Research component integration, Copilotkit implementation, SEO copilotkit implementation, Wix SEO metadata complete, Wix SEO metadata review
This commit is contained in:
ajaysi
@@ -16,6 +16,9 @@ from models.blog_models import (
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GroundingChunk,
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GroundingSupport,
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Citation,
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ResearchConfig,
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ResearchMode,
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ResearchProvider,
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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 fastapi import HTTPException
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@@ -24,6 +27,7 @@ from .keyword_analyzer import KeywordAnalyzer
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from .competitor_analyzer import CompetitorAnalyzer
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from .content_angle_generator import ContentAngleGenerator
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from .data_filter import ResearchDataFilter
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from .research_strategies import get_strategy_for_mode
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class ResearchService:
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@@ -44,7 +48,6 @@ class ResearchService:
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Includes intelligent caching for exact keyword matches.
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"""
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try:
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from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
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from services.cache.research_cache import research_cache
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topic = request.topic or ", ".join(request.keywords)
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@@ -79,62 +82,104 @@ class ResearchService:
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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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blog_writer_logger.log_operation_start("research_api_call", api_name="research", operation="research")
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# Single comprehensive research prompt - Gemini handles Google Search automatically
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research_prompt = f"""
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Research the topic "{topic}" in the {industry} industry for {target_audience} audience. Provide a comprehensive analysis including:
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1. Current trends and insights (2024-2025)
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2. Key statistics and data points with sources
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3. Industry expert opinions and quotes
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4. Recent developments and news
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5. Market analysis and forecasts
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6. Best practices and case studies
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7. Keyword analysis: primary, secondary, and long-tail opportunities
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8. Competitor analysis: top players and content gaps
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9. Content angle suggestions: 5 compelling angles for blog posts
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Focus on factual, up-to-date information from credible sources.
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Include specific data points, percentages, and recent developments.
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Structure your response with clear sections for each analysis area.
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"""
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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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strategy = get_strategy_for_mode(research_mode)
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# Single Gemini call with native Google Search grounding - no fallbacks
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# Validation is handled inside generate_grounded_content when validate_subsequent_operations=True
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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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user_id=user_id,
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validate_subsequent_operations=True # Validates Google Grounding + 3 LLM calls
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)
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api_duration_ms = (time.time() - api_start_time) * 1000
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logger.info(f"Research: mode={research_mode.value}, provider={config.provider.value}")
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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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# 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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# Extract sources from grounding metadata
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sources = self._extract_sources_from_grounding(gemini_result)
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# Route to appropriate 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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from services.subscription.preflight_validator import validate_exa_research_operations
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from services.database import get_db
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from services.subscription import PricingService
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import os
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import time
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# Pre-flight validation
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db_val = next(get_db())
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try:
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pricing_service = PricingService(db_val)
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gpt_provider = os.getenv("GPT_PROVIDER", "google")
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validate_exa_research_operations(pricing_service, user_id, gpt_provider)
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finally:
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db_val.close()
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# Execute Exa search
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api_start_time = time.time()
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try:
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exa_provider = ExaResearchProvider()
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raw_result = await exa_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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# Track usage
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cost = raw_result.get('cost', {}).get('total', 0.005) if isinstance(raw_result.get('cost'), dict) else 0.005
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exa_provider.track_exa_usage(user_id, cost)
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# Log API call performance
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blog_writer_logger.log_api_call(
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"exa_search",
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"search_and_contents",
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api_duration_ms,
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token_usage={},
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content_length=len(raw_result.get('content', ''))
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)
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# Extract content for downstream analysis
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content = raw_result.get('content', '')
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sources = raw_result.get('sources', [])
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search_widget = "" # Exa doesn't provide search widgets
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search_queries = raw_result.get('search_queries', [])
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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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if config.provider != ResearchProvider.EXA:
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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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# Extract grounding metadata for detailed UI display
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grounding_metadata = self._extract_grounding_metadata(gemini_result)
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# Extract search widget and queries for UI display
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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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# Parse the comprehensive response for different analysis components
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content = gemini_result.get("content", "")
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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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competitor_analysis = self.competitor_analyzer.analyze(content, user_id=user_id)
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suggested_angles = self.content_angle_generator.generate(content, topic, industry, user_id=user_id)
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@@ -261,7 +306,6 @@ class ResearchService:
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Research method with progress updates for real-time feedback.
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"""
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try:
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from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
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from services.cache.research_cache import research_cache
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from services.cache.persistent_research_cache import persistent_research_cache
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from api.blog_writer.task_manager import task_manager
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@@ -293,66 +337,100 @@ class ResearchService:
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logger.info(f"Returning cached research result for keywords: {request.keywords}")
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return BlogResearchResponse(**cached_result)
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# User ID validation (validation logic is now in Google Grounding provider)
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# User ID validation
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if not user_id:
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await task_manager.update_progress(task_id, "❌ Error: User ID is required for research operation")
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raise ValueError("user_id is required for research operation. Please provide Clerk user ID.")
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# Cache miss - proceed with API call
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await task_manager.update_progress(task_id, "🌐 Cache miss - connecting to Google Search grounding...")
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logger.info(f"Cache miss - making API call for keywords: {request.keywords}")
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gemini = GeminiGroundedProvider()
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# Single comprehensive research prompt - Gemini handles Google Search automatically
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research_prompt = f"""
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Research the topic "{topic}" in the {industry} industry for {target_audience} audience. Provide a comprehensive analysis including:
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1. Current trends and insights (2024-2025)
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2. Key statistics and data points with sources
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3. Industry expert opinions and quotes
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4. Recent developments and news
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5. Market analysis and forecasts
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6. Best practices and case studies
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7. Keyword analysis: primary, secondary, and long-tail opportunities
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8. Competitor analysis: top players and content gaps
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9. Content angle suggestions: 5 compelling angles for blog posts
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Focus on factual, up-to-date information from credible sources.
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Include specific data points, percentages, and recent developments.
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Structure your response with clear sections for each analysis area.
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"""
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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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strategy = get_strategy_for_mode(research_mode)
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await task_manager.update_progress(task_id, "🤖 Making AI request to Gemini with Google Search grounding...")
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# Single Gemini call with native Google Search grounding - no fallbacks
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# Validation is handled inside generate_grounded_content when validate_subsequent_operations=True
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try:
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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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user_id=user_id,
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validate_subsequent_operations=True # Validates Google Grounding + 3 LLM calls
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)
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except HTTPException as http_error:
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# Re-raise HTTPException so it can be properly handled by task manager
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logger.error(f"Subscription limit exceeded for 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 # Re-raise HTTPException to preserve status code and error details
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logger.info(f"Research: mode={research_mode.value}, provider={config.provider.value}")
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await task_manager.update_progress(task_id, "📊 Processing research results and extracting insights...")
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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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# 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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# Extract grounding metadata for detailed UI display
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grounding_metadata = self._extract_grounding_metadata(gemini_result)
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# Extract search widget and queries for UI display
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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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# Route to appropriate 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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from services.subscription.preflight_validator import validate_exa_research_operations
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from services.database import get_db
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from services.subscription import PricingService
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import os
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await task_manager.update_progress(task_id, "🌐 Connecting to Exa neural search...")
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# Pre-flight validation
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db_val = next(get_db())
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try:
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pricing_service = PricingService(db_val)
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gpt_provider = os.getenv("GPT_PROVIDER", "google")
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validate_exa_research_operations(pricing_service, user_id, gpt_provider)
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except HTTPException as http_error:
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logger.error(f"Subscription limit exceeded for Exa 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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finally:
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db_val.close()
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# Execute Exa search
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await task_manager.update_progress(task_id, "🤖 Executing Exa neural search...")
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try:
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exa_provider = ExaResearchProvider()
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raw_result = await exa_provider.search(
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research_prompt, topic, industry, target_audience, config, user_id
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)
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# Track usage
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cost = raw_result.get('cost', {}).get('total', 0.005) if isinstance(raw_result.get('cost'), dict) else 0.005
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exa_provider.track_exa_usage(user_id, cost)
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# Extract content for downstream analysis
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content = raw_result.get('content', '')
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sources = raw_result.get('sources', [])
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search_widget = "" # Exa doesn't provide search widgets
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search_queries = raw_result.get('search_queries', [])
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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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if config.provider != ResearchProvider.EXA:
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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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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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# 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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# Parse the comprehensive response for different analysis components
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content = gemini_result.get("content", "")
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keyword_analysis = self.keyword_analyzer.analyze(content, request.keywords, user_id=user_id)
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competitor_analysis = self.competitor_analyzer.analyze(content, user_id=user_id)
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suggested_angles = self.content_angle_generator.generate(content, topic, industry, user_id=user_id)
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