Search vector store

curl --request POST \
  --url https://api.openai.com/v1/vector_stores/{vector_store_id}/search \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "query": "<string>",
  "rewrite_query": false,
  "max_num_results": 10,
  "filters": {
    "type": "eq",
    "key": "<string>",
    "value": "<string>"
  },
  "ranking_options": {
    "ranker": "auto",
    "score_threshold": 0
  }
}
'

import requests

url = "https://api.openai.com/v1/vector_stores/{vector_store_id}/search"

payload = {
    "query": "<string>",
    "rewrite_query": False,
    "max_num_results": 10,
    "filters": {
        "type": "eq",
        "key": "<string>",
        "value": "<string>"
    },
    "ranking_options": {
        "ranker": "auto",
        "score_threshold": 0
    }
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    query: '<string>',
    rewrite_query: false,
    max_num_results: 10,
    filters: {type: 'eq', key: '<string>', value: '<string>'},
    ranking_options: {ranker: 'auto', score_threshold: 0}
  })
};

fetch('https://api.openai.com/v1/vector_stores/{vector_store_id}/search', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.openai.com/v1/vector_stores/{vector_store_id}/search",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'query' => '<string>',
    'rewrite_query' => false,
    'max_num_results' => 10,
    'filters' => [
        'type' => 'eq',
        'key' => '<string>',
        'value' => '<string>'
    ],
    'ranking_options' => [
        'ranker' => 'auto',
        'score_threshold' => 0
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.openai.com/v1/vector_stores/{vector_store_id}/search"

	payload := strings.NewReader("{\n  \"query\": \"<string>\",\n  \"rewrite_query\": false,\n  \"max_num_results\": 10,\n  \"filters\": {\n    \"type\": \"eq\",\n    \"key\": \"<string>\",\n    \"value\": \"<string>\"\n  },\n  \"ranking_options\": {\n    \"ranker\": \"auto\",\n    \"score_threshold\": 0\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.openai.com/v1/vector_stores/{vector_store_id}/search")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"query\": \"<string>\",\n  \"rewrite_query\": false,\n  \"max_num_results\": 10,\n  \"filters\": {\n    \"type\": \"eq\",\n    \"key\": \"<string>\",\n    \"value\": \"<string>\"\n  },\n  \"ranking_options\": {\n    \"ranker\": \"auto\",\n    \"score_threshold\": 0\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.openai.com/v1/vector_stores/{vector_store_id}/search")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"query\": \"<string>\",\n  \"rewrite_query\": false,\n  \"max_num_results\": 10,\n  \"filters\": {\n    \"type\": \"eq\",\n    \"key\": \"<string>\",\n    \"value\": \"<string>\"\n  },\n  \"ranking_options\": {\n    \"ranker\": \"auto\",\n    \"score_threshold\": 0\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "object": "vector_store.search_results.page",
  "search_query": [
    "<string>"
  ],
  "data": [
    {
      "file_id": "<string>",
      "filename": "<string>",
      "score": 0.5,
      "attributes": {},
      "content": [
        {
          "type": "text",
          "text": "<string>"
        }
      ]
    }
  ],
  "has_more": true,
  "next_page": "<string>"
}

POST

vector_stores

{vector_store_id}

Search vector store

curl --request POST \
  --url https://api.openai.com/v1/vector_stores/{vector_store_id}/search \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "query": "<string>",
  "rewrite_query": false,
  "max_num_results": 10,
  "filters": {
    "type": "eq",
    "key": "<string>",
    "value": "<string>"
  },
  "ranking_options": {
    "ranker": "auto",
    "score_threshold": 0
  }
}
'

import requests

url = "https://api.openai.com/v1/vector_stores/{vector_store_id}/search"

payload = {
    "query": "<string>",
    "rewrite_query": False,
    "max_num_results": 10,
    "filters": {
        "type": "eq",
        "key": "<string>",
        "value": "<string>"
    },
    "ranking_options": {
        "ranker": "auto",
        "score_threshold": 0
    }
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    query: '<string>',
    rewrite_query: false,
    max_num_results: 10,
    filters: {type: 'eq', key: '<string>', value: '<string>'},
    ranking_options: {ranker: 'auto', score_threshold: 0}
  })
};

fetch('https://api.openai.com/v1/vector_stores/{vector_store_id}/search', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.openai.com/v1/vector_stores/{vector_store_id}/search",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'query' => '<string>',
    'rewrite_query' => false,
    'max_num_results' => 10,
    'filters' => [
        'type' => 'eq',
        'key' => '<string>',
        'value' => '<string>'
    ],
    'ranking_options' => [
        'ranker' => 'auto',
        'score_threshold' => 0
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.openai.com/v1/vector_stores/{vector_store_id}/search"

	payload := strings.NewReader("{\n  \"query\": \"<string>\",\n  \"rewrite_query\": false,\n  \"max_num_results\": 10,\n  \"filters\": {\n    \"type\": \"eq\",\n    \"key\": \"<string>\",\n    \"value\": \"<string>\"\n  },\n  \"ranking_options\": {\n    \"ranker\": \"auto\",\n    \"score_threshold\": 0\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.openai.com/v1/vector_stores/{vector_store_id}/search")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"query\": \"<string>\",\n  \"rewrite_query\": false,\n  \"max_num_results\": 10,\n  \"filters\": {\n    \"type\": \"eq\",\n    \"key\": \"<string>\",\n    \"value\": \"<string>\"\n  },\n  \"ranking_options\": {\n    \"ranker\": \"auto\",\n    \"score_threshold\": 0\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.openai.com/v1/vector_stores/{vector_store_id}/search")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"query\": \"<string>\",\n  \"rewrite_query\": false,\n  \"max_num_results\": 10,\n  \"filters\": {\n    \"type\": \"eq\",\n    \"key\": \"<string>\",\n    \"value\": \"<string>\"\n  },\n  \"ranking_options\": {\n    \"ranker\": \"auto\",\n    \"score_threshold\": 0\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "object": "vector_store.search_results.page",
  "search_query": [
    "<string>"
  ],
  "data": [
    {
      "file_id": "<string>",
      "filename": "<string>",
      "score": 0.5,
      "attributes": {},
      "content": [
        {
          "type": "text",
          "text": "<string>"
        }
      ]
    }
  ],
  "has_more": true,
  "next_page": "<string>"
}

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Path Parameters

vector_store_id

string

required

The ID of the vector store to search.

Example:

"vs_abc123"

Body

application/json

query

required

A query string for a search

rewrite_query

boolean

default:false

Whether to rewrite the natural language query for vector search.

max_num_results

integer

default:10

The maximum number of results to return. This number should be between 1 and 50 inclusive.

Required range: 1 <= x <= 50

filters

Comparison Filter · object

A filter to apply based on file attributes.

Comparison Filter
Compound Filter

Show child attributes

ranking_options

object

Ranking options for search.

Show child attributes

Response

200 - application/json

object

enum<string>

required

The object type, which is always vector_store.search_results.page

Available options:

vector_store.search_results.page

search_query

string[]

required

The query used for this search.

data

object[]

required

The list of search result items.

Show child attributes

has_more

boolean

required

Indicates if there are more results to fetch.

next_page

string | null

required

The token for the next page, if any.

API

Assistants

Audio

Batch

Chat

Completions

Embeddings

Evals

Files

Fine-tuning

Images

Models

Moderations

API Reference

Audit Logs

Certificates

Usage

Invites

Projects

Users

Realtime

Responses

Uploads

Vector stores

Search vector store

Authorizations

Path Parameters

Body

Response