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Fix linting

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This commit is contained in:
Zamil Majdy
2024-05-20 18:42:36 +07:00
parent 3a60504138
commit c8e16f3fe1
4 changed files with 42 additions and 34 deletions
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55
autogpts/autogpt/autogpt/agents/prompt_strategies/code_flow.py
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@@ -28,41 +28,48 @@ class CodeFlowAgentActionProposal(BaseModel):
immediate_plan: str = Field(
...,
description="We will be running an iterative process to execute the plan, "
"Write the partial / immediate plan to execute your plan as detailed and efficiently as possible without the help of the reasoning/intelligence. "
"The plan should describe the output of the immediate plan, so that the next iteration can be executed by taking the output into account. "
"Try to do as much as possible without making any assumption or uninformed guesses. Avoid large output at all costs!!!"
"Format: Objective[Objective of this iteration, explain what's the use of this iteration for the next one] Plan[Plan that does not require any reasoning or intelligence] Output[Output of the plan / should be small, avoid whole file output]",
)
"Write the partial / immediate plan to execute your plan as detailed and "
"efficiently as possible without the help of the reasoning/intelligence. "
"The plan should describe the output of the immediate plan, so that the next "
"iteration can be executed by taking the output into account. "
"Try to do as much as possible without making any assumption or uninformed "
"guesses. Avoid large output at all costs!!!\n"
"Format: Objective[Objective of this iteration, explain what's the use of this "
"iteration for the next one] Plan[Plan that does not require any reasoning or "
"intelligence] Output[Output of the plan / should be small, avoid whole file "
"output]", )
python_code: str = Field(
...,
description=(
"Write the fully-functional Python code of the immediate plan. The output will be an `async def main() -> str` function of the immediate plan that return the string output, the output will be passed into the LLM context window so avoid returning the whole content!. "
..., description=(
"Write the fully-functional Python code of the immediate plan. "
"The output will be an `async def main() -> str` function of the immediate "
"plan that return the string output, the output will be passed into the "
"LLM context window so avoid returning the whole content!. "
"Use ONLY the listed available functions and built-in Python features. "
"Leverage the given magic functions to implement function calls for which the "
"arguments can't be determined yet. Example:`async def main() -> str:\n return await provided_function('arg1', 'arg2').split('\\n')[0]`"
),
)
"Leverage the given magic functions to implement function calls for which "
"the arguments can't be determined yet. "
"Example:`async def main() -> str:\n"
" return await provided_function('arg1', 'arg2').split('\\n')[0]`"), )
FINAL_INSTRUCTION: str = (
"You have to give the answer in the from of JSON schema specified previously. "
"For the `python_code` field, you have to write Python code to execute your plan as efficiently as possible. "
"Your code will be executed directly without any editing: "
"if it doesn't work you will be held responsible. "
"For the `python_code` field, you have to write Python code to execute your plan "
"as efficiently as possible. Your code will be executed directly without any "
"editing, if it doesn't work you will be held responsible. "
"Use ONLY the listed available functions and built-in Python features. "
"Do not make uninformed assumptions (e.g. about the content or format of an unknown file). "
"Leverage the given magic functions to implement function calls for which the "
"arguments can't be determined yet. Reduce the amount of unnecessary data passed into "
"these magic functions where possible, because magic costs money and magically "
"processing large amounts of data is expensive. "
"If you think are done with the task, you can simply call "
"Do not make uninformed assumptions "
"(e.g. about the content or format of an unknown file). Leverage the given magic "
"functions to implement function calls for which the arguments can't be determined "
"yet. Reduce the amount of unnecessary data passed into these magic functions "
"where possible, because magic costs money and magically processing large amounts "
"of data is expensive. If you think are done with the task, you can simply call "
"finish(reason='your reason') to end the task, "
"a function that has one `finish` command, don't mix finish with other functions! "
"If you still need to do other functions, let the next cycle execute the `finish` function. "
"If you still need to do other functions, "
"let the next cycle execute the `finish` function. "
"Avoid hard-coding input values as input, and avoid returning large outputs. "
"The code that you have been executing in the past cycles can also be buggy, "
"so if you see undesired output, you can always try to re-plan, and re-code. "
)
"so if you see undesired output, you can always try to re-plan, and re-code. ")
class CodeFlowAgentPromptStrategy(PromptStrategy):

6
autogpts/autogpt/autogpt/agents/prompt_strategies/one_shot.py
View File

@@ -29,7 +29,8 @@ _RESPONSE_INTERFACE_NAME = "AssistantResponse"
class AssistantThoughts(ModelWithSummary):
past_action_summary: str = Field(
...,
description="Summary of the last action you took, if there is none, you can leave it empty",
description="Summary of the last action you took, if there is none, "
"you can leave it empty",
)
observations: str = Field(
..., description="Relevant observations from your last actions (if any)"
@@ -39,7 +40,8 @@ class AssistantThoughts(ModelWithSummary):
self_criticism: str = Field(..., description="Constructive self-criticism")
plan: list[str] = Field(
...,
description="Short list that conveys the long-term plan, considering the progress on your task so far",
description="Short list that conveys the long-term plan, "
"considering the progress on your task so far",
)
speak: str = Field(..., description="Summary of thoughts, to say to user")

7
autogpts/autogpt/tests/unit/test_code_flow_strategy.py
View File

@@ -51,13 +51,12 @@ async def test_code_flow_build_prompt():
prompt = str(
prompt_strategy.build_prompt(
task="Figure out from file1.csv and file2.csv how much was spent on utilities",
task="Figure out from file.csv how much was spent on utilities",
messages=[],
ai_profile=ai_profile,
ai_directives=ai_directives,
commands=commands,
)
)
))
assert "DummyGPT" in prompt
assert "async def web_search(query: str, num_results: int = None)" in prompt
@@ -79,7 +78,7 @@ async def test_code_flow_parse_response():
],
"speak": "This is what the AI would say."
},
"immediate_plan": "Objective[objective1] Plan[plan1] Output[out1]",
"immediate_plan": "Objective[objective1] Plan[plan1] Output[out1]",
"python_code": "async def main() -> str:\n return 'You passed the test.'",
}
"""

8
autogpts/autogpt/tests/unit/test_function_code_validation.py
View File

@@ -16,7 +16,7 @@ async def test_code_validation():
},
return_type="str",
return_desc="Information matching the query",
function_desc="Read a webpage and return the information matching the query",
function_desc="Read a webpage and return the info matching the query",
is_async=True,
),
"web_search": FunctionDef(
@@ -34,7 +34,7 @@ async def test_code_validation():
arg_descs={},
return_type="str",
return_desc="Answer in the text format",
function_desc="Get the number of contributors to the autogpt github repository",
function_desc="Get the num of contributors to the autogpt github repo",
is_async=False,
),
},
@@ -47,7 +47,7 @@ def crawl_info(url: str, query: str) -> str | None:
if info:
return info
urls = await read_webpage(url, "Find (if any) possible relevant URLS to crawl for finding the info of " + query + " return list of URLs separated by newline")
urls = await read_webpage(url, "autogpt github contributor page")
for url in urls.split('\\n'):
info = await crawl_info(url, query)
if info:
@@ -59,7 +59,7 @@ def hehe():
return 'hehe'
def main() -> str:
query = "Find the number of contributors to the autogpt github repository, or if any, list of urls that can be crawled to find the number of contributors"
query = "Find the number of contributors to the autogpt github repository"
for title, url in ("autogpt github contributor page"):
info = await crawl_info(url, query)
if info: