Build production-ready semantic search systems
For decades, semantic similarity was a concept foreign to computers. If you wanted to teach a computer that two sentences are synonymous, you had to convey that information through hard-coded, inflexible rules.
Modern search systems don’t need rules. Without being explicitly taught how to do it, they can handle typos and spelling variations, and understand the semantic relationships between sentences just like humans do. Thanks to meaning-based search systems, we can now compute the meaning of a text as a whole, and determine the semantic similarity between two documents.
These systems use vector search, a type of document search. It embeds documents as points in a high-dimensional space representing their semantics, that is, their abstract meaning. We can then find out how similar two documents are in meaning by measuring the distance between their corresponding points in the embedding space. This way, vector search can help to:
- Find the texts most likely to contain the answer to a query.
- Find the texts most similar to a given text.
- Return relevant texts directly to the user.
By building vector search into your product, you can provide a Google-like search experience right in your documents. Get the best results from your curated database — no matter if it contains millions or even billions of documents.
With deepset Cloud — our enterprise platform for AI teams — you can start building intelligent and fast search systems today without vendor lock-in. Use the latest embedding models for your vector search to index the documents in your database. By applying our built-in advanced evaluation techniques to your system, you can be sure you are delivering the best experience to your users.
Whether you’re looking to build a more intuitive document search, a recommendation system based on meaning, or an application that leverages retrieval-augmented generative AI, deepset Cloud is the fastest and most robust solution for implementing production-grade vector search.
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