AI Analysis and Content Strategy fixes. Enhanced Strategy Routes refactoring.
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ajaysi
@@ -31,7 +31,11 @@ from .research_strategies import get_strategy_for_mode
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class ResearchService:
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"""Service for conducting comprehensive research using Google Search grounding."""
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"""Service for conducting comprehensive research using Exa neural search.
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Currently supports Exa as the primary and only provider for testing and debugging.
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Google Search grounding code is preserved for future use.
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"""
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def __init__(self):
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self.keyword_analyzer = KeywordAnalyzer()
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@@ -43,9 +47,11 @@ class ResearchService:
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async def research(self, request: BlogResearchRequest, user_id: str) -> BlogResearchResponse:
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"""
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Stage 1: Research & Strategy (AI Orchestration)
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Uses ONLY Gemini's native Google Search grounding - ONE API call for everything.
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Uses Exa neural search as the primary research provider.
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Follows LinkedIn service pattern for efficiency and cost optimization.
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Includes intelligent caching for exact keyword matches.
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Note: Currently Exa-only for testing. Failures will raise errors instead of falling back.
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"""
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try:
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from services.cache.research_cache import research_cache
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@@ -88,7 +94,7 @@ class ResearchService:
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# Determine research mode and get appropriate strategy
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research_mode = request.research_mode or ResearchMode.BASIC
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config = request.config or ResearchConfig(mode=research_mode, provider=ResearchProvider.GOOGLE)
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config = request.config or ResearchConfig(mode=research_mode, provider=ResearchProvider.EXA)
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strategy = get_strategy_for_mode(research_mode)
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logger.info(f"Research: mode={research_mode.value}, provider={config.provider.value}")
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@@ -96,7 +102,11 @@ class ResearchService:
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# Build research prompt based on strategy
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research_prompt = strategy.build_research_prompt(topic, industry, target_audience, config)
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# Route to appropriate provider
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# Currently Exa-only for testing - fail if other providers are requested
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if config.provider != ResearchProvider.EXA:
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raise ValueError(f"Only Exa provider is currently supported for testing. Requested provider: {config.provider.value}")
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# Route to Exa provider
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if config.provider == ResearchProvider.EXA:
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# Exa research workflow
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from .exa_provider import ExaResearchProvider
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@@ -145,13 +155,9 @@ class ResearchService:
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grounding_metadata = None # Exa doesn't provide grounding metadata
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except RuntimeError as e:
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if "EXA_API_KEY not configured" in str(e):
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logger.warning("Exa not configured, falling back to Google")
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config.provider = ResearchProvider.GOOGLE
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# Continue to Google flow below
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raw_result = None
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else:
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raise
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# Fail fast - no fallback for testing/debugging
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logger.error(f"Exa research failed: {e}")
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raise RuntimeError(f"Exa research failed: {e}. Please ensure EXA_API_KEY is configured.") from e
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elif config.provider == ResearchProvider.TAVILY:
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# Tavily research workflow
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@@ -231,41 +237,13 @@ class ResearchService:
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grounding_metadata = None # Tavily doesn't provide grounding metadata
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except RuntimeError as e:
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if "TAVILY_API_KEY not configured" in str(e):
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logger.warning("Tavily not configured, falling back to Google")
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config.provider = ResearchProvider.GOOGLE
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# Continue to Google flow below
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raw_result = None
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else:
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raise
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if config.provider not in [ResearchProvider.EXA, ResearchProvider.TAVILY]:
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# Google research (existing flow) or fallback from Exa
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from .google_provider import GoogleResearchProvider
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import time
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api_start_time = time.time()
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google_provider = GoogleResearchProvider()
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gemini_result = await google_provider.search(
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research_prompt, topic, industry, target_audience, config, user_id
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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 and content
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sources = self._extract_sources_from_grounding(gemini_result)
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content = gemini_result.get("content", "")
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search_widget = gemini_result.get("search_widget", "") or ""
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search_queries = gemini_result.get("search_queries", []) or []
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grounding_metadata = self._extract_grounding_metadata(gemini_result)
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# Fail fast - no fallback for testing/debugging
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logger.error(f"Tavily research failed: {e}")
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raise RuntimeError(f"Tavily research failed: {e}. Please ensure TAVILY_API_KEY is configured.") from e
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# Validate that we have content and sources before proceeding
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if 'content' not in locals() or 'sources' not in locals():
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raise RuntimeError(f"{config.provider.value} research did not return content or sources. Research failed.")
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# Continue with common analysis (same for both providers)
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keyword_analysis = self.keyword_analyzer.analyze(content, request.keywords, user_id=user_id)
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@@ -434,7 +412,7 @@ class ResearchService:
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# Determine research mode and get appropriate strategy
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research_mode = request.research_mode or ResearchMode.BASIC
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config = request.config or ResearchConfig(mode=research_mode, provider=ResearchProvider.GOOGLE)
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config = request.config or ResearchConfig(mode=research_mode, provider=ResearchProvider.EXA)
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strategy = get_strategy_for_mode(research_mode)
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logger.info(f"Research: mode={research_mode.value}, provider={config.provider.value}")
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@@ -442,7 +420,11 @@ class ResearchService:
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# Build research prompt based on strategy
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research_prompt = strategy.build_research_prompt(topic, industry, target_audience, config)
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# Route to appropriate provider
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# Currently Exa-only for testing - fail if other providers are requested
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if config.provider != ResearchProvider.EXA:
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raise ValueError(f"Only Exa provider is currently supported for testing. Requested provider: {config.provider.value}")
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# Route to Exa provider
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if config.provider == ResearchProvider.EXA:
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# Exa research workflow
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from .exa_provider import ExaResearchProvider
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@@ -495,13 +477,10 @@ class ResearchService:
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grounding_metadata = None # Exa doesn't provide grounding metadata
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except RuntimeError as e:
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if "EXA_API_KEY not configured" in str(e):
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logger.warning("Exa not configured, falling back to Google")
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await task_manager.update_progress(task_id, "⚠️ Exa not configured, falling back to Google Search")
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config.provider = ResearchProvider.GOOGLE
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# Continue to Google flow below
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else:
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raise
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# Fail fast - no fallback for testing/debugging
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logger.error(f"Exa research failed: {e}")
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await task_manager.update_progress(task_id, f"❌ Exa research failed: {str(e)}")
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raise RuntimeError(f"Exa research failed: {e}. Please ensure EXA_API_KEY is configured.") from e
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elif config.provider == ResearchProvider.TAVILY:
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# Tavily research workflow
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@@ -581,43 +560,18 @@ class ResearchService:
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grounding_metadata = None # Tavily doesn't provide grounding metadata
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except RuntimeError as e:
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if "TAVILY_API_KEY not configured" in str(e):
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logger.warning("Tavily not configured, falling back to Google")
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await task_manager.update_progress(task_id, "⚠️ Tavily not configured, falling back to Google Search")
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config.provider = ResearchProvider.GOOGLE
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# Continue to Google flow below
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else:
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raise
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if config.provider not in [ResearchProvider.EXA, ResearchProvider.TAVILY]:
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# Google research (existing flow)
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from .google_provider import GoogleResearchProvider
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await task_manager.update_progress(task_id, "🌐 Connecting to Google Search grounding...")
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google_provider = GoogleResearchProvider()
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await task_manager.update_progress(task_id, "🤖 Making AI request to Gemini with Google Search grounding...")
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try:
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gemini_result = await google_provider.search(
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research_prompt, topic, industry, target_audience, config, user_id
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)
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except HTTPException as http_error:
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logger.error(f"Subscription limit exceeded for Google research: {http_error.detail}")
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await task_manager.update_progress(task_id, f"❌ Subscription limit exceeded: {http_error.detail.get('message', str(http_error.detail)) if isinstance(http_error.detail, dict) else str(http_error.detail)}")
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raise
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await task_manager.update_progress(task_id, "📊 Processing research results and extracting insights...")
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# Extract sources and content
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# Handle None result case
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if gemini_result is None:
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logger.error("gemini_result is None after search - this should not happen if HTTPException was raised")
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raise ValueError("Research result is None - search operation failed unexpectedly")
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sources = self._extract_sources_from_grounding(gemini_result)
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content = gemini_result.get("content", "") if isinstance(gemini_result, dict) else ""
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search_widget = gemini_result.get("search_widget", "") or "" if isinstance(gemini_result, dict) else ""
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search_queries = gemini_result.get("search_queries", []) or [] if isinstance(gemini_result, dict) else []
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grounding_metadata = self._extract_grounding_metadata(gemini_result)
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# Fail fast - no fallback for testing/debugging
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logger.error(f"Tavily research failed: {e}")
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await task_manager.update_progress(task_id, f"❌ Tavily research failed: {str(e)}")
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raise RuntimeError(f"Tavily research failed: {e}. Please ensure TAVILY_API_KEY is configured.") from e
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# Validate that we have content and sources before proceeding
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if config.provider == ResearchProvider.EXA and ('content' not in locals() or 'sources' not in locals()):
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await task_manager.update_progress(task_id, "❌ Exa research did not return content or sources")
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raise RuntimeError("Exa research did not return content or sources. Research failed.")
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elif config.provider == ResearchProvider.TAVILY and ('content' not in locals() or 'sources' not in locals()):
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await task_manager.update_progress(task_id, "❌ Tavily research did not return content or sources")
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raise RuntimeError("Tavily research did not return content or sources. Research failed.")
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# Continue with common analysis (same for both providers)
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await task_manager.update_progress(task_id, "🔍 Analyzing keywords and content angles...")
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