gochat/internal/service/conversation_insight_service.go

package service

import (
	"context"
	"fmt"
	"strings"

	"github.com/gochat/gochat/internal/llm"
	"github.com/gochat/gochat/internal/repository"
	applogger "github.com/gochat/gochat/pkg/logger"
)

// ConversationInsightService provides AI-powered conversation analysis features:
// - Participant analysis (role, sentiment, engagement)
// - Action items extraction (tasks, deadlines, owners)
// - Label/priority suggestion (auto-tagging conversations)
//
// Reference: Chatwoot Captain::Llm::ConversationInsightService + M12 PRD

// --- Request/Response DTOs ---

// ParticipantAnalysisRequest is the input for analyzing conversation participants.
type ParticipantAnalysisRequest struct {
	ConversationID uint `json:"conversation_id" validate:"required"`
}

// ParticipantInfo holds analysis results for a single participant.
type ParticipantInfo struct {
	Name      string   `json:"name"`
	Role      string   `json:"role"`       // "customer", "agent", "manager"
	Sentiment string   `json:"sentiment"`  // "positive", "neutral", "negative"
	Topics    []string `json:"topics"`     // main topics discussed
	Engagement float64 `json:"engagement"` // engagement score 0-1
}

// ParticipantAnalysisResult holds the participant analysis result.
type ParticipantAnalysisResult struct {
	Participants []ParticipantInfo `json:"participants"`
	Summary      string            `json:"summary"` // brief overall participant dynamics summary
}

// ActionItemsRequest is the input for extracting action items from a conversation.
type ActionItemsRequest struct {
	ConversationID uint `json:"conversation_id" validate:"required"`
}

// ActionItem holds a single action item extracted from a conversation.
type ActionItem struct {
	Description string `json:"description"`
	Owner       string `json:"owner,omitempty"` // person responsible
	Deadline    string `json:"deadline,omitempty"`
	Priority    string `json:"priority"` // "high", "medium", "low"
	Status      string `json:"status"`  // "pending", "in_progress", "completed"
}

// ActionItemsResult holds the extracted action items.
type ActionItemsResult struct {
	Items []ActionItem `json:"items"`
}

// LabelSuggestionRequest is the input for suggesting labels/priority for a conversation.
type LabelSuggestionRequest struct {
	ConversationID uint   `json:"conversation_id" validate:"required"`
	AssistantID    uint   `json:"assistant_id,omitempty"` // optional: use assistant guidelines
}

// InsightLabelSuggestionResult holds suggested labels and priority.
type InsightLabelSuggestionResult struct {
	Labels   []string `json:"labels"`
	Priority string   `json:"priority"` // "urgent", "high", "medium", "low"
	Reason   string   `json:"reason"`   // brief explanation of the suggestion
}

// ConversationInsightService orchestrates AI-powered conversation analysis.
type ConversationInsightService struct {
	conversationRepo *repository.ConversationRepo
	messageRepo      *repository.MessageRepo
	assistantRepo    *repository.CaptainAssistantRepo
	llmProvider      llm.Provider
	promptBuilder    *SystemPromptBuilder
}

// NewConversationInsightService creates a new ConversationInsightService.
func NewConversationInsightService(
	conversationRepo *repository.ConversationRepo,
	messageRepo *repository.MessageRepo,
	assistantRepo *repository.CaptainAssistantRepo,
	llmProvider llm.Provider,
) *ConversationInsightService {
	return &ConversationInsightService{
		conversationRepo: conversationRepo,
		messageRepo:      messageRepo,
		assistantRepo:    assistantRepo,
		llmProvider:      llmProvider,
		promptBuilder:    NewSystemPromptBuilder(),
	}
}

// --- Participant Analysis ---
// Reference: M12 PRD §Captain AI — Participant Analysis

// AnalyzeParticipants analyzes the participants in a conversation.
func (s *ConversationInsightService) AnalyzeParticipants(ctx context.Context, accountID uint, req *ParticipantAnalysisRequest) (*ParticipantAnalysisResult, error) {
	// Fetch conversation messages
	contextStr, err := s.fetchConversationContext(ctx, req.ConversationID)
	if err != nil {
		return nil, fmt.Errorf("fetch conversation context: %w", err)
	}
	if contextStr == "" {
		return nil, fmt.Errorf("no messages found for conversation %d", req.ConversationID)
	}

	systemPrompt := `You are a conversation analysis AI. Analyze the participants in the following conversation.
For each participant, identify:
1. Their role (customer, agent, manager, etc.)
2. Their overall sentiment (positive, neutral, negative)
3. The main topics they discussed (2-3 keywords)
4. Their engagement level (0-1 score based on message frequency and depth)

Also provide a brief summary of the overall participant dynamics.

Return your analysis as JSON in this exact format:
{
  "participants": [
    {"name": "...", "role": "...", "sentiment": "...", "topics": ["..."], "engagement": 0.0}
  ],
  "summary": "..."
}`

	llmResp, err := s.llmProvider.ChatCompletion(ctx, llm.ChatRequest{
		Model:       "gpt-4",
		Messages: []llm.ChatMessage{
			{Role: "system", Content: systemPrompt},
			{Role: "user", Content: contextStr},
		},
		Temperature: 0.3,
		MaxTokens:   1024,
	})
	if err != nil {
		applogger.L().Errorf("AnalyzeParticipants LLM: %v", err)
		return nil, fmt.Errorf("participant analysis failed: %w", err)
	}

	if len(llmResp.Choices) == 0 {
		return nil, fmt.Errorf("no LLM response")
	}

	content := llmResp.Choices[0].Message.Content
	// Try to parse JSON, fallback to text extraction
	var result ParticipantAnalysisResult
	if err := parseJSONResponse(content, &result); err != nil {
		// Fallback: extract structured data from plain text
		result = extractParticipantInfoFromText(content)
	}

	return &result, nil
}

// --- Action Items Extraction ---
// Reference: M12 PRD §Captain AI — Action Items

// ExtractActionItems extracts action items from a conversation.
func (s *ConversationInsightService) ExtractActionItems(ctx context.Context, accountID uint, req *ActionItemsRequest) (*ActionItemsResult, error) {
	contextStr, err := s.fetchConversationContext(ctx, req.ConversationID)
	if err != nil {
		return nil, fmt.Errorf("fetch conversation context: %w", err)
	}
	if contextStr == "" {
		return nil, fmt.Errorf("no messages found for conversation %d", req.ConversationID)
	}

	systemPrompt := `You are an action item extraction AI. Analyze the conversation and extract all actionable items.
For each action item, identify:
1. Description: what needs to be done
2. Owner: who is responsible (if mentioned)
3. Deadline: any mentioned deadline
4. Priority: high, medium, or low
5. Status: pending, in_progress, or completed (if mentioned)

Return as JSON array:
{
  "items": [
    {"description": "...", "owner": "...", "deadline": "...", "priority": "...", "status": "..."}
  ]
}`

	llmResp, err := s.llmProvider.ChatCompletion(ctx, llm.ChatRequest{
		Model:       "gpt-4",
		Messages: []llm.ChatMessage{
			{Role: "system", Content: systemPrompt},
			{Role: "user", Content: contextStr},
		},
		Temperature: 0.2,
		MaxTokens:   512,
	})
	if err != nil {
		applogger.L().Errorf("ExtractActionItems LLM: %v", err)
		return nil, fmt.Errorf("action item extraction failed: %w", err)
	}

	if len(llmResp.Choices) == 0 {
		return nil, fmt.Errorf("no LLM response")
	}

	var result ActionItemsResult
	content := llmResp.Choices[0].Message.Content
	if err := parseJSONResponse(content, &result); err != nil {
		// Fallback: try to extract items from plain text
		result = extractActionItemsFromText(content)
	}

	return &result, nil
}

// --- Label/Priority Suggestion ---
// Reference: M12 PRD §Captain AI — Recommendation Engine

// SuggestLabels suggests labels and priority for a conversation.
func (s *ConversationInsightService) SuggestLabels(ctx context.Context, accountID uint, req *LabelSuggestionRequest) (*LabelSuggestionResult, error) {
	contextStr, err := s.fetchConversationContext(ctx, req.ConversationID)
	if err != nil {
		return nil, fmt.Errorf("fetch conversation context: %w", err)
	}
	if contextStr == "" {
		return nil, fmt.Errorf("no messages found for conversation %d", req.ConversationID)
	}

	// Optionally use assistant guidelines for context-aware suggestions
	var assistantPrompt string
	if req.AssistantID > 0 {
		assistant, err := s.assistantRepo.GetByID(ctx, req.AssistantID)
		if err == nil && assistant != nil {
			cfg, _ := assistant.GetConfig()
			assistantPrompt = s.promptBuilder.BuildAssistantPrompt(assistant, cfg)
		}
	}

	systemPrompt := `You are a conversation categorization AI. Analyze the conversation and suggest:
1. Appropriate labels/tags (3-5 concise labels that categorize the conversation topic, type, and urgency)
2. Priority level: urgent, high, medium, or low
3. A brief reason explaining why these labels and priority were chosen

Return as JSON:
{
  "labels": ["label1", "label2", ...],
  "priority": "...",
  "reason": "..."
}`

	if assistantPrompt != "" {
		systemPrompt = assistantPrompt + "\n\n" + systemPrompt
	}

	llmResp, err := s.llmProvider.ChatCompletion(ctx, llm.ChatRequest{
		Model:       "gpt-4",
		Messages: []llm.ChatMessage{
			{Role: "system", Content: systemPrompt},
			{Role: "user", Content: contextStr},
		},
		Temperature: 0.3,
		MaxTokens:   256,
	})
	if err != nil {
		applogger.L().Errorf("SuggestLabels LLM: %v", err)
		return nil, fmt.Errorf("label suggestion failed: %w", err)
	}

	if len(llmResp.Choices) == 0 {
		return nil, fmt.Errorf("no LLM response")
	}

	var result LabelSuggestionResult
	content := llmResp.Choices[0].Message.Content
	if err := parseJSONResponse(content, &result); err != nil {
		result = extractLabelSuggestionFromText(content)
	}

	return &result, nil
}

// --- Helper Methods ---

// fetchConversationContext retrieves recent messages and formats them for LLM input.
func (s *ConversationInsightService) fetchConversationContext(ctx context.Context, conversationID uint) (string, error) {
	msgs, _, err := s.messageRepo.FindByConversation(ctx, conversationID, 0, 50)
	if err != nil {
		return "", fmt.Errorf("fetch messages: %w", err)
	}

	if len(msgs) == 0 {
		return "", nil
	}

	var builder strings.Builder
	for _, msg := range msgs {
		sender := "Customer"
		if msg.SenderType == "agent" || msg.SenderType == "user" {
			sender = "Agent"
		}
		builder.WriteString(fmt.Sprintf("[%s]: %s\n", sender, msg.Content))
	}

	return builder.String(), nil
}