Vector Search
Create vector search endpoints and indexes, run similarity queries, and manage embeddings.
Example prompts
Section titled “Example prompts”“Create a storage-optimized vector search endpoint for my RAG app”
“Query my document index for articles similar to machine learning best practices”
“Create a delta sync index on my knowledge_base table using the content column”
manage_vs_data
Section titled “manage_vs_data”Description: Manage Vector Search index data: upsert, delete, scan, sync.
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
action | str | Yes | — |
index_name | str | Yes | — |
inputs_json | Optional[Union[str, list]] | No | — |
primary_keys | Optional[List[str]] | No | — |
num_results | int | No | — |
manage_vs_endpoint
Section titled “manage_vs_endpoint”Description: Manage Vector Search endpoints: create, get, list, delete.
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
action | str | Yes | — |
name | Optional[str] | No | — |
endpoint_type | str | No | — |
manage_vs_index
Section titled “manage_vs_index”Description: Manage Vector Search indexes: create, get, list, delete.
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
action | str | Yes | — |
name | Optional[str] | No | — |
endpoint_name | Optional[str] | No | — |
primary_key | Optional[str] | No | — |
index_type | str | No | — |
delta_sync_index_spec | Optional[Dict[str, Any]] | No | — |
direct_access_index_spec | Optional[Dict[str, Any]] | No | — |
query_vs_index
Section titled “query_vs_index”Description: Query a Vector Search index for similar documents.
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
index_name | str | Yes | — |
columns | List[str] | Yes | — |
query_text | Optional[str] | No | — |
query_vector | Optional[List[float]] | No | — |
num_results | int | No | — |
filters_json | Optional[Union[str, dict]] | No | — |
filter_string | Optional[str] | No | — |
query_type | Optional[str] | No | — |