Vector Search and SEO: What You Should Know

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Vector Search and SEO: What You Should Know

While traditional search engines matched keywords, they didn't understand the meaning behind your search query. Vector search is a powerful technology, changing how we navigate information.

Understanding Vector Search

Vector search, also known as semantic search, improves search accuracy by understanding the semantics (meanings) of the data and relationship between its parts. Unlike traditional search, vector search handles synonyms, typing errors, ambiguous language, along with a range of not-so-clear queries. This is due to the fact that it focuses on the meaning behind the search, rather than the key phrases used.

It uses Large Language Models (LLMs) to transform data into mathematical vectors, which is also referred to as vector embedding.

Vector Search and SEO: What You Should Know
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Understanding Vector Databases

These are specialised databases that store, manage, and search vectors efficiently. They handle large datasets and perform fast vector similarity searches. The majority of AI apps today use vector databases.

Can you imagine if AI knew your habits, preferences, and health data? It could use this knowledge to suggest products, services, even recipes to fit your individual preferences. Standard AI models don't have the ability to learn this way. 

When you query a vector database, it uses the query's vector representation to find nearest neighbours within the database.

Nearest Neighbour Search

It measures the distance between our query vector and the other vectors in the database, analysing them one at a time. This can be slow for large datasets, which is why it often relies on nearest neighbour algorithms that prioritise efficiency, finding neighbours that are close to the query, reducing search time.

Approximate Nearest Neighbour Search

How you search for nearest neighbours is determined by how the data has been stored in the vector databset. As a result, the algorithm uses two approaches, indexing and sketching. Indexing creates hierarchical data structure for raster exploration. Sketching doesn't search the entire dataset, but rather creates a compressed version of the data and then compares it to the query.

The most common nearest neighbour search is indexing, where the vector search relies on hierarchical data structure. Remember these are just machines, they love structure, which is why you need to align your SEO strategy accordingly. Creating hierarchy on your website, combined with schema mark-up, ensures your website is seen in vector searches.

Conclusion

Vector search is a leap forward in how we search and retrieve information online. When you understand the relationship between vector search and SEO, it is able to deliver relevant and accurate results. It has revolutionised search engines turning them into sophisticated and smart technologies that evolve with user behaviour. Are you interested in aligning your SEO strategy with vector search? Contact Genie Crawl today to find out more.

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