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GET
/
v1
/
knowledge
/
{kb_id}
/
search
Search In Knowledge Base
curl --request GET \
  --url https://api.xpander.ai/v1/knowledge/{kb_id}/search \
  --header 'x-api-key: <api-key>'
import requests

url = "https://api.xpander.ai/v1/knowledge/{kb_id}/search"

headers = {"x-api-key": "<api-key>"}

response = requests.get(url, headers=headers)

print(response.text)
const options = {method: 'GET', headers: {'x-api-key': '<api-key>'}};

fetch('https://api.xpander.ai/v1/knowledge/{kb_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.xpander.ai/v1/knowledge/{kb_id}/search",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "GET",
CURLOPT_HTTPHEADER => [
"x-api-key: <api-key>"
],
]);

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

curl_close($curl);

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

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

func main() {

url := "https://api.xpander.ai/v1/knowledge/{kb_id}/search"

req, _ := http.NewRequest("GET", url, nil)

req.Header.Add("x-api-key", "<api-key>")

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

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

fmt.Println(string(body))

}
HttpResponse<String> response = Unirest.get("https://api.xpander.ai/v1/knowledge/{kb_id}/search")
.header("x-api-key", "<api-key>")
.asString();
require 'uri'
require 'net/http'

url = URI("https://api.xpander.ai/v1/knowledge/{kb_id}/search")

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

request = Net::HTTP::Get.new(url)
request["x-api-key"] = '<api-key>'

response = http.request(request)
puts response.read_body
[
  {
    "content": "<string>",
    "score": 123
  }
]
{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}
Perform semantic search across documents in a knowledge base using vector similarity. Returns the most relevant document chunks based on your query.

Path Parameters

kb_id
string
required
Unique identifier of the knowledge base to search (UUID format)

Query Parameters

search_query
string
required
The search query text to find relevant documents
top_k
integer
default:5
Number of results to return (default: 5, maximum: 50)

Response

Returns a flat array of search results, each containing:
content
string
The extracted text content from the matching document
score
number
Relevance score (float, higher indicates better match)

Example Request

curl --request GET \
  --url 'https://api.xpander.ai/v1/knowledge/<kb-id>/search?search_query=pricing+plans&top_k=5' \
  --header 'x-api-key: <your-api-key>'

Example Response

[
  {
    "content": "The extracted text content from the matching document...",
    "score": 81.41
  }
]

Notes

  • Search only returns results from documents with status: "completed"
  • Pending or failed documents are excluded from search results
  • Empty results indicate no matching content in the knowledge base
  • Returns 404 if the knowledge base is not found

Authorizations

x-api-key
string
header
required

API Key for authentication

Path Parameters

kb_id
string
required

Query Parameters

search_query
string
required

Query to search in the VDB

use_bubble
boolean | null
default:false

Should use bubble search to return the result capped

bubble_size
integer | null
default:1000

Bubble size (padding + result + margin)

top_k
integer | null
default:10

Top K Results

Response

Successful Response

content
string
required

The content of the document

score
number
required

The search result score