SEO Dashboard Fixes and content planning refactoring
This commit is contained in:
ajaysi
@@ -16,6 +16,7 @@ from models.blog_models import (
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GroundingSupport,
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Citation,
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)
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from services.blog_writer.logger_config import blog_writer_logger, log_function_call
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from .keyword_analyzer import KeywordAnalyzer
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from .competitor_analyzer import CompetitorAnalyzer
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@@ -32,6 +33,7 @@ class ResearchService:
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self.content_angle_generator = ContentAngleGenerator()
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self.data_filter = ResearchDataFilter()
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@log_function_call("research_operation")
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async def research(self, request: BlogResearchRequest) -> BlogResearchResponse:
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"""
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Stage 1: Research & Strategy (AI Orchestration)
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@@ -47,6 +49,16 @@ class ResearchService:
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industry = request.industry or (request.persona.industry if request.persona and request.persona.industry else "General")
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target_audience = getattr(request.persona, 'target_audience', 'General') if request.persona else 'General'
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# Log research parameters
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blog_writer_logger.log_operation_start(
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"research",
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topic=topic,
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industry=industry,
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target_audience=target_audience,
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keywords=request.keywords,
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keyword_count=len(request.keywords)
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)
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# Check cache first for exact keyword match
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cached_result = research_cache.get_cached_result(
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keywords=request.keywords,
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@@ -56,10 +68,12 @@ class ResearchService:
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if cached_result:
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logger.info(f"Returning cached research result for keywords: {request.keywords}")
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blog_writer_logger.log_operation_end("research", 0, success=True, cache_hit=True)
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return BlogResearchResponse(**cached_result)
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# Cache miss - proceed with API call
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logger.info(f"Cache miss - making API call for keywords: {request.keywords}")
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blog_writer_logger.log_operation_start("gemini_api_call", api_name="gemini_grounded", operation="research")
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gemini = GeminiGroundedProvider()
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# Single comprehensive research prompt - Gemini handles Google Search automatically
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@@ -82,11 +96,23 @@ class ResearchService:
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"""
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# Single Gemini call with native Google Search grounding - no fallbacks
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import time
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api_start_time = time.time()
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gemini_result = await gemini.generate_grounded_content(
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prompt=research_prompt,
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content_type="research",
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max_tokens=2000
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)
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api_duration_ms = (time.time() - api_start_time) * 1000
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# Log API call performance
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blog_writer_logger.log_api_call(
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"gemini_grounded",
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"generate_grounded_content",
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api_duration_ms,
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token_usage=gemini_result.get("token_usage", {}),
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content_length=len(gemini_result.get("content", ""))
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)
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# Extract sources from grounding metadata
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sources = self._extract_sources_from_grounding(gemini_result)
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@@ -105,6 +131,17 @@ class ResearchService:
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suggested_angles = self.content_angle_generator.generate(content, topic, industry)
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logger.info(f"Research completed successfully with {len(sources)} sources and {len(search_queries)} search queries")
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# Log analysis results
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blog_writer_logger.log_performance(
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"research_analysis",
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len(content),
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"characters",
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sources_count=len(sources),
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search_queries_count=len(search_queries),
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keyword_analysis_keys=len(keyword_analysis),
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suggested_angles_count=len(suggested_angles)
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)
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# Create the response
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response = BlogResearchResponse(
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@@ -146,7 +183,47 @@ class ResearchService:
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error_message = str(e)
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logger.error(f"Research failed: {error_message}")
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# Return a graceful failure response instead of raising
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# Log error with full context
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blog_writer_logger.log_error(
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e,
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"research",
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context={
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"topic": topic,
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"keywords": request.keywords,
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"industry": industry,
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"target_audience": target_audience
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}
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)
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# Import custom exceptions for better error handling
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from services.blog_writer.exceptions import (
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ResearchFailedException,
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APIRateLimitException,
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APITimeoutException,
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ValidationException
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)
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# Determine if this is a retryable error
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retry_suggested = True
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user_message = "Research failed. Please try again with different keywords or check your internet connection."
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if isinstance(e, APIRateLimitException):
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retry_suggested = True
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user_message = f"Rate limit exceeded. Please wait {e.context.get('retry_after', 60)} seconds before trying again."
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elif isinstance(e, APITimeoutException):
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retry_suggested = True
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user_message = "Research request timed out. Please try again with a shorter query or check your internet connection."
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elif isinstance(e, ValidationException):
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retry_suggested = False
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user_message = "Invalid research request. Please check your input parameters and try again."
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elif "401" in error_message or "403" in error_message:
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retry_suggested = False
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user_message = "Authentication failed. Please check your API credentials."
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elif "400" in error_message:
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retry_suggested = False
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user_message = "Invalid request. Please check your input parameters."
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# Return a graceful failure response with enhanced error information
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return BlogResearchResponse(
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success=False,
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sources=[],
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@@ -155,9 +232,18 @@ class ResearchService:
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suggested_angles=[],
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search_widget="",
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search_queries=[],
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error_message=error_message
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error_message=user_message,
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retry_suggested=retry_suggested,
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error_code=getattr(e, 'error_code', 'RESEARCH_FAILED'),
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actionable_steps=getattr(e, 'actionable_steps', [
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"Try with different keywords",
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"Check your internet connection",
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"Wait a few minutes and try again",
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"Contact support if the issue persists"
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])
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)
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@log_function_call("research_with_progress")
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async def research_with_progress(self, request: BlogResearchRequest, task_id: str) -> BlogResearchResponse:
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"""
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Research method with progress updates for real-time feedback.
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@@ -291,7 +377,47 @@ class ResearchService:
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error_message = str(e)
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logger.error(f"Research failed: {error_message}")
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# Return a graceful failure response instead of raising
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# Log error with full context
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blog_writer_logger.log_error(
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e,
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"research",
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context={
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"topic": topic,
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"keywords": request.keywords,
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"industry": industry,
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"target_audience": target_audience
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}
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)
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# Import custom exceptions for better error handling
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from services.blog_writer.exceptions import (
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ResearchFailedException,
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APIRateLimitException,
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APITimeoutException,
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ValidationException
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)
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# Determine if this is a retryable error
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retry_suggested = True
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user_message = "Research failed. Please try again with different keywords or check your internet connection."
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if isinstance(e, APIRateLimitException):
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retry_suggested = True
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user_message = f"Rate limit exceeded. Please wait {e.context.get('retry_after', 60)} seconds before trying again."
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elif isinstance(e, APITimeoutException):
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retry_suggested = True
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user_message = "Research request timed out. Please try again with a shorter query or check your internet connection."
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elif isinstance(e, ValidationException):
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retry_suggested = False
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user_message = "Invalid research request. Please check your input parameters and try again."
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elif "401" in error_message or "403" in error_message:
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retry_suggested = False
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user_message = "Authentication failed. Please check your API credentials."
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elif "400" in error_message:
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retry_suggested = False
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user_message = "Invalid request. Please check your input parameters."
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# Return a graceful failure response with enhanced error information
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return BlogResearchResponse(
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success=False,
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sources=[],
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@@ -300,7 +426,15 @@ class ResearchService:
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suggested_angles=[],
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search_widget="",
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search_queries=[],
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error_message=error_message
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error_message=user_message,
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retry_suggested=retry_suggested,
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error_code=getattr(e, 'error_code', 'RESEARCH_FAILED'),
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actionable_steps=getattr(e, 'actionable_steps', [
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"Try with different keywords",
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"Check your internet connection",
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"Wait a few minutes and try again",
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"Contact support if the issue persists"
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])
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)
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def _extract_sources_from_grounding(self, gemini_result: Dict[str, Any]) -> List[ResearchSource]:
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