package search

import (
	"context"
	"encoding/json"
	"fmt"
	"strings"

	"github.com/opensearch-project/opensearch-go/v2"
	"github.com/opensearch-project/opensearch-go/v2/opensearchapi"
)

// SearchRequest represents an API search request
type SearchRequest struct {
	Query   string            `json:"q"`
	Mode    string            `json:"mode"` // keyword, semantic, hybrid
	Limit   int               `json:"limit"`
	Offset  int               `json:"offset"`
	Filters SearchFilters     `json:"filters"`
	Rerank  bool              `json:"rerank"`
	Include SearchInclude     `json:"include"`
}

// SearchFilters for narrowing results
type SearchFilters struct {
	Language       []string `json:"language"`
	CountryHint    []string `json:"country_hint"`
	SourceCategory []string `json:"source_category"`
	DocType        []string `json:"doc_type"`
	SchoolLevel    []string `json:"school_level"`
	Subjects       []string `json:"subjects"`
	State          []string `json:"state"`
	MinTrustScore  float64  `json:"min_trust_score"`
	DateFrom       string   `json:"date_from"`
}

// SearchInclude specifies what to include in response
type SearchInclude struct {
	Snippets    bool `json:"snippets"`
	Highlights  bool `json:"highlights"`
	ContentText bool `json:"content_text"`
}

// SearchResult represents a single search result
type SearchResult struct {
	DocID       string   `json:"doc_id"`
	Title       string   `json:"title"`
	URL         string   `json:"url"`
	Domain      string   `json:"domain"`
	Language    string   `json:"language"`
	DocType     string   `json:"doc_type"`
	SchoolLevel string   `json:"school_level"`
	Subjects    []string `json:"subjects"`
	Scores      Scores   `json:"scores"`
	Snippet     string   `json:"snippet,omitempty"`
	Highlights  []string `json:"highlights,omitempty"`
}

// Scores contains all scoring components
type Scores struct {
	BM25     float64 `json:"bm25"`
	Semantic float64 `json:"semantic"`
	Rerank   float64 `json:"rerank"`
	Trust    float64 `json:"trust"`
	Quality  float64 `json:"quality"`
	Final    float64 `json:"final"`
}

// SearchResponse is the API response
type SearchResponse struct {
	QueryID    string         `json:"query_id"`
	Results    []SearchResult `json:"results"`
	Pagination Pagination     `json:"pagination"`
}

// Pagination info
type Pagination struct {
	Limit         int `json:"limit"`
	Offset        int `json:"offset"`
	TotalEstimate int `json:"total_estimate"`
}

// EmbeddingProvider interface for generating embeddings
type EmbeddingProvider interface {
	Embed(ctx context.Context, text string) ([]float32, error)
	IsEnabled() bool
	Dimension() int
}

// Service handles search operations
type Service struct {
	client            *opensearch.Client
	indexName         string
	embeddingProvider EmbeddingProvider
	semanticEnabled   bool
}

// NewService creates a new search service
func NewService(url, username, password, indexName string) (*Service, error) {
	cfg := opensearch.Config{
		Addresses: []string{url},
		Username:  username,
		Password:  password,
	}

	client, err := opensearch.NewClient(cfg)
	if err != nil {
		return nil, err
	}

	return &Service{
		client:          client,
		indexName:       indexName,
		semanticEnabled: false,
	}, nil
}

// SetEmbeddingProvider configures the embedding provider for semantic search
func (s *Service) SetEmbeddingProvider(provider EmbeddingProvider) {
	if provider != nil && provider.IsEnabled() {
		s.embeddingProvider = provider
		s.semanticEnabled = true
	}
}

// IsSemanticEnabled returns true if semantic search is available
func (s *Service) IsSemanticEnabled() bool {
	return s.semanticEnabled && s.embeddingProvider != nil
}

// Search performs a search query
func (s *Service) Search(ctx context.Context, req *SearchRequest) (*SearchResponse, error) {
	// Determine search mode
	mode := req.Mode
	if mode == "" {
		mode = "keyword" // Default to keyword search
	}

	// For semantic/hybrid modes, generate query embedding
	var queryEmbedding []float32
	var embErr error
	if (mode == "semantic" || mode == "hybrid") && s.IsSemanticEnabled() {
		queryEmbedding, embErr = s.embeddingProvider.Embed(ctx, req.Query)
		if embErr != nil {
			// Fall back to keyword search if embedding fails
			mode = "keyword"
		}
	} else if mode == "semantic" || mode == "hybrid" {
		// Semantic requested but not enabled, fall back
		mode = "keyword"
	}

	// Build OpenSearch query based on mode
	var query map[string]interface{}
	switch mode {
	case "semantic":
		query = s.buildSemanticQuery(req, queryEmbedding)
	case "hybrid":
		query = s.buildHybridQuery(req, queryEmbedding)
	default:
		query = s.buildQuery(req)
	}

	queryJSON, err := json.Marshal(query)
	if err != nil {
		return nil, err
	}

	searchReq := opensearchapi.SearchRequest{
		Index: []string{s.indexName},
		Body:  strings.NewReader(string(queryJSON)),
	}

	res, err := searchReq.Do(ctx, s.client)
	if err != nil {
		return nil, err
	}
	defer res.Body.Close()

	// Parse response
	var osResponse struct {
		Hits struct {
			Total struct {
				Value int `json:"value"`
			} `json:"total"`
			Hits []struct {
				ID     string                 `json:"_id"`
				Score  float64                `json:"_score"`
				Source map[string]interface{} `json:"_source"`
				Highlight map[string][]string `json:"highlight,omitempty"`
			} `json:"hits"`
		} `json:"hits"`
	}

	if err := json.NewDecoder(res.Body).Decode(&osResponse); err != nil {
		return nil, err
	}

	// Convert to SearchResults
	results := make([]SearchResult, 0, len(osResponse.Hits.Hits))
	for _, hit := range osResponse.Hits.Hits {
		result := s.hitToResult(hit.Source, hit.Score, hit.Highlight, req.Include)
		results = append(results, result)
	}

	return &SearchResponse{
		QueryID: fmt.Sprintf("q-%d", ctx.Value("request_id")),
		Results: results,
		Pagination: Pagination{
			Limit:         req.Limit,
			Offset:        req.Offset,
			TotalEstimate: osResponse.Hits.Total.Value,
		},
	}, nil
}

// buildQuery constructs the OpenSearch query
func (s *Service) buildQuery(req *SearchRequest) map[string]interface{} {
	// Main query
	must := []map[string]interface{}{}
	filter := []map[string]interface{}{}

	// Text search
	if req.Query != "" {
		must = append(must, map[string]interface{}{
			"multi_match": map[string]interface{}{
				"query":  req.Query,
				"fields": []string{"title^3", "content_text"},
				"type":   "best_fields",
			},
		})
	}

	// Filters
	if len(req.Filters.Language) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"language": req.Filters.Language},
		})
	}

	if len(req.Filters.CountryHint) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"country_hint": req.Filters.CountryHint},
		})
	}

	if len(req.Filters.SourceCategory) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"source_category": req.Filters.SourceCategory},
		})
	}

	if len(req.Filters.DocType) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"doc_type": req.Filters.DocType},
		})
	}

	if len(req.Filters.SchoolLevel) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"school_level": req.Filters.SchoolLevel},
		})
	}

	if len(req.Filters.Subjects) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"subjects": req.Filters.Subjects},
		})
	}

	if len(req.Filters.State) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"state": req.Filters.State},
		})
	}

	if req.Filters.MinTrustScore > 0 {
		filter = append(filter, map[string]interface{}{
			"range": map[string]interface{}{
				"trust_score": map[string]interface{}{"gte": req.Filters.MinTrustScore},
			},
		})
	}

	if req.Filters.DateFrom != "" {
		filter = append(filter, map[string]interface{}{
			"range": map[string]interface{}{
				"fetch_time": map[string]interface{}{"gte": req.Filters.DateFrom},
			},
		})
	}

	// Build bool query
	boolQuery := map[string]interface{}{}
	if len(must) > 0 {
		boolQuery["must"] = must
	}
	if len(filter) > 0 {
		boolQuery["filter"] = filter
	}

	// Construct full query
	query := map[string]interface{}{
		"query": map[string]interface{}{
			"bool": boolQuery,
		},
		"from": req.Offset,
		"size": req.Limit,
		"_source": []string{
			"doc_id", "title", "url", "domain", "language",
			"doc_type", "school_level", "subjects",
			"trust_score", "quality_score", "snippet_text",
		},
	}

	// Add highlighting if requested
	if req.Include.Highlights {
		query["highlight"] = map[string]interface{}{
			"fields": map[string]interface{}{
				"title":        map[string]interface{}{},
				"content_text": map[string]interface{}{"fragment_size": 150, "number_of_fragments": 3},
			},
		}
	}

	// Add function score for trust/quality boosting
	query["query"] = map[string]interface{}{
		"function_score": map[string]interface{}{
			"query": query["query"],
			"functions": []map[string]interface{}{
				{
					"field_value_factor": map[string]interface{}{
						"field":    "trust_score",
						"factor":   1.5,
						"modifier": "sqrt",
						"missing":  0.5,
					},
				},
				{
					"field_value_factor": map[string]interface{}{
						"field":    "quality_score",
						"factor":   1.0,
						"modifier": "sqrt",
						"missing":  0.5,
					},
				},
			},
			"score_mode": "multiply",
			"boost_mode": "multiply",
		},
	}

	return query
}

// buildSemanticQuery constructs a pure vector search query using k-NN
func (s *Service) buildSemanticQuery(req *SearchRequest, embedding []float32) map[string]interface{} {
	filter := s.buildFilters(req)

	// k-NN query for semantic search
	knnQuery := map[string]interface{}{
		"content_embedding": map[string]interface{}{
			"vector": embedding,
			"k":      req.Limit + req.Offset, // Get enough results for pagination
		},
	}

	// Add filter if present
	if len(filter) > 0 {
		knnQuery["content_embedding"].(map[string]interface{})["filter"] = map[string]interface{}{
			"bool": map[string]interface{}{
				"filter": filter,
			},
		}
	}

	query := map[string]interface{}{
		"knn":   knnQuery,
		"from":  req.Offset,
		"size":  req.Limit,
		"_source": []string{
			"doc_id", "title", "url", "domain", "language",
			"doc_type", "school_level", "subjects",
			"trust_score", "quality_score", "snippet_text",
		},
	}

	// Add highlighting if requested
	if req.Include.Highlights {
		query["highlight"] = map[string]interface{}{
			"fields": map[string]interface{}{
				"title":        map[string]interface{}{},
				"content_text": map[string]interface{}{"fragment_size": 150, "number_of_fragments": 3},
			},
		}
	}

	return query
}

// buildHybridQuery constructs a combined BM25 + vector search query
func (s *Service) buildHybridQuery(req *SearchRequest, embedding []float32) map[string]interface{} {
	filter := s.buildFilters(req)

	// Build the bool query for BM25
	must := []map[string]interface{}{}
	if req.Query != "" {
		must = append(must, map[string]interface{}{
			"multi_match": map[string]interface{}{
				"query":  req.Query,
				"fields": []string{"title^3", "content_text"},
				"type":   "best_fields",
			},
		})
	}

	boolQuery := map[string]interface{}{}
	if len(must) > 0 {
		boolQuery["must"] = must
	}
	if len(filter) > 0 {
		boolQuery["filter"] = filter
	}

	// Convert embedding to []interface{} for JSON
	embeddingInterface := make([]interface{}, len(embedding))
	for i, v := range embedding {
		embeddingInterface[i] = v
	}

	// Hybrid query using script_score to combine BM25 and cosine similarity
	// This is a simpler approach than OpenSearch's neural search plugin
	query := map[string]interface{}{
		"query": map[string]interface{}{
			"script_score": map[string]interface{}{
				"query": map[string]interface{}{
					"bool": boolQuery,
				},
				"script": map[string]interface{}{
					"source": "cosineSimilarity(params.query_vector, 'content_embedding') + 1.0 + _score * 0.5",
					"params": map[string]interface{}{
						"query_vector": embeddingInterface,
					},
				},
			},
		},
		"from": req.Offset,
		"size": req.Limit,
		"_source": []string{
			"doc_id", "title", "url", "domain", "language",
			"doc_type", "school_level", "subjects",
			"trust_score", "quality_score", "snippet_text",
		},
	}

	// Add highlighting if requested
	if req.Include.Highlights {
		query["highlight"] = map[string]interface{}{
			"fields": map[string]interface{}{
				"title":        map[string]interface{}{},
				"content_text": map[string]interface{}{"fragment_size": 150, "number_of_fragments": 3},
			},
		}
	}

	return query
}

// buildFilters constructs the filter array for queries
func (s *Service) buildFilters(req *SearchRequest) []map[string]interface{} {
	filter := []map[string]interface{}{}

	if len(req.Filters.Language) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"language": req.Filters.Language},
		})
	}

	if len(req.Filters.CountryHint) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"country_hint": req.Filters.CountryHint},
		})
	}

	if len(req.Filters.SourceCategory) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"source_category": req.Filters.SourceCategory},
		})
	}

	if len(req.Filters.DocType) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"doc_type": req.Filters.DocType},
		})
	}

	if len(req.Filters.SchoolLevel) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"school_level": req.Filters.SchoolLevel},
		})
	}

	if len(req.Filters.Subjects) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"subjects": req.Filters.Subjects},
		})
	}

	if len(req.Filters.State) > 0 {
		filter = append(filter, map[string]interface{}{
			"terms": map[string]interface{}{"state": req.Filters.State},
		})
	}

	if req.Filters.MinTrustScore > 0 {
		filter = append(filter, map[string]interface{}{
			"range": map[string]interface{}{
				"trust_score": map[string]interface{}{"gte": req.Filters.MinTrustScore},
			},
		})
	}

	if req.Filters.DateFrom != "" {
		filter = append(filter, map[string]interface{}{
			"range": map[string]interface{}{
				"fetch_time": map[string]interface{}{"gte": req.Filters.DateFrom},
			},
		})
	}

	return filter
}

// hitToResult converts an OpenSearch hit to SearchResult
func (s *Service) hitToResult(source map[string]interface{}, score float64, highlight map[string][]string, include SearchInclude) SearchResult {
	result := SearchResult{
		DocID:       getString(source, "doc_id"),
		Title:       getString(source, "title"),
		URL:         getString(source, "url"),
		Domain:      getString(source, "domain"),
		Language:    getString(source, "language"),
		DocType:     getString(source, "doc_type"),
		SchoolLevel: getString(source, "school_level"),
		Subjects:    getStringArray(source, "subjects"),
		Scores: Scores{
			BM25:    score,
			Trust:   getFloat(source, "trust_score"),
			Quality: getFloat(source, "quality_score"),
			Final:   score, // MVP: final = BM25 * trust * quality (via function_score)
		},
	}

	if include.Snippets {
		result.Snippet = getString(source, "snippet_text")
	}

	if include.Highlights && highlight != nil {
		if h, ok := highlight["content_text"]; ok {
			result.Highlights = h
		}
	}

	return result
}

// Helper functions
func getString(m map[string]interface{}, key string) string {
	if v, ok := m[key].(string); ok {
		return v
	}
	return ""
}

func getFloat(m map[string]interface{}, key string) float64 {
	if v, ok := m[key].(float64); ok {
		return v
	}
	return 0.0
}

func getStringArray(m map[string]interface{}, key string) []string {
	if v, ok := m[key].([]interface{}); ok {
		result := make([]string, 0, len(v))
		for _, item := range v {
			if s, ok := item.(string); ok {
				result = append(result, s)
			}
		}
		return result
	}
	return nil
}