
# Summary

1. [Introduction to the Multi-Agent System
2. [Understanding the Hive-Mind Architecture
3. [Setting Up the Project Configuration
4. [Prioritizing and Sequencing Tasks
5. [Generating Code with Chat Functions
6. [Refining Code through Iterative Processes
7. [Creating High-Quality Unit Tests
8. [Evaluating Code and Test Results
9. [Managing Project Completeness
10. [Generating Task Descriptions
11. [Creating a FullStackCoderAgent
12. [Understanding the Full Stack Development Process
13. [Utilizing Refinement Context for Improvement
14. [Analyzing Task Dependencies and Guidelines
15. [Implementing Promising Regions and Heuristics
16. [Deploying a Multi-Goal Project
17. [Measuring and Reporting Project Progress
18. [Handling Code Compilation and Execution
19. [Unit Testing as a Quality Assurance Measure
20. [Conclusion and Future Directions


Generate Weighted Graphs:
Create accurate weighted graphs representing the relationships between different code components. These graphs serve as the foundation for determining the optimal order of code execution in complex scenarios.

Train Knowledge Extraction Model:
Train the Knowledge Extraction model (PKE) to estimate the distances between code components. This model helps determine the optimal sequence for executing code, while also predicting valuable heuristics to guide the process.

Enhance Pathfinding with PKE-RRT:
Utilize the PKE-RRT algorithm to improve the Multi-Goal Path Finding (MGPF) process. Leverage PKE model outputs to prioritize goals and enhance the exploration of different code execution paths. Achieve rapid and sub-optimal solutions efficiently.

Optimize Code Generation Workflow:
Apply the Chain-of-Thought Prompting (CoT) strategy to transformer-based code generation. Implement the Code Chain-of-Thought~(CodeCoT) approach to enhance code accuracy through iterative self-examination and refinement.

Achieve Versatile Code Generation:
Execute the Vanilla CodeCoT technique to iteratively generate code and test cases. Empower the model to adapt and self-reflect, resulting in improved code accuracy across various Large Language Model (LLM) variants.

Iterative Self-Examination:
Employ Self-exam CodeCoT for code generation. Enable LLMs to generate code, create test cases, and refine outputs iteratively. The process includes analyzing erroneous code to enhance code accuracy.

Improve LLM Adaptation:
Adapt Large Language Models (LLMs) using few-shot learning techniques. Enhance LLM performance by fine-tuning on specific tasks, thereby enhancing its effectiveness across diverse coding scenarios.

Enhanced MGPF Planning:
Utilize dynamic graph heuristics for Multi-Goal Path Finding (MGPF). Incorporate predicted regions and guidelines to facilitate tree exploration, leading to efficient sub-optimal solutions in challenging scenarios.

Evaluate CodeCoT Techniques:
Assess the performance of CodeCoT techniques on LLM variants. Measure improvements in code generation accuracy through Vanilla CodeCoT and Self-exam CodeCoT approaches.

Enhance LLM Generalization:
Test Large Language Model (LLM) generalization using CoT approaches. Evaluate LLM performance on diverse tasks beyond its training data. Analyze the impact of CodeCoT techniques on LLM adaptability.

Empower LLM Reasoning with CoT:
Apply Chain-of-Thought Prompting (CoT) to improve LLM reasoning in multi-step code generation. Implement CodeCoT techniques to enhance code accuracy and reasoning capabilities.

Assess PKE-RRT Efficiency:
Analyze PKE-RRT algorithm efficiency for Multi-Goal Path Finding (MGPF). Evaluate calculation time, path cost, sample number, and success rate in various goal scenarios to gauge algorithm performance.

LLM Adaptation for Specific Tasks:
Fine-tune LLMs for specific coding tasks using CodeCoT strategies. Enhance LLM performance and accuracy by adapting to task-specific data and scenarios.

Refine Code Generation Accuracy:
Implement CoT strategies to refine code generation accuracy in LLMs. Utilize self-examination and iterative refinement to mitigate errors caused by task variation.

Dynamic Graph-Based MGPF:
Perform graph-based optimization for Multi-Goal Path Finding (MGPF). Utilize predicted graph weights from the PKE model to optimize the visiting order of goals, achieving efficient and accurate pathfinding.

CoT Techniques for Enhanced Performance:
Analyze the effect of Chain-of-Thought (CoT) techniques on transformer-based code generation. Evaluate how CodeCoT enhances LLM performance and adaptability across diverse coding tasks.

Evaluate LLM Adaptation Metrics:
Measure Large Language Model (LLM) adaptation metrics using CodeCoT. Analyze code generation accuracy, reasoning capabilities, and efficiency improvements across different LLM variants.

Test PKE-RRT in Complex Scenarios:
Assess PKE-RRT algorithm performance in complex Multi-Goal Path Finding (MGPF) scenarios. Measure efficiency in scenarios with varying numbers of goals and analyze calculation time and path cost.

Multi-Task Learning Insights:
Investigate the effectiveness of multi-task learning using PKE. Study how the PKE model enhances code accuracy and pathfinding through local path estimation and heuristic prediction.

CoT Impact on LLM Tasks:
Evaluate the impact of Chain-of-Thought (CoT) techniques on LLM tasks. Measure LLM versatility and performance enhancements in code generation tasks using CodeCoT approaches.

@dataclass
class ComplexMultiLineTemplate(TypedTemplate):
    class_name: str = None
    attributes: list = None
    methods: list = None

    source = """class {{ class_name }}:
    def __init__(self{% for attr in attributes %}, {{ attr.name }}: {{ attr.type }}{% endfor %}):
        {% for attr in attributes -%}
        self.{{ attr.name }} = {{ attr.name }}
        {% endfor %}
    {%- for method in methods %}
    def {{ method.name }}(self{% for param in method.params %}, {{ param.name }}: {{ param.type }}{% endfor %}):
        return "{{ faker_sentence() }}"  # Simulating logic with Faker sentence{% endfor %}"""

@pytest.fixture
def rendered_complex_multiline_template():
    faker = Faker()
    Attribute = namedtuple("Attribute", ["name", "type"])
    Method = namedtuple("Method", ["name", "params"])
    Param = namedtuple("Param", ["name", "type"])

    attributes = [Attribute(name=faker.word(), type=faker.word()) for _ in range(3)]
    methods = [
        Method(
            name=faker.word(),
            params=[Param(name=faker.word(), type=faker.word()) for _ in range(2)],
        )
        for _ in range(3)
    ]

    template = ComplexMultiLineTemplate(
        class_name=faker.word().capitalize(), attributes=attributes, methods=methods
    )

    return template.render()

def chat(
    prompt=DEFAULT_PROMPT,
    sys_msg=DEFAULT_SYS_MSG,
    msgs=None,
    funcs=None,
    model=DEFAULT_MODEL,
    max_retry=DEFAULT_MAX_RETRY,
    backoff_factor=DEFAULT_BACKOFF_FACTOR,
    initial_wait=DEFAULT_INITIAL_WAIT,
    raw_msg=False,
) -> Union[str, dict]:
    """
    Customized completion function that interacts with the OpenAI API, capable of handling prompts, system messages,
    and specific functions. If the content length is too long, it will shorten the content and retry.
    """
    openai.api_key = os.getenv("OPENAI_API_KEY")

# Usage
project_config_file = 'project_config.yaml'
project_directory = '/path/to/full_stack_project'

project_manager = ProjectManagerAgent(project_config_file)
project_manager.plan_full_stack_project(project_directory)

# Create an instance of FullStackCoderAgent
coder_agent = FullStackCoderAgent()

# Define task descriptions and prompts
task_descriptions = ["Backend API", "Frontend UI", "Database Setup", "User Authentication", "Deployment"]
prompts = [f"Create code for: {task}" for task in task_descriptions]

# Generate and save code for each task
for task_description, prompt in zip(task_descriptions, prompts):
    final_code = coder_agent.create_code(prompt)

class ProjectManagerAgent:
    """
    The ProjectManagerAgent class serves as a comprehensive agent for managing multi-goal projects.
    It assists in prioritizing tasks, estimating project completeness, and providing insights into
    the status of the project by analyzing code files.
    """

    def __init__(self, yaml_path: str):
        self.yaml_path = yaml_path
        self.project_config = self.load_yaml()

    def load_yaml(self) -> Dict:
        with open(self.yaml_path, 'r') as file:
            config = yaml.safe_load(file)
        return config['pke']

    def extract_components(self, key: str) -> Dict:
        return self.project_config[key]

    def prioritize_tasks(self) -> List[str]:
        task_dependencies = self.extract_components('task_dependencies')
        promising_regions = self.extract_components('promising_regions')
        guidelines = self.extract_components('guidelines')
        heuristics = self.extract_components('heuristics')

        # Create a priority queue to store tasks based on their importance
        tasks_queue = deque()

        # Logic to prioritize tasks based on the extracted information
        for task, dependencies in task_dependencies.items():
            if all(dep in promising_regions for dep in dependencies) and task in guidelines:
                tasks_queue.append((heuristics.get(task, 0), task))

        # Sorting tasks based on heuristics
        tasks_queue = sorted(tasks_queue, key=lambda x: x[0], reverse=True)

        # Returning prioritized tasks
        return [task[1] for task in tasks_queue]

    def plan_full_stack_project(self, project_directory: str):
        prioritized_tasks = self.prioritize_tasks()
        task_descriptions = self.generate_task_descriptions()

        # Utilize chat() to generate code for each prioritized task
        for task_description in task_descriptions:
            prompt = f"Create code for: {task_description}"
            final_code = chat(prompt=prompt, sys_msg="Your system message here")  # Use the chat() function here

            # Determine file paths based on task description
            task_file_path = os.path.join(project_directory, f"{task_description.lower().replace(' ', '_')}.py")

            # Write generated code to task file
            with open(task_file_path, 'w') as task_file:
                task_file.write(final_code)

            print(f"Generated code for {task_description}")

        print("Project code generation complete.")

def estimate_completeness(self, source_directory: str) -> float:
        """Estimate project completeness based on lines of code (LOC) in source files."""
        total_lines = 0
        completed_lines = 0

        for root, _, files in os.walk(source_directory):
            for file in files:
                if file.endswith('.py'):
                    file_path = os.path.join(root, file)
                    with open(file_path, 'r') as code_file:
                        lines = code_file.readlines()
                        total_lines += len(lines)
                        completed_lines += sum(1 for line in lines if line.strip())

        completeness_percentage = (completed_lines / total_lines) * 100 if total_lines > 0 else 0
        return completeness_percentage

    def generate_task_descriptions(self) -> List[str]:
        """Generate user-friendly task descriptions for prioritized tasks."""
        prioritized_tasks = self.prioritize_tasks()
        task_descriptions = []

        for task in prioritized_tasks:
            description = self.extract_components('task_descriptions').get(task, '')
            task_descriptions.append(description)

        return task_descriptions


# Usage
project_config_file = 'project_config.yaml'
project_directory = '/path/to/full_stack_project'

project_manager = ProjectManagerAgent(project_config_file)
project_manager.plan_full_stack_project(project_directory)

# Create an instance of FullStackCoderAgent
coder_agent = FullStackCoderAgent()

# Define task descriptions and prompts
task_descriptions = ["Backend API", "Frontend UI", "Database Setup", "User Authentication", "Deployment"]
prompts = [f"Create code for: {task}" for task in task_descriptions]

# Generate and save code for each task
for task_description, prompt in zip(task_descriptions, prompts):
    final_code = coder_agent.create_code(prompt)

import unittest

class FullStackCoderAgent:
    """
    The FullStackCoderAgent class empowers the creation of high-quality code by combining
    advanced techniques to generate, evaluate, and refine code along with its corresponding tests.

    """

    def __init__(self):
        self.refinement_iterations = 0

    def generate_code(self, prompt: str, refinement_context=None) -> str:
        response = chat(prompt=prompt, msgs=refinement_context)
        return response["content"]

    def generate_tests(self) -> unittest.TestCase:
        class CodeTests(unittest.TestCase):
            def test_functionality(self):
                self.assertEqual(function_under_test(5), 8)  # Example test

        return CodeTests

    def evaluate_code(self, code: str) -> dict:
        compiled_code = compile(parsed_code, filename="<ast>", mode="exec")
        globals_dict = {}
        eval(compiled_code, globals_dict)
        return globals_dict.get('function_under_test')

    def evaluate_tests(self, tests: unittest.TestCase) -> bool:
        suite = unittest.TestLoader().loadTestsFromTestCase(tests)
        test_result = unittest.TextTestRunner().run(suite)
        return test_result.wasSuccessful()

    def create_code(self, prompt: str) -> str:
        refinement_context = None

        while self.refinement_iterations < MAX_REFINEMENT_ITERATIONS:
            code = self.generate_code(prompt, refinement_context)
            function_under_test = self.evaluate_code(code)
            tests = self.generate_tests()
            
            if self.evaluate_tests(tests):
                return code

            refinement_context = [{"role": "system", "content": "Refinement needed"}]
            self.refinement_iterations += 1

        raise Exception("Maximum refinement iterations reached")

# Usage
agent = FullStackCoderAgent()
final_code = agent.create_code(prompt)
print(final_code)

CREATE NOVEL PRODUCTION QUALITY CODE FOR MY CODE GENERATION UNICORN STARTUP, PLEASE =)


from typing import Type

def generate_typed_template_from_chat_response(
    template_class: Type[TypedTemplate],
    prompt=DEFAULT_PROMPT,
    sys_msg=DEFAULT_SYS_MSG,
    msgs=None,
    funcs=None,
    model=DEFAULT_MODEL,
    max_retry=DEFAULT_MAX_RETRY,
    backoff_factor=DEFAULT_BACKOFF_FACTOR,
    initial_wait=DEFAULT_INITIAL_WAIT,
    raw_msg=False
):
    response = chat(
        prompt=prompt,
        sys_msg=sys_msg,
        msgs=msgs,
        funcs=funcs,
        model=model,
        max_retry=max_retry,
        backoff_factor=backoff_factor,
        initial_wait=initial_wait,
        raw_msg=raw_msg,
    )

    if not isinstance(response, dict):
        raise TypeError("Expected a dictionary response from chat function")

    # Detect the names and types of attributes from the TypedTemplate class
    detected_attributes = {
        attr_name: attr_type
        for attr_name, attr_type in template_class.__annotations__.items()
        if attr_name not in ["class_name", "attributes", "methods"]
    }
    
    # Extract the corresponding values from the chat response
    kwargs = {name: response.get(name) for name, _ in detected_attributes.items()}

    # Initialize the TypedTemplate with the detected values
    template = template_class(**kwargs)

    return template.render()


from typing import Type

def generate_typed_template_from_chat_response(
    template_class: Type[TypedTemplate],
    prompt=DEFAULT_PROMPT,
    sys_msg=DEFAULT_SYS_MSG,
    msgs=None,
    funcs=None,
    model=DEFAULT_MODEL,
    max_retry=DEFAULT_MAX_RETRY,
    backoff_factor=DEFAULT_BACKOFF_FACTOR,
    initial_wait=DEFAULT_INITIAL_WAIT,
    raw_msg=False
):
    response = chat(
        prompt=prompt,
        sys_msg=sys_msg,
        msgs=msgs,
        funcs=funcs,
        model=model,
        max_retry=max_retry,
        backoff_factor=backoff_factor,
        initial_wait=initial_wait,
        raw_msg=raw_msg,
    )

    if not isinstance(response, dict):
        raise TypeError("Expected a dictionary response from chat function")

    # Detect the names and types of attributes from the TypedTemplate class
    detected_attributes = {
        attr_name: attr_type
        for attr_name, attr_type in template_class.__annotations__.items()
        if attr_name not in ["class_name", "attributes", "methods"]
    }
    
    # Extract the corresponding values from the chat response
    kwargs = {name: response.get(name) for name, _ in detected_attributes.items()}

    # Initialize the TypedTemplate with the detected values
    template = template_class(**kwargs)

    return template.render()
