package eino

import (
	"context"
	"errors"
	"rag/dao"
	"sort"

	"github.com/cloudwego/eino/callbacks"
	"github.com/cloudwego/eino/components/embedding"
	"github.com/cloudwego/eino/components/retriever"
	"github.com/cloudwego/eino/schema"
	"github.com/gogf/gf/v2/util/gconv"
	"github.com/pgvector/pgvector-go"
)

type PGVectorRetrieverConfig struct {
	Embedder     embedding.Embedder
	DefaultTopK  int
	DefaultIndex string
	DSLInfo      map[string]any
}

type PGVectorRetriever struct {
	embedder embedding.Embedder
	topK     int
	index    string
	dslInfo  map[string]any
}

func NewPGVectorRetriever(config *PGVectorRetrieverConfig) (*PGVectorRetriever, error) {
	if config.Embedder == nil {
		return nil, errors.New("embedder is required")
	}
	if config.DefaultTopK <= 0 {
		config.DefaultTopK = 5
	}

	return &PGVectorRetriever{
		embedder: config.Embedder,
		topK:     config.DefaultTopK,
		index:    config.DefaultIndex,
		dslInfo:  config.DSLInfo,
	}, nil
}

func (r *PGVectorRetriever) Retrieve(ctx context.Context, query string, opts ...retriever.Option) ([]*schema.Document, error) {
	options := &retriever.Options{
		Index:     &r.index,
		TopK:      &r.topK,
		DSLInfo:   r.dslInfo,
		Embedding: r.embedder,
	}
	options = retriever.GetCommonOptions(options, opts...)

	ctx = callbacks.OnStart(ctx, &retriever.CallbackInput{
		Query: query,
		TopK:  *options.TopK,
	})

	// ==========================================
	// 🔥 双路检索：向量 + 全文
	// ==========================================
	docsVector, err := r.doRetrieveVector(ctx, query, options)
	if err != nil {
		callbacks.OnError(ctx, err)
		return nil, err
	}

	docsFulltext, err := r.doRetrieveMeilisearch(ctx, query, options)
	if err != nil {
		callbacks.OnError(ctx, err)
		return nil, err
	}

	// 合并 + 去重
	docs := mergeAndDeduplicate(docsVector, docsFulltext)

	// 排序（distance 越小越靠前）
	sort.Slice(docs, func(i, j int) bool {
		d1 := gconv.Float64(docs[i].MetaData["distance"])
		d2 := gconv.Float64(docs[j].MetaData["distance"])
		return d1 < d2
	})

	// 最多保留 topK
	if len(docs) > *options.TopK {
		docs = docs[:*options.TopK]
	}

	callbacks.OnEnd(ctx, &retriever.CallbackOutput{Docs: docs})
	return docs, nil
}

// ==========================================
// 1. 向量检索（PG）
// ==========================================
func (r *PGVectorRetriever) doRetrieveVector(ctx context.Context, query string, opts *retriever.Options) ([]*schema.Document, error) {
	vectors, err := opts.Embedding.EmbedStrings(ctx, []string{query})
	if err != nil {
		return nil, err
	}
	if len(vectors) == 0 {
		return nil, errors.New("empty query vector")
	}

	queryVec := pgvector.NewVector(gconv.Float32s(vectors[0]))
	topK := *opts.TopK
	datasetIds := gconv.Int64s(opts.DSLInfo["dataset_ids"])

	rows, err := dao.DocumentChunk.GetAllByVector(ctx, datasetIds, queryVec, topK)
	if err != nil {
		return nil, err
	}

	docs := make([]*schema.Document, 0, len(rows))
	for _, row := range rows {
		docs = append(docs, &schema.Document{
			ID:      gconv.String(row["id"]),
			Content: gconv.String(row["content"]),
			MetaData: map[string]any{
				"dataset_id":  gconv.Int64(row["dataset_id"]),
				"document_id": gconv.Int64(row["document_id"]),
				"distance":    gconv.Float64(row["distance"]),
				"retrieve_by": "vector",
			},
		})
	}
	return docs, nil
}

// ==========================================
// 2. 全文检索（Meilisearch）🔥 新增
// ==========================================
func (r *PGVectorRetriever) doRetrieveMeilisearch(ctx context.Context, query string, opts *retriever.Options) ([]*schema.Document, error) {
	topK := *opts.TopK
	datasetIds := gconv.Int64s(opts.DSLInfo["dataset_ids"])

	// 调用你已有的 Meilisearch DAO
	rows, err := dao.DocumentChunk.SearchByKeywords(ctx, query, datasetIds, topK)
	if err != nil {
		return nil, err
	}

	docs := make([]*schema.Document, 0, len(rows))
	for _, row := range rows {
		docs = append(docs, &schema.Document{
			ID:      gconv.String(row["id"]),
			Content: gconv.String(row["content"]),
			MetaData: map[string]any{
				"dataset_id":  gconv.Int64(row["dataset_id"]),
				"document_id": gconv.Int64(row["document_id"]),
				"distance":    0.1, // 全文结果给高分
				"retrieve_by": "fulltext",
			},
		})
	}
	return docs, nil
}

// ==========================================
// 合并去重
// ==========================================
func mergeAndDeduplicate(vecDocs, fullDocs []*schema.Document) []*schema.Document {
	idMap := make(map[string]*schema.Document)
	for _, d := range vecDocs {
		idMap[d.ID] = d
	}
	for _, d := range fullDocs {
		if _, exists := idMap[d.ID]; !exists {
			idMap[d.ID] = d
		}
	}
	merged := make([]*schema.Document, 0, len(idMap))
	for _, d := range idMap {
		merged = append(merged, d)
	}
	return merged
}