Query Encoders Reference

Query encoders produce dense or sparse vector representations at query time to drive similarity(...) retrieval.

1. `precomputed_user(...)`

Retrieves a pre-trained user embedding vector from the designated embedding table (e.g. Collaborative Filtering / ALS factor matrix).

precomputed_user(input_user_id=$user_id)

Parameter	Type	Required	Description
`input_user_id`	`string \| $param`	Yes	ID of the user whose embedding vector should be looked up.

Retrieves a precomputed item embedding vector (used for item-to-item similarity recommendations).

precomputed_item(input_item_id=$item_id)

Parameter	Type	Required	Description
`input_item_id`	`string \| $param`	Yes	ID of the seed item.

Dynamically pools the embedding vectors of items the user has recently interacted with into a composite user representation.

interaction_pooling(
  input_user_id=$user_id,
  [pooling_function='mean'],
  [truncate_interactions=10]
)

Parameter	Type	Required	Default	Description
`input_user_id`	`string \| $param`	Yes	—	User ID.
`pooling_function`	`string`	No	`'mean'`	Reduction function: `'mean'`, `'sum'`, or `'max'`.
`truncate_interactions`	`integer`	No	`10`	Maximum number of recent interaction item vectors to pool.

Clusters recent interaction item embeddings into $k$ distinct interest clusters to generate multiple candidate queries for multi-interest users.

interaction_round_robin(
  input_user_id=$user_id,
  [pooling_function='mean'],
  [num_clusters=5]
)

Computes query representations from tabular attribute dictionaries or profile features.

user_attribute_pooling(
  input_user_id=$user_id,
  input_user_features=$features
)