Smart Answers Detailed Pipeline Setup

Typically, you can use the default pipelines included with Fusion AI. This topic provides information you can use to customize these pipelines. See also Configure The Smart Answers Pipelines.

"question-answering" index pipeline

question-answering default index pipeline

"question-answering" query pipeline

question-answering default query pipeline

Index pipeline setup

Stages in the default "question-answering" index pipeline

question-answering default index pipeline

Typically, only one custom index stage needs to be configured in your index pipeline:

If you are using a dynamic schema, make sure this stage is added after the Solr Dynamic Field Name Mapping stage.

There are several required parameters:

Query pipeline setup

Stages in the default "question-answering" query pipeline

question-answering default query pipeline

In the "question-answering" query pipeline, all stages (except the Solr Query stage) can be tuned for better Smart Answers:

Optionally, you can also:

The Query Fields stage

The first stage is Query Fields which should have one main parameter specified:

  • Return Fields - Since documents should be retrieved with vectors and clusters, make sure to include the documents’ Vector Field and Clusters Field, which are compressed_document_vector_s and document_clusters_ss by default.

The Rewrite Pagination Parameters for Reranking stage

The Rewrite Pagination Parameters for Reranking query stage is used to specify how many results from Solr should be used to create a pool of candidates for further re-ranking via model. It’s done under the hood, so users would still see the number of results controlled by "start" and "rows" query parameters.

  • Number of Results - Number of results to request from Solr, to be re-ranked by downstream stages. Default value: 500. This parameter affects both results accuracy and query time performance. Increasing the number can lead to better accuracy but worse query time performance and vice versa.

    This parameter can be dynamically overwritten by rowsFromSolrToRerank raw query parameter in the request.

The Filter Stop Words stage

When limited data is provided to train FAQ or coldstart models, the model may not be able to learn the weights of stop words accurately. Especially when using Solr to retrieve the top x results (if Number of Clusters is set to 0 in the stop words may have high impact on Solr. User can use this stage to provide customized stop words list by providing them in Words to filter part of the stage.

See Filter Stop Words for reference information. See also Best Practices.

The Escape Query stage

The Escape Query query stage escapes Lucene/Solr reserved characters in a query. In the Smart Answers use case, queries are natural-language questions. They tend to be longer than regular queries and with punctuation which we don’t want to interpret as Lucene/Solr query operators.

The Query-Document Vectors Distance stage

The Query-Document Vectors Distance query stage computes the distance between a query vector and each candidate document vector. It should be added after the Solr Query stage.

  • Query Vector Context Key - The context key which stores dense vectors. It should be set to the same value as _Vector Context Key _in the Default value: query_vector.

  • Document Vector Field - The field which stores dense vectors or their compressed representations. It should be the same as _Vector Field _in Default: compressed_document_vector_s.

  • Keep document vector field - This option allows you to keep or drop returned document vectors. In most cases it makes sense to drop document vectors after computing vector distances, to improve run time performance. Default: False (unchecked).

  • Distance Type - Choose one of the supported distance types. For a FAQ solution, cosine_similarity is recommended, which produces values with a maximum value of 1. Higher value means higher similarity. Default: cosine_similarity.

  • Document Vectors Distance Field - The result document field that contains the distance between its vector and the query vector. Default: vectors_distance.

The Compute Mathematical Expression stage

We can use the Compute Mathematical Expression query stage to combine the Solr score with the vectors similarity score to borrow Solr’s keywords matching capabilities. This stage should be set before the Result Document Field Sorting stage. Result field name in this stage and Sort field in the Result Document Field Sorting stage should be set to the same value.

  • Math expression - Specify a mathematical formula for combining the Solr score and the vector similarity score. Note that Solr scores do not have an upper bound, so it is best to rescale it by using the max_score value, which is the max Solr score from all returned docs. vectors_distance is the cosine similarity in our case and it already has an upper bound of 1.0. The complete list of supported mathematical expressions is listed in the Oracle documentation.

    Default: 0.3 * score / max_score + 0.7 * vectors_distance

    We recommend adjusting the ratios 0.3 and 0.7 to other values based on query-time evaluation results (mentioned in the section above). Feel free to try other math expressions (such as log10) to scale the Solr score as well.
  • Result field name - The document field name that contains the combined score. The same value should be set in the Result Document Field Sorting stage, in the Sort Field parameter. Default: ensemble_score

The Result Document Field Sorting stage

Finally, the Result Document Field Sorting stage sorts results based on vector distances obtained from the previous Vectors distance per Query/Document stage or ensemble score from Compute mathematical expression stage.

  • Sort Field - The field to use for document sorting. Default value: ensemble_score

  • Sort order - The sorting order: (asc)ending or (desc)ending. If cosine_similarity is used with higher values meaning higher similarity, then this should be set to desc. Default: desc.

Configuring the number of clusters

Another important parameter to choose is the Number of clusters parameter in the in the query pipeline. There are two options for utilizing the dense vectors from a query pipeline:

  • If set Number of clusters parameter to 0 (default option), then we use Solr to return the top x results, then re-rank using vector cosine similarity, or combine the Solr score with the vector score using our Compute mathematical expression stage. If you choose this option, then adjust Number of Results parameter (Default value 500) in the Rewrite Pagination Parameters for Reranking stage in the query pipeline. This parameter controls how many documents to be returned from Solr to Fusion and re-ranked under the hood. You can adjust this parameter to find a good balance between relevancy and query speed.

  • If you set the Number of clusters parameter to a value x greater than 0, when a query comes in, Fusion transfers the query into a dense vector and find the closest x clusters to which the query belongs. The pipeline then obtains documents from the same clusters as query and re-ranks them based on the vector cosine similarity between the query and the indexed answers/questions. The good default values can be 1 cluster per document and 10 clusters per query.

Documents from the clusters might be obtained in the following ways: * By using Solr search. Then search space is narrowed by both clusters and Solr. And Solr score might be used in the ensemble in the same way as in the first option described above. * By using only clusters. Then all documents from certain clusters are retrieved for re-ranking. Solr doesn’t narrow the search space, but it’s also not used for ensemble.

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 Extract Short Answers from Longer Documents.