Fix: Agent orchestrator initialization, singleton LLM loading, and dashboard activity logging
This commit is contained in:
ajaysi
@@ -13,12 +13,17 @@ from abc import ABC, abstractmethod
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# txtai imports for native agent framework
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try:
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from txtai import Agent, LLM
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TXTAI_AVAILABLE = Agent.__module__ != "txtai.agent.placeholder"
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from txtai.pipeline import Agent, LLM
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TXTAI_AVAILABLE = True
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except ImportError:
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TXTAI_AVAILABLE = False
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# Fallback implementation for development
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logging.warning("txtai not available, using fallback implementation")
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try:
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from txtai import Agent, LLM
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TXTAI_AVAILABLE = True
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except ImportError:
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TXTAI_AVAILABLE = False
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Agent = None
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LLM = None
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logging.warning("txtai not available")
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# Optional MLflow integration
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try:
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@@ -121,8 +126,10 @@ class BaseALwrityAgent(ABC):
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if TXTAI_AVAILABLE:
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try:
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if not self.llm:
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# Hardening: Explicitly set task to avoid 'text2text-generation' default failures
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self.llm = LLM(model_name, task="text-generation")
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# Allow txtai to auto-detect the correct task for the model
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# But force language-generation (txtai mapping for text-generation) for known models
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task_hint = "language-generation" if any(x in model_name for x in ["Qwen", "Instruct", "GPT", "Llama"]) else None
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self.llm = LLM(path=model_name, task=task_hint) if task_hint else LLM(path=model_name)
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self.txtai_agent = self._create_txtai_agent()
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logger.info(f"Initialized txtai agent for {agent_type} - {self.agent_id}")
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@@ -777,59 +784,8 @@ class StrategyOrchestratorAgent(BaseALwrityAgent):
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if not TXTAI_AVAILABLE:
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return None
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return Agent(
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llm=self.llm,
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tools=[
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{
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"name": "market_signal_detector",
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"description": "Detects market changes and competitor activities",
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"target": self._market_signal_detector_tool
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},
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{
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"name": "google_trends_fetcher",
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"description": "Fetches Google Trends data and embeds it into SIF for retrieval",
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"target": self._google_trends_fetcher_tool
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},
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{
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"name": "agent_coordinator",
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"description": "Coordinates actions between multiple agents",
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"target": self._agent_coordinator_tool
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},
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{
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"name": "performance_analyzer",
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"description": "Analyzes marketing performance metrics",
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"target": self._performance_analyzer_tool
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},
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{
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"name": "strategy_synthesizer",
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"description": "Synthesizes unified strategies from multiple inputs",
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"target": self._strategy_synthesizer_tool
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},
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{
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"name": "task_delegator",
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"description": "Delegates specific tasks to specialized agents (content, competitor, seo, social)",
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"target": self._delegate_task_tool
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}
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],
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max_iterations=15,
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system=self.get_effective_system_prompt(f"""You are the Marketing Strategy Orchestrator for ALwrity user {self.user_id}.
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Your role is to coordinate all marketing agents, analyze market signals,
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and synthesize unified strategies.
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Key Responsibility: DELEGATE tasks to specialized agents.
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- Content Strategy Agent: For content analysis, gaps, and optimization.
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- Competitor Response Agent: For monitoring and counter-strategies.
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- SEO Optimization Agent: For technical SEO and keywords.
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- Social Amplification Agent: For social trends and distribution.
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Use the 'task_delegator' tool to assign work to these agents.
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Do not just plan; EXECUTE by delegating.
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Always prioritize user goals and maintain safety constraints.
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Coordinate multi-agent responses to market changes effectively."""
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)
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)
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llm = getattr(self.llm, "llm", self.llm)
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return Agent(llm=llm, tools=[], max_iterations=15)
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async def _market_signal_detector_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""Tool for detecting market signals"""
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@@ -47,7 +47,7 @@ logger = get_service_logger(__name__)
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class AgentTeamConfiguration:
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"""Configuration for the complete agent team"""
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user_id: str
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shared_llm: str = "Qwen/Qwen3-4B-Instruct-2507"
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shared_llm: str = "Qwen/Qwen2.5-3B-Instruct" # Updated to a stable model known for text-generation
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max_iterations: int = 15
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enable_safety: bool = True
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enable_performance_monitoring: bool = True
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@@ -70,6 +70,9 @@ class ALwrityAgentOrchestrator:
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self.performance_monitor: Optional[AgentPerformanceMonitor] = None
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self.safety_framework: Optional[Dict[str, Any]] = None
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# Initialize execution history for tracking agent activities
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self.execution_history: List[Dict[str, Any]] = []
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# Initialize components
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self._initialize_components()
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@@ -80,8 +83,14 @@ class ALwrityAgentOrchestrator:
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try:
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# Initialize shared LLM
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if TXTAI_AVAILABLE:
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# Hardening: Explicitly set task to avoid 'text2text-generation' default failures
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self.llm = LLM(self.config.shared_llm, task="text-generation")
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try:
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# Allow auto-detection of task
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self.llm = LLM(self.config.shared_llm)
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except Exception as e:
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logger.error(
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f"Failed to initialize shared LLM '{self.config.shared_llm}': {e}"
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)
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raise
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else:
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self.llm = None
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@@ -117,6 +126,36 @@ class ALwrityAgentOrchestrator:
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status = onboarding_service.get_onboarding_status(self.user_id)
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if not status.get("is_completed", False):
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logger.info(f"Skipping agent initialization for user {self.user_id} - Onboarding incomplete")
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self.execution_history.append({
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"user_id": self.user_id,
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"timestamp": datetime.utcnow().isoformat(),
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"agent_id": "system",
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"action": "system_check",
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"status": "pending",
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"details": "Agent initialization paused. Waiting for user onboarding completion.",
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"agent_name": "System Orchestrator",
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"agent_type": "orchestrator"
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})
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# Persist this check to DB so it survives refreshes
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try:
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from services.database import get_session_for_user
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from services.agent_activity_service import AgentActivityService
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db = get_session_for_user(self.user_id)
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if db:
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try:
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activity_service = AgentActivityService(db, self.user_id)
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run = activity_service.start_run(agent_type="system_orchestrator", prompt="System Check")
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activity_service.finish_run(
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run_id=run.id,
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success=True,
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result_summary="Agent initialization paused. Waiting for user onboarding completion."
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)
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finally:
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db.close()
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except Exception:
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pass
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return
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except Exception as e:
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logger.warning(f"Could not check onboarding status for {self.user_id}: {e}")
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@@ -141,10 +180,14 @@ class ALwrityAgentOrchestrator:
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except Exception:
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pass
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# Track successful initializations
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initialized_agents = []
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# Content Strategy Agent
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if enabled_by_key.get("content_strategist", True):
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self.content_agent = ContentStrategyAgent(self.user_id, self.config.shared_llm, llm=self.llm)
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self.agents['content'] = self.content_agent
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initialized_agents.append("Content Strategist")
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# Strategy Architect Agent
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if enabled_by_key.get("strategy_architect", True):
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@@ -152,43 +195,37 @@ class ALwrityAgentOrchestrator:
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intel_service = TxtaiIntelligenceService(self.user_id)
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self.strategy_agent = StrategyArchitectAgent(intel_service, self.user_id)
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self.agents['strategy'] = self.strategy_agent
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initialized_agents.append("Strategy Architect")
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# Competitor Response Agent
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if enabled_by_key.get("competitor_analyst", True):
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self.competitor_agent = CompetitorResponseAgent(self.user_id, self.config.shared_llm, llm=self.llm)
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self.agents['competitor'] = self.competitor_agent
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initialized_agents.append("Competitor Analyst")
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# SEO Optimization Agent
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if enabled_by_key.get("seo_specialist", True):
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self.seo_agent = SEOOptimizationAgent(self.user_id, self.config.shared_llm, llm=self.llm)
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self.agents['seo'] = self.seo_agent
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initialized_agents.append("SEO Specialist")
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# Social Amplification Agent
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if enabled_by_key.get("social_media_manager", True):
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self.social_agent = SocialAmplificationAgent(self.user_id, self.config.shared_llm, llm=self.llm)
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self.agents['social'] = self.social_agent
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# Strategy Architect Agent
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if enabled_by_key.get("strategy_architect", True):
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from services.intelligence.txtai_service import TxtaiIntelligenceService
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intel_service = TxtaiIntelligenceService(self.user_id)
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self.strategy_agent = StrategyArchitectAgent(intel_service, self.user_id)
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self.agents['strategy'] = self.strategy_agent
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initialized_agents.append("Social Media Manager")
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# Trend Surfer Agent
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if enabled_by_key.get("trend_surfer", True):
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# TrendSurferAgent needs TxtaiIntelligenceService, which we might need to get from SIF or initialize
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# For now, we assume SIF integration is handled elsewhere or we pass a mock/stub if needed
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# But wait, TrendSurferAgent constructor is (intelligence_service, user_id)
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# We need to get the intelligence service here.
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# Since AgentOrchestrator doesn't hold TxtaiIntelligenceService directly (SIFIntegrationService does),
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# this is tricky.
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# However, SIFIntegrationService initializes AgentOrchestrator.
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# Let's import TxtaiIntelligenceService and initialize it here for the agent
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from services.intelligence.txtai_service import TxtaiIntelligenceService
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intel_service = TxtaiIntelligenceService(self.user_id)
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self.trend_surfer_agent = TrendSurferAgent(intel_service, self.user_id)
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self.agents['trend'] = self.trend_surfer_agent
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# TrendSurferAgent needs TxtaiIntelligenceService
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try:
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from services.intelligence.txtai_service import TxtaiIntelligenceService
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intel_service = TxtaiIntelligenceService(self.user_id)
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self.trend_surfer_agent = TrendSurferAgent(intel_service, self.user_id)
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self.agents['trend'] = self.trend_surfer_agent
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initialized_agents.append("Trend Surfer")
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except Exception as e:
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logger.error(f"Failed to initialize TrendSurferAgent: {e}")
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# Content Guardian Agent
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if enabled_by_key.get("content_guardian", True):
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@@ -206,12 +243,52 @@ class ALwrityAgentOrchestrator:
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sif_service=None # SIF service is optional/circular dependency handling
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)
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self.agents['guardian'] = self.content_guardian_agent
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initialized_agents.append("Content Guardian")
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logger.info(f"Initialized ContentGuardianAgent for user {self.user_id}")
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except Exception as e:
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logger.error(f"Failed to initialize ContentGuardianAgent: {e}")
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logger.info(f"Created {len(self.agents)} specialized agents for user {self.user_id}")
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# Log initialization activity
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if initialized_agents:
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# 1. Add to in-memory history
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self.execution_history.append({
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"user_id": self.user_id,
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"timestamp": datetime.utcnow().isoformat(),
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"agent_id": "system",
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"action": "agent_initialization",
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"status": "completed",
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"details": f"Successfully initialized agent team: {', '.join(initialized_agents)}",
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"agent_name": "System Orchestrator",
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"agent_type": "orchestrator"
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})
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# 2. Add to persistent database history (so dashboard sees it on refresh)
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try:
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from services.database import get_session_for_user
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from services.agent_activity_service import AgentActivityService
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db = get_session_for_user(self.user_id)
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if db:
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try:
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activity_service = AgentActivityService(db, self.user_id)
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# Create a run record
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run = activity_service.start_run(
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agent_type="system_orchestrator",
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prompt="Initialize Autonomous Agent Team"
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)
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# Immediately finish it
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activity_service.finish_run(
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run_id=run.id,
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success=True,
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result_summary=f"Successfully initialized agent team: {', '.join(initialized_agents)}"
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)
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finally:
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db.close()
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except Exception as e:
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logger.error(f"Failed to log initialization activity to DB: {e}")
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except Exception as e:
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logger.error(f"Error creating specialized agents for user {self.user_id}: {e}")
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raise e
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@@ -258,6 +335,11 @@ class ALwrityAgentOrchestrator:
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"4. Provide specific action recommendations.\n\n"
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"Return a comprehensive strategy with specific actions, priorities, and expected outcomes."
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)
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# Ensure orchestrator is initialized
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if not self.orchestrator_agent:
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self._create_orchestrator_agent()
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orchestrator_prompt = self.orchestrator_agent.build_task_prompt(instruction=instruction, task_context=context)
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result = await self.orchestrator_agent.run(orchestrator_prompt)
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@@ -322,6 +404,7 @@ class ALwrityAgentOrchestrator:
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"timestamp": datetime.utcnow().isoformat(),
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"agent_statuses": agent_statuses,
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"performance_summary": performance_summary,
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"recent_activity": self.get_execution_history(limit=5),
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"market_signals_active": self.config.enable_market_signals,
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"safety_enabled": self.config.enable_safety,
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"performance_monitoring_enabled": self.config.enable_performance_monitoring
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@@ -342,6 +425,10 @@ class ALwrityAgentOrchestrator:
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# Helper methods
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def get_execution_history(self, limit: int = 100) -> List[Dict[str, Any]]:
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"""Get execution history for this orchestrator"""
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return self.execution_history[-limit:]
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async def _prepare_orchestrator_context(self, market_context: Dict[str, Any]) -> Dict[str, Any]:
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"""Prepare comprehensive context for orchestrator"""
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context = {
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@@ -382,7 +469,13 @@ class AgentOrchestrationService:
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self.orchestrators[user_id] = ALwrityAgentOrchestrator(config)
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logger.info(f"Created new orchestrator for user: {user_id}")
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return self.orchestrators[user_id]
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# Ensure initialization happened, if not try again (e.g. if onboarding was just completed)
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orchestrator = self.orchestrators[user_id]
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if not orchestrator.agents and not orchestrator.execution_history:
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logger.info(f"Orchestrator for {user_id} has no agents. Attempting re-initialization.")
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orchestrator._create_specialized_agents()
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return orchestrator
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async def execute_marketing_strategy(self, user_id: str, market_context: Dict[str, Any]) -> Dict[str, Any]:
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"""Execute marketing strategy for a user"""
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@@ -13,12 +13,19 @@ from abc import ABC, abstractmethod
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# txtai imports for native agent framework
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try:
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from txtai import Agent, LLM
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TXTAI_AVAILABLE = Agent.__module__ != "txtai.agent.placeholder"
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from txtai.pipeline import Agent, LLM
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TXTAI_AVAILABLE = True
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except ImportError:
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TXTAI_AVAILABLE = False
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# Fallback implementation for development
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logging.warning("txtai not available, using fallback implementation")
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try:
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from txtai import Agent, LLM
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TXTAI_AVAILABLE = True
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except ImportError:
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TXTAI_AVAILABLE = False
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Agent = None
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LLM = None
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logging.warning("txtai not available")
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_core_llm_cache = {}
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# Optional MLflow integration
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try:
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@@ -162,12 +169,15 @@ class BaseALwrityAgent(ABC):
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_prompt_context_cache: Dict[str, Dict[str, Any]] = {}
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_profile_cache: Dict[str, Dict[str, Any]] = {}
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def __init__(self, user_id: str, agent_type: str, model_name: str = "Qwen/Qwen3-4B-Instruct-2507", llm: Any = None, enable_tracing: bool = True):
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def __init__(self, user_id: str, agent_type: str, model_name: str = "Qwen/Qwen3-4B-Instruct-2507", llm: Any = None, enable_tracing: bool = True, **kwargs):
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self.user_id = user_id
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self.agent_type = agent_type
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self.model_name = model_name
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self.agent_id = f"{agent_type}_{user_id}_{datetime.utcnow().strftime('%Y%m%d%H%M%S')}"
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self.enable_tracing = enable_tracing
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self.kwargs = kwargs
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# Optional task hint; do not enforce
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self.llm_task = str(self.kwargs.get("task") or "").strip()
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self.performance = AgentPerformance(
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agent_id=self.agent_id,
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total_actions=0,
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@@ -191,26 +201,90 @@ class BaseALwrityAgent(ABC):
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self._agent_profile = self._load_agent_profile_overrides()
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self._prompt_context = self._load_prompt_context()
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# Note: We cannot await async methods in __init__.
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# If _create_txtai_agent is async, it must be called explicitly after initialization
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# or we must use a factory method.
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# For now, we will handle sync initialization here, and specialized agents
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# must handle their async initialization separately or via a sync wrapper.
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if TXTAI_AVAILABLE:
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try:
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if not self.llm:
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# Create new LLM if not provided
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# Hardening: Explicitly set task to avoid 'text2text-generation' default failures
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raw_llm = LLM(model_name, task="text-generation")
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# Wrap it
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try:
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# Use global cache for core agent LLMs too
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cache_key = f"{model_name}:language-generation"
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if cache_key in _core_llm_cache:
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raw_llm = _core_llm_cache[cache_key]
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else:
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# Use language-generation for txtai internal mapping
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raw_llm = LLM(path=model_name, task="language-generation")
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_core_llm_cache[cache_key] = raw_llm
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except Exception as e:
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logger.error(f"Failed to initialize LLM for {agent_type}: {e}")
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raise
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self.llm = TrackingLLMWrapper(raw_llm, self.user_id, self.model_name)
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try:
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self.txtai_agent = self._create_txtai_agent()
|
||||
logger.info(f"Initialized txtai agent for {agent_type} - {self.agent_id}")
|
||||
# _create_txtai_agent might be async or sync
|
||||
# CRITICAL FIX: We cannot await here. If it's async, we must run it in a loop or warn.
|
||||
# Given specialized agents define it as async, we need a sync wrapper or run_until_complete.
|
||||
|
||||
if asyncio.iscoroutinefunction(self._create_txtai_agent):
|
||||
try:
|
||||
# Check if we are in a running loop
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
except RuntimeError:
|
||||
loop = None
|
||||
|
||||
if loop and loop.is_running():
|
||||
# We are already in a loop (e.g. server), we can't block.
|
||||
# This is a design flaw in initializing async agents in __init__.
|
||||
# We will defer initialization or use a sync wrapper if possible.
|
||||
logger.warning(f"Cannot await async _create_txtai_agent for {agent_type} in __init__ within running loop. Initializing via create_task (agent may not be ready immediately).")
|
||||
|
||||
# Create a task to initialize it
|
||||
async def async_init():
|
||||
try:
|
||||
self.txtai_agent = await self._create_txtai_agent()
|
||||
logger.info(f"Async initialized txtai agent for {agent_type} - {self.agent_id}")
|
||||
except Exception as e:
|
||||
logger.error(f"Async initialization failed for {agent_type}: {e}")
|
||||
|
||||
loop.create_task(async_init())
|
||||
# Temporarily set to None or a placeholder, but we can't set it to the result yet
|
||||
self.txtai_agent = None
|
||||
else:
|
||||
# No running loop, we can run_until_complete
|
||||
if not loop:
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
self.txtai_agent = loop.run_until_complete(self._create_txtai_agent())
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to handle async initialization for {agent_type}: {e}")
|
||||
# Try fallback to sync run if possible
|
||||
try:
|
||||
self.txtai_agent = asyncio.run(self._create_txtai_agent())
|
||||
except Exception as e2:
|
||||
logger.error(f"Even asyncio.run failed: {e2}")
|
||||
raise e
|
||||
else:
|
||||
self.txtai_agent = self._create_txtai_agent()
|
||||
|
||||
if self.txtai_agent:
|
||||
logger.info(f"Initialized txtai agent for {agent_type} - {self.agent_id}")
|
||||
else:
|
||||
raise RuntimeError(f"txtai agent creation returned None for {agent_type}")
|
||||
except Exception as inner_e:
|
||||
logger.warning(f"Could not initialize specific txtai agent for {agent_type}: {inner_e}")
|
||||
self.txtai_agent = self._create_fallback_agent()
|
||||
logger.error(f"Could not initialize specific txtai agent for {agent_type}: {inner_e}")
|
||||
# Fail fast: Re-raise exception
|
||||
raise inner_e
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to initialize txtai agent for {agent_type}: {e}")
|
||||
self.txtai_agent = self._create_fallback_agent()
|
||||
# Fail fast: Re-raise exception
|
||||
raise e
|
||||
else:
|
||||
self.txtai_agent = self._create_fallback_agent()
|
||||
raise RuntimeError(f"txtai not available for {agent_type}")
|
||||
|
||||
# Initialize safety framework
|
||||
self.safety_framework = get_safety_framework(user_id)
|
||||
@@ -253,6 +327,10 @@ class BaseALwrityAgent(ABC):
|
||||
return "strategy_orchestrator"
|
||||
return value
|
||||
|
||||
def _resolve_llm_task(self, requested_task: Optional[str]) -> str:
|
||||
# No enforcement; return provided value or empty string
|
||||
return str(requested_task or "").strip()
|
||||
|
||||
def _load_agent_profile_overrides(self) -> Dict[str, Any]:
|
||||
cache_key = f"{self.user_id}:{self.agent_key}"
|
||||
cached = BaseALwrityAgent._profile_cache.get(cache_key)
|
||||
@@ -880,8 +958,8 @@ class BaseALwrityAgent(ABC):
|
||||
class StrategyOrchestratorAgent(BaseALwrityAgent):
|
||||
"""Central orchestrator agent that coordinates all marketing agents"""
|
||||
|
||||
def __init__(self, user_id: str, market_detector: Any = None, performance_monitor: Any = None, llm: Any = None):
|
||||
super().__init__(user_id, "StrategyOrchestrator", llm=llm)
|
||||
def __init__(self, user_id: str, market_detector: Any = None, performance_monitor: Any = None, llm: Any = None, **kwargs):
|
||||
super().__init__(user_id, "StrategyOrchestrator", llm=llm, **kwargs)
|
||||
self.market_detector = market_detector
|
||||
self.performance_monitor = performance_monitor
|
||||
self.sub_agents = {}
|
||||
@@ -896,66 +974,10 @@ class StrategyOrchestratorAgent(BaseALwrityAgent):
|
||||
if not TXTAI_AVAILABLE:
|
||||
return None
|
||||
|
||||
return Agent(
|
||||
llm=self.llm,
|
||||
tools=[
|
||||
{
|
||||
"name": "market_signal_detector",
|
||||
"description": "Detects market changes and competitor activities",
|
||||
"target": self._market_signal_detector_tool
|
||||
},
|
||||
{
|
||||
"name": "google_trends_fetcher",
|
||||
"description": "Fetches Google Trends data and embeds it into SIF for retrieval",
|
||||
"target": self._google_trends_fetcher_tool
|
||||
},
|
||||
{
|
||||
"name": "agent_coordinator",
|
||||
"description": "Coordinates actions between multiple agents",
|
||||
"target": self._agent_coordinator_tool
|
||||
},
|
||||
{
|
||||
"name": "performance_analyzer",
|
||||
"description": "Analyzes marketing performance metrics",
|
||||
"target": self._performance_analyzer_tool
|
||||
},
|
||||
{
|
||||
"name": "strategy_synthesizer",
|
||||
"description": "Synthesizes unified strategies from multiple inputs",
|
||||
"target": self._strategy_synthesizer_tool
|
||||
},
|
||||
{
|
||||
"name": "task_delegator",
|
||||
"description": "Delegates specific tasks to specialized agents (content, competitor, seo, social)",
|
||||
"target": self._delegate_task_tool
|
||||
},
|
||||
{
|
||||
"name": "kickoff_gsc_first_pass",
|
||||
"description": "Kicks off first-pass execution by invoking SEO/Content default GSC plans",
|
||||
"target": self._kickoff_gsc_first_pass_tool
|
||||
}
|
||||
],
|
||||
max_iterations=15,
|
||||
system=self.get_effective_system_prompt(f"""You are the Marketing Strategy Orchestrator for ALwrity user {self.user_id}.
|
||||
|
||||
Your role is to coordinate all marketing agents, analyze market signals,
|
||||
and synthesize unified strategies.
|
||||
|
||||
Key Responsibility: DELEGATE tasks to specialized agents.
|
||||
- Content Strategy Agent: For content analysis, gaps, and optimization.
|
||||
- Competitor Response Agent: For monitoring and counter-strategies.
|
||||
- SEO Optimization Agent: For technical SEO and keywords.
|
||||
- Social Amplification Agent: For social trends and distribution.
|
||||
|
||||
Use the 'task_delegator' tool to assign work to these agents.
|
||||
Do not just plan; EXECUTE by delegating.
|
||||
|
||||
Always prioritize user goals and maintain safety constraints.
|
||||
Coordinate multi-agent responses to market changes effectively.
|
||||
|
||||
First, call 'kickoff_gsc_first_pass' to ground the plan on live GSC signals."""
|
||||
)
|
||||
)
|
||||
_llm_for_agent = self.llm
|
||||
for _ in range(3):
|
||||
_llm_for_agent = getattr(_llm_for_agent, "llm", _llm_for_agent)
|
||||
return Agent(llm=_llm_for_agent, tools=[], max_iterations=15)
|
||||
|
||||
async def _market_signal_detector_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Tool for detecting market signals"""
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -18,8 +18,8 @@ class TrendSurferAgent(SIFBaseAgent):
|
||||
"Surfs" the trends detected by MarketSignalDetector to propose timely content.
|
||||
"""
|
||||
|
||||
def __init__(self, intelligence_service: TxtaiIntelligenceService, user_id: str):
|
||||
super().__init__(intelligence_service, user_id, agent_type="trend_surfer")
|
||||
def __init__(self, intelligence_service: TxtaiIntelligenceService, user_id: str, **kwargs):
|
||||
super().__init__(intelligence_service, user_id, agent_type="trend_surfer", **kwargs)
|
||||
self.user_id = user_id
|
||||
self.signal_detector = MarketSignalDetector(user_id)
|
||||
self.trends_service = GoogleTrendsService()
|
||||
|
||||
@@ -25,8 +25,9 @@ except ImportError:
|
||||
|
||||
class SharedLLMWrapper:
|
||||
"""Wraps the shared ALwrity LLM service to look like a txtai LLM."""
|
||||
def __init__(self, user_id: str):
|
||||
def __init__(self, user_id: str, task: Optional[str] = None):
|
||||
self.user_id = user_id
|
||||
self.task = task
|
||||
|
||||
def generate(self, prompt: str, **kwargs) -> str:
|
||||
"""Generate text using the shared LLM provider."""
|
||||
@@ -41,25 +42,54 @@ class SharedLLMWrapper:
|
||||
def __call__(self, prompt: str, **kwargs) -> str:
|
||||
return self.generate(prompt, **kwargs)
|
||||
|
||||
_local_llm_cache = {}
|
||||
|
||||
class LocalLLMWrapper:
|
||||
"""
|
||||
Lazily loads a local LLM via txtai.
|
||||
This prevents blocking server startup with heavy model loads.
|
||||
Lazily loads a local LLM via txtai and caches it globally.
|
||||
This prevents blocking server startup and redundant model loads.
|
||||
"""
|
||||
def __init__(self, model_path: str, task: str = "text-generation"):
|
||||
def __init__(self, model_path: str, task: str = None):
|
||||
self.model_path = model_path
|
||||
self.task = task
|
||||
self._llm = None
|
||||
# No self._llm here, we use the global cache
|
||||
|
||||
@property
|
||||
def llm(self):
|
||||
if self._llm is None:
|
||||
if LLM is None:
|
||||
raise ImportError("txtai.pipeline.LLM is not available")
|
||||
logger.info(f"Loading local LLM: {self.model_path} with task: {self.task}")
|
||||
# Explicitly set task to avoid 'text2text-generation' default failures
|
||||
self._llm = LLM(path=self.model_path, task=self.task)
|
||||
return self._llm
|
||||
# Create a cache key based on model path and task
|
||||
cache_key = f"{self.model_path}:{self.task}"
|
||||
|
||||
if cache_key in _local_llm_cache:
|
||||
return _local_llm_cache[cache_key]
|
||||
|
||||
if LLM is None:
|
||||
raise ImportError("txtai.pipeline.LLM is not available")
|
||||
|
||||
task_to_use = (self.task or "language-generation").strip()
|
||||
# Explicitly force language-generation for known models if auto-detect fails
|
||||
if any(x in self.model_path for x in ["Qwen", "Instruct", "GPT", "Llama"]):
|
||||
task_to_use = "language-generation"
|
||||
if task_to_use == "text-generation":
|
||||
task_to_use = "language-generation"
|
||||
|
||||
logger.info(f"Loading local LLM (singleton): {self.model_path} (task={task_to_use})")
|
||||
try:
|
||||
_local_llm_cache[cache_key] = LLM(path=self.model_path, task=task_to_use)
|
||||
except Exception as e:
|
||||
try:
|
||||
import transformers
|
||||
from transformers.pipelines import SUPPORTED_TASKS
|
||||
logger.error(
|
||||
f"LocalLLMWrapper init failed (model={self.model_path}, requested_task={task_to_use}, "
|
||||
f"transformers={getattr(transformers, '__version__', 'unknown')}, "
|
||||
f"supported_tasks={sorted(list(SUPPORTED_TASKS.keys()))[:50]})"
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
logger.error(f"Failed to initialize LocalLLMWrapper: {e}")
|
||||
raise e
|
||||
|
||||
return _local_llm_cache[cache_key]
|
||||
|
||||
def __call__(self, prompt: str, **kwargs) -> str:
|
||||
return self.llm(prompt, **kwargs)
|
||||
@@ -75,13 +105,9 @@ class SIFBaseAgent(BaseALwrityAgent):
|
||||
|
||||
# 2. Local LLM for internal agent work (default for SIF agents)
|
||||
if llm is None:
|
||||
if TXTAI_AVAILABLE and LLM is not None:
|
||||
# Use Lazy Local LLM when txtai LLM is available
|
||||
# Hardening: Specify 'text-generation' task to avoid text2text defaults
|
||||
llm = LocalLLMWrapper(model_name, task="text-generation")
|
||||
else:
|
||||
# Fallback to Shared if txtai or LLM is not available
|
||||
llm = self.shared_llm
|
||||
if not (TXTAI_AVAILABLE and LLM is not None):
|
||||
raise RuntimeError("txtai LLM is required for SIF agents but is not available")
|
||||
llm = LocalLLMWrapper(model_name, task="text-generation")
|
||||
|
||||
super().__init__(user_id, agent_type, model_name, llm)
|
||||
self.intelligence = intelligence_service
|
||||
@@ -98,14 +124,16 @@ class SIFBaseAgent(BaseALwrityAgent):
|
||||
capabilities via a standard agent interface if available.
|
||||
"""
|
||||
if not TXTAI_AVAILABLE or Agent is None:
|
||||
logger.debug(f"[{self.__class__.__name__}] txtai Agent not available, using fallback agent")
|
||||
return self._create_fallback_agent()
|
||||
raise RuntimeError(f"[{self.__class__.__name__}] txtai Agent not available")
|
||||
|
||||
try:
|
||||
return Agent(llm=self.llm, tools=[])
|
||||
_llm_for_agent = self.llm
|
||||
for _ in range(3):
|
||||
_llm_for_agent = getattr(_llm_for_agent, "llm", _llm_for_agent)
|
||||
return Agent(llm=_llm_for_agent, tools=[])
|
||||
except Exception as e:
|
||||
logger.warning(f"[{self.__class__.__name__}] Failed to create txtai Agent: {e}")
|
||||
return self._create_fallback_agent()
|
||||
logger.error(f"[{self.__class__.__name__}] Failed to create txtai Agent: {e}")
|
||||
raise
|
||||
|
||||
class StrategyArchitectAgent(SIFBaseAgent):
|
||||
"""Agent for discovering content pillars and identifying strategic gaps."""
|
||||
|
||||
Reference in New Issue
Block a user