Offering a powerful search is a key success factor for all products that involve content or user data and rely on search functionality to facilitate information retrieval. Handling search bars as a trivial development step that might be solved with out-of-the-box tools might be a huge mistake that leads to user frustration and disenchantment with the product.
When users encounter difficulties in finding the information they need, frustration and dissatisfaction can quickly set in. This can lead to a negative impression of the product and, in some cases, prompt users to abandon it altogether. Irrelevant results can cause confusion, hinder user progress, and make it difficult for users to trust the product as a reliable source of information.
Luckily, the technologies behind search engines are progressing and a powerful and smooth search experience is today fairly possible to achieve for all digital products, and not anymore exclusive for huge companies such as Google or Amazon.
Searching through keyword matches might be a good approach for specific scenarios, for example when the user knows the potential results very well and wants to retrieve those using a specific wording. However, the majority of the search scenarios are well less certain and, with huge amounts of data available, users most likely need to retrieve information they have never seen before.
Semantic search has revolutionized the way users interact with search engines by going beyond keyword matching and understanding the meaning and context of search queries. One of the main advantages of context-aware search engines is their ability to comprehend the intent behind a search query and understand the relationship between words and phrases. This way, advanced search engines can provide results that are conceptually related to the user’s query, delivering a more comprehensive and valuable search experience.
We built a robust and flexible semantic search engine ready to be integrated into existing systems and products. Unlike standard APIs, our solution will be fully adapted to your needs. Some possible customizations include but are not limited to:
In the core of the solution for semantic search is the notion of embeddings which are high dimensional vectors. In other words: numerical representation of text data. With the help of embeddings, semantic similarity can be computed as the spatial proximity between these vectors. In this context, vector databases are tools designed to index and store vector embeddings for fast retrieval and similarity search. The resulting matches can be fed into a LLM (large language model) in order to have an integrated response with the features listed above.
By drawing mostly on open source models and embeddings, our engine can provide a high level of security and data privacy, as it can be deployed on your own infrastructure. Those products handling user data don’t need to compromise its safety to offer powerful AI functionalities.