package vector

import (
	"context"
	"fmt"

	"engimind/internal/models"
)

// ContextChunk is a search result with source metadata.
type ContextChunk struct {
	Text     string  `json:"text"`
	SourceID string  `json:"sourceId"`
	Score    float32 `json:"score"`
}

// RAGService orchestrates embedding + vector search for retrieval.
type RAGService struct {
	embedding *EmbeddingService
	store     *QdrantStore
}

// NewRAGService creates a RAG service.
func NewRAGService(embedding *EmbeddingService, store *QdrantStore) *RAGService {
	return &RAGService{embedding: embedding, store: store}
}

// CollectionName returns the Qdrant collection name for a project.
func CollectionName(projectID string) string {
	return fmt.Sprintf("engimind_%s", projectID)
}

// IndexDocument chunks and indexes a parsed document.
func (s *RAGService) IndexDocument(ctx context.Context, projectID string, source models.SourceFile, content string, embeddingCfg EmbeddingConfig) error {
	colName := CollectionName(projectID)
	if err := s.store.EnsureCollection(ctx, colName); err != nil {
		return err
	}

	textChunks := ChunkText(content, 500, 50)
	var chunks []Chunk
	for i, text := range textChunks {
		vec, err := s.embedding.GetEmbedding(
			text, embeddingCfg.BaseURL, embeddingCfg.Model,
			embeddingCfg.APIKey, embeddingCfg.Provider,
		)
		if err != nil {
			return fmt.Errorf("embed chunk %d: %w", i, err)
		}
		chunks = append(chunks, Chunk{
			ID:       fmt.Sprintf("%s-chunk-%d", source.ID, i),
			SourceID: source.ID,
			Text:     text,
			Vector:   vec,
		})
	}

	return s.store.Insert(ctx, colName, chunks)
}

// SearchContext retrieves relevant text chunks for a query.
func (s *RAGService) SearchContext(ctx context.Context, projectID, question string, topK int, embeddingCfg EmbeddingConfig) ([]ContextChunk, error) {
	queryVec, err := s.embedding.GetEmbedding(
		question, embeddingCfg.BaseURL, embeddingCfg.Model,
		embeddingCfg.APIKey, embeddingCfg.Provider,
	)
	if err != nil {
		return nil, fmt.Errorf("embed query: %w", err)
	}

	colName := CollectionName(projectID)
	results, err := s.store.Search(ctx, colName, queryVec, uint64(topK))
	if err != nil {
		return nil, err
	}

	contextChunks := make([]ContextChunk, len(results))
	for i, r := range results {
		contextChunks[i] = ContextChunk{
			Text:     r.Text,
			SourceID: r.SourceID,
		}
	}
	return contextChunks, nil
}

// EmbeddingConfig holds the config needed to call an embedding API.
type EmbeddingConfig struct {
	BaseURL  string
	Model    string
	APIKey   string
	Provider string
}