43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
import json
|
||
import os
|
||
import re
|
||
from pydantic import BaseModel, Field
|
||
from typing import List, Dict
|
||
import requests
|
||
import numpy as np
|
||
from loguru import logger
|
||
|
||
from configs.model_config import (
|
||
VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD)
|
||
from .base_tool import BaseToolModel
|
||
|
||
|
||
|
||
from dev_opsgpt.service.kb_api import search_docs
|
||
|
||
|
||
class DocRetrieval(BaseToolModel):
|
||
name = "DocRetrieval"
|
||
description = "采用向量化对本地知识库进行检索"
|
||
|
||
class ToolInputArgs(BaseModel):
|
||
query: str = Field(..., description="检索的关键字或问题")
|
||
knowledge_base_name: str = Field(..., description="知识库名称", examples=["samples"])
|
||
search_top: int = Field(VECTOR_SEARCH_TOP_K, description="检索返回的数量")
|
||
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)
|
||
|
||
class ToolOutputArgs(BaseModel):
|
||
"""Output for MetricsQuery."""
|
||
title: str = Field(..., description="检索网页标题")
|
||
snippet: str = Field(..., description="检索内容的判断")
|
||
link: str = Field(..., description="检索网页地址")
|
||
|
||
@classmethod
|
||
def run(cls, query, knowledge_base_name, search_top=VECTOR_SEARCH_TOP_K, score_threshold=SCORE_THRESHOLD):
|
||
"""excute your tool!"""
|
||
docs = search_docs(query, knowledge_base_name, search_top, score_threshold)
|
||
return_docs = []
|
||
for idx, doc in enumerate(docs):
|
||
return_docs.append({"index": idx, "snippet": doc.page_content, "title": doc.metadata.get("source"), "link": doc.metadata.get("source")})
|
||
return return_docs
|