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geekan / MetaGPT

🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
https://deepwisdom.ai/
MIT License
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Research Role Resource Exhaustion Error When Using Google Gemini API through 'serper' with engine: google #1165

Closed ming188199 closed 2 months ago

ming188199 commented 3 months ago

Bug description

Encountered an AttributeError during the execution of a script intended for researching publicly listed companies. This error specifically arises after attempting to initiate sessions with configured proxies, halting the execution of the script.

config2.yaml

llm:
  api_type: "gemini"  # 默认支持模型gemini-pro
  model: "gemini-pro"
  api_key: "[HIDDEN]"
  proxy: "http://127.0.0.1:20171"
  # api_type: "openai"  # 或 azure / ollama 等
  # base_url: "https://api.openai-forward.com/v1"
  # api_key: "[HIDDEN]"
  # model: "gpt-4-1106-preview"  # 或 gpt-3.5-turbo-1106 / gpt-4-1106-preview
  # proxy: "http://127.0.0.1:20171"
search:
  # api_type: 'serpapi' # serpapi/google/serper/ddg
  # api_key: '583547*0478af9'
  # cse_id: 'YOUR_CSE_ID' # 仅适用于google
  proxy: "http://127.0.0.1:20171"
  params:
    engine: google 
    google_domain: 'google.com'
    gl: cn # us cn
    hl: zh-cn # en zh-cn
browser:
  engine: 'playwright' # playwright/selenium
  # 对于 playwright 引擎，请查阅 https://playwright.dev/python/docs/api/class-browsertype
  # 对于 selenium 引擎，请查阅 https://www.selenium.dev/documentation/webdriver/browsers
  browser_type: 'chromium' # playwright: chromium/firefox/webkit; selenium: chrome/firefox/edge/ie
proxy: "http://127.0.0.1:20171"

Bug solved method

Environment information

#!/usr/bin/env python

import asyncio

from metagpt.roles.researcher import RESEARCH_PATH, Researcher

async def main():
    topic = "上市公司研究报告"
    role = Researcher(language="zh-cn")
    await role.run(topic)
    print(f"save report to {RESEARCH_PATH / f'{topic}.md'}.")

if __name__ == "__main__":
    asyncio.run(main())


- installation method: 

**Screenshots or logs**
<!-- Screenshots or logs of the bug can help us understand the problem more quickly -->

2024-04-09 07:13:39.192 | INFO     | metagpt.const:get_metagpt_package_root:29 - Package root set to /Users/kk/Documents/github/AGI
2024-04-09 07:13:42.572 | INFO     | metagpt.provider.google_gemini_api:__init_gemini:60 - Use proxy: http://127.0.0.1:20171
2024-04-09 07:13:42.577 | INFO     | metagpt.roles.researcher:_act:56 - David(Researcher): to do CollectLinks(David)
["Natural language processing", "Machine learning"]
2024-04-09 07:13:51.104 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.000 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 37, completion_tokens: 10
Warning: model not found. Using cl100k_base encoding.
Warning: model not found. Using cl100k_base encoding.
["What are the applications of natural language processing in machine learning?", "How can natural language processing improve machine learning models?", "What are the challenges in integrating natural language processing with machine learning?", "What are the future trends in natural language processing and machine learning?"]
2024-04-09 07:13:56.608 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.000 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 1165, completion_tokens: 54
[3, 1, 0, 4, 2, 7, 6, 5]
2024-04-09 07:14:02.168 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 866, completion_tokens: 25
[0, 1, 3, 4, 5, 6, 7]
2024-04-09 07:14:12.430 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 813, completion_tokens: 22
[0, 1, 3, 4, 5, 6, 7]
2024-04-09 07:14:17.339 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 844, completion_tokens: 22
[0, 1, 3, 4, 6, 7]
2024-04-09 07:14:21.642 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 902, completion_tokens: 19
2024-04-09 07:14:21.649 | INFO     | metagpt.roles.researcher:_act:56 - David(Researcher): to do WebBrowseAndSummarize(David)
Warning: model not found. Using cl100k_base encoding.
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Not relevant.
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2024-04-09 07:15:02.769 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 131, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
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2024-04-09 07:15:02.912 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 132, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
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2024-04-09 07:15:02.955 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 132, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
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2024-04-09 07:15:03.013 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 129, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
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Not relevant.
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2024-04-09 07:15:04.649 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 131, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
Warning: model not found. Using cl100k_base encoding.
2024-04-09 07:15:04.818 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 132, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
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Not relevant.
2024-04-09 07:15:05.061 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.001 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 129, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
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2024-04-09 07:15:05.575 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.002 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 1627, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
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Not relevant.
Not relevant.
2024-04-09 07:15:06.473 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.002 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 131, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
Warning: model not found. Using cl100k_base encoding.
2024-04-09 07:15:06.595 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.002 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 132, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
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Not relevant.
Not relevant.
Not relevant.
2024-04-09 07:15:08.302 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.002 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 1258, completion_tokens: 4
Warning: model not found. Using cl100k_base encoding.
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2024-04-09 07:15:08.479 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.002 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 131, completion_tokens: 4
2024-04-09 07:15:08.616 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.002 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 132, completion_tokens: 4
The provided text does not directly address the challenges in integrating natural language processing with machine learning. Therefore, I cannot answer the question using the provided text.
2024-04-09 07:15:09.565 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.003 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 1338, completion_tokens: 31
Warning: model not found. Using cl100k_base encoding.
Warning: model not found. Using cl100k_base encoding.
2024-04-09 07:15:10.003 | WARNING  | metagpt.utils.common:wrapper:649 - There is a exception in role's execution, in order to resume, we delete the newest role communication message in the role's memory.
Traceback (most recent call last):
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/utils/common.py", line 640, in wrapper
    return await func(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/role.py", line 550, in run
    rsp = await self.react()
          ^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/researcher.py", line 105, in react
    msg = await super().react()
          ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/role.py", line 519, in react
    rsp = await self._act_by_order()
          ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/role.py", line 473, in _act_by_order
    rsp = await self._act()
          ^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/researcher.py", line 74, in _act
    summaries = await asyncio.gather(*todos)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/actions/research.py", line 228, in run
    summary = await self._aask(prompt, [system_text])
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/actions/action.py", line 93, in _aask
    return await self.llm.aask(prompt, system_msgs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/provider/base_llm.py", line 150, in aask
    rsp = await self.acompletion_text(message, stream=stream, timeout=self.get_timeout(timeout))
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/tenacity/_asyncio.py", line 88, in async_wrapped
    return await fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/tenacity/_asyncio.py", line 47, in __call__
    do = self.iter(retry_state=retry_state)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/tenacity/__init__.py", line 314, in iter
    return fut.result()
           ^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/concurrent/futures/_base.py", line 449, in result
    return self.__get_result()
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/tenacity/_asyncio.py", line 50, in __call__
    result = await fn(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/provider/base_llm.py", line 200, in acompletion_text
    return await self._achat_completion_stream(messages, timeout=self.get_timeout(timeout))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/provider/google_gemini_api.py", line 141, in _achat_completion_stream
    resp: AsyncGenerateContentResponse = await self.llm.generate_content_async(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/generativeai/generative_models.py", line 263, in generate_content_async
    iterator = await self._async_client.stream_generate_content(
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/retry/retry_unary_async.py", line 230, in retry_wrapped_func
    return await retry_target(
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/retry/retry_unary_async.py", line 160, in retry_target
    _retry_error_helper(
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/retry/retry_base.py", line 212, in _retry_error_helper
    raise final_exc from source_exc
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/retry/retry_unary_async.py", line 155, in retry_target
    return await target()
           ^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/grpc_helpers_async.py", line 189, in error_remapped_callable
    await call.wait_for_connection()
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/grpc_helpers_async.py", line 79, in wait_for_connection
    raise exceptions.from_grpc_error(rpc_error) from rpc_error
google.api_core.exceptions.ResourceExhausted: 429 Resource has been exhausted (e.g. check quota).

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/kk/Documents/github/AGI/MetaGPT/上市公司研究报告.py", line 24, in <module>
    asyncio.run(main())
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/asyncio/runners.py", line 190, in run
    return runner.run(main)
           ^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/asyncio/runners.py", line 118, in run
    return self._loop.run_until_complete(task)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete
    return future.result()
           ^^^^^^^^^^^^^^^
  File "/Users/kk/Documents/github/AGI/MetaGPT/上市公司研究报告.py", line 18, in main
    await role.run(topic)
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/utils/common.py", line 662, in wrapper
    raise Exception(format_trackback_info(limit=None))
Exception: Traceback (most recent call last):
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/utils/common.py", line 640, in wrapper
    return await func(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/role.py", line 550, in run
    rsp = await self.react()
          ^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/researcher.py", line 105, in react
    msg = await super().react()
          ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/role.py", line 519, in react
    rsp = await self._act_by_order()
          ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/role.py", line 473, in _act_by_order
    rsp = await self._act()
          ^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/roles/researcher.py", line 74, in _act
    summaries = await asyncio.gather(*todos)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/actions/research.py", line 228, in run
    summary = await self._aask(prompt, [system_text])
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/actions/action.py", line 93, in _aask
    return await self.llm.aask(prompt, system_msgs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/provider/base_llm.py", line 150, in aask
    rsp = await self.acompletion_text(message, stream=stream, timeout=self.get_timeout(timeout))
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/tenacity/_asyncio.py", line 88, in async_wrapped
    return await fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/tenacity/_asyncio.py", line 47, in __call__
    do = self.iter(retry_state=retry_state)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/tenacity/__init__.py", line 314, in iter
    return fut.result()
           ^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/concurrent/futures/_base.py", line 449, in result
    return self.__get_result()
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/tenacity/_asyncio.py", line 50, in __call__
    result = await fn(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/provider/base_llm.py", line 200, in acompletion_text
    return await self._achat_completion_stream(messages, timeout=self.get_timeout(timeout))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/metagpt/provider/google_gemini_api.py", line 141, in _achat_completion_stream
    resp: AsyncGenerateContentResponse = await self.llm.generate_content_async(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/generativeai/generative_models.py", line 263, in generate_content_async
    iterator = await self._async_client.stream_generate_content(
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/retry/retry_unary_async.py", line 230, in retry_wrapped_func
    return await retry_target(
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/retry/retry_unary_async.py", line 160, in retry_target
    _retry_error_helper(
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/retry/retry_base.py", line 212, in _retry_error_helper
    raise final_exc from source_exc
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/retry/retry_unary_async.py", line 155, in retry_target
    return await target()
           ^^^^^^^^^^^^^^
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/grpc_helpers_async.py", line 189, in error_remapped_callable
    await call.wait_for_connection()
  File "/Users/kk/opt/anaconda3/envs/MetaGPT/lib/python3.11/site-packages/google/api_core/grpc_helpers_async.py", line 79, in wait_for_connection
    raise exceptions.from_grpc_error(rpc_error) from rpc_error
google.api_core.exceptions.ResourceExhausted: 429 Resource has been exhausted (e.g. check quota).
seehi commented 2 months ago

It seems the error is caused by RateLimited, but there isn't a retry attempt in the code. You can try to use retry_if_exception_type() instead of retry_if_exception_type(ConnectionError) in the retry to see if that helps.

ming188199 commented 2 months ago

It work a little, and it print so many warning -- 'Warning: model not found. Using cl100k_base encoding.' the result is :

I apologize, but the provided context does not contain any information on "上市公司详细研究报告." Therefore, I cannot generate a research report on the topic using the provided context.
Warning: model not found. Using cl100k_base encoding.
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Not relevant.
2024-04-09 15:33:50.231 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.004 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 133, completion_tokens: 4
 learning (ML) is a powerful tool that can be used to improve natural language processing (NLP) in a number of ways. Here are a few examples: contextual understanding because we use many different words to express the same idea. Some of these words may convey exactly the same meaning, while some may be levels of complexity (small, little, tiny, minute) and different people use synonyms to denote slightly different meanings within their personal vocabulary. This can make it difficult for NLP models to understand the intended meaning of a sentence.
3. **Irony and sarcasm:** Irony and sarcasm present problems for machine learning models because they generally use words and phrases that, strictly by definition, may be positive or negative, but actually connote the opposite. This can make it difficult for NLP models to understand

* **Named entity recognition (NER)**: ML can be used to train models to identify and classify named entities in text, such as people, places, and organizations. This information can be used to improve the accuracy of search results, recommendation systems, and other NLP applications.
* **Part-of-speech tagging (POS)**: ML can be used to train models to identify the part of speech of each word in a sentence. This information can be used to improve the accuracy of grammar checkers, spell checkers, and other NLP applications.
* **Sentiment analysis**: ML can be used to train models to determine the sentiment of a piece of text, such as whether it is positive, negative, or neutral. This information can be used to improve the accuracy of customer service chatbots, social media monitoring tools, and other NLP applications.
* **Machine translation**: ML can be used to train models to translate text from one language to another. This information can be used to improve the accuracy of machine translation services, such as Google Translate and Microsoft Translator.

These are just a few examples of how ML can be used to improve NLP. As ML continues to develop, we can expect to see even more innovative and powerful NLP applications emerge.
 the intended meaning of a sentence.
4. **Ambiguity:** Ambiguity in NLP refers to sentences and phrases that potentially have two or more possible interpretations. This can make it difficult for NLP models to understand the intended meaning of a sentence.
5. **Errors in text and speech:** Misspelled or misused words can create problems for text analysis. Autocorrect and grammar correction applications can handle common mistakes, but don’t always understand the writer’s intention. With spoken language, mispronunciations, different accents, stutters, etc., can be difficult for a machine to understand.
6. **Colloquialisms and slang:** Informal phrases, expressions, idioms, and culture-specific lingo present a number of problems for NLP – especially for models intended for broad use. Because as formal language, colloquialisms may have no “dictionary definition” at all, and these expressions may even have different meanings in different geographic areas. Furthermore, cultural slang is constantly morphing and expanding, so new words pop up every day.
7. **Domain-specific language:** Different businesses and industries often use very different language. An NLP processing model needed for healthcare, for example, would be very different than one used to process legal documents. This can make it difficult for NLP models to understand the intended meaning of a sentence.
8. **Low-resource languages:** AI machine learning NLP applications have been largely built for the most common, widely used languages. However, many languages, especially those spoken by people with less access to technology often go overlooked and under processed. This can make it difficult for NLP models to understand the intended meaning of a sentence.
9. **Lack of research and development:** Machine learning requires A LOT of data to function to its outer limits – billions of pieces of training data. The more data NLP models are trained on, the smarter they become. That said, data (and human language!) is only growing by the day, as are new machine learning techniques and custom algorithms. All of the problems above will require more research and new techniques in order to improve on them.
2024-04-09 15:33:53.554 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.005 | Max budget: $10.000 | Current cost: $0.001, prompt_tokens: 3935, completion_tokens: 300
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**What are the different types of machine learning algorithms?**

There are three2024-04-09 15:33:54.148 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.006 | Max budget: $10.000 | Current cost: $0.001, prompt_tokens: 1893, completion_tokens: 666
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Not relevant.
 main types of machine learning algorithms:

1. **Supervised learning** algorithms learn from labeled data, which means that each data point has a known output. The algorithm learns to map the input data to the output data.
2. **Unsupervised learning** algorithms learn from unlabeled data, which means that each data point does not have a known output. The algorithm learns to find patterns and structures in the data.
3. **Reinforcement learning** algorithms learn by interacting with their environment. The algorithm receives feedback from the environment and learns to take actions that maximize the reward.
Not relevant.
2024-04-09 15:33:55.554 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.006 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 131, completion_tokens: 4
2024-04-09 15:33:56.174 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.007 | Max budget: $10.000 | Current cost: $0.001, prompt_tokens: 3038, completion_tokens: 136
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The text provided discusses the challenges in developing machine learning models for natural language processing,**Types of Machine Learning Algorithms**

Machine learning algorithms are classified into four main including ambiguity and polysemy, context understanding, and the need for large datasets.

**Ambiguity and Polysemy:** Natural language is rife with words types:

1. **Supervised Learning:** Algorithms learn from labeled data, where the target output is known. They can be used for classification and regression tasks. Examples include:
    - Decision Trees
    - Support Vector Machines
    - Random Forests
    - Naive Bayes

2. **Unsupervised Learning:** Algorithms analyze unlabeled data to discover patterns, group similar data, or reduce dimensions. Examples include:
    - K-means
    - Hierarchical clustering
    - Dimensionality Reduction Methods (PCA, t-SNE)

3. **Semi-supervised Learning:** Algorithms combine labeled and unlabeled data for training, leveraging the limited labeled data to improve performance.

4. **Reinforcement Learning:** Algorithms learn through trial and error by interacting with an environment to maximize rewards. They are used in robotics, game playing, and autonomous systems.
 and phrases that have multiple meanings based on context. Resolving ambiguity and polysemy is a significant challenge in NLP. For example, the word "bank" could refer to a financial institution or the side of a river.

**Context Understanding:** Understanding the context in which words and phrases are used is crucial. It involves not just the immediate context but also the broader discourse. For example, the meaning of the word "they" can vary depending on the context in which it is used.

**Need for Large Datasets:** Machine learning models for NLP require large datasets to train on. This is because natural language is highly variable, and models need to be able to generalize to new data. However, collecting and annotating large datasets can be time-consuming and expensive.
2024-04-09 15:34:03.476 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.008 | Max budget: $10.000 | Current cost: $0.001, prompt_tokens: 5228, completion_tokens: 192
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**What are the challenges in developing machine learning models for natural language processing?**

According to the reference information, the challenges in developing machine learning models for natural language processing include:

* **Ambiguity and Contextual Understanding:** Natural language is inherently complex and often ambiguous. Words and phrases can have multiple meanings depending on the context in which they are used. AI systems need to accurately interpret the intended meaning by considering the broader context, including the surrounding words, sentence structure, and even the speaker's intent or background knowledge.
* **Contextual Inference and Reasoning:** Understanding natural language requires the ability to make inferences and reason about the world. For example, to understand the sentence "The cat is on the mat," an AI system needs to know that cats are typically found on mats and that "on" implies a physical relationship between the cat and the mat.
* **Scalability and Efficiency:** Natural language processing models can be computationally expensive, especially for large datasets. Developing scalable and efficient models is crucial for real-world applications.
* **Data Sparsity and Lack of Labeled Data:** Natural language data is often sparse, meaning that many words and phrases occur infrequently. This can make it difficult to train machine learning models that can generalize well to new data. Additionally, labeled data for natural language processing tasks is often limited and expensive to acquire.
* **Evaluation and Metrics:** Evaluating the performance of natural language processing models is challenging due to the subjective nature of language. There is no single metric that can capture all aspects of language understanding and generation.
2024-04-09 15:34:10.714 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.009 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 1070, completion_tokens: 316
2024-04-09 15:34:10.725 | INFO     | metagpt.roles.researcher:_act:56 - David(Researcher): to do ConductResearch(David)
I apologize, but the provided context does not contain any information related to the topic "前妻不让奶奶单独见孩子，说要算在孩子父亲探视权数量里面，可以打官司告她吗？". Therefore, I cannot generate a research report on this topic using the provided information.
2024-04-09 15:34:15.525 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.010 | Max budget: $10.000 | Current cost: $0.001, prompt_tokens: 2593, completion_tokens: 63
save report to /Users/kk/Documents/github/AGI/data/research.
ming188199 commented 2 months ago

but I use the yaml as follow can work. 

llm:
# gpt3.5
  api_type: "openai"  # or azure / ollama / open_llm etc. Check LLMType for more options
  model: "gpt-3.5-turbo-1106"  # or gpt-3.5-turbo-1106 / gpt-4-1106-preview
  base_url: "https://api.openai-forward.com/v1"  # or forward url / other llm url
  api_key: "sk-t5yC
seehi commented 2 months ago

It depends on the capabilities of the LLM.

ming188199 commented 2 months ago

It depends on the capabilities of the LLM.

It seems gemini can't search any thing.

gemini

log

I don't konw what it mean like this:

2024-04-09 15:33:50.231 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.004 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 133, completion_tokens: 4
 learning (ML) is a powerful tool that can be used to improve natural language processing (NLP) in a number of ways. Here are a few examples: contextual understanding because we use many different words to express the same idea. Some of these words may convey exactly the same meaning, while some may be levels of complexity (small, little, tiny, minute) and different people use synonyms to denote slightly different meanings within their personal vocabulary. This can make it difficult for NLP models to understand the intended meaning of a sentence.
3. **Irony and sarcasm:** Irony and sarcasm present problems for machine learning models because they generally use words and phrases that, strictly by definition, may be positive or negative, but actually connote the opposite. This can make it difficult for NLP models to understand

output

I apologize, but the provided context does not contain any information related to the topic "前妻不让奶奶单独见孩子，说要算在孩子父亲探视权数量里面，可以打官司告她吗？". Therefore, I cannot generate a research report on this topic using the provided information.

gpt3.5

log

2024-04-09 15:44:04.703 | INFO     | metagpt.utils.cost_manager:update_cost:57 - Total running cost: $0.000 | Max budget: $10.000 | Current cost: $0.000, prompt_tokens: 115, completion_tokens: 9
```json
[
    "child custody rights and grandparents visitation rights in Louisiana",
    "legal rights of grandparents in child custody cases",
    "how to file a legal action for grandparents visitation rights in Louisiana",
    "best interest of the child in child custody and visitation rights cases"
]
## output
```markdown
# Research Report: 在中国大陆，前妻不让奶奶单独见孩子，说要算在孩子父亲探视权数量里面，可以打官司告她吗？

## Introduction
在中国大陆，前妻不让奶奶单独见孩子，说要算在孩子父亲探视权数量里面，可以打官司告她吗？这个问题涉及到家庭法律和探视权的问题。根据中国的相关法律，父母对孩子的监护权是平等的，但在离婚后，监护权可能会根据具体情况做出裁定。本报告将探讨在中国大陆前妻不让奶奶单独见孩子的法律规定以及可能的法律救济途径。
......
## Conclusion
在中国大陆，前妻不让奶奶单独见孩子的情况涉及到家庭法律和探视权的问题。根据相关法律规定，父母对孩子的监护权是平等的，但在离婚后，监护权可能会根据具体情况做出裁定。如果前妻限制奶奶探视孩子，父亲可以通过法律途径寻求解决。具体的法律救济途径需要咨询专业律师以获取详细建议。

## References
- 66law.cn. (n.d.). Retrieved from https://m.66law.cn/laws/1767189.aspx
- Sina.cn. (2021, September 25). Retrieved from https://news.sina.cn/2021-09-25/detail-iktzscyx6280372.d.html
- Xinhua News. (2023, May 31). Retrieved from http://js.news.cn/2023-05/31/c_1129659112.htm
- CNLaw. (n.d.). Retrieved from http://www.cnlaw.net/doc/83651.htm
- Baidu. (n.d.). Retrieved from https://zhidao.baidu.com/question/2277149080150868148.html?
- Dalvlaw. (n.d.). Retrieved from https://www.dalvlaw.com/laws/195895.html
ming188199 commented 2 months ago

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