mirror of
https://github.com/leonvanzyl/autocoder.git
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1607fc8175
Enable the orchestrator to assign 1-3 features per coding agent subprocess, selected via dependency chain extension + same-category fill. This reduces cold-start overhead and leverages shared context across related features. Orchestrator (parallel_orchestrator.py): - Add batch tracking: _batch_features and _feature_to_primary data structures - Add build_feature_batches() with dependency chain + category fill algorithm - Add start_feature_batch() and _spawn_coding_agent_batch() methods - Update _on_agent_complete() for batch cleanup across all features - Update stop_feature() with _feature_to_primary lookup - Update get_ready_features() to exclude all batch feature IDs - Update main loop to build batches then spawn per available slot CLI and agent layer: - Add --feature-ids (comma-separated) and --batch-size CLI args - Add feature_ids parameter to run_autonomous_agent() with batch prompt selection - Add get_batch_feature_prompt() with sequential workflow instructions WebSocket layer (server/websocket.py): - Add BATCH_CODING_AGENT_START_PATTERN and BATCH_FEATURES_COMPLETE_PATTERN - Add _handle_batch_agent_start() and _handle_batch_agent_complete() methods - Add featureIds field to all agent_update messages - Track current_feature_id updates as agent moves through batch Frontend (React UI): - Add featureIds to ActiveAgent and WSAgentUpdateMessage types - Update KanbanColumn and DependencyGraph agent-feature maps for batch - Update AgentCard to show "Batch: #X, #Y, #Z" with active feature highlight - Add "Features per Agent" segmented control (1-3) in SettingsModal Settings integration (full stack): - Add batch_size to schemas, settings router, agent router, process manager - Default batch_size=3, user-configurable 1-3 via settings UI - batch_size=1 is functionally identical to pre-batching behavior Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
"""
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Settings Router
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===============
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API endpoints for global settings management.
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Settings are stored in the registry database and shared across all projects.
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"""
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import mimetypes
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import os
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import sys
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from fastapi import APIRouter
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from ..schemas import ModelInfo, ModelsResponse, SettingsResponse, SettingsUpdate
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from ..services.chat_constants import ROOT_DIR
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# Mimetype fix for Windows - must run before StaticFiles is mounted
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mimetypes.add_type("text/javascript", ".js", True)
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# Ensure root is on sys.path for registry import
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if str(ROOT_DIR) not in sys.path:
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sys.path.insert(0, str(ROOT_DIR))
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from registry import (
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AVAILABLE_MODELS,
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DEFAULT_MODEL,
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get_all_settings,
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set_setting,
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)
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router = APIRouter(prefix="/api/settings", tags=["settings"])
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def _parse_yolo_mode(value: str | None) -> bool:
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"""Parse YOLO mode string to boolean."""
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return (value or "false").lower() == "true"
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def _is_glm_mode() -> bool:
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"""Check if GLM API is configured via environment variables."""
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base_url = os.getenv("ANTHROPIC_BASE_URL", "")
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# GLM mode is when ANTHROPIC_BASE_URL is set but NOT pointing to Ollama
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return bool(base_url) and not _is_ollama_mode()
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def _is_ollama_mode() -> bool:
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"""Check if Ollama API is configured via environment variables."""
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base_url = os.getenv("ANTHROPIC_BASE_URL", "")
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return "localhost:11434" in base_url or "127.0.0.1:11434" in base_url
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@router.get("/models", response_model=ModelsResponse)
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async def get_available_models():
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"""Get list of available models.
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Frontend should call this to get the current list of models
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instead of hardcoding them.
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"""
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return ModelsResponse(
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models=[ModelInfo(id=m["id"], name=m["name"]) for m in AVAILABLE_MODELS],
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default=DEFAULT_MODEL,
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)
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def _parse_int(value: str | None, default: int) -> int:
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"""Parse integer setting with default fallback."""
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if value is None:
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return default
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try:
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return int(value)
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except (ValueError, TypeError):
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return default
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def _parse_bool(value: str | None, default: bool = False) -> bool:
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"""Parse boolean setting with default fallback."""
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if value is None:
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return default
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return value.lower() == "true"
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@router.get("", response_model=SettingsResponse)
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async def get_settings():
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"""Get current global settings."""
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all_settings = get_all_settings()
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return SettingsResponse(
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yolo_mode=_parse_yolo_mode(all_settings.get("yolo_mode")),
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model=all_settings.get("model", DEFAULT_MODEL),
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glm_mode=_is_glm_mode(),
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ollama_mode=_is_ollama_mode(),
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testing_agent_ratio=_parse_int(all_settings.get("testing_agent_ratio"), 1),
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playwright_headless=_parse_bool(all_settings.get("playwright_headless"), default=True),
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batch_size=_parse_int(all_settings.get("batch_size"), 3),
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)
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@router.patch("", response_model=SettingsResponse)
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async def update_settings(update: SettingsUpdate):
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"""Update global settings."""
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if update.yolo_mode is not None:
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set_setting("yolo_mode", "true" if update.yolo_mode else "false")
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if update.model is not None:
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set_setting("model", update.model)
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if update.testing_agent_ratio is not None:
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set_setting("testing_agent_ratio", str(update.testing_agent_ratio))
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if update.playwright_headless is not None:
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set_setting("playwright_headless", "true" if update.playwright_headless else "false")
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if update.batch_size is not None:
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set_setting("batch_size", str(update.batch_size))
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# Return updated settings
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all_settings = get_all_settings()
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return SettingsResponse(
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yolo_mode=_parse_yolo_mode(all_settings.get("yolo_mode")),
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model=all_settings.get("model", DEFAULT_MODEL),
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glm_mode=_is_glm_mode(),
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ollama_mode=_is_ollama_mode(),
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testing_agent_ratio=_parse_int(all_settings.get("testing_agent_ratio"), 1),
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playwright_headless=_parse_bool(all_settings.get("playwright_headless"), default=True),
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batch_size=_parse_int(all_settings.get("batch_size"), 3),
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)
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