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Upload agents/programmatic_seo_agent.py with huggingface_hub

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  1. agents/programmatic_seo_agent.py +142 -0
agents/programmatic_seo_agent.py ADDED
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+ import asyncio
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+ from typing import Dict, List, Any
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+ from core.agent import BaseAgent
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+ from core.models import AgentConfig, Task, AgentMessage, SEOData
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+ import logging
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+ import random
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+ from datetime import datetime
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+
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+ logger = logging.getLogger(__name__)
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+
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+
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+ class ProgrammaticSEOAgent(BaseAgent):
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+ """Programmatic SEO Agent responsible for creating long-tail content at scale"""
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+
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+ def __init__(self, config: AgentConfig):
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+ super().__init__(config)
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+ self.content_templates = []
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+ self.long_tail_keywords = []
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+ self.generated_pages = []
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+ self.scaling_strategies = []
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+
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+ async def execute(self):
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+ """Execute programmatic SEO functions"""
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+ logger.info(f"{self.name} executing programmatic SEO...")
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+
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+ # Generate content templates
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+ await self.create_templates()
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+
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+ # Identify long-tail keywords
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+ await self.find_long_tail_keywords()
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+
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+ # Generate scalable content
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+ await self.generate_scalable_content()
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+
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+ # Implement scaling strategies
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+ await self.implement_scaling()
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+
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+ async def create_templates(self):
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+ """Create content templates for programmatic generation"""
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+ logger.info(f"{self.name} creating content templates...")
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+
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+ # Simulate template creation
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+ templates = [
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+ {
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+ "name": "product_comparison_template",
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+ "structure": ["title", "intro", "feature_comparison", "pros_cons", "conclusion"],
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+ "variables": ["product1", "product2", "category", "features"],
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+ "target_keywords": ["best [product] comparison", "[product1] vs [product2]"]
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+ },
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+ {
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+ "name": "local_service_template",
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+ "structure": ["title", "intro", "services", "process", "contact"],
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+ "variables": ["service", "location", "business_name"],
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+ "target_keywords": ["[service] [location]", "[service] near me"]
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+ },
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+ {
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+ "name": "how_to_template",
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+ "structure": ["title", "intro", "steps", "tips", "faq"],
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+ "variables": ["task", "tools", "time"],
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+ "target_keywords": ["how to [task]", "[task] tutorial"]
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+ }
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+ ]
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+
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+ self.content_templates.extend(templates)
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+
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+ # Log template creation
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+ logger.info(f"Created {len(templates)} content templates")
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+
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+ async def find_long_tail_keywords(self):
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+ """Identify long-tail keywords for programmatic content"""
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+ logger.info(f"{self.name} finding long-tail keywords...")
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+
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+ # Simulate long-tail keyword discovery
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+ long_tail_keywords = [
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+ {"keyword": "best running shoes for flat feet 2024", "volume": 120, "difficulty": "low", "intent": "commercial"},
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+ {"keyword": "how to fix leaky faucet in apartment", "volume": 85, "difficulty": "low", "intent": "informational"},
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+ {"keyword": "affordable web design services chicago", "volume": 210, "difficulty": "medium", "intent": "commercial"},
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+ {"keyword": "organic dog food brands comparison", "volume": 180, "difficulty": "low", "intent": "commercial"},
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+ {"keyword": "beginner yoga poses for seniors", "volume": 95, "difficulty": "low", "intent": "informational"}
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+ ]
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+
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+ self.long_tail_keywords.extend(long_tail_keywords)
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+
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+ # Log keyword discovery
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+ logger.info(f"Found {len(long_tail_keywords)} long-tail keywords")
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+
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+ async def generate_scalable_content(self):
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+ """Generate content using templates and variables"""
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+ logger.info(f"{self.name} generating scalable content...")
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+
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+ # Simulate content generation using templates
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+ generated_content = []
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+ for i in range(5): # Generate 5 sample pages
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+ content = {
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+ "title": f"Generated Page {i+1}",
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+ "url": f"/generated-page-{i+1}",
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+ "template_used": random.choice(self.content_templates)["name"],
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+ "target_keyword": random.choice(self.long_tail_keywords)["keyword"],
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+ "status": "published",
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+ "word_count": random.randint(800, 1500)
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+ }
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+ generated_content.append(content)
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+
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+ self.generated_pages.extend(generated_content)
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+
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+ # Log content generation
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+ logger.info(f"Generated {len(generated_content)} scalable content pages")
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+
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+ async def implement_scaling(self):
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+ """Implement strategies for scaling content generation"""
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+ logger.info(f"{self.name} implementing scaling strategies...")
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+
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+ # Simulate scaling strategies
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+ scaling_strategies = [
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+ {"strategy": "automated_keyword_research", "implementation": "api_integration"},
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+ {"strategy": "template_variety_expansion", "implementation": "new_template_creation"},
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+ {"strategy": "content_personalization", "implementation": "user_data_integration"},
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+ {"strategy": "multi_format_content", "implementation": "video_audio_transcripts"}
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+ ]
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+
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+ self.scaling_strategies.extend(scaling_strategies)
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+
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+ # Send scaling updates to SEO Director
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+ await self.send_message(
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+ recipient="seo_director",
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+ content=f"Scaling strategies implemented: {len(scaling_strategies)}",
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+ message_type="info"
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+ )
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+
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+ async def _execute_task_logic(self, task: Task) -> Dict[str, Any]:
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+ """Execute specific task logic for Programmatic SEO agent"""
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+ if task.type == "create_templates":
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+ await self.create_templates()
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+ return {"status": "completed", "result": self.content_templates}
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+ elif task.type == "find_keywords":
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+ await self.find_long_tail_keywords()
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+ return {"status": "completed", "result": self.long_tail_keywords[:5]} # Return first 5
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+ elif task.type == "generate_content":
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+ await self.generate_scalable_content()
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+ return {"status": "completed", "result": self.generated_pages}
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+ else:
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+ return {"status": "error", "message": f"Unknown task type: {task.type}"}