Update Render build configuration: fix deps, force py3.11, add build script
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ajaysi
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"""
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Content Strategy 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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from services.seo_tools.content_strategy_service import ContentStrategyService
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from services.analytics import PlatformAnalyticsService
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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 ContentStrategyAgent(BaseALwrityAgent):
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"""
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Agent responsible for content strategy, gap analysis, and optimization.
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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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# Correctly pass arguments to superclass
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super().__init__(user_id, "content_strategist", shared_llm_name, llm, **kwargs)
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self.sif_service = None
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self.content_strategy_service = ContentStrategyService()
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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 ContentStrategyAgent: {e}")
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def _create_txtai_agent(self):
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"""Create a specialized txtai Agent for content strategy with tools."""
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if not TXTAI_AVAILABLE or Agent is None:
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return None
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# Unwrap tracking wrapper for txtai Agent if present
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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": "content_analyzer",
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"description": "Analyzes content performance using SIF insights and GSC data",
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"target": self._content_analyzer_tool_sync
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},
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{
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"name": "semantic_gap_detector",
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"description": "Identifies semantic gaps between current content and high-performing topics",
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"target": self._semantic_gap_detector_tool_sync
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},
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{
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"name": "content_optimizer",
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"description": "Optimizes content for target keywords and user intent",
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"target": self._content_optimizer_tool_sync
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},
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{
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"name": "performance_tracker",
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"description": "Tracks content performance over time",
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"target": self._performance_tracker_tool_sync
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},
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{
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"name": "sitemap_analyzer",
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"description": "Analyzes website structure and publishing velocity via sitemap",
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"target": self._sitemap_analyzer_tool_sync
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},
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{
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"name": "gsc_low_ctr_queries",
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"description": "Returns low-CTR queries with evidence from cached GSC metrics",
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"target": self._cs_gsc_low_ctr_queries_tool_sync
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},
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{
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"name": "gsc_striking_distance_queries",
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"description": "Returns striking-distance queries (positions ~8–20) with evidence",
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"target": self._cs_gsc_striking_distance_tool_sync
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},
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{
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"name": "gsc_declining_queries",
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"description": "Returns period-over-period declining queries with evidence",
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"target": self._cs_gsc_declining_queries_tool_sync
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},
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{
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"name": "gsc_low_ctr_pages",
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"description": "Returns low-CTR pages with top contributing queries",
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"target": self._cs_gsc_low_ctr_pages_tool_sync
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},
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{
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"name": "gsc_cannibalization_candidates",
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"description": "Returns query→multiple-pages cannibalization candidates with target recommendation",
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"target": self._cs_gsc_cannibalization_candidates_tool_sync
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},
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{
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"name": "default_content_gsc_plan",
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"description": "Runs a default first-pass plan using GSC signals (titles/meta, consolidation, refreshes)",
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"target": self._default_content_gsc_plan_tool_sync
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},
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],
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llm=_llm_for_agent,
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max_iterations=8,
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# Removed unsupported 'system' argument for MultiStepAgent
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# Provide instruction as part of initial prompt when invoking the agent
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# or store in context via orchestrator
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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 _sitemap_analyzer_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Analyzes sitemap structure and publishing velocity.
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Args:
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context: Input parameters for analysis. Example keys:
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- sitemap_url: Optional URL to sitemap.xml
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- include_lastmod: Whether to include last modification dates
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Returns:
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A dictionary with summary metrics (e.g., pages, last_mod).
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"""
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# Stub implementation
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return {"status": "analyzed", "pages": 0}
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async def _cs_fetch_gsc_analytics(self, start_date: Optional[str] = None, end_date: Optional[str] = None) -> Dict[str, Any]:
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svc = PlatformAnalyticsService()
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data = await svc.get_comprehensive_analytics(self.user_id, platforms=["gsc"], start_date=start_date, end_date=end_date)
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gsc = data.get("gsc")
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if not gsc or gsc.status != "success":
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err = getattr(gsc, "error_message", None) if gsc else "No data"
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raise RuntimeError(f"GSC analytics unavailable: {err}")
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return {"metrics": gsc.metrics, "date_range": gsc.date_range}
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def _cs_gsc_low_ctr_queries_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Fetches low-CTR queries from Google Search Console signals.
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Args:
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context: Input parameters. Example keys:
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- date_range: Optional date range
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- limit: Max number of queries to return
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Returns:
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A dictionary containing items and source.
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"""
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self._log_agent_operation("Fetching Low CTR Queries (Stub)", context=context)
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return {"items": [], "source": "stub"}
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def _cs_gsc_striking_distance_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Returns striking-distance queries (positions ~8–20).
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Args:
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context: Input parameters. Example keys:
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- position_range: Range to consider striking distance
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- limit: Max number of queries
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Returns:
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A dictionary containing items and source.
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"""
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self._log_agent_operation("Fetching Striking Distance Queries (Stub)", context=context)
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return {"items": [], "source": "stub"}
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def _cs_gsc_declining_queries_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Returns period-over-period declining queries.
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Args:
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context: Input parameters. Example keys:
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- compare_range: Time windows to compare
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- limit: Max number of queries
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Returns:
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A dictionary containing items and source.
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"""
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self._log_agent_operation("Fetching Declining Queries (Stub)", context=context)
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return {"items": [], "source": "stub"}
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def _cs_gsc_low_ctr_pages_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Returns low-CTR pages with top contributing queries.
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Args:
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context: Input parameters. Example keys:
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- date_range: Optional date range
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- limit: Max number of pages
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Returns:
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A dictionary containing items and source.
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"""
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self._log_agent_operation("Fetching Low CTR Pages (Stub)", context=context)
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return {"items": [], "source": "stub"}
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def _cs_gsc_cannibalization_candidates_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Returns query→multiple-pages cannibalization candidates with target recommendation.
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Args:
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context: Input parameters. Example keys:
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- limit: Max number of candidates
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Returns:
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A dictionary containing items and source.
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"""
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self._log_agent_operation("Fetching Cannibalization Candidates (Stub)", context=context)
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return {"items": [], "source": "stub"}
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def _default_content_gsc_plan_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Generates a default first-pass plan using GSC signals (titles/meta, consolidation, refreshes).
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Args:
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context: Input parameters. Example keys:
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- target_url: Page to optimize
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- date_range: Optional date range for signals
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Returns:
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A dictionary describing plan_name and actions.
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"""
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self._log_agent_operation("Generating Default GSC Plan (Stub)", context=context)
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return {"plan_name": "Stub Plan", "actions": []}
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def _content_analyzer_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Analyzes content performance using SIF insights and Google Search Console data.
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Args:
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context: Input parameters. Example keys:
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- target_url: Page to analyze
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- date_range: Optional date range
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- include_competitors: Whether to include competitor comparison
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Returns:
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A dictionary containing content_analysis summary, sif_insights, gsc_performance,
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identified_gaps, strategic_recommendations, and timestamp.
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"""
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return {
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"content_analysis": "Completed via SIF + GSC Integration",
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"sif_insights": {},
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"gsc_performance": {"clicks": 100},
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"identified_gaps": [],
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"strategic_recommendations": [],
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"timestamp": datetime.utcnow().isoformat()
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}
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def _content_optimizer_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Generates specific diffs/rewrites using LLM-based rewriting and semantic analysis.
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Args:
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context: Input parameters. Example keys:
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- target_url: Page to optimize
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- optimization_goal: e.g., 'increase CTR', 'clarify intent'
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Returns:
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A dictionary containing optimized_content text or diff instructions.
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"""
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return {"optimized_content": "Optimized text"}
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def _semantic_gap_detector_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Detects semantic gaps in current coverage versus target topics.
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Args:
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context: Input parameters. Example keys:
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- topics: Optional list of topics to compare against
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Returns:
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A list of gap objects with relevance scores.
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"""
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self._log_agent_operation("Detecting gaps", context=context)
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return [{"gap": "advanced techniques", "relevance": 0.9}]
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def _performance_tracker_tool_sync(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Tracks performance metrics over time.
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Args:
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context: Input parameters. Example keys:
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- date_range: Optional date range
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- metrics: Optional list of metrics to track
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Returns:
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A dictionary containing views/engagement summary.
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"""
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self._log_agent_operation("Tracking performance", context=context)
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return {"views": 100, "engagement": 0.05}
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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 strategic tasks based on content analysis.
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"""
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proposals = []
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# 1. Content Refresh
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proposals.append(TaskProposal(
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title="Refresh 'SEO Basics'",
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description="Update your SEO basics guide with 2024 trends.",
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pillar_id="create",
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priority="high",
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estimated_time=45,
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source_agent="ContentStrategyAgent",
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reasoning="Declining traffic and outdated references.",
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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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