Smart Answers

Fusion Smart Answers brings the benefits of a versatile, scalable Semantic Search platform to provide cutting edge relevancy for your applications. This system makes use of advanced Deep Learning techniques to provide the power of Neural dense vectors search. Semantically rich models encode queries and documents into vectors in the same digital vector space in such way that query and the most relevant information are located near each other. It pushes search boundaries beyond classical token-based matching mechanisms by leveraging the contextual information and query understanding.

Virtual assistants and Chatbots can incorporate Smart Answers to enable self-service for employees and customers and save cost on answering incoming queries.

E-commerce can utilize Smart Answers to handle zero-result search queries and improve the overall relevancy by recommending products that are most likely of users interest.

Training data can be explicitly provided as query/response pairs or constructed from the collected signals data. Even if you don’t have an existing set of recorded interactions, you can rely on Smart Answers cold start solution. It uses various pre-trained models to get OOTB semantic capabilities as well as provides a possibility to train unsupervised models for specific domains.

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.

Support for the Fusion 4.2 implementation of Smart Answers will be discontinued by end of year 2020. Upgrade to Fusion 5.1 or higher for ongoing Smart Answers support.

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.

Zero-result search problem in E-commerce:

Queries that lead to zero-results is a huge problem for E-commerce that leads to income loss and decrease of the overall users experience. Semantic search doesn’t have this problem as it doesn’t operate on the exact token matches as classical search. Instead search is done in the vector space which can find relevant products even if there is no token overlapping.

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

As the solution utilizes state of the art Deep Learning techniques, it is able to capture semantic and contextual information into query understanding. This makes it very suitable to work with long queries or natural language questions.

Getting started

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

The Supervised solution The Cold Start solution

Use this solution when you already have a collection of query/response pairs or if you can construct such a dataset from the signals data.

  • Input: A dataset of query/response pairs.

  • Training module: Deep learning

See Supervised Solution for details.

Use this solution when you have no historical training data or fewer than 200 query/response pairs.

  • Training module: Word2vec

See Cold Start Solution for details.

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

"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 <appname>-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