Search V2 (Search through Texts)

WorqHat Search V2 is an AI-powered search solution designed to deliver a fast and relevant search experience for end-users. Unlike traditional search engines that require extensive resources and development time, WorqHat Search V2 offers a ready-to-go solution that is highly customizable and easy to install. By addressing common search challenges such as poor fulfillment and typo handling, WorqHat Search V2 ensures that users can find the information they need quickly and accurately. With features like instant search, typo handling, relative rankings, and user behavior understanding, WorqHat Search V2 empowers businesses of all sizes to enhance their search capabilities and improve user satisfaction.

​

How does it work?

WorqHat Search V2 leverages advanced AI algorithms to provide a powerful search experience that goes beyond traditional keyword matching. Here’s how it works:

1. Data Analysis: The AI-powered backend of WorqHat Search V2 begins by analyzing the available content and training data. This includes text from various sources such as documents, web pages, or user-generated content.

2. Natural Language Processing (NLP): The AI algorithms employ NLP techniques to understand the context and meaning of the user’s search query. This goes beyond simple keyword matching and takes into account the relationships between words, phrases, and concepts.

3. Semantic Understanding: By applying semantic analysis, the AI algorithms extract the underlying semantic structure of the content and user queries. This allows for a more nuanced understanding of the intent behind the search and enables the system to generate optimized answers.

4. Training Data Integration: WorqHat Search V2 is designed to continuously learn and improve over time. As users interact with the system and provide feedback, the AI algorithms analyze this data and incorporate it into the training process. This iterative learning approach helps to refine the search results and make them more accurate and relevant.

5. Optimized Answers: Based on the analysis of the user’s search query, content, and training data, WorqHat Search V2 generates optimized answers that best match the user’s intent. These answers are ranked based on their relevance and presented to the user in a prioritized manner.

6. Feedback Loop: WorqHat Search V2 encourages user feedback to further enhance its AI capabilities. By allowing users to rate the relevance and usefulness of the search results, the system can continuously refine its algorithms and improve the overall search experience.

By harnessing the power of AI, WorqHat Search V2 delivers a sophisticated search solution that analyzes user queries, content, and training data to generate optimized answers. This advanced approach enables businesses to provide a more intelligent and intuitive search experience for their users, leading to improved satisfaction and engagement.

​

Use Cases

• E-commerce Product Search: Online retailers rely on keyword-based search to help customers find desired products quickly. Users can enter keywords or specific terms, and the search engine matches these against product names, descriptions, and attributes. Typo tolerance ensures that minor spelling errors or variations in product names do not hinder the search results.

• Document Retrieval: Document management systems and knowledge bases utilize keyword-based search to enable users to find relevant documents or information. Users can enter keywords or phrases, and the search engine matches them against indexed content, including document titles, tags, and content. Typo tolerance allows for accurate retrieval, even with minor spelling mistakes.

• Content Discovery: Online content platforms, such as news websites or article directories, employ keyword-based search to help users discover relevant articles or content. Users can enter keywords or topics of interest, and the search engine matches them against article titles, summaries, and content. Typo tolerance ensures that variations in spelling or typing errors do not hinder the search experience.

• Customer Support Knowledge Base: Companies use keyword-based search in their customer support knowledge bases to help users find relevant articles or FAQs. Users can enter keywords related to their query, and the search engine matches them against the indexed knowledge base content. Typo tolerance ensures that users get accurate search results, even if they make minor spelling errors.

• Job Portals: Job search platforms utilize keyword-based search to enable users to find relevant job listings. Users can enter job titles, skills, or specific keywords, and the search engine matches them against job titles, descriptions, and requirements. Typo tolerance helps users find relevant job opportunities, even with minor spelling mistakes in their search queries.

​

How to use Search V2 AI

You can use the following Endpoints on any Codebase, including client side codebases as long as you are able to send the Headers and the Request Body to the API Endpoint. It’s that easy! Just send a POST Request to the API Endpoint with the Headers and the Request Body, and you are good to go!

Visit the API Reference to learn how to implement Search V2 AI in your projects. Get access to Sample Code, API Endpoints and run it right within the browser to test it out.

​

Train Your Own Data

The Search V2 AI is a large model that is trained on a large dataset. However, this model is mostly focused on Optimising Time and give quicker responses. That is why, this model is completely dependent on the training data provided by the user. The more training data you provide, the better the model will perform and return a better response on search queries.