Embed

Agent embeds corpus passages and user queries with the same input_type, then wonders why retrieval recall is poor.

Set input_type='search_document' when embedding corpus passages for indexing and input_type='search_query' when embedding the query at search time. The asymmetric input_type is required for the retrieval embedding space to align.

A downstream classifier is trained on embeddings produced with input_type='search_document', then serves with input_type='classification'.

Use input_type='classification' for classification features and input_type='clustering' for clustering, and keep the same input_type at train and inference time. Mixing input_types between training and serving shifts the embedding distribution and degrades the model.

Operator changes the requested embedding dimension (output_dimension) for new documents but the existing index was built at the old dimension.

All vectors in one index must share the same dimensionality. Changing the output dimension requires a full re-embed and re-index; never mix dimensions in a single index. Validate vector length on write.