f1-blog-pipeline 0.1.0

A library for generating F1 blog posts from RSS feeds using AI

F1 Blog Pipeline

A Python library for generating F1 blog posts from RSS feeds using AI. Automatically extracts articles from F1 news sources and generates comprehensive blog posts using Google's Gemini API.

Features

• 📰 RSS Feed Parsing with date filtering support
• 🤖 AI-Powered Blog Generation using Gemini API
• 🌐 Web Scraping with Playwright for full article extraction
• 📅 Flexible Date Filtering (today, yesterday, specific dates, date ranges)
• 🔧 Library-First Design - use programmatically in your applications
• ⚙️ Configurable feeds, extraction settings, and generation parameters
• 🛡️ Robust Error Handling with partial results on failures

Installation

Basic Installation

# Clone the repository
git clone https://github.com/yourusername/f1-blog-pipeline.git
cd f1-blog-pipeline

# Install dependencies
pip install -r requirements.txt

# Install Playwright browsers
playwright install chromium

Development Installation

# Install in editable mode
pip install -e .

Quick Start

from f1_blog_pipeline import F1BlogPipeline

# Initialize pipeline with API key
pipeline = F1BlogPipeline(api_key='your-gemini-api-key')

# Run full pipeline
result = pipeline.run_full_pipeline(filter_date='today')

# Check results
if result.success:
    print(f"Extracted {result.articles_extracted} articles")
    print(f"Blog: {len(result.blog_content)} characters")

Configuration

Environment Variables

Create a .env file (optional):

# Required
GEMINI_API_KEY=your_gemini_api_key_here

# Optional
GEMINI_MODEL=gemini-2.0-flash

Note: The library does NOT load .env files automatically. Your application is responsible for credential management (use python-dotenv or your own config system).

Security: Never commit .env files to version control. The .env file is in .gitignore by default. Always use .env for local development and CI/CD secrets management for production deployments.

Custom Feeds

custom_feeds = {
    "Autosport": "https://www.autosport.com/rss/feed/f1",
    "The Race": "https://www.the-race.com/feed/",
    "RaceFans": "https://www.racefans.net/feed/"
}

pipeline = F1BlogPipeline(
    api_key='your-key',
    feeds=custom_feeds,
    max_per_feed=10
)

Configuration File

Copy config.yaml.example to config.yaml and customize:

feeds:
  Autosport: "https://www.autosport.com/rss/feed/f1"
  Motorsport: "https://www.motorsport.com/rss/f1/news"

extraction:
  max_per_feed: 5
  timeout: 30
  delay: 2

generation:
  model: "gemini-2.0-flash"
  temperature: 0.7

Usage

Basic Pipeline

from f1_blog_pipeline import F1BlogPipeline

# Initialize
pipeline = F1BlogPipeline(api_key='your-key')

# Run full pipeline
result = pipeline.run_full_pipeline(filter_date='today')

# Access results
print(result.success)              # True/False
print(result.articles_extracted)   # Number of articles
print(result.blog_content)         # Generated blog markdown
print(result.errors)               # List of errors if any

Individual Stages

# Just extract articles
articles = pipeline.extract_articles(filter_date='today')

# Generate blog from existing articles
blog = pipeline.generate_blog(articles, author='Your Name')

Custom Configuration

import logging

pipeline = F1BlogPipeline(
    api_key='your-key',
    feeds={'Source': 'https://...'},
    max_per_feed=10,
    timeout=45000,
    delay=3,
    model='gemini-2.0-pro',
    temperature=0.8,
    max_tokens=8000,
    output_dir='./output',
    logger_level=logging.DEBUG
)

Credential Management

The library is designed to work with any credential management strategy:

# Option 1: Pass API key directly
pipeline = F1BlogPipeline(api_key='sk-...')

# Option 2: Use environment variable (your app sets it)
import os
os.environ['GEMINI_API_KEY'] = get_key_from_your_config()
pipeline = F1BlogPipeline()  # Falls back to env var

# Option 3: Load from .env in your app (library doesn't do this)
from dotenv import load_dotenv
load_dotenv()  # YOUR responsibility, not library's
pipeline = F1BlogPipeline()

Date Filtering

# Today's articles only
result = pipeline.run_full_pipeline(filter_date='today')

# Yesterday
result = pipeline.run_full_pipeline(filter_date='yesterday')

# Specific date
result = pipeline.run_full_pipeline(filter_date='2026-02-22')

# Last N days
result = pipeline.run_full_pipeline(days_back=7)

# No filter (latest articles per feed)
result = pipeline.run_full_pipeline()

Examples

See the examples/ directory for detailed usage examples:

• basic_usage.py - Basic library usage
• custom_feeds.py - Custom RSS feeds
• date_range.py - Date filtering examples

Output Formats

Extracted Articles

The pipeline saves extracted articles to articles/extracted_articles.txt in this format:

================================================================================
Source: Autosport
Title: Hamilton explains Mercedes 2026 strategy shift
Authors: ['Scott Mitchell-Malm']
URL: https://www.autosport.com/f1/news/...

[Full article text content with proper formatting and paragraphs...]

================================================================================
Source: Motorsport
Title: Red Bull confirms major technical update for Bahrain
...

Generated Blog Post

The pipeline generates a markdown blog post with YAML frontmatter:

---
title: "2026 Testing Unpacked: Regulation Chaos and Team Performance"
date: 2026-02-22
author: "Adrian"
tags: [f1, testing, 2026-season, bahrain, ferrari, mercedes, red-bull]
summary: "Welcome to the 2026 era. If pre-season testing in Bahrain..."
---

# 2026 Testing Unpacked: Regulation Chaos and Team Performance

Welcome to the 2026 era. If pre-season testing in Bahrain has taught us anything...

## Team Performance Analysis

**Mercedes** emerged as the early pace-setters:
- Russell topped FP1 with a 1:31.247
- New power unit showing promising reliability
- Aerodynamic package generating consistent downforce

**Ferrari** showed mixed results:
- Leclerc struggled with rear stability in high-speed corners
- Team working on suspension geometry adjustments
- Power unit performance on par with expectations

## Technical Updates

The new 2026 regulations have forced teams to rethink...

Project Structure

f1_app/
├── f1_blog_pipeline/          # Main library package
│   ├── __init__.py           # Public API
│   ├── pipeline.py           # Pipeline orchestrator
│   ├── core/                 # Core modules
│   │   ├── rss_parser.py     # RSS feed parsing with date filtering
│   │   ├── article_extractor.py  # Article extraction
│   │   ├── blog_generator.py     # AI blog generation
│   │   └── text_cleaner.py       # Text cleaning utilities
├── examples/                 # Usage examples
├── articles/                 # Extracted articles (generated)
├── posts/                    # Generated blog posts (generated)
├── requirements.txt          # Dependencies
├── pyproject.toml           # Package configuration
├── config.yaml.example      # Example configuration
└── README.md                # This file

API Reference

F1BlogPipeline

Main pipeline class for orchestrating the entire workflow.

class F1BlogPipeline:
    def __init__(
        api_key: Optional[str] = None,
        feeds: Optional[Dict[str, str]] = None,
        max_per_feed: int = 5,
        timeout: int = 30000,
        delay: int = 2,
        model: str = "gemini-2.0-flash",
        temperature: float = 0.7,
        max_tokens: int = 4000,
        output_dir: Optional[str] = None,
        logger_level: Optional[int] = None
    )

PipelineResult

Result object returned by pipeline execution.

@dataclass
class PipelineResult:
    success: bool                          # Overall success
    articles_extracted: int                # Number of articles
    articles: List[Dict[str, Any]]        # Extracted articles
    blog_content: Optional[str]           # Generated blog
    errors: List[Dict[str, str]]          # Errors
    warnings: List[str]                   # Warnings

Advanced Usage

Batch Processing

Generate blog posts for multiple dates:

from datetime import datetime, timedelta
from f1_blog_pipeline import F1BlogPipeline

pipeline = F1BlogPipeline(api_key='your-key')

# Generate posts for last 7 days
for days_ago in range(7):
    date = datetime.now() - timedelta(days=days_ago)
    date_str = date.strftime('%Y-%m-%d')
    
    result = pipeline.run_full_pipeline(filter_date=date_str)
    if result.success:
        with open(f'posts/blog_{date_str}.md', 'w') as f:
            f.write(result.blog_content)
        print(f"✓ Generated blog for {date_str}")

Custom Model Configuration

Test different Gemini models:

pipeline = F1BlogPipeline(
    api_key='your-key',
    model='gemini-2.0-pro',
    temperature=0.9,  # More creative
    max_tokens=8000   # Longer output
)
result = pipeline.run_full_pipeline(filter_date='today')

Available models:

• gemini-2.0-flash (fast, good quality, cost-effective)
• gemini-2.0-pro (higher quality, slower, more expensive)
• gemini-2.0-flash-exp-preview (experimental features)

Error Handling

The pipeline continues on partial failures:

result = pipeline.run_full_pipeline(filter_date='today')

if result.success:
    print(f"Success! {result.articles_extracted} articles")
else:
    print("Pipeline failed")
    for error in result.errors:
        print(f"[{error['stage']}] {error['message']}")

# Check warnings even on success
for warning in result.warnings:
    print(f"Warning: {warning}")

Requirements

• Python 3.10+
• google-genai >= 0.4.0
• playwright >= 1.40.0
• python-dotenv >= 1.0.0

Troubleshooting

Playwright Browsers Not Installed

playwright install chromium

API Key Not Found

# Option 1: Pass directly
pipeline = F1BlogPipeline(api_key='your-key')

# Option 2: Use environment variable
import os
os.environ['GEMINI_API_KEY'] = 'your-key'
pipeline = F1BlogPipeline()

# Option 3: Load from .env file
from dotenv import load_dotenv
load_dotenv()
pipeline = F1BlogPipeline()

Bot Detection / 403 Errors

Increase delay between requests:

pipeline = F1BlogPipeline(delay=5)  # 5 seconds
result = pipeline.run_full_pipeline(filter_date='today')

License

MIT

Contributing

Contributions welcome! Please open an issue or submit a pull request.

Acknowledgments

• Powered by Google Gemini API
• Browser automation by Playwright
• RSS feeds from Autosport, Motorsport.com, and other F1 news sources