{"id":9754,"date":"2026-04-16T08:55:18","date_gmt":"2026-04-16T08:55:18","guid":{"rendered":"https:\/\/demo.themeinprogress.eu\/internal-linking-related-contents-pro\/?page_id=9754"},"modified":"2026-04-30T13:11:54","modified_gmt":"2026-04-30T13:11:54","slug":"how-to-configure-and-use-the-ai-engine","status":"publish","type":"page","link":"https:\/\/demo.themeinprogress.eu\/internal-linking-related-contents-pro\/how-to-configure-and-use-the-ai-engine\/","title":{"rendered":"How to configure and use the AI Engine"},"content":{"rendered":"
Important:<\/b> this feature is planned for version 1.4.1<\/b>, with release expected by June 30, 2026<\/b>.
Some details, screenshots, or options shown on this page may change before the official release.<\/div>\n

How does it work?<\/h3>\n

This option, available only for WordPress posts, uses embeddings to analyze your site content and automatically suggest relevant internal links between articles.<\/p>\n

Unlike other available search engines, which rely on traditional approaches such as categories or tags to identify related content, this system is able to understand the overall meaning of texts and identify truly relevant connections between articles.<\/p>\n

In this way, the system identifies related posts by comparing embeddings using a cosine similarity algorithm, which measures how close two pieces of content are from a semantic perspective.<\/p>\n

This feature will not be available while the activation plugin<\/a> is installed on your website.<\/p>\n

If you are using the activation plugin, please complete the license activation process and remove it once the license has been correctly activated.<\/p>\n

Important:<\/b> This feature is included with all plans purchased before the official release, expected by June 30, 2026<\/b>. After the official release, the Personal<\/b> plan will no longer include this feature.<\/div>\n

What are embeddings?<\/h3>\n

Embeddings are numerical representations of textual content. In practice, each article is transformed into a vector, that is, a sequence of numbers that describes its meaning.<\/p>\n

This allows the system to compare content at a semantic level: texts that are similar in meaning will have similar numerical representations, even if they use different words.<\/p>\n

Why use embeddings?<\/h3>\n

Using embeddings allows you to overcome the limitations of methods based solely on keywords, categories, or article titles.<\/p>\n

In particular, it prevents articles in the same category from always showing the same related content, improving the variety and quality of suggestions.<\/p>\n

By analyzing the content of each post, suggestions become more accurate because they take into account context and meaning, allowing you to link truly related content even when they do not share identical terms or belong to the same categories.<\/p>\n

This approach improves the quality of internal linking and remains effective even on sites with a large number of contents.<\/p>\n

How to get started<\/h3>\n

To begin, you need to select a provider for generating embeddings.<\/p>\n

You can use OpenAI APIs, which require an API key and active credits, or choose a local model using Ollama, which requires a more advanced setup and configuration.<\/p>\n

\"\"<\/a>
Select the AI provider from the available options<\/figcaption><\/figure>\n

Regarding OpenAI, you can generate a new API key by following these steps:<\/p>\n

    \n
  1. Go to the OpenAI website<\/a> and log into your account<\/li>\n
  2. Click the “Create API key” button in the top right corner<\/li>\n
  3. In the popup, enter an optional name for the key<\/li>\n
  4. Click “Create” to confirm and copy the API key<\/li>\n<\/ol>\n

    It is important to store the API key securely, as it will not be possible to view it again later.<\/p>\n

    Once you have the API key, you will need to purchase credits to use it within your site.<\/p>\n

    Credits can be purchased using the payment methods supported by the platform.<\/p>\n

    In most cases, a valid payment card is required; the availability of prepaid cards or other methods (such as direct debit) may vary depending on the country and account configuration.<\/p>\n

    If you do not have access to a traditional card, you can consider alternatives such as Revolut, which allows you to generate virtual cards usable for this type of purchase.<\/p>\n

    This is a commonly adopted solution, but it is always recommended to verify compatibility with your account.<\/p>\n

    OpenAI supports long content through automatic chunking, but extremely long articles may require many API calls and can fail due to server timeout, rate limits, or runtime constraints. Articles above tens of thousands of words should be considered edge cases.<\/em><\/p>\n

    Embeddings generation<\/h3>\n

    Once the provider has been selected, you can choose a compatible embeddings model directly from the plugin settings.<\/p>\n

    \"\"<\/a>
    Select an OpenAI embeddings model<\/figcaption><\/figure>\n

    OpenAI provides different embeddings models depending on the balance you want between cost, performance, and storage usage.<\/p>\n