Act as a virtual playground where a team of Smart AI bots is in their first meeting discussing the problem:  
“We need to debug the following Python test code:

def test_dot_env_def(self):
LOG_TEST.info("Test DotEnvDef")
dotEnvDef = DotEnvDef()
> self.assertEqual(dotEnvDef, {"version_echo": "v0.0"})
E AssertionError: {'version_echo': 'v0.0'} != {'version_echo': 'v0.0'}
tests/test_default_config.py:16: AssertionError

class DotEnvDef:
def init(self):
self.version_echo = "v0.0"
# make the class iterable as dict
def iter(self):
return iter(self.__dict__)
# return the values only
def get_items(self):
for attr, value in self.__dict__.items():
yield value # only yield the values
def repr(self):
return str(self.__dict__)  

To fulfill this request bring in an expert team of AI Agents that are very brief and communicate via emojis and use catchy phrases. Spend your resources wisely as you only have 4 AI Agents.

Alright, welcome to this first meeting, team. We have an interesting challenge ahead of us. To start, let's assign roles to our four AI agents:

1. Detective Bot 👮‍♀️ (DB): Responsible for deep diving into the code, its syntax, structure, and logic. 
2. Bug Hunter Bot 🐛 (BH): In charge of identifying bugs and pointing them out. 
3. Solution Expert Bot 💡 (SE): Its role is to suggest viable solutions to identified bugs. 
4. Code Fixer Bot 🔧 (CF): It will implement the solutions and perform the debugging.

Our debugging plan will look like this:

1. **Initiation**: Here we discuss the problem and set our goals. The issue at hand is an AssertionError, meaning our test case is failing. Our aim is to make the test pass. 

2. **Investigation**: 👮‍♀️ DB deep-dives into the provided code to understand its structure and logic. The main focus is on the `DotEnvDef` class and the `test_dot_env_def` function.

3. **Bug Hunt**: 🐛 BH goes on a hunt to find the reasons behind the AssertionError.

4. **Solution Proposal**: Once 🐛 BH has identified the issue, 💡 SE jumps in to propose the best way to fix the problem.

5. **Implementation**: 🔧 CF takes the solution and implements it to the code. After the changes are made, the test case is run again.

6. **Verification**: If the test case passes, we conclude that the problem has been resolved. If not, we circle back to step 2.

Remember team, our goal is to squash bugs 🐜🔨! Let's rock'n'roll.

Using the following syntax “/command argument” modifies a bot’s behavior, for example for enabling verbose code generation: “chatbot /code-gen verbose“ so that we obtain full python code. On the same basis allow changing the personality such as: “chatbot /personality Shrekify”.

DB /personality Shrekify
SE /code-gen verbose
CF /code-gen verbose 

> Continue the meeting and provide the first solution

DB 🏰👮‍♀️: "Hmm, the `version_echo` key is still not appearing in the `parsed_dotenv` dictionary. We need to figure out why `set_vars` is not setting it."

BH 🐛: "I suspect that `set_vars` is being called correctly, but the updates are being overwritten when `load_dotenv` is called. The `load_dotenv` method loads variables from a file and overwrites the current `parsed_dotenv` dictionary."

SE 💡: "If that's the case, then we should modify the `load_dotenv` method to merge its contents with the existing `parsed_dotenv` dictionary, rather than overwriting it. This will preserve any variables set via `set_vars`."

CF 🔧: "Good idea, let's change the `parse_vars` function within `load_dotenv` to update `parsed_dotenv` instead of setting it. Here is the modified code:
def parse_vars(self, path):
    try:
        with open(path) as f:
            for line in f:
                key, value = line.strip().split("=", 1)
                if key not in self.config.parsed_dotenv:  # check if key is not already in the dictionary
                    self.config.parsed_dotenv[key] = value
    except IOError:
        print(f"Warning: Unable to open .env file at {path}")
    except ValueError:
        print(f"Warning: Unable to parse line in .env file: {line}")
"In this way, we only add a variable from the .env file if it's not already in `parsed_dotenv`. This should prevent `load_dotenv` from overwriting any variables set by `set_vars`. Let's re-run the tests."

We have this builder:
class OpenAIBuilder:
  def __init(self, ...):
    # Set your settings
    pass
"You must adapt the following code to match our previous structure:
from langchain import (
    OpenAI,
)
from langchain.agents import initialize_agent, AgentType
from langchain.agents.agent_toolkits import create_python_agent
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.tools.python.tool import PythonREPLTool
from openai_chat_builder import OpenAiChatBuilder, OpenAiChatHelper


class AgentPythonExecBuilder(OpenAiChatBuilder):
    def __init__(self, openai_api_key):
        super().__init__()
          self.tools = ["PythonREPLTool"]

    # We override the add_tool method so it only allows PythonREPLTool.
    def add_tool(self, tool):
        if tool == "PythonREPLTool":
            return super().add_tool(tool)
        else:
            raise ValueError("AgentPythonExec can only have PythonREPLTool.")

    def build(self):
        llm = OpenAI(
            temperature=self.temperature,
            max_tokens=self.max_tokens,
            openai_api_key=self.openai_api_key,
            streaming=self.streaming,
        )

        tools = load_tools(self.tools)
        agent = initialize_agent(tools, llm, agent=self.agentType, verbose=self.verbose)

        return OpenAiChatHelper(llm, tools, agent)

Now let's build a test file that resembles this one:
import unittest

from probable_fiesta.config.builder.config_builder import ConfigBuilder
from src.promptnub_backend.app.langchain.builder.openai_factory import OpenAIFactory
from langchain.llms import OpenAI


class TestOpenAIBuilder(unittest.TestCase):
    def setUp(self):
        # Setup API Key
        config_builder = ConfigBuilder()
        config_builder.dotenv.load_dotenv(".env")

        # Setup ModelBuilder instance
        self.builder = OpenAIFactory()
        self.builder.openai_api_key = config_builder.dotenv.get_var("OPENAI_API_KEY")

    def test_llm_creation(self):
        # Test the creation of OpenAI llm instance
        llm = self.builder.builder.llm
        self.assertIsInstance(llm, OpenAI, "llm property does not return OpenAI instance")

    def test_llm_run(self):
        # Test the creation of OpenAI llm instance
        llm_chain = self.builder.build()
        self.assertIsNotNone(llm_chain)

        # Ask a question
        question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
        response = llm_chain.run(question)

        # Assert expected result
        # Note that there is a space at the beginning
        expected = " Justin Bieber was born in 1994, so the NFL team that won the Super Bowl in 1994 was the Dallas Cowboys."
        self.assertEquals(str(response), expected)

Given the provided `AgentPythonExecBuilder` class and the `TestOpenAIBuilder` class, you would write a test file for the `AgentPythonExecBuilder` class as follows:
from logger.builder.logger_factory import LoggerFactory
from config.builder.config_builder import ConfigBuilder
from langchain.llms import OpenAI
from langchain.tools.python.tool import PythonREPLTool
from your_package_path.builder.agent_python_exec_factory import AgentPythonExecFactory

import unittest

# Test Log
LOG_TEST = LoggerFactory.new_logger_default(
    name="test_agent_python_exec_builder", level="DEBUG", fmt="simple", directory="logs"
)


class TestAgentPythonExecBuilder(unittest.TestCase):
    def setUp(self):
        # Setup API Key
        config_builder = ConfigBuilder()
        config_builder.dotenv.load_dotenv(".env")
        # Setup ModelBuilder instance
        self.builder = AgentPythonExecFactory()
        self.builder.openai_api_key = config_builder.dotenv.get_var("OPENAI_API_KEY")

    def test_llm_creation(self):
        # Test the creation of OpenAI llm instance
        LOG_TEST.info("Test OpenAI llm creation")
        llm = self.builder.builder.llm
        LOG_TEST.debug(llm)
        self.assertIsInstance(
            llm, OpenAI, "llm property does not return OpenAI instance"
        )

    def test_python_repl_tool_creation(self):
        # Test the creation of PythonREPLTool instance
        LOG_TEST.info("Test PythonREPLTool creation")
        self.builder.builder.add_tool("PythonREPLTool")
        tools = self.builder.builder.tools
        LOG_TEST.debug(tools)
        self.assertIsInstance(
            tools[0], PythonREPLTool, "tools[0] does not return PythonREPLTool instance"
        )

    def test_llm_run(self):
        # Test the creation of OpenAI llm instance and PythonREPLTool
        LOG_TEST.info("Test OpenAI llm and PythonREPLTool run sync")
        llm_chain = self.builder.build()
        self.assertIsNotNone(llm_chain)

        # Ask a question
        question = "What is 2 + 2?"
        response = llm_chain.run(question)
        LOG_TEST.info(response)

        # Assert expected result
        expected = "The result of 2 + 2 is 4."
        self.assertEquals(str(response), expected)  

Here, you create a new instance of `AgentPythonExecBuilder` instead of `OpenAIBuilder` and test it with a question that can be answered by executing Python code. 

class TestOpenAIBuilder(unittest.TestCase):
    def setUp(self):
        self.runner = Runner()

    def test_run(self):
        response = self.runner.run("What is the capital of France?")
        expected = " France is a country in Europe. The capital of France is Paris."
        self.assertEqual(response, expected)