296 lines
9.0 KiB
Python
296 lines
9.0 KiB
Python
"""
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LLM适配器 - 使用DeepSeek LLM API检测品牌引用
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"""
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import asyncio
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import json
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import logging
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import random
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import re
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from typing import Optional
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from app.schemas.scoring import CitationResult
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from app.config import settings
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logger = logging.getLogger(__name__)
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BRAND_CITATION_PROMPT = """分析以下AI搜索查询中是否提到了目标品牌。
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查询关键词: {keyword}
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目标品牌: {brand_name}
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品牌别名: {brand_aliases}
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返回JSON格式:
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{{"cited": true/false, "position": 1, "citation_text": "...", "sentiment": "positive/neutral/negative", "confidence": 0.95}}
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"""
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class LLMAdapterError(Exception):
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"""LLM适配器异常"""
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pass
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class LLMAdapter:
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"""LLM适配器 - 使用 OpenAI 兼容协议检测品牌引用（支持百炼/DashScope/DeepSeek）"""
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def __init__(self, api_key: Optional[str] = None, max_retries: int = 3):
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"""
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初始化LLM适配器
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Args:
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api_key: API密钥，默认优先使用 OPENAI_API_KEY（百炼/DashScope），其次 DEEPSEEK_API_KEY
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max_retries: 最大重试次数
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"""
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self.api_key = (
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api_key
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or getattr(settings, 'OPENAI_API_KEY', None)
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or getattr(settings, 'DEEPSEEK_API_KEY', None)
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)
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# base_url 优先 OPENAI_BASE_URL，其次 DEEPSEEK_BASE_URL
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self.base_url = (
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getattr(settings, 'OPENAI_BASE_URL', None)
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or getattr(settings, 'DEEPSEEK_BASE_URL', 'https://api.deepseek.com/v1')
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)
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# model 优先 OPENAI_MODEL，其次 DEFAULT_LLM_MODEL
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self.model = (
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getattr(settings, 'OPENAI_MODEL', None)
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or getattr(settings, 'DEFAULT_LLM_MODEL', 'qwen3-coder-plus')
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or 'qwen3-coder-plus'
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)
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self.max_retries = max_retries
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self._client = None
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@property
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def client(self):
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"""延迟初始化 OpenAI 兼容客户端"""
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if self._client is None:
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try:
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from openai import OpenAI
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self._client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url,
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)
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except ImportError:
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raise LLMAdapterError("请安装openai库: pip install openai")
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return self._client
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def _build_prompt(self, keyword: str, brand_name: str, brand_aliases: list[str]) -> str:
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"""构建Prompt"""
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aliases_str = ", ".join(brand_aliases) if brand_aliases else "无"
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return BRAND_CITATION_PROMPT.format(
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keyword=keyword,
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brand_name=brand_name,
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brand_aliases=aliases_str
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)
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async def query_brand_citation(
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self,
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keyword: str,
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brand_name: str,
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brand_aliases: list[str]
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) -> CitationResult:
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"""
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使用LLM检测品牌引用
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Args:
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keyword: 查询关键词
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brand_name: 目标品牌名称
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brand_aliases: 品牌别名列表
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Returns:
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CitationResult: 包含cited, position, citation_text, sentiment, confidence
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Raises:
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LLMAdapterError: API调用或解析失败
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"""
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if not settings.ENABLE_LLM:
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logger.info("LLM调用已禁用 (ENABLE_LLM=False)，返回模拟数据")
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return self._get_mock_result(keyword, brand_name, brand_aliases)
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prompt = self._build_prompt(keyword, brand_name, brand_aliases)
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last_error = None
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for attempt in range(self.max_retries):
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try:
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response = await self._call_deepseek(prompt)
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return self._parse_response(response)
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except Exception as e:
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last_error = e
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logger.warning(
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f"LLM API调用失败 (尝试 {attempt + 1}/{self.max_retries}): {e}"
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)
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raise LLMAdapterError(f"LLM API调用失败，已重试{self.max_retries}次: {last_error}")
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def _get_mock_result(
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self,
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keyword: str,
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brand_name: str,
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brand_aliases: list[str]
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) -> CitationResult:
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"""
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生成模拟结果（当LLM禁用时使用）
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随机决定是否引用，模拟真实场景的数据分布
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"""
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cited = random.random() < 0.6
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sentiment_options = ["positive", "neutral", "negative"]
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sentiment = random.choice(sentiment_options)
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if cited:
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position = random.randint(1, 10)
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citation_text = f'模拟引用：在搜索"{keyword}"时，提到了{brand_name}品牌及其相关产品。'
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else:
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position = None
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citation_text = ""
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return CitationResult(
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cited=cited,
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position=position,
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citation_text=citation_text,
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sentiment=sentiment,
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confidence=round(random.uniform(0.7, 0.99), 2)
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)
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async def _call_deepseek(self, prompt: str) -> dict:
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"""
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调用DeepSeek API
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Args:
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prompt: 提示词
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Returns:
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API响应的JSON解析结果
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Raises:
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LLMAdapterError: API调用失败
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"""
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try:
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# 在线程池中执行同步的API调用
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response = await asyncio.to_thread(
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self._sync_call_deepseek,
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prompt
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)
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return response
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except json.JSONDecodeError as e:
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raise LLMAdapterError(f"JSON解析失败: {e}")
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except Exception as e:
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raise LLMAdapterError(f"API调用失败: {e}")
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def _sync_call_deepseek(self, prompt: str) -> dict:
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"""
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同步调用DeepSeek API（在线程池中执行）
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Args:
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prompt: 提示词
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Returns:
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API响应的JSON解析结果
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"""
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "user",
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"content": prompt
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}
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],
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temperature=0.1,
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max_tokens=500,
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)
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content = response.choices[0].message.content
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if not content:
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raise LLMAdapterError("API返回空响应")
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# 提取JSON（可能包裹在```json block中）
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json_str = self._extract_json(content)
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return json.loads(json_str)
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def _extract_json(self, text: str) -> str:
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"""从文本中提取JSON"""
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# 尝试直接解析
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try:
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json.loads(text)
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return text
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except json.JSONDecodeError:
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pass
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# 尝试从代码块中提取
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json_pattern = r'```(?:json)?\s*([\s\S]*?)\s*```'
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match = re.search(json_pattern, text)
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if match:
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return match.group(1).strip()
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# 尝试找到第一个{到最后一个}之间的内容
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first_brace = text.find('{')
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last_brace = text.rfind('}')
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if first_brace != -1 and last_brace != -1 and last_brace > first_brace:
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return text[first_brace:last_brace + 1]
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raise LLMAdapterError(f"无法从响应中提取JSON: {text[:200]}")
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def _parse_response(self, response: dict) -> CitationResult:
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"""
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解析API响应
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Args:
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response: API返回的字典
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Returns:
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CitationResult对象
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Raises:
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LLMAdapterError: 解析失败
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"""
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try:
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required_fields = ['cited', 'sentiment', 'confidence']
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for field in required_fields:
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if field not in response:
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raise LLMAdapterError(f"响应缺少必需字段: {field}")
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cited = bool(response['cited'])
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sentiment = str(response.get('sentiment', 'neutral')).lower()
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if sentiment not in ['positive', 'neutral', 'negative']:
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sentiment = 'neutral'
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# 验证position
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position = response.get('position')
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if position is not None:
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position = int(position)
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if position < 1:
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position = None
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# 验证confidence
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confidence = float(response.get('confidence', 0.5))
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confidence = max(0.0, min(1.0, confidence))
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citation_text = response.get('citation_text')
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if citation_text and len(citation_text) > 500:
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citation_text = citation_text[:500]
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return CitationResult(
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cited=cited,
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position=position,
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citation_text=citation_text,
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sentiment=sentiment,
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confidence=confidence
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)
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except (ValueError, TypeError) as e:
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raise LLMAdapterError(f"解析响应失败: {e}")
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async def close(self):
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"""关闭客户端连接"""
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if self._client is not None:
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try:
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# OpenAI/DeepSeek客户端不需要显式关闭
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pass
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except Exception as e:
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logger.warning(f"关闭LLM客户端时出错: {e}")
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finally:
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self._client = None
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