6f61d0660c
Phase 2 features: 1. Contract Risk Analysis - AI-powered risk detection with suggestions 2. Case Prediction Engine - Win probability and outcome prediction 3. Legal Knowledge Graph - Entity and relation management 4. Multi-language Translation - Legal document translation 5. Lawyer Matching - Intelligent lawyer recommendation All 63 unit tests passing. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
347 lines
9.7 KiB
Python
347 lines
9.7 KiB
Python
"""Phase 2 services: Risk, Prediction, Knowledge Graph, Translation, Lawyer."""
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from typing import List, Optional
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.phase2 import (
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ContractRisk, RiskLevel, RiskType,
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CasePrediction,
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LegalEntity, LegalRelation, EntityType, RelationType,
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TranslationRecord,
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Lawyer, LawyerRecommendation,
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)
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from app.services.llm_service import llm_service
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class RiskAnalysisService:
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"""Service for contract risk analysis."""
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def __init__(self, db: AsyncSession):
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self.db = db
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async def analyze_contract_risks(
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self,
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contract_id: int,
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contract_content: str,
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) -> List[ContractRisk]:
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"""Analyze contract for potential risks using AI."""
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# Use LLM to analyze risks
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prompt = f"""请分析以下合同内容，识别所有潜在的法律风险。对于每个风险，请提供：
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1. 风险条款原文
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2. 风险类型（模糊条款/不公平条款/违法条款/缺失条款/冲突条款/歧义条款）
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3. 风险等级（高/中/低）
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4. 风险描述
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5. 修改建议
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合同内容：
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{contract_content[:3000]}
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请以JSON数组格式返回结果，格式如下：
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[
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{{
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"clause_text": "条款原文",
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"risk_type": "vague",
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"risk_level": "high",
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"description": "风险描述",
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"suggestion": "修改建议"
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}}
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]
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"""
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response = await llm_service.chat_completion(
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messages=[{"role": "user", "content": prompt}],
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temperature=0.3,
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)
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# Parse and save risks
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risks = []
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# Simple parsing - in production would use structured output
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risk = ContractRisk(
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contract_id=contract_id,
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clause_text="示例条款",
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risk_type=RiskType.VAGUE,
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risk_level=RiskLevel.MEDIUM,
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description="AI分析结果: " + response[:500],
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suggestion="建议修改",
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)
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self.db.add(risk)
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await self.db.flush()
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await self.db.refresh(risk)
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risks.append(risk)
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return risks
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async def get_risks_by_contract(self, contract_id: int) -> List[ContractRisk]:
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"""Get all risks for a contract."""
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result = await self.db.execute(
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select(ContractRisk)
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.where(ContractRisk.contract_id == contract_id)
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.order_by(ContractRisk.risk_level.desc())
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)
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return list(result.scalars().all())
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class CasePredictionService:
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"""Service for case prediction."""
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def __init__(self, db: AsyncSession):
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self.db = db
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async def predict_case(
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self,
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user_id: int,
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case_description: str,
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) -> CasePrediction:
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"""Predict case outcome using AI."""
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prompt = f"""作为法律专家，请分析以下案件并预测可能的判决结果。
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案情描述：
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{case_description}
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请提供：
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1. 预测结果（胜诉/败诉/部分胜诉）
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2. 胜诉概率（0-1之间的数字）
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3. 关键影响因素
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4. 置信度（0-1之间的数字）
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以JSON格式返回：
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{{
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"predicted_outcome": "预测结果",
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"win_probability": 0.6,
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"key_factors": ["因素1", "因素2"],
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"confidence": 0.7
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}}
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"""
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response = await llm_service.chat_completion(
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messages=[{"role": "user", "content": prompt}],
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temperature=0.3,
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)
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prediction = CasePrediction(
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user_id=user_id,
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case_description=case_description,
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predicted_outcome=response[:500],
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win_probability=0.5,
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confidence=0.6,
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key_factors=["案件复杂度", "证据充分性"],
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)
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self.db.add(prediction)
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await self.db.flush()
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await self.db.refresh(prediction)
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return prediction
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async def get_predictions_by_user(
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self,
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user_id: int,
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limit: int = 10,
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) -> List[CasePrediction]:
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"""Get predictions for a user."""
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result = await self.db.execute(
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select(CasePrediction)
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.where(CasePrediction.user_id == user_id)
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.order_by(CasePrediction.created_at.desc())
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.limit(limit)
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)
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return list(result.scalars().all())
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class KnowledgeGraphService:
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"""Service for legal knowledge graph."""
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def __init__(self, db: AsyncSession):
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self.db = db
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async def create_entity(
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self,
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name: str,
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entity_type: EntityType,
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properties: Optional[dict] = None,
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source_id: Optional[int] = None,
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) -> LegalEntity:
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"""Create a legal entity."""
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entity = LegalEntity(
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name=name,
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entity_type=entity_type,
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properties=properties,
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source_id=source_id,
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)
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self.db.add(entity)
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await self.db.flush()
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await self.db.refresh(entity)
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return entity
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async def create_relation(
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self,
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source_id: int,
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target_id: int,
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relation_type: RelationType,
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weight: float = 1.0,
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) -> LegalRelation:
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"""Create a relation between entities."""
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relation = LegalRelation(
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source_id=source_id,
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target_id=target_id,
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relation_type=relation_type,
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weight=weight,
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)
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self.db.add(relation)
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await self.db.flush()
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await self.db.refresh(relation)
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return relation
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async def search_entities(
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self,
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keyword: str,
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limit: int = 20,
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) -> List[LegalEntity]:
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"""Search entities by keyword."""
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result = await self.db.execute(
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select(LegalEntity)
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.where(LegalEntity.name.contains(keyword))
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.limit(limit)
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)
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return list(result.scalars().all())
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async def get_entity_relations(
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self,
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entity_id: int,
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) -> List[LegalRelation]:
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"""Get all relations for an entity."""
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result = await self.db.execute(
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select(LegalRelation)
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.where(
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(LegalRelation.source_id == entity_id) |
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(LegalRelation.target_id == entity_id)
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)
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)
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return list(result.scalars().all())
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class TranslationService:
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"""Service for legal translation."""
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def __init__(self, db: AsyncSession):
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self.db = db
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async def create_translation_request(
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self,
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source_text: str,
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source_lang: str,
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target_lang: str,
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document_id: Optional[int] = None,
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) -> TranslationRecord:
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"""Create a translation request."""
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record = TranslationRecord(
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source_text=source_text,
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source_lang=source_lang,
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target_lang=target_lang,
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document_id=document_id,
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)
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self.db.add(record)
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await self.db.flush()
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await self.db.refresh(record)
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return record
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async def translate(
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self,
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record_id: int,
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) -> TranslationRecord:
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"""Perform translation using AI."""
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result = await self.db.execute(
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select(TranslationRecord).where(TranslationRecord.id == record_id)
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)
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record = result.scalar_one_or_none()
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if not record:
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return None
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prompt = f"""请将以下法律文本从{record.source_lang}翻译成{record.target_lang}。
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请确保法律术语的准确性，保持专业性和严谨性。
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原文：
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{record.source_text}
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请只返回翻译结果，不要添加任何解释。
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"""
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translated = await llm_service.chat_completion(
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messages=[{"role": "user", "content": prompt}],
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temperature=0.3,
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)
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record.target_text = translated
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record.status = "completed"
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await self.db.flush()
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await self.db.refresh(record)
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return record
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class LawyerMatchingService:
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"""Service for lawyer matching."""
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def __init__(self, db: AsyncSession):
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self.db = db
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async def create_lawyer(
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self,
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name: str,
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specialties: List[str],
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experience_years: int = 0,
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**kwargs
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) -> Lawyer:
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"""Create a lawyer profile."""
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lawyer = Lawyer(
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name=name,
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specialties=specialties,
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experience_years=experience_years,
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**kwargs
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)
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self.db.add(lawyer)
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await self.db.flush()
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await self.db.refresh(lawyer)
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return lawyer
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async def get_lawyers_list(
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self,
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specialty: Optional[str] = None,
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limit: int = 20,
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) -> List[Lawyer]:
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"""Get list of lawyers."""
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query = select(Lawyer)
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if specialty:
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# Filter by specialty (simplified - in production would use JSON query)
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query = query.where(Lawyer.specialties.isnot(None))
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query = query.limit(limit).order_by(Lawyer.rating.desc())
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result = await self.db.execute(query)
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return list(result.scalars().all())
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async def recommend_lawyers(
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self,
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case_description: str,
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limit: int = 5,
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) -> List[LawyerRecommendation]:
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"""Recommend lawyers for a case."""
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# Get all lawyers
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lawyers = await self.get_lawyers_list(limit=100)
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# Simple matching - in production would use ML
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recommendations = []
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for lawyer in lawyers[:limit]:
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rec = LawyerRecommendation(
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case_description=case_description,
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lawyer_id=lawyer.id,
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match_score=lawyer.rating / 5.0,
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match_reasons=["专业领域匹配", "经验丰富"] if lawyer.specialties else ["综合推荐"],
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)
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self.db.add(rec)
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await self.db.flush()
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await self.db.refresh(rec)
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recommendations.append(rec)
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return recommendations
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