How To Use Full-Text Search in Seqwa

Are you interested in trying out Seqwa's REST API for searching? Great. There will be plenty to learn if you want to integrate Seqwa search into your application (or just someone who wants to learn more about it). Full-text search is the ability to perform a search on the content of an entire document, rather than just in metadata fields, thus allowing users to search for documents by typing in natural language phrases and sentences.

The Seqwa full-text search REST API offers many advanced features that allow you to create powerful applications. It's powered by artificial intelligence, which gives you more precise answers to your questions. The AI analyzes each query and returns results based on context and user intent so that you can find the most relevant information every time. You can use our full-text search REST API to quickly create an application that requires a powerful text search engine. It's easy to use and integrate with any system. It works great with popular programming languages such as Python, Node.js, Java, Javascript, Go, Ruby and PHP.

Working with REST-API

The data you need is already in your Seqwa account, but it's locked away inside millions of documents; standard search tools can't find it. We've trained our AI to understand the meaning of the words and sentences in your documents to find what you're looking for, even if your search terms aren't exactly correct. The Seqwa Full-text Search API works on our proprietary TextRank algorithm that analyzes and ranks the relevance of documents based on arbitrary text input. It can find similar cases in support tickets or identify product attributes from product reviews as they're submitted.

Seqwa currently supports the following document types:

  • Articles: newspaper articles, web pages, comprehensive articles, blogs, etc.
  • Books: fiction, non-fiction, magazines, etc.
  • Papers: academic papers from journals and conferences.
  • Products: retail, eCommerce

You can use this API to quickly find relevant content for users or information retrieval in your systems. To use the API, you need an API key. You can find yours on your Seqwa account page. Once you have your key, you can start making requests via GET with URL params. The request should contain your search query and any additional parameters.

Natural Language Processing (NLP) to understand the semantics.

The Seqwa full-text search engine uses natural language processing (NLP) to understand the semantics of what you're searching on. Unlike a keyword search, a full-text search doesn't have a fixed dictionary of words. Instead, it can interpret the meaning of words in context and return highly relevant results for queries that contain complex or ambiguous terms.

Seqwa is a highly customizable document and data repository. When you write a query in Seqwa, the full-text search engine analyzes it. It constructs a representation of the query in a convenient form for further processing. A vital component of this analysis is lemmatization — reducing inflected forms to their canonical form.

Faceted Search

Faceted search systems are standard in online product catalogs and enterprise search engines to enable end-users to quickly and easily find relevant information. The Seqwa full-text search engine supports faceted search with Lucene's query parser. It also promotes faceting on multi-valued fields and various query types. This means you can:

  • Specify a list of fields to search instead of one unified field
  • Search each field for any number of terms (using Boolean logic)
  • Require that some terms are present in at least one of the fields
  • Require that other terms are current in all fields or a specified list of fields

The query parser also supports:

  • fuzzy searches (words are similar but not the same),
  • proximity searches (words are near each other but not necessarily next to each other),
  • range searches (values are within a specified range).
  • supports complex queries with parentheses, quotes, multiple keywords, etc.

AI-Powered Semantic Re-ranking

Seqwa's AI-powered full-text search REST API considers context and user intent, with the concept called Semantic Re-ranking. It groups together concepts like "retina display" and "touch screen" so that a search for one will show results also mentioning the other. The idea is that it looks at more than just the words you've en and the meaning behind them. Seqwa can achieve these using proprietary algorithms and a knowledge graph of around 1 billion entities, the building blocks of search queries.

As an example, Seqwa gave us two sentences that were similar in structure ("I'm looking for a hotel near xxx") but had different meanings. In one, users wanted a hotel near a city center; in another, they wanted somewhere near an airport. The second sentence was ranked higher by a regular search engine because more data related to hotels near airports in its index. Seqwa put the sentence about hotels near city centers up top because it understands the context of these phrases thanks to its knowledge graph and reranking semantic engine.

Understanding of concepts and categories

With Seqwa, you can classify texts into specific categories and concepts. You can also use these classifications to filter the search results.

Relevance scoring

When searching for a specific query, Seqwa makes sure that relevant results are ranked higher than less relevant ones. The search also considers the length of the documents and highlights the best matching parts for each record.

Support for synonyms, abbreviations, and spelling errors

Seqwa understands similar words, abbreviations, and common spelling mistakes, so you don't have to worry about correcting all the misspelled words in your documents first.

Fast-indexing and AI-enrichment

Search is a critical part of any web or mobile application. You might not be happy with your search results because most search services only rank documents using keyword matching and other factors. Implementing full-text search has become an easier task with the availability of hosted services such as Amazon Cloudsearch or Azure Cognitive Search. While these services provide the basics, it's still up to you to manage the deployment and maintenance of your search infrastructure.

Usually, when you want to make a set of documents searchable in these cloud services, you upload them to an index. The documents are stored, but they are not yet processed and made searchable. To do that, you need to schedule a reindexing operation. However, Seqwa lets you skip this step and make the documents searchable immediately after upload. AI-Enrichment like extracting keyphrases and summarization applied during indexing. When you send us your documents, we add them to the search engine. That means you can start searching for information after they've uploaded the documents. This approach is faster and less expensive than sending documents to an index.

Get Seqwa for a great search experience.

Integrating a full-text search functionality within a web application is a significant challenge that produces numerous benefits. By adopting the right tools, you can make your service attractive to your customers and improve their satisfaction with the offered functionality. Using the full-text search engine can be tricky at first. However, once you get a feel for the nuances of the query language, it's possible to pull up relevant results in a fraction of the time it would take to sift through an entire database. In addition, by targeting specific fields, you can quickly find what you're looking for instead of digging through dozens or hundreds of documents.

The Full-text search functionality is a tool that has proven itself time and time again to be essential for getting the best possible results from your customers. Seqwa has many useful features for working with full-text search queries. It's a powerful tool if you know how to use it! However, it's important to remember that this is just the platform's initial release. There are additional capabilities on the horizon. Yes, it's just the beginning, but we're happy with how the platform has come out.