```
multi_engineer_v2/
├── __pycache__
│   └── ...
├── chat
│   └── ...
├── code_execution
│   └── ...
├── efc
│   └── ...
├── file_operations
│   └── ...
├── llm
│   └── ...
├── smart_edit
│   └── ...
├── src
│   └── ...
├── tests
│   └── ...
├── tools
│   └── ...
├── utils
│   └── ...
├── __init__.py
├── .env
├── app_state.py
├── config.py
├── main.py
├── pytest.ini
├── readme.md
├── requirements.txt
├── setup.py
```

```
workbench_ui/
├── __pycache__
│   └── ...
└── static
    ├── script.js
    └── style.css
├── test.py
├── __init__.py
├── .env
├── .sesskey
├── app.py
├── cli_integration.py
├── debug_import.py
├── requirements.txt
└── test_multi_engineer.py
```

here i have created these file trees of the projects i will be moving. Can you update the following guide to reflect a more specific roadmap below?

```
Tailored Poetry Migration Plan for Your Multi-Modal CLI Tool and Workbench UI  Thank you for clarifying the project components. Based on your structure, we'll focus on migrating the following two distinct projects into a unified root directory managed by Poetry:  1. multi_engineer_v2: Your multi-modal CLI tool (referred to as the Backend). 2. workbench_ui: Your current UI project containing app.py, style.css, script.js, and cli_integration.py (referred to as the Frontend).  This tailored plan ensures seamless dependency management, environment isolation, and prepares the foundation for Phase 2 of your roadmap. Let's proceed step-by-step.  ---  ## Table of Contents  1. Project Structure Overview 2. Prerequisites 3. Step-by-Step Migration - Step 1: Create a New Root Directory - Step 2: Initialize Poetry in the Root Directory - Step 3: Set Up Frontend and Backend as Separate Poetry Projects - Step 4: Configure Poetry Workspaces - Step 5: Define Dependencies for Frontend and Backend - Step 6: Migrate Existing Code into Frontend and Backend Directories - Step 7: Adjust Import Paths and Project Configurations - Step 8: Set Up Shared Code (Optional) - Step 9: Update .gitignore - Step 10: Verify the Setup 4. Phase 2 Integration Considerations 5. Additional Recommendations 6. Conclusion  ---  ## 1. Project Structure Overview  Before initiating the migration, it's crucial to visualize the intended project structure. This clarity ensures organized management and scalability.  ### Desired Directory Structure:  my_project/ ├── backend/ │ ├── cli.py │ ├── multi_engineer_v2/ │ │ ├── init.py │ │ ├── cli_engine.py │ │ └── [Other Backend Files] │ └── pyproject.toml ├── frontend/ │ ├── app.py │ ├── script.js │ ├── style.css │ ├── cli_integration.py │ ├── [Other Frontend Files] │ └── pyproject.toml ├── shared/ # Optional for shared utilities │ ├── utils.py │ └── pyproject.toml ├── poetry.lock └── pyproject.toml # Root Poetry configuration  - backend/: Contains your CLI tool multi_engineer_v2 and associated backend scripts. - frontend/: Houses your workbench_ui project with app.py, style.css, script.js, and cli_integration.py. - shared/: (Optional) For any shared modules or utilities between frontend and backend. - pyproject.toml: Managed by Poetry to define workspace and project configurations. - poetry.lock: Ensures consistent dependency versions across environments.  ---  ## 2. Prerequisites  Ensure the following are met before proceeding:  - Python Version: Python 3.8+ installed on your system. - Poetry Installation: Install Poetry using the official Poetry installation guide.

    curl -sSL https://install.python-poetry.org | python3 -


    After installation, add Poetry to your system's PATH if not done automatically:

    export PATH="$HOME/.local/bin:$PATH"


- Version Control: Use Git to track changes and ensure you have backups before making significant modifications.

---

## 3. Step-by-Step Migration

Follow the steps below to migrate your projects into a unified Poetry-managed repository.

### Step 1: Create a New Root Directory

Organize your projects under a single root for better management.

mkdir my_project
cd my_project


### Step 2: Initialize Poetry in the Root Directory

Initialize Poetry without creating a new project. This setup prepares the root for using Poetry workspaces.

poetry init --no-interaction


This command generates a minimal pyproject.toml file in the my_project/ directory.

### Step 3: Set Up Frontend and Backend as Separate Poetry Projects

Create separate directories for frontend and backend, initializing each as its own Poetry project.

#### 3.1 Set Up Backend

mkdir backend
cd backend
poetry init --no-interaction


This creates a backend/pyproject.toml.

#### 3.2 Set Up Frontend

Navigate back to the root and set up the frontend.

cd ..
mkdir frontend
cd frontend
poetry init --no-interaction


This creates a frontend/pyproject.toml.

#### 3.3 (Optional) Set Up Shared Code

If you anticipate shared utilities between frontend and backend:

cd ..
mkdir shared
cd shared
poetry init --no-interaction


This creates a shared/pyproject.toml.

### Step 4: Configure Poetry Workspaces

Poetry workspaces allow managing multiple Poetry projects within a single repository, facilitating shared dependencies and streamlined workflow.

#### 4.1 Update Root pyproject.toml

Edit my_project/pyproject.toml to include the frontend/, backend/, and optionally shared/ directories as workspace members.

[tool.poetry]
name = "my_project"
version = "0.1.0"
description = "Multi-Modal CLI Tool and Workbench UI"
authors = ["Your Name <you@example.com>"]

[tool.poetry.dependencies]
python = "^3.8"

[tool.poetry.dev-dependencies]

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

[tool.poetry.workspace]
members = ["backend", "frontend", "shared"]


Note: Ensure you have Poetry version >=1.2 to utilize workspaces. You can check your Poetry version with:

poetry --version


Upgrade if necessary:

poetry self update


### Step 5: Define Dependencies for Frontend and Backend

Define project-specific dependencies within each pyproject.toml.

#### 5.1 Backend Dependencies

Navigate to backend/ and add necessary dependencies.

cd backend


Assuming your backend uses FastAPI, Uvicorn, and other packages:

poetry add fastapi uvicorn


If additional dependencies like SQLAlchemy, requests, or other utilities are needed:

poetry add sqlalchemy requests


For development dependencies (e.g., testing frameworks):

poetry add --dev pytest


#### 5.2 Frontend Dependencies

Navigate to frontend/ and add necessary dependencies.

cd ../frontend


If your frontend uses FastAPI to serve the UI, and possibly other Python packages:

poetry add fastapi jinja2


If your frontend involves JavaScript frameworks or assets, manage those dependencies separately using npm or yarn.

npm init -y
npm install react react-dom


Important: Poetry manages Python dependencies. Use npm or yarn for JavaScript dependencies within the frontend/ directory.

#### 5.3 Shared Dependencies (Optional)

If you have shared utilities:

cd ../shared
poetry add pydantic
poetry add --dev black


Note: Shared dependencies can be referenced in both frontend and backend via Poetry's path dependencies if placed inside the workspace.

### Step 6: Migrate Existing Code into Frontend and Backend Directories

Move your existing multi_engineer_v2 (CLI tool) and workbench_ui (Frontend) into the appropriate directories.

#### 6.1 Move Backend Code (multi_engineer_v2)

Assuming multi_engineer_v2 is your CLI tool:

mv path/to/current/multi_engineer_v2 backend/


Ensure that the CLI tool's entry point (e.g., cli.py) is correctly placed within backend/multi_engineer_v2/.

Directory Structure after Move:

my_project/
├── backend/
│   ├── multi_engineer_v2/
│   │   ├── init.py
│   │   ├── cli_engine.py
│   │   └── [Other Backend Files]
│   ├── cli.py
│   └── pyproject.toml
├── frontend/
│   ├── app.py
│   ├── script.js
│   ├── style.css
│   ├── cli_integration.py
│   └── pyproject.toml
├── shared/
│   ├── utils.py
│   └── pyproject.toml
└── pyproject.toml


#### 6.2 Move Frontend Code (workbench_ui)

mv path/to/current/workbench_ui/* frontend/


Ensure that all frontend-specific files (app.py, style.css, script.js, cli_integration.py) are correctly placed within frontend/.

Note: If workbench_ui has subdirectories (e.g., for templates or static assets), ensure they are moved accordingly.

#### 6.3 Verify Moved Files

After moving, your directories should contain:

- Backend:
  - backend/multi_engineer_v2/ with CLI tool files.
  - backend/app.py (if applicable), else adjust as needed.
- Frontend:
  - frontend/app.py
  - frontend/script.js
  - frontend/style.css
  - frontend/cli_integration.py
  - Any other frontend-specific files.

### Step 7: Adjust Import Paths and Project Configurations

After moving files, ensure that all import paths and configurations are correctly adjusted.

#### 7.1 Update app.py Imports in Frontend

If frontend/app.py or frontend/cli_integration.py imports modules from multi_engineer_v2 or shared utilities, adjust the import statements accordingly.

Example:

# frontend/app.py

# Before:
# from cli_integration import some_function

# After:
from backend.multi_engineer_v2.cli_engine import some_function


Note: This direct import implies that the frontend depends on the backend's internal modules. Consider whether the frontend should interact with the backend via APIs instead for better decoupling.

#### 7.2 Configure Dependencies Between Frontend and Backend

If there's a need for the frontend to access backend modules directly (not recommended for decoupled systems), add path dependencies.

Example (frontend/pyproject.toml):

[tool.poetry.dependencies]
python = "^3.8"
backend = { path = "../backend", develop = true }
shared = { path = "../shared", develop = true }


Recommendation:

It's generally better to have the frontend interact with the backend via API endpoints rather than direct imports. This approach promotes decoupling and scalability.

### Step 8: Set Up Shared Code (Optional)

If you have utilities or modules that both frontend and backend use, place them in the shared/ directory.

#### 8.1 Develop Shared Modules

Example (shared/utils.py):

import uuid
import re

def generate_unique_id():
    """Generates a unique identifier using UUID4."""
    return str(uuid.uuid4())

def requires_approval(response):
    """Determines if the AI response requires user approval based on predefined criteria."""
    # Example criteria: contains file operations
    return bool(re.search(r'\bcreate|modify|delete\b', response, re.IGNORECASE))

def extract_action_description(response):
    """Extracts a description of the action from the AI response."""
    return response.strip()


#### 8.2 Reference Shared Modules in Frontend and Backend

Ensure that both projects can access the shared modules.

Example Imports:

# backend/multi_engineer_v2/cli_engine.py
from shared.utils import generate_unique_id, requires_approval, extract_action_description

# frontend/cli_integration.py
from shared.utils import some_shared_function


### Step 9: Update .gitignore

Create or update the .gitignore in the root directory to exclude virtual environments, build artifacts, and other non-essential files.

Create my_project/.gitignore:

# Poetry
poetry.lock

# Python
pycache/
.py[cod]
.pyo
.pyd
venv/
.env
.env/
.env

# Node.js (for frontend if applicable)
frontend/node_modules/
frontend/package-lock.json
frontend/yarn.lock

# Build artifacts
backend/dist/
backend/build/
backend/multi_engineer_v2/.egg-info/
frontend/dist/
frontend/build/
frontend/.egg-info/

# IDEs and Editors
.vscode/
.idea/
.sublime-project
.sublime-workspace

# OS Files
.DS_Store
Thumbs.db

# Logs
*.log

# Coverage reports
.coverage
htmlcov/
coverage.xml


Note: Adjust paths and entries based on your specific project needs.

### Step 10: Verify the Setup

After completing the migration, ensure that everything functions as expected.

#### 10.1 Install Dependencies

- Backend:

    cd backend
    poetry install


- Frontend:

    cd ../frontend
    poetry install


    If using JavaScript dependencies:

    npm install


- Shared (If Applicable):

    cd ../shared
    poetry install


#### 10.2 Activate Virtual Environments

Poetry manages virtual environments automatically. To activate:

- Backend:

    cd backend
    poetry shell


- Frontend:

    cd ../frontend
    poetry shell


- Shared:

    cd ../shared
    poetry shell


#### 10.3 Run Backend Server (multi_engineer_v2)

Assuming backend/cli.py is the entry point for your CLI tool:

cd backend
poetry run python cli.py


If backend/cli.py starts a server (e.g., FastAPI):

poetry run uvicorn cli:app --reload --host 0.0.0.0 --port 8000


#### 10.4 Run Frontend (workbench_ui)

Assuming frontend/app.py serves the UI using FastAPI:

cd frontend
poetry run uvicorn app:app --reload --host 0.0.0.0 --port 5001


Verify:

- Access your Backend CLI tool functionalities via http://localhost:8000.
- Access your Frontend UI via http://localhost:5001.
- Ensure both are operating independently without dependency conflicts.

#### 10.5 Test Shared Modules (If Applicable)

Ensure that shared utilities are accessible from both frontend and backend.

Example:

- Backend (backend/multi_engineer_v2/cli_engine.py):

    from shared.utils import generate_unique_id, requires_approval, extract_action_description

    def some_backend_function(response):
        if requires_approval(response):
            action_id = generate_unique_id()
            description = extract_action_description(response)
            # Proceed with action handling


- Frontend (frontend/cli_integration.py):

    from shared.utils import some_shared_function

    def some_frontend_function():
        result = some_shared_function()
        # Proceed with frontend logic


---

## 4. Phase 2 Integration Considerations

With Phase 1 setting up a robust structure, Phase 2 can focus on deeper integrations and enhancements.

### 4.1 API Communication Between Frontend and Backend

Instead of importing backend modules directly into the frontend, set up API endpoints in the backend and communicate via HTTP requests from the frontend.

#### Backend (backend/cli.py)

Define API endpoints using FastAPI to expose backend functionalities.

from fastapi import FastAPI
from shared.utils import generate_unique_id, requires_approval, extract_action_description

app = FastAPI()

@app.get("/api/health")
def health_check():
    return {"status": "Backend is running."}

@app.post("/api/execute_command")
def execute_command(command: str):
    # Process the command using multi_engineer_v2 logic
    response = multi_engineer_v2.run_command(command)
    
    if requires_approval(response):
        action_id = generate_unique_id()
        description = extract_action_description(response)
        # Store pending approval logic here
        return {"action_id": action_id, "description": description, "requires_approval": True}
    
    return {"response": response, "requires_approval": False}


#### Frontend (frontend/cli_integration.py)

Use HTTP requests to interact with the backend APIs.

import requests

def send_command(command: str):
    response = requests.post("http://localhost:8000/api/execute_command", json={"command": command})
    return response.json()

def health_check():
    response = requests.get("http://localhost:8000/api/health")
    return response.json()


### 4.2 Real-Time Communication with WebSockets

Leverage WebSockets for real-time updates, notifications, or live interactions between frontend and backend.

#### Backend (backend/cli.py)

Define WebSocket endpoints to handle real-time events.

from fastapi import WebSocket, WebSocketDisconnect

active_connections = []

@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    active_connections.append(websocket)
    try:
        while True:
            data = await websocket.receive_text()
            # Handle incoming data or commands
    except WebSocketDisconnect:
        active_connections.remove(websocket)


#### Frontend (frontend/script.js)

Establish WebSocket connections to receive real-time updates.

const socket = new WebSocket("ws://localhost:8000/ws");

socket.onopen = () => {
    console.log("WebSocket connection established.");
    socket.send("Frontend connected.");
};

socket.onmessage = (event) => {
    const data = event.data;
    console.log("Received from backend:", data);
    // Update UI based on received data
};

socket.onerror = (error) => {
    console.error("WebSocket Error:", error);
};

socket.onclose = () => {
    console.warn("WebSocket connection closed.");
};


### 4.3 Authentication and Security

Implement authentication to secure API endpoints and WebSocket communications.

#### Backend (backend/cli.py)

Use OAuth2 with JWT tokens for secure authentication.

from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
import jwt

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

def authenticate_user(username: str, password: str):
    # Implement user authentication logic
    return user

def create_access_token(data: dict):
    # Implement JWT token creation
    return token

@app.post("/token")
def login(form_data: OAuth2PasswordRequestForm = Depends()):
    user = authenticate_user(form_data.username, form_data.password)
    if not user:
        raise HTTPException(
            status_code=status.HTTP_401_UNAUTHORIZED,
            detail="Invalid credentials.",
            headers={"WWW-Authenticate": "Bearer"},
        )
    token = create_access_token(data={"sub": user.username})
    return {"access_token": token, "token_type": "bearer"}

def get_current_user(token: str = Depends(oauth2_scheme)):
    # Implement token validation and retrieve current user
    return user

@app.post("/api/protected_endpoint")
def protected_endpoint(user: User = Depends(get_current_user)):
    return {"message": f"Hello, {user.username}!"}


#### Frontend (frontend/script.js)

Handle authentication tokens and include them in API requests.

// Example: Storing and using JWT tokens

// After successful login
function storeToken(token) {
    localStorage.setItem('access_token', token);
}

function getToken() {
    return localStorage.getItem('access_token');
}

// Including the token in API requests
function authenticatedRequest(endpoint, data) {
    const token = getToken();
    return fetch(http://localhost:8000${endpoint}, {
        method: 'POST',
        headers: {
            'Authorization': Bearer ${token},
            'Content-Type': 'application/json'
        },
        body: JSON.stringify(data)
    });
}


### 4.4 Shared Utilities and Modules

Ensure that shared code is efficiently utilized across both frontend and backend.

#### Example (shared/utils.py):

import uuid
import re

def generate_unique_id():
    """Generates a unique identifier using UUID4."""
    return str(uuid.uuid4())

def requires_approval(response):
    """Determines if the AI response requires user approval based on predefined criteria."""
    # Example criteria: contains file operations
    return bool(re.search(r'\bcreate|modify|delete\b', response, re.IGNORECASE))

def extract_action_description(response):
    """Extracts a description of the action from the AI response."""
    return response.strip()


---

## 4. Phase 2 Integration Considerations

With a solid foundation from Phase 1, Phase 2 can delve into deeper integrations and feature enhancements.

### 4.1 Seamless API Communication

Ensure that your frontend and backend are communicating effectively via well-defined API endpoints. Utilize tools like Swagger UI (integrated with FastAPI) for API documentation.

### 4.2 Enhance Real-Time Features

Implement advanced real-time functionalities, such as live collaboration, instant notifications, or dynamic content updates. WebSockets can be expanded to handle these use cases.

### 4.3 Implement Robust Authentication

Strengthen security by implementing role-based access control (RBAC), refreshing tokens, and securing WebSocket communications.

### 4.4 Optimize Frontend Performance

Leverage bundlers like Webpack or tools like Vite for efficient asset management and faster load times. If using React, consider integrating Redux or Context API for state management.

### 4.5 Develop Comprehensive Testing Suites

Expand your testing strategy to include integration tests, end-to-end tests (e.g., using Selenium or Cypress), and continuous testing in your CI/CD pipeline.

---

## 5. Additional Recommendations

### 5.1 Utilize Environment Variables

Manage sensitive configurations and environment-specific settings using .env files.

- Add python-dotenv:

    cd shared
    poetry add python-dotenv


- Create .env in backend/ and/or frontend/:

    DATABASE_URL=postgresql://user:password@localhost:5432/mydatabase
    SECRET_KEY=your-secret-key


- Load Environment Variables in Python:

    Example (backend/cli.py):

    from dotenv import load_dotenv
    import os

    load_dotenv()

    DATABASE_URL = os.getenv("DATABASE_URL")
    SECRET_KEY = os.getenv("SECRET_KEY")


### 5.2 Implement Testing Frameworks

Ensure code reliability by integrating testing tools.

- Backend:

    cd backend
    poetry add --dev pytest


    Example Test (backend/tests/test_cli.py):

    from fastapi.testclient import TestClient
    from cli import app

    client = TestClient(app)

    def test_health_check():
        response = client.get("/api/health")
        assert response.status_code == 200
        assert response.json() == {"status": "Backend is running."}


- Frontend:

    If using JavaScript frameworks, integrate testing tools like Jest.

### 5.3 Continuous Integration (CI)

Set up CI pipelines using platforms like GitHub Actions, GitLab CI/CD, or Jenkins to automate testing and deployment.

Example GitHub Actions Workflow (.github/workflows/ci.yml):

name: CI

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  backend-tests:
    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.8'

    - name: Install Poetry
      run: |
        curl -sSL https://install.python-poetry.org | python3 -
        echo "::add-path::$HOME/.local/bin"

    - name: Install Backend Dependencies
      run: |
        cd backend
        poetry install

    - name: Run Backend Tests
      run: |
        cd backend
        poetry run pytest

  frontend-tests:
    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.8'

    - name: Install Poetry
      run: |
        curl -sSL https://install.python-poetry.org | python3 -
        echo "::add-path::$HOME/.local/bin"

    - name: Install Frontend Dependencies
      run: |
        cd frontend
        poetry install
        npm install

    - name: Run Frontend Tests
      run: |
        cd frontend
        poetry run pytest  # If applicable
        # Add npm test if using JavaScript tests
        # npm test


### 5.4 Documentation

Maintain comprehensive documentation for ease of onboarding and maintenance.

- Use README.md: Include setup instructions, project structure overview, and usage guidelines.

- Generate API Docs: Utilize FastAPI's automatic documentation generation.

    Access via http://localhost:8000/docs once the backend server is running.

### 5.5 Dependency Management Best Practices

- Regularly Update Dependencies:

    cd backend
    poetry update

    cd ../frontend
    poetry update

    cd ../shared
    poetry update


- Lock Dependencies: Commit poetry.lock to version control to ensure consistent environments across different setups.

### 5.6 Optimize Virtual Environment Usage

Configure Poetry to manage virtual environments effectively.

- Create Virtual Environments Within Projects:

    poetry config virtualenvs.in-project true


    This setting ensures that each project (frontend/, backend/, shared/) maintains its own .venv directory, enhancing isolation.

---

## 6. Conclusion

Migrating your multi_engineer_v2 (Backend) and workbench_ui (Frontend) projects into a unified Poetry-managed repository offers numerous benefits:

- Isolation: Each project has its own dependencies and virtual environment, preventing conflicts.
- Clarity: A well-organized directory structure makes the project easy to navigate and maintain.
- Scalability: Easily add more components or shared libraries as your project grows.
- Consistency: Ensures all team members have a consistent setup, reducing “it works on my machine” issues.
- Enhanced Workflow: Leverage Poetry’s powerful features for dependency management and virtual environment handling.

By following this tailored migration plan, you'll establish a robust foundation that not only resolves current dependency conflicts but also sets the stage for seamless integrations and feature enhancements in Phase 2 of your roadmap.

Feel free to implement these changes and reach out if you encounter any challenges or require further assistance during the migration process!
```