core

type Chunk struct {
	Content    string         `json:"content"`
	StartPos   int            `json:"start_pos"`
	EndPos     int            `json:"end_pos"`
	Metadata   map[string]any `json:"metadata"`
	TokenCount int            `json:"token_count"`
}

type Clearable interface {
	ClearAll(ctx context.Context) error
}

type ContextProvider interface {
	GenerateContext(ctx context.Context, doc Document, chunk string) (string, error)
}

type CrossEncoderProvider interface {
	Score(ctx context.Context, pairs []QueryDocPair) ([]float64, error)
}

type Document struct {
	ID        string         `json:"id"`
	Content   string         `json:"content"`
	Metadata  map[string]any `json:"metadata,omitempty"`
	Embedding []float64      `json:"embedding,omitempty"`
}

type DocumentLister interface {
	ListDocumentIDs(ctx context.Context, limit int, offset int) ([]string, error)
}

type Edge struct {
	ID         string         `json:"id"`
	Source     string         `json:"source"`
	Target     string         `json:"target"`
	Type       string         `json:"type"`
	Properties map[string]any `json:"properties,omitempty"`
	Weight     float64        `json:"weight"`
}

type EmbeddingProvider interface {
	EmbedQuery(ctx context.Context, query string) ([]float64, error)
	EmbedDocuments(ctx context.Context, documents []string) ([][]float64, error)
	Name() string
}

type EmbeddingProviderType string

type EvalMetrics struct {
	ContextRelevance float64 `json:"context_relevance"`
	Faithfulness     float64 `json:"faithfulness"`
	AnswerRelevancy  float64 `json:"answer_relevancy"`
	RecallAtK        float64 `json:"recall_at_k"`
	MRR              float64 `json:"mrr"`
}

type GraphEmbedder interface {
	Embed(ctx context.Context, text string) ([]float64, error)
}

type GraphRetrievalResult struct {
	ID           string         `json:"id"`
	Content      string         `json:"content"`
	Score        float64        `json:"score"`
	GraphScore   float64        `json:"graph_score"`
	VectorScore  float64        `json:"vector_score"`
	Source       string         `json:"source"`
	Metadata     map[string]any `json:"metadata,omitempty"`
	RelatedNodes []*Node        `json:"related_nodes,omitempty"`
}

type LLMRerankerProvider interface {
	ScoreRelevance(ctx context.Context, query, document string) (float64, error)
}

type LowLevelSearchResult struct {
	ID       string         `json:"id"`
	Score    float64        `json:"score"`
	Metadata map[string]any `json:"metadata"`
}

type LowLevelVectorStore interface {
	Store(ctx context.Context, id string, vector []float64, metadata map[string]any) error
	Search(ctx context.Context, query []float64, topK int, filter map[string]any) ([]LowLevelSearchResult, error)
	Delete(ctx context.Context, id string) error
}

type Node struct {
	ID         string         `json:"id"`
	Type       string         `json:"type"`
	Label      string         `json:"label"`
	Properties map[string]any `json:"properties,omitempty"`
	Embedding  []float64      `json:"embedding,omitempty"`
	CreatedAt  time.Time      `json:"created_at"`
}

type QueryDocPair struct {
	Query    string
	Document string
}

type QueryLLMProvider interface {
	Complete(ctx context.Context, prompt string) (string, error)
}

type RAGError struct {
	Code    types.ErrorCode
	Message string
	Cause   error
}

type RerankProvider interface {
	RerankSimple(ctx context.Context, query string, documents []string, topN int) ([]types.RerankResult, error)
	Name() string
}

type RerankProviderType string

type Reranker interface {
	Rerank(ctx context.Context, query string, results []RetrievalResult) ([]RetrievalResult, error)
}

type RetrievalMetrics struct {
	TraceID          string        `json:"trace_id"`
	RunID            string        `json:"run_id"`
	SpanID           string        `json:"span_id,omitempty"`
	RetrievalLatency time.Duration `json:"retrieval_latency_ms"`
	RerankLatency    time.Duration `json:"rerank_latency_ms"`
	TopK             int           `json:"topk"`
	HitCount         int           `json:"hit_count"`
	ContextTokens    int           `json:"context_tokens"`
	Strategy         string        `json:"strategy"`
}

type RetrievalResult struct {
	Document    Document `json:"document"`
	BM25Score   float64  `json:"bm25_score"`
	VectorScore float64  `json:"vector_score"`
	HybridScore float64  `json:"hybrid_score"`
	RerankScore float64  `json:"rerank_score,omitempty"`
	FinalScore  float64  `json:"final_score"`
}

type SearchResult struct {
	ID       string
	Distance float64
	Score    float64
}

type Tokenizer interface {
	CountTokens(text string) int
	Encode(text string) []int
}

type VectorSearchResult struct {
	Document Document `json:"document"`
	Score    float64  `json:"score"`
	Distance float64  `json:"distance"`
}

type VectorStore interface {
	AddDocuments(ctx context.Context, docs []Document) error
	Search(ctx context.Context, queryEmbedding []float64, topK int) ([]VectorSearchResult, error)
	DeleteDocuments(ctx context.Context, ids []string) error
	UpdateDocument(ctx context.Context, doc Document) error
	Count(ctx context.Context) (int, error)
}

type VectorStoreAdapter struct {
	// contains filtered or unexported fields
}

type VectorStoreType string

type WebRetrievalResult struct {
	URL     string  `json:"url"`
	Title   string  `json:"title"`
	Content string  `json:"content"`
	Score   float64 `json:"score"`
}

type WebSearchFunc func(ctx context.Context, query string, maxResults int) ([]WebRetrievalResult, error)