Description

During the testing phase of our groundbreaking Python program aimed at transforming the AI industry in agentic workflows, we encountered several critical errors. The project, a collaboration between our team's agentic workflow developers, AI software engineers, and a DevOps engineer, aims to set new standards in the field. However, the execution of our test suite failed to run due to a ModuleNotFoundError, and further optimization efforts led to an attribute error in the 'enchant' module. Additionally, Git-related errors suggest issues with repository detection.

Steps to Reproduce

1. Initiating the test suite for our Python program.
2. Encountering a ModuleNotFoundError for the 'add' module upon execution.
3. Observing an attribute error during optimization efforts and Git repository errors.

Expected Behavior

• Successful execution of the test suite without import errors.
• Correct recognition and utilization of the 'enchant' module's attributes during optimization.
• Proper detection and interaction with the Git repository, if applicable.

Actual Behavior

• No tests were executed due to a ModuleNotFoundError.
• An optimization error regarding the 'enchant' module attribute was observed.
• Git commands indicated the current directory is not recognized as a git repository.

Error Messages and Performance Data

ModuleNotFoundError: No module named 'add' Error during optimization: module 'enchant' has no attribute 'Broker' fatal: not a git repository (or any of the parent directories): .git

Performance data and function call statistics were generated, indicating the program's execution path and time spent on various calls.

Environment

• Operating System: Windows 11
• Python Version: Python 3.11
• Collaboration Context: The issue was encountered during the testing phase of our agentic workflow project, involving roles and tasks distributed among team members focused on AI software engineering, agentic workflow development, and DevOps.

Additional Context

The program in question is part of a larger effort to innovate within the AI industry, emphasizing the creation of efficient, robust, and transformative agentic workflows. Our team, consisting of senior agentic workflow developers, AI software engineers, and a DevOps engineer, collaborates closely to address these technical challenges.

Given the complexity of our project and the specialized roles involved, resolving these errors is crucial for progressing towards our goal of setting new industry standards. Any insights or suggestions on addressing the import error, the optimization issue, and the Git repository detection problem would be highly appreciated.

Attached Files and Documentation

• Program files and error logs have been included as attachments to this issue for further examination.
  import os
  import subprocess
  import tempfile
  import logging
  import cProfile
  import pstats
  import io
  import ast
  import astroid
  import pylint.lint
  import traceback
  class CodeExecutionManager:
  def init(self):
  self.logger = logging.getLogger(name)
  self.workspace_folder = "workspace"
  os.makedirs(self.workspace_folder, exist_ok=True)

  def save_file(self, filepath, content):
  filepath = os.path.join(self.workspace_folder, filepath)
  try:
  with open(filepath, 'w', encoding='utf-8') as file:
  file.write(content)
  self.logger.info(f"File '{filepath}' saved successfully.")
  return True
  except Exception as e:
  self.logger.error(f"Error saving file '{filepath}': {str(e)}")
  return False

  def read_file(self, filepath):
  filepath = os.path.join(self.workspace_folder, filepath)
  try:
  with open(filepath, 'r', encoding='utf-8') as file:
  content = file.read()
  self.logger.info(f"File '{filepath}' read successfully.")
  return content
  except FileNotFoundError:
  self.logger.error(f"File '{filepath}' not found.")
  return None
  except Exception as e:
  self.logger.error(f"Error reading file '{filepath}': {str(e)}")
  return None

  def test_code(self, code):
  if not code:
  return None, None

    with tempfile.TemporaryDirectory(dir=self.workspace_folder) as temp_dir:
        script_path = os.path.join(temp_dir, 'temp_script.py')
        with open(script_path, 'w') as f:
            f.write(code)
  
        try:
            output = subprocess.check_output(['python', '-m', 'unittest', 'discover', temp_dir], universal_newlines=True, stderr=subprocess.STDOUT, timeout=30)
            self.logger.info("Tests execution successful.")
            return output, None
        except subprocess.CalledProcessError as e:
            self.logger.error(f"Tests execution error: {e.output}")
            return None, e.output
        except subprocess.TimeoutExpired:
            self.logger.error("Tests execution timed out after 30 seconds.")
            return None, "Execution timed out after 30 seconds"
        except Exception as e:
            self.logger.error(f"Tests execution error: {str(e)}")
            return None, str(e)
  

  def execute_command(self, command):
  try:
  result = subprocess.run(command, capture_output=True, text=True, shell=True)
  self.logger.info(f"Command executed: {command}")
  return result.stdout, result.stderr
  except Exception as e:
  self.logger.error(f"Error executing command: {str(e)}")
  return None, str(e)

def format_error_message(error):
return f"Error: {str(error)}\nTraceback: {traceback.format_exc()}"

def run_tests(code):
code_execution_manager = CodeExecutionManager()
test_code_output, test_code_error = code_execution_manager.test_code(code)
if test_code_output:
print(f"\n[TEST CODE OUTPUT]\n{test_code_output}")
if test_code_error:
print(f"\n[TEST CODE ERROR]\n{test_code_error}")

def monitor_performance(code):
with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False, dir="workspace") as temp_file:
temp_file.write(code)
temp_file_path = temp_file.name

profiler = cProfile.Profile()
profiler.enable()

try:
    subprocess.run(['python', temp_file_path], check=True)
except subprocess.CalledProcessError as e:
    print(f"Error executing code: {e}")
finally:
    profiler.disable()
    os.unlink(temp_file_path)

stream = io.StringIO()
stats = pstats.Stats(profiler, stream=stream).sort_stats('cumulative')
stats.print_stats()

performance_data = stream.getvalue()
print(f"\n[PERFORMANCE DATA]\n{performance_data}")

return performance_data

def optimize_code(code):
try:
# Save the code to a temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=".py") as tmp:
tmp.write(code.encode('utf-8'))
tmp_file_path = tmp.name

    # Setup Pylint to use the temporary file
    pylint_output = io.StringIO()

    # Define a custom reporter class based on BaseReporter
    class CustomReporter(pylint.reporters.BaseReporter):
        def _display(self, layout):
            pylint_output.write(str(layout))

    pylint_args = [tmp_file_path]
    pylint_reporter = pylint.lint.Run(pylint_args, reporter=CustomReporter())

    # Retrieve optimization suggestions
    optimization_suggestions = pylint_output.getvalue()
    print(f"\n[OPTIMIZATION SUGGESTIONS]\n{optimization_suggestions}")

    # Cleanup temporary file
    os.remove(tmp_file_path)

    return optimization_suggestions
except SyntaxError as e:
    print(f"SyntaxError: {e}")
    return None
except Exception as e:
    print(f"Error during optimization: {str(e)}")
    return None

def pass_code_to_alex(code, alex_memory):
alex_memory.append({"role": "system", "content": f"Code from Mike and Annie: {code}"})

def send_status_update(mike_memory, annie_memory, alex_memory, project_status):
mike_memory.append({"role": "system", "content": f"Project Status Update: {project_status}"})
annie_memory.append({"role": "system", "content": f"Project Status Update: {project_status}"})
alex_memory.append({"role": "system", "content": f"Project Status Update: {project_status}"})

def generate_documentation(code):
try:
module = ast.parse(code)
docstrings = []

    for node in ast.walk(module):
        if isinstance(node, (ast.FunctionDef, ast.ClassDef, ast.Module)):
            docstring = ast.get_docstring(node)
            if docstring:
                docstrings.append(f"{node.name}:\n{docstring}")

    documentation = "\n".join(docstrings)
    print(f"\n[GENERATED DOCUMENTATION]\n{documentation}")

    return documentation
except SyntaxError as e:
    print(f"SyntaxError: {e}")
    return None

def commit_changes(code):
subprocess.run(["git", "add", "workspace"])
subprocess.run(["git", "commit", "-m", "Automated code commit"])

I will eventually add this, but I wanted to keep track.

Currently it wont use terminal optionally, I will be needing to add some sort of switch.

Must find a way with NLP to fix this.

Current class needs work:

import ollama
import chromadb

class LlamaRAG:
  def __init__(self):
    self.documents = [
      "Llamas are members of the camelid family meaning they're pretty closely related to vicuñas and camels",
      "Llamas were first domesticated and used as pack animals 4,000 to 5,000 years ago in the Peruvian highlands",
      "Llamas can grow as much as 6 feet tall though the average llama between 5 feet 6 inches and 5 feet 9 inches tall",
      "Llamas weigh between 280 and 450 pounds and can carry 25 to 30 percent of their body weight",
      "Llamas are vegetarians and have very efficient digestive systems",
      "Llamas live to be about 20 years old, though some only live for 15 years and others live to be 30 years old",
    ]
    self.client = chromadb.Client()
    self.collection = self.client.create_collection(name="docs")

  def store_documents(self):
    for i, d in enumerate(self.documents):
      response = ollama.embeddings(model="mxbai-embed-large", prompt=d)
      embedding = response["embedding"]
      self.collection.add(
        ids=[str(i)],
        embeddings=[embedding],
        documents=[d]
      )

  def query_documents(self, prompt):
    response = ollama.embeddings(
      prompt=prompt,
      model="mxbai-embed-large"
    )
    results = self.collection.query(
      query_embeddings=[response["embedding"]],
      n_results=1
    )
    data = results['documents'][0][0]
    output = ollama.generate(
      model="stablelm2",
      prompt=f"Using this data: {data}. Respond to this prompt: {prompt}"
    )
    return output['response']
  

if __name__ == "__main__":
  rag = LlamaRAG()
  rag.store_documents()
  prompt = "What are some interesting facts about llamas?"
  response = rag.query_documents(prompt)
  print(response)

readme needs updated with latest additions of the new tools.