94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
import ai_client
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from typing import List, Optional
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from models import Ticket, Track, WorkerContext
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from file_cache import ASTParser
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class ConductorEngine:
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"""
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Orchestrates the execution of tickets within a track.
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"""
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def __init__(self, track: Track):
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self.track = track
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def run_linear(self):
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"""
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Executes tickets sequentially according to their dependencies.
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Iterates through the track's executable tickets until no more can be run.
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"""
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while True:
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executable = self.track.get_executable_tickets()
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if not executable:
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# Check if we are finished or blocked
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all_done = all(t.status == "completed" for t in self.track.tickets)
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if all_done:
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print("Track completed successfully.")
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else:
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print("No more executable tickets. Track may be blocked or finished.")
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break
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for ticket in executable:
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print(f"Executing ticket {ticket.id}: {ticket.description}")
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# For now, we use a default model name or take it from config
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context = WorkerContext(
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ticket_id=ticket.id,
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model_name="gemini-2.5-flash-lite",
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messages=[]
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)
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run_worker_lifecycle(ticket, context)
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def confirm_execution(payload: str) -> bool:
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"""
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Placeholder for external confirmation function.
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In a real scenario, this might trigger a UI prompt.
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"""
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return True
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def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files: List[str] = None):
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"""
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Simulates the lifecycle of a single agent working on a ticket.
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Calls the AI client and updates the ticket status based on the response.
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"""
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context_injection = ""
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if context_files:
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parser = ASTParser(language="python")
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for i, file_path in enumerate(context_files):
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try:
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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if i == 0:
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view = parser.get_curated_view(content)
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else:
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view = parser.get_skeleton(content)
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context_injection += f"\nFile: {file_path}\n{view}\n"
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except Exception as e:
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context_injection += f"\nError reading {file_path}: {e}\n"
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# Build a prompt for the worker
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user_message = (
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f"You are assigned to Ticket {ticket.id}.\n"
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f"Task Description: {ticket.description}\n"
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)
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if context_injection:
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user_message += f"\nContext Files:\n{context_injection}\n"
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user_message += (
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"Please complete this task. If you are blocked and cannot proceed, "
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"start your response with 'BLOCKED' and explain why."
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)
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# In a real scenario, we would pass md_content from the aggregator
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# and manage the conversation history in the context.
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response = ai_client.send(
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md_content="",
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user_message=user_message,
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base_dir=".",
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pre_tool_callback=confirm_execution if ticket.step_mode else None
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)
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if "BLOCKED" in response.upper():
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ticket.mark_blocked(response)
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else:
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ticket.mark_complete()
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return response
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