feat: LinkedIn LLM alignment - Phase 1-3 complete
Phase 1: Dead Code Cleanup - Remove GeminiGroundedProvider import and property from linkedin_service.py - Remove fallback_provider property (gemini_provider imports) - Fix routers/linkedin.py edit endpoint to use llm_text_gen - Delete dead LinkedInImageEditor class - Remove dead _transform_gemini_sources from content_generator.py Phase 2: Research Infrastructure Alignment - Add user_id to _conduct_research() for pre-flight validation - Add validate_exa_research_operations() before Exa/Tavily calls - Pass user_id to provider.simple_search() for usage tracking - Inject research content into LLM prompts via _build_research_context() - Fix Google engine path to fallback to Exa - Add Exa → Tavily fallback on research failure Phase 3: Cosmetic Cleanup - Rename _generate_prompts_with_gemini → _generate_prompts_with_llm - Rename _build_gemini_prompt → _build_image_prompt - Rename _parse_gemini_response → _parse_llm_response - Remove all Gemini references from LinkedIn code (0 remaining) - Update docstrings and log messages Additional: - Research caching using existing ResearchCache - Shared ExaContentResearchProvider in services/research/ - Persona service uses llm_text_gen instead of gemini_structured_json_response - LinkedInWriter.tsx ChatMessage → ChatMsg type mapping fix - RegisterLinkedInActionsEnhanced.tsx content_format_rules typing fix
This commit is contained in:
ajaysi
@@ -7,9 +7,10 @@ proper error handling, monitoring, and documentation.
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"""
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from fastapi import APIRouter, HTTPException, Depends, BackgroundTasks, Request
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from fastapi.responses import JSONResponse
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from fastapi.responses import JSONResponse, FileResponse
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from typing import Dict, Any, Optional
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import time
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import json
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from loguru import logger
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from pathlib import Path
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@@ -17,11 +18,17 @@ from models.linkedin_models import (
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LinkedInPostRequest, LinkedInArticleRequest, LinkedInCarouselRequest,
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LinkedInVideoScriptRequest, LinkedInCommentResponseRequest,
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LinkedInPostResponse, LinkedInArticleResponse, LinkedInCarouselResponse,
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LinkedInVideoScriptResponse, LinkedInCommentResponseResult
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LinkedInVideoScriptResponse, LinkedInCommentResponseResult,
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LinkedInEditContentRequest, LinkedInEditContentResponse
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)
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from services.llm_providers.main_text_generation import llm_text_gen
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from services.linkedin_service import LinkedInService
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from services.linkedin.carousel import LinkedInCarouselPDFRenderer
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from middleware.auth_middleware import get_current_user
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from utils.text_asset_tracker import save_and_track_text_content
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from models.api_monitoring import APIRequest
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from sqlalchemy import func
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from collections import defaultdict
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# Initialize the LinkedIn service instance
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linkedin_service = LinkedInService()
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@@ -29,6 +36,34 @@ from services.subscription.monitoring_middleware import DatabaseAPIMonitor
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from services.database import get_db as get_db_dependency
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from sqlalchemy.orm import Session
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# Simple in-memory rate limiter: {user_id: [timestamp, ...]}
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_rate_limit_store: Dict[str, list] = defaultdict(list)
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RATE_LIMIT_MAX_REQUESTS = 30
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RATE_LIMIT_WINDOW = 60 # seconds
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def check_rate_limit(user_id: str) -> Optional[int]:
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"""Returns retry-after seconds if rate limited, None otherwise."""
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now = time.time()
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window_start = now - RATE_LIMIT_WINDOW
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timestamps = _rate_limit_store[user_id]
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# Prune old entries
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_rate_limit_store[user_id] = [t for t in timestamps if t > window_start]
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if len(_rate_limit_store[user_id]) >= RATE_LIMIT_MAX_REQUESTS:
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return int(_rate_limit_store[user_id][0] + RATE_LIMIT_WINDOW - now)
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_rate_limit_store[user_id].append(now)
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return None
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ERROR_CODES = {
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'VALIDATION': 'LINKEDIN_ERR_001',
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'GENERATION_FAILED': 'LINKEDIN_ERR_002',
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'RATE_LIMITED': 'LINKEDIN_ERR_003',
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'SAVE_FAILED': 'LINKEDIN_ERR_004',
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'NOT_FOUND': 'LINKEDIN_ERR_404',
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}
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def error_response(code: str, message: str) -> dict:
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return {"code": code, "message": message}
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# Initialize router
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router = APIRouter(
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prefix="/api/linkedin",
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@@ -112,10 +147,10 @@ async def generate_post(
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# Validate request
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if not request.topic.strip():
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raise HTTPException(status_code=422, detail="Topic cannot be empty")
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Topic cannot be empty"))
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if not request.industry.strip():
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raise HTTPException(status_code=422, detail="Industry cannot be empty")
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Industry cannot be empty"))
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# Extract user_id
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user_id = None
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@@ -124,22 +159,30 @@ async def generate_post(
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if not user_id:
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user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
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# Rate limit check
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retry_after = check_rate_limit(user_id or 'anonymous')
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if retry_after:
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raise HTTPException(
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status_code=429,
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detail=error_response(ERROR_CODES['RATE_LIMITED'], f"Rate limit exceeded. Retry after {retry_after} seconds."),
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headers={"Retry-After": str(retry_after)}
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)
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# Generate post content
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response = await linkedin_service.generate_linkedin_post(request)
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if not response.success:
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raise HTTPException(status_code=500, detail=error_response(ERROR_CODES['GENERATION_FAILED'], response.error or "Post generation failed"))
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# Log successful request
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duration = time.time() - start_time
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 200
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)
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if not response.success:
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raise HTTPException(status_code=500, detail=response.error)
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# Save and track text content (non-blocking)
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# Save and track text content
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if user_id and response.data and response.data.content:
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try:
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# Combine all text content
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text_content = response.data.content
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if response.data.call_to_action:
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text_content += f"\n\nCall to Action: {response.data.call_to_action}"
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@@ -166,7 +209,7 @@ async def generate_post(
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subdirectory="posts"
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)
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except Exception as track_error:
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logger.warning(f"Failed to track LinkedIn post asset: {track_error}")
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logger.error(f"Failed to track LinkedIn post asset: {track_error}")
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logger.info(f"Successfully generated LinkedIn post in {duration:.2f} seconds")
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return response
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@@ -177,14 +220,13 @@ async def generate_post(
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duration = time.time() - start_time
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logger.error(f"Error generating LinkedIn post: {str(e)}")
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# Log failed request
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 500
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)
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raise HTTPException(
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status_code=500,
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detail=f"Failed to generate LinkedIn post: {str(e)}"
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detail=error_response(ERROR_CODES['GENERATION_FAILED'], f"Failed to generate LinkedIn post: {str(e)}")
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)
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@@ -222,10 +264,10 @@ async def generate_article(
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# Validate request
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if not request.topic.strip():
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raise HTTPException(status_code=422, detail="Topic cannot be empty")
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Topic cannot be empty"))
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if not request.industry.strip():
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raise HTTPException(status_code=422, detail="Industry cannot be empty")
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Industry cannot be empty"))
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# Extract user_id
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user_id = None
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@@ -234,17 +276,16 @@ async def generate_article(
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if not user_id:
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user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
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# Rate limit check
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retry_after = check_rate_limit(user_id or 'anonymous')
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if retry_after:
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raise HTTPException(status_code=429, detail=error_response(ERROR_CODES['RATE_LIMITED'], f"Rate limit exceeded. Retry after {retry_after} seconds."), headers={"Retry-After": str(retry_after)})
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# Generate article content
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response = await linkedin_service.generate_linkedin_article(request)
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# Log successful request
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duration = time.time() - start_time
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 200
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)
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if not response.success:
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raise HTTPException(status_code=500, detail=response.error)
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raise HTTPException(status_code=500, detail=error_response(ERROR_CODES['GENERATION_FAILED'], response.error or "Article generation failed"))
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# Save and track text content (non-blocking)
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if user_id and response.data:
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@@ -282,7 +323,7 @@ async def generate_article(
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file_extension=".md"
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)
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except Exception as track_error:
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logger.warning(f"Failed to track LinkedIn article asset: {track_error}")
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logger.error(f"Failed to track LinkedIn article asset: {track_error}")
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logger.info(f"Successfully generated LinkedIn article in {duration:.2f} seconds")
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return response
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@@ -300,7 +341,7 @@ async def generate_article(
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raise HTTPException(
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status_code=500,
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detail=f"Failed to generate LinkedIn article: {str(e)}"
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detail=error_response(ERROR_CODES['GENERATION_FAILED'], f"Failed to generate LinkedIn article: {str(e)}")
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)
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@@ -337,13 +378,13 @@ async def generate_carousel(
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# Validate request
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if not request.topic.strip():
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raise HTTPException(status_code=422, detail="Topic cannot be empty")
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Topic cannot be empty"))
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if not request.industry.strip():
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raise HTTPException(status_code=422, detail="Industry cannot be empty")
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Industry cannot be empty"))
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if request.slide_count < 3 or request.slide_count > 15:
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raise HTTPException(status_code=422, detail="Slide count must be between 3 and 15")
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if request.number_of_slides < 3 or request.number_of_slides > 15:
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Number of slides must be between 3 and 15"))
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# Extract user_id
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user_id = None
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@@ -352,18 +393,23 @@ async def generate_carousel(
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if not user_id:
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user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
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# Rate limit check
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retry_after = check_rate_limit(user_id or 'anonymous')
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if retry_after:
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raise HTTPException(status_code=429, detail=error_response(ERROR_CODES['RATE_LIMITED'], f"Rate limit exceeded. Retry after {retry_after} seconds."), headers={"Retry-After": str(retry_after)})
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# Generate carousel content
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response = await linkedin_service.generate_linkedin_carousel(request)
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if not response.success:
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raise HTTPException(status_code=500, detail=error_response(ERROR_CODES['GENERATION_FAILED'], response.error or "Carousel generation failed"))
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# Log successful request
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duration = time.time() - start_time
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 200
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)
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if not response.success:
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raise HTTPException(status_code=500, detail=response.error)
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# Save and track text content (non-blocking)
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if user_id and response.data:
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try:
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@@ -381,10 +427,10 @@ async def generate_carousel(
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source_module="linkedin_writer",
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title=f"LinkedIn Carousel: {response.data.title[:80] if response.data.title else request.topic[:80]}",
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description=f"LinkedIn carousel for {request.industry} industry",
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prompt=f"Topic: {request.topic}\nIndustry: {request.industry}\nSlides: {getattr(request, 'number_of_slides', request.slide_count if hasattr(request, 'slide_count') else 5)}",
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prompt=f"Topic: {request.topic}\nIndustry: {request.industry}\nSlides: {request.number_of_slides}",
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tags=["linkedin", "carousel", request.industry.lower().replace(' ', '_')],
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asset_metadata={
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"slide_count": len(response.data.slides),
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"number_of_slides": len(response.data.slides),
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"has_cover": response.data.cover_slide is not None,
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"has_cta": response.data.cta_slide is not None
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},
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@@ -392,7 +438,7 @@ async def generate_carousel(
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file_extension=".md"
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)
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except Exception as track_error:
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logger.warning(f"Failed to track LinkedIn carousel asset: {track_error}")
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logger.error(f"Failed to track LinkedIn carousel asset: {track_error}")
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logger.info(f"Successfully generated LinkedIn carousel in {duration:.2f} seconds")
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return response
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@@ -410,10 +456,82 @@ async def generate_carousel(
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raise HTTPException(
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status_code=500,
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detail=f"Failed to generate LinkedIn carousel: {str(e)}"
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detail=error_response(ERROR_CODES['GENERATION_FAILED'], f"Failed to generate LinkedIn carousel: {str(e)}")
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)
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@router.post(
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"/generate-carousel-pdf",
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summary="Render Carousel as PDF",
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description="""
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Render previously generated LinkedIn carousel content as a PDF document.
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Takes carousel content (slides with title, content, visual_elements) and
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renders them into visually appealing slide images composed into a PDF
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ready for LinkedIn upload (1.91:1 aspect ratio, max 300 slides, max 100MB).
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"""
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)
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async def generate_carousel_pdf(
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request: LinkedInCarouselRequest,
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background_tasks: BackgroundTasks,
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http_request: Request,
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db: Session = Depends(get_db),
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current_user: Optional[Dict[str, Any]] = Depends(get_current_user)
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):
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"""Generate carousel content and render as PDF."""
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start_time = time.time()
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try:
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user_id = None
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if current_user:
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user_id = str(current_user.get('id', '') or current_user.get('sub', ''))
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if not user_id:
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user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
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# First generate carousel content
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content_result = await linkedin_service.generate_linkedin_carousel(request)
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if not content_result.success or not content_result.data:
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raise HTTPException(status_code=500, detail=content_result.error or "Carousel generation failed")
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carousel_data = content_result.data.model_dump()
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# Then render to PDF
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renderer = LinkedInCarouselPDFRenderer()
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pdf_result = await renderer.render_carousel_to_pdf(
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carousel_data=carousel_data,
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color_scheme=request.color_scheme,
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user_id=user_id,
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)
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if not pdf_result.get('success'):
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raise HTTPException(status_code=500, detail=pdf_result.get('error', 'PDF rendering failed'))
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duration = time.time() - start_time
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background_tasks.add_task(log_api_request, http_request, db, duration, 200)
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pdf_path = pdf_result.get('pdf_path')
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if pdf_path:
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return FileResponse(
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path=pdf_path,
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media_type="application/pdf",
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filename=f"linkedin_carousel_{request.topic[:30].replace(' ', '_')}.pdf"
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)
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return JSONResponse(content={
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'success': True,
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'pdf_bytes': pdf_result.get('pdf_bytes'),
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'metadata': pdf_result.get('metadata'),
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})
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except HTTPException:
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raise
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except Exception as e:
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duration = time.time() - start_time
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logger.error(f"Error generating carousel PDF: {str(e)}")
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raise HTTPException(status_code=500, detail=error_response(ERROR_CODES['GENERATION_FAILED'], f"Failed to generate carousel PDF: {str(e)}"))
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@router.post(
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"/generate-video-script",
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response_model=LinkedInVideoScriptResponse,
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@@ -447,14 +565,14 @@ async def generate_video_script(
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# Validate request
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if not request.topic.strip():
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raise HTTPException(status_code=422, detail="Topic cannot be empty")
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Topic cannot be empty"))
|
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|
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if not request.industry.strip():
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raise HTTPException(status_code=422, detail="Industry cannot be empty")
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Industry cannot be empty"))
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video_duration = getattr(request, 'video_duration', getattr(request, 'video_length', 60))
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if video_duration < 15 or video_duration > 300:
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raise HTTPException(status_code=422, detail="Video length must be between 15 and 300 seconds")
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raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Video length must be between 15 and 300 seconds"))
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# Extract user_id
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user_id = None
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@@ -463,18 +581,23 @@ async def generate_video_script(
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if not user_id:
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user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
|
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|
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# Rate limit check
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retry_after = check_rate_limit(user_id or 'anonymous')
|
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if retry_after:
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raise HTTPException(status_code=429, detail=error_response(ERROR_CODES['RATE_LIMITED'], f"Rate limit exceeded. Retry after {retry_after} seconds."), headers={"Retry-After": str(retry_after)})
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# Generate video script content
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response = await linkedin_service.generate_linkedin_video_script(request)
|
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if not response.success:
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raise HTTPException(status_code=500, detail=error_response(ERROR_CODES['GENERATION_FAILED'], response.error or "Video script generation failed"))
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# Log successful request
|
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duration = time.time() - start_time
|
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background_tasks.add_task(
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log_api_request, http_request, db, duration, 200
|
||||
)
|
||||
|
||||
if not response.success:
|
||||
raise HTTPException(status_code=500, detail=response.error)
|
||||
|
||||
# Save and track text content (non-blocking)
|
||||
if user_id and response.data:
|
||||
try:
|
||||
@@ -514,7 +637,7 @@ async def generate_video_script(
|
||||
file_extension=".md"
|
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)
|
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except Exception as track_error:
|
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logger.warning(f"Failed to track LinkedIn video script asset: {track_error}")
|
||||
logger.error(f"Failed to track LinkedIn video script asset: {track_error}")
|
||||
|
||||
logger.info(f"Successfully generated LinkedIn video script in {duration:.2f} seconds")
|
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return response
|
||||
@@ -532,7 +655,7 @@ async def generate_video_script(
|
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|
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raise HTTPException(
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status_code=500,
|
||||
detail=f"Failed to generate LinkedIn video script: {str(e)}"
|
||||
detail=error_response(ERROR_CODES['GENERATION_FAILED'], f"Failed to generate LinkedIn video script: {str(e)}")
|
||||
)
|
||||
|
||||
|
||||
@@ -572,10 +695,10 @@ async def generate_comment_response(
|
||||
post_context = getattr(request, 'post_context', getattr(request, 'original_post', ''))
|
||||
|
||||
if not original_comment.strip():
|
||||
raise HTTPException(status_code=422, detail="Original comment cannot be empty")
|
||||
raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Original comment cannot be empty"))
|
||||
|
||||
if not post_context.strip():
|
||||
raise HTTPException(status_code=422, detail="Post context cannot be empty")
|
||||
raise HTTPException(status_code=422, detail=error_response(ERROR_CODES['VALIDATION'], "Post context cannot be empty"))
|
||||
|
||||
# Extract user_id
|
||||
user_id = None
|
||||
@@ -584,18 +707,23 @@ async def generate_comment_response(
|
||||
if not user_id:
|
||||
user_id = http_request.headers.get("X-User-ID") or http_request.headers.get("Authorization")
|
||||
|
||||
# Rate limit check
|
||||
retry_after = check_rate_limit(user_id or 'anonymous')
|
||||
if retry_after:
|
||||
raise HTTPException(status_code=429, detail=error_response(ERROR_CODES['RATE_LIMITED'], f"Rate limit exceeded. Retry after {retry_after} seconds."), headers={"Retry-After": str(retry_after)})
|
||||
|
||||
# Generate comment response
|
||||
response = await linkedin_service.generate_linkedin_comment_response(request)
|
||||
|
||||
if not response.success:
|
||||
raise HTTPException(status_code=500, detail=error_response(ERROR_CODES['GENERATION_FAILED'], response.error or "Comment response generation failed"))
|
||||
|
||||
# Log successful request
|
||||
duration = time.time() - start_time
|
||||
background_tasks.add_task(
|
||||
log_api_request, http_request, db, duration, 200
|
||||
)
|
||||
|
||||
if not response.success:
|
||||
raise HTTPException(status_code=500, detail=response.error)
|
||||
|
||||
# Save and track text content (non-blocking)
|
||||
if user_id and hasattr(response, 'response') and response.response:
|
||||
try:
|
||||
@@ -626,7 +754,7 @@ async def generate_comment_response(
|
||||
file_extension=".md"
|
||||
)
|
||||
except Exception as track_error:
|
||||
logger.warning(f"Failed to track LinkedIn comment response asset: {track_error}")
|
||||
logger.error(f"Failed to track LinkedIn comment response asset: {track_error}")
|
||||
|
||||
logger.info(f"Successfully generated LinkedIn comment response in {duration:.2f} seconds")
|
||||
return response
|
||||
@@ -644,7 +772,7 @@ async def generate_comment_response(
|
||||
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Failed to generate LinkedIn comment response: {str(e)}"
|
||||
detail=error_response(ERROR_CODES['GENERATION_FAILED'], f"Failed to generate LinkedIn comment response: {str(e)}")
|
||||
)
|
||||
|
||||
|
||||
@@ -691,6 +819,128 @@ async def get_content_types():
|
||||
}
|
||||
|
||||
|
||||
@router.post(
|
||||
"/edit-content",
|
||||
response_model=LinkedInEditContentResponse,
|
||||
summary="Edit LinkedIn Content with AI",
|
||||
description="""
|
||||
Apply AI-powered edits to LinkedIn content.
|
||||
|
||||
Supported edit types:
|
||||
- professionalize: Rewrite content with professional business language
|
||||
- optimize_engagement: Optimize hook and structure for maximum engagement
|
||||
- add_hashtags: Generate relevant, industry-specific hashtags
|
||||
- adjust_tone: Rewrite content in a different tone (professional, conversational, authoritative, etc.)
|
||||
- expand: Add depth, examples, and insights to content
|
||||
- condense: Shorten content while preserving key messages
|
||||
- add_cta: Generate a contextual call-to-action
|
||||
"""
|
||||
)
|
||||
async def edit_linkedin_content(
|
||||
request: LinkedInEditContentRequest,
|
||||
current_user: Optional[Dict[str, Any]] = Depends(get_current_user)
|
||||
):
|
||||
"""Edit LinkedIn content using AI-powered text generation."""
|
||||
try:
|
||||
# Extract user_id for subscription checking
|
||||
user_id = None
|
||||
if current_user:
|
||||
user_id = str(current_user.get('id', '') or current_user.get('sub', ''))
|
||||
|
||||
if not request.content.strip():
|
||||
return LinkedInEditContentResponse(
|
||||
success=False, error="Content cannot be empty", edit_type=request.edit_type
|
||||
)
|
||||
|
||||
# Build the system prompt based on edit type
|
||||
system_prompts = {
|
||||
"professionalize": "You are a professional business writer. Rewrite the following LinkedIn content to be more professional, polished, and industry-appropriate. Maintain the original message but use sophisticated business language, improve sentence structure, and ensure a confident executive presence.",
|
||||
"optimize_engagement": "You are a LinkedIn engagement strategist. Rewrite the following content to maximize engagement. Strengthen the hook in the first 2 lines, add thought-provoking elements, improve readability with shorter sentences, and ensure the content encourages comments and shares.",
|
||||
"add_hashtags": "You are a LinkedIn hashtag strategist. Generate 5 highly relevant, industry-specific hashtags for the following content. Return the original content unchanged, followed by two newlines and the hashtags on a single line.",
|
||||
"adjust_tone": "You are a LinkedIn tone specialist. Rewrite the following content in the specified tone while preserving all key information and the overall message.",
|
||||
"expand": "You are a LinkedIn content strategist. Expand the following content by adding relevant examples, data points, actionable insights, and deeper analysis. Maintain the original structure but add substantial value while keeping it LinkedIn-appropriate (under 3000 characters).",
|
||||
"condense": "You are a LinkedIn editing specialist. Condense the following content to be more concise and impactful. Remove filler words, tighten sentences, and preserve only the strongest points. Keep the core message intact.",
|
||||
"add_cta": "You are a LinkedIn conversion strategist. Add a compelling, contextual call-to-action to the following content. The CTA should feel natural, not salesy, and should encourage meaningful engagement (comments, connections, or discussions)."
|
||||
}
|
||||
|
||||
system_prompt = system_prompts.get(request.edit_type)
|
||||
if not system_prompt:
|
||||
return LinkedInEditContentResponse(
|
||||
success=False, error=f"Unknown edit type: {request.edit_type}", edit_type=request.edit_type
|
||||
)
|
||||
|
||||
# Build the user prompt with context
|
||||
user_prompt = f"Content to edit:\n\n{request.content}\n\n"
|
||||
if request.industry:
|
||||
user_prompt += f"Industry: {request.industry}\n"
|
||||
if request.tone:
|
||||
user_prompt += f"Target tone: {request.tone}\n"
|
||||
if request.target_audience:
|
||||
user_prompt += f"Target audience: {request.target_audience}\n"
|
||||
if request.parameters:
|
||||
user_prompt += f"Additional context: {json.dumps(request.parameters)}\n"
|
||||
|
||||
user_prompt += "\nReturn ONLY the edited content without any explanations, labels, or markdown formatting."
|
||||
|
||||
# Generate edited content using provider-agnostic gateway
|
||||
temperature = {
|
||||
"professionalize": 0.3,
|
||||
"optimize_engagement": 0.7,
|
||||
"add_hashtags": 0.4,
|
||||
"adjust_tone": 0.5,
|
||||
"expand": 0.7,
|
||||
"condense": 0.3,
|
||||
"add_cta": 0.6,
|
||||
}.get(request.edit_type, 0.5)
|
||||
|
||||
max_tokens = {
|
||||
"expand": 2048,
|
||||
"professionalize": 1024,
|
||||
"optimize_engagement": 1024,
|
||||
"adjust_tone": 1024,
|
||||
"condense": 1024,
|
||||
"add_cta": 1024,
|
||||
"add_hashtags": 512,
|
||||
}.get(request.edit_type, 1024)
|
||||
|
||||
edited = llm_text_gen(
|
||||
prompt=user_prompt,
|
||||
system_prompt=system_prompt,
|
||||
user_id=user_id,
|
||||
flow_type=f"linkedin_edit_{request.edit_type}",
|
||||
max_tokens=max_tokens,
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
if not edited:
|
||||
return LinkedInEditContentResponse(
|
||||
success=False, error="AI editing returned empty result", edit_type=request.edit_type
|
||||
)
|
||||
|
||||
edited = edited.strip()
|
||||
|
||||
# For add_hashtags, ensure hashtags are separated from content
|
||||
if request.edit_type == "add_hashtags":
|
||||
if not edited.endswith("\n\n"):
|
||||
# Hashtags might be inline; separate them
|
||||
pass
|
||||
|
||||
logger.info(f"LinkedIn content edited successfully via {request.edit_type}")
|
||||
return LinkedInEditContentResponse(
|
||||
success=True,
|
||||
content=edited,
|
||||
edit_type=request.edit_type,
|
||||
provider="llm_text_gen",
|
||||
model="provider-agnostic"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error editing LinkedIn content: {str(e)}", exc_info=True)
|
||||
return LinkedInEditContentResponse(
|
||||
success=False, error=f"Editing failed: {str(e)}", edit_type=request.edit_type
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/usage-stats",
|
||||
summary="Get Usage Statistics",
|
||||
@@ -699,30 +949,29 @@ async def get_content_types():
|
||||
async def get_usage_stats(db: Session = Depends(get_db)):
|
||||
"""Get usage statistics for LinkedIn content generation."""
|
||||
try:
|
||||
# This would query the database for actual usage stats
|
||||
# For now, returning mock data
|
||||
base = db.query(APIRequest).filter(APIRequest.path.like('/api/linkedin/%'))
|
||||
total = base.count()
|
||||
successful = base.filter(APIRequest.status_code < 400).count()
|
||||
|
||||
avg_dur = base.with_entities(func.avg(APIRequest.duration)).scalar() or 0
|
||||
|
||||
content_types = {
|
||||
"posts": base.filter(APIRequest.path.like('%generate-post')).count(),
|
||||
"articles": base.filter(APIRequest.path.like('%generate-article')).count(),
|
||||
"carousels": base.filter(APIRequest.path.like('%generate-carousel')).count(),
|
||||
"video_scripts": base.filter(APIRequest.path.like('%generate-video-script')).count(),
|
||||
"comment_responses": base.filter(APIRequest.path.like('%generate-comment-response')).count(),
|
||||
}
|
||||
|
||||
return {
|
||||
"total_requests": 1250,
|
||||
"content_types": {
|
||||
"posts": 650,
|
||||
"articles": 320,
|
||||
"carousels": 180,
|
||||
"video_scripts": 70,
|
||||
"comment_responses": 30
|
||||
},
|
||||
"success_rate": 0.96,
|
||||
"average_generation_time": 4.2,
|
||||
"top_industries": [
|
||||
"Technology",
|
||||
"Healthcare",
|
||||
"Finance",
|
||||
"Marketing",
|
||||
"Education"
|
||||
]
|
||||
"total_requests": total,
|
||||
"content_types": content_types,
|
||||
"success_rate": round(successful / max(total, 1), 2),
|
||||
"average_generation_time": round(float(avg_dur), 2),
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error retrieving usage stats: {str(e)}")
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail="Failed to retrieve usage statistics"
|
||||
detail=error_response(ERROR_CODES['GENERATION_FAILED'], "Failed to retrieve usage statistics")
|
||||
)
|
||||
Reference in New Issue
Block a user