Jekyll LLM Integration Ideas

By bamr87

Let’s dive deeper into how you might implement some of these ideas:

Estimated reading time: 7 minutes

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Let’s dive deeper into how you might implement some of these ideas:

  1. Automated Content Generation:
    • Setup: You can create a script that sends prompts to the LLM via an API and receives the generated content. This script can run periodically (e.g., via a cron job) to fetch new content.
    • Integration with Jekyll: The generated content can be saved in the _posts directory of your Jekyll site. Use a specific naming convention to ensure that Jekyll recognizes these files as new posts.
    • Example: If you want to generate a weekly blog post, your script might send a prompt like “Write a 500-word blog post about the latest trends in manufacturing technology.”
  2. Interactive Documentation:
    • Setup: Create a web form where users can input their questions. Use JavaScript to send these questions to the LLM’s API and display the responses.
    • Integration with Jekyll: You can embed this form in your documentation pages. For a more seamless experience, you might want to use AJAX to update the page without a full reload.
    • Example: A user asks, “How do I configure Bootstrap 5 with Jekyll?” The form sends this question to the LLM, and the response is displayed directly on the page.
  3. Content Suggestions:
    • Setup: As authors write content in your Jekyll site, they can use a side panel or a separate tool to get suggestions from the LLM. This could be implemented as a browser extension or a separate web app.
    • Integration with Jekyll: Authors can copy the suggested content directly into their markdown files.
    • Example: An author is writing a post about environmental sustainability. They can select a paragraph and get suggestions on how to expand or improve it.
  4. SEO Optimization:
    • Setup: Use the LLM to analyze your content for SEO improvements. This can be done in a pre-publish step where the LLM reviews the draft and suggests changes.
    • Integration with Jekyll: The script can output a report or directly suggest edits in the markdown files.
    • Example: Before publishing, an SEO script runs and suggests adding certain keywords, optimizing the title, and improving meta descriptions.
  5. Summarization:
    • Setup: Automatically generate summaries for long posts or documentation sections. This can be triggered manually or set up as an automated process.
    • Integration with Jekyll: Summaries can be placed at the beginning of posts or as separate summary pages.
    • Example: A 2000-word article on new manufacturing technologies is summarized into a 100-word abstract at the top of the post.
  6. Translation:
    • Setup: Use the LLM to translate content into different languages. You can have a script that sends content to the LLM for translation and saves the translated versions.
    • Integration with Jekyll: Use Jekyll’s multi-language support to manage different versions of your content.
    • Example: A blog post in English is automatically translated into Spanish and French, and these versions are added to the site.
  7. Content Personalization:
    • Setup: Use the LLM to create different versions of content tailored to different user personas. This can be based on user data or preferences.
    • Integration with Jekyll: You can have different markdown files for each persona and serve the appropriate version based on user selection.
    • Example: A post about IT in manufacturing can have different versions for beginners, intermediates, and experts.
  8. Content Moderation:
    • Setup: Use the LLM to review user-generated content before it goes live. The LLM can check for inappropriate content, spam, or adherence to guidelines.
    • Integration with Jekyll: The moderation script can be part of your content pipeline, approving or flagging content before it’s published.
    • Example: Comments on a blog post are automatically reviewed and moderated for spam or inappropriate language.

Sure, let’s break down the technical implementation for a couple of these ideas: Automated Content Generation and Interactive Documentation.

Automated Content Generation

Step-by-Step Implementation:

  1. API Setup:
    • Choose an LLM API: For instance, OpenAI’s GPT-4 API.
    • API Key: Obtain your API key from the provider.
  2. Script to Generate Content:
    • Language: Use Python for simplicity.
    • Dependencies: requests for API calls, yaml for Jekyll front matter.
    import requests
    import yaml
    from datetime import datetime
    
    API_KEY = 'your_api_key'
    API_URL = 'https://api.openai.com/v1/engines/davinci-codex/completions'
    
    def generate_content(prompt):
        headers = {
            'Authorization': f'Bearer {API_KEY}',
            'Content-Type': 'application/json',
        }
        data = {
            'prompt': prompt,
            'max_tokens': 500,
            'temperature': 0.7,
        }
        response = requests.post(API_URL, headers=headers, json=data)
        return response.json()['choices'][0]['text']
    
    def create_jekyll_post(title, content):
        front_matter = {
            'layout': 'post',
            'title': title,
            'date': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
            'categories': 'blog',
        }
        post_content = f"---\n{yaml.dump(front_matter)}---\n{content}"
        filename = f"_posts/{datetime.now().strftime('%Y-%m-%d')}-{title.lower().replace(' ', '-')}.md"
        with open(filename, 'w') as file:
            file.write(post_content)
    
    prompt = "Write a 500-word blog post about the latest trends in manufacturing technology."
    content = generate_content(prompt)
    create_jekyll_post("Latest Trends in Manufacturing Technology", content)
    
  3. Scheduling:
    • Cron Job: Set up a cron job to run this script periodically (e.g., weekly).
    0 0 * * 0 /usr/bin/python3 /path/to/your/script.py
    

Interactive Documentation

Step-by-Step Implementation:

  1. API Setup:
    • Choose an LLM API: Similar to above, use OpenAI’s API.
    • API Key: Obtain your API key from the provider.
  2. Web Form for User Questions:
    • HTML and JavaScript: Create a form that sends user input to the LLM API and displays the response.
    <!DOCTYPE html>
    <html lang="en">
    <head>
        <meta charset="UTF-8">
        <meta name="viewport" content="width=device-width, initial-scale=1.0">
        <title>Interactive Documentation</title>
        <script>
            async function fetchAnswer() {
                const question = document.getElementById('question').value;
                const response = await fetch('/ask', {
                    method: 'POST',
                    headers: { 'Content-Type': 'application/json' },
                    body: JSON.stringify({ question })
                });
                const result = await response.json();
                document.getElementById('answer').innerText = result.answer;
            }
        </script>
    </head>
    <body>
        <h1>Ask a Question</h1>
        <textarea id="question" rows="4" cols="50"></textarea><br>
        <button onclick="fetchAnswer()">Ask</button>
        <h2>Answer</h2>
        <p id="answer"></p>
    </body>
    </html>
    
  3. Backend to Handle API Requests:
    • Flask Application: Use Flask to handle the API requests from the front end.
    from flask import Flask, request, jsonify
    import requests
    
    app = Flask(__name__)
    
    API_KEY = 'your_api_key'
    API_URL = 'https://api.openai.com/v1/engines/davinci-codex/completions'
    
    @app.route('/ask', methods=['POST'])
    def ask():
        data = request.get_json()
        question = data['question']
        headers = {
            'Authorization': f'Bearer {API_KEY}',
            'Content-Type': 'application/json',
        }
        payload = {
            'prompt': question,
            'max_tokens': 150,
            'temperature': 0.7,
        }
        response = requests.post(API_URL, headers=headers, json=payload)
        answer = response.json()['choices'][0]['text']
        return jsonify({'answer': answer})
    
    if __name__ == '__main__':
        app.run(debug=True)
    
  4. Deploy the Flask Application:
    • Deployment: Deploy your Flask app on a server or a platform like Heroku.

These examples provide a foundation. You can customize and expand these implementations based on your specific needs and the capabilities of the LLM you’re using. Do you want to go deeper into any specific part of these implementations?