Search API
Perform a vector similarity search (VSS) operation on a dataset.
The search operation will return the most relevant matches based on cosine similarity with the input text. The datasets queries should have an embedding column, and the appropriate embedding model loaded.
Authorizations
Body
anyOptional
Responses
200
Search completed successfully
application/json
Responseany
400
Invalid request parameters
application/json
500
Internal server error
application/json
post
/v1/searchPOST /v1/search HTTP/1.1
Host: data.spiceai.io
X-API-KEY: YOUR_API_KEY
Content-Type: application/json
Accept: */*
Content-Length: 157
{
"datasets": [
"app_messages"
],
"text": "Tokyo plane tickets",
"where": "user=1234321",
"additional_columns": [
"timestamp"
],
"limit": 3,
"keywords": [
"plane",
"tickets"
]
}{
"results": [
{
"matches": {
"message": "I booked use some tickets"
},
"dataset": "app_messages",
"primary_key": {
"id": "6fd5a215-0881-421d-ace0-b293b83452b5"
},
"data": {
"timestamp": 1724716542
},
"score": 0.914321
},
{
"matches": {
"message": "direct to Narata"
},
"dataset": "app_messages",
"primary_key": {
"id": "8a25595f-99fb-4404-8c82-e1046d8f4c4b"
},
"data": {
"timestamp": 1724715881
},
"score": 0.83221
},
{
"matches": {
"message": "Yes, we're sitting together"
},
"dataset": "app_messages",
"primary_key": {
"id": "8421ed84-b86d-4b10-b4da-7a432e8912c0"
},
"data": {
"timestamp": 1724716123
},
"score": 0.787654321
}
],
"duration_ms": 42
}Last updated
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