Preparing Your Aident Workspace...

Casos de usoPrecios Documentación

ESEmpezar ahora

AppsDatabricks ToolsDatabricks Vectorsearch Vector Search Indexes Query Index

Databricks Tools

Databricks Vectorsearch Vector Search Indexes Query Index

Usa Databricks Vectorsearch Vector Search Indexes Query Index con Databricks Tools mediante Aident Loadout. Revisa sus entradas, salidas, configuración, precio y riesgo antes de ejecutarla.

Action de escrituraNivel de riesgo 2 de 5

Producto

Aident Loadout Aident Playbook Editor Open-CUAK

Compañía

Apps Skills Casos de uso Documentación Blog Precios Descargas Política de privacidad Términos de servicio

Alternativas

vs Composio vs Zapier

llms.txtObtén un resumen con IA de Aident

© Aident Labs, 2024-2026

Conexión con clave de API

Usar Databricks Vectorsearch Vector Search Indexes Query Index

Descripción del proveedor

Tool to query vector search index to find similar vectors and return associated documents. Use when performing similarity search, hybrid keyword-similarity search, or full-text search on Databricks Vector Search indexes. Supports filtering, reranking, and returns configurable co…

Precios y límites

Precio: Action gratuita
Pago unificado: No compatible actualmente

Entradas

columnsarray
Obligatorio
List of column names to include in the response. These columns will be returned along with the similarity scores for matched documents
filters
Filter conditions to apply to results. Use dictionary format for standard endpoints (e.g., {'field': value}), or SQL string format for storage-optimized endpoints (e.g., "field = 'value'")
rerankerobject
Reranking configuration object.
index_namestring
Obligatorio
Name of the vector index to query. Use the full three-level name format (catalog.schema.index_name)
page_tokenstring
Token for pagination. Pass the next_page_token from a previous response to retrieve the next page of results. Maximum total retrievable results is 10,000 across all pages
query_textstring
Text query to search with. Required for Delta Sync Index using model endpoint. Automatically converted to embeddings. Must provide either query_vector or query_text
query_typestring
Type of search to perform. ANN (default): Approximate nearest neighbor search. HYBRID: Combines keyword and similarity search. FULL_TEXT: Full-text search mode
debug_levelinteger
Set to ≥1 to get latency information in response. Required for reranker timing information
num_resultsinteger
Obligatorio
Maximum number of results to return. Maximum is 200 for hybrid search, 10,000 total retrievable with pagination
filters_jsonstring
JSON string representing query filters (alternative format)
query_vectorarray
Vector embeddings to search with. Required for Direct Vector Access Index and Delta Sync Index using self-managed vectors. Must provide either query_vector or query_text
score_thresholdnumber
Minimum similarity score threshold for results. Defaults to 0.0. Only results with similarity scores above this threshold will be returned

Salida observable

data
Obligatorio
Data from the action execution
errorstring
Error if any occurred during the execution of the action
successfulboolean
Obligatorio
Whether or not the action execution was successful or not

Skills que usan esta Action

Casos de uso relacionados

Aún no hay casos de uso relacionados verificados.

Fuente oficial

Fuente del proveedor

Skills que usan esta Action

Aún no hay Skills publicadas que hagan referencia explícita a esta Action.