656f596d7e
Core modules: - Laws: CRUD, search, AI-powered QA - Analysis: legal research and case management - Contracts: lifecycle management with templates - Signatures: electronic signature workflow Infrastructure: - FastAPI + SQLite + async SQLAlchemy - Docker deployment support - 54 unit tests passing Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
"""LLM service for AI-powered features."""
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from typing import List, Dict, Any, Optional
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import httpx
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from app.core.config import settings
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class LLMService:
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"""Service for LLM API interactions."""
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def __init__(self):
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self.api_base = settings.LLM_API_BASE
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self.api_key = settings.LLM_API_KEY
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self.model = settings.LLM_MODEL
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async def chat_completion(
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self,
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messages: List[Dict[str, str]],
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temperature: float = 0.7,
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max_tokens: int = 2000,
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) -> str:
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"""Get chat completion from LLM."""
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if not self.api_key:
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# Return mock response for testing
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return "这是一个模拟的法律回复。"
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async with httpx.AsyncClient() as client:
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response = await client.post(
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f"{self.api_base}/chat/completions",
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headers={
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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},
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json={
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"model": self.model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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},
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timeout=60.0,
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)
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response.raise_for_status()
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data = response.json()
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return data["choices"][0]["message"]["content"]
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async def legal_qa(
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self,
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question: str,
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context: Optional[str] = None,
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) -> str:
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"""Answer a legal question."""
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system_prompt = """你是一个专业的法律助手。请根据以下原则回答问题：
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1. 准确引用相关法律条文
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2. 解释法律条文的含义和适用条件
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3. 提供实用的法律建议
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4. 如不确定，请明确说明"""
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messages = [
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{"role": "system", "content": system_prompt},
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]
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if context:
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messages.append({
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"role": "user",
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"content": f"参考以下法律内容：\n{context}\n\n问题：{question}"
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})
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else:
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messages.append({"role": "user", "content": question})
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return await self.chat_completion(messages)
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async def analyze_legal_issue(
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self,
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issue_description: str,
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relevant_laws: Optional[List[str]] = None,
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) -> str:
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"""Analyze a legal issue and provide insights."""
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system_prompt = """你是一个专业的法律分析师。请根据以下结构分析法律问题：
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1. 问题定性
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2. 相关法律依据
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3. 法律分析
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4. 风险提示
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5. 建议"""
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user_content = f"请分析以下法律问题：\n{issue_description}"
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if relevant_laws:
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user_content += f"\n\n相关法律：\n" + "\n".join(relevant_laws)
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_content},
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]
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return await self.chat_completion(messages, temperature=0.5)
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async def review_contract(
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self,
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contract_content: str,
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contract_type: Optional[str] = None,
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) -> str:
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"""Review a contract and identify potential issues."""
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system_prompt = """你是一个专业的合同审查专家。请审查合同并：
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1. 识别潜在风险条款
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2. 指出不明确的条款
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3. 提出修改建议
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4. 检查法律合规性"""
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user_content = f"请审查以下合同内容：\n{contract_content}"
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if contract_type:
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user_content = f"合同类型：{contract_type}\n\n{user_content}"
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_content},
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]
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return await self.chat_completion(messages, max_tokens=3000)
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# Singleton instance
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llm_service = LLMService()
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