API reference›Vector Store

Search Collection

POST /v1/vector_store/{id}/search View .md

Searches items in a vector store collection for the closest embeddings matches.

Path parameters

id string required

The ID of the vector store collection.

Body

application/json

Search Vector Store Request Payload

input string required

The text query to search against the embeddings items in the vector store collection.

Responses

application/json · search-vector-store-response-payload

2 fields

results array of objects

A list of collection items that best match the embeddings search query, sorted by most relevant first.
3 fields
- id string
  
  The unique ID of the vector store collection item.
- created string
  
  The UTC creation timestamp of the collection item in ISO 8601 format.
- content string
  
  The readable content text of the vector store collection item.
usage object

An object containing usage information for this embeddings request.
2 fields
- prompt_tokens integer
  
  Number of tokens in the provided input.
- total_tokens integer
  
  Total number of tokens used in the request.

curl https://api.vultrinference.com/v1/vector_store/{id}/search \
  -H "Authorization: Bearer $VULTR_INFERENCE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "string"
  }'

import os
import requests

response = requests.post(
    "https://api.vultrinference.com/v1/vector_store/{id}/search",
    headers={"Authorization": f"Bearer {os.environ['VULTR_INFERENCE_API_KEY']}"},
    json={
        "input": "string",
    },
)
print(response.json())

const response = await fetch("https://api.vultrinference.com/v1/vector_store/{id}/search", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.VULTR_INFERENCE_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "input": "string"
  }),
});
console.log(await response.json());

Response · 200

{
    "results": [
        {
            "id": "string",
            "created": "string",
            "content": "string"
        }
    ],
    "usage": {
        "prompt_tokens": 0,
        "total_tokens": 0
    }
}