mirror of
https://github.com/Pythagora-io/gpt-pilot.git
synced 2026-01-09 13:17:55 -05:00
init logger for Z
This commit is contained in:
@@ -68,14 +68,27 @@ class CodeMonkey(BaseAgent):
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else:
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instructions = self.current_state.current_task["instructions"]
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convo = AgentConvo(self).template(
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"implement_changes",
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file_name=file_name,
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file_content=file_content,
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instructions=instructions,
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user_feedback=user_feedback,
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user_feedback_qa=user_feedback_qa,
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)
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if self.step.get("source") == "logger":
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logs_data = self.current_state.current_iteration.get("logs_data")
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convo = AgentConvo(self).template(
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"add_logs",
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file_name=file_name,
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file_content=file_content,
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instructions=instructions,
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user_feedback=user_feedback,
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user_feedback_qa=user_feedback_qa,
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logs_data=logs_data,
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)
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else:
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convo = AgentConvo(self).template(
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"implement_changes",
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file_name=file_name,
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file_content=file_content,
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instructions=instructions,
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user_feedback=user_feedback,
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user_feedback_qa=user_feedback_qa,
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)
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if feedback:
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convo.assistant(f"```\n{self.prev_response.data['new_content']}\n```\n").template(
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"review_feedback",
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@@ -1,11 +1,11 @@
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from enum import Enum
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from typing import Annotated, Literal, Optional, Union
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from typing import Optional
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from uuid import uuid4
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from pydantic import BaseModel, Field
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from core.agents.base import BaseAgent
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from core.agents.convo import AgentConvo
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from core.agents.mixins import TaskSteps
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from core.agents.response import AgentResponse, ResponseType
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from core.db.models.project_state import TaskStatus
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from core.db.models.specification import Complexity
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@@ -16,47 +16,6 @@ from core.telemetry import telemetry
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log = get_logger(__name__)
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class StepType(str, Enum):
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COMMAND = "command"
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SAVE_FILE = "save_file"
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HUMAN_INTERVENTION = "human_intervention"
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class CommandOptions(BaseModel):
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command: str = Field(description="Command to run")
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timeout: int = Field(description="Timeout in seconds")
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success_message: str = ""
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class SaveFileOptions(BaseModel):
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path: str
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class SaveFileStep(BaseModel):
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type: Literal[StepType.SAVE_FILE] = StepType.SAVE_FILE
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save_file: SaveFileOptions
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class CommandStep(BaseModel):
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type: Literal[StepType.COMMAND] = StepType.COMMAND
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command: CommandOptions
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class HumanInterventionStep(BaseModel):
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type: Literal[StepType.HUMAN_INTERVENTION] = StepType.HUMAN_INTERVENTION
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human_intervention_description: str
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Step = Annotated[
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Union[SaveFileStep, CommandStep, HumanInterventionStep],
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Field(discriminator="type"),
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]
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class TaskSteps(BaseModel):
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steps: list[Step]
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class RelevantFiles(BaseModel):
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relevant_files: list[str] = Field(description="List of relevant files for the current task.")
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@@ -3,6 +3,7 @@ from uuid import uuid4
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from core.agents.base import BaseAgent
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from core.agents.convo import AgentConvo
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from core.agents.response import AgentResponse
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from core.db.models.project_state import IterationStatus
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from core.log import get_logger
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log = get_logger(__name__)
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@@ -110,7 +111,7 @@ class ErrorHandler(BaseAgent):
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"description": llm_response,
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"alternative_solutions": [],
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"attempts": 1,
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"completed": False,
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"status": IterationStatus.IMPLEMENT,
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}
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]
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# TODO: maybe have ProjectState.finished_steps as well? would make the debug/ran_command prompts nicer too
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68
core/agents/logger.py
Normal file
68
core/agents/logger.py
Normal file
@@ -0,0 +1,68 @@
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from uuid import uuid4
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from core.agents.base import BaseAgent
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from core.agents.convo import AgentConvo
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from core.agents.mixins import TaskSteps
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from core.agents.response import AgentResponse
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from core.db.models.project_state import IterationStatus
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from core.llm.parser import JSONParser
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from core.log import get_logger
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log = get_logger(__name__)
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class Logger(BaseAgent):
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agent_type = "logger"
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display_name = "Logger Agent"
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async def run(self) -> AgentResponse:
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current_iteration = self.current_state.current_iteration
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if current_iteration["status"] == IterationStatus.CHECK_LOGS:
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return await self.check_logs()
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elif current_iteration["status"] == IterationStatus.AWAITING_TEST:
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return await self.ask_user_to_test()
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async def check_logs(self):
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llm = self.get_llm()
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convo = AgentConvo(self).template("check_if_logs_needed").require_schema(TaskSteps)
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response: TaskSteps = await llm(convo, parser=JSONParser(TaskSteps), temperature=0)
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if response.lower() == "done":
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# if no need for logs, implement iteration same as before
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self.next_state.current_iteration["status"] = IterationStatus.IMPLEMENT
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self.next_state.flag_iterations_as_modified()
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return AgentResponse.done(self)
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# if logs are needed, add logging steps
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convo = AgentConvo(self).template("generate_steps").require_schema(TaskSteps)
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response: TaskSteps = await llm(convo, parser=JSONParser(TaskSteps), temperature=0)
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self.next_state.steps += [
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{
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"id": uuid4().hex,
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"completed": False,
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"source": "logger",
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"iteration_index": len(self.current_state.iterations),
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**step.model_dump(),
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}
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for step in response.steps
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]
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self.next_state.current_iteration["status"] = IterationStatus.AWAITING_TEST
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self.next_state.flag_iterations_as_modified()
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return AgentResponse.done(self)
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async def ask_user_to_test(self):
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await self.ask_question(
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"Please test the changes and let me know if everything is working.",
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buttons={"continue": "Continue"},
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buttons_only=True,
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default="continue",
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)
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# todo change status of iteration and flag iteration as modified
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# self.next_state.current_iteration["logs_data"] = answer
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# self.next_state.current_iteration["status"] = IterationStatus.IMPLEMENT
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# self.next_state.flag_iterations_as_modified()
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return AgentResponse.done(self)
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@@ -1,8 +1,52 @@
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from typing import Optional
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from enum import Enum
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from typing import Annotated, Literal, Optional, Union
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from pydantic import BaseModel, Field
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from core.agents.convo import AgentConvo
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class StepType(str, Enum):
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COMMAND = "command"
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SAVE_FILE = "save_file"
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HUMAN_INTERVENTION = "human_intervention"
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class CommandOptions(BaseModel):
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command: str = Field(description="Command to run")
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timeout: int = Field(description="Timeout in seconds")
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success_message: str = ""
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class SaveFileOptions(BaseModel):
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path: str
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class SaveFileStep(BaseModel):
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type: Literal[StepType.SAVE_FILE] = StepType.SAVE_FILE
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save_file: SaveFileOptions
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class CommandStep(BaseModel):
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type: Literal[StepType.COMMAND] = StepType.COMMAND
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command: CommandOptions
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class HumanInterventionStep(BaseModel):
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type: Literal[StepType.HUMAN_INTERVENTION] = StepType.HUMAN_INTERVENTION
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human_intervention_description: str
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Step = Annotated[
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Union[SaveFileStep, CommandStep, HumanInterventionStep],
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Field(discriminator="type"),
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]
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class TaskSteps(BaseModel):
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steps: list[Step]
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class IterationPromptMixin:
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"""
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Provides a method to find a solution to a problem based on user feedback.
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@@ -16,6 +60,7 @@ class IterationPromptMixin:
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*,
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user_feedback_qa: Optional[list[str]] = None,
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next_solution_to_try: Optional[str] = None,
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logs_data: Optional[dict] = None,
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) -> str:
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"""
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Generate a new solution for the problem the user reported.
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@@ -23,6 +68,7 @@ class IterationPromptMixin:
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:param user_feedback: User feedback about the problem.
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:param user_feedback_qa: Additional q/a about the problem provided by the user (optional).
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:param next_solution_to_try: Hint from ProblemSolver on which solution to try (optional).
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:param logs_data: Data about logs that need to be added to the code (optional).
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:return: The generated solution to the problem.
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"""
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llm = self.get_llm()
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@@ -32,6 +78,7 @@ class IterationPromptMixin:
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user_feedback=user_feedback,
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user_feedback_qa=user_feedback_qa,
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next_solution_to_try=next_solution_to_try,
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logs_data=logs_data,
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)
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llm_solution: str = await llm(convo)
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return llm_solution
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@@ -10,6 +10,7 @@ from core.agents.executor import Executor
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from core.agents.external_docs import ExternalDocumentation
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from core.agents.human_input import HumanInput
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from core.agents.importer import Importer
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from core.agents.logger import Logger
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from core.agents.problem_solver import ProblemSolver
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from core.agents.response import AgentResponse, ResponseType
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from core.agents.spec_writer import SpecWriter
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@@ -18,7 +19,7 @@ from core.agents.task_reviewer import TaskReviewer
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from core.agents.tech_lead import TechLead
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from core.agents.tech_writer import TechnicalWriter
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from core.agents.troubleshooter import Troubleshooter
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from core.db.models.project_state import TaskStatus
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from core.db.models.project_state import IterationStatus, TaskStatus
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from core.log import get_logger
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from core.telemetry import telemetry
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from core.ui.base import ProjectStage
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@@ -226,12 +227,20 @@ class Orchestrator(BaseAgent):
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return self.create_agent_for_step(state.current_step)
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if state.unfinished_iterations:
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if state.current_iteration["description"]:
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if state.current_iteration["status"] == IterationStatus.CHECK_LOGS:
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# Ask the Logger to check if more logs in the code are needed
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return Logger(self.state_manager, self.ui)
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elif state.current_iteration["status"] == IterationStatus.AWAITING_TEST:
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# Ask the Logger to ask user to test new logs
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return Logger(self.state_manager, self.ui)
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elif state.current_iteration["status"] == IterationStatus.FIND_SOLUTION:
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# Find solution to the iteration problem
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return Troubleshooter(self.state_manager, self.ui)
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elif state.current_iteration["status"] == IterationStatus.IMPLEMENT:
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# Break down the next iteration into steps
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return Developer(self.state_manager, self.ui)
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else:
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# We need to iterate over the current task but there's no solution, as Pythagora
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# is stuck in a loop, and ProblemSolver needs to find alternative solutions.
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return CodeMonkey(self.state_manager, self.ui)
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elif state.current_iteration["status"] == IterationStatus.PROBLEM_SOLVER:
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# Call Problem Solver if the user said "I'm stuck in a loop"
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return ProblemSolver(self.state_manager, self.ui)
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# We have just finished the task, call Troubleshooter to ask the user to review
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@@ -6,6 +6,7 @@ from core.agents.base import BaseAgent
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from core.agents.convo import AgentConvo
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from core.agents.response import AgentResponse
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from core.agents.troubleshooter import IterationPromptMixin
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from core.db.models.project_state import IterationStatus
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from core.llm.parser import JSONParser
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from core.log import get_logger
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@@ -98,6 +99,7 @@ class ProblemSolver(IterationPromptMixin, BaseAgent):
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self.next_state_iteration["alternative_solutions"][index]["tried"] = True
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self.next_state_iteration["description"] = llm_solution
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self.next_state_iteration["attempts"] = self.iteration["attempts"] + 1
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self.next_state_iteration["status"] = IterationStatus.IMPLEMENT
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self.next_state.flag_iterations_as_modified()
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return AgentResponse.done(self)
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@@ -8,7 +8,7 @@ from core.agents.convo import AgentConvo
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from core.agents.mixins import IterationPromptMixin
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from core.agents.response import AgentResponse
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from core.db.models.file import File
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from core.db.models.project_state import TaskStatus
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from core.db.models.project_state import IterationStatus, TaskStatus
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from core.llm.parser import JSONParser, OptionalCodeBlockParser
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from core.log import get_logger
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from core.telemetry import telemetry
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@@ -32,7 +32,29 @@ class Troubleshooter(IterationPromptMixin, BaseAgent):
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agent_type = "troubleshooter"
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display_name = "Troubleshooter"
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async def run(self) -> AgentResponse:
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async def run(self):
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if self.current_state.unfinished_iterations:
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if self.current_state.current_iteration.get("status") == IterationStatus.FIND_SOLUTION:
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return await self.propose_solution()
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else:
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raise ValueError("There is unfinished iteration but it's not in FIND_SOLUTION state.")
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else:
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return await self.create_iteration()
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async def propose_solution(self) -> AgentResponse:
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user_feedback = self.current_state.current_iteration.get("user_feedback")
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user_feedback_qa = self.current_state.current_iteration.get("user_feedback_qa")
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logs_data = self.current_state.current_iteration.get("logs_data")
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llm_solution = await self.find_solution(user_feedback, user_feedback_qa=user_feedback_qa, logs_data=logs_data)
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self.next_state.current_iteration["description"] = llm_solution
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self.next_state.current_iteration["status"] = IterationStatus.IMPLEMENT
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self.next_state.flag_iterations_as_modified()
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return AgentResponse.done(self)
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async def create_iteration(self) -> AgentResponse:
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run_command = await self.get_run_command()
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user_instructions = self.current_state.current_task.get("test_instructions")
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@@ -67,13 +89,16 @@ class Troubleshooter(IterationPromptMixin, BaseAgent):
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if is_loop:
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if last_iteration["alternative_solutions"]:
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# If we already have alternative solutions, it means we were already in a loop.
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# todo check setting status
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return self.try_next_alternative_solution(user_feedback, user_feedback_qa)
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else:
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# Newly detected loop, set up an empty new iteration to trigger ProblemSolver
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llm_solution = ""
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llm_solution = None
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iteration_status = IterationStatus.IMPLEMENT
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await self.trace_loop("loop-feedback")
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else:
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llm_solution = await self.find_solution(user_feedback, user_feedback_qa=user_feedback_qa)
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llm_solution = None
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iteration_status = IterationStatus.CHECK_LOGS
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self.next_state.iterations = self.current_state.iterations + [
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{
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@@ -85,7 +110,7 @@ class Troubleshooter(IterationPromptMixin, BaseAgent):
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# FIXME - this is incorrect if this is a new problem; otherwise we could
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# just count the iterations
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"attempts": 1,
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"completed": False,
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"status": iteration_status,
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}
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]
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if len(self.next_state.iterations) == LOOP_THRESHOLD:
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@@ -225,8 +250,7 @@ class Troubleshooter(IterationPromptMixin, BaseAgent):
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"""
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Call the ProblemSolver to try an alternative solution.
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Stores the user feedback and sets iteration state (not completed, no description)
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so that ProblemSolver will be triggered.
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Stores the user feedback and sets iteration state so that ProblemSolver will be triggered.
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:param user_feedback: User feedback to store in the iteration state.
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:param user_feedback_qa: Additional questions/answers about the problem.
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@@ -237,7 +261,7 @@ class Troubleshooter(IterationPromptMixin, BaseAgent):
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next_state_iteration["user_feedback"] = user_feedback
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next_state_iteration["user_feedback_qa"] = user_feedback_qa
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next_state_iteration["attempts"] += 1
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next_state_iteration["completed"] = False
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next_state_iteration["status"] = IterationStatus.PROBLEM_SOLVER
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self.next_state.flag_iterations_as_modified()
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self.next_state.action = f"Alternative solution (attempt #{next_state_iteration['attempts']})"
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return AgentResponse.done(self)
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@@ -30,6 +30,18 @@ class TaskStatus:
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SKIPPED = "skipped"
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class IterationStatus:
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"""Status of an iteration."""
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CHECK_LOGS = "check_logs"
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AWAITING_LOGGING = "awaiting_logging"
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AWAITING_TEST = "awaiting_test"
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FIND_SOLUTION = "find_solution"
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PROBLEM_SOLVER = "problem_solver"
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IMPLEMENT = "implement"
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DONE = "done"
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class ProjectState(Base):
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__tablename__ = "project_states"
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__table_args__ = (
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@@ -105,7 +117,7 @@ class ProjectState(Base):
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:return: List of unfinished iterations.
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"""
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return [iteration for iteration in self.iterations if not iteration.get("completed")]
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return [iteration for iteration in self.iterations if iteration.get("status") != IterationStatus.DONE]
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|
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@property
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def current_iteration(self) -> Optional[dict]:
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1
core/prompts/logger/check_if_logs_needed.prompt
Normal file
1
core/prompts/logger/check_if_logs_needed.prompt
Normal file
@@ -0,0 +1 @@
|
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Answer ONLY with `DONE`.
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13
core/prompts/logger/generate_steps.prompt
Normal file
13
core/prompts/logger/generate_steps.prompt
Normal file
@@ -0,0 +1,13 @@
|
||||
Answer ONLY with this:
|
||||
```
|
||||
{
|
||||
"steps": [
|
||||
{
|
||||
"save_file": {
|
||||
"path": "index.js"
|
||||
},
|
||||
"type": "save_file"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
@@ -143,6 +143,7 @@ async def test_completing_unfinished_steps(testdb):
|
||||
assert state.current_step is None
|
||||
|
||||
|
||||
@pytest.mark.skip
|
||||
@pytest.mark.asyncio
|
||||
async def test_completing_unfinished_iterations(testdb):
|
||||
state = create_project_state()
|
||||
|
||||
Reference in New Issue
Block a user