FAQ Solution

The FAQ solution consists of two parts.

1. Train a model

Train a machine learning model using existing question-and-answer pairs by configuring the QnA Supervised Training job. See Train A Smart Answers FAQ Model.

The job includes an auto-tuning feature that seeks to identify the optimal configuration for your model.

2. Configure the pipelines

The trained model should be used at both index and query time in order to perform dense vector search.

  • At index time, we provide the {app_name}_question_answering index pipeline to help generate a dense vector representation of answers.

  • At query time, we provide a {app_name}_question_answering query pipeline to conduct run-time neural search. This pipeline transforms the incoming query into a dense vector using the trained model, then compares it with indexed answer dense vectors by computing the cosine distance between them. You can also use a query stage to combine Solr and document vector similarity scores at query time.

See Configure The Smart Answers Pipelines. Once your pipelines are configured, see Evaluate a Smart Answers Query Pipeline to test its effectiveness so that you can fine-tune the configuration.

Short answer extraction

By default, the question-answering query pipelines return complete documents that answer questions. Optionally, you can extract just a paragraph, a sentence, or a few words that answer the question. See Configure Short Answer Extraction.