docqa

type Attributes struct {
	Position Range
	Evidence []Range
}

type Client interface {
	// GetLLMResponse prompts the LLM and gets its text response, hopefully (not certainly) with the given schema.
	GetLLMResponse(systemPrompt string, userPrompt string, schema map[string]any) (string, LLMUsage, error)
}

type Entity interface {
	PureData
	Attr() *Attributes
	// contains filtered or unexported methods
}

type LLMType interface {
	Parse(value string) (Entity, error)
	Instructions() LLMTypeInstructions
}

type LLMTypeInstructions struct {
	OneLiner string
	Details  []string
}

type LLMUsage struct {
	InputTokens  int
	OutputTokens int
}

type Protocol interface {
	// Schema creates a jsonschema given a set of keyed questions.
	Schema(qs map[string]Question) map[string]any
	// SystemPrompt builds a system prompt for the given set of [Question]s.
	SystemPrompt(qs map[string]Question) string
	// ParseResponse takes a raw LLM response and parses it into lists of [Entity], keyed by question key.
	ParseResponse(resp string) (map[string][]Entity, error)
}

type PureData interface {
	Validate() error
	Clone() PureData
	Eq(PureData) bool
}

type Question struct {
	// Question is the one-liner, for example `Who is the author of this document?`.
	Question string `json:"question"`
	// Details provides extra details, examples, and instructions on how to answer this question, as bullet points.
	Details []string `json:"details"`
	// AllowedTypeKeys lists all the keys of the types which the LLM may respond with.
	AllowedTypeKeys []string `json:"allowed_type_keys"`
}

type Range struct {
	Start int `json:"start"`
	End   int `json:"end"`
}

type RoleAndTask struct {
	Role string
	Task string
}

type Value interface {
	PureData
}

type ValueEntity[T Value] struct {
	Attributes Attributes
	Value      T
}