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Subscription dashboard improvements, AI text generation limit, and other fixes.

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This commit is contained in:
ajaysi
2025-11-01 18:01:14 +05:30
parent cdb41aec1b
commit de4328175d
64 changed files with 5809 additions and 444 deletions
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59
backend/services/blog_writer/research/research_service.py
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@@ -4,7 +4,8 @@ Research Service - Core research functionality for AI Blog Writer.
Handles Google Search grounding, caching, and research orchestration.
"""
from typing import Dict, Any, List
from typing import Dict, Any, List, Optional
from datetime import datetime
from loguru import logger
from models.blog_models import (
@@ -17,6 +18,7 @@ from models.blog_models import (
Citation,
)
from services.blog_writer.logger_config import blog_writer_logger, log_function_call
from fastapi import HTTPException
from .keyword_analyzer import KeywordAnalyzer
from .competitor_analyzer import CompetitorAnalyzer
@@ -34,7 +36,7 @@ class ResearchService:
self.data_filter = ResearchDataFilter()
@log_function_call("research_operation")
async def research(self, request: BlogResearchRequest) -> BlogResearchResponse:
async def research(self, request: BlogResearchRequest, user_id: str) -> BlogResearchResponse:
"""
Stage 1: Research & Strategy (AI Orchestration)
Uses ONLY Gemini's native Google Search grounding - ONE API call for everything.
@@ -71,6 +73,10 @@ class ResearchService:
blog_writer_logger.log_operation_end("research", 0, success=True, cache_hit=True)
return BlogResearchResponse(**cached_result)
# User ID validation (validation logic is now in Google Grounding provider)
if not user_id:
raise ValueError("user_id is required for research operation. Please provide Clerk user ID.")
# Cache miss - proceed with API call
logger.info(f"Cache miss - making API call for keywords: {request.keywords}")
blog_writer_logger.log_operation_start("gemini_api_call", api_name="gemini_grounded", operation="research")
@@ -96,12 +102,15 @@ class ResearchService:
"""
# Single Gemini call with native Google Search grounding - no fallbacks
# Validation is handled inside generate_grounded_content when validate_subsequent_operations=True
import time
api_start_time = time.time()
gemini_result = await gemini.generate_grounded_content(
prompt=research_prompt,
content_type="research",
max_tokens=2000
max_tokens=2000,
user_id=user_id,
validate_subsequent_operations=True # Validates Google Grounding + 3 LLM calls
)
api_duration_ms = (time.time() - api_start_time) * 1000
@@ -126,9 +135,9 @@ class ResearchService:
# Parse the comprehensive response for different analysis components
content = gemini_result.get("content", "")
keyword_analysis = self.keyword_analyzer.analyze(content, request.keywords)
competitor_analysis = self.competitor_analyzer.analyze(content)
suggested_angles = self.content_angle_generator.generate(content, topic, industry)
keyword_analysis = self.keyword_analyzer.analyze(content, request.keywords, user_id=user_id)
competitor_analysis = self.competitor_analyzer.analyze(content, user_id=user_id)
suggested_angles = self.content_angle_generator.generate(content, topic, industry, user_id=user_id)
logger.info(f"Research completed successfully with {len(sources)} sources and {len(search_queries)} search queries")
@@ -179,6 +188,9 @@ class ResearchService:
return filtered_response
except HTTPException:
# Re-raise HTTPException (subscription errors) - let task manager handle it
raise
except Exception as e:
error_message = str(e)
logger.error(f"Research failed: {error_message}")
@@ -244,7 +256,7 @@ class ResearchService:
)
@log_function_call("research_with_progress")
async def research_with_progress(self, request: BlogResearchRequest, task_id: str) -> BlogResearchResponse:
async def research_with_progress(self, request: BlogResearchRequest, task_id: str, user_id: str) -> BlogResearchResponse:
"""
Research method with progress updates for real-time feedback.
"""
@@ -281,6 +293,11 @@ class ResearchService:
logger.info(f"Returning cached research result for keywords: {request.keywords}")
return BlogResearchResponse(**cached_result)
# User ID validation (validation logic is now in Google Grounding provider)
if not user_id:
await task_manager.update_progress(task_id, "❌ Error: User ID is required for research operation")
raise ValueError("user_id is required for research operation. Please provide Clerk user ID.")
# Cache miss - proceed with API call
await task_manager.update_progress(task_id, "🌐 Cache miss - connecting to Google Search grounding...")
logger.info(f"Cache miss - making API call for keywords: {request.keywords}")
@@ -307,11 +324,20 @@ class ResearchService:
await task_manager.update_progress(task_id, "🤖 Making AI request to Gemini with Google Search grounding...")
# Single Gemini call with native Google Search grounding - no fallbacks
gemini_result = await gemini.generate_grounded_content(
prompt=research_prompt,
content_type="research",
max_tokens=2000
)
# Validation is handled inside generate_grounded_content when validate_subsequent_operations=True
try:
gemini_result = await gemini.generate_grounded_content(
prompt=research_prompt,
content_type="research",
max_tokens=2000,
user_id=user_id,
validate_subsequent_operations=True # Validates Google Grounding + 3 LLM calls
)
except HTTPException as http_error:
# Re-raise HTTPException so it can be properly handled by task manager
logger.error(f"Subscription limit exceeded for research: {http_error.detail}")
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)}")
raise # Re-raise HTTPException to preserve status code and error details
await task_manager.update_progress(task_id, "📊 Processing research results and extracting insights...")
# Extract sources from grounding metadata
@@ -327,9 +353,9 @@ class ResearchService:
await task_manager.update_progress(task_id, "🔍 Analyzing keywords and content angles...")
# Parse the comprehensive response for different analysis components
content = gemini_result.get("content", "")
keyword_analysis = self.keyword_analyzer.analyze(content, request.keywords)
competitor_analysis = self.competitor_analyzer.analyze(content)
suggested_angles = self.content_angle_generator.generate(content, topic, industry)
keyword_analysis = self.keyword_analyzer.analyze(content, request.keywords, user_id=user_id)
competitor_analysis = self.competitor_analyzer.analyze(content, user_id=user_id)
suggested_angles = self.content_angle_generator.generate(content, topic, industry, user_id=user_id)
await task_manager.update_progress(task_id, "💾 Caching results for future use...")
logger.info(f"Research completed successfully with {len(sources)} sources and {len(search_queries)} search queries")
@@ -373,6 +399,9 @@ class ResearchService:
return filtered_response
except HTTPException:
# Re-raise HTTPException (subscription errors) - let task manager handle it
raise
except Exception as e:
error_message = str(e)
logger.error(f"Research failed: {error_message}")