[Bug]: KeyError: 'user_feedback_qa'
Opened this issue · 10 comments
Version
VisualStudio Code extension
Operating System
Windows 10
What happened?
[Pythagora] Stopping Pythagora due to error:
File `core/cli/main.py`, line 37, in run_project
success = await orca.run()
File `core/agents/orchestrator.py`, line 67, in run
response = await agent.run()
File `core/agents/code_monkey.py`, line 32, in run
return await self.implement_changes()
File `core/agents/code_monkey.py`, line 63, in implement_changes
user_feedback_qa = iterations[-1]["user_feedback_qa"]
KeyError: 'user_feedback_qa'
(env) E:\Project\gpt-pilot>
``` ----------happens when i answer no no no no multiple times on some stupid suggestions, cant undo cant go back any steps always leads to the above
Same issue using repository except it happened without any inputs during code review. Restarting immediately fails again with the same message
same here
Seems to be working again after making this change to core/agents/code_monkey.py L60:
if len(iterations) > 0:
if "description" in iterations[-1]:
instructions = iterations[-1]["description"]
if "user_feedback" in iterations[-1]:
user_feedback = iterations[-1]["user_feedback"]
if "user_feedback_qa" in iterations[-1]:
user_feedback_qa = iterations[-1]["user_feedback_qa"]Resolved., Similar resolution to @DaveTacker
`from os.path import basename
from pydantic import BaseModel, Field
from core.agents.base import BaseAgent
from core.agents.convo import AgentConvo
from core.agents.response import AgentResponse, ResponseType
from core.config import DESCRIBE_FILES_AGENT_NAME
from core.llm.parser import JSONParser, OptionalCodeBlockParser
from core.log import get_logger
log = get_logger(name)
class FileDescription(BaseModel):
summary: str = Field(
description="Detailed description summarized what the file is about, and what the major classes, functions, elements or other functionality is implemented."
)
references: list[str] = Field(
description="List of references the file imports or includes (only files local to the project), where each element specifies the project-relative path of the referenced file, including the file extension."
)
class CodeMonkey(BaseAgent):
agent_type = "code-monkey"
display_name = "Code Monkey"
async def run(self) -> AgentResponse:
if self.prev_response and self.prev_response.type == ResponseType.DESCRIBE_FILES:
return await self.describe_files()
else:
return await self.implement_changes()
async def implement_changes(self) -> AgentResponse:
file_name = self.step["save_file"]["path"]
current_file = await self.state_manager.get_file_by_path(file_name)
file_content = current_file.content.content if current_file else ""
task = self.current_state.current_task
if self.prev_response and self.prev_response.type == ResponseType.CODE_REVIEW_FEEDBACK:
attempt = self.prev_response.data["attempt"] + 1
feedback = self.prev_response.data["feedback"]
log.debug(f"Fixing file {file_name} after review feedback: {feedback} ({attempt}. attempt)")
await self.send_message(f"Reworking changes I made to {file_name} ...")
else:
log.debug(f"Implementing file {file_name}")
await self.send_message(f"{'Updating existing' if file_content else 'Creating new'} file {file_name} ...")
self.next_state.action = (
f'Update file "{basename(file_name)}"' if file_content else f'Create file "{basename(file_name)}"'
)
attempt = 1
feedback = None
iterations = self.current_state.iterations
user_feedback = None
user_feedback_qa = None
llm = self.get_llm()
if iterations:
instructions = iterations[-1].get("description")
user_feedback = iterations[-1].get("user_feedback")
user_feedback_qa = iterations[-1].get("user_feedback_qa")
else:
instructions = self.current_state.current_task["instructions"]
convo = AgentConvo(self).template(
"implement_changes",
file_name=file_name,
file_content=file_content,
instructions=instructions,
user_feedback=user_feedback,
user_feedback_qa=user_feedback_qa,
)
if feedback:
convo.assistant(f"```\n{self.prev_response.data['new_content']}\n```\n").template(
"review_feedback",
content=self.prev_response.data["approved_content"],
original_content=file_content,
rework_feedback=feedback,
)
response: str = await llm(convo, temperature=0, parser=OptionalCodeBlockParser())
# FIXME: provide a counter here so that we don't have an endless loop here
return AgentResponse.code_review(self, file_name, task["instructions"], file_content, response, attempt)
async def describe_files(self) -> AgentResponse:
llm = self.get_llm(DESCRIBE_FILES_AGENT_NAME)
to_describe = {
file.path: file.content.content for file in self.current_state.files if not file.meta.get("description")
}
for file in self.next_state.files:
content = to_describe.get(file.path)
if content is None:
continue
if content == "":
file.meta = {
**file.meta,
"description": "Empty file",
"references": [],
}
continue
log.debug(f"Describing file {file.path}")
await self.send_message(f"Describing file {file.path} ...")
convo = (
AgentConvo(self)
.template(
"describe_file",
path=file.path,
content=content,
)
.require_schema(FileDescription)
)
llm_response: FileDescription = await llm(convo, parser=JSONParser(spec=FileDescription))
file.meta = {
**file.meta,
"description": llm_response.summary,
"references": llm_response.references,
}
return AgentResponse.done(self)
`
Here is the whole corrected code_monkey.py in gpt-pilot/core/agents
from os.path import basename
from pydantic import BaseModel, Field
from core.agents.base import BaseAgent
from core.agents.convo import AgentConvo
from core.agents.response import AgentResponse, ResponseType
from core.config import DESCRIBE_FILES_AGENT_NAME
from core.llm.parser import JSONParser, OptionalCodeBlockParser
from core.log import get_logger
log = get_logger(name)
class FileDescription(BaseModel):
summary: str = Field(
description="Detailed description summarized what the file is about, and what the major classes, functions, elements or other functionality is implemented."
)
references: list[str] = Field(
description="List of references the file imports or includes (only files local to the project), where each element specifies the project-relative path of the referenced file, including the file extension."
)
class CodeMonkey(BaseAgent):
agent_type = "code-monkey"
display_name = "Code Monkey"
async def run(self) -> AgentResponse:
if self.prev_response and self.prev_response.type == ResponseType.DESCRIBE_FILES:
return await self.describe_files()
else:
return await self.implement_changes()
async def implement_changes(self) -> AgentResponse:
file_name = self.step["save_file"]["path"]
current_file = await self.state_manager.get_file_by_path(file_name)
file_content = current_file.content.content if current_file else ""
task = self.current_state.current_task
if self.prev_response and self.prev_response.type == ResponseType.CODE_REVIEW_FEEDBACK:
attempt = self.prev_response.data["attempt"] + 1
feedback = self.prev_response.data["feedback"]
log.debug(f"Fixing file {file_name} after review feedback: {feedback} ({attempt}. attempt)")
await self.send_message(f"Reworking changes I made to {file_name} ...")
else:
log.debug(f"Implementing file {file_name}")
await self.send_message(f"{'Updating existing' if file_content else 'Creating new'} file {file_name} ...")
self.next_state.action = (
f'Update file "{basename(file_name)}"' if file_content else f'Create file "{basename(file_name)}"'
)
attempt = 1
feedback = None
iterations = self.current_state.iterations
user_feedback = None
user_feedback_qa = None
llm = self.get_llm()
if iterations:
if "description" in iterations[-1]:
instructions = iterations[-1]["description"]
if "user_feedback" in iterations[-1]:
user_feedback = iterations[-1]["user_feedback"]
if "user_feedback_qa" in iterations[-1]:
user_feedback_qa = iterations[-1]["user_feedback_qa"]
else:
instructions = self.current_state.current_task["instructions"]
convo = AgentConvo(self).template(
"implement_changes",
file_name=file_name,
file_content=file_content,
instructions=instructions,
user_feedback=user_feedback,
user_feedback_qa=user_feedback_qa,
)
if feedback:
convo.assistant(f"```\n{self.prev_response.data['new_content']}\n```\n").template(
"review_feedback",
content=self.prev_response.data["approved_content"],
original_content=file_content,
rework_feedback=feedback,
)
response: str = await llm(convo, temperature=0, parser=OptionalCodeBlockParser())
# FIXME: provide a counter here so that we don't have an endless loop here
return AgentResponse.code_review(self, file_name, task["instructions"], file_content, response, attempt)
async def describe_files(self) -> AgentResponse:
llm = self.get_llm(DESCRIBE_FILES_AGENT_NAME)
to_describe = {
file.path: file.content.content for file in self.current_state.files if not file.meta.get("description")
}
for file in self.next_state.files:
content = to_describe.get(file.path)
if content is None:
continue
if content == "":
file.meta = {
**file.meta,
"description": "Empty file",
"references": [],
}
continue
log.debug(f"Describing file {file.path}")
await self.send_message(f"Describing file {file.path} ...")
convo = (
AgentConvo(self)
.template(
"describe_file",
path=file.path,
content=content,
)
.require_schema(FileDescription)
)
llm_response: FileDescription = await llm(convo, parser=JSONParser(spec=FileDescription))
file.meta = {
**file.meta,
"description": llm_response.summary,
"references": llm_response.references,
}
return AgentResponse.done(self)
`from os.path import basename
from pydantic import BaseModel, Field
from core.agents.base import BaseAgent
from core.agents.convo import AgentConvo
from core.agents.response import AgentResponse, ResponseType
from core.config import DESCRIBE_FILES_AGENT_NAME
from core.llm.parser import JSONParser, OptionalCodeBlockParser
from core.log import get_logger
log = get_logger(name)
class FileDescription(BaseModel):
summary: str = Field(
description="Detailed description summarized what the file is about, and what the major classes, functions, elements or other functionality is implemented."
)
references: list[str] = Field(
description="List of references the file imports or includes (only files local to the project), where each element specifies the project-relative path of the referenced file, including the file extension."
)
class CodeMonkey(BaseAgent):
agent_type = "code-monkey"
display_name = "Code Monkey"
async def run(self) -> AgentResponse:
if self.prev_response and self.prev_response.type == ResponseType.DESCRIBE_FILES:
return await self.describe_files()
else:
return await self.implement_changes()
async def implement_changes(self) -> AgentResponse:
file_name = self.step["save_file"]["path"]
current_file = await self.state_manager.get_file_by_path(file_name)
file_content = current_file.content.content if current_file else ""
task = self.current_state.current_task
if self.prev_response and self.prev_response.type == ResponseType.CODE_REVIEW_FEEDBACK:
attempt = self.prev_response.data["attempt"] + 1
feedback = self.prev_response.data["feedback"]
log.debug(f"Fixing file {file_name} after review feedback: {feedback} ({attempt}. attempt)")
await self.send_message(f"Reworking changes I made to {file_name} ...")
else:
log.debug(f"Implementing file {file_name}")
await self.send_message(f"{'Updating existing' if file_content else 'Creating new'} file {file_name} ...")
self.next_state.action = (
f'Update file "{basename(file_name)}"' if file_content else f'Create file "{basename(file_name)}"'
)
attempt = 1
feedback = None
iterations = self.current_state.iterations
user_feedback = None
user_feedback_qa = None
llm = self.get_llm()
if iterations:
last_iteration = iterations[-1]
instructions = last_iteration.get("description", "")
user_feedback = last_iteration.get("user_feedback", None)
user_feedback_qa = last_iteration.get("user_feedback_qa", None)
else:
instructions = self.current_state.current_task["instructions"]
convo = AgentConvo(self).template(
"implement_changes",
file_name=file_name,
file_content=file_content,
instructions=instructions,
user_feedback=user_feedback,
user_feedback_qa=user_feedback_qa,
)
if feedback:
convo.assistant(f"```\n{self.prev_response.data['new_content']}\n```\n").template(
"review_feedback",
content=self.prev_response.data["approved_content"],
original_content=file_content,
rework_feedback=feedback,
)
response: str = await llm(convo, temperature=0, parser=OptionalCodeBlockParser())
# FIXME: provide a counter here so that we don't have an endless loop here
return AgentResponse.code_review(self, file_name, task["instructions"], file_content, response, attempt)
async def describe_files(self) -> AgentResponse:
llm = self.get_llm(DESCRIBE_FILES_AGENT_NAME)
to_describe = {
file.path: file.content.content for file in self.current_state.files if not file.meta.get("description")
}
for file in self.next_state.files:
content = to_describe.get(file.path)
if content is None:
continue
if content == "":
file.meta = {
**file.meta,
"description": "Empty file",
"references": [],
}
continue
log.debug(f"Describing file {file.path}")
await self.send_message(f"Describing file {file.path} ...")
convo = (
AgentConvo(self)
.template(
"describe_file",
path=file.path,
content=content,
)
.require_schema(FileDescription)
)
llm_response: FileDescription = await llm(convo, parser=JSONParser(spec=FileDescription))
file.meta = {
**file.meta,
"description": llm_response.summary,
"references": llm_response.references,
}
return AgentResponse.done(self)
`
This version of the CodeMonkey class ensures that the keys description, user_feedback, and user_feedback_qa are accessed safely using the get method, preventing KeyError exceptions if these keys are missing.
This is strange because I had the same solution from chatgtp but it didn't work and even after copying your code still does not work. pythagora won't load the window to upload or create a new application. I wonder how its gonna be before the bug is ironed out. I have tried this in both windows 10 and 11. please help mybe am doing something wrong
Is it exactly the same error?
yes
On the side note: I have realised that the errors are caused by skipping filling out .env variables. That means you have to have all the services set before you start coding and fillout all the required information, then you are good to go. I have not had those errors anymore.
Is it exactly the same error?
yesOn the side note: I have realised that the errors are caused by skipping filling out .env variables. That means you have to have all the services set before you start coding and fillout all the required information, then you are good to go. I have not had those errors anymore.
I had the same problem. How do you ensure to have all the services set before you start coding and fillout all the required information?? I have everything set up in .env and I still receive these errors.