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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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5
backend/services/blog_writer/research/competitor_analyzer.py
View File

@@ -11,7 +11,7 @@ from loguru import logger
class CompetitorAnalyzer:
"""Analyzes competitors and market intelligence from research content."""
def analyze(self, content: str) -> Dict[str, Any]:
def analyze(self, content: str, user_id: str = None) -> Dict[str, Any]:
"""Parse comprehensive competitor analysis from the research content using AI."""
competitor_prompt = f"""
Analyze the following research content and extract competitor insights:
@@ -57,7 +57,8 @@ class CompetitorAnalyzer:
competitor_analysis = llm_text_gen(
prompt=competitor_prompt,
json_struct=competitor_schema
json_struct=competitor_schema,
user_id=user_id
)
if isinstance(competitor_analysis, dict) and 'error' not in competitor_analysis:

5
backend/services/blog_writer/research/content_angle_generator.py
View File

@@ -11,7 +11,7 @@ from loguru import logger
class ContentAngleGenerator:
"""Generates strategic content angles from research content."""
def generate(self, content: str, topic: str, industry: str) -> List[str]:
def generate(self, content: str, topic: str, industry: str, user_id: str = None) -> List[str]:
"""Parse strategic content angles from the research content using AI."""
angles_prompt = f"""
Analyze the following research content and create strategic content angles for: {topic} in {industry}
@@ -65,7 +65,8 @@ class ContentAngleGenerator:
angles_result = llm_text_gen(
prompt=angles_prompt,
json_struct=angles_schema
json_struct=angles_schema,
user_id=user_id
)
if isinstance(angles_result, dict) and 'content_angles' in angles_result:

5
backend/services/blog_writer/research/keyword_analyzer.py
View File

@@ -11,7 +11,7 @@ from loguru import logger
class KeywordAnalyzer:
"""Analyzes keywords from research content using AI-powered extraction."""
def analyze(self, content: str, original_keywords: List[str]) -> Dict[str, Any]:
def analyze(self, content: str, original_keywords: List[str], user_id: str = None) -> Dict[str, Any]:
"""Parse comprehensive keyword analysis from the research content using AI."""
# Use AI to extract and analyze keywords from the rich research content
keyword_prompt = f"""
@@ -64,7 +64,8 @@ class KeywordAnalyzer:
keyword_analysis = llm_text_gen(
prompt=keyword_prompt,
json_struct=keyword_schema
json_struct=keyword_schema,
user_id=user_id
)
if isinstance(keyword_analysis, dict) and 'error' not in keyword_analysis:

59
backend/services/blog_writer/research/research_service.py
View File

@@ -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}")