MetaGPT Meet a tenacity.RetryError mistake using llama2 as chat model

I am using llama as my model and the deply it by llama-cpp-python.llama-cpp-python offers a web server which aims to act as a drop-in replacement for the OpenAI API. .I find that there is a mistake when code run for some time.The conmand is python startup.py "Write a cli snake game" --code_review True .

I check llama logs,they are normal I think.

The mistakes and all logs are as follows.

2023-07-29 21:08:05.514 | INFO     | metagpt.config:__init__:44 - Config loading done.
2023-07-29 21:08:06.185 | INFO     | metagpt.software_company:invest:39 - Investment: $3.0.
2023-07-29 21:08:06.185 | INFO     | metagpt.roles.role:_act:166 - Alice(Product Manager): ready to WritePRD
 Please provide the missing information to complete the product requirements document.

---

## Original Requirements
The boss wants us to create a CLI snake game that can be played in the terminal. The game should have the following features:

## Product Goals
Our goals for this product are:

1. To provide an engaging and entertaining gaming experience for users.
2. To create a game that is easy to learn but challenging to master.
3. To design a game that can be played in the terminal, making it accessible to users who prefer a more traditional gaming experience.

## User Stories
As a user, I want to:

1. Be able to control the snake's movement using arrow keys or WASD keys.
2. See the game board and the snake's position in real-time.
3. Be able to eat food pellets to grow the snake and avoid colliding with walls or its own body.
4. Experience a sense of progression and achievement as I complete levels and unlock new skins for the snake.
5. Have the option to play against other players in a multiplayer mode.

## Competitive Analysis
We have analyzed several similar CLI games and identified the following strengths and weaknesses:

Strengths:

1. Easy to learn and pick up.
2. Provides a sense of progression and achievement.
3. Offers a variety of game modes and skins.

Weaknesses:

1. Limited customization options.
2. Lack of multiplayer features.
3. Outdated graphics and design.


## Competitive Quadrant Chart
Here is a quadrant chart comparing our product to its competitors:
```mermaid
quadrantChart
    title Reach and engagement of campaigns
    x-axis Low Reach --> High Reach
    y-axis Low Engagement --> High Engagement
    quadrant-1 We should expand
    quadrant-2 Need to promote
    quadrant-3 Re-evaluate
    quadrant-4 May be improved
    "Campaign: A": [0.3, 0.6]
    "Campaign B": [0.45, 0.23]
    "Campaign C": [0.57, 0.69]
    "Campaign D": [0.78, 0.34]
    "Campaign E": [0.40, 0.34]
    "Campaign F": [0.35, 0.78]
    "Our Target Product": [0.5, 0.6]

Requirement Analysis

The product should be a CLI snake game that can be played in the terminal. It should have the following requirements:

The game should have a simple and intuitive control scheme.
The game board should be displayed in real-time, with the snake's position and food pellets visible.
The snake should move smoothly and respond to user input accurately.
The game should have a variety of levels with increasing difficulty.
The game should include multiplayer features, allowing users to play against each other.
The game should have customization options, such as skins and backgrounds.
The game should be accessible to users with disabilities.

Requirement Pool

Here are the requirements for our product, prioritized based on importance and feasibility:

P0: Create a simple and intuitive control scheme for the snake's movement. P1: Design a user-friendly game board that displays the snake's position and food pellets in real-time. P2: Implement smooth and accurate snake movement based on user input. P3: Develop a variety of levels with increasing difficulty to keep users engaged. P4: Include multiplayer features, allowing users to play against each other. P5: Add customization options, such as skins and backgrounds. P6: Ensure the game is accessible to users with disabilities.

UI Design draft

Here is a simple design for our CLI snake game:

Game Board:

   _______
  /       \
 /         \
/___________\
|   |   |   |
|   |   |   |
|  
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is . 
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is .
Warning: model not found. Using cl100k_base encoding.
You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is . 
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is .

Please provide the missing information in the format specified above.
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is .

Please provide the information in the format example.
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is .
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is .
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is .
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is .
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is . 
Warning: model not found. Using cl100k_base encoding.
 You are a Product Manager, named Alice, your goal is Efficiently create a successful product, and the constraint is .
Warning: model not found. Using cl100k_base encoding.
Traceback (most recent call last):
  File "/home/zjh/.conda/envs/metagpt/lib/python3.9/site-packages/tenacity-8.2.2-py3.9.egg/tenacity/_asyncio.py", line 50, in __call__
    result = await fn(*args, **kwargs)
  File "/home/zjh/MetaGPT/metagpt/actions/action.py", line 57, in _aask_v1
    content = await self.llm.aask(prompt, system_msgs)
  File "/home/zjh/MetaGPT/metagpt/provider/base_gpt_api.py", line 44, in aask
    rsp = await self.acompletion_text(message, stream=True)
  File "/home/zjh/MetaGPT/metagpt/provider/openai_api.py", line 32, in wrapper
    return await f(*args, **kwargs)
  File "/home/zjh/MetaGPT/metagpt/provider/openai_api.py", line 218, in acompletion_text
    return await self._achat_completion_stream(messages)
  File "/home/zjh/MetaGPT/metagpt/provider/openai_api.py", line 169, in _achat_completion_stream
    usage = self._calc_usage(messages, full_reply_content)
  File "/home/zjh/MetaGPT/metagpt/provider/openai_api.py", line 224, in _calc_usage
    prompt_tokens = count_message_tokens(messages, self.model)
  File "/home/zjh/MetaGPT/metagpt/utils/token_counter.py", line 55, in count_message_tokens
    raise NotImplementedError(
NotImplementedError: num_tokens_from_messages() is not implemented for model gpt-3.5. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/zjh/MetaGPT/startup.py", line 36, in <module>
    fire.Fire(main)
  File "/home/zjh/.conda/envs/metagpt/lib/python3.9/site-packages/fire-0.4.0-py3.9.egg/fire/core.py", line 141, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/home/zjh/.conda/envs/metagpt/lib/python3.9/site-packages/fire-0.4.0-py3.9.egg/fire/core.py", line 466, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
  File "/home/zjh/.conda/envs/metagpt/lib/python3.9/site-packages/fire-0.4.0-py3.9.egg/fire/core.py", line 681, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "/home/zjh/MetaGPT/startup.py", line 32, in main
    asyncio.run(startup(idea, investment, n_round, code_review))
  File "/home/zjh/.conda/envs/metagpt/lib/python3.9/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/home/zjh/.conda/envs/metagpt/lib/python3.9/asyncio/base_events.py", line 642, in run_until_complete
    return future.result()
  File "/home/zjh/MetaGPT/startup.py", line 20, in startup
    await company.run(n_round=n_round)
  File "/home/zjh/MetaGPT/metagpt/software_company.py", line 60, in run
    await self.environment.run()
  File "/home/zjh/MetaGPT/metagpt/environment.py", line 56, in run
    await asyncio.gather(*futures)
  File "/home/zjh/MetaGPT/metagpt/roles/role.py", line 239, in run
    rsp = await self._react()
  File "/home/zjh/MetaGPT/metagpt/roles/role.py", line 208, in _react
    return await self._act()
  File "/home/zjh/MetaGPT/metagpt/roles/role.py", line 167, in _act
    response = await self._rc.todo.run(self._rc.important_memory)
  File "/home/zjh/MetaGPT/metagpt/actions/write_prd.py", line 145, in run
    prd = await self._aask_v1(prompt, "prd", OUTPUT_MAPPING)
  File "/home/zjh/.conda/envs/metagpt/lib/python3.9/site-packages/tenacity-8.2.2-py3.9.egg/tenacity/_asyncio.py", line 88, in async_wrapped
    return await fn(*args, **kwargs)
  File "/home/zjh/.conda/envs/metagpt/lib/python3.9/site-packages/tenacity-8.2.2-py3.9.egg/tenacity/_asyncio.py", line 47, in __call__
    do = self.iter(retry_state=retry_state)
  File "/home/zjh/.conda/envs/metagpt/lib/python3.9/site-packages/tenacity-8.2.2-py3.9.egg/tenacity/__init__.py", line 326, in iter
    raise retry_exc from fut.exception()
tenacity.RetryError: RetryError[<Future at 0x7ff56e57f0d0 state=finished raised NotImplementedError>]