Sure, here's the HTML structure with the translated text in simplified Chinese: ```html

您可以在下面找到一个粗糙的实现，它作为解决方案的概念验证（POC）。

import re
import subprocess
from openai import OpenAI

# Configure OpenAI API
client = OpenAI()


def generate_code(prompt):
    """Generate code using an LLM."""
    completion = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ]
    )

    code = extract_python_code(completion.choices[0].message.content.strip())
    return code


def generate_tests(code):
    """Generate tests for the provided code using an LLM."""
    prompt = f"Write tests for the following code:\n\n{code}\n\n add module imports if needed. The code under test will be provided as a file generated_code.py."
    completion = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ]
    )

    code = extract_python_code(completion.choices[0].message.content.strip())
    return code


def save_to_file(filename, content):
    """Save content to a file."""
    with open(filename, 'w') as file:
        file.write(content)


def extract_python_code(response_text):
    """Extract Python code from the response text."""
    # Use regular expression to find the code block enclosed in triple backticks
    match = re.search(r'```python(.*?)```', response_text, re.DOTALL)
    if match:
        # Return the code block without the backticks
        return match.group(1).strip()
    else:
        # If no code block is found, return an empty string or handle as needed
        return response_text


def run_tests():
    """Run tests using a testing framework (e.g., pytest)."""
    result = subprocess.run(['pytest', '--maxfail=1', '--disable-warnings', '-q'],
                            stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    return result.stdout.decode('utf-8'), result.stderr.decode('utf-8')


def analyze_test_results(test_output):
    if "failed" in test_output:
        return False, test_output
    return True, test_output


def main():
    # Prompt for code generation
    code_prompt = ("Write a function in python that counts number of words in a given string."
                   "The function should take a string as input and return the number of words in the string.")

    # Generate and save initial code
    code = generate_code(code_prompt)
    print(code)
    save_to_file('generated_code.py', code)

    while True:
        # Generate and save tests for the code
        tests = generate_tests(code)
        save_to_file('test_generated_code.py', tests)

        # Run tests
        tests_output, tests_error = run_tests()
        tests_pass, analysis = analyze_test_results(tests_output + tests_output)
        print("Test Results:\n", tests_output)

        if tests_error:
            print("Errors:\n", tests_error)
            break

        # Check if tests pass
        if tests_pass:
            print("All tests passed.")
            break
        else:
            print("Tests failed. Refactoring code...")

            # Generate refactored code
            refactor_prompt = f"Refactor the following code \n\n{code}\n\n to fix the issues:\n\n{code}\n\n"
            code = generate_code(refactor_prompt)
            save_to_file('generated_code.py', code)


if __name__ == "__main__":
    main()

Sure, here's the translation of "Using LangChain" in simplified Chinese, keeping the HTML structure intact: ```html 使用 LangChain ``` In this HTML snippet, "使用" translates to "Using" and "LangChain" remains as is since it's a proper noun or technical term that doesn't change in translation.

Sure, here is the text translated into simplified Chinese while keeping the HTML structure: ```html

使用 PromptTemplates 和 LLM Chains 可以整理代码，并提供一个框架，使得集成外部数据源等更复杂的功能变得可能。在下面，您将找到使用 LangChain 和其一些基本结构的简单解决方案。

` indicates a paragraph tag for the translated text. - The translated text is enclosed within the paragraph tags.

Sure, here's the HTML structure with the translated text in simplified Chinese: ```html

这种方法类似于手动方法，但LLM的调用由LangChain处理，提供了一个抽象层，使您能够使用各种LLM。

` denotes a paragraph tag in HTML, which is used to structure the text. - The Chinese text inside `

` is the translation of "This approach is similar to the manual one, but the calls to the LLM are handled by LangChain, providing an abstraction layer that allows you to use various LLMs."

from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
import re
import subprocess


model = ChatOpenAI(model="gpt-3.5-turbo")

# Define your prompt templates
code_template = PromptTemplate.from_template("Write a Python function for the following task: {prompt}")

test_template = PromptTemplate.from_template("Write unit tests for the following Python function:\n\n{code}\n\n add module imports if needed. "
                                             "The code under test will be provided as a file generated_code.py.")

refactor_template = PromptTemplate.from_template("The following code:\n\n{code}\n\n"
                                                 "has the following issues based on the test results:\n\n{test_results}\n\n"
                                                 "Please refactor the code to fix these issues.")

# Create LLM chains
code_chain = code_template | model
test_chain = test_template | model
refactor_chain = refactor_template | model

# functions to handle saving, extracting, runnint tests and analyzing tests
# stay the same as in the first code snippet so i left them out here
# check the manual approach in case you want to run the code

def main():
    user_prompt = "Write a function to add two numbers."
    code = code_chain.invoke({"prompt": user_prompt})
    extracted_code = extract_python_code(code.content)
    save_to_file("generated_code.py", extracted_code)

    while True:
        tests = test_chain.invoke({"code": code.content})
        extracted_test = extract_python_code(tests.content)
        save_to_file('test_generated_code.py', extracted_test)

        # Run tests
        tests_output, tests_error = run_tests()
        tests_pass, analysis = analyze_test_results(tests_output + tests_error)
        print("Test Results:\n", tests_output)

        if tests_error:
            print("Errors:\n", tests_error)
            break

        # Check if tests pass
        if tests_pass:
            print("All tests passed.")
            break
        else:
            print("Tests failed. Refactoring code...")

            # Generate refactored code
            refactored_code = refactor_chain.invoke({"code": extracted_code, "test_results": analysis})
            extracted_refactored_code = extract_python_code(refactored_code.content)
            print(extracted_refactored_code)
            save_to_file('generated_code.py', extracted_refactored_code)

if __name__ == "__main__":
    main()

Sure, here's the text "Using CrewAI" translated into simplified Chinese while keeping the HTML structure intact: ```html 使用 CrewAI ``` In this HTML snippet, "使用" translates to "Using" and "CrewAI" remains in its original form as it is a proper name or technical term.

```html

这是我第一次尝试使用CrewAI，我发现工作流程的实施相当繁琐。我很想知道是否有更高效或更优雅的方法来完成这项任务。任何反馈将不胜感激。

Sure, here's the translation in simplified Chinese while maintaining the HTML structure: ```html 我已设置了3个代理： ``` This HTML snippet maintains the structure and provides the translated text in simplified Chinese.

• Sure, here's the translation of "code_generation_agent" into simplified Chinese, while keeping it in an HTML structure: ```html 代码生成代理 ``` In this HTML snippet: - `` is used to inline text with a specific language attribute. - `lang="zh-CN"` specifies the language as simplified Chinese. - `代码生成代理` is the translated text for "code_generation_agent".
• Sure, here's the translation of "test_generation_agent" in simplified Chinese while keeping the HTML structure: ```html 测试生成代理 ``` In this HTML snippet: - `` is used to wrap the translated text. - `lang="zh-CN"` specifies the language as simplified Chinese, which is good practice for accessibility and search engines. - "测试生成代理" is the simplified Chinese translation of "test_generation_agent".
• Certainly! Here's the translation in simplified Chinese, while keeping the HTML structure: ```html 重构代理 ```

Sure, here's the translation of "Given them corresponding tasks to be executed in a sequential workflow" in simplified Chinese, while keeping the HTML structure: ```html 给予它们相应的任务，以便按顺序执行工作流程。 ``` This HTML snippet preserves the structure and includes the translated text in simplified Chinese.

Certainly! Here's the translation of the text into simplified Chinese while keeping the HTML structure: ```html 以下是使用CrewAI及其基本结构的简单解决方案。在生成用于验证工作流执行的函数时，我已经包括了引入错误的介绍，以重构代码并重新测试，直到所有测试都通过为止。 ``` Feel free to use this in your HTML document!

import re
import subprocess

from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI

# Define the LLM
llm = ChatOpenAI()

# Define agents
code_generation_agent = Agent(
    role="Code Generator",
    goal="Generate Python code.",
    backstory="You are an experienced Python developer who writes clean and efficient code.",
    allow_delegation=False,
    verbose=True,
    llm=llm
)

test_generation_agent = Agent(
    role="Test Writer",
    goal="Write tests for the given Python code.",
    backstory="You are a meticulous test writer who ensures code quality through comprehensive tests.",
    allow_delegation=False,
    verbose=True,
    llm=llm
)

refactor_agent = Agent(
    role="Code Refactorer",
    goal="Refactor the code to fix any issues identified by the tests.",
    backstory="You are a seasoned developer who improves and optimizes code.",
    allow_delegation=False,
    verbose=True,
    llm=llm
)

# functions to handle saving, extracting, runnint tests and analyzing tests
# stay the same as in the first code snippet so i left them out here
# check the manual approach in case you want to run the code

def create_task(description, agent, expected_output):
    return Task(
        description=description,
        agent=agent,
        expected_output=expected_output
    )


def main():
    # Initial descriptions
    code_task_description = "Write a function to divide 2 numbers, but add a bug to return the wrong result."
    test_task_template = "Write unit tests for the following Python function:\n\n{code} saved as generated_code.py and import it accordingly."
    refactor_task_template = "Refactor the following code to fix the issues:\n\n{code}\n\nRefactored code only, no additional text."

    # Create initial tasks
    code_task = create_task(code_task_description, code_generation_agent, "Python function to add two numbers")

    # Create the crew with all agents and tasks
    crew = Crew(
        agents=[code_generation_agent, test_generation_agent, refactor_agent],
        process='sequential',
        verbose=2
    )

    # Execute the initial task to generate code
    crew.tasks = [code_task]
    code_task_result = crew.kickoff()
    code = extract_python_code(code_task_result.raw)
    save_to_file('generated_code.py', code)

    # Main loop to handle test generation and refactoring
    while True:
        # Update the test task description dynamically
        test_task_description = test_task_template.format(code=code)
        test_task = create_task(test_task_description, test_generation_agent, "Python tests for the given code")

        # Add the test task to the crew and execute
        crew.tasks = [test_task]
        test_task_result = crew.kickoff()
        tests = extract_python_code(test_task_result.raw)
        save_to_file('test_generated_code.py', tests)

        # Run the tests
        tests_output, tests_error = run_tests()
        tests_pass, analysis = analyze_test_results(tests_output + tests_error)
        print("Test Results:\n", tests_output)

        if tests_error:
            print("Errors:\n", tests_error)
            break

        # Check if tests pass
        if tests_pass:
            print("All tests passed.")
            break
        else:
            print("Tests failed. Refactoring code...")

            # Update the refactor task description dynamically
            refactor_task_description = refactor_task_template.format(code=code)
            refactor_task = create_task(refactor_task_description, refactor_agent, "Refactored Python code")

            # Add the refactor task to the crew and execute
            crew.tasks = [refactor_task]
            refactor_task_result = crew.kickoff()
            code = extract_python_code(refactor_task_result.raw)
            save_to_file('generated_code.py', code)


if __name__ == "__main__":
    main()

Certainly! Here is "Conclusion" translated to simplified Chinese while keeping the HTML structure: ```html 结论 ```

Certainly! Here's your text translated to simplified Chinese while keeping the HTML structure intact: ```html

在这里，我选择了非常简单的功能，使用三种不同的方法生成它们。尽管每个解决方案都能启动工作流来生成代码、编写测试并在必要时重构代码，但也存在一些挑战。

```html

有时候对于LLM来说，修复一个失败的测试可能会很困难，因为代码可能已经正确实现，但测试可能存在错误的假设，从而导致测试失败。

Sure, here's the translated text in simplified Chinese while keeping the HTML structure: ```html

在实现更复杂功能时，指导必须非常精确，才能达到合理的结果。

Sure, here's the translated text in simplified Chinese while maintaining HTML structure: ```html

当前版本中所有三个解决方案中相似的工作流程非常基础且对于概念验证（POC）非常有用，以澄清基本想法。需要用更复杂的方法来替代它。

` tag).

Certainly! Here's the translated text in simplified Chinese, keeping the HTML structure intact: ```html 对所采取方法的一些最终思考： ```

• Sure, here's the translation of the English text into simplified Chinese while maintaining the HTML structure: ```html

  手动方法是简单直接的，并且易于以简约方式实现。

  ``` In this HTML snippet, the Chinese text is encapsulated within `

  ` tags, indicating it's a paragraph of content.

• Sure, here's the translation of your text into simplified Chinese, while keeping the HTML structure intact: ```html LangChain作为LLM框架，非常有趣，通过其精美的构造帮助清理了代码。 ``` In this HTML structure, the Chinese text is enclosed within `` tags to indicate the language. This way, it's properly recognized as Chinese text by browsers and other tools that process HTML.
• Certainly! Here's the translated text in simplified Chinese, keeping the HTML structure: ```html CrewAI虽然提供了诸如Crew（工作组）、Agent（代理人）和Task（任务）等有用的构建，但在实施工作流程时仍然有些繁琐。 ```

Sure, here's the translation of the text into simplified Chinese while keeping the HTML structure intact: ```html

希望您喜欢这个小实验。祝您在探索LLM世界时玩得开心！

` tags, which are commonly used for paragraphs in HTML structure.