40 lines
2.0 KiB
Python
40 lines
2.0 KiB
Python
from pydantic import BaseModel, Field
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from loguru import logger
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from coagent.llm_models.llm_config import EmbedConfig
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from .base_tool import BaseToolModel
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from coagent.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(5, description="检索返回的数量")
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score_threshold: float = Field(1.0, 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=5, score_threshold=1.0, embed_config: EmbedConfig=EmbedConfig(), kb_root_path: str=""):
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"""excute your tool!"""
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try:
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docs = search_docs(query, knowledge_base_name, search_top, score_threshold,
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kb_root_path=kb_root_path, embed_engine=embed_config.embed_engine,
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embed_model=embed_config.embed_model, embed_model_path=embed_config.embed_model_path,
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model_device=embed_config.model_device
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)
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except Exception as e:
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logger.exception(e)
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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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