# AI-Mem Orchestr8 Implementation Plan

## Overview

This plan implements the AI-Mem Orchestr8 system - a comprehensive AI memory management platform that extends the current AI-Mem cookiecutter templates with CLI lifecycle management, backend API for team synchronization, web frontend for browsing shared thoughts, and Kubernetes deployment via the orchestr8 platform.

## Current State Analysis

### What Exists Now:
- **AI-Mem Template System**: Mature cookiecutter templates for Claude Code configurations and thoughts repositories
- **19 Specialized Components**: 6 AI agents + 13 workflow commands with conditional features
- **Cross-platform Sync Scripts**: Git-based synchronization utilities (bash, PowerShell, batch)
- **Flexible Configuration**: 9 example configurations for different use cases
- **Orchestr8 Platform**: Enterprise GitOps platform with module system, security, and monitoring
- **Claude Code Integration Points**: Memory system, sub-agents, SDK for custom workflows

### What's Missing:
- CLI tool for lifecycle management (on roadmap)
- Backend API for team synchronization (on roadmap) 
- Web frontend for browsing thoughts (on roadmap)
- Kubernetes deployment integration (on roadmap)
- Real-time collaboration features
- Semantic search capabilities

## Desired End State

After implementation, teams will have:

1. **Unified CLI Experience**: `aimem` command that manages templates, synchronization, team collaboration, and deployment
2. **Collaborative Web Platform**: Real-time thought browsing, editing, and team management
3. **Enterprise Deployment**: Kubernetes-native deployment via orchestr8 with security and monitoring
4. **Seamless Integration**: Backward compatibility with existing AI-Mem workflows while adding team collaboration

### Success Verification:
- Teams can deploy AI-Mem to Kubernetes with one command: `aimem deploy --env production`
- Multiple users can collaborate on shared thoughts with real-time synchronization
- Semantic search across team knowledge bases works effectively
- Existing AI-Mem users can upgrade without losing functionality

## What We're NOT Doing

- Replacing the existing cookiecutter template system (maintaining backward compatibility)
- Building a new AI assistant (integrating with Claude Code, not competing)
- Creating a Git alternative (using Git as the foundation with enhancements)
- Supporting non-Kubernetes deployments in initial release (focusing on orchestr8 integration)

## Implementation Approach

**Strategy**: Incremental development with each phase delivering standalone value while building toward the complete system. Focus on backward compatibility and seamless migration paths.

**Key Principles**:
- Maintain existing AI-Mem functionality throughout
- Build on proven orchestr8 module patterns
- Integrate with Claude Code workflows, don't replace them
- Prioritize team collaboration and search capabilities

---

## Phase 1: AI-Mem CLI Foundation

### Overview
Extend the existing AI-Mem system with a comprehensive CLI that provides lifecycle management while maintaining full backward compatibility.

### Changes Required:

#### 1. CLI Infrastructure
**File**: `src/aimem/__init__.py`
**Changes**: Create main CLI package structure

```python
# CLI entry point with subcommands
import click
from .commands import init, sync, search, team, deploy, status

@click.group()
@click.version_option()
def cli():
    """AI-Mem Orchestr8 CLI for managing AI memory lifecycles."""
    pass

cli.add_command(init)
cli.add_command(sync)
cli.add_command(search)
cli.add_command(team)
cli.add_command(deploy)
cli.add_command(status)
```

#### 2. Template Management Integration
**File**: `src/aimem/commands/init.py`
**Changes**: Integrate with existing cookiecutter templates

```python
@click.command()
@click.option('--template', type=click.Choice(['claude-config', 'thoughts-repo', 'full']))
@click.option('--config-file', help='Path to configuration YAML')
@click.option('--remote', help='Enable remote synchronization')
def init(template, config_file, remote):
    """Initialize AI-Mem project with templates."""
    # Use existing cookiecutter integration
    # Add remote sync configuration
    # Set up local CLI configuration
```

#### 3. Enhanced Sync Capabilities  
**File**: `src/aimem/commands/sync.py`
**Changes**: Extend existing sync scripts with remote capabilities

```python
@click.command()
@click.option('--remote/--local', default=False, help='Sync with remote backend')
@click.option('--conflict-resolution', type=click.Choice(['manual', 'auto']))
def sync(remote, conflict_resolution):
    """Sync thoughts with local git and optional remote backend."""
    # Leverage existing sync-thoughts scripts
    # Add remote API integration
    # Handle conflict resolution
```

#### 4. Local Search Implementation
**File**: `src/aimem/commands/search.py` 
**Changes**: Create local search across thoughts directories

```python
@click.command()
@click.argument('query')
@click.option('--type', type=click.Choice(['fulltext', 'semantic']))
@click.option('--path', help='Restrict search to path')
def search(query, type, path):
    """Search across thoughts with full-text or semantic matching."""
    # Implement ripgrep-based full-text search
    # Add basic semantic search with sentence-transformers
    # Return ranked results with snippets
```

#### 5. Project Configuration
**File**: `pyproject.toml`
**Changes**: Add CLI dependencies and entry point

```toml
[project]
name = "aimem"
version = "0.1.0"
dependencies = [
    "click>=8.0",
    "cookiecutter>=2.0", 
    "pyyaml>=6.0",
    "requests>=2.28",
    "rich>=13.0",
    "gitpython>=3.1"
]

[project.scripts]
aimem = "aimem:cli"

[tool.uv]
dev-dependencies = [
    "pytest>=7.0",
    "black>=23.0",
    "ruff>=0.1"
]
```

### Success Criteria:

#### Automated Verification:
- [ ] CLI installs successfully: `uv tool install .`  
- [ ] All commands show help: `aimem --help` and subcommands
- [ ] Template generation works: `aimem init --template claude-config`
- [ ] Local sync functions: `aimem sync` in generated directory
- [ ] Search returns results: `aimem search "implementation"`
- [ ] Unit tests pass: `uv run pytest`
- [ ] Linting passes: `uv run ruff check src/`

#### Manual Verification:
- [ ] Existing AI-Mem users can run CLI without breaking current workflows
- [ ] Generated templates match existing cookiecutter output exactly
- [ ] Search results are relevant and well-formatted
- [ ] Error messages are helpful and actionable

---

## Phase 2: Backend API Service

### Overview
Build the FastAPI backend service for team collaboration, remote synchronization, and search capabilities.

### Changes Required:

#### 1. API Foundation
**File**: `backend/src/aimem_api/main.py`
**Changes**: Create FastAPI application with core structure

```python
from fastapi import FastAPI, Depends
from fastapi.middleware.cors import CORSMiddleware
from .routers import thoughts, search, sync, teams
from .auth import get_current_user
from .database import init_db

app = FastAPI(title="AI-Mem API", version="0.1.0")

app.add_middleware(CORSMiddleware, allow_origins=["*"])

app.include_router(thoughts.router, prefix="/api/v1", dependencies=[Depends(get_current_user)])
app.include_router(search.router, prefix="/api/v1", dependencies=[Depends(get_current_user)])
app.include_router(sync.router, prefix="/api/v1", dependencies=[Depends(get_current_user)])
app.include_router(teams.router, prefix="/api/v1", dependencies=[Depends(get_current_user)])

@app.on_event("startup")
async def startup():
    await init_db()
```

#### 2. Database Models
**File**: `backend/src/aimem_api/models.py`
**Changes**: SQLAlchemy models for thoughts and teams

```python
from sqlalchemy import Column, String, Text, DateTime, UUID, JSONB
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.dialects.postgresql import UUID
import uuid

Base = declarative_base()

class Thought(Base):
    __tablename__ = "thoughts"
    
    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
    path = Column(String, nullable=False, index=True)
    title = Column(String, nullable=False)
    content = Column(Text, nullable=False)
    metadata = Column(JSONB, default={})
    team_id = Column(UUID(as_uuid=True), nullable=False, index=True)
    created_at = Column(DateTime, nullable=False)
    updated_at = Column(DateTime, nullable=False)
```

#### 3. Thoughts API
**File**: `backend/src/aimem_api/routers/thoughts.py`
**Changes**: CRUD operations for thoughts

```python
from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.orm import Session
from ..database import get_db
from ..schemas import ThoughtCreate, ThoughtUpdate, ThoughtResponse

router = APIRouter()

@router.get("/thoughts/", response_model=List[ThoughtResponse])
async def list_thoughts(
    team_id: UUID,
    skip: int = 0, 
    limit: int = 100,
    db: Session = Depends(get_db)
):
    """List thoughts with pagination and filtering."""
    # Implementation with filtering, pagination
    pass

@router.post("/thoughts/", response_model=ThoughtResponse)
async def create_thought(
    thought: ThoughtCreate,
    db: Session = Depends(get_db)
):
    """Create a new thought."""
    # Implementation with validation, metadata extraction
    pass
```

#### 4. Search Service
**File**: `backend/src/aimem_api/services/search.py`
**Changes**: Full-text and semantic search implementation

```python
from sentence_transformers import SentenceTransformer
import numpy as np
from ..database import get_db

class SearchService:
    def __init__(self):
        self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
    
    async def search_thoughts(
        self, 
        query: str, 
        search_type: str = "fulltext",
        team_id: UUID = None
    ):
        """Search thoughts with full-text or semantic matching."""
        if search_type == "semantic":
            return await self._semantic_search(query, team_id)
        else:
            return await self._fulltext_search(query, team_id)
```

#### 5. WebSocket Integration
**File**: `backend/src/aimem_api/websocket.py`
**Changes**: Real-time synchronization

```python
from fastapi import WebSocket, WebSocketDisconnect
import json

class ConnectionManager:
    def __init__(self):
        self.active_connections: Dict[str, List[WebSocket]] = {}
    
    async def connect(self, websocket: WebSocket, team_id: str):
        await websocket.accept()
        if team_id not in self.active_connections:
            self.active_connections[team_id] = []
        self.active_connections[team_id].append(websocket)
    
    async def broadcast_to_team(self, team_id: str, message: dict):
        if team_id in self.active_connections:
            for connection in self.active_connections[team_id]:
                await connection.send_text(json.dumps(message))
```

#### 6. Docker Configuration
**File**: `backend/Dockerfile`
**Changes**: Production-ready container

```dockerfile
FROM python:3.11-slim

WORKDIR /app

COPY pyproject.toml ./
RUN pip install uv && uv pip install --system .

COPY src/ ./src/
CMD ["uvicorn", "aimem_api.main:app", "--host", "0.0.0.0", "--port", "8000"]
```

### Success Criteria:

#### Automated Verification:
- [ ] API starts successfully: `uvicorn aimem_api.main:app`
- [ ] Health check returns 200: `curl localhost:8000/health`
- [ ] OpenAPI docs accessible: `curl localhost:8000/docs`
- [ ] Database migrations run: `alembic upgrade head`
- [ ] All tests pass: `pytest backend/tests/`
- [ ] API performance tests pass: `pytest backend/tests/test_performance.py`

#### Manual Verification:
- [ ] CRUD operations work correctly via API
- [ ] Search returns relevant results with good performance
- [ ] WebSocket connections handle real-time updates
- [ ] Authentication and authorization work properly
- [ ] API handles concurrent requests without issues

---

## Phase 3: Web Frontend Interface

### Overview
Build the Next.js web frontend for collaborative thought management with real-time synchronization.

### Changes Required:

#### 1. Next.js Application Setup
**File**: `frontend/package.json`
**Changes**: Project dependencies and scripts

```json
{
  "name": "aimem-frontend",
  "version": "0.1.0",
  "scripts": {
    "dev": "next dev",
    "build": "next build",
    "start": "next start"
  },
  "dependencies": {
    "next": "14.0.0",
    "react": "18.0.0",
    "react-dom": "18.0.0",
    "@tanstack/react-query": "^5.0.0",
    "@monaco-editor/react": "^4.6.0",
    "socket.io-client": "^4.7.0",
    "tailwindcss": "^3.3.0",
    "@radix-ui/react-*": "^1.0.0"
  }
}
```

#### 2. Main Layout Component  
**File**: `frontend/src/components/Layout.tsx`
**Changes**: App layout with sidebar and content areas

```tsx
interface LayoutProps {
  children: React.ReactNode;
}

export function Layout({ children }: LayoutProps) {
  return (
    <div className="flex h-screen bg-gray-50">
      <Sidebar />
      <main className="flex-1 flex flex-col overflow-hidden">
        <Header />
        <div className="flex-1 flex overflow-hidden">
          {children}
        </div>
      </main>
      <RightPanel />
    </div>
  );
}
```

#### 3. Thought Tree Browser
**File**: `frontend/src/components/ThoughtTree.tsx`
**Changes**: File tree with virtual scrolling

```tsx
interface ThoughtTreeProps {
  thoughts: ThoughtNode[];
  onSelect: (thought: Thought) => void;
}

export function ThoughtTree({ thoughts, onSelect }: ThoughtTreeProps) {
  const [expandedNodes, setExpandedNodes] = useState<Set<string>>(new Set());
  
  return (
    <div className="h-full overflow-auto">
      <VirtualizedTree 
        nodes={thoughts}
        expandedNodes={expandedNodes}
        onNodeClick={onSelect}
        renderNode={({ node, level }) => (
          <ThoughtNode 
            node={node} 
            level={level}
            isExpanded={expandedNodes.has(node.id)}
          />
        )}
      />
    </div>
  );
}
```

#### 4. Monaco Editor Integration
**File**: `frontend/src/components/ThoughtEditor.tsx`
**Changes**: Collaborative markdown editor

```tsx
import { Editor } from '@monaco-editor/react';
import { useWebSocket } from '../hooks/useWebSocket';

interface ThoughtEditorProps {
  thought: Thought;
  onSave: (content: string) => void;
}

export function ThoughtEditor({ thought, onSave }: ThoughtEditorProps) {
  const [content, setContent] = useState(thought.content);
  const { send, collaborators } = useWebSocket(thought.team_id);
  
  const handleContentChange = useCallback((value: string | undefined) => {
    if (value !== undefined) {
      setContent(value);
      // Send cursor position and selection to other users
      send({
        type: 'content_change',
        thoughtId: thought.id,
        content: value
      });
    }
  }, [thought.id, send]);
  
  return (
    <div className="flex flex-col h-full">
      <EditorHeader 
        thought={thought}
        collaborators={collaborators}
      />
      <Editor
        height="100%"
        defaultLanguage="markdown"
        value={content}
        onChange={handleContentChange}
        options={{
          wordWrap: 'on',
          minimap: { enabled: false },
          lineNumbers: 'on'
        }}
      />
    </div>
  );
}
```

#### 5. Search Interface
**File**: `frontend/src/components/Search.tsx`
**Changes**: Multi-modal search with filters

```tsx
export function SearchInterface() {
  const [query, setQuery] = useState('');
  const [filters, setFilters] = useState<SearchFilters>({});
  const [searchType, setSearchType] = useState<'fulltext' | 'semantic'>('fulltext');
  
  const { data: results, isLoading } = useQuery({
    queryKey: ['search', query, filters, searchType],
    queryFn: () => searchAPI.search(query, { ...filters, type: searchType }),
    enabled: query.length > 2
  });
  
  return (
    <div className="flex flex-col h-full">
      <SearchInput 
        value={query}
        onChange={setQuery}
        placeholder="Search thoughts..."
      />
      <SearchFilters 
        filters={filters}
        onChange={setFilters}
        searchType={searchType}
        onTypeChange={setSearchType}
      />
      <SearchResults 
        results={results}
        isLoading={isLoading}
      />
    </div>
  );
}
```

#### 6. Team Dashboard
**File**: `frontend/src/pages/dashboard.tsx`
**Changes**: Team overview and management

```tsx
export default function Dashboard() {
  const { data: team } = useQuery({
    queryKey: ['team'],
    queryFn: teamsAPI.getCurrentTeam
  });
  
  const { data: stats } = useQuery({
    queryKey: ['team-stats'],  
    queryFn: teamsAPI.getTeamStats
  });
  
  return (
    <Layout>
      <div className="p-6">
        <DashboardHeader team={team} />
        <StatsGrid stats={stats} />
        <RecentActivity teamId={team?.id} />
        <TeamMembers team={team} />
      </div>
    </Layout>
  );
}
```

### Success Criteria:

#### Automated Verification:
- [ ] Frontend builds successfully: `npm run build`
- [ ] No TypeScript errors: `npx tsc --noEmit`
- [ ] No linting errors: `npx eslint src/`
- [ ] Unit tests pass: `npm run test`
- [ ] E2E tests pass: `npx playwright test`

#### Manual Verification:
- [ ] Thought tree loads and displays correctly
- [ ] Monaco editor supports markdown editing with syntax highlighting
- [ ] Search returns relevant results for both fulltext and semantic queries
- [ ] Real-time collaboration works with multiple users
- [ ] Team dashboard shows accurate statistics and activity
- [ ] Mobile responsive design works on different screen sizes

---

## Phase 4: Orchestr8 Module Integration

### Overview
Package the AI-Mem system as an orchestr8 module with Kubernetes deployment, security policies, and monitoring integration.

### Changes Required:

#### 1. Module Specification
**File**: `o8-module.yaml`
**Changes**: Define AI-Mem as orchestr8 module

```yaml
apiVersion: orchestr8.io/v1alpha1
kind: Module
metadata:
  name: aimem
  description: "AI Memory Management for team collaboration"
  version: "0.1.0"
spec:
  displayName: "AI-Mem"
  category: "ai-tools"
  
  dependencies:
    - name: postgresql-operator
      version: ">=1.8.0"
    - name: redis-operator  
      version: ">=6.2.0"
      
  resources:
    requests:
      cpu: "500m"
      memory: "1Gi"
    limits:
      cpu: "2"
      memory: "4Gi"
      
  networking:
    ingress:
      enabled: true
      host: "aimem.{domain}"
      tls: true
    service:
      type: ClusterIP
      ports:
        - name: http
          port: 80
          targetPort: 3000
        - name: api
          port: 8080
          targetPort: 8000
          
  security:
    networkPolicies: true
    podSecurityStandard: "restricted"
    rbac:
      enabled: true
      permissions:
        - apiGroups: [""]
          resources: ["configmaps", "secrets"]
          verbs: ["get", "list", "watch"]
          
  monitoring:
    prometheus:
      enabled: true
      path: "/metrics"
      port: 8000
    grafana:
      dashboard: true
      
  storage:
    - name: thoughts-data
      type: persistent
      size: "10Gi"
      accessModes: ["ReadWriteOnce"]
```

#### 2. Kubernetes Manifests
**File**: `manifests/base/deployment.yaml`
**Changes**: Core Kubernetes deployment

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: aimem-backend
  labels:
    app: aimem-backend
spec:
  replicas: 2
  selector:
    matchLabels:
      app: aimem-backend
  template:
    metadata:
      labels:
        app: aimem-backend
    spec:
      securityContext:
        runAsNonRoot: true
        runAsUser: 1000
        fsGroup: 1000
      containers:
      - name: backend
        image: aimem/backend:latest
        ports:
        - containerPort: 8000
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: aimem-secrets
              key: database-url
        resources:
          requests:
            cpu: 250m
            memory: 512Mi
          limits:
            cpu: 1000m
            memory: 2Gi
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 5
---
apiVersion: apps/v1  
kind: Deployment
metadata:
  name: aimem-frontend
spec:
  replicas: 2
  selector:
    matchLabels:
      app: aimem-frontend
  template:
    metadata:
      labels:
        app: aimem-frontend
    spec:
      containers:
      - name: frontend
        image: aimem/frontend:latest
        ports:
        - containerPort: 3000
        env:
        - name: API_URL
          value: "http://aimem-backend:8000"
```

#### 3. ArgoCD Application
**File**: `argocd-apps/modules/aimem.yaml`
**Changes**: ArgoCD application definition following orchestr8 patterns

```yaml
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
  name: aimem
  namespace: argocd
  finalizers:
  - resources-finalizer.argocd.argoproj.io
spec:
  project: orchestr8-modules
  source:
    repoURL: https://github.com/ai-mem/ai-mem
    path: manifests/overlays/production
    targetRevision: main
  destination:
    server: https://kubernetes.default.svc
    namespace: aimem
  syncPolicy:
    automated:
      prune: true
      selfHeal: true
    syncOptions:
    - CreateNamespace=true
    - RespectIgnoreDifferences=true
  ignoreDifferences:
  - group: apps
    kind: Deployment
    jsonPointers:
    - /spec/replicas
```

#### 4. Security Policies
**File**: `manifests/base/network-policy.yaml`
**Changes**: Network isolation and security

```yaml
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: aimem-network-policy
spec:
  podSelector:
    matchLabels:
      app.kubernetes.io/name: aimem
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          name: istio-system
  - from:
    - podSelector:
        matchLabels:
          app.kubernetes.io/name: aimem
    ports:
    - protocol: TCP
      port: 8000
    - protocol: TCP
      port: 3000
  egress:
  - to:
    - namespaceSelector:
        matchLabels:
          name: postgresql
    ports:
    - protocol: TCP
      port: 5432
  - to: []
    ports:
    - protocol: TCP
      port: 443  # HTTPS outbound
```

#### 5. Istio Configuration
**File**: `manifests/base/istio.yaml`
**Changes**: Service mesh integration with authentication

```yaml
apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: aimem
spec:
  hosts:
  - aimem.orchestr8.local
  gateways:
  - orchestr8-gateway
  http:
  - match:
    - uri:
        prefix: /api/
    route:
    - destination:
        host: aimem-backend
        port:
          number: 8000
  - match:
    - uri:
        prefix: /
    route:
    - destination:
        host: aimem-frontend
        port:
          number: 3000
---
apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
  name: aimem-authz
spec:
  selector:
    matchLabels:
      app.kubernetes.io/name: aimem
  rules:
  - from:
    - source:
        principals: ["cluster.local/ns/istio-system/sa/oauth2-proxy"]
```

#### 6. Monitoring Configuration
**File**: `manifests/base/monitoring.yaml`
**Changes**: Prometheus and Grafana integration

```yaml
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: aimem-metrics
spec:
  selector:
    matchLabels:
      app: aimem-backend
  endpoints:
  - port: http
    path: /metrics
    interval: 30s
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: aimem-grafana-dashboard
data:
  dashboard.json: |
    {
      "dashboard": {
        "title": "AI-Mem Metrics",
        "panels": [
          {
            "title": "Request Rate",
            "targets": [
              {
                "expr": "rate(http_requests_total{service=\"aimem-backend\"}[5m])"
              }
            ]
          }
        ]
      }
    }
```

### Success Criteria:

#### Automated Verification:
- [ ] Module validates successfully: `o8 module validate .`
- [ ] Kubernetes manifests are valid: `kubectl apply --dry-run=client -f manifests/`
- [ ] Helm chart renders correctly: `helm template ./charts/aimem`
- [ ] Security policies pass OPA validation: `conftest verify --policy security-policies manifests/`
- [ ] All containers build: `docker build -t aimem/backend backend/ && docker build -t aimem/frontend frontend/`

#### Manual Verification:
- [ ] Module deploys successfully to Kubernetes cluster
- [ ] All pods start and pass health checks
- [ ] Ingress routes traffic correctly to frontend and backend
- [ ] Authentication works through Istio and OAuth2 proxy
- [ ] Monitoring dashboards show metrics correctly
- [ ] Network policies allow expected traffic and block unauthorized access

---

## Phase 5: Advanced Features & Production Readiness

### Overview
Add advanced features like semantic search, AI-powered organization, and enterprise-grade operational features.

### Changes Required:

#### 1. Semantic Search Enhancement
**File**: `backend/src/aimem_api/services/semantic_search.py`
**Changes**: Advanced vector search with embeddings

```python
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
import asyncio

class SemanticSearchService:
    def __init__(self):
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
        self.vector_db = QdrantClient(url="http://qdrant:6333")
    
    async def index_thought(self, thought: Thought):
        """Index thought for semantic search."""
        embedding = self.model.encode(thought.content)
        await self.vector_db.upsert(
            collection_name="thoughts",
            points=[{
                "id": str(thought.id),
                "vector": embedding.tolist(),
                "payload": {
                    "title": thought.title,
                    "path": thought.path,
                    "team_id": str(thought.team_id)
                }
            }]
        )
    
    async def semantic_search(
        self, 
        query: str, 
        team_id: str, 
        limit: int = 20
    ):
        """Perform semantic search across thoughts."""
        query_embedding = self.model.encode(query)
        results = await self.vector_db.search(
            collection_name="thoughts",
            query_vector=query_embedding.tolist(),
            query_filter={
                "must": [{"key": "team_id", "match": {"value": team_id}}]
            },
            limit=limit
        )
        return results
```

#### 2. AI-Powered Organization
**File**: `backend/src/aimem_api/services/ai_organizer.py`  
**Changes**: Claude integration for automatic organization

```python
from anthropic import Anthropic
import asyncio
from typing import List

class AIOrganizerService:
    def __init__(self):
        self.client = Anthropic()
    
    async def suggest_tags(self, thought: Thought) -> List[str]:
        """Use Claude to suggest relevant tags."""
        prompt = f"""
        Based on this thought document, suggest 3-5 relevant tags:
        
        Title: {thought.title}
        Content: {thought.content[:1000]}...
        
        Return only the tags as a comma-separated list.
        """
        
        message = await self.client.messages.create(
            model="claude-3-haiku-20240307",
            max_tokens=100,
            messages=[{"role": "user", "content": prompt}]
        )
        
        tags = [tag.strip() for tag in message.content[0].text.split(',')]
        return tags
    
    async def detect_duplicates(self, thought: Thought, team_id: str) -> List[Thought]:
        """Find potentially duplicate thoughts."""
        # Use semantic search to find similar thoughts
        semantic_service = SemanticSearchService()
        similar_thoughts = await semantic_service.semantic_search(
            thought.content, team_id, limit=5
        )
        
        # Use Claude to assess if they're actual duplicates
        duplicates = []
        for similar in similar_thoughts:
            if similar.score > 0.8:  # High similarity threshold
                duplicates.append(similar)
                
        return duplicates
```

#### 3. Advanced Git Integration
**File**: `backend/src/aimem_api/services/git_service.py`
**Changes**: Sophisticated git operations and conflict resolution

```python
import git
from typing import List, Dict
import asyncio
from pathlib import Path

class GitService:
    def __init__(self, repo_path: str):
        self.repo = git.Repo(repo_path)
    
    async def smart_merge(self, branch: str) -> Dict[str, any]:
        """Perform intelligent merge with conflict detection."""
        try:
            # Check for conflicts before merging
            conflicts = await self._detect_conflicts(branch)
            
            if conflicts:
                return {
                    "success": False,
                    "conflicts": conflicts,
                    "resolution_suggestions": await self._suggest_resolutions(conflicts)
                }
            
            # Perform merge
            self.repo.git.merge(branch)
            return {"success": True, "merged_files": self._get_changed_files()}
            
        except git.GitCommandError as e:
            return {"success": False, "error": str(e)}
    
    async def _detect_conflicts(self, branch: str) -> List[str]:
        """Detect potential merge conflicts."""
        # Get files changed in both branches
        base = self.repo.merge_base(self.repo.head, branch)[0]
        head_files = set(self.repo.git.diff('--name-only', base, 'HEAD').split())
        branch_files = set(self.repo.git.diff('--name-only', base, branch).split())
        
        return list(head_files.intersection(branch_files))
```

#### 4. Enterprise Analytics Dashboard
**File**: `frontend/src/pages/analytics.tsx`
**Changes**: Team analytics and insights

```tsx
import { LineChart, BarChart, PieChart } from '@/components/charts';

export default function Analytics() {
  const { data: analytics } = useQuery({
    queryKey: ['team-analytics'],
    queryFn: analyticsAPI.getTeamAnalytics,
    refetchInterval: 300000 // 5 minutes
  });
  
  return (
    <Layout>
      <div className="p-6 space-y-6">
        <AnalyticsHeader />
        
        <div className="grid grid-cols-1 lg:grid-cols-2 xl:grid-cols-3 gap-6">
          <MetricCard
            title="Thoughts Created"
            value={analytics?.thoughtsThisWeek}
            change={analytics?.thoughtsChange}
            trend="up"
          />
          
          <MetricCard
            title="Active Contributors" 
            value={analytics?.activeUsers}
            change={analytics?.usersChange}
            trend="stable"
          />
          
          <MetricCard
            title="Search Queries"
            value={analytics?.searchQueries}
            change={analytics?.searchChange}
            trend="up"
          />
        </div>
        
        <div className="grid grid-cols-1 xl:grid-cols-2 gap-6">
          <ChartCard title="Thought Creation Over Time">
            <LineChart 
              data={analytics?.thoughtsOverTime}
              xKey="date"
              yKey="count"
            />
          </ChartCard>
          
          <ChartCard title="Most Active Authors">
            <BarChart
              data={analytics?.topAuthors}
              xKey="name"
              yKey="thoughtCount"
            />
          </ChartCard>
        </div>
        
        <div className="grid grid-cols-1 xl:grid-cols-3 gap-6">
          <ChartCard title="Thought Types Distribution">
            <PieChart
              data={analytics?.thoughtTypes}
              nameKey="type"
              valueKey="count"
            />
          </ChartCard>
          
          <AIInsights insights={analytics?.aiInsights} />
          
          <TeamHealth metrics={analytics?.teamHealth} />
        </div>
      </div>
    </Layout>
  );
}
```

#### 5. Backup & Disaster Recovery  
**File**: `backend/src/aimem_api/services/backup_service.py`
**Changes**: Automated backup and recovery system

```python
import asyncio
import boto3
from datetime import datetime, timedelta
import tarfile
import os

class BackupService:
    def __init__(self):
        self.s3_client = boto3.client('s3')
        self.bucket_name = os.getenv('BACKUP_BUCKET')
    
    async def create_backup(self, team_id: str) -> str:
        """Create full backup of team data."""
        timestamp = datetime.utcnow().strftime('%Y%m%d_%H%M%S')
        backup_name = f"aimem_backup_{team_id}_{timestamp}.tar.gz"
        
        # Export thoughts from database
        thoughts_data = await self._export_thoughts(team_id)
        
        # Export git repository
        git_archive = await self._create_git_archive(team_id)
        
        # Create compressed backup
        backup_path = f"/tmp/{backup_name}"
        with tarfile.open(backup_path, 'w:gz') as tar:
            tar.add(thoughts_data, arcname='thoughts.json')
            tar.add(git_archive, arcname='git_repo.tar')
        
        # Upload to S3
        await self._upload_backup(backup_path, backup_name)
        
        # Cleanup local files
        os.remove(backup_path)
        os.remove(thoughts_data)
        os.remove(git_archive)
        
        return backup_name
    
    async def restore_backup(self, backup_name: str, team_id: str):
        """Restore team data from backup."""
        # Download from S3
        backup_path = await self._download_backup(backup_name)
        
        # Extract and restore
        with tarfile.open(backup_path, 'r:gz') as tar:
            tar.extractall('/tmp/restore')
        
        # Restore thoughts to database
        await self._restore_thoughts('/tmp/restore/thoughts.json', team_id)
        
        # Restore git repository
        await self._restore_git_repo('/tmp/restore/git_repo.tar', team_id)
```

#### 6. CLI Production Features
**File**: `src/aimem/commands/deploy.py`
**Changes**: Production deployment and management

```python
@click.command()
@click.option('--env', type=click.Choice(['local', 'staging', 'production']))
@click.option('--domain', help='Custom domain for deployment')
@click.option('--replicas', default=2, help='Number of replicas')
@click.option('--backup-schedule', help='Backup cron schedule')
def deploy(env, domain, replicas, backup_schedule):
    """Deploy AI-Mem to Kubernetes via orchestr8."""
    
    # Validate environment
    if env == 'production':
        if not click.confirm('Deploy to production?'):
            return
    
    # Generate deployment configuration
    config = {
        'environment': env,
        'domain': domain or f'aimem.{env}.orchestr8.local',
        'replicas': replicas,
        'backup': {
            'enabled': True,
            'schedule': backup_schedule or '0 2 * * *'  # Daily at 2 AM
        }
    }
    
    # Use o8-cli to deploy
    result = subprocess.run([
        'o8', 'module', 'deploy', 
        '--env', env,
        '--config', json.dumps(config)
    ], capture_output=True)
    
    if result.returncode == 0:
        click.echo(f"✅ Deployed successfully to {env}")
        click.echo(f"🌐 Available at: https://{config['domain']}")
    else:
        click.echo(f"❌ Deployment failed: {result.stderr.decode()}")
```

### Success Criteria:

#### Automated Verification:
- [ ] Vector search performs semantic queries: `curl -X POST localhost:8000/api/v1/search/semantic`
- [ ] AI organization suggests relevant tags: Test tag suggestion API
- [ ] Backup creation completes: `aimem backup create --team team-id`
- [ ] Analytics dashboard loads data: Check analytics endpoints
- [ ] Production deployment succeeds: `aimem deploy --env production`

#### Manual Verification:
- [ ] Semantic search returns contextually relevant results
- [ ] AI-powered features provide useful suggestions and insights
- [ ] Backup and restore operations preserve all data correctly
- [ ] Analytics dashboard provides actionable team insights
- [ ] Production deployment is stable and performs well under load
- [ ] Enterprise features meet security and compliance requirements

---

## Testing Strategy

### Unit Tests:
- **CLI Commands**: Test all aimem commands with various inputs and error conditions
- **API Endpoints**: Test CRUD operations, search functionality, and authentication
- **Database Models**: Test data validation, relationships, and migrations
- **Search Services**: Test both fulltext and semantic search accuracy

### Integration Tests:
- **API Integration**: Test complete workflows from CLI through API to database
- **WebSocket Communication**: Test real-time synchronization between multiple clients
- **Git Operations**: Test sync, conflict resolution, and merge operations
- **Kubernetes Deployment**: Test deployment, scaling, and failover scenarios

### Manual Testing Steps:
1. **Setup New Team**: Use CLI to initialize team, invite members, deploy to Kubernetes
2. **Collaborative Editing**: Multiple users edit same thought, verify conflict resolution
3. **Search Accuracy**: Create diverse thoughts, test search relevance and performance
4. **Load Testing**: Simulate 100+ concurrent users, verify system stability
5. **Disaster Recovery**: Test backup creation and restoration procedures

## Performance Considerations

### Backend Optimization:
- **Database Connection Pooling**: AsyncPG with 20 connections per pod
- **Query Optimization**: Indexes on commonly searched fields, read replicas for search
- **Caching Strategy**: Redis for API responses, thought metadata, search results
- **Vector Search**: Qdrant with HNSW indexing for sub-second semantic search

### Frontend Performance:
- **Virtual Scrolling**: Handle 10,000+ thoughts in tree view efficiently  
- **Code Splitting**: Lazy load editor, analytics, and advanced features
- **State Management**: Efficient updates with React Query and Zustand
- **Bundle Optimization**: Tree shaking, compression, CDN asset delivery

### Kubernetes Scaling:
- **Horizontal Pod Autoscaling**: CPU/memory based scaling with min 2, max 10 replicas
- **Resource Management**: Appropriate requests/limits to prevent resource starvation
- **Database Scaling**: PostgreSQL with read replicas and connection pooling
- **Monitoring**: Prometheus metrics with alerting for performance degradation

## Migration Notes

### From Current AI-Mem:
1. **Seamless CLI Migration**: Existing users can install `aimem` CLI alongside current setup
2. **Template Compatibility**: New CLI generates identical templates to existing cookiecutter
3. **Sync Script Integration**: Existing sync scripts continue working, with optional remote sync
4. **Gradual Adoption**: Teams can adopt remote features incrementally

### Data Migration:
- **Thought Import**: CLI command to bulk import existing thought directories
- **Team Setup**: Automatic team creation with existing member detection
- **Git History Preservation**: Full git history maintained during migration
- **Configuration Transfer**: Automatic migration of existing AI-Mem configurations

## References

- Original AI-Mem README: `/projects/ai-mem/README.md`
- Orchestr8 Module Specification: `/projects/orchestr8/specs/o8-module-spec-v1.yaml`
- Claude Code Memory Documentation: `/projects/claude-code-docs/docs/memory.md`
- Claude Code Sub-agents Guide: `/projects/claude-code-docs/docs/sub-agents.md`
- Orchestr8 Security Policies: `/projects/orchestr8/docs/SECURITY.md`