feat(nordagpt): add memory service — fact extraction, summaries, CRUD, prompt injection

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

blueprints/chat/routes.py

@@ -645,3 +645,38 @@ def chat_feedback():
 def chat_analytics():
     """Redirect to consolidated analytics chat tab."""
     return redirect(url_for('admin.user_insights', tab='chat'))
+
+
+# ============================================================
+# MEMORY CRUD API
+# ============================================================
+
+@bp.route('/api/chat/memory', methods=['GET'])
+@login_required
+@member_required
+def get_user_memory_api():
+    """API: Get user's memory facts and conversation summaries."""
+    try:
+        from memory_service import get_user_memory, get_conversation_summaries
+        return jsonify({
+            'facts': get_user_memory(current_user.id, limit=20),
+            'summaries': get_conversation_summaries(current_user.id, limit=10)
+        })
+    except Exception as e:
+        logger.error(f"Error fetching user memory: {e}")
+        return jsonify({'error': str(e)}), 500
+
+
+@bp.route('/api/chat/memory/<int:fact_id>', methods=['DELETE'])
+@login_required
+@member_required
+def delete_memory_fact(fact_id):
+    """API: Soft-delete a memory fact."""
+    try:
+        from memory_service import delete_user_fact
+        if delete_user_fact(current_user.id, fact_id):
+            return jsonify({'status': 'ok'})
+        return jsonify({'error': 'Nie znaleziono'}), 404
+    except Exception as e:
+        logger.error(f"Error deleting memory fact {fact_id}: {e}")
+        return jsonify({'error': str(e)}), 500

memory_service.py

@@ -0,0 +1,452 @@
+"""
+Memory Service
+==============
+
+Manages persistent user memory for NordaGPT conversations.
+
+Features:
+- Extract key facts from conversations using Flash-Lite
+- Generate and update conversation summaries
+- Format memory for injection into AI prompts
+- CRUD operations for user memory facts
+
+Author: Norda Biznes Development Team
+"""
+
+import json
+import logging
+from datetime import datetime
+from typing import List, Dict, Any, Optional
+
+from database import SessionLocal, AIUserMemory, AIConversationSummary, AIChatMessage
+
+logger = logging.getLogger(__name__)
+
+FLASH_LITE_MODEL = 'gemini-3.1-flash-lite-preview'
+
+
+def get_user_memory(user_id: int, limit: int = 10) -> List[Dict[str, Any]]:
+    """
+    Returns list of active, non-expired memory facts for a user.
+
+    Sorted by confidence desc, then by created_at desc (recency).
+
+    Args:
+        user_id: User ID
+        limit: Maximum number of facts to return
+
+    Returns:
+        List of dicts with fact data
+    """
+    db = SessionLocal()
+    try:
+        now = datetime.now()
+        query = db.query(AIUserMemory).filter(
+            AIUserMemory.user_id == user_id,
+            AIUserMemory.is_active == True,
+            (AIUserMemory.expires_at == None) | (AIUserMemory.expires_at > now)
+        ).order_by(
+            AIUserMemory.confidence.desc(),
+            AIUserMemory.created_at.desc()
+        ).limit(limit)
+
+        facts = query.all()
+        return [
+            {
+                'id': f.id,
+                'fact': f.fact,
+                'category': f.category or 'general',
+                'confidence': float(f.confidence) if f.confidence else 1.0,
+                'created_at': f.created_at.isoformat() if f.created_at else None,
+                'expires_at': f.expires_at.isoformat() if f.expires_at else None,
+                'source_conversation_id': f.source_conversation_id
+            }
+            for f in facts
+        ]
+    finally:
+        db.close()
+
+
+def get_conversation_summaries(user_id: int, limit: int = 5) -> List[Dict[str, Any]]:
+    """
+    Returns list of recent conversation summaries for a user.
+
+    Args:
+        user_id: User ID
+        limit: Maximum number of summaries to return
+
+    Returns:
+        List of dicts with summary data
+    """
+    db = SessionLocal()
+    try:
+        summaries = db.query(AIConversationSummary).filter(
+            AIConversationSummary.user_id == user_id
+        ).order_by(
+            AIConversationSummary.updated_at.desc()
+        ).limit(limit).all()
+
+        return [
+            {
+                'id': s.id,
+                'conversation_id': s.conversation_id,
+                'summary': s.summary,
+                'key_topics': s.key_topics or [],
+                'created_at': s.created_at.isoformat() if s.created_at else None,
+                'updated_at': s.updated_at.isoformat() if s.updated_at else None
+            }
+            for s in summaries
+        ]
+    finally:
+        db.close()
+
+
+def format_memory_for_prompt(user_id: int) -> str:
+    """
+    Formats user memory and conversation summaries as a string for AI prompt injection.
+
+    Returns empty string if no memory exists.
+
+    Args:
+        user_id: User ID
+
+    Returns:
+        Formatted memory string or empty string
+    """
+    facts = get_user_memory(user_id, limit=10)
+    summaries = get_conversation_summaries(user_id, limit=5)
+
+    if not facts and not summaries:
+        return ""
+
+    lines = ["\n# PAMIĘĆ O UŻYTKOWNIKU"]
+
+    if facts:
+        lines.append("Znane fakty:")
+        for f in facts:
+            category = f.get('category', 'general')
+            fact_text = f.get('fact', '')
+            lines.append(f"- [{category}] {fact_text}")
+
+    if summaries:
+        lines.append("\nOstatnie rozmowy:")
+        for s in summaries:
+            # Format date from updated_at
+            date_str = ''
+            if s.get('updated_at'):
+                try:
+                    dt = datetime.fromisoformat(s['updated_at'])
+                    date_str = dt.strftime('%Y-%m-%d')
+                except Exception:
+                    date_str = s['updated_at'][:10] if s.get('updated_at') else ''
+
+            summary_text = s.get('summary', '')
+            topics = s.get('key_topics', [])
+            topics_str = ', '.join(topics) if topics else ''
+
+            if topics_str:
+                lines.append(f"- {date_str}: {summary_text} (tematy: {topics_str})")
+            else:
+                lines.append(f"- {date_str}: {summary_text}")
+
+    lines.append("\nWykorzystuj tę wiedzę do personalizacji odpowiedzi.")
+
+    return "\n".join(lines) + "\n"
+
+
+def extract_facts_async(
+    conversation_id: int,
+    user_id: int,
+    user_context: Optional[Dict[str, Any]],
+    gemini_service
+) -> None:
+    """
+    Calls Flash-Lite to extract key facts from conversation and saves to DB.
+
+    Deduplicates against existing facts. Max 3 facts per extraction.
+    Handles all errors gracefully.
+
+    Args:
+        conversation_id: Conversation ID to extract facts from
+        user_id: User ID
+        user_context: Optional user context dict
+        gemini_service: GeminiService instance for API calls
+    """
+    db = SessionLocal()
+    try:
+        # Load last 10 messages from the conversation
+        messages = db.query(AIChatMessage).filter(
+            AIChatMessage.conversation_id == conversation_id
+        ).order_by(AIChatMessage.created_at.desc()).limit(10).all()
+
+        if not messages:
+            return
+
+        # Build conversation text (chronological order)
+        conversation_text = ""
+        for msg in reversed(messages):
+            role = "Użytkownik" if msg.role == 'user' else "Asystent"
+            conversation_text += f"{role}: {msg.content}\n"
+
+        # Load existing facts for deduplication
+        existing_facts = get_user_memory(user_id, limit=20)
+        existing_texts = [f['fact'] for f in existing_facts]
+        existing_str = "\n".join(f"- {t}" for t in existing_texts) if existing_texts else "(brak)"
+
+        # Build extraction prompt
+        prompt = f"""Przeanalizuj poniższy fragment rozmowy i wyodrębnij maksymalnie 3 kluczowe fakty o UŻYTKOWNIKU (nie asystencie).
+
+Fakty powinny dotyczyć: preferencji użytkownika, jego firmy, branży, zainteresowań biznesowych, planów, potrzeb.
+
+ISTNIEJĄCE FAKTY (nie duplikuj):
+{existing_str}
+
+ROZMOWA:
+{conversation_text}
+
+Odpowiedz TYLKO w formacie JSON (bez markdown, bez komentarzy):
+[
+  {{"fact": "treść faktu po polsku", "category": "kategoria"}},
+  ...
+]
+
+Kategorie: business, interest, preference, contact, plan, general
+Jeśli nie ma nowych faktów wartych zapamiętania, zwróć pustą tablicę: []"""
+
+        # Call Flash-Lite
+        response_text = gemini_service.generate_text(
+            prompt=prompt,
+            feature='memory_extraction',
+            user_id=user_id,
+            temperature=0.3,
+            model=FLASH_LITE_MODEL
+        )
+
+        if not response_text:
+            return
+
+        # Strip markdown code fences if present
+        cleaned = response_text.strip()
+        if cleaned.startswith("```"):
+            # Remove first and last fence lines
+            lines = cleaned.split("\n")
+            lines = [l for l in lines if not l.strip().startswith("```")]
+            cleaned = "\n".join(lines).strip()
+
+        # Parse JSON
+        try:
+            extracted = json.loads(cleaned)
+        except json.JSONDecodeError:
+            logger.warning(f"Memory extraction: JSON parse failed for user {user_id}: {cleaned[:200]}")
+            return
+
+        if not isinstance(extracted, list):
+            return
+
+        # Save new facts (max 3, deduplicate)
+        saved_count = 0
+        for item in extracted[:3]:
+            if not isinstance(item, dict):
+                continue
+            fact_text = item.get('fact', '').strip()
+            category = item.get('category', 'general')
+
+            if not fact_text:
+                continue
+
+            # Simple deduplication: skip if very similar to existing
+            is_duplicate = any(
+                _similarity_check(fact_text, existing)
+                for existing in existing_texts
+            )
+            if is_duplicate:
+                continue
+
+            memory = AIUserMemory(
+                user_id=user_id,
+                fact=fact_text,
+                category=category,
+                source_conversation_id=conversation_id,
+                confidence=0.8,
+                is_active=True
+            )
+            db.add(memory)
+            saved_count += 1
+
+        if saved_count > 0:
+            db.commit()
+            logger.info(f"Memory: saved {saved_count} facts for user {user_id}")
+
+    except Exception as e:
+        logger.warning(f"Memory extraction failed for user {user_id}, conv {conversation_id}: {e}")
+    finally:
+        db.close()
+
+
+def summarize_conversation_async(
+    conversation_id: int,
+    user_id: int,
+    gemini_service
+) -> None:
+    """
+    Generates or updates conversation summary using Flash-Lite.
+
+    Handles all errors gracefully.
+
+    Args:
+        conversation_id: Conversation ID to summarize
+        user_id: User ID
+        gemini_service: GeminiService instance for API calls
+    """
+    db = SessionLocal()
+    try:
+        # Load messages from the conversation
+        messages = db.query(AIChatMessage).filter(
+            AIChatMessage.conversation_id == conversation_id
+        ).order_by(AIChatMessage.created_at.asc()).all()
+
+        if not messages:
+            return
+
+        # Build conversation text
+        conversation_text = ""
+        for msg in messages:
+            role = "Użytkownik" if msg.role == 'user' else "Asystent"
+            conversation_text += f"{role}: {msg.content[:300]}\n"
+
+        # Trim if too long
+        if len(conversation_text) > 4000:
+            conversation_text = conversation_text[:4000] + "..."
+
+        prompt = f"""Napisz krótkie podsumowanie (max 2 zdania) poniższej rozmowy, skupiając się na głównym temacie i potrzebach użytkownika. Podaj też listę 3-5 słów kluczowych opisujących tematykę.
+
+ROZMOWA:
+{conversation_text}
+
+Odpowiedz TYLKO w formacie JSON (bez markdown):
+{{"summary": "podsumowanie rozmowy", "key_topics": ["temat1", "temat2", "temat3"]}}"""
+
+        response_text = gemini_service.generate_text(
+            prompt=prompt,
+            feature='memory_summary',
+            user_id=user_id,
+            temperature=0.3,
+            model=FLASH_LITE_MODEL
+        )
+
+        if not response_text:
+            return
+
+        # Strip markdown code fences
+        cleaned = response_text.strip()
+        if cleaned.startswith("```"):
+            lines = cleaned.split("\n")
+            lines = [l for l in lines if not l.strip().startswith("```")]
+            cleaned = "\n".join(lines).strip()
+
+        # Parse JSON
+        try:
+            result = json.loads(cleaned)
+        except json.JSONDecodeError:
+            logger.warning(f"Summary: JSON parse failed for conv {conversation_id}: {cleaned[:200]}")
+            return
+
+        if not isinstance(result, dict):
+            return
+
+        summary_text = result.get('summary', '').strip()
+        key_topics = result.get('key_topics', [])
+
+        if not summary_text:
+            return
+
+        # Upsert summary (unique on conversation_id)
+        existing = db.query(AIConversationSummary).filter(
+            AIConversationSummary.conversation_id == conversation_id
+        ).first()
+
+        if existing:
+            existing.summary = summary_text
+            existing.key_topics = key_topics
+            existing.updated_at = datetime.now()
+        else:
+            summary = AIConversationSummary(
+                conversation_id=conversation_id,
+                user_id=user_id,
+                summary=summary_text,
+                key_topics=key_topics
+            )
+            db.add(summary)
+
+        db.commit()
+        logger.info(f"Memory: updated summary for conv {conversation_id}")
+
+    except Exception as e:
+        logger.warning(f"Summary generation failed for conv {conversation_id}: {e}")
+    finally:
+        db.close()
+
+
+def delete_user_fact(user_id: int, fact_id: int) -> bool:
+    """
+    Soft-deletes a fact (sets is_active=False).
+
+    Args:
+        user_id: User ID (for ownership check)
+        fact_id: Fact ID to delete
+
+    Returns:
+        True if deleted, False if not found or not owned by user
+    """
+    db = SessionLocal()
+    try:
+        fact = db.query(AIUserMemory).filter(
+            AIUserMemory.id == fact_id,
+            AIUserMemory.user_id == user_id,
+            AIUserMemory.is_active == True
+        ).first()
+
+        if not fact:
+            return False
+
+        fact.is_active = False
+        db.commit()
+        logger.info(f"Memory: soft-deleted fact {fact_id} for user {user_id}")
+        return True
+
+    except Exception as e:
+        logger.warning(f"Memory delete failed for fact {fact_id}: {e}")
+        return False
+    finally:
+        db.close()
+
+
+def _similarity_check(new_fact: str, existing_fact: str, threshold: float = 0.6) -> bool:
+    """
+    Simple word-overlap similarity check for deduplication.
+
+    Args:
+        new_fact: New fact text
+        existing_fact: Existing fact text
+        threshold: Overlap ratio threshold (0-1)
+
+    Returns:
+        True if facts are considered duplicates
+    """
+    new_words = set(new_fact.lower().split())
+    existing_words = set(existing_fact.lower().split())
+
+    if not new_words or not existing_words:
+        return False
+
+    # Remove short/common words
+    stop_words = {'w', 'z', 'i', 'a', 'to', 'na', 'do', 'jest', 'się', 'że', 'nie', 'jak', 'co'}
+    new_words -= stop_words
+    existing_words -= stop_words
+
+    if not new_words or not existing_words:
+        return False
+
+    intersection = new_words & existing_words
+    overlap = len(intersection) / min(len(new_words), len(existing_words))
+    return overlap >= threshold

nordabiz_chat.py

@@ -87,6 +87,14 @@ except ImportError:
     SMART_ROUTER_AVAILABLE = False
     logger.warning("Smart Router or Context Builder not available - using full context fallback")
 
+# Import memory service for user fact storage and prompt injection
+try:
+    from memory_service import format_memory_for_prompt, extract_facts_async, summarize_conversation_async
+    MEMORY_SERVICE_AVAILABLE = True
+except ImportError:
+    MEMORY_SERVICE_AVAILABLE = False
+    logger.warning("Memory service not available - user memory will not be injected")
+
 
 class NordaBizChatEngine:
     """
@@ -319,6 +327,25 @@ class NordaBizChatEngine:
             db.commit()
             db.refresh(ai_msg)
 
+            # Async memory extraction in background thread
+            if MEMORY_SERVICE_AVAILABLE:
+                import threading
+                _conv_id = conversation_id
+                _user_id = user_id
+                _msg_count = conversation.message_count or 0
+                _uctx = user_context
+                _gsvc = self.gemini_service
+
+                def _extract_memory():
+                    try:
+                        extract_facts_async(_conv_id, _user_id, _uctx, _gsvc)
+                        if _msg_count % 5 == 0 and _msg_count > 0:
+                            summarize_conversation_async(_conv_id, _user_id, _gsvc)
+                    except Exception as e:
+                        logger.warning(f"Async memory extraction failed: {e}")
+
+                threading.Thread(target=_extract_memory, daemon=True).start()
+
             return ai_msg
 
         finally:
@@ -983,7 +1010,15 @@ ZASADY PERSONALIZACJI:
 - NIE ujawniaj danych technicznych (user_id, company_id, rola systemowa)
 """
 
-        system_prompt = user_identity + f"""Jesteś pomocnym asystentem portalu Norda Biznes - katalogu firm zrzeszonych w stowarzyszeniu Norda Biznes z Wejherowa.
+        # Inject user memory (facts + conversation summaries) into prompt
+        user_memory_text = ""
+        if MEMORY_SERVICE_AVAILABLE and user_context and user_context.get('user_id'):
+            try:
+                user_memory_text = format_memory_for_prompt(user_context['user_id'])
+            except Exception as e:
+                logger.warning(f"Memory load failed: {e}")
+
+        system_prompt = user_identity + user_memory_text + f"""Jesteś pomocnym asystentem portalu Norda Biznes - katalogu firm zrzeszonych w stowarzyszeniu Norda Biznes z Wejherowa.
 
 📊 MASZ DOSTĘP DO BAZY WIEDZY:
 - Liczba firm: {context['total_companies']}
@@ -1629,6 +1664,25 @@ W dyskusji [Artur Wiertel](link) pytał o moderację. Pełna treść: [moje uwag
             db.commit()
             db.refresh(ai_msg)
 
+            # Async memory extraction in background thread
+            if MEMORY_SERVICE_AVAILABLE:
+                import threading
+                _conv_id = conversation_id
+                _user_id = user_id
+                _msg_count = conversation.message_count or 0
+                _uctx = user_context
+                _gsvc = self.gemini_service
+
+                def _extract_memory_stream():
+                    try:
+                        extract_facts_async(_conv_id, _user_id, _uctx, _gsvc)
+                        if _msg_count % 5 == 0 and _msg_count > 0:
+                            summarize_conversation_async(_conv_id, _user_id, _gsvc)
+                    except Exception as e:
+                        logger.warning(f"Async memory extraction failed: {e}")
+
+                threading.Thread(target=_extract_memory_stream, daemon=True).start()
+
             yield {
                 'type': 'done',
                 'message_id': ai_msg.id,