The Ray/Seldon Vectorize Field stage invokes a machine learning model to encode a string field to a vector representation. In Managed Fusion 5.9.11 and earlier, this stage is called Seldon Vectorize Field.

This feature is only available in Managed Fusion 5.9.x for versions 5.9.6+.

For more information on setting up vector search with Ray or Seldon, see Configure Ray/Seldon vector search.

Configure Ray/Seldon vector search

You can use Seldon or Ray models to vectorize text for Neural Hybrid Search.

This feature is only available in Managed Fusion 5.9.x for versions 5.9.6+.

Prerequisites

Seldon

To vectorize text with Seldon, you’ll first need to develop and deploy a machine learning model.

Ray

This feature is only available in Managed Fusion 5.9.12 and later versions of Managed Fusion 5.9.

To vectorize text with Ray, you first need to develop and deploy a machine learning model with Ray.

Configure index pipeline

Sign into Managed Fusion, go to Indexing > Index Pipelines, then select an existing pipeline or create a new one.
Click Add a new pipeline stage, then select Ray/Seldon Vectorize Field. In Managed Fusion 5.9.11 and earlier, this stage is called Seldon Vectorize Field.
Fill in the required fields:
1. Enter a Model ID. This is the name of the model you developed and deployed.
2. Enter the Model Input Field. For example, text.
3. Enter the Model Output Vector Field. For example, vector.
4. Enter the Source Field. For example, body_t.
5. Enter the Destination Field. For example, body_512_v.
Click Save.
Make sure the Ray/Seldon Vectorize Field stage is ordered before the Solr Indexer stage.

Index data using the new pipeline. Confirm that your vector field is indexed by checking to see that the field is present in documents.

Configure query pipeline

Go to Querying > Query Pipelines, then select an existing pipeline.
Click Add a new pipeline stage, then select Ray/Seldon Vectorize Query. In Managed Fusion 5.9.11 and earlier, this stage is called Seldon Vectorize Query.
Fill in the required fields, making sure to search against the field into which you indexed the vectors:
1. Enter a Model ID. This is the name of the model you developed and deployed.
2. Enter the Query Input.
3. Enter the Model Input Field. For example, text.
4. Enter the Model Output Vector Field. For example, vector.
5. Enter the Vector Context Key. For example, vector.
Click Save.
Make sure the Ray/Seldon Vectorize Query stage is ordered before the Solr Query stage.

Perform vector searches

After setting up the stages, you can perform vector searches via the knn query parser as you would with Solr. Specify the search vector and include it in the query. For example, change the q parameter to a knn query parser string.The Ray/Seldon Vectorize Query stage will encode user queries using the specified model and modify the q parameter to use the knn query parser, turning the query into a vector search.

ImportantThis index stage must be placed before the Solr Indexer stage.

Configuration

When entering configuration values in the UI, use unescaped characters, such as \t for the tab character. When entering configuration values in the API, use escaped characters, such as \\t for the tab character.

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