141 lines
5.0 KiB
Python
141 lines
5.0 KiB
Python
"""
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Social Amplification Agent implementation.
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"""
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from loguru import logger
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from .base import SIFBaseAgent, TXTAI_AVAILABLE, Agent
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from services.intelligence.agents.core_agent_framework import BaseALwrityAgent, TaskProposal
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try:
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from services.intelligence.sif_integration import SIFIntegrationService
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SIF_AVAILABLE = True
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except ImportError:
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SIF_AVAILABLE = False
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class SocialAmplificationAgent(BaseALwrityAgent):
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"""
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Agent responsible for social media monitoring, content adaptation, and distribution.
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"""
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def __init__(self, user_id: str, shared_llm_name: str, llm: Any = None, **kwargs):
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super().__init__(user_id, "social_media_manager", shared_llm_name, llm, **kwargs)
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self.sif_service = None
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if SIF_AVAILABLE:
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try:
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self.sif_service = SIFIntegrationService(user_id)
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except Exception as e:
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logger.warning(f"Failed to initialize SIF service for SocialAmplificationAgent: {e}")
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def _create_txtai_agent(self):
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"""Create a specialized txtai Agent for social media."""
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if not TXTAI_AVAILABLE or Agent is None:
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return None
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_llm_for_agent = getattr(self.llm, "llm", self.llm)
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return Agent(
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tools=[
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{
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"name": "social_monitor",
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"description": "Monitors social trends and conversations",
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"target": self._social_monitor_tool
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},
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{
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"name": "content_adapter",
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"description": "Adapts long-form content for social platforms",
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"target": self._content_adapter_tool
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},
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{
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"name": "engagement_optimizer",
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"description": "Optimizes posts for engagement (hashtags, timing)",
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"target": self._engagement_optimizer_tool
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},
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{
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"name": "distribution_manager",
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"description": "Manages posting schedule",
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"target": self._distribution_manager_tool
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}
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],
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llm=_llm_for_agent,
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max_iterations=10,
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# Removed unsupported 'system' argument
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# Instruction will be provided via orchestrator context or initial prompt
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# Instruction should be provided during invocation or via orchestrator context
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)
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# Tool Implementations
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def _social_monitor_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Social monitoring tool using SIF.
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Args:
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context: Dictionary containing monitoring criteria like 'topics' or 'platforms'.
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"""
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# Stub implementation
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return {
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"trends": ["AI in marketing", "Content automation"],
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"source": "stub",
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"timestamp": datetime.utcnow().isoformat()
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}
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def _content_adapter_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Adapts content for specific platforms.
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Args:
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context: Dictionary containing 'content' and 'platform' (e.g., 'linkedin', 'twitter').
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"""
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# Stub implementation
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return {"adapted_content": "Social post"}
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def _engagement_optimizer_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Optimizes content for engagement (hashtags, timing, hook).
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Args:
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context: Dictionary containing 'content' to optimize.
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"""
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# Stub implementation
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return {
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"optimization_suggestions": ["Use questions"],
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"estimated_engagement_score": 8.5,
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"timestamp": datetime.utcnow().isoformat()
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}
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def _distribution_manager_tool(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Manages distribution (scheduling/posting).
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Args:
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context: Dictionary containing 'post_content' and 'schedule_time'.
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"""
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# Stub implementation
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return {
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"distribution_plan": [],
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"status": "scheduled",
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"timestamp": datetime.utcnow().isoformat()
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}
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async def propose_daily_tasks(self, context: Dict[str, Any]) -> List[TaskProposal]:
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"""
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Propose social media tasks.
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"""
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proposals = []
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# 1. Social Post Creation
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proposals.append(TaskProposal(
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title="Create LinkedIn Thread",
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description="Summarize your latest blog post into a 5-tweet thread.",
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pillar_id="distribute",
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priority="medium",
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estimated_time=20,
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source_agent="SocialAmplificationAgent",
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reasoning="Repurpose existing content.",
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action_type="navigate",
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action_url="/content-planning-dashboard"
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))
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return proposals
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