- Index pipeline setup
- Query pipeline setup
"question-answering" index pipeline
"question-answering" query pipeline
Index pipeline setup
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
In the "question-answering" query pipeline, all stages (except the Solr Query stage) can be tuned for better Smart Answers:
You can also configure the number of clusters used in deep encoding.
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
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.Note
This parameter can be dynamically overwritten by
rowsFromSolrToRerankraw 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.
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
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:
Document Vector Field - The field which stores dense vectors or their compressed representations. It should be the same as _Vector Field _in Default:
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:
Distance Type - Choose one of the supported distance types. For a FAQ solution,
cosine_similarityis recommended, which produces values with a maximum value of 1. Higher value means higher similarity. Default:
Document Vectors Distance Field - The result document field that contains the distance between its vector and the query vector. Default:
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_scorevalue, which is the max Solr score from all returned docs.
vectors_distanceis 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.
0.3 * score / max_score + 0.7 * vectors_distanceNote
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:
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:
Sort order - The sorting order: (asc)ending or (desc)ending. If
cosine_similarityis used with higher values meaning higher similarity, then this should be set to
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.