.document_id == document_id, JudicialPractice.document_id.is_(None)))
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rows = q.limit(limit).all()
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return [
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{"id": r.id, "court_name": r.court_name, "case_number": r.case_number, "summary": r.summary}
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for r in rows
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]
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def _find_target_document_for_ref(self, suggested_title: str, target_article: str, jurisdiction_id: str) -> str | None:
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"""Поиск id документа в БД по suggested_title или target_article. Упрощённый подбор."""
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if not self.db:
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return None
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from sqlalchemy import or_
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q = self.db.query(LegalDocument.id).filter(LegalDocument.jurisdiction_id == jurisdiction_id)
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cond = []
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if suggested_title:
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cond.append(LegalDocument.title.ilike(f"%{suggested_title[:80]}%"))
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cond.append(LegalDocument.short_name.ilike(f"%{suggested_title[:60]}%"))
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if target_article:
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cond.append(LegalDocument.title.ilike(f"%{target_article[:60]}%"))
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cond.append(LegalDocument.content.ilike(f"%{target_article[:60]}%"))
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if cond:
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q = q.filter(or_(*cond))
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row = q.first()
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return row[0] if row else None
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def run(
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self,
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*,
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document_id: str | None = None,
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jurisdiction_id: str,
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title: str,
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content: str | None = None,
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existing_document_type: str | None = None,
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skip_agents: list[str] | None = None,
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save_cross_references: bool = True,
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) -> dict[str, Any]:
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"""
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Запуск полного анализа. Возвращает сводку по всем агентам.
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Если document_id задан и save_cross_references=True, перекрёстные ссылки пишутся в БД.
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"""
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skip = set(skip_agents or [])
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juris_code = self._get_jurisdiction_code(jurisdiction_id)
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ctx: dict[str, Any] = {
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"document_id": document_id,
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"jurisdiction_id": jurisdiction_id,
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"jurisdiction_code": juris_code,
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"title": title,
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"content": content or "",
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"existing_document_ids": [],
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}
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if self.db:
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ctx["existing_document_ids"] = [r[0] for r in self.db.query(LegalDocument.id).filter(LegalDocument.jurisdiction_id == jurisdiction_id).limit(500).all()]
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results: dict[str, AgentResult] = {}
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# 1. Классификатор
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if "classifier" not in skip:
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res = self.agents["classifier"].run(ctx)
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results["classifier"] = res
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ctx["document_type"] = res.data.get("document_type", "other") if res.success else "other"
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else:
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ctx["document_type"] = existing_document_type or "other"
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# 2. Нормы
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if "norm_compliance" not in skip:
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results["norm_compliance"] = self.agents["norm_compliance"].run(ctx)
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# 3. Перекрёстные ссылки
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if "cross_reference" not in skip:
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res = self.agents["cross_reference"].run(ctx)
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results["cross_reference"] = res
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refs = (res.data.get("references") or []) if res.success else []
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if save_cross_references and document_id and self.db and refs:
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for r in refs:
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target_id = self._find_target_document_for_ref(
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r.get("suggested_title", ""), r.get("target_article", ""), jurisdiction_id
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)
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if not target_id or target_id == document_id:
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continue
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# проверка на дубликат
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ex = self.db.query(CrossReference).filter(
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CrossReference.source_document_id == document_id,
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CrossReference.target_document_id == target_id,
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).first()
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if not ex:
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self.db.add(CrossReference(
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source_document_id=document_id,
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target_document_id=target_id,
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quote_excerpt=r.get("quote_excerpt"),
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target_article=r.get("target_article"),
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relevance=r.get("relevance"),
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created_by_agent="CrossReferenceAgent",
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))
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try:
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self.db.commit()
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except Exception:
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if self.db:
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self.db.rollback()
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# 4. Комментарии (подбор из БД + агент)
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if "comment_enrichment" not in skip:
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ctx["comment_candidates"] = self._get_comment_candidates(jurisdiction_id, document_id)
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ctx["article_ref"] = "" # при желании можно извлечь из content
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results["comment_enrichment"] = self.agents["comment_enrichment"].run(ctx)
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# 5. Судебная практика
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if "judicial_practice" not in skip:
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ctx["practice_candidates"] = self._get_practice_candidates(jurisdiction_id, document_id)
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results["judicial_practice"] = self.agents["judicial_practice"].run(ctx)
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# 6. Форматирование
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if "formatting" not in skip:
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results["formatting"] = self.agents["formatting"].run(ctx)
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# Сборка analysis_json и compliance_notes
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analysis = {}
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compliance_parts = []
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for k, v in results.items():
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if v.success and v.data:
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analysis[k] = v.data
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if k == "norm_compliance" and v.data.get("summary"):
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compliance_parts.append(v.data["summary"])
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if k == "norm_compliance" and v.data.get("issues"):
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for i in v.data["issues"]:
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compliance_parts.append(f"Замечание: {i}")
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return {
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"document_type": ctx.get("document_type", "other"),
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"analysis_json": json.dumps(analysis, ensure_ascii=False) if analysis else None,
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"compliance_notes": "\n".join(compliance_parts) if compliance_parts else None,
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"results": {k: {"success": v.success, "data": v.data, "error": v.error} for k, v in results.items()},
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}
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