Search Knowledge Base

POST /v2/memory/knowledge/search

Search Knowledge Base

cURL

curl --request POST \
  --url https://api.velt.dev/v2/memory/knowledge/search \
  --header 'Content-Type: application/json' \
  --header 'x-velt-api-key: <x-velt-api-key>' \
  --header 'x-velt-auth-token: <x-velt-auth-token>' \
  --data '
{
  "data": {
    "query": "<string>",
    "sourceId": [
      "<string>"
    ],
    "limit": 123
  }
}
'

import requests

url = "https://api.velt.dev/v2/memory/knowledge/search"

payload = { "data": {
        "query": "<string>",
        "sourceId": ["<string>"],
        "limit": 123
    } }
headers = {
    "x-velt-api-key": "<x-velt-api-key>",
    "x-velt-auth-token": "<x-velt-auth-token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {
    'x-velt-api-key': '<x-velt-api-key>',
    'x-velt-auth-token': '<x-velt-auth-token>',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({data: {query: '<string>', sourceId: ['<string>'], limit: 123}})
};

fetch('https://api.velt.dev/v2/memory/knowledge/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.velt.dev/v2/memory/knowledge/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([
    'data' => [
        'query' => '<string>',
        'sourceId' => [
                '<string>'
        ],
        'limit' => 123
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "x-velt-api-key: <x-velt-api-key>",
    "x-velt-auth-token: <x-velt-auth-token>"
  ],
]);

$response = curl_exec($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.velt.dev/v2/memory/knowledge/search"

	payload := strings.NewReader("{\n  \"data\": {\n    \"query\": \"<string>\",\n    \"sourceId\": [\n      \"<string>\"\n    ],\n    \"limit\": 123\n  }\n}")

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

	req.Header.Add("x-velt-api-key", "<x-velt-api-key>")
	req.Header.Add("x-velt-auth-token", "<x-velt-auth-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.velt.dev/v2/memory/knowledge/search")
  .header("x-velt-api-key", "<x-velt-api-key>")
  .header("x-velt-auth-token", "<x-velt-auth-token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"data\": {\n    \"query\": \"<string>\",\n    \"sourceId\": [\n      \"<string>\"\n    ],\n    \"limit\": 123\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.velt.dev/v2/memory/knowledge/search")

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

request = Net::HTTP::Post.new(url)
request["x-velt-api-key"] = '<x-velt-api-key>'
request["x-velt-auth-token"] = '<x-velt-auth-token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"data\": {\n    \"query\": \"<string>\",\n    \"sourceId\": [\n      \"<string>\"\n    ],\n    \"limit\": 123\n  }\n}"

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

{
  "result": {
    "results": [
      {
        "sourceId": "src_9a8...",
        "text": "Always cite a peer-reviewed source...",
        "score": 0.18
      }
    ],
    "recordsSearched": 1
  }
}

Use this API to run a semantic vector search over the content of your ingested knowledge base: the chunked, embedded text extracted from the files you’ve ingested. This is distinct from searching learned judgments (which search and ask read over). The endpoint is workspace-scoped.

Endpoint

POST https://api.velt.dev/v2/memory/knowledge/search

Headers

x-velt-api-key · string · required

Your API key.

x-velt-auth-token · string · required

Your Auth Token.

Body

Params

data · object · required

Show properties

query · string · required

The natural-language search query. It is embedded and matched against your knowledge chunks by cosine nearest-neighbour.

sourceId · string | string[]

Restrict the search to one or more knowledge sources. A single id restricts to that source; an array of 2 to 30 ids fans out one prefiltered nearest-neighbour search per id and merges/re-ranks the combined results by distance. Omit to search the entire workspace knowledge base.

limit · integer

Maximum number of results to return.

This endpoint is workspace-scoped. organizationId and documentId are not accepted; the strict schema rejects them rather than silently ignoring them. An unknown or cross-workspace sourceId returns empty results (never an error or a cross-tenant read). On embedding failure, the endpoint falls back to a recency query.

Example Requests

Search a single source

{
  "data": {
    "query": "citation policy",
    "sourceId": "src_9a8...",
    "limit": 10
  }
}

Search across multiple sources

{
  "data": {
    "query": "citation policy",
    "sourceId": ["src_9a8...", "src_2b1..."],
    "limit": 10
  }
}

Search the whole workspace knowledge base

{
  "data": {
    "query": "citation policy",
    "limit": 10
  }
}

Response

Each result includes the matched sourceId, the chunk text, and a score: the cosine distance, where lower is more relevant. recordsSearched reports how many sources were queried.

Success Response

{
  "result": {
    "results": [
      {
        "sourceId": "src_9a8...",
        "text": "Always cite a peer-reviewed source...",
        "score": 0.18
      }
    ],
    "recordsSearched": 1
  }
}

Failure Response

{
  "error": {
    "message": "ERROR_MESSAGE",
    "status": "INVALID_ARGUMENT"
  }
}

{
  "result": {
    "results": [
      {
        "sourceId": "src_9a8...",
        "text": "Always cite a peer-reviewed source...",
        "score": 0.18
      }
    ],
    "recordsSearched": 1
  }
}