// Package ai provides AI integration for embeddings and context injection.
package ai

import (
	"context"
	"encoding/json"
	"fmt"
	"net/http"
	"os"
	"strings"
	"time"
)

// OpenAIClient implements AI operations using OpenAI API.
type OpenAIClient struct {
	config     *Config
	httpClient *http.Client
}

// NewOpenAIClient creates a new OpenAI client.
func NewOpenAIClient(config *Config) *OpenAIClient {
	apiKey := config.APIKey
	if apiKey == "" {
		apiKey = os.Getenv("OPENAI_API_KEY")
	}

	baseURL := config.BaseURL
	if baseURL == "" {
		baseURL = "https://api.openai.com/v1"
	}

	return &OpenAIClient{
		config: &Config{
			Provider:    config.Provider,
			Model:       config.Model,
			Dimensions:  config.Dimensions,
			APIKey:      apiKey,
			BaseURL:     baseURL,
			BatchSize:   config.BatchSize,
			Temperature: config.Temperature,
		},
		httpClient: &http.Client{Timeout: 60 * time.Second},
	}
}

// EmbeddingRequest represents an embedding API request.
type EmbeddingRequest struct {
	Model      string   `json:"model"`
	Input      []string `json:"input"`
	Dimensions int      `json:"dimensions,omitempty"`
}

// EmbeddingResponse represents an embedding API response.
type EmbeddingResponse struct {
	Object string `json:"object"`
	Data   []struct {
		Object    string    `json:"object"`
		Index     int       `json:"index"`
		Embedding []float32 `json:"embedding"`
	} `json:"data"`
	Model string `json:"model"`
	Usage struct {
		PromptTokens int `json:"prompt_tokens"`
		TotalTokens  int `json:"total_tokens"`
	} `json:"usage"`
	Error *APIError `json:"error,omitempty"`
}

// APIError represents an API error.
type APIError struct {
	Message string `json:"message"`
	Type    string `json:"type"`
	Code    string `json:"code"`
}

func (e *APIError) Error() string {
	return e.Message
}

// Embed generates embeddings for texts.
func (c *OpenAIClient) Embed(ctx context.Context, texts []string) ([][]float32, error) {
	if c.config.APIKey == "" {
		return nil, fmt.Errorf("OpenAI API key not configured")
	}

	model := c.config.Model
	if model == "" {
		model = "text-embedding-3-small"
	}

	batchSize := c.config.BatchSize
	if batchSize == 0 {
		batchSize = 100
	}

	var allEmbeddings [][]float32

	// Process in batches
	for i := 0; i < len(texts); i += batchSize {
		end := i + batchSize
		if end > len(texts) {
			end = len(texts)
		}

		batch := texts[i:end]
		embeddings, err := c.embedBatch(ctx, model, batch)
		if err != nil {
			return nil, err
		}

		allEmbeddings = append(allEmbeddings, embeddings...)
	}

	return allEmbeddings, nil
}

// embedBatch processes a single batch of texts.
func (c *OpenAIClient) embedBatch(ctx context.Context, model string, texts []string) ([][]float32, error) {
	req := EmbeddingRequest{
		Model: model,
		Input: texts,
	}

	// Set dimensions if specified (for text-embedding-3 models)
	if c.config.Dimensions > 0 && strings.HasPrefix(model, "text-embedding-3") {
		req.Dimensions = c.config.Dimensions
	}

	body, err := json.Marshal(req)
	if err != nil {
		return nil, fmt.Errorf("failed to marshal request: %w", err)
	}

	httpReq, err := http.NewRequestWithContext(ctx, "POST", c.config.BaseURL+"/embeddings", strings.NewReader(string(body)))
	if err != nil {
		return nil, fmt.Errorf("failed to create request: %w", err)
	}

	httpReq.Header.Set("Content-Type", "application/json")
	httpReq.Header.Set("Authorization", "Bearer "+c.config.APIKey)

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

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

	if embeddingResp.Error != nil {
		return nil, embeddingResp.Error
	}

	// Extract embeddings in order
	embeddings := make([][]float32, len(texts))
	for _, data := range embeddingResp.Data {
		embeddings[data.Index] = data.Embedding
	}

	return embeddings, nil
}

// ChatRequest represents a chat completion request.
type ChatRequest struct {
	Model       string        `json:"model"`
	Messages    []ChatMessage `json:"messages"`
	Temperature float64       `json:"temperature,omitempty"`
	MaxTokens   int           `json:"max_tokens,omitempty"`
}

// ChatMessage represents a chat message.
type ChatMessage struct {
	Role    string `json:"role"`
	Content string `json:"content"`
}

// ChatResponse represents a chat completion response.
type ChatResponse struct {
	ID      string `json:"id"`
	Object  string `json:"object"`
	Created int64  `json:"created"`
	Model   string `json:"model"`
	Choices []struct {
		Index   int         `json:"index"`
		Message ChatMessage `json:"message"`
		Finish  string      `json:"finish_reason"`
	} `json:"choices"`
	Usage struct {
		PromptTokens     int `json:"prompt_tokens"`
		CompletionTokens int `json:"completion_tokens"`
		TotalTokens      int `json:"total_tokens"`
	} `json:"usage"`
	Error *APIError `json:"error,omitempty"`
}

// QueryWithContext generates a response with context injection.
func (c *OpenAIClient) QueryWithContext(ctx context.Context, query string, contextDocs []string) (string, error) {
	if c.config.APIKey == "" {
		return "", fmt.Errorf("OpenAI API key not configured")
	}

	model := c.config.Model
	if model == "" || strings.Contains(model, "embedding") {
		model = "gpt-4o-mini"
	}

	// Build context
	contextText := strings.Join(contextDocs, "\n\n---\n\n")

	// Build messages
	messages := []ChatMessage{
		{
			Role: "system",
			Content: "You are a helpful assistant that answers questions based on the provided context. " +
				"Use the context to provide accurate and relevant answers. " +
				"If the context doesn't contain relevant information, say so.",
		},
		{
			Role:    "user",
			Content: fmt.Sprintf("Context:\n%s\n\nQuestion: %s", contextText, query),
		},
	}

	req := ChatRequest{
		Model:    model,
		Messages: messages,
	}

	if c.config.Temperature > 0 {
		req.Temperature = c.config.Temperature
	}

	body, err := json.Marshal(req)
	if err != nil {
		return "", fmt.Errorf("failed to marshal request: %w", err)
	}

	httpReq, err := http.NewRequestWithContext(ctx, "POST", c.config.BaseURL+"/chat/completions", strings.NewReader(string(body)))
	if err != nil {
		return "", fmt.Errorf("failed to create request: %w", err)
	}

	httpReq.Header.Set("Content-Type", "application/json")
	httpReq.Header.Set("Authorization", "Bearer "+c.config.APIKey)

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

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

	if chatResp.Error != nil {
		return "", chatResp.Error
	}

	if len(chatResp.Choices) == 0 {
		return "", fmt.Errorf("no response generated")
	}

	return chatResp.Choices[0].Message.Content, nil
}

// MockClient implements AI operations without external API calls.
type MockClient struct {
	dimensions int
}

// NewMockClient creates a mock client for testing.
func NewMockClient(dimensions int) *MockClient {
	return &MockClient{dimensions: dimensions}
}

// Embed generates mock embeddings.
func (c *MockClient) Embed(ctx context.Context, texts []string) ([][]float32, error) {
	embeddings := make([][]float32, len(texts))
	for i := range texts {
		// Generate deterministic but varied embeddings
		embedding := make([]float32, c.dimensions)
		for j := range embedding {
			embedding[j] = float32(i*100+j) / float32(c.dimensions*100)
		}
		embeddings[i] = embedding
	}
	return embeddings, nil
}

// QueryWithContext returns a mock response.
func (c *MockClient) QueryWithContext(ctx context.Context, query string, context []string) (string, error) {
	return "This is a mock response.", nil
}