Add LinkedIn content generation service to backend
Co-authored-by: ajay.calsoft <ajay.calsoft@gmail.com>
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backend/docs/LINKEDIN_CONTENT_GENERATION.md
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backend/docs/LINKEDIN_CONTENT_GENERATION.md
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# LinkedIn Content Generation Service
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A comprehensive FastAPI-based service for generating professional LinkedIn content using AI. This service has been migrated from the legacy Streamlit implementation to provide robust API endpoints for LinkedIn content creation.
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## Overview
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The LinkedIn Content Generation Service provides AI-powered tools for creating various types of LinkedIn content:
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- **Posts**: Short-form professional posts with research-backed content
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- **Articles**: Long-form articles with SEO optimization
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- **Carousels**: Multi-slide visual content
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- **Video Scripts**: Structured scripts for LinkedIn videos
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- **Comment Responses**: Professional responses to LinkedIn comments
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## Features
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### 🚀 Core Capabilities
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- **Multi-format Content Generation**: Posts, articles, carousels, video scripts, and comment responses
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- **Research Integration**: Automated research using multiple search engines (Metaphor, Google, Tavily)
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- **AI-Powered Optimization**: Industry-specific content optimization using Gemini AI
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- **SEO Enhancement**: Built-in SEO optimization for LinkedIn articles
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- **Engagement Prediction**: AI-based engagement metrics prediction
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- **Professional Tone Control**: Multiple tone options (professional, conversational, authoritative, etc.)
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### 🛠 Technical Features
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- **FastAPI Integration**: RESTful API with automatic documentation
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- **Comprehensive Error Handling**: Robust exception handling and logging
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- **Database Monitoring**: Request logging and performance monitoring
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- **Async/Await Support**: Non-blocking operations for better performance
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- **Pydantic Validation**: Strong request/response validation
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- **Structured JSON Responses**: Consistent API response format
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## API Endpoints
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### Base URL
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```
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/api/linkedin
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```
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### Available Endpoints
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| Endpoint | Method | Description |
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|----------|--------|-------------|
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| `/health` | GET | Health check for service status |
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| `/generate-post` | POST | Generate LinkedIn posts |
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| `/generate-article` | POST | Generate LinkedIn articles |
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| `/generate-carousel` | POST | Generate LinkedIn carousels |
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| `/generate-video-script` | POST | Generate video scripts |
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| `/generate-comment-response` | POST | Generate comment responses |
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| `/content-types` | GET | Get available content types |
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| `/usage-stats` | GET | Get usage statistics |
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## Quick Start
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### 1. Prerequisites
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```bash
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# Install dependencies
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pip install -r requirements.txt
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# Set environment variables
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export GEMINI_API_KEY="your_gemini_api_key"
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export DATABASE_URL="sqlite:///./alwrity.db"
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```
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### 2. Start the Service
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```bash
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# Start FastAPI server
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uvicorn app:app --host 0.0.0.0 --port 8000 --reload
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```
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### 3. Access Documentation
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- **Interactive API Docs**: http://localhost:8000/docs
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- **Alternative Docs**: http://localhost:8000/redoc
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## Usage Examples
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### Generate a LinkedIn Post
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```python
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import requests
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# Request payload
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payload = {
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"topic": "Artificial Intelligence in Healthcare",
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"industry": "Healthcare",
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"post_type": "thought_leadership",
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"tone": "professional",
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"target_audience": "Healthcare executives",
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"key_points": ["AI diagnostics", "Patient outcomes", "Cost reduction"],
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"include_hashtags": True,
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"include_call_to_action": True,
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"research_enabled": True,
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"max_length": 2000
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}
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# Make request
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response = requests.post(
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"http://localhost:8000/api/linkedin/generate-post",
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json=payload
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)
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# Process response
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if response.status_code == 200:
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data = response.json()
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print(f"Generated post: {data['data']['content']}")
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print(f"Hashtags: {[h['hashtag'] for h in data['data']['hashtags']]}")
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else:
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print(f"Error: {response.status_code}")
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```
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### Generate a LinkedIn Article
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```python
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payload = {
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"topic": "Digital Transformation in Manufacturing",
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"industry": "Manufacturing",
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"tone": "professional",
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"target_audience": "Manufacturing leaders",
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"key_sections": ["Current challenges", "Technology solutions", "Implementation strategies"],
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"include_images": True,
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"seo_optimization": True,
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"research_enabled": True,
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"word_count": 1500
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}
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response = requests.post(
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"http://localhost:8000/api/linkedin/generate-article",
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json=payload
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)
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```
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### Generate a LinkedIn Carousel
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```python
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payload = {
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"topic": "5 Ways to Improve Team Productivity",
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"industry": "Business Management",
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"slide_count": 8,
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"tone": "professional",
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"target_audience": "Team leaders and managers",
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"key_takeaways": ["Clear communication", "Goal setting", "Tool optimization"],
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"include_cover_slide": True,
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"include_cta_slide": True,
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"visual_style": "modern"
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}
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response = requests.post(
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"http://localhost:8000/api/linkedin/generate-carousel",
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json=payload
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)
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```
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## Request/Response Models
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### LinkedIn Post Request
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```json
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{
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"topic": "string",
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"industry": "string",
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"post_type": "professional|thought_leadership|industry_news|personal_story|company_update|poll",
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"tone": "professional|conversational|authoritative|inspirational|educational|friendly",
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"target_audience": "string (optional)",
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"key_points": ["string"] (optional),
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"include_hashtags": true,
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"include_call_to_action": true,
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"research_enabled": true,
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"search_engine": "metaphor|google|tavily",
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"max_length": 3000
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}
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```
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### LinkedIn Post Response
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```json
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{
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"success": true,
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"data": {
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"content": "Generated post content...",
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"character_count": 1250,
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"hashtags": [
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{
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"hashtag": "#AIinHealthcare",
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"category": "industry",
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"popularity_score": 0.9
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}
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],
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"call_to_action": "What's your experience with AI in healthcare?",
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"engagement_prediction": {
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"estimated_likes": 120,
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"estimated_comments": 15,
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"estimated_shares": 8
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}
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},
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"research_sources": [
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{
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"title": "AI in Healthcare: Current Trends",
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"url": "https://example.com/ai-healthcare",
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"content": "Summary of AI healthcare trends...",
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"relevance_score": 0.95
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}
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],
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"generation_metadata": {
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"generation_time": 3.2,
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"timestamp": "2025-01-27T10:00:00Z",
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"model_used": "gemini-2.0-flash-001"
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}
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}
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```
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## Configuration
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### Environment Variables
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| Variable | Description | Required | Default |
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|----------|-------------|----------|---------|
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| `GEMINI_API_KEY` | Google Gemini API key | Yes | - |
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| `DATABASE_URL` | Database connection string | No | `sqlite:///./alwrity.db` |
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| `LOG_LEVEL` | Logging level | No | `INFO` |
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### Content Generation Settings
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The service supports various customization options:
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#### Post Types
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- `professional`: Standard professional posts
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- `thought_leadership`: Industry insights and expertise
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- `industry_news`: News and updates
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- `personal_story`: Personal experiences and stories
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- `company_update`: Company news and announcements
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- `poll`: Interactive polls
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#### Tone Options
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- `professional`: Formal business tone
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- `conversational`: Casual but professional
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- `authoritative`: Expert and confident
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- `inspirational`: Motivational and uplifting
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- `educational`: Informative and teaching
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- `friendly`: Warm and approachable
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#### Search Engines
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- `metaphor`: Metaphor AI search (recommended)
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- `google`: Google Search API
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- `tavily`: Tavily AI search
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## Architecture
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### Service Structure
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```
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backend/
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├── models/
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│ └── linkedin_models.py # Pydantic models for requests/responses
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├── services/
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│ └── linkedin_service.py # Core business logic
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├── routers/
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│ └── linkedin.py # FastAPI route handlers
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├── middleware/
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│ └── monitoring_middleware.py # Request monitoring
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└── docs/
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└── LINKEDIN_CONTENT_GENERATION.md
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```
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### Key Components
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#### LinkedInContentService
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The core service class that handles all content generation logic:
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- **Content Generation**: AI-powered content creation
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- **Research Integration**: Multi-source research capabilities
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- **Error Handling**: Comprehensive exception management
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- **Logging**: Detailed operation logging
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#### Request Models
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Pydantic models for strong typing and validation:
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- `LinkedInPostRequest`
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- `LinkedInArticleRequest`
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- `LinkedInCarouselRequest`
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- `LinkedInVideoScriptRequest`
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- `LinkedInCommentResponseRequest`
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#### Response Models
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Structured response models with metadata:
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- `LinkedInPostResponse`
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- `LinkedInArticleResponse`
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- `LinkedInCarouselResponse`
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- `LinkedInVideoScriptResponse`
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- `LinkedInCommentResponseResult`
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## Performance Considerations
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### Response Times
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- **Posts**: 3-8 seconds (with research)
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- **Articles**: 15-45 seconds (depending on length)
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- **Carousels**: 5-15 seconds
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- **Video Scripts**: 3-10 seconds
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- **Comment Responses**: 1-3 seconds
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### Rate Limiting
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The service respects API rate limits:
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- Gemini API: Built-in retry logic with exponential backoff
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- Research APIs: Configurable rate limiting
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### Caching
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- Research results caching (planned)
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- Response caching for similar requests (planned)
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## Error Handling
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### Common Error Scenarios
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#### 422 Validation Error
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```json
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{
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"detail": [
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{
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"loc": ["body", "topic"],
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"msg": "ensure this value has at least 3 characters",
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"type": "value_error.any_str.min_length"
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}
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]
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}
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```
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#### 500 Internal Server Error
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```json
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{
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"success": false,
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"error": "Content generation failed: API key not configured",
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"generation_metadata": {
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"service_version": "1.0.0",
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"timestamp": "2025-01-27T10:00:00Z"
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}
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}
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```
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### Error Recovery
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- Automatic retry logic for transient failures
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- Graceful fallback for content generation
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- Detailed error logging for debugging
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## Monitoring and Logging
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### Request Monitoring
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All API requests are logged with:
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- Request path and method
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- Response time and status code
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- User information (if available)
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- Request/response sizes
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### Performance Metrics
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- Generation time tracking
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- Success/failure rates
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- Popular content types
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- Error frequency analysis
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### Health Checks
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```bash
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curl http://localhost:8000/api/linkedin/health
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```
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## Migration from Streamlit
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### Key Changes
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1. **Architecture**: Streamlit UI → FastAPI REST API
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2. **Dependencies**: Integrated with existing backend services
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3. **Error Handling**: Enhanced exception handling and logging
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4. **Monitoring**: Database-backed request monitoring
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5. **Validation**: Strong request/response validation
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6. **Documentation**: Automatic API documentation
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### Compatibility
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- All original functionality preserved
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- Enhanced features and capabilities
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- Better integration with existing systems
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- Improved performance and scalability
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## Testing
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### Running Tests
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```bash
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# Structure validation
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python3 validate_linkedin_structure.py
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# Full functionality tests (requires dependencies)
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python3 test_linkedin_endpoints.py
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```
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### Test Coverage
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- ✅ Post generation
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- ✅ Article generation
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- ✅ Carousel generation
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- ✅ Video script generation
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- ✅ Comment response generation
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- ✅ Error handling
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- ✅ Structure validation
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## Troubleshooting
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### Common Issues
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#### 1. Import Errors
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```bash
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ModuleNotFoundError: No module named 'pydantic'
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```
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**Solution**: Install dependencies
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```bash
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pip install -r requirements.txt
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```
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#### 2. API Key Issues
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```bash
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Error: GEMINI_API_KEY environment variable is not set
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```
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**Solution**: Set the environment variable
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```bash
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export GEMINI_API_KEY="your_api_key_here"
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```
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#### 3. Database Connection Issues
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```bash
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Error creating database session
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```
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**Solution**: Check database configuration and permissions
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#### 4. Generation Timeouts
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**Solution**: Increase timeout settings or reduce content complexity
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### Debug Mode
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Enable debug logging:
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```bash
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export LOG_LEVEL=DEBUG
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```
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## Future Enhancements
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### Planned Features
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- [ ] Real search engine integration (Metaphor, Google, Tavily)
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- [ ] Content scheduling and calendar integration
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- [ ] A/B testing capabilities
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- [ ] Advanced analytics and reporting
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- [ ] Multi-language support
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- [ ] Custom templates and brand voice
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- [ ] LinkedIn API integration for direct posting
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- [ ] Content performance tracking
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### Performance Improvements
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- [ ] Response caching
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- [ ] Parallel processing for multiple requests
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- [ ] Background job processing
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- [ ] CDN integration for static assets
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## Support
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For issues and questions:
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1. Check the [troubleshooting section](#troubleshooting)
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2. Review the API documentation at `/docs`
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3. Check the logs for detailed error information
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4. Validate your request format against the examples
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## License
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This LinkedIn Content Generation Service is part of the ALwrity platform and follows the same licensing terms.
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