Search

curl -X POST "http://localhost:8080/search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": [0.01, -0.02, "... match store dimension ..."],
    "top_k": 5
  }'

{
  "results": [
    {
      "id": "doc-1",
      "score": 0.894123,
      "label": "Close Match",
      "text": "Hello world"
    }
  ]
}

POST

curl -X POST "http://localhost:8080/search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": [0.01, -0.02, "... match store dimension ..."],
    "top_k": 5
  }'

{
  "results": [
    {
      "id": "doc-1",
      "score": 0.894123,
      "label": "Close Match",
      "text": "Hello world"
    }
  ]
}

Overview

Search the local store using a numeric query vector. The query length must match the store dimension.

array

required

JSON array of floats (same length as store dimension).

number

default:"10"

Maximum results to return. Capped at 100.

number

default:"0"

Minimum score (0–1). Used when kiosk_mode is true.

boolean

default:"false"

When true, filters results below threshold.

curl -X POST "http://localhost:8080/search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": [0.01, -0.02, "... match store dimension ..."],
    "top_k": 5
  }'

{
  "results": [
    {
      "id": "doc-1",
      "score": 0.894123,
      "label": "Close Match",
      "text": "Hello world"
    }
  ]
}

Result fields

Field	Description
`id`	Matched item id
`score`	Similarity score (0–1), rounded to 6 decimals
`label`	Human-readable relevance label
`text`	Stored text, if any

The CLI and Python SDK can embed plain-text queries locally before calling this endpoint. See CLI: search and Python: search_text().

System

AI Generation

Data Operations

Delete

Search

Overview

Result fields

​Overview

​Result fields

​Related

Overview

Result fields

Related