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feat: LinkedIn LLM alignment - Phase 1-3 complete

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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
2026-06-12 18:58:53 +05:30
parent e54aaa7a3e
commit 63a0df2536
37 changed files with 2891 additions and 1355 deletions
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4
backend/services/linkedin/image_prompts/__init__.py
View File

@@ -2,8 +2,8 @@
LinkedIn Image Prompts Package
This package provides AI-powered image prompt generation for LinkedIn content
using Google's Gemini API. It creates three distinct prompt styles optimized
for professional business image generation.
using the provider-agnostic llm_text_gen gateway. It creates three distinct
prompt styles optimized for professional business image generation.
"""
from .linkedin_prompt_generator import LinkedInPromptGenerator

75
backend/services/linkedin/image_prompts/linkedin_prompt_generator.py
View File

@@ -1,9 +1,10 @@
"""
LinkedIn Image Prompt Generator Service
This service generates AI-optimized image prompts for LinkedIn content using Gemini's
capabilities. It creates three distinct prompt styles (professional, creative, industry-specific)
following best practices for image generation.
This service generates AI-optimized image prompts for LinkedIn content using
the provider-agnostic llm_text_gen gateway. It creates three distinct prompt
styles (professional, creative, industry-specific) following best practices
for image generation.
"""
import asyncio
@@ -13,14 +14,14 @@ from loguru import logger
# Import existing infrastructure
from ...onboarding.api_key_manager import APIKeyManager
from ...llm_providers.gemini_provider import gemini_text_response
from ...llm_providers.main_text_generation import llm_text_gen
class LinkedInPromptGenerator:
"""
Generates AI-optimized image prompts for LinkedIn content.
This service creates three distinct prompt styles following Gemini API best practices:
This service creates three distinct prompt styles following best practices:
1. Professional Style - Corporate aesthetics, clean lines, business colors
2. Creative Style - Engaging visuals, vibrant colors, social media appeal
3. Industry-Specific Style - Tailored to specific business sectors
@@ -31,10 +32,9 @@ class LinkedInPromptGenerator:
Initialize the LinkedIn Prompt Generator.
Args:
api_key_manager: API key manager for Gemini authentication
api_key_manager: API key manager for authentication
"""
self.api_key_manager = api_key_manager or APIKeyManager()
self.model = "gemini-2.0-flash-exp"
# Prompt generation configuration
self.max_prompt_length = 500
@@ -49,7 +49,8 @@ class LinkedInPromptGenerator:
async def generate_three_prompts(
self,
linkedin_content: Dict[str, Any],
aspect_ratio: str = "1:1"
aspect_ratio: str = "1:1",
user_id: str = None
) -> List[Dict[str, Any]]:
"""
Generate three AI-optimized image prompts for LinkedIn content.
@@ -57,6 +58,7 @@ class LinkedInPromptGenerator:
Args:
linkedin_content: LinkedIn content context (topic, industry, content_type, content)
aspect_ratio: Desired image aspect ratio
user_id: User ID for subscription checking
Returns:
List of three prompt objects with style, prompt, and description
@@ -65,11 +67,11 @@ class LinkedInPromptGenerator:
start_time = datetime.now()
logger.info(f"Generating image prompts for LinkedIn content: {linkedin_content.get('topic', 'Unknown')}")
# Generate prompts using Gemini
prompts = await self._generate_prompts_with_gemini(linkedin_content, aspect_ratio)
# Generate prompts using provider-agnostic gateway
prompts = await self._generate_prompts_with_llm(linkedin_content, aspect_ratio, user_id)
if not prompts or len(prompts) < 3:
logger.warning("Gemini prompt generation failed, using fallback prompts")
logger.warning("Prompt generation failed, using fallback prompts")
prompts = self._get_fallback_prompts(linkedin_content, aspect_ratio)
# Ensure exactly 3 prompts
@@ -92,62 +94,65 @@ class LinkedInPromptGenerator:
logger.error(f"Error generating LinkedIn image prompts: {str(e)}")
return self._get_fallback_prompts(linkedin_content, aspect_ratio)
async def _generate_prompts_with_gemini(
async def _generate_prompts_with_llm(
self,
linkedin_content: Dict[str, Any],
aspect_ratio: str
aspect_ratio: str,
user_id: str = None
) -> List[Dict[str, Any]]:
"""
Generate image prompts using Gemini AI.
Generate image prompts using provider-agnostic llm_text_gen.
Args:
linkedin_content: LinkedIn content context
aspect_ratio: Image aspect ratio
user_id: User ID for subscription checking
Returns:
List of generated prompts
"""
try:
# Build the prompt for Gemini
gemini_prompt = self._build_gemini_prompt(linkedin_content, aspect_ratio)
# Build the prompt
prompt = self._build_image_prompt(linkedin_content, aspect_ratio)
# Generate response using Gemini
response = gemini_text_response(
prompt=gemini_prompt,
temperature=0.7,
top_p=0.8,
n=1,
# Generate response using provider-agnostic gateway
response = llm_text_gen(
prompt=prompt,
system_prompt="You are an expert AI image prompt engineer specializing in LinkedIn content optimization.",
user_id=user_id,
flow_type="linkedin_image_prompts",
max_tokens=1000,
system_prompt="You are an expert AI image prompt engineer specializing in LinkedIn content optimization."
temperature=0.7
)
if not response:
logger.warning("No response from Gemini prompt generation")
logger.warning("No response from prompt generation")
return []
# Parse Gemini response into structured prompts
prompts = self._parse_gemini_response(response, linkedin_content)
# Parse response into structured prompts
response_text = response if isinstance(response, str) else str(response or "")
prompts = self._parse_llm_response(response_text, linkedin_content)
return prompts
except Exception as e:
logger.error(f"Error in Gemini prompt generation: {str(e)}")
logger.error(f"Error in prompt generation: {str(e)}")
return []
def _build_gemini_prompt(
def _build_image_prompt(
self,
linkedin_content: Dict[str, Any],
aspect_ratio: str
) -> str:
"""
Build comprehensive prompt for Gemini to generate image prompts.
Build comprehensive prompt for LLM to generate image prompts.
Args:
linkedin_content: LinkedIn content context
aspect_ratio: Image aspect ratio
Returns:
Formatted prompt for Gemini
Formatted prompt for LLM
"""
topic = linkedin_content.get('topic', 'business')
industry = linkedin_content.get('industry', 'business')
@@ -428,16 +433,16 @@ class LinkedInPromptGenerator:
else:
return 'Informational & Awareness'
def _parse_gemini_response(
def _parse_llm_response(
self,
response: str,
linkedin_content: Dict[str, Any]
) -> List[Dict[str, Any]]:
"""
Parse Gemini response into structured prompt objects.
Parse LLM response into structured prompt objects.
Args:
response: Raw response from Gemini
response: Raw response from LLM
linkedin_content: LinkedIn content context
Returns:
@@ -462,7 +467,7 @@ class LinkedInPromptGenerator:
return self._parse_response_manually(response, linkedin_content)
except Exception as e:
logger.error(f"Error parsing Gemini response: {str(e)}")
logger.error(f"Error parsing LLM response: {str(e)}")
return self._parse_response_manually(response, linkedin_content)
def _parse_response_manually(
@@ -474,7 +479,7 @@ class LinkedInPromptGenerator:
Manually parse response if JSON parsing fails.
Args:
response: Raw response from Gemini
response: Raw response from LLM
linkedin_content: LinkedIn content context
Returns: