Raw embedding vector query input for direct vector similarity search. Accepts a pre-computed embedding vector and uses it directly for similarity search without any inference. Useful for programmatic use cases such as taxonomy enrichment where embeddings are already available. Use Cases: - Taxonomy enrichment (passing pre-computed document embeddings) - Programmatic similarity search with known vectors - Cross-collection matching with pre-extracted features - ColBERT/multi-vector search with pre-computed token embeddings Examples: Single dense vector: json {\"input_mode\": \"vector\", \"value\": [0.1, 0.2, 0.3, ...]} Multi-dense vector (ColBERT — list of token embeddings): json {\"input_mode\": \"vector\", \"value\": [[0.1, 0.2], [0.3, 0.4], ...]} Template-based (from taxonomy input mapping): json {\"input_mode\": \"vector\", \"value\": \"{{INPUT.query_image}}\"}
| Name | Type | Description | Notes |
|---|---|---|---|
| input_mode | str | Discriminator field. Always 'vector' for raw embedding queries. | [optional] [default to 'vector'] |
| value | Value2 | [optional] |
from mixpeek.models.stage_defs_vector_query_input import StageDefsVectorQueryInput
# TODO update the JSON string below
json = "{}"
# create an instance of StageDefsVectorQueryInput from a JSON string
stage_defs_vector_query_input_instance = StageDefsVectorQueryInput.from_json(json)
# print the JSON string representation of the object
print(StageDefsVectorQueryInput.to_json())
# convert the object into a dict
stage_defs_vector_query_input_dict = stage_defs_vector_query_input_instance.to_dict()
# create an instance of StageDefsVectorQueryInput from a dict
stage_defs_vector_query_input_from_dict = StageDefsVectorQueryInput.from_dict(stage_defs_vector_query_input_dict)