Lets look at some code files and functions then a high level targeted refactor idea and step through updating related improvements:

first step lets review and update our tools.py to make sure formating and older deprecated tool references as update to our current consolidated ones. Here are two new ones:

```
async def project_activate(existing_project_path: str, 
                           file_names: Optional[List[str]] = None, 
                           return_structure: bool = False, 
                           **kwargs) -> Dict[str, Union[str, List[str], Dict[str, str]]]:
    """
    Activates an existing project, granting permission to operate within its directory.
    Returns project file structure and/or content of specified files. Updates the AppState
    with the activated project and its tree structure.

    Args:
        existing_project_path (str): The path to the existing project directory.
        file_names (List[str], optional): A list of file names within the project to read and return their content.
        return_structure (bool, optional): If True, returns the full project structure. Defaults to False.
        **kwargs: Additional keyword arguments.

    Returns:
        Dict[str, Union[str, List[str], Dict[str, str]]]: A dictionary containing:
            - 'status': 'activated' if successful, 'error' if an issue occurred.
            - 'message': Additional information, especially useful for error descriptions.
            - 'project_name': The name of the activated project.
            - 'project_structure': List of all files in the project as relative paths (if return_structure is True).
            - 'file_contents': Dict of file contents with relative paths as keys (if file_names provided).

    Note:
        This function updates the AppState with the activated project path and its tree structure.
    """
    # Initialize a lock for thread-safe operations on AppState
    state_lock = Lock()
    
    try:
        # Validate the project path
        if not os.path.isdir(existing_project_path):
            return {
                "status": "error",
                "message": f"The specified path does not exist or is not a directory: {existing_project_path}"
            }

        # Get the absolute path
        project_path = os.path.abspath(existing_project_path)

        # Acquire lock before modifying AppState
        async with state_lock:
            # Update app state with the activated project
            app_state = get_app_state()
            app_state.active_project = project_path

            # Initialize result dictionary
            result = {
                "status": "activated",
                "project_name": os.path.basename(project_path)
            }

            # Handle specified file contents
            if file_names:
                file_contents = {}
                file_context = get_file_context()
                for file_name in file_names:
                    # Ensure file_name is a relative path
                    normalized_file_name = os.path.normpath(file_name)
                    # Prevent path traversal outside the project directory
                    if os.path.isabs(normalized_file_name) or normalized_file_name.startswith(".."):
                        logging.warning(f"Invalid file path requested: {file_name}")
                        continue

                    full_path = os.path.join(project_path, normalized_file_name)
                    if os.path.isfile(full_path):
                        try:
                            # Asynchronously read file content
                            async with aiofiles.open(full_path, 'r') as f:
                                content = await f.read()
                            file_contents[normalized_file_name] = content
                            # Update file context
                            file_context.add_or_update_file(normalized_file_name, content, "read")
                        except Exception as read_err:
                            logging.error(f"Error reading file '{file_name}': {read_err}")
                            continue
                    else:
                        logging.warning(f"File not found: {file_name}")
                result["file_contents"] = file_contents

            # Handle project structure
            if return_structure:
                try:
                    project_structure = await list_files_for_project(directory=project_path)
                    result["project_structure"] = project_structure
                except Exception as struct_err:
                    logging.error(f"Error retrieving project structure: {struct_err}")
                    result["project_structure"] = []

            # Update the active project's tree structure
            app_state.update_active_project_tree()

        return result

    except Exception as e:
        logging.error(f"Error in project_activate: {e}")
        return {
            "status": "error",
            "message": str(e)
        }

    finally:
        # Access the status from the result or default to 'error'
        status = locals().get('result', {}).get('status', 'error')
        status_message = 'successful' if status == 'activated' else 'failed'
        logging.info(f"Project activation {status_message} for: {existing_project_path}")

async def project_create(project_name: str, directory: Optional[str] = None, file_structure: Optional[List[str]] = None) -> Dict[str, Union[str, List[str], str]]:
    try:
        project_path = os.path.join(directory or os.getcwd(), project_name)


        # Check if the project already exists
        if os.path.exists(project_path):
            logging.warning(f"Project {project_name} already exists at {project_path}")
            return {"status": "error", "message": f"Project {project_name} already exists."}

        # Get user approval
        logging.info("Requesting user approval")
        try:
            user_approved = await get_user_approval_for_project(project_name, project_path, file_structure or [])
            logging.info(f"User approval result: {user_approved}")
        except Exception as e:
            logging.error(f"Error during user approval: {str(e)}", exc_info=True)
            return {"status": "error", "message": f"Error during user approval: {str(e)}"}

        if not user_approved:
            logging.info("Project creation cancelled by user")
            return {"status": "cancelled", "message": "Project creation cancelled by user."}
        
        os.makedirs(project_path, exist_ok=True)
        logging.info(f"Created project directory: {project_path}")

    
        file_context = get_file_context()
        app_state = get_app_state()
        app_state.active_project = project_path  # Set the newly created project as active
        file_creation_messages = []
   

        if file_structure:
            relative_file_structure = [to_relative_path(item, project_path) for item in file_structure]
        else:
            relative_file_structure = []

        created_files = []
        skipped_files = []

        for item in relative_file_structure:
            full_path = os.path.join(project_path, item)
            try:
                if item.endswith('/'):
                    os.makedirs(full_path, exist_ok=True)
                    logging.info(f"Created directory: {item}")
                else:
                    # Check if the path is within the project directory and doesn't contain backslashes
                    if os.path.commonpath([project_path, os.path.abspath(full_path)]) != project_path or '\\' in item:
                        raise ValueError(f"Invalid path: {item}")
                    
                    os.makedirs(os.path.dirname(full_path), exist_ok=True)
                    with open(full_path, 'w') as f:
                        pass
                    created_files.append(item)
                    logging.info(f"Created file: {item}")

                    # Add file to context history
                    file_context.add_or_update_file(item, "", "create")
                    # update_message = file_context.add_or_update_file(item, "", "create")
                    # file_creation_messages.append(f"File created: {item}\nFile Context Update: {update_message}")
            except Exception as e:
                logging.warning(f"Failed to create {item}: {str(e)}")
                skipped_files.append(item)


        # # Consolidate all file creation messages into a single response
        # if file_creation_messages:
        #     consolidated_response = "Project structure created:\n" + "\n".join(file_creation_messages)
        #     if skipped_files:
        #         consolidated_response += f"\n\nSkipped files:\n" + "\n".join(skipped_files)


        app_state.update_active_project_tree()

        return {
            "project_name": project_name,
            "file_structure": relative_file_structure,
            "created_files": created_files,
            "skipped_files": skipped_files,
            "status": "created"
        }
    except Exception as e:
        logging.error(f"Error in project_create: {str(e)}")
        return {
            "status": "error",
            "message": str(e)
        }
    

async def list_files_for_project(directory: str = ".", 
                                 ignore_dir_patterns: Optional[List[str]] = None, 
                                 ignore_file_patterns: Optional[List[str]] = None, 
                                 max_depth: int = 5) -> List[str]:
    """
    Lists all files within the given directory up to a specified depth,
    excluding directories and files that match the ignore patterns.

    Args:
        directory (str): The root directory to list files from.
        ignore_dir_patterns (List[str], optional): Patterns for directories to ignore.
            Defaults to ['.git', 'pycache', 'bin', 'lib'].
        ignore_file_patterns (List[str], optional): Patterns for files to ignore.
            Defaults to ['.pyc'].
        max_depth (int, optional): Maximum directory depth to traverse. Defaults to 5.

    Returns:
        List[str]: A list of relative file paths.
    """
    if ignore_dir_patterns is None:
        ignore_dir_patterns = ['.git', 'pycache', 'bin', 'lib']
    if ignore_file_patterns is None:
        ignore_file_patterns = ['.pyc']

    try:
        files = []
        for root, dirs, filenames in os.walk(directory):
            # Calculate current depth
            relative_path = os.path.relpath(root, directory)
            depth = relative_path.count(os.sep) if relative_path != '.' else 0
            if depth >= max_depth:
                dirs[:] = []  # Prevent further traversal
                continue

            # Exclude directories matching ignore patterns
            dirs[:] = [d for d in dirs if not any(fnmatch.fnmatch(d, pattern) for pattern in ignore_dir_patterns)]

            for filename in filenames:
                if not any(fnmatch.fnmatch(filename, pattern) for pattern in ignore_file_patterns):
                    files.append(os.path.relpath(os.path.join(root, filename), directory))

        return files

    except Exception as e:
        logging.error(f"Error listing files in '{directory}': {e}")
        raise  # Propagate exception for higher-level handling

```

here is our current tools for reference:

```
def initialize_tool_manager():
    # Initialize ToolManager and add tools
    tool_manager = ToolManager()
       
    tool_manager.add_tool(Tool("project_create", project_create, "Create a new project with specified structure", 
                            {"project_name": {"type": "string", "description": "The name of the new project directory"},
                             "directory": {"type": "string", "description": "The path where the project should be created", "optional": True},
                             "file_structure": {"type": "array", "items": {"type": "string"}, "description": "List of files and directories to create (using relative paths)", "optional": True}}))
    
    tool_manager.add_tool(Tool("project_activate", project_activate, "Activate an existing project and retrieve its structure or file contents", 
                            {"existing_project_path": {"type": "string", "description": "The path to the existing project directory"},
                             "file_names": {"type": "array", "items": {"type": "string"}, "description": "List of file names to read content from (using relative paths)", "optional": True},
                             "return_structure": {"type": "boolean", "description": "If true, returns the full project structure", "optional": True}}))
    
    tool_manager.add_tool(Tool("smart_edit", smart_edit, "Edit a file or creates or new file using natural language instructions", 
                            {"file_path": {"type": "string", "description": "The relative path to the file to edit within the active project"},
                             "instructions": {"type": "string", "description": "Natural language instruction for the edit"},
                             "model_name": {"type": "string", "description": "Name of the model to use for editing", "default": "llama"}}))
    
    tool_manager.add_tool(Tool("execute_code", execute_code, "Execute Python code in a virtual environment and manage long-running processes", 
                            {"code": {"type": "string", "description": "The Python code to execute"},
                             "timeout": {"type": "integer", "description": "Timeout in seconds, default is 10"}}))
    
    tool_manager.add_tool(Tool("stop_process", stop_process, "Stop a running process by its ID", 
                            {"process_id": {"type": "string", "description": "The ID of the process to stop"}}))

    tool_manager.add_tool(Tool("tavily_search", tavily_search, "Perform a web search using the Tavily API", 
                            {"query": {"type": "string", "description": "The search query"}}))

    return tool_manager
```

here is our Tool class we may need to update for old tool references or update to make sure our current tools are properly supported:

```
class Tool:
    def __init__(self, name: str, function: Callable, description: str, parameters: Dict[str, Any]):
        self.name = name
        self.function = function
        self.description = description
        self.parameters = parameters

class ToolManager:
    def __init__(self):
        self.tools: Dict[str, Tool] = {}
        self.logger = logging.getLogger(__name__)

    def add_tool(self, tool: Tool):
        if tool.name == "execute_code":
            original_function = tool.function
            async def wrapped_execute_code(**kwargs):
                timeout = kwargs.get('timeout', 10)
                if isinstance(timeout, str):
                    try:
                        timeout = int(timeout)
                    except ValueError:
                        timeout = 10
                kwargs['timeout'] = timeout
                return await original_function(**kwargs)
            tool.function = wrapped_execute_code
        self.tools[tool.name] = tool

    def get_tool_descriptions(self, format="default") -> List[Dict[str, Any]]:
        if format == "claude":
            return [
                {
                    "name": tool.name,
                    "description": tool.description,
                    "input_schema": {
                        "type": "object",
                        "properties": tool.parameters,
                        "required": list(tool.parameters.keys())
                    }
                }
                for tool in self.tools.values()
            ]
        else:
            return [
                {
                    "type": "function",
                    "function": {
                        "name": tool.name,
                        "description": tool.description,
                        "parameters": {
                            "type": "object",
                            "properties": tool.parameters,
                            "required": list(tool.parameters.keys())
                        }
                    }
                }
                for tool in self.tools.values()
            ]

    def _tool_to_dict(self, tool: Tool) -> Dict[str, Any]:
        return {
            "type": "function",
            "function": {
                "name": tool.name,
                "description": tool.description,
                "parameters": {
                    "type": "object",
                    "properties": tool.parameters,
                    "required": list(tool.parameters.keys())
                }
            }
        }
     
    async def execute_tool(self, tool_call: Dict[str, Any], model_name: str) -> Any:
        if isinstance(tool_call, dict) and 'function' in tool_call:
            # Handle embedded function calls (Llama format)
            function_name = tool_call["function"]
            arguments = tool_call["arguments"]
        elif hasattr(tool_call, 'function') and hasattr(tool_call.function, 'name'):
            # Handle structured tool calls (GPT/Claude format)
            function_name = tool_call.function.name
            arguments = json.loads(tool_call.function.arguments)
        else:
            raise ValueError("Unsupported tool call format")

        if not isinstance(arguments, dict):
            raise ValueError("Invalid function call format: arguments must be a JSON object")

        logging.info(f"Executing tool: {function_name} with arguments: {arguments}")

        if function_name not in self.tools:
            error_msg = f"Unknown tool: {function_name}"
            logging.error(error_msg)
            raise ValueError(error_msg)

        tool = self.tools[function_name]
        try:
            if "content" in arguments and isinstance(arguments["content"], str):
                arguments["content"] = arguments["content"].replace('\\n', '\n')

            # Handle the case where 'file_names' is passed instead of 'file_name'
            if function_name == 'smart_edit':
                if 'file_names' in arguments:
                    if isinstance(arguments['file_names'], list):
                        arguments['file_name'] = arguments['file_names'][0]
                    else:
                        arguments['file_name'] = arguments['file_names']
                    del arguments['file_names']
                arguments['model_name'] = model_name

            if asyncio.iscoroutinefunction(tool.function):
                result = await tool.function(**arguments)
            else:
                result = tool.function(**arguments)

            # Format and log the result
            formatted_result = self.format_result(function_name, result, arguments)
            # logging.info(f"Tool Output ({function_name}):\n{formatted_result}")

            return formatted_result
        except Exception as e:
            error_msg = f"Error executing tool {function_name}: {str(e)}"
            logging.error(error_msg, exc_info=True)
            raise RuntimeError(error_msg)

    def format_result(self, function_name: str, result: Any, arguments: Dict[str, Any]) -> str:
        if function_name == "create_file":
            return f"File created: {arguments['file_name']}\nContent:\n{arguments['content']}"
        elif function_name == "smart_edit":
            try:
                result_json = json.loads(result)
                if result_json["status"] == "success":
                    confidence_info = ""
                    if 'confidence' in result_json:
                        confidence = result_json['confidence']
                        confidence_info = (
                            f"Confidence Scores:\n"
                            f"Overall: {confidence.get('overall', 0):.2f}\n"
                            f"Name: {confidence.get('name', 0):.2f}\n"
                            f"Content: {confidence.get('content', 0):.2f}\n"
                            f"Context: {confidence.get('context', 0):.2f}\n\n"
                        )
                    return (
                        f"Smart Edit Result:\n"
                        f"Status: {result_json['status']}\n"
                        f"Message: {result_json['message']}\n\n"
                        f"Changes:\n{result_json['diff']}\n\n"
                        f"Update Plans:\n{json.dumps(result_json['update_result'], indent=2)}"
                        f"{confidence_info}"
                      
                    )
                elif result_json["status"] == "cancelled":
                    return f"Smart Edit Cancelled:\n{result_json['message']}\n\nProposed Changes:\n{result_json['diff']}"
                else:
                    return f"Smart Edit Error:\n{result_json['message']}"
            except json.JSONDecodeError:
                return f"Error parsing smart_edit result: {result}"
        elif function_name == "execute_code":
            process_id, execution_result = result
            return f"Code executed with Process ID: {process_id}\n{execution_result}"
        else:
            return str(result)
```

here is our smart_edit so you can see what this function returns:

```
async def smart_edit(*, file_path: str, instructions: str, model_name: str) -> str:
    """
    Perform a smart edit on a file using AI-generated updates.

    Args:
        file_path (str): Relative path to the file within the active project.
        instructions (str): Natural language instructions for the edit.
        model_name (str): Name of the AI model to use for generating updates.

    Returns:
        str: JSON string containing the result of the edit operation.
    """
    try:
        logging.info(f"Starting smart_edit for file: {file_path}")
        app_state = get_app_state()
        project_root = app_state.active_project
        full_file_path = os.path.join(project_root, file_path)

        if not os.path.isfile(full_file_path):
            return json.dumps({
                "status": "error",
                "message": f"File not found: {file_path}"
            })

        file_ops = FileScopeOperations(full_file_path)
        content = file_ops._read_file()
        logging.info(f"Original content:\n{content}")

        efc = generate_hybrid_efc(content)
        logging.info(f"Generated EFC: {json.dumps(efc, indent=2)}")

        block_context = find_best_matching_block(efc, instructions)
        logging.info(f"Best matching block: {json.dumps(block_context, indent=2)}")

        update_scope = determine_update_scope(block_context, instructions)
        logging.info(f"Determined update scope: {update_scope}")
        
        confidence_scores = block_context.get('confidence', {})
        logging.info(f"Initial confidence scores: {json.dumps(confidence_scores, indent=2)}")
        
        logging.debug(f"Update scope: {update_scope}")
        if update_scope == 'file':
            logging.debug("Executing file-level edit")
            update_result = await file_ops.process_file_level_edit(instructions, model_name)
        elif update_scope in ['function', 'class']:
            logging.debug(f"Executing {update_scope}-level edit")
            update_result = await generate_block_update_plan(efc, instructions, content, model_name, confidence_scores)
        else:
            logging.error(f"Invalid update scope: {update_scope}")
            return json.dumps({"status": "error", "message": f"Invalid update scope: {update_scope}"})

        logging.info(f"Update result: {json.dumps(update_result, indent=2)}")

        if 'error' in update_result:
            return json.dumps({"status": "error", "message": update_result['error']})

        if 'update_plans' not in update_result:
            return json.dumps({"status": "error", "message": "Invalid update plan structure"})

        new_content = apply_block_updates(content, update_result['update_plans'], efc)
        logging.info(f"New content after updates:\n{new_content}")

        diff = generate_smart_edit_diff(content, new_content, update_result, update_scope, confidence_scores)

        final_confidence_scores = update_result['update_plans'][0].get('confidence_scores', {})
        logging.info(f"Final confidence scores: {json.dumps(final_confidence_scores, indent=2)}")

        # User approval and file writing logic
        try:
            if not await get_user_approval(diff, confidence_scores):
                return json.dumps({"status": "cancelled", "message": "Update cancelled by user", "diff": diff})
        except EOFError:
            return json.dumps({"status": "error", "message": "Failed to apply the update: User input was interrupted"})

        with open(full_file_path, 'w') as file:
            file.write(new_content)

        # Update the cache with the new content
        file_context = get_file_context()
        file_context.add_or_update_file(file_path, new_content, "smart_edit")
        app_state.update_active_project_tree()

        result = json.dumps({
            "status": "success",
            "message": f"File updated successfully: {file_path}",
            "diff": diff,
            "update_result": update_result,
            "confidence": confidence_scores
        })
        logging.info(f"Smart edit completed successfully: {result}")
        return result

    except Exception as e:
        logging.error(f"Error in smart_edit: {str(e)}", exc_info=True)
        error_result = json.dumps({
            "status": "error",
            "message": f"Error during smart update: {str(e)}",
            "details": str(e)
        })
        logging.info(f"Smart edit error result: {error_result}")
        return error_result
```

and finally for now this is our chat.py where you can get a sense of how things operate for the CLI tool conversation flow and where the tools are executed after they are called and executed internally.

chat.py
```
async def chat_with_model(user_input: str, tool_manager: ToolManager, model_name: str) -> str:
    logging.info(f"Processing user input: {user_input}")
    logging.info(f"Using model: {model_name}")
    logging.info(f"Available tools: {[tool['function']['name'] for tool in tool_manager.get_tool_descriptions()]}")

    app_state = get_app_state()
    # Add the new user input to the history
    app_state.conversation_history.append({"role": "user", "content": user_input})

    file_context = get_file_context()
    model_config = get_model_config(model_name)

    ai_client = AIClientFactory.get_ai_client(model_name)
    logging.info(f"Using AI client: {type(ai_client).__name__}")

    optimized_history = app_state.conversation_history[:-1]  # Exclude the latest user message

    # System message includes context awareness prompt
    file_context_summary = file_context.generate_context_awareness_prompt(app_state)
    logging.info(f"context awareness prompt: {file_context_summary}")
    system_message = f"{model_config['base_system_prompt']}\n\n{file_context_summary}"

    tools = tool_manager.get_tool_descriptions(format="claude" if model_name.lower() in ['sonnet', 'claude-3-sonnet-20240229'] else "default")
    logging.info(f"History passed to AI client: {optimized_history}")

    try:
        logging.info("Creating message with AI client")
        response = await ai_client.create_message(
            prompt=user_input,
            system_message=system_message,
            tools=tools,
            history=optimized_history
        )
        logging.info("Message created successfully")

        if hasattr(response, 'usage'):
            if model_name.lower() in ['sonnet', 'claude-3-sonnet-20240229']:
                update_token_usage(model_name, {
                    "prompt_tokens": response.usage.input_tokens,
                    "completion_tokens": response.usage.output_tokens,
                    "total_tokens": response.usage.input_tokens + response.usage.output_tokens,
                    "cache_creation_tokens": getattr(response.usage, 'cache_creation_input_tokens', 0),
                    "cache_read_tokens": getattr(response.usage, 'cache_read_input_tokens', 0)
                })
            else:
                # For Llama and GPT
                update_token_usage(model_name, {
                    "prompt_tokens": response.usage.prompt_tokens,
                    "completion_tokens": response.usage.completion_tokens,
                    "total_tokens": response.usage.total_tokens
                })
    except Exception as e:
        logging.error(f"API Error: {str(e)}")
        return f"I'm sorry, there was an error communicating with the AI. Please try again. Error: {str(e)}"

    # Extract assistant response and function calls
    if hasattr(ai_client, 'extract_response'):
        assistant_response = ai_client.extract_response(response)
        if asyncio.iscoroutine(assistant_response):
            assistant_response = await assistant_response
    else:
        assistant_response = response.content[0].text if response.content else ""

    logging.info(f"EXTRACTED RESPONSE: {assistant_response}")

    # **Process file updates and modify assistant_response accordingly**
    assistant_response = await process_file_updates(assistant_response, file_context, app_state)

    # Extract and execute function calls
    function_calls = ai_client.extract_function_calls(response)
    function_results = await execute_function_calls(function_calls, tool_manager, model_name)

    # Integrate function results into assistant response
    if not assistant_response and function_results:
        assistant_response = "I've performed the following actions based on your request:\n\n"
    assistant_response = integrate_function_results(assistant_response, function_results, model_config)

    # **Append the finalized assistant response once**
    app_state.conversation_history.append({"role": "assistant", "content": assistant_response})
    logging.info(f"Conversation history: {app_state.conversation_history}")
    logging.info(f"Final assistant response: {assistant_response}")
    return assistant_response


async def process_file_updates(assistant_response: str, file_context, app_state: Any) -> str:
    updated_files = detect_file_updates(assistant_response)
    notifications = []

    for relative_path, new_content in updated_files.items():
        existing_content = file_context.get_file_content(relative_path)

        if new_content is not None:
            if existing_content is not None:
                # Existing file: calculate diff
                diff = calculate_diff(existing_content, new_content)
                if diff.strip():
                    # Include acknowledgment and diff in the assistant response
                    ack_message = f"I've updated `{relative_path}` to the context."
                    diff_message = f"\n### Diff for {relative_path}\n```diff\n{diff}\n```"
                    assistant_response += f"\n{ack_message}{diff_message}"
                else:
                    # No changes detected
                    no_change_msg = f"\nNo changes detected in {relative_path}."
                    assistant_response += no_change_msg
            else:
                # First instance: notification about addition
                notification_message = f"\nI've added `{relative_path}` to the context."
                assistant_response += notification_message

            # Update the FileContextTracker with new content
            file_context.add_or_update_file(relative_path, new_content, "updated via function call")

            # Optionally, remove the processed content from assistant_response
            assistant_response = remove_full_content_from_response(assistant_response, relative_path)
        else:
            # Handle additions where no new content is provided
            notification_message = f"\nI've added `{relative_path}` to the context."
            assistant_response += notification_message

            # Update the FileContextTracker to reflect the addition
            file_context.add_or_update_file(relative_path, "", "added via function call")

    return assistant_response.strip()


def detect_file_updates(assistant_message: str) -> Dict[str, Optional[str]]:
    """
    Detects and extracts file updates from the assistant message.
    Returns a dictionary mapping relative_path to new_content or None for additions.
    """
    updates = {}

    # 1. Handle additions: "Added `file1.txt` to context."
    pattern_added = r"Added\s+`?(?P<path>[^`]+)`?\s+to context\."
    for match in re.finditer(pattern_added, assistant_message):
        relative_path = match.group('path').strip()
        updates[relative_path] = None  # None indicates addition without content
        logging.debug(f"Detected addition: {relative_path}")

    # 2. Handle fresh content: "Fresh content for file1.txt: ```diff\n...```"
    pattern_fresh = r"Fresh content for\s+`?(?P<path>[^`]+)`?:\s*```(?:diff|markdown)?\n(?P<content>[\s\S]*?)```"
    for match in re.finditer(pattern_fresh, assistant_message):
        relative_path = match.group('path').strip()
        content = match.group('content').strip()
        updates[relative_path] = content
        logging.debug(f"Detected fresh content for {relative_path}: {content}")

    # 3. Handle updates with diffs: "I've updated `file1.txt` to the context.\n### Diff for file1.txt\n```diff\n...```"
    pattern_update = r"I've updated\s+`?(?P<path>[^`]+)`?\s+to the context\.\n### Diff for\s+`?\1`?\s+```diff\n(?P<diff>[\s\S]*?)```"
    for match in re.finditer(pattern_update, assistant_message):
        relative_path = match.group('path').strip()
        diff = match.group('diff').strip()
        new_content = extract_new_content_from_diff(diff)
        if new_content:
            updates[relative_path] = new_content
            logging.debug(f"Detected update for {relative_path}: {new_content}")

    return updates


def extract_new_content_from_diff(diff: str) -> Optional[str]:
    """
    Parses a unified diff and extracts the new content.
    This function assumes that the diff follows the standard unified diff format.
    """
    new_lines = []
    for line in diff.splitlines():
        if line.startswith('+') and not line.startswith('+++'):
            new_lines.append(line[1:])
    return '\n'.join(new_lines) if new_lines else None

def calculate_diff(old_content: str, new_content: str) -> str:
    old_lines = old_content.splitlines()
    new_lines = new_content.splitlines()
    diff = difflib.unified_diff(old_lines, new_lines, lineterm='')
    return '\n'.join(diff)


def remove_full_content_from_response(assistant_message: str, relative_path: str) -> str:
    """
    Removes the full file content from the assistant message, leaving only the relevant response.
    """
    # Pattern captures only the 'Fresh content...' part
    pattern = rf"\s*Fresh content for {re.escape(relative_path)}:.*?```.*?\n[\s\S]*?```"
    cleaned_message = re.sub(pattern, "", assistant_message, flags=re.MULTILINE)
    # Replace multiple spaces with a single space and strip leading/trailing spaces
    cleaned_message = re.sub(r'\s{2,}', ' ', cleaned_message).strip()
    return cleaned_message


async def execute_function_calls(function_calls, tool_manager: ToolManager, model_name: str):
    results = []
    if asyncio.iscoroutine(function_calls):
        function_calls = await function_calls
    for function_call in function_calls:
        try:
            if isinstance(function_call, dict):
                # Llama format
                func_name = function_call.get('function')
                arguments = function_call.get('arguments', {})
            else:
                # GPT-4 format
                func_name = function_call.function.name
                arguments = json.loads(function_call.function.arguments)

            result = await tool_manager.execute_tool({"function": func_name, "arguments": arguments}, model_name)
            results.append((func_name, arguments, result))
        except Exception as e:
            logging.error(f"Error executing function: {str(e)}")
            results.append((func_name, arguments, f"Error executing function: {str(e)}"))
    return results


def integrate_function_results(assistant_response: str, function_results: List[Tuple[str, Dict, str]], model_config: Dict) -> str:
    if not assistant_response and function_results:
        assistant_response = "I've performed the following actions based on your request:\n\n"

    for func_name, args, result in function_results:
        if model_config.get('use_tool_calls', False):
            assistant_response += f"\n\nResult of {func_name}: {result}"
        else:
            # For models that embed function calls in the response
            placeholder = f"<function={func_name}>{json.dumps(args)}</function>"
            if placeholder in assistant_response:
                assistant_response = assistant_response.replace(placeholder, str(result))
            else:
                assistant_response += f"\n\nResult of {func_name}: {result}"

    return assistant_response
```

first we are going to focus on making sure our Tool class is playing nice with our current tools especially (project create, project activate, smart edit) and being returned to chat properly, any deprecated tools are no longer handed then I will provide a high level sequencing issue and simplification/refactor needed in teh chat.py i discovered from live testing. Does all this make sense so far?