package memory

import (
	"bytes"
	"context"
	"crypto/sha256"
	"encoding/json"
	"fmt"
	"io"
	"math"
	"net/http"
	"strings"
	"time"

	"github.com/nextlevelbuilder/goclaw/internal/config"
)

// ContentHash returns a short SHA256 hex digest of the content (first 16 bytes).
func ContentHash(text string) string {
	h := sha256.Sum256([]byte(text))
	return fmt.Sprintf("%x", h[:16])
}

// TextChunk is a chunk of text with line number metadata.
type TextChunk struct {
	Text      string
	StartLine int
	EndLine   int
}

// ChunkText splits text into chunks at paragraph boundaries with optional overlap.
// Each chunk includes its starting line number in the source file.
// When overlap > 0, trailing lines from the previous chunk are prepended to the next chunk
// so that context at chunk boundaries is preserved for semantic search.
func ChunkText(text string, maxChunkLen, overlap int) []TextChunk {
	if maxChunkLen <= 0 {
		maxChunkLen = 1000
	}
	if overlap < 0 {
		overlap = 0
	}
	// Clamp overlap to half the chunk size to prevent infinite loop
	if overlap >= maxChunkLen/2 {
		overlap = maxChunkLen / 2
	}

	lines := strings.Split(text, "\n")
	var chunks []TextChunk
	var current strings.Builder
	startLine := 1

	// overlapLines holds trailing lines from the previous chunk to prepend to the next.
	var overlapLines []string
	overlapStartLine := 0

	flush := func(endLine int) {
		content := strings.TrimSpace(current.String())
		if content != "" {
			chunks = append(chunks, TextChunk{
				Text:      content,
				StartLine: startLine,
				EndLine:   endLine,
			})
		}

		// Compute overlap lines to carry into the next chunk.
		overlapLines = nil
		overlapStartLine = 0
		if overlap > 0 && endLine > 0 {
			charCount := 0
			for j := endLine - 1; j >= startLine-1 && j >= 0; j-- {
				lineLen := len(lines[j])
				if charCount+lineLen > overlap {
					break
				}
				charCount += lineLen + 1 // +1 for newline
				overlapLines = append(overlapLines, lines[j])
				overlapStartLine = j + 1 // 1-based
			}
			// Reverse to restore original order
			for left, right := 0, len(overlapLines)-1; left < right; left, right = left+1, right-1 {
				overlapLines[left], overlapLines[right] = overlapLines[right], overlapLines[left]
			}
		}

		current.Reset()
		// Seed next chunk with overlap content
		if len(overlapLines) > 0 {
			startLine = overlapStartLine
			for k, ol := range overlapLines {
				if k > 0 {
					current.WriteString("\n")
				}
				current.WriteString(ol)
			}
		} else {
			startLine = endLine + 1
		}
	}

	for i, line := range lines {
		lineNum := i + 1

		// Paragraph boundary: empty line
		if strings.TrimSpace(line) == "" && current.Len() > 0 {
			if current.Len() >= maxChunkLen/2 {
				flush(lineNum - 1)
				continue
			}
		}

		if current.Len() > 0 {
			current.WriteString("\n")
		}
		current.WriteString(line)

		// Force flush if too large
		if current.Len() >= maxChunkLen {
			flush(lineNum)
		}
	}

	if current.Len() > 0 {
		flush(len(lines))
	}

	return chunks
}

// EmbeddingProvider generates vector embeddings for text.
type EmbeddingProvider interface {
	// Name returns the provider identifier (e.g., "openai", "voyage").
	Name() string

	// Model returns the model used for embeddings.
	Model() string

	// Embed generates embeddings for a batch of texts.
	Embed(ctx context.Context, texts []string) ([][]float32, error)
}

// OpenAIEmbeddingProvider uses the OpenAI-compatible embedding API.
// Works with OpenAI, OpenRouter, and any compatible endpoint.
type OpenAIEmbeddingProvider struct {
	name       string
	model      string
	apiKey     string
	apiURL     string
	dimensions int // optional: truncate output to this many dimensions (0 = use model default)
	httpClient *http.Client
}

// NewOpenAIEmbeddingProvider creates a provider for OpenAI-compatible embedding APIs.
func NewOpenAIEmbeddingProvider(name, apiKey, apiURL, model string) *OpenAIEmbeddingProvider {
	if apiURL == "" {
		apiURL = "https://api.openai.com/v1"
	}
	if model == "" {
		model = "text-embedding-3-small"
	}
	// Rewrite loopback hosts to host.docker.internal when running inside Docker,
	// centralized here so no call site (verify handler, runtime memory embedding)
	// can reach the container itself instead of a host service like Ollama.
	apiURL = config.DockerLocalhost(apiURL)

	return &OpenAIEmbeddingProvider{
		name:       name,
		model:      model,
		apiKey:     apiKey,
		apiURL:     apiURL,
		httpClient: &http.Client{Timeout: 60 * time.Second},
	}
}

// WithDimensions sets the output dimensions for models that support dimension truncation.
func (p *OpenAIEmbeddingProvider) WithDimensions(d int) *OpenAIEmbeddingProvider {
	p.dimensions = d
	return p
}

func (p *OpenAIEmbeddingProvider) Name() string  { return p.name }
func (p *OpenAIEmbeddingProvider) Model() string { return p.model }

func (p *OpenAIEmbeddingProvider) Embed(ctx context.Context, texts []string) ([][]float32, error) {
	reqBody := map[string]any{
		"input": texts,
		"model": p.model,
	}
	if p.dimensions > 0 {
		reqBody["dimensions"] = p.dimensions
	}

	bodyJSON, err := json.Marshal(reqBody)
	if err != nil {
		return nil, fmt.Errorf("marshal request: %w", err)
	}

	req, err := http.NewRequestWithContext(ctx, http.MethodPost, p.apiURL+"/embeddings", bytes.NewReader(bodyJSON))
	if err != nil {
		return nil, fmt.Errorf("create request: %w", err)
	}

	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("Authorization", "Bearer "+p.apiKey)

	resp, err := p.httpClient.Do(req)
	if err != nil {
		return nil, fmt.Errorf("embedding request: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		body, _ := io.ReadAll(resp.Body)
		return nil, fmt.Errorf("embedding API error %d: %s", resp.StatusCode, string(body))
	}

	var result struct {
		Data []struct {
			Embedding []float32 `json:"embedding"`
			Index     *int      `json:"index"`
		} `json:"data"`
	}

	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
		return nil, fmt.Errorf("decode response: %w", err)
	}

	if len(result.Data) != len(texts) {
		return nil, fmt.Errorf("embedding response count: got %d vectors for %d inputs", len(result.Data), len(texts))
	}

	embeddings := make([][]float32, len(result.Data))
	indexed := false
	for _, d := range result.Data {
		indexed = indexed || d.Index != nil
	}
	seen := make([]bool, len(result.Data))
	for responseIndex, d := range result.Data {
		targetIndex := responseIndex
		if indexed {
			if d.Index == nil || *d.Index < 0 || *d.Index >= len(result.Data) || seen[*d.Index] {
				return nil, fmt.Errorf("embedding response has invalid index at position %d", responseIndex)
			}
			targetIndex = *d.Index
		}
		seen[targetIndex] = true
		if len(d.Embedding) == 0 {
			return nil, fmt.Errorf("embedding response has empty vector at index %d", targetIndex)
		}
		if p.dimensions > 0 && len(d.Embedding) != p.dimensions {
			return nil, fmt.Errorf("embedding response dimension at index %d: got %d, want %d", targetIndex, len(d.Embedding), p.dimensions)
		}
		for _, value := range d.Embedding {
			if math.IsNaN(float64(value)) || math.IsInf(float64(value), 0) {
				return nil, fmt.Errorf("embedding response has non-finite value at index %d", targetIndex)
			}
		}
		embeddings[targetIndex] = d.Embedding
	}

	return embeddings, nil
}

// CosineSimilarity computes the cosine similarity between two vectors.
// Returns a value between -1 and 1 (1 = identical).
func CosineSimilarity(a, b []float32) float64 {
	if len(a) != len(b) || len(a) == 0 {
		return 0
	}

	var dot, normA, normB float64
	for i := range a {
		dot += float64(a[i]) * float64(b[i])
		normA += float64(a[i]) * float64(a[i])
		normB += float64(b[i]) * float64(b[i])
	}

	denom := math.Sqrt(normA) * math.Sqrt(normB)
	if denom == 0 {
		return 0
	}

	return dot / denom
}