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Fusion 5.12
    Fusion 5.12

    Smart Answers

    Fusion Smart Answers brings the benefits of a versatile, scalable enterprise search platform to virtual assistants and chatbots.

    This robust Q&A system makes use of advanced deep learning techniques to match question/answer pairs and enable self-service for employees and customers, increasing search relevance and saving cost on answering incoming queries.

    These features bring traditional search relevancy development and data science together into an easy-to-use configuration framework that leverages Fusion’s indexing and querying pipelines.

    Even if you do not have an existing set of recorded interactions for question/answer pairs, you can still build a robust Q&A system using our cold start solution, which improves relevancy by using machine learning (dense-vector semantic search) based on content documents.

    Example business use cases

    Call center or IT support
    • Embed this system as a self-help feature on your Help page or Contact Us page to reduce the call center load.

    • Make it available to your customer support team to find the answers to already-solved problems.

    Questions about products for e-commerce:

    E-commerce websites can use this system to search "how to" content, product user manuals, or existing product questions. For example, provides a search function on questions about each product.

    Search in Slack, email conversations, or SharePoint FAQ docs

    You can achieve fast knowledge extraction by applying this solution to these types of knowledge repositories.

    Improve search for long queries

    The cold start solution utilizes word embedding methods to capture semantic and contextual information for long queries or natural language questions. The cold start solution doesn’t require FAQ training data. Instead, it can use word vector training combined with Solr scores using our provided query pipeline.

    Getting started

    To get started, you need a trained machine learning model. There are two methods for generating a model, depending on the kind of data you already have:

    The FAQ solution The cold start solution

    Use this solution when you already have a collection of questions that have been asked before, and their answers.

    • Input: A dataset of question/answer pairs.

    • Training module: Deep learning

    See FAQ Solution for details.

    Use this solution when you have no historical FAQ data, or fewer than 200 question/answer pairs.

    • Input: A body of content that can be used to answer questions.

    • Training module: Word2vec

    See Cold Start Solution for details.

    Each method requires a different model training procedure, but the model deployment procedure is the same for both.

    The system combines the power of Solr and neural dense vectors search from deep learning. Neural dense vector retrieval transfers documents into a digital vector space to incorporate semantic and contextual information into query understanding.

    "Question-answering" pipelines and stages

    Once you have trained and deployed your model, you can use one of the default pipelines that are automatically created with your Fusion app. Both pipelines are called APP_NAME-question-answering.

    You must perform some basic configuration on these pipelines. For advanced configuration tips, see Detailed Pipeline Setup.

    "-question-answering" and "-question-answering-dual-fields" index pipelines

    question-answering default index pipeline

    "-question-answering" and "-question-answering-dual-fields" query pipelines

    question-answering default query pipeline