97 lines
3.8 KiB
Python
97 lines
3.8 KiB
Python
import dashscope
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from http import HTTPStatus
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from backend.core.config import settings
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import logging
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# 获取当前模块的专用 Logger
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# __name__ 会自动识别为 "backend.services.crawler_service" 这样的路径
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logger = logging.getLogger(__name__)
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class LLMService:
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"""
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LLM 服务封装层
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负责与 DashScope (通义千问/GTE) 交互，包括 Embedding 和 Rerank
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"""
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def __init__(self):
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dashscope.api_key = settings.DASHSCOPE_API_KEY
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def get_embedding(self, text: str, dimension: int = 1536):
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"""生成文本向量 (Bi-Encoder)"""
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try:
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resp = dashscope.TextEmbedding.call(
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model=dashscope.TextEmbedding.Models.text_embedding_v4, # 或 v4，视你的数据库维度而定
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input=text,
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dimension=dimension
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)
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if resp.status_code == HTTPStatus.OK:
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return resp.output['embeddings'][0]['embedding']
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else:
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logger.error(f"Embedding API Error: {resp}")
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return None
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except Exception as e:
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logger.error(f"Embedding Exception: {e}")
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return None
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def rerank(self, query: str, documents: list, top_n: int = 5):
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"""
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执行重排序 (Cross-Encoder)
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Args:
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query: 用户问题
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documents: 粗排召回的切片列表 (List[dict])，必须包含 'content' 字段
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top_n: 最终返回多少个结果
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Returns:
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List[dict]: 排序后并截取 Top N 的文档列表，包含新的 'score'
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"""
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if not documents:
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return []
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# 1. 准备输入数据
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# Rerank API 需要纯文本列表，但我们需要保留 documents 里的 meta_info 和 id
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# 所以我们提取 content 给 API，拿到 index 后再映射回去
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doc_contents = [doc.get('content', '') for doc in documents]
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# 如果文档太多（比如超过 100 个），建议先截断，避免 API 超时或报错
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if len(doc_contents) > 50:
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doc_contents = doc_contents[:50]
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documents = documents[:50]
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try:
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# 2. 调用 DashScope GTE-Rerank
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resp = dashscope.TextReRank.call(
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model='gte-rerank',
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query=query,
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documents=doc_contents,
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top_n=top_n,
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return_documents=False # 我们只需要索引和分数，不需要它把文本再传回来
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)
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if resp.status_code == HTTPStatus.OK:
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# 3. 结果重组
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# API 返回结构示例: output.results = [{'index': 2, 'relevance_score': 0.98}, {'index': 0, ...}]
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reranked_results = []
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for item in resp.output.results:
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# 根据 API 返回的 index 找到原始文档对象
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original_doc = documents[item.index]
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# 更新分数为 Rerank 的精准分数 (通常是 0~1 之间的置信度)
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original_doc['score'] = item.relevance_score
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# 标记来源，方便调试知道这是 Rerank 过的
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original_doc['reranked'] = True
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reranked_results.append(original_doc)
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return reranked_results
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else:
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logger.error(f"Rerank API Error: {resp}")
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# 降级策略：如果 Rerank 挂了，直接返回粗排的前 N 个
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return documents[:top_n]
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except Exception as e:
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logger.error(f"Rerank Exception: {e}")
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# 降级策略
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return documents[:top_n]
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llm_service = LLMService() |