Post
552
Article: https://huggingface.co/blog/JLouisBiz/semantical-website-links
You don't need to do the tedious work of finding all those links on your huge website.
Automating semantic links on websites using Large Language Models (LLMs) enhances user experience and efficiency. Here's a simplified workflow:
1. Store LLM embeddings in PostgreSQL: Use the vector data type to store text embeddings generated by an LLM.
2. Divide page texts into chunks for processing.
3. Generate embeddings using an LLM for each chunk of text.
4. Create template markup around specific terms needing links.
An automated program then:
- Converts marked-up terms to their corresponding LLMs' embeddings,
- Compares these with stored database embeddings (using cosine similarity),
- Identifies the most relevant page based on highest similarity score, and
- Automatically adds a link from the original content to this contextually related information.
This process improves navigation by directing users to highly contextual pages. It saves time as it automates creating semantic links while maintaining accuracy.
You don't need to do the tedious work of finding all those links on your huge website.
Automating semantic links on websites using Large Language Models (LLMs) enhances user experience and efficiency. Here's a simplified workflow:
1. Store LLM embeddings in PostgreSQL: Use the vector data type to store text embeddings generated by an LLM.
2. Divide page texts into chunks for processing.
3. Generate embeddings using an LLM for each chunk of text.
4. Create template markup around specific terms needing links.
An automated program then:
- Converts marked-up terms to their corresponding LLMs' embeddings,
- Compares these with stored database embeddings (using cosine similarity),
- Identifies the most relevant page based on highest similarity score, and
- Automatically adds a link from the original content to this contextually related information.
This process improves navigation by directing users to highly contextual pages. It saves time as it automates creating semantic links while maintaining accuracy.
