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POST
/
v5
/
vector-stores
/
{vector_store_name}
/
query
Query Vectors
curl --request POST \
  --url https://api.egp.scale.com/v5/vector-stores/{vector_store_name}/query \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "text": "<string>",
  "query_type": "semantic",
  "top_k": 10,
  "filter": {},
  "rerank": false,
  "rerank_model": "<string>",
  "rerank_top_n": 123,
  "include_vectors": false
}
'
{
  "vectors": [
    {
      "id": "<string>",
      "text": "<string>",
      "score": 123,
      "metadata": {},
      "vector": [
        123
      ]
    }
  ],
  "metadata": {
    "search_type": "<string>",
    "total_query_time_ms": 123,
    "embedding_model": "<string>",
    "embedding_time_ms": 123,
    "index_query_time_ms": 123,
    "reranking_model": "<string>",
    "reranking_time_ms": 123
  }
}

Authorizations

​
x-api-key
string
header
required

Headers

​
x-selected-account-id
string | null

Path Parameters

​
vector_store_name
string
required

The name of the vector store

Body

application/json

Request to query documents.

​
text
string
required

Text query for automatic embedding (required)

​
query_type
enum<string>
default:semantic

Query type: semantic, lexical, or hybrid

Available options:
semantic,
lexical,
hybrid
​
top_k
integer
default:10

Number of search results to return

Required range: x >= 1
​
filter
Filter · object

Metadata filter expression

​
rerank
boolean
default:false

Enable reranking of search results

​
rerank_model
string

Reranking model to use (uses system default if not specified)

​
rerank_top_n
integer

Number of results after reranking (defaults to top_k)

​
include_vectors
boolean
default:false

Include embedding vectors in response

Response

Successful Response

Response for query operation.

​
vectors
VectorDocumentResponse · object[]
required

Array of matching documents

​
metadata
QueryMetadata · object
required

Query execution metadata