retriever

type Fakeretriever struct {
	Name string
	Docs []langchaingoschema.Document
}

type KnowledgeBaseRetriever struct {
	base.BaseNode
	Instance *apiretriever.KnowledgeBaseRetriever
	Finish   func()
}

type MultiQueryRetriever struct {
	base.BaseNode
	Instance *apiretriever.MultiQueryRetriever
}

type Reference struct {
	// Question row
	Question string `json:"question" example:"q: 旷工最小计算单位为多少天？"`
	// Answer row
	Answer string `json:"answer" example:"旷工最小计算单位为 0.5 天。"`
	// vector search score
	Score float32 `json:"score" example:"0.34"`
	// the qa file fullpath
	QAFilePath string `json:"qa_file_path" example:"dataset/dataset-playground/v1/qa.csv"`
	// line number in the qa file
	QALineNumber int `json:"qa_line_number" example:"7"`
	// source file name, only file name, not full path
	FileName string `json:"file_name" example:"员工考勤管理制度-2023.pdf"`
	// page number in the source file
	PageNumber int `json:"page_number" example:"1"`
	// related content in the source file or in webpage
	Content string `` /* 144-byte string literal not displayed */
	// Title of the webpage
	Title string `json:"title,omitempty" example:"开始使用 Microsoft 帐户 – Microsoft"`
	// URL of the webpage
	URL string `json:"url,omitempty" example:"https://www.microsoft.com/zh-cn/welcome"`
	// RerankScore
	RerankScore float32        `json:"rerank_score,omitempty" example:"0.58124"`
	Metadata    map[string]any `json:"-"`
}

type RerankRequestBody struct {
	Query    string   `json:"question"`
	Passages []string `json:"answers"`
}

type RerankRetriever struct {
	base.BaseNode
	Instance *apiretriever.RerankRetriever
}