552 lines
20 KiB
Python
552 lines
20 KiB
Python
"""
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OASIS simulation manager
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Manages parallel Twitter and Reddit dual-platform simulation
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Uses preset scripts + LLM-generated configuration parameters
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"""
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import os
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import json
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import shutil
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from typing import Dict, Any, List, Optional
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from dataclasses import dataclass, field
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from datetime import datetime
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from enum import Enum
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from ..config import Config
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from ..utils.logger import get_logger
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from .entity_reader import EntityReader, FilteredEntities
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from .oasis_profile_generator import OasisProfileGenerator, OasisAgentProfile
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from .simulation_config_generator import SimulationConfigGenerator, SimulationParameters
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from ..utils.locale import t
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logger = get_logger("mirofish.simulation")
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class SimulationStatus(str, Enum):
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"""Simulation status"""
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CREATED = "created"
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PREPARING = "preparing"
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READY = "ready"
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RUNNING = "running"
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PAUSED = "paused"
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STOPPED = "stopped" # Simulation manually stopped
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COMPLETED = "completed" # Simulation naturally completed
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FAILED = "failed"
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class PlatformType(str, Enum):
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"""Platform type"""
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TWITTER = "twitter"
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REDDIT = "reddit"
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@dataclass
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class SimulationState:
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"""Simulation state"""
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simulation_id: str
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project_id: str
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graph_id: str
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# Platform enable status
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enable_twitter: bool = True
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enable_reddit: bool = True
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# Status
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status: SimulationStatus = SimulationStatus.CREATED
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# Preparation phase data
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entities_count: int = 0
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profiles_count: int = 0
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entity_types: List[str] = field(default_factory=list)
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# Config generation info
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config_generated: bool = False
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config_reasoning: str = ""
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# Runtime data
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current_round: int = 0
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twitter_status: str = "not_started"
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reddit_status: str = "not_started"
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# Timestamps
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created_at: str = field(default_factory=lambda: datetime.now().isoformat())
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updated_at: str = field(default_factory=lambda: datetime.now().isoformat())
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# Error info
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error: Optional[str] = None
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def to_dict(self) -> Dict[str, Any]:
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"""Full state dictionary (internal use)"""
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return {
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"simulation_id": self.simulation_id,
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"project_id": self.project_id,
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"graph_id": self.graph_id,
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"enable_twitter": self.enable_twitter,
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"enable_reddit": self.enable_reddit,
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"status": self.status.value,
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"entities_count": self.entities_count,
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"profiles_count": self.profiles_count,
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"entity_types": self.entity_types,
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"config_generated": self.config_generated,
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"config_reasoning": self.config_reasoning,
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"current_round": self.current_round,
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"twitter_status": self.twitter_status,
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"reddit_status": self.reddit_status,
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"created_at": self.created_at,
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"updated_at": self.updated_at,
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"error": self.error,
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}
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def to_simple_dict(self) -> Dict[str, Any]:
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"""Simplified state dictionary (for API responses)"""
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return {
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"simulation_id": self.simulation_id,
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"project_id": self.project_id,
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"graph_id": self.graph_id,
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"status": self.status.value,
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"entities_count": self.entities_count,
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"profiles_count": self.profiles_count,
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"entity_types": self.entity_types,
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"config_generated": self.config_generated,
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"error": self.error,
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}
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class SimulationManager:
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"""
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Simulation manager
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Core features:
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1. Read entities from the graph store and filter them
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2. Generate OASIS Agent Profiles
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3. Use LLM to intelligently generate simulation configuration parameters
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4. Prepare all files required by the preset scripts
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"""
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# Simulation data storage directory
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SIMULATION_DATA_DIR = os.path.join(
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os.path.dirname(__file__), "../../uploads/simulations"
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)
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def __init__(self):
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# Ensure directory exists
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os.makedirs(self.SIMULATION_DATA_DIR, exist_ok=True)
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# In-memory simulation state cache
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self._simulations: Dict[str, SimulationState] = {}
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def _get_simulation_dir(self, simulation_id: str) -> str:
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"""Get the simulation data directory"""
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sim_dir = os.path.join(self.SIMULATION_DATA_DIR, simulation_id)
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os.makedirs(sim_dir, exist_ok=True)
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return sim_dir
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def _save_simulation_state(self, state: SimulationState):
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"""Save the simulation state to a file"""
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sim_dir = self._get_simulation_dir(state.simulation_id)
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state_file = os.path.join(sim_dir, "state.json")
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state.updated_at = datetime.now().isoformat()
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with open(state_file, "w", encoding="utf-8") as f:
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json.dump(state.to_dict(), f, ensure_ascii=False, indent=2)
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self._simulations[state.simulation_id] = state
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def _load_simulation_state(self, simulation_id: str) -> Optional[SimulationState]:
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"""Load the simulation state from a file"""
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if simulation_id in self._simulations:
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return self._simulations[simulation_id]
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sim_dir = self._get_simulation_dir(simulation_id)
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state_file = os.path.join(sim_dir, "state.json")
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if not os.path.exists(state_file):
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return None
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with open(state_file, "r", encoding="utf-8") as f:
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data = json.load(f)
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state = SimulationState(
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simulation_id=simulation_id,
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project_id=data.get("project_id", ""),
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graph_id=data.get("graph_id", ""),
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enable_twitter=data.get("enable_twitter", True),
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enable_reddit=data.get("enable_reddit", True),
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status=SimulationStatus(data.get("status", "created")),
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entities_count=data.get("entities_count", 0),
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profiles_count=data.get("profiles_count", 0),
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entity_types=data.get("entity_types", []),
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config_generated=data.get("config_generated", False),
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config_reasoning=data.get("config_reasoning", ""),
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current_round=data.get("current_round", 0),
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twitter_status=data.get("twitter_status", "not_started"),
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reddit_status=data.get("reddit_status", "not_started"),
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created_at=data.get("created_at", datetime.now().isoformat()),
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updated_at=data.get("updated_at", datetime.now().isoformat()),
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error=data.get("error"),
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)
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self._simulations[simulation_id] = state
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return state
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def create_simulation(
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self,
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project_id: str,
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graph_id: str,
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enable_twitter: bool = True,
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enable_reddit: bool = True,
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) -> SimulationState:
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"""
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Create a new simulation
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Args:
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project_id: Project ID
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graph_id: graph store ID
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enable_twitter: Whether to enable Twitter simulation
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enable_reddit: Whether to enable Reddit simulation
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Returns:
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SimulationState
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"""
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import uuid
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simulation_id = f"sim_{uuid.uuid4().hex[:12]}"
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state = SimulationState(
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simulation_id=simulation_id,
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project_id=project_id,
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graph_id=graph_id,
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enable_twitter=enable_twitter,
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enable_reddit=enable_reddit,
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status=SimulationStatus.CREATED,
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)
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self._save_simulation_state(state)
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logger.info(
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f"Created simulation: {simulation_id}, project={project_id}, graph={graph_id}"
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)
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return state
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def prepare_simulation(
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self,
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simulation_id: str,
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simulation_requirement: str,
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document_text: str,
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defined_entity_types: Optional[List[str]] = None,
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use_llm_for_profiles: bool = True,
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progress_callback: Optional[callable] = None,
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parallel_profile_count: int = 3,
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) -> SimulationState:
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"""
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Prepare the simulation environment (fully automated)
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Steps:
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1. Read and filter entities from the graph store
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2. Generate an OASIS Agent Profile for each entity (optional LLM enhancement, supports parallelism)
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3. Use LLM to intelligently generate simulation configuration parameters (time, activity, posting frequency, etc.)
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4. Save the configuration file and Profile files
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5. Copy preset scripts to the simulation directory
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Args:
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simulation_id: Simulation ID
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simulation_requirement: Simulation requirement description (used for LLM configuration generation)
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document_text: Original document content (used for LLM to understand context)
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defined_entity_types: Predefined entity types (optional)
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use_llm_for_profiles: Whether to use LLM to generate detailed personas
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progress_callback: Progress callback function (stage, progress, message)
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parallel_profile_count: Number of personas to generate in parallel, default 3
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Returns:
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SimulationState
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"""
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state = self._load_simulation_state(simulation_id)
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if not state:
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raise ValueError(t("api.simulationNotFound", id=simulation_id))
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try:
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state.status = SimulationStatus.PREPARING
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self._save_simulation_state(state)
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sim_dir = self._get_simulation_dir(simulation_id)
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# ========== Phase 1: Read and filter entities ==========
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if progress_callback:
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progress_callback("reading", 0, t("progress.connectingGraph"))
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reader = EntityReader()
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if progress_callback:
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progress_callback("reading", 30, t("progress.readingNodeData"))
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filtered = reader.filter_defined_entities(
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graph_id=state.graph_id,
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defined_entity_types=defined_entity_types,
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enrich_with_edges=True,
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)
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state.entities_count = filtered.filtered_count
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state.entity_types = list(filtered.entity_types)
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if progress_callback:
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progress_callback(
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"reading",
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100,
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t("progress.readingComplete", count=filtered.filtered_count),
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current=filtered.filtered_count,
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total=filtered.filtered_count,
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)
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if filtered.filtered_count == 0:
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state.status = SimulationStatus.FAILED
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state.error = "No matching entities found; please check whether the graph is correctly built"
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self._save_simulation_state(state)
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return state
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# ========== Phase 2: Generate Agent Profiles ==========
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total_entities = len(filtered.entities)
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if progress_callback:
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progress_callback(
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"generating_profiles",
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0,
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t("progress.startGenerating"),
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current=0,
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total=total_entities,
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)
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# Pass graph_id to enable graph retrieval for richer context
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generator = OasisProfileGenerator(graph_id=state.graph_id)
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def profile_progress(current, total, msg):
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if progress_callback:
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progress_callback(
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"generating_profiles",
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int(current / total * 100),
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msg,
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current=current,
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total=total,
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item_name=msg,
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)
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# Set real-time save file path (prefer Reddit JSON format)
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realtime_output_path = None
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realtime_platform = "reddit"
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if state.enable_reddit:
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realtime_output_path = os.path.join(sim_dir, "reddit_profiles.json")
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realtime_platform = "reddit"
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elif state.enable_twitter:
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realtime_output_path = os.path.join(sim_dir, "twitter_profiles.csv")
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realtime_platform = "twitter"
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profiles = generator.generate_profiles_from_entities(
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entities=filtered.entities,
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use_llm=use_llm_for_profiles,
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progress_callback=profile_progress,
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graph_id=state.graph_id, # Pass graph_id for graph retrieval
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parallel_count=parallel_profile_count, # Parallel generation count
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realtime_output_path=realtime_output_path, # Real-time save path
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output_platform=realtime_platform, # Output format
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)
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state.profiles_count = len(profiles)
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# Save Profile files (note: Twitter uses CSV format, Reddit uses JSON format)
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# Reddit was already saved in real time during generation; save again here to ensure completeness
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if progress_callback:
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progress_callback(
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"generating_profiles",
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95,
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t("progress.savingProfiles"),
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current=total_entities,
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total=total_entities,
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)
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if state.enable_reddit:
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generator.save_profiles(
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profiles=profiles,
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file_path=os.path.join(sim_dir, "reddit_profiles.json"),
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platform="reddit",
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)
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if state.enable_twitter:
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# Twitter uses CSV format! This is required by OASIS
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generator.save_profiles(
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profiles=profiles,
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file_path=os.path.join(sim_dir, "twitter_profiles.csv"),
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platform="twitter",
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)
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if progress_callback:
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progress_callback(
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"generating_profiles",
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100,
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t("progress.profilesComplete", count=len(profiles)),
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current=len(profiles),
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total=len(profiles),
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)
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# ========== Phase 3: LLM intelligent simulation config generation ==========
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if progress_callback:
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progress_callback(
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"generating_config",
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0,
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t("progress.analyzingRequirements"),
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current=0,
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total=3,
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)
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config_generator = SimulationConfigGenerator()
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if progress_callback:
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progress_callback(
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"generating_config",
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30,
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t("progress.callingLLMConfig"),
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current=1,
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total=3,
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)
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sim_params = config_generator.generate_config(
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simulation_id=simulation_id,
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project_id=state.project_id,
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graph_id=state.graph_id,
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simulation_requirement=simulation_requirement,
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document_text=document_text,
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entities=filtered.entities,
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enable_twitter=state.enable_twitter,
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enable_reddit=state.enable_reddit,
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)
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if progress_callback:
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progress_callback(
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"generating_config",
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70,
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t("progress.savingConfigFiles"),
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current=2,
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total=3,
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)
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# Save config files
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config_path = os.path.join(sim_dir, "simulation_config.json")
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with open(config_path, "w", encoding="utf-8") as f:
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f.write(sim_params.to_json())
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state.config_generated = True
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state.config_reasoning = sim_params.generation_reasoning
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if progress_callback:
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progress_callback(
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"generating_config",
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100,
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t("progress.configComplete"),
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current=3,
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total=3,
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)
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# Note: run scripts stay in backend/scripts/, no longer copied to simulation dir
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# When starting simulation, simulation_runner runs scripts from scripts/ dir
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# Update state
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state.status = SimulationStatus.READY
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self._save_simulation_state(state)
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logger.info(
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f"Simulation preparation complete: {simulation_id}, "
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f"entities={state.entities_count}, profiles={state.profiles_count}"
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)
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return state
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except Exception as e:
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logger.error(f"Simulation preparation failed: {simulation_id}, error={str(e)}")
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import traceback
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logger.error(traceback.format_exc())
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state.status = SimulationStatus.FAILED
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state.error = str(e)
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self._save_simulation_state(state)
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raise
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def get_simulation(self, simulation_id: str) -> Optional[SimulationState]:
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"""Get simulation state"""
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return self._load_simulation_state(simulation_id)
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def list_simulations(
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self, project_id: Optional[str] = None
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) -> List[SimulationState]:
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"""List all simulations"""
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simulations = []
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if os.path.exists(self.SIMULATION_DATA_DIR):
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for sim_id in os.listdir(self.SIMULATION_DATA_DIR):
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# Skip hidden files (e.g. .DS_Store) and non-directory files
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sim_path = os.path.join(self.SIMULATION_DATA_DIR, sim_id)
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if sim_id.startswith(".") or not os.path.isdir(sim_path):
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continue
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state = self._load_simulation_state(sim_id)
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if state:
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if project_id is None or state.project_id == project_id:
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simulations.append(state)
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return simulations
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def get_profiles(
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self, simulation_id: str, platform: str = "reddit"
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) -> List[Dict[str, Any]]:
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"""Get the simulation's Agent Profile"""
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state = self._load_simulation_state(simulation_id)
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if not state:
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raise ValueError(t("api.simulationNotFound", id=simulation_id))
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sim_dir = self._get_simulation_dir(simulation_id)
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profile_path = os.path.join(sim_dir, f"{platform}_profiles.json")
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if not os.path.exists(profile_path):
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return []
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with open(profile_path, "r", encoding="utf-8") as f:
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return json.load(f)
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def get_simulation_config(self, simulation_id: str) -> Optional[Dict[str, Any]]:
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"""Get the simulation configuration"""
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sim_dir = self._get_simulation_dir(simulation_id)
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config_path = os.path.join(sim_dir, "simulation_config.json")
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if not os.path.exists(config_path):
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return None
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with open(config_path, "r", encoding="utf-8") as f:
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return json.load(f)
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def get_run_instructions(self, simulation_id: str) -> Dict[str, str]:
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"""Get run instructions"""
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sim_dir = self._get_simulation_dir(simulation_id)
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config_path = os.path.join(sim_dir, "simulation_config.json")
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scripts_dir = os.path.abspath(
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os.path.join(os.path.dirname(__file__), "../../scripts")
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)
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return {
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"simulation_dir": sim_dir,
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"scripts_dir": scripts_dir,
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"config_file": config_path,
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"commands": {
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"twitter": f"python {scripts_dir}/run_twitter_simulation.py --config {config_path}",
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"reddit": f"python {scripts_dir}/run_reddit_simulation.py --config {config_path}",
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"parallel": f"python {scripts_dir}/run_parallel_simulation.py --config {config_path}",
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},
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"instructions": (
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f"1. Activate conda environment: conda activate MiroFish\n"
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f"2. Run simulation (scripts located at {scripts_dir}):\n"
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f" - Run Twitter only: python {scripts_dir}/run_twitter_simulation.py --config {config_path}\n"
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f" - Run Reddit only: python {scripts_dir}/run_reddit_simulation.py --config {config_path}\n"
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f" - Run both platforms in parallel: python {scripts_dir}/run_parallel_simulation.py --config {config_path}"
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),
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}
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