43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
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import json
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import os
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import re
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from pydantic import BaseModel, Field
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from typing import List, Dict
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import requests
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import numpy as np
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from loguru import logger
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from configs.model_config import (
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VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD)
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from .base_tool import BaseToolModel
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from dev_opsgpt.service.kb_api import search_docs
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class DocRetrieval(BaseToolModel):
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name = "DocRetrieval"
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description = "采用向量化对本地知识库进行检索"
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class ToolInputArgs(BaseModel):
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query: str = Field(..., description="检索的关键字或问题")
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knowledge_base_name: str = Field(..., description="知识库名称", examples=["samples"])
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search_top: int = Field(VECTOR_SEARCH_TOP_K, description="检索返回的数量")
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score_threshold: float = Field(SCORE_THRESHOLD, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=1)
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class ToolOutputArgs(BaseModel):
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"""Output for MetricsQuery."""
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title: str = Field(..., description="检索网页标题")
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snippet: str = Field(..., description="检索内容的判断")
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link: str = Field(..., description="检索网页地址")
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@classmethod
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def run(cls, query, knowledge_base_name, search_top=VECTOR_SEARCH_TOP_K, score_threshold=SCORE_THRESHOLD):
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"""excute your tool!"""
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docs = search_docs(query, knowledge_base_name, search_top, score_threshold)
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return_docs = []
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for idx, doc in enumerate(docs):
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return_docs.append({"index": idx, "snippet": doc.page_content, "title": doc.metadata.get("source"), "link": doc.metadata.get("source")})
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return return_docs
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