Neural search is the most advanced technology that empowers search engines with the capability to comprehend user queries better and return the most accurate search results. It is a search technology that makes use of deep learning algorithms to provide more relevant results. Neural search understands language in the same way human beings do, using natural language processing (NLP) and machine learning techniques. Neural search has become very popular in recent years because it can improve search accuracy by predicting a user’s intent more accurately.
Understanding the drivers of better results with neural search:
Several factors contribute to better results with neural search. Here are some of the most important factors:
1. Word embedding: Neural search uses word embedding to understand language better. Word embeddings are a set of representations used in NLP to numerically represent words. In a search query, words may have multiple meanings, and word embedding helps neural search to understand the appropriate meaning of the word in context.
2. Contextual word similarity: In addition to word embedding, neural search also employs contextual word similarity to better understand the meaning of words in the query. With contextual word similarity techniques, words within the same query can be evaluated within the context of each other to get a clearer understanding of user intent.
3. Neural networks: Neural networks are machine learning algorithms that are capable of learning and adapting to new information. Neural search uses these algorithms to continually learn and improve the search results delivered to users.
4. Multi-modal learning: Multi-modal learning refers to the use of multiple media channels to enhance learning. With neural search, multi-modal learning techniques can be applied, allowing the search engine to combine visual and textual data when processing a user’s query.
5. Query expansion: Query expansion is a technique that increases the coverage and recall of a search engine by adding more terms to a user’s query. Neural search uses query expansion to provide more accurate results to users, even when their queries are imprecise.
To use the features of neural search in your project, you need a client. Here’s an example of a client using PHP:
$client = new \GuzzleHttp\Client(); $requestUrl = "https://api.neuralsearch.ai/v2/search"; $headers = [ "X-Api-Key" => "your-api-key", "Content-Type" => "application/json" ]; $requestBody = [ "q" => "what is the weather like today?", "indexName" => "your-index-name", "k" => 10 ]; $response = $client->request("POST", $requestUrl, [ "headers" => $headers, "json" => $requestBody ]); $results = json_decode($response->getBody()->getContents(), true);
In this PHP client example, the API endpoint is defined by $requestUrl. Before that, we set headers to send authorization key and content-type to indicate the format of the request body. Finally, we set the request body with the query we want to search, the index name we’re searching in, and the maximum number of results we need in the response.
How WPSOLR can help:
WPSOLR is a popular search plugin for WordPress that integrates seamlessly with popular WordPress themes. It provides an intuitive interface to configure the search options, and it supports various search engines, including neural search. With the WPSOLR plugin, you can quickly enable neural search on your WordPress website and benefit from its advanced capabilities.
Neural search is a powerful technology that can significantly improve the accuracy of search results. Several factors contribute to better results with neural search, including word embedding, contextual word similarity, neural networks, multi-modal learning, and query expansion. By using a client, such as the PHP client we provided an example of, you can integrate neural search technology into your project. With the WPSOLR plugin, you can take advantage of neural search in WordPress and provide the best search experience for your users.