refactor: extract run_task_implementation_agent
This commit is contained in:
AI Christianson
parent
d8dcc8ca86
commit
51fa86b5c4
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@ -364,273 +364,7 @@ def create_agent(
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from ra_aid.agents.research_agent import run_research_agent, run_web_research_agent
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from ra_aid.agents.research_agent import run_research_agent, run_web_research_agent
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from ra_aid.agents.implementation_agent import run_task_implementation_agent
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def run_task_implementation_agent(
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base_task: str,
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tasks: list,
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task: str,
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plan: str,
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related_files: list,
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model,
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*,
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expert_enabled: bool = False,
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web_research_enabled: bool = False,
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memory: Optional[Any] = None,
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thread_id: Optional[str] = None,
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) -> Optional[str]:
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"""Run an implementation agent for a specific task.
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Args:
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base_task: The main task being implemented
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tasks: List of tasks to implement
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plan: The implementation plan
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related_files: List of related files
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model: The LLM model to use
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expert_enabled: Whether expert mode is enabled
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web_research_enabled: Whether web research is enabled
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memory: Optional memory instance to use
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thread_id: Optional thread ID (defaults to new UUID)
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Returns:
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Optional[str]: The completion message if task completed successfully
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"""
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thread_id = thread_id or str(uuid.uuid4())
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logger.debug("Starting implementation agent with thread_id=%s", thread_id)
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logger.debug(
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"Implementation configuration: expert=%s, web=%s",
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expert_enabled,
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web_research_enabled,
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)
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logger.debug("Task details: base_task=%s, current_task=%s", base_task, task)
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logger.debug("Related files: %s", related_files)
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if memory is None:
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memory = MemorySaver()
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if thread_id is None:
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thread_id = str(uuid.uuid4())
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tools = get_implementation_tools(
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expert_enabled=expert_enabled,
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web_research_enabled=get_config_repository().get("web_research_enabled", False),
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)
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agent = create_agent(model, tools, checkpointer=memory, agent_type="planner")
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current_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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working_directory = os.getcwd()
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# Make sure key_facts is defined before using it
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try:
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key_facts = format_key_facts_dict(get_key_fact_repository().get_facts_dict())
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except RuntimeError as e:
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logger.error(f"Failed to access key fact repository: {str(e)}")
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key_facts = ""
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# Get formatted research notes using repository
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try:
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repository = get_research_note_repository()
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notes_dict = repository.get_notes_dict()
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formatted_research_notes = format_research_notes_dict(notes_dict)
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except RuntimeError as e:
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logger.error(f"Failed to access research note repository: {str(e)}")
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formatted_research_notes = ""
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# Get latest project info
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try:
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project_info = get_project_info(".")
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formatted_project_info = format_project_info(project_info)
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except Exception as e:
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logger.warning("Failed to get project info: %s", str(e))
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formatted_project_info = "Project info unavailable"
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# Get environment inventory information
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env_inv = get_env_inv()
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# Get model configuration to check for reasoning_assist_default
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provider = get_config_repository().get("provider", "")
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model_name = get_config_repository().get("model", "")
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logger.debug("Checking for reasoning_assist_default on %s/%s", provider, model_name)
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model_config = {}
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provider_models = models_params.get(provider, {})
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if provider_models and model_name in provider_models:
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model_config = provider_models[model_name]
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# Check if reasoning assist is explicitly enabled/disabled
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force_assistance = get_config_repository().get("force_reasoning_assistance", False)
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disable_assistance = get_config_repository().get(
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"disable_reasoning_assistance", False
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)
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if force_assistance:
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reasoning_assist_enabled = True
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elif disable_assistance:
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reasoning_assist_enabled = False
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else:
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# Fall back to model default
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reasoning_assist_enabled = model_config.get("reasoning_assist_default", False)
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logger.debug("Reasoning assist enabled: %s", reasoning_assist_enabled)
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# Initialize implementation guidance section
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implementation_guidance_section = ""
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# If reasoning assist is enabled, make a one-off call to the expert model
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if reasoning_assist_enabled:
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try:
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logger.info(
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"Reasoning assist enabled for model %s, getting implementation guidance",
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model_name,
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)
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# Collect tool descriptions
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tool_metadata = []
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from ra_aid.tools.reflection import get_function_info as get_tool_info
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for tool in tools:
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try:
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tool_info = get_tool_info(tool.func)
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name = tool.func.__name__
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description = inspect.getdoc(tool.func)
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tool_metadata.append(
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f"Tool: {name}\\nDescription: {description}\\n"
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)
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except Exception as e:
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logger.warning(f"Error getting tool info for {tool}: {e}")
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# Format tool metadata
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formatted_tool_metadata = "\\n".join(tool_metadata)
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# Initialize expert model
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expert_model = initialize_expert_llm(provider, model_name)
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# Format the reasoning assist prompt for implementation
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reasoning_assist_prompt = REASONING_ASSIST_PROMPT_IMPLEMENTATION.format(
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current_date=current_date,
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working_directory=working_directory,
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task=task,
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key_facts=key_facts,
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key_snippets=format_key_snippets_dict(
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get_key_snippet_repository().get_snippets_dict()
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),
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research_notes=formatted_research_notes,
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related_files="\\n".join(related_files),
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env_inv=env_inv,
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tool_metadata=formatted_tool_metadata,
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)
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# Show the reasoning assist query in a panel
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console.print(
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Panel(
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Markdown(
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"Consulting with the reasoning model on the best implementation approach."
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),
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title="📝 Thinking about implementation...",
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border_style="yellow",
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)
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)
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logger.debug("Invoking expert model for implementation reasoning assist")
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# Make the call to the expert model
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response = expert_model.invoke(reasoning_assist_prompt)
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# Check if the model supports think tags
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supports_think_tag = model_config.get("supports_think_tag", False)
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supports_thinking = model_config.get("supports_thinking", False)
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# Process response content
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content = None
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if hasattr(response, "content"):
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content = response.content
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else:
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# Fallback if content attribute is missing
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content = str(response)
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# Process the response content using the centralized function
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content, extracted_thinking = process_thinking_content(
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content=content,
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supports_think_tag=supports_think_tag,
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supports_thinking=supports_thinking,
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panel_title="💭 Implementation Thinking",
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panel_style="yellow",
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logger=logger,
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)
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# Display the implementation guidance in a panel
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console.print(
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Panel(
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Markdown(content),
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title="Implementation Guidance",
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border_style="blue",
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)
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)
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# Format the implementation guidance section for the prompt
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implementation_guidance_section = f"""<implementation guidance>
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{content}
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</implementation guidance>"""
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logger.info("Received implementation guidance")
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except Exception as e:
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logger.error("Error getting implementation guidance: %s", e)
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implementation_guidance_section = ""
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prompt = IMPLEMENTATION_PROMPT.format(
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current_date=current_date,
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working_directory=working_directory,
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base_task=base_task,
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task=task,
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tasks=tasks,
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plan=plan,
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related_files=related_files,
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key_facts=key_facts,
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key_snippets=format_key_snippets_dict(
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get_key_snippet_repository().get_snippets_dict()
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),
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research_notes=formatted_research_notes,
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work_log=get_work_log_repository().format_work_log(),
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expert_section=EXPERT_PROMPT_SECTION_IMPLEMENTATION if expert_enabled else "",
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human_section=(
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HUMAN_PROMPT_SECTION_IMPLEMENTATION
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if get_config_repository().get("hil", False)
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else ""
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),
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web_research_section=(
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WEB_RESEARCH_PROMPT_SECTION_CHAT
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if get_config_repository().get("web_research_enabled", False)
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else ""
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),
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env_inv=env_inv,
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project_info=formatted_project_info,
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implementation_guidance_section=implementation_guidance_section,
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)
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config_values = get_config_repository().get_all()
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recursion_limit = get_config_repository().get(
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"recursion_limit", DEFAULT_RECURSION_LIMIT
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)
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run_config = {
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"configurable": {"thread_id": thread_id},
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"recursion_limit": recursion_limit,
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}
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run_config.update(config_values)
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try:
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logger.debug("Implementation agent completed successfully")
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none_or_fallback_handler = init_fallback_handler(agent, tools)
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_result = run_agent_with_retry(agent, prompt, none_or_fallback_handler)
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if _result:
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# Log task implementation completion
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log_work_event(f"Completed implementation of task: {task}")
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return _result
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except (KeyboardInterrupt, AgentInterrupt):
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raise
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except Exception as e:
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logger.error("Implementation agent failed: %s", str(e), exc_info=True)
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raise
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_CONTEXT_STACK = []
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_CONTEXT_STACK = []
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@ -3,27 +3,30 @@ Agent package for various specialized agents.
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This package contains agents responsible for specific tasks such as
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This package contains agents responsible for specific tasks such as
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cleaning up key facts and key snippets in the database when they
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cleaning up key facts and key snippets in the database when they
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exceed certain thresholds, as well as performing research tasks
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exceed certain thresholds, as well as performing research tasks,
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and planning implementation.
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planning implementation, and implementing specific tasks.
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Includes agents for:
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Includes agents for:
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- Key facts garbage collection
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- Key facts garbage collection
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- Key snippets garbage collection
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- Key snippets garbage collection
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- Research tasks
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- Implementation tasks
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- Planning tasks
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- Planning tasks
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- Research tasks
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"""
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"""
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from typing import Optional
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from typing import Optional
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from ra_aid.agents.implementation_agent import run_task_implementation_agent
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from ra_aid.agents.key_facts_gc_agent import run_key_facts_gc_agent
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from ra_aid.agents.key_facts_gc_agent import run_key_facts_gc_agent
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from ra_aid.agents.key_snippets_gc_agent import run_key_snippets_gc_agent
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from ra_aid.agents.key_snippets_gc_agent import run_key_snippets_gc_agent
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from ra_aid.agents.research_agent import run_research_agent, run_web_research_agent
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from ra_aid.agents.planning_agent import run_planning_agent
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from ra_aid.agents.planning_agent import run_planning_agent
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from ra_aid.agents.research_agent import run_research_agent, run_web_research_agent
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__all__ = [
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__all__ = [
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"run_key_facts_gc_agent",
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"run_key_facts_gc_agent",
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"run_key_snippets_gc_agent",
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"run_key_snippets_gc_agent",
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"run_planning_agent",
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"run_research_agent",
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"run_research_agent",
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"run_web_research_agent",
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"run_task_implementation_agent",
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"run_planning_agent"
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"run_web_research_agent"
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]
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]
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@ -0,0 +1,316 @@
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"""
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Implementation agent for executing specific implementation tasks.
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This module provides functionality for running a task implementation agent
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to execute specific tasks based on a plan. The agent can be configured with
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expert guidance and web research options.
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"""
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import inspect
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import os
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import uuid
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from datetime import datetime
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from typing import Any, Optional, List
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from rich.console import Console
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from rich.markdown import Markdown
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from rich.panel import Panel
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from ra_aid.agent_context import agent_context, is_completed, reset_completion_flags, should_exit
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# Import agent_utils functions at runtime to avoid circular imports
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from ra_aid import agent_utils
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from ra_aid.database.repositories.key_fact_repository import get_key_fact_repository
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from ra_aid.database.repositories.key_snippet_repository import get_key_snippet_repository
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from ra_aid.database.repositories.human_input_repository import get_human_input_repository
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from ra_aid.database.repositories.research_note_repository import get_research_note_repository
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from ra_aid.database.repositories.config_repository import get_config_repository
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from ra_aid.database.repositories.work_log_repository import get_work_log_repository
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from ra_aid.env_inv_context import get_env_inv
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from ra_aid.exceptions import AgentInterrupt
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from ra_aid.llm import initialize_expert_llm
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from ra_aid.logging_config import get_logger
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from ra_aid.model_formatters import format_key_facts_dict
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from ra_aid.model_formatters.key_snippets_formatter import format_key_snippets_dict
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from ra_aid.model_formatters.research_notes_formatter import format_research_notes_dict
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from ra_aid.models_params import models_params, DEFAULT_TOKEN_LIMIT
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from ra_aid.project_info import format_project_info, get_project_info
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from ra_aid.prompts.expert_prompts import EXPERT_PROMPT_SECTION_IMPLEMENTATION
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from ra_aid.prompts.human_prompts import HUMAN_PROMPT_SECTION_IMPLEMENTATION
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from ra_aid.prompts.implementation_prompts import IMPLEMENTATION_PROMPT
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from ra_aid.prompts.reasoning_assist_prompt import REASONING_ASSIST_PROMPT_IMPLEMENTATION
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from ra_aid.prompts.web_research_prompts import WEB_RESEARCH_PROMPT_SECTION_CHAT
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from ra_aid.tool_configs import get_implementation_tools
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from ra_aid.tools.memory import get_related_files, log_work_event
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from ra_aid.text.processing import process_thinking_content
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logger = get_logger(__name__)
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console = Console()
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def run_task_implementation_agent(
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base_task: str,
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tasks: list,
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task: str,
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plan: str,
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related_files: list,
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model,
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*,
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expert_enabled: bool = False,
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web_research_enabled: bool = False,
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memory: Optional[Any] = None,
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thread_id: Optional[str] = None,
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) -> Optional[str]:
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"""Run an implementation agent for a specific task.
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|
|
||||||
|
Args:
|
||||||
|
base_task: The main task being implemented
|
||||||
|
tasks: List of tasks to implement
|
||||||
|
task: The current task to implement
|
||||||
|
plan: The implementation plan
|
||||||
|
related_files: List of related files
|
||||||
|
model: The LLM model to use
|
||||||
|
expert_enabled: Whether expert mode is enabled
|
||||||
|
web_research_enabled: Whether web research is enabled
|
||||||
|
memory: Optional memory instance to use
|
||||||
|
thread_id: Optional thread ID (defaults to new UUID)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Optional[str]: The completion message if task completed successfully
|
||||||
|
"""
|
||||||
|
thread_id = thread_id or str(uuid.uuid4())
|
||||||
|
logger.debug("Starting implementation agent with thread_id=%s", thread_id)
|
||||||
|
logger.debug(
|
||||||
|
"Implementation configuration: expert=%s, web=%s",
|
||||||
|
expert_enabled,
|
||||||
|
web_research_enabled,
|
||||||
|
)
|
||||||
|
logger.debug("Task details: base_task=%s, current_task=%s", base_task, task)
|
||||||
|
logger.debug("Related files: %s", related_files)
|
||||||
|
|
||||||
|
if memory is None:
|
||||||
|
from langgraph.checkpoint.memory import MemorySaver
|
||||||
|
memory = MemorySaver()
|
||||||
|
|
||||||
|
if thread_id is None:
|
||||||
|
thread_id = str(uuid.uuid4())
|
||||||
|
|
||||||
|
tools = get_implementation_tools(
|
||||||
|
expert_enabled=expert_enabled,
|
||||||
|
web_research_enabled=get_config_repository().get("web_research_enabled", False),
|
||||||
|
)
|
||||||
|
|
||||||
|
agent = agent_utils.create_agent(model, tools, checkpointer=memory, agent_type="planner")
|
||||||
|
|
||||||
|
current_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||||
|
working_directory = os.getcwd()
|
||||||
|
|
||||||
|
# Make sure key_facts is defined before using it
|
||||||
|
try:
|
||||||
|
key_facts = format_key_facts_dict(get_key_fact_repository().get_facts_dict())
|
||||||
|
except RuntimeError as e:
|
||||||
|
logger.error(f"Failed to access key fact repository: {str(e)}")
|
||||||
|
key_facts = ""
|
||||||
|
|
||||||
|
# Get formatted research notes using repository
|
||||||
|
try:
|
||||||
|
repository = get_research_note_repository()
|
||||||
|
notes_dict = repository.get_notes_dict()
|
||||||
|
formatted_research_notes = format_research_notes_dict(notes_dict)
|
||||||
|
except RuntimeError as e:
|
||||||
|
logger.error(f"Failed to access research note repository: {str(e)}")
|
||||||
|
formatted_research_notes = ""
|
||||||
|
|
||||||
|
# Get latest project info
|
||||||
|
try:
|
||||||
|
project_info = get_project_info(".")
|
||||||
|
formatted_project_info = format_project_info(project_info)
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning("Failed to get project info: %s", str(e))
|
||||||
|
formatted_project_info = "Project info unavailable"
|
||||||
|
|
||||||
|
# Get environment inventory information
|
||||||
|
env_inv = get_env_inv()
|
||||||
|
|
||||||
|
# Get model configuration to check for reasoning_assist_default
|
||||||
|
provider = get_config_repository().get("provider", "")
|
||||||
|
model_name = get_config_repository().get("model", "")
|
||||||
|
logger.debug("Checking for reasoning_assist_default on %s/%s", provider, model_name)
|
||||||
|
|
||||||
|
model_config = {}
|
||||||
|
provider_models = models_params.get(provider, {})
|
||||||
|
if provider_models and model_name in provider_models:
|
||||||
|
model_config = provider_models[model_name]
|
||||||
|
|
||||||
|
# Check if reasoning assist is explicitly enabled/disabled
|
||||||
|
force_assistance = get_config_repository().get("force_reasoning_assistance", False)
|
||||||
|
disable_assistance = get_config_repository().get(
|
||||||
|
"disable_reasoning_assistance", False
|
||||||
|
)
|
||||||
|
|
||||||
|
if force_assistance:
|
||||||
|
reasoning_assist_enabled = True
|
||||||
|
elif disable_assistance:
|
||||||
|
reasoning_assist_enabled = False
|
||||||
|
else:
|
||||||
|
# Fall back to model default
|
||||||
|
reasoning_assist_enabled = model_config.get("reasoning_assist_default", False)
|
||||||
|
|
||||||
|
logger.debug("Reasoning assist enabled: %s", reasoning_assist_enabled)
|
||||||
|
|
||||||
|
# Initialize implementation guidance section
|
||||||
|
implementation_guidance_section = ""
|
||||||
|
|
||||||
|
# If reasoning assist is enabled, make a one-off call to the expert model
|
||||||
|
if reasoning_assist_enabled:
|
||||||
|
try:
|
||||||
|
logger.info(
|
||||||
|
"Reasoning assist enabled for model %s, getting implementation guidance",
|
||||||
|
model_name,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Collect tool descriptions
|
||||||
|
tool_metadata = []
|
||||||
|
from ra_aid.tools.reflection import get_function_info as get_tool_info
|
||||||
|
|
||||||
|
for tool in tools:
|
||||||
|
try:
|
||||||
|
tool_info = get_tool_info(tool.func)
|
||||||
|
name = tool.func.__name__
|
||||||
|
description = inspect.getdoc(tool.func)
|
||||||
|
tool_metadata.append(
|
||||||
|
f"Tool: {name}\nDescription: {description}\n"
|
||||||
|
)
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Error getting tool info for {tool}: {e}")
|
||||||
|
|
||||||
|
# Format tool metadata
|
||||||
|
formatted_tool_metadata = "\n".join(tool_metadata)
|
||||||
|
|
||||||
|
# Initialize expert model
|
||||||
|
expert_model = initialize_expert_llm(provider, model_name)
|
||||||
|
|
||||||
|
# Format the reasoning assist prompt for implementation
|
||||||
|
reasoning_assist_prompt = REASONING_ASSIST_PROMPT_IMPLEMENTATION.format(
|
||||||
|
current_date=current_date,
|
||||||
|
working_directory=working_directory,
|
||||||
|
task=task,
|
||||||
|
key_facts=key_facts,
|
||||||
|
key_snippets=format_key_snippets_dict(
|
||||||
|
get_key_snippet_repository().get_snippets_dict()
|
||||||
|
),
|
||||||
|
research_notes=formatted_research_notes,
|
||||||
|
related_files="\n".join(related_files),
|
||||||
|
env_inv=env_inv,
|
||||||
|
tool_metadata=formatted_tool_metadata,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Show the reasoning assist query in a panel
|
||||||
|
console.print(
|
||||||
|
Panel(
|
||||||
|
Markdown(
|
||||||
|
"Consulting with the reasoning model on the best implementation approach."
|
||||||
|
),
|
||||||
|
title="📝 Thinking about implementation...",
|
||||||
|
border_style="yellow",
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
logger.debug("Invoking expert model for implementation reasoning assist")
|
||||||
|
# Make the call to the expert model
|
||||||
|
response = expert_model.invoke(reasoning_assist_prompt)
|
||||||
|
|
||||||
|
# Check if the model supports think tags
|
||||||
|
supports_think_tag = model_config.get("supports_think_tag", False)
|
||||||
|
supports_thinking = model_config.get("supports_thinking", False)
|
||||||
|
|
||||||
|
# Process response content
|
||||||
|
content = None
|
||||||
|
|
||||||
|
if hasattr(response, "content"):
|
||||||
|
content = response.content
|
||||||
|
else:
|
||||||
|
# Fallback if content attribute is missing
|
||||||
|
content = str(response)
|
||||||
|
|
||||||
|
# Process the response content using the centralized function
|
||||||
|
content, extracted_thinking = process_thinking_content(
|
||||||
|
content=content,
|
||||||
|
supports_think_tag=supports_think_tag,
|
||||||
|
supports_thinking=supports_thinking,
|
||||||
|
panel_title="💭 Implementation Thinking",
|
||||||
|
panel_style="yellow",
|
||||||
|
logger=logger,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Display the implementation guidance in a panel
|
||||||
|
console.print(
|
||||||
|
Panel(
|
||||||
|
Markdown(content),
|
||||||
|
title="Implementation Guidance",
|
||||||
|
border_style="blue",
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
# Format the implementation guidance section for the prompt
|
||||||
|
implementation_guidance_section = f"""<implementation guidance>
|
||||||
|
{content}
|
||||||
|
</implementation guidance>"""
|
||||||
|
|
||||||
|
logger.info("Received implementation guidance")
|
||||||
|
except Exception as e:
|
||||||
|
logger.error("Error getting implementation guidance: %s", e)
|
||||||
|
implementation_guidance_section = ""
|
||||||
|
|
||||||
|
prompt = IMPLEMENTATION_PROMPT.format(
|
||||||
|
current_date=current_date,
|
||||||
|
working_directory=working_directory,
|
||||||
|
base_task=base_task,
|
||||||
|
task=task,
|
||||||
|
tasks=tasks,
|
||||||
|
plan=plan,
|
||||||
|
related_files=related_files,
|
||||||
|
key_facts=key_facts,
|
||||||
|
key_snippets=format_key_snippets_dict(
|
||||||
|
get_key_snippet_repository().get_snippets_dict()
|
||||||
|
),
|
||||||
|
research_notes=formatted_research_notes,
|
||||||
|
work_log=get_work_log_repository().format_work_log(),
|
||||||
|
expert_section=EXPERT_PROMPT_SECTION_IMPLEMENTATION if expert_enabled else "",
|
||||||
|
human_section=(
|
||||||
|
HUMAN_PROMPT_SECTION_IMPLEMENTATION
|
||||||
|
if get_config_repository().get("hil", False)
|
||||||
|
else ""
|
||||||
|
),
|
||||||
|
web_research_section=(
|
||||||
|
WEB_RESEARCH_PROMPT_SECTION_CHAT
|
||||||
|
if get_config_repository().get("web_research_enabled", False)
|
||||||
|
else ""
|
||||||
|
),
|
||||||
|
env_inv=env_inv,
|
||||||
|
project_info=formatted_project_info,
|
||||||
|
implementation_guidance_section=implementation_guidance_section,
|
||||||
|
)
|
||||||
|
|
||||||
|
config_values = get_config_repository().get_all()
|
||||||
|
recursion_limit = get_config_repository().get(
|
||||||
|
"recursion_limit", 100
|
||||||
|
)
|
||||||
|
run_config = {
|
||||||
|
"configurable": {"thread_id": thread_id},
|
||||||
|
"recursion_limit": recursion_limit,
|
||||||
|
}
|
||||||
|
run_config.update(config_values)
|
||||||
|
|
||||||
|
try:
|
||||||
|
logger.debug("Implementation agent completed successfully")
|
||||||
|
none_or_fallback_handler = agent_utils.init_fallback_handler(agent, tools)
|
||||||
|
_result = agent_utils.run_agent_with_retry(agent, prompt, none_or_fallback_handler)
|
||||||
|
if _result:
|
||||||
|
# Log task implementation completion
|
||||||
|
log_work_event(f"Completed implementation of task: {task}")
|
||||||
|
return _result
|
||||||
|
except (KeyboardInterrupt, AgentInterrupt):
|
||||||
|
raise
|
||||||
|
except Exception as e:
|
||||||
|
logger.error("Implementation agent failed: %s", str(e), exc_info=True)
|
||||||
|
raise
|
||||||
|
|
@ -66,7 +66,6 @@ You must:
|
||||||
Do so by incrementally and systematically exploring the filesystem with careful directory listing tool calls.
|
Do so by incrementally and systematically exploring the filesystem with careful directory listing tool calls.
|
||||||
You can use fuzzy file search to quickly find relevant files matching a search pattern.
|
You can use fuzzy file search to quickly find relevant files matching a search pattern.
|
||||||
Use ripgrep_search extensively to do *exhaustive* searches for all references to anything that might be changed as part of the base level task.
|
Use ripgrep_search extensively to do *exhaustive* searches for all references to anything that might be changed as part of the base level task.
|
||||||
Prefer to use ripgrep_search with context params rather than reading whole files in order to preserve context tokens.
|
|
||||||
Call emit_key_facts and emit_key_snippet on key information/facts/snippets of code you discover about this project during your research. This is information you will be writing down to be able to efficiently complete work in the future, so be on the lookout for these and make it count.
|
Call emit_key_facts and emit_key_snippet on key information/facts/snippets of code you discover about this project during your research. This is information you will be writing down to be able to efficiently complete work in the future, so be on the lookout for these and make it count.
|
||||||
While it is important to emit key facts and snippets, only emit ones that are truly important info about the project or this task. Do not excessively emit key facts or snippets. Be strategic about it.
|
While it is important to emit key facts and snippets, only emit ones that are truly important info about the project or this task. Do not excessively emit key facts or snippets. Be strategic about it.
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -347,7 +347,7 @@ def request_task_implementation(task_spec: str) -> str:
|
||||||
try:
|
try:
|
||||||
print_task_header(task_spec)
|
print_task_header(task_spec)
|
||||||
# Run implementation agent
|
# Run implementation agent
|
||||||
from ..agent_utils import run_task_implementation_agent
|
from ..agents.implementation_agent import run_task_implementation_agent
|
||||||
|
|
||||||
reset_completion_flags()
|
reset_completion_flags()
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -237,7 +237,7 @@ def test_request_implementation_uses_key_fact_repository(reset_memory, mock_func
|
||||||
def test_request_task_implementation_uses_key_fact_repository(reset_memory, mock_functions):
|
def test_request_task_implementation_uses_key_fact_repository(reset_memory, mock_functions):
|
||||||
"""Test that request_task_implementation uses KeyFactRepository correctly."""
|
"""Test that request_task_implementation uses KeyFactRepository correctly."""
|
||||||
# Mock running the implementation agent
|
# Mock running the implementation agent
|
||||||
with patch('ra_aid.agent_utils.run_task_implementation_agent'):
|
with patch('ra_aid.agents.implementation_agent.run_task_implementation_agent'):
|
||||||
# Call the function
|
# Call the function
|
||||||
result = request_task_implementation("test task")
|
result = request_task_implementation("test task")
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue