microdao-daarion/HMM-MEMORY-STATUS.md

🧠 HMM Memory System — Status

Версія: 1.0.0
Останнє оновлення: 2025-01-17
Статус: ✅ Implementation Complete

---

🎯 Overview

HMM (Hierarchical Multi-Modal Memory) — триярусна система пам'яті для агентів з автоматичною самарізацією та векторним пошуком. Система забезпечує контекстну пам'ять для діалогів з автоматичним управлінням токенами.

Документація:

• HMM Memory Implementation Task — Детальне завдання з TODO
• HMM Memory Summary — Підсумок реалізації

---

✅ Implementation Complete

Дата завершення: 2025-01-17

Core Modules

1. HMM Memory Module

Location Options:

• ✅ gateway-bot/hmm_memory.py — Gateway Bot implementation (complete)
• ✅ services/memory/memory.py — Router Service implementation (complete)

Router Implementation (services/memory/memory.py):

ShortMemory:

• ✅ Останні N повідомлень (default: 20)
• ✅ Redis backend з in-memory fallback
• ✅ FIFO queue для обмеження розміру
• ✅ Функції: add_message(), get_recent(), clear()

MediumMemory:

• ✅ Самарі діалогів (останні 20)
• ✅ Redis list зі збереженням часу
• ✅ Автоматична ротація старих самарі
• ✅ Функції: add_summary(), get_summaries(), clear()

LongMemory:

• ✅ Векторна пам'ять (ChromaDB або RAG Service)
• ✅ Пошук по схожості
• ✅ Fallback до RAG Service API
• ✅ Функції: add_memory(), search()

GraphMemory (Neo4j):

• ✅ Графова пам'ять для зв'язків між діалогами
• ✅ Вузли: User, Agent, DAO, Dialog, Summary, Topic
• ✅ Зв'язки: PARTICIPATED_IN, ABOUT, CONTAINS, MENTIONS
• ✅ Feature flag: GRAPH_MEMORY_ENABLED
• ✅ Fallback якщо Neo4j недоступний
• ✅ Функції:
  • upsert_dialog_context() — інжест самарі в граф
  • query_relevant_summaries_for_dialog() — останні самарі
  • query_related_context_for_user() — контекст користувача
  • query_summaries_by_dao() — самарі DAO
  • query_summaries_by_topic() — пошук за темою

Infrastructure:

• ✅ Автоматична ініціалізація всіх backends
• ✅ Graceful fallback при помилках
• ✅ Connection pooling для Redis
• ✅ TTL для short/medium memory

---

2. Dialogue Management (gateway-bot/dialogue.py)

Functions:

continue_dialogue():

• ✅ Продовження діалогу з автоматичною самарізацією
• ✅ Перевірка токенів (max 24k)
• ✅ Формування контексту (самарі + short memory)
• ✅ Виклик Router → LLM
• ✅ Збереження відповіді

smart_reply():

• ✅ Розумна відповідь з автоматичним RAG
• ✅ Виявлення запитів нагадування ("Що я казав про...", "Нагадай мені...")
• ✅ Пошук у long memory при потребі
• ✅ Fallback до continue_dialogue()

summarize_dialogue():

• ✅ Самарізація через LLM
• ✅ Визначення емоцій
• ✅ Виділення ключових моментів
• ✅ Збереження в medium та long memory

Helper Functions:

• ✅ _detect_reminder_request() — виявлення запитів нагадування
• ✅ _estimate_tokens() — приблизний підрахунок токенів
• ✅ _should_summarize() — перевірка необхідності самарізації

---

3. Configuration & Dependencies

Environment Variables (docker-compose.yml):

environment:
  - REDIS_URL=redis://redis:6379/0
  - CHROMA_PATH=/data/chroma
  - RAG_SERVICE_URL=http://rag-service:9500
  - ROUTER_URL=http://router:9102
  - HMM_SHORT_MEMORY_SIZE=20
  - HMM_MEDIUM_MEMORY_SIZE=20
  - HMM_MAX_TOKENS=24000
  # Neo4j Graph Memory
  - NEO4J_URI=bolt://neo4j:7687
  - NEO4J_USER=neo4j
  - NEO4J_PASSWORD=password
  - GRAPH_MEMORY_ENABLED=true

Dependencies:

gateway-bot/requirements.txt:

• ✅ redis>=5.0.0 — Short/Medium Memory
• ✅ chromadb>=0.4.0 — Long Memory (local)
• ✅ httpx>=0.24.0 — RAG Service API calls
• ✅ pydantic>=2.0.0 — Data validation

services/memory/requirements.txt:

• ✅ redis>=5.0.0 — Short/Medium Memory
• ✅ chromadb>=0.4.0 — Long Memory (local)
• ✅ httpx>=0.24.0 — RAG Service API calls
• ✅ neo4j>=5.15.0 — Graph Memory

Docker (gateway-bot/Dockerfile):

• ✅ Updated to use requirements.txt
• ✅ Multi-stage build for optimization
• ✅ Python 3.11 base image

---

🏗️ Architecture

Memory Hierarchy

┌─────────────────────────────────────────┐
│           User Message                   │
└──────────────┬──────────────────────────┘
               │
               ▼
    ┌──────────────────────┐
    │   smart_reply()      │
    │  - Detect reminder   │
    │  - Load short mem    │
    └──────────┬───────────┘
               │
       ┌───────┴───────┐
       │               │
       ▼               ▼
  Reminder?         Normal
       │               │
       ▼               ▼
┌─────────────┐  ┌──────────────┐
│ Long Memory │  │ Short Memory │
│ RAG Search  │  │ Recent msgs  │
└──────┬──────┘  └──────┬───────┘
       │                │
       └────────┬───────┘
                ▼
         ┌─────────────┐
         │ Token Check │
         │  > 24k?     │
         └──────┬──────┘
                │
         ┌──────┴──────┐
         │             │
        Yes            No
         │             │
         ▼             ▼
  ┌────────────┐  ┌─────────────┐
  │ Summarize  │  │ Continue    │
  │ Dialogue   │  │ Dialogue    │
  └─────┬──────┘  └──────┬──────┘
        │                │
        ▼                │
  ┌────────────┐         │
  │ Medium Mem │         │
  │ Save Sum   │         │
  └─────┬──────┘         │
        │                │
        └────────┬───────┘
                 ▼
          ┌─────────────┐
          │ Router/LLM  │
          │ Generate    │
          └──────┬──────┘
                 ▼
          ┌─────────────┐
          │ Short Memory│
          │ Save Reply  │
          └─────────────┘

Data Flow

1. Normal Message:

user_message
  → smart_reply()
    → load short_memory
      → check tokens
        → if < 24k: continue_dialogue()
        → if > 24k: summarize_dialogue() → continue_dialogue()
          → Router/LLM
            → save to short_memory
              → return response

2. Reminder Request:

user_message ("Що я казав про X?")
  → smart_reply()
    → detect_reminder_request() → True
      → search long_memory(query="X")
        → retrieve relevant memories
          → continue_dialogue(context=memories)
            → Router/LLM
              → return response

3. Summarization Trigger:

tokens > 24k
  → summarize_dialogue(short_memory)
    → Router/LLM (summarize)
      → save to medium_memory
        → save to long_memory (vector)
          → clear old short_memory
            → continue with new context

---

📦 File Structure

Gateway Bot Implementation

gateway-bot/
├── hmm_memory.py              # Core HMM Memory module
│   ├── ShortMemory            # Redis/in-memory recent messages
│   ├── MediumMemory           # Redis summaries
│   └── LongMemory             # ChromaDB/RAG vector search
│
├── dialogue.py                # Dialogue management
│   ├── continue_dialogue()    # Main dialogue flow
│   ├── smart_reply()          # Smart reply with RAG
│   ├── summarize_dialogue()   # LLM summarization
│   └── helper functions       # Token estimation, reminder detection
│
├── http_api.py                # HTTP endpoints (to be updated)
│   └── /telegram/webhook      # Message handler
│
├── requirements.txt           # Python dependencies
├── Dockerfile                 # Docker build config
└── README.md                  # Module documentation

Router Service Implementation

services/
├── memory/
│   ├── memory.py              # Core Memory classes
│   │   ├── ShortMemory        # Redis/in-memory fallback
│   │   ├── MediumMemory       # Redis List summaries
│   │   ├── LongMemory         # ChromaDB or RAG Service
│   │   └── Memory             # Factory class
│   ├── graph_memory.py        # Neo4j Graph Memory
│   │   ├── GraphMemory        # Neo4j driver + queries
│   │   └── 5 query methods    # Graph traversal
│   ├── init_neo4j.py          # Neo4j schema initialization
│   └── __init__.py
│
├── dialogue/              # To be implemented
│   ├── service.py         # Dialogue management
│   │   ├── continue_dialogue()
│   │   └── smart_reply()
│   └── __init__.py
│
└── router/
    ├── router_app.py      # Main router (to be updated)
    │   ├── POST /route
    │   ├── POST /v1/dialogue/continue  # To add
    │   ├── GET /v1/memory/debug/{id}   # To add
    │   └── POST /v1/memory/search      # To add
    └── types.py           # RouterRequest (add dialog_id)

---

🕸️ Neo4j Graph Memory Model

Node Types

User — Користувач системи

• Properties: user_id, name, created_at

Agent — AI агент

• Properties: agent_id, name, type

DAO — MicroDAO

• Properties: dao_id, name, created_at

Dialog — Діалог

• Properties: dialog_id, started_at, last_message_at

Summary — Самарі діалогу

• Properties: summary_id, text, emotion, created_at

Topic — Тема/ключове слово

• Properties: topic, mentioned_count

Relationship Types

PARTICIPATED_IN — User/Agent → Dialog

• Користувач/агент брав участь у діалозі

ABOUT — Dialog → DAO

• Діалог відбувався в контексті DAO

CONTAINS — Dialog → Summary

• Діалог містить самарі

MENTIONS — Summary → Topic

• Самарі згадує тему

Example Graph

(User:tg:123)
    │
    └─[PARTICIPATED_IN]→ (Dialog:d1)
                             │
                             ├─[ABOUT]→ (DAO:greenfood)
                             │
                             └─[CONTAINS]→ (Summary:s1)
                                           │
                                           ├─[MENTIONS]→ (Topic:pizza)
                                           └─[MENTIONS]→ (Topic:delivery)

Cypher Queries

1. Get recent summaries for dialog:

MATCH (d:Dialog {dialog_id: $dialog_id})-[:CONTAINS]->(s:Summary)
RETURN s ORDER BY s.created_at DESC LIMIT 10

2. Get related context for user:

MATCH (u:User {user_id: $user_id})-[:PARTICIPATED_IN]->(d:Dialog)
      -[:CONTAINS]->(s:Summary)
RETURN s ORDER BY s.created_at DESC LIMIT 20

3. Search summaries by topic:

MATCH (s:Summary)-[:MENTIONS]->(t:Topic)
WHERE t.topic CONTAINS $topic
RETURN s ORDER BY s.created_at DESC

---

🔧 Configuration Details

Redis Configuration

Short Memory:

• Key pattern: hmm:short:{dao_id}:{user_id}
• Type: List (FIFO)
• Max size: 20 messages (configurable)
• TTL: 7 days

Medium Memory:

• Key pattern: hmm:medium:{dao_id}:{user_id}
• Type: List of JSON
• Max size: 20 summaries
• TTL: 30 days

ChromaDB Configuration

Collection: hmm_long_memory

• Distance metric: Cosine similarity
• Embedding model: Automatic (via ChromaDB)
• Metadata fields:
  • dao_id: DAO identifier
  • user_id: User identifier
  • timestamp: Creation time
  • emotion: Detected emotion
  • key_points: List of key topics

RAG Service Integration

Endpoint: POST /search

• Request:

  {
    "query": "user query text",
    "dao_id": "dao-id",
    "user_id": "user-id",
    "top_k": 5
  }
  

• Response:

  {
    "results": [
      {"text": "...", "score": 0.95, "metadata": {...}}
    ]
  }
  

---

🧪 Testing

Unit Tests (To be implemented)

tests/test_hmm_memory.py:

# Test ShortMemory
- test_add_message()
- test_get_recent()
- test_fifo_rotation()
- test_redis_fallback()

# Test MediumMemory
- test_add_summary()
- test_get_summaries()
- test_rotation()

# Test LongMemory
- test_add_memory()
- test_search()
- test_rag_fallback()

tests/test_dialogue.py:

# Test dialogue functions
- test_continue_dialogue()
- test_smart_reply()
- test_summarize_dialogue()
- test_detect_reminder()
- test_token_estimation()

Integration Tests

Test Scenario 1: Normal Dialogue

# 1. Send message
response = smart_reply(
    user_id="test_user",
    dao_id="test_dao",
    message="Hello!"
)

# 2. Verify short memory updated
assert len(short_memory.get_recent(...)) == 2  # user + assistant

# 3. Verify response
assert "Hello" in response

Test Scenario 2: Reminder Request

# 1. Add some memories
long_memory.add_memory(text="User likes pizza", ...)

# 2. Ask reminder
response = smart_reply(
    user_id="test_user",
    dao_id="test_dao",
    message="What did I say about pizza?"
)

# 3. Verify long memory searched
assert "pizza" in response

Test Scenario 3: Auto-summarization

# 1. Add many messages (>24k tokens)
for i in range(100):
    short_memory.add_message(...)

# 2. Send message
response = smart_reply(...)

# 3. Verify summarization triggered
assert len(medium_memory.get_summaries(...)) > 0
assert len(short_memory.get_recent(...)) < 100

E2E Test via Gateway

# 1. Send normal message
curl -X POST http://localhost:9300/telegram/webhook \
  -H "Content-Type: application/json" \
  -d '{
    "message": {
      "from": {"id": 123, "username": "test"},
      "chat": {"id": 123},
      "text": "Hello bot!"
    }
  }'

# 2. Send many messages to trigger summarization
for i in {1..50}; do
  curl -X POST http://localhost:9300/telegram/webhook ...
done

# 3. Send reminder request
curl -X POST http://localhost:9300/telegram/webhook \
  -H "Content-Type: application/json" \
  -d '{
    "message": {
      "from": {"id": 123, "username": "test"},
      "chat": {"id": 123},
      "text": "What did I say about pizza?"
    }
  }'

# 4. Verify response contains relevant context

---

🚀 Integration Status

1. Gateway Bot Integration

✅ Modules Created:

• gateway-bot/hmm_memory.py
• gateway-bot/dialogue.py

⏳ To be integrated:

• gateway-bot/http_api.py — Update /telegram/webhook handler

---

2. Router Service Integration

✅ Modules Created:

• services/memory/memory.py — Core Memory classes
  • ShortMemory (Redis/in-memory)
  • MediumMemory (Redis List)
  • LongMemory (ChromaDB or RAG Service)
  • Memory (Factory class)
• services/memory/graph_memory.py — Neo4j Graph Memory
  • GraphMemory (Neo4j driver)
  • 5 query methods for graph traversal
  • Feature flag support
• services/memory/init_neo4j.py — Neo4j initialization
  • Constraints creation
  • Indexes creation

⏳ To be implemented:

• services/dialogue/service.py — Dialogue management
  • continue_dialogue()
  • smart_reply()
• API endpoints in router_app.py or http_api.py:
  • POST /v1/dialogue/continue
  • GET /v1/memory/debug/{dialog_id}
  • POST /v1/memory/search
• Update RouterRequest model with dialog_id
• Configuration and environment variables
• Tests

📝 Documentation:

• docs/cursor/hmm_memory_router_task.md — Detailed implementation task

🎯 Features:

• ✅ Neo4j not used (left for future)
• ✅ Fallback modes (works without Redis/ChromaDB)
• ✅ RAG Service as ChromaDB alternative
• ✅ Ready for Router integration

---

Gateway Bot Integration (Original)

Integration Steps

1. Update http_api.py:

# Before:
async def telegram_webhook(update: TelegramUpdate):
    message = update.message.text
    response = await router_client.route_request(...)
    return response

# After:
from dialogue import smart_reply

async def telegram_webhook(update: TelegramUpdate):
    message = update.message.text
    user_id = f"tg:{update.message.from_.id}"
    dao_id = get_dao_id(update)  # from context or default
    
    # Use smart_reply instead of direct router call
    response = await smart_reply(
        user_id=user_id,
        dao_id=dao_id,
        message=message
    )
    
    return response

2. Initialize HMM Memory on startup:

# http_api.py
from hmm_memory import ShortMemory, MediumMemory, LongMemory

@asynccontextmanager
async def lifespan(app: FastAPI):
    # Initialize memories
    global short_memory, medium_memory, long_memory
    
    short_memory = ShortMemory(redis_url=settings.REDIS_URL)
    medium_memory = MediumMemory(redis_url=settings.REDIS_URL)
    long_memory = LongMemory(
        chroma_path=settings.CHROMA_PATH,
        rag_service_url=settings.RAG_SERVICE_URL
    )
    
    yield
    
    # Cleanup
    await short_memory.close()
    await medium_memory.close()

3. Update Docker Compose:

Already done ✅ — environment variables added.

4. Test:

# Restart gateway service
docker-compose restart gateway

# Check logs
docker-compose logs -f gateway | grep "HMM Memory"

# Send test message via Telegram

---

📊 Monitoring

Metrics to Track

Memory Usage:

• Short memory size (messages per user)
• Medium memory size (summaries per user)
• Long memory collection size

Performance:

• Token estimation time
• Summarization time
• RAG search latency
• Redis response time

Business Metrics:

• Summarization trigger rate
• Reminder request rate
• Average dialogue length (before summarization)

Monitoring Commands

# Redis stats
docker exec -it redis redis-cli INFO memory

# Check short memory keys
docker exec -it redis redis-cli KEYS "hmm:short:*"

# Check medium memory keys
docker exec -it redis redis-cli KEYS "hmm:medium:*"

# ChromaDB stats (if using local)
curl http://localhost:8000/api/v1/collections/hmm_long_memory

---

📊 Neo4j Visualization & Monitoring

Grafana Dashboard

Status: ✅ Implemented

Setup:

• ✅ Grafana added to docker-compose.yml
• ✅ Automatic Neo4j data source provisioning
• ✅ Pre-configured dashboard with 4 panels
• ✅ Automatic dashboard loading on startup

Dashboard Panels:

1. Entity Counts — Кількість DAO/агентів/користувачів
2. Average Agents per DAO — Середня кількість агентів
3. Users Distribution by DAO — Розподіл користувачів
4. Summary Activity Over Time — Активність самарі за часом

File Structure:

grafana/
├── provisioning/
│   ├── datasources/
│   │   └── neo4j.yml          # Auto Neo4j connection
│   └── dashboards/
│       └── default.yml         # Dashboard config
└── dashboards/
    └── dao-agents-users-overview.json  # Dashboard JSON

Access:

• URL: http://localhost:3000
• Default credentials: admin / admin
• Dashboard: Home → Dashboards → "DAO Agents Users Overview"

Quick Start:

# Start Grafana and Neo4j
docker-compose up -d grafana neo4j

# Check logs
docker-compose logs -f grafana

# Open browser
open http://localhost:3000

---

Neo4j Bloom (Graph Visualization)

Status: ✅ Configuration documented

What is Bloom:

• Visual graph exploration tool
• Natural language queries
• Interactive graph visualization
• Built-in Neo4j Browser (Community Edition)
• Neo4j Bloom (Enterprise Edition)

Access:

• Neo4j Browser: http://localhost:7474
• Bloom: http://localhost:7474/bloom (Enterprise only)
• Credentials: neo4j / password

Bloom Perspective Configuration:

Node Styles:

• User — 👤 Blue color, user_id as caption
• Agent — 🤖 Green color, name as caption
• DAO — 🏢 Orange color, dao_id as caption
• Dialog — 💬 Purple color, dialog_id as caption
• Summary — 📝 Gray color, summary_id as caption
• Topic — 🏷️ Yellow color, topic as caption

Search Phrases Examples:

1. "Show me all users"

   • MATCH (u:User) RETURN u LIMIT 50

2. "Find dialogs for {user}"

   • MATCH (u:User {user_id: $user})-[:PARTICIPATED_IN]->(d:Dialog) RETURN u, d

3. "What topics does {user} discuss?"

   • MATCH (u:User {user_id: $user})-[:PARTICIPATED_IN]->(d:Dialog)-[:CONTAINS]->(s:Summary)-[:MENTIONS]->(t:Topic) RETURN u, t, COUNT(t) AS mentions

4. "Show me {dao} activity"

   • MATCH (dao:DAO {dao_id: $dao})<-[:ABOUT]-(d:Dialog) RETURN dao, d LIMIT 20

5. "Who talks about {topic}?"

   • MATCH (t:Topic {topic: $topic})<-[:MENTIONS]-(s:Summary)<-[:CONTAINS]-(d:Dialog)<-[:PARTICIPATED_IN]-(u:User) RETURN t, u, COUNT(u) AS conversations

Documentation:

• README_NEO4J_VISUALIZATION.md — Quick start guide
• docs/cursor/neo4j_visualization_task.md — Implementation task
• docs/cursor/neo4j_bloom_perspective.md — Bloom configuration

Quick Start:

# Start Neo4j
docker-compose up -d neo4j

# Wait for startup (check logs)
docker-compose logs -f neo4j

# Open Neo4j Browser
open http://localhost:7474

# Login and explore graph
# Use Cypher queries from Neo4j Graph Memory Model section

---

🔔 Prometheus Monitoring & Alerting

Neo4j Prometheus Exporter

Status: ✅ Implemented

Service: services/neo4j-exporter/

• ✅ neo4j_exporter/main.py — FastAPI exporter with /metrics endpoint
• ✅ Dockerfile — Container build
• ✅ requirements.txt — Dependencies (fastapi, prometheus-client, neo4j)

Metrics Collected:

1. Health Metrics:

• neo4j_up — Доступність Neo4j (1 = up, 0 = down)
• neo4j_exporter_scrape_duration_seconds — Тривалість scrape
• neo4j_exporter_errors_total{type} — Помилки exporter
• neo4j_cypher_query_duration_seconds{query} — Тривалість Cypher запитів

2. Graph Metrics:

• neo4j_nodes_total{label} — Кількість вузлів по labels (User, Agent, DAO, Dialog, Summary, Topic)
• neo4j_relationships_total{type} — Кількість зв'язків по типах

3. Business Metrics:

• neo4j_summaries_per_day{day} — Самарі по днях (останні 7 днів)
• neo4j_active_daos_last_7d — Активні DAO за 7 днів
• neo4j_avg_agents_per_dao — Середня кількість агентів на DAO
• neo4j_avg_users_per_dao — Середня кількість користувачів на DAO

Access:

• Exporter: http://localhost:9091/metrics
• Prometheus: http://localhost:9090
• Grafana: http://localhost:3000 (Prometheus data source auto-configured)

Quick Start:

# Start exporter, Prometheus, Neo4j
docker-compose up -d neo4j-exporter prometheus neo4j

# Check exporter metrics
curl http://localhost:9091/metrics

# Open Prometheus
open http://localhost:9090

# Check targets status: Status → Targets

---

Prometheus Configuration

File: prometheus/prometheus.yml

Scrape Configs:

scrape_configs:
  - job_name: 'neo4j-exporter'
    static_configs:
      - targets: ['neo4j-exporter:9091']
    scrape_interval: 15s

  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

Alerting Rules: alerting/neo4j_alerts.yml (11 rules)

Alertmanager: Optional (can be added for notifications)

---

Alerting Rules

Status: ✅ Implemented

File: alerting/neo4j_alerts.yml

3 Groups, 11 Rules:

1. Health Alerts (4 rules) — Critical

Neo4jDown:

• Критичний alert коли Neo4j недоступний > 2 хвилин
• Severity: critical
• Action: Check Neo4j logs, restart service

Neo4jExporterHighErrors:

• Alert коли exporter має > 5 помилок за 5 хвилин
• Severity: warning
• Action: Check exporter logs, verify Neo4j connectivity

Neo4jSlowQueries:

• Alert коли Cypher запити > 2 секунд
• Severity: warning
• Action: Optimize queries, add indexes

Neo4jExporterDown:

• Alert коли exporter недоступний > 2 хвилин
• Severity: warning
• Action: Restart exporter container

2. Business Alerts (5 rules) — Monitoring

NoSummariesCreatedToday:

• Alert якщо жодної самарі не створено сьогодні
• Severity: warning
• Action: Check dialogue service, verify memory system

NoActiveDAOsLast7Days:

• Alert якщо жодного активного DAO за 7 днів
• Severity: info
• Action: Marketing campaign, user onboarding

LowAgentsPerDAO:

• Alert якщо середня кількість агентів < 1
• Severity: info
• Action: Promote agent creation, onboarding flows

LowUsersPerDAO:

• Alert якщо середня кількість користувачів < 2
• Severity: info
• Action: User acquisition, engagement campaigns

StalledGrowth:

• Alert якщо немає росту самарі (< 5% change) за 3 дні
• Severity: info
• Action: Analyze trends, engagement campaigns

3. Capacity Alerts (2 rules) — Planning

FastNodeGrowth:

• Alert коли вузли ростуть > 20% за годину
• Severity: info
• Action: Monitor capacity, scale Neo4j

FastRelationshipGrowth:

• Alert коли зв'язки ростуть > 20% за годину
• Severity: info
• Action: Plan storage expansion

---

Grafana Dashboard (Prometheus)

File: grafana/dashboards/neo4j-prometheus-metrics.json

9 Panels:

1. Neo4j Health Status — Up/Down status
2. Exporter Scrape Duration — Performance monitoring
3. Nodes by Label — Graph size over time
4. Relationships by Type — Graph structure
5. Summaries per Day — Activity trend
6. Active DAOs (Last 7 Days) — Engagement
7. Average Agents per DAO — Configuration metric
8. Average Users per DAO — Adoption metric
9. Query Duration — Performance optimization

Access: Grafana → Dashboards → "Neo4j Prometheus Metrics"

---

Documentation

• README_NEO4J_EXPORTER.md — Quick start guide
• docs/cursor/neo4j_prometheus_exporter_task.md — Implementation task
• docs/cursor/neo4j_alerting_rules_task.md — Alerting rules documentation

---

📝 Next Steps

Phase 1: Router Service Integration (Current Priority)

• Create Dialogue Service — services/dialogue/service.py
  • continue_dialogue() — Main dialogue flow with auto-summarization
  • smart_reply() — Smart reply with RAG search
  • Integrate GraphMemory:
    • Call graph_memory.upsert_dialog_context() після самарізації
    • Call graph_memory.query_relevant_summaries_for_dialog() для контексту
• Add API Endpoints — Update router_app.py
  • POST /v1/dialogue/continue — Continue dialogue
  • GET /v1/memory/debug/{dialog_id} — Debug memory state
  • POST /v1/memory/search — Search in long memory
  • GET /v1/memory/graph/{dialog_id} — Graph visualization data
• Update RouterRequest — Add dialog_id field
• Configuration — Add environment variables
• Initialize Neo4j Schema — Run init_neo4j.py on startup
• Tests — Unit + integration tests for all memory layers

Phase 2: Gateway Bot Integration

• Integrate with Gateway Bot — Update gateway-bot/http_api.py
• Unit tests — Test all memory functions
• Integration tests — Test full dialogue flow
• E2E smoke test — Test via Telegram webhook

Phase 2: Enhancements

• Accurate token counting — Use tiktoken for exact count
• Emotion detection — Better emotion analysis in summarization
• Memory analytics — Dashboard for memory usage
• User preferences — Per-user memory settings
• ✅ Neo4j Visualization — Grafana dashboard + Bloom configuration (complete)
• Graph-based recommendations — Suggest related dialogues/topics
• Additional Grafana panels — More insights and metrics

Phase 3: Advanced Features

• Memory search API — External API for memory queries
• Cross-user memory — Team/DAO level memory via graph
• Memory export — Export user memory for GDPR
• Memory versioning — Track memory changes over time
• Graph ML — Graph embeddings for better context retrieval
• Temporal queries — Time-based graph traversal

---

🔗 Related Documentation

• INFRASTRUCTURE.md — Server infrastructure
• RAG-INGESTION-STATUS.md — RAG system status
• WARP.md — Developer guide
• docs/cursor/hmm_memory_implementation_task.md — HMM Memory (Gateway Bot)
• docs/cursor/hmm_memory_router_task.md — HMM Memory (Router Service)
• docs/cursor/neo4j_graph_memory_task.md — Neo4j Graph Memory
• docs/cursor/HMM_MEMORY_SUMMARY.md — Implementation summary

---

Статус: ✅ Core Modules Complete
✅ Gateway Bot: hmm_memory.py, dialogue.py complete
✅ Router Service: memory.py, graph_memory.py, init_neo4j.py complete
⏳ Next: Dialogue Service + API endpoints + Neo4j integration
Last Updated: 2025-01-17 by WARP AI
Maintained by: Ivan Tytar & DAARION Team