nordabiz/nordabiz_chat.py

#!/usr/bin/env python3
"""
Norda Biznes AI Chat Engine
============================

Multi-turn conversational AI for company directory queries.

Features:
- Answer questions about member companies
- Find companies by service, competency, or need
- Concise, helpful responses
- Full conversation history tracking
- Cost tracking per message

Author: Norda Biznes Development Team
Created: 2025-11-23
"""

import os
import time
from datetime import datetime
from typing import Dict, List, Any, Optional
import google.generativeai as genai
import gemini_service
from search_service import search_companies

from database import (
    SessionLocal,
    Company,
    Category,
    Service,
    CompanyService,
    Competency,
    CompanyCompetency,
    Certification,
    Award,
    CompanyEvent,
    AIChatConversation,
    AIChatMessage
)

# Import feedback learning service for few-shot learning
try:
    from feedback_learning_service import get_feedback_learning_service
    FEEDBACK_LEARNING_AVAILABLE = True
except ImportError:
    FEEDBACK_LEARNING_AVAILABLE = False


class NordaBizChatEngine:
    """
    AI Chat Assistant for Norda Biznes company directory

    Helps users find companies, services, and business partners.
    """

    def __init__(self, gemini_api_key: Optional[str] = None, use_global_service: bool = True):
        """
        Initialize Norda Biznes Chat Engine

        Args:
            gemini_api_key: Google Gemini API key (uses env var if not provided)
            use_global_service: Use global gemini_service for automatic cost tracking (default: True)
        """
        self.use_global_service = use_global_service

        if use_global_service:
            # Use global gemini_service for automatic cost tracking to ai_api_costs table
            self.gemini_service = gemini_service.get_gemini_service()
            self.model_name = "gemini-2.5-flash"
            self.model = None

            # Initialize tokenizer for cost calculation (still needed for per-message tracking)
            api_key = gemini_api_key or os.getenv('GOOGLE_GEMINI_API_KEY')
            if api_key and api_key != 'TWOJ_KLUCZ_API_TUTAJ':
                genai.configure(api_key=api_key)
                self.tokenizer = genai.GenerativeModel(self.model_name)
            else:
                self.tokenizer = None
        else:
            # Legacy: direct API access (no centralized cost tracking)
            api_key = gemini_api_key or os.getenv('GOOGLE_GEMINI_API_KEY')
            if not api_key or api_key == 'TWOJ_KLUCZ_API_TUTAJ':
                raise ValueError("GOOGLE_GEMINI_API_KEY not found in environment")

            genai.configure(api_key=api_key)
            self.model_name = "gemini-2.5-flash"
            self.model = genai.GenerativeModel(self.model_name)
            self.tokenizer = self.model
            self.gemini_service = None

    def start_conversation(
        self,
        user_id: int,
        title: Optional[str] = None,
        conversation_type: str = 'general'
    ) -> AIChatConversation:
        """
        Start new conversation

        Args:
            user_id: User ID
            title: Optional conversation title
            conversation_type: Type of conversation (default: 'general')

        Returns:
            AIChatConversation: New conversation object
        """
        db = SessionLocal()

        try:
            # Auto-generate title if not provided
            if not title:
                title = f"Rozmowa - {datetime.now().strftime('%Y-%m-%d %H:%M')}"

            conversation = AIChatConversation(
                user_id=user_id,
                started_at=datetime.now(),
                conversation_type=conversation_type,
                title=title,
                is_active=True,
                message_count=0,
                model_name=self.model_name
            )

            db.add(conversation)
            db.commit()
            db.refresh(conversation)

            return conversation

        finally:
            db.close()

    def send_message(
        self,
        conversation_id: int,
        user_message: str,
        user_id: Optional[int] = None
    ) -> AIChatMessage:
        """
        Send message and get AI response

        Args:
            conversation_id: Conversation ID
            user_message: User's message text
            user_id: User ID for cost tracking (optional)

        Returns:
            AIChatMessage: AI response message
        """
        db = SessionLocal()
        start_time = time.time()

        try:
            # Get conversation
            conversation = db.query(AIChatConversation).filter_by(
                id=conversation_id
            ).first()

            if not conversation:
                raise ValueError(f"Conversation {conversation_id} not found")

            # Save user message
            user_msg = AIChatMessage(
                conversation_id=conversation_id,
                created_at=datetime.now(),
                role='user',
                content=user_message,
                edited=False,
                regenerated=False
            )
            db.add(user_msg)
            db.commit()

            # Build context from conversation history and relevant companies
            context = self._build_conversation_context(db, conversation, user_message)

            # Get AI response with cost tracking
            response = self._query_ai(
                context,
                user_message,
                user_id=user_id
            )

            # Calculate metrics for per-message tracking in AIChatMessage table
            latency_ms = int((time.time() - start_time) * 1000)
            if self.tokenizer:
                input_tokens = self.tokenizer.count_tokens(user_message).total_tokens
                output_tokens = self.tokenizer.count_tokens(response).total_tokens
                cost_usd = self._calculate_cost(input_tokens, output_tokens)
            else:
                # Fallback if tokenizer not available
                input_tokens = len(user_message.split()) * 2  # Rough estimate
                output_tokens = len(response.split()) * 2
                cost_usd = 0.0

            # Save AI response
            ai_msg = AIChatMessage(
                conversation_id=conversation_id,
                created_at=datetime.now(),
                role='assistant',
                content=response,
                tokens_input=input_tokens,
                tokens_output=output_tokens,
                cost_usd=cost_usd,
                latency_ms=latency_ms,
                edited=False,
                regenerated=False
            )
            db.add(ai_msg)

            # Update conversation
            conversation.message_count += 2
            conversation.updated_at = datetime.now()
            db.commit()
            db.refresh(ai_msg)

            return ai_msg

        finally:
            db.close()

    def get_conversation_history(
        self,
        conversation_id: int
    ) -> List[Dict[str, Any]]:
        """
        Get all messages in conversation

        Args:
            conversation_id: Conversation ID

        Returns:
            List of message dicts
        """
        db = SessionLocal()

        try:
            messages = db.query(AIChatMessage).filter_by(
                conversation_id=conversation_id
            ).order_by(AIChatMessage.created_at).all()

            return [
                {
                    'id': msg.id,
                    'role': msg.role,
                    'content': msg.content,
                    'created_at': msg.created_at.isoformat(),
                    'tokens_input': msg.tokens_input,
                    'tokens_output': msg.tokens_output,
                    'cost_usd': float(msg.cost_usd) if msg.cost_usd else 0.0,
                    'latency_ms': msg.latency_ms
                }
                for msg in messages
            ]

        finally:
            db.close()

    def _build_conversation_context(
        self,
        db,
        conversation: AIChatConversation,
        current_message: str
    ) -> Dict[str, Any]:
        """
        Build context for AI with ALL companies (not pre-filtered)

        This allows AI to intelligently select relevant companies instead of
        relying on keyword-based search pre-filtering.

        Args:
            db: Database session
            conversation: Current conversation
            current_message: User's current message (for reference only)

        Returns:
            Context dict with ALL companies and categories
        """
        # Load ALL active companies - let AI do the intelligent filtering
        all_companies = db.query(Company).filter_by(status='active').all()

        context = {
            'conversation_type': conversation.conversation_type,
            'total_companies': len(all_companies)
        }

        # Get all categories with company counts
        categories = db.query(Category).all()
        context['categories'] = [
            {
                'name': cat.name,
                'slug': cat.slug,
                'company_count': db.query(Company).filter_by(category_id=cat.id, status='active').count()
            }
            for cat in categories
        ]

        # Include ALL companies in compact format to minimize tokens
        # AI will intelligently select the most relevant ones
        context['all_companies'] = [
            self._company_to_compact_dict(c)
            for c in all_companies
        ]

        # Add conversation history (last 10 messages for context)
        messages = db.query(AIChatMessage).filter_by(
            conversation_id=conversation.id
        ).order_by(AIChatMessage.created_at.desc()).limit(10).all()

        context['recent_messages'] = [
            {'role': msg.role, 'content': msg.content}
            for msg in reversed(messages)
        ]

        return context

    def _company_to_compact_dict(self, c: Company) -> Dict[str, Any]:
        """
        Convert company to compact dictionary for AI context.
        Optimized to minimize tokens while keeping all important data.

        Args:
            c: Company object

        Returns:
            Compact dict with essential company info
        """
        compact = {
            'name': c.name,
            'cat': c.category.name if c.category else None,
        }

        # Only include non-empty fields to save tokens
        if c.description_short:
            compact['desc'] = c.description_short
        if c.founding_history:
            compact['history'] = c.founding_history  # Owners, founders, history
        if c.services:
            services = [cs.service.name for cs in c.services if cs.service]
            if services:
                compact['svc'] = services
        if c.competencies:
            competencies = [cc.competency.name for cc in c.competencies if cc.competency]
            if competencies:
                compact['comp'] = competencies
        if c.website:
            compact['web'] = c.website
        if c.phone:
            compact['tel'] = c.phone
        if c.email:
            compact['mail'] = c.email
        if c.address_city:
            compact['city'] = c.address_city
        if c.year_established:
            compact['year'] = c.year_established
        if c.certifications:
            certs = [cert.name for cert in c.certifications if cert.is_active]
            if certs:
                compact['cert'] = certs[:3]  # Limit to 3 certs

        return compact

    # Słownik synonimów i powiązanych terminów dla lepszego wyszukiwania
    KEYWORD_SYNONYMS = {
        # IT / Web
        'strony': ['www', 'web', 'internet', 'witryny', 'seo', 'e-commerce', 'ecommerce', 'sklep', 'portal'],
        'internetowe': ['www', 'web', 'online', 'cyfrowe', 'seo', 'marketing'],
        'aplikacje': ['software', 'programowanie', 'systemy', 'crm', 'erp', 'app'],
        'it': ['informatyka', 'komputery', 'software', 'systemy', 'serwis'],
        'programowanie': ['software', 'kod', 'developer', 'aplikacje'],
        # Budownictwo
        'budowa': ['budownictwo', 'konstrukcje', 'remonty', 'wykończenia', 'dach', 'elewacja'],
        'dom': ['budynek', 'mieszkanie', 'nieruchomości', 'budownictwo'],
        'remont': ['wykończenie', 'naprawa', 'renowacja', 'modernizacja'],
        # Transport / Logistyka
        'transport': ['przewóz', 'logistyka', 'spedycja', 'dostawa', 'kurier'],
        'samochód': ['auto', 'pojazd', 'motoryzacja', 'serwis', 'naprawa'],
        # Usługi
        'księgowość': ['rachunkowość', 'finanse', 'podatki', 'biuro rachunkowe', 'kadry'],
        'prawo': ['prawnik', 'adwokat', 'radca', 'kancelaria', 'notariusz'],
        'marketing': ['reklama', 'promocja', 'seo', 'social media', 'branding'],
        # Produkcja
        'produkcja': ['wytwarzanie', 'fabryka', 'zakład', 'przemysł'],
        'metal': ['stal', 'obróbka', 'spawanie', 'cnc', 'ślusarstwo'],
        'drewno': ['stolarka', 'meble', 'tartak', 'carpentry'],
    }

    def _find_relevant_companies(self, db, message: str) -> List[Company]:
        """
        Find companies relevant to user's message

        Uses unified SearchService with:
        - Synonym expansion for better keyword matching
        - NIP/REGON direct lookup
        - PostgreSQL FTS with fuzzy matching (when available)
        - Fallback scoring for SQLite

        Args:
            db: Database session
            message: User's message

        Returns:
            List of relevant Company objects
        """
        # Use unified SearchService for better search results
        results = search_companies(db, message, limit=10)

        # Extract Company objects from SearchResult
        return [result.company for result in results]

    def _query_ai(
        self,
        context: Dict[str, Any],
        user_message: str,
        user_id: Optional[int] = None
    ) -> str:
        """
        Query Gemini AI with full company database context

        Args:
            context: Context dict with ALL companies
            user_message: User's message
            user_id: User ID for cost tracking

        Returns:
            AI response text
        """
        import json

        # Build system prompt with ALL companies
        system_prompt = f"""Jesteś pomocnym asystentem portalu Norda Biznes - katalogu firm zrzeszonych w stowarzyszeniu Norda Biznes z Wejherowa.

📊 MASZ DOSTĘP DO PEŁNEJ BAZY DANYCH:
- Liczba firm: {context['total_companies']}
- Kategorie: {', '.join([f"{cat['name']} ({cat['company_count']})" for cat in context.get('categories', [])])}

🎯 TWOJA ROLA:
- Analizujesz CAŁĄ bazę firm i wybierasz najlepsze dopasowania do pytania użytkownika
- Odpowiadasz zwięźle (2-3 zdania), chyba że użytkownik prosi o szczegóły
- Podajesz konkretne nazwy firm z kontaktem
- Możesz wyszukiwać po: nazwie firmy, usługach, kompetencjach, właścicielach (w history), mieście

📋 FORMAT DANYCH (skróty):
- name: nazwa firmy
- cat: kategoria
- desc: krótki opis
- history: historia firmy, właściciele, założyciele
- svc: usługi
- comp: kompetencje
- web/tel/mail: kontakt
- city: miasto
- cert: certyfikaty

⚠️ WAŻNE:
- ZAWSZE podawaj nazwę firmy i kontakt (tel/web/mail jeśli dostępne)
- Jeśli pytanie o osobę (np. "kto to Roszman") - szukaj w polu "history"
- Odpowiadaj PO POLSKU
"""

        # Add feedback-based learning context (few-shot examples)
        if FEEDBACK_LEARNING_AVAILABLE:
            try:
                feedback_service = get_feedback_learning_service()
                learning_context = feedback_service.format_for_prompt()
                if learning_context:
                    system_prompt += learning_context
            except Exception as e:
                # Don't fail if feedback learning has issues
                import logging
                logging.getLogger(__name__).warning(f"Feedback learning error: {e}")

        # Add ALL companies in compact JSON format
        if context.get('all_companies'):
            system_prompt += "\n\n🏢 PEŁNA BAZA FIRM (wybierz najlepsze):\n"
            system_prompt += json.dumps(context['all_companies'], ensure_ascii=False, indent=None)
            system_prompt += "\n"

        # Add conversation history
        full_prompt = system_prompt + "\n\n# HISTORIA ROZMOWY:\n"
        for msg in context.get('recent_messages', []):
            role_name = "Użytkownik" if msg['role'] == 'user' else "Ty"
            full_prompt += f"{role_name}: {msg['content']}\n"

        full_prompt += f"\nUżytkownik: {user_message}\nTy: "

        # Get response with automatic cost tracking to ai_api_costs table
        if self.use_global_service and self.gemini_service:
            response_text = self.gemini_service.generate_text(
                prompt=full_prompt,
                feature='ai_chat',
                user_id=user_id,
                temperature=0.7
            )
            return response_text
        else:
            # Legacy: direct API call (no centralized cost tracking)
            response = self.model.generate_content(full_prompt)
            return response.text

    def _calculate_cost(self, input_tokens: int, output_tokens: int) -> float:
        """
        Calculate cost in USD

        Args:
            input_tokens: Number of input tokens
            output_tokens: Number of output tokens

        Returns:
            Total cost in USD
        """
        # Gemini 2.5 Flash pricing (per 1M tokens)
        input_cost = (input_tokens / 1_000_000) * 0.075
        output_cost = (output_tokens / 1_000_000) * 0.30
        return input_cost + output_cost