Query Knowledge Engine

Streams a response to a natural language query using Server-Sent Events

POST /api/v1/knowledge-engines/{id}/query

Query Knowledge Engine

cURL

curl --request POST \
  --url https://api.getdecisional.ai/api/v1/knowledge-engines/{id}/query \
  --header 'Authorization: Basic <encoded-value>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "query": "<string>",
  "name": "<string>",
  "advanced_reasoning": true,
  "model": "llama-v3-70b",
  "context_id": "<string>"
}
'

import requests

url = "https://api.getdecisional.ai/api/v1/knowledge-engines/{id}/query"

payload = {
    "query": "<string>",
    "name": "<string>",
    "advanced_reasoning": True,
    "model": "llama-v3-70b",
    "context_id": "<string>"
}
headers = {
    "Authorization": "Basic <encoded-value>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Basic <encoded-value>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    query: '<string>',
    name: '<string>',
    advanced_reasoning: true,
    model: 'llama-v3-70b',
    context_id: '<string>'
  })
};

fetch('https://api.getdecisional.ai/api/v1/knowledge-engines/{id}/query', 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.getdecisional.ai/api/v1/knowledge-engines/{id}/query",
  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>',
    'name' => '<string>',
    'advanced_reasoning' => true,
    'model' => 'llama-v3-70b',
    'context_id' => '<string>'
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Basic <encoded-value>",
    "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.getdecisional.ai/api/v1/knowledge-engines/{id}/query"

	payload := strings.NewReader("{\n  \"query\": \"<string>\",\n  \"name\": \"<string>\",\n  \"advanced_reasoning\": true,\n  \"model\": \"llama-v3-70b\",\n  \"context_id\": \"<string>\"\n}")

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

	req.Header.Add("Authorization", "Basic <encoded-value>")
	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.getdecisional.ai/api/v1/knowledge-engines/{id}/query")
  .header("Authorization", "Basic <encoded-value>")
  .header("Content-Type", "application/json")
  .body("{\n  \"query\": \"<string>\",\n  \"name\": \"<string>\",\n  \"advanced_reasoning\": true,\n  \"model\": \"llama-v3-70b\",\n  \"context_id\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.getdecisional.ai/api/v1/knowledge-engines/{id}/query")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Basic <encoded-value>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"query\": \"<string>\",\n  \"name\": \"<string>\",\n  \"advanced_reasoning\": true,\n  \"model\": \"llama-v3-70b\",\n  \"context_id\": \"<string>\"\n}"

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

200

Example

"<string>"

Query a knowledge engine with a natural language question. This endpoint uses Server-Sent Events (SSE) to stream the response back to the client.

Request Format

query · string · required

The natural language query to ask the knowledge engine

name · string · required

A name for this query workflow

context_id · string

Context ID used in case of thread mode for chat based workflows

advanced_reasoning · boolean

Flag to enable advanced reasoning for the query

model · string

The model to use for the query

Example Request

{
  "query": "What were the company's revenue figures for 2023?",
  "name": "Revenue Analysis",
  "advanced_reasoning": true,
  "model": "llama-v3-70b"
}

Response Format

The response is streamed using Server-Sent Events (SSE) with the following event types:

• message: Contains intermediate response chunks as they are generated
• error: Contains error messages if something goes wrong
• done: Final event containing the complete response with metadata

Example Response Stream

// Intermediate messages
event: message
data: "Based on the available information, "
event: message
data: "the company's revenue in 2023 was "
event: message
data: "$1.2 billion, representing a 15% increase from 2022."
// Final response with complete result
event: done
data: {
"id": "wf_abc123xyz789",
"name": "Revenue Analysis",
"query": "What were the company's revenue figures for 2023?",
"type": "query",
"knowledge_engine_id": "kng_abc123xyz789",
"status": "processed",
"response": "Based on the available information, the company's revenue in 2023 was $1.2 billion, representing a 15% increase from 2022.",
"citations": [
{
"text": "In fiscal year 2023, total revenue reached $1.2B, up 15% YoY",
"source": "Annual Report 2023",
"page": 45
}
],
"created_at": 1679644800
}

Notes

• The streaming response allows for real-time display of the AI’s response as it’s being generated
• The final done event includes the complete response along with metadata and citations
• If an error occurs, the stream will emit an error event and close the connection
• Clients should handle connection closure appropriately using the close event

The streaming response requires a client that supports Server-Sent Events (SSE). Most modern browsers and HTTP clients support this feature.

Response Object

id · string

Unique identifier for the workflow

name · string

Name of the workflow

query · string

The original query that was asked

type · string

Type of workflow (always “query”)

knowledge_engine_id · string

ID of the knowledge engine that was queried

status · string

Status of the workflow (“processed” when complete)

response · string

The complete response text

citations · array

Array of citations supporting the response

Show Citation Object

text · string

The relevant text from the source

source · string

Name of the source document

page · number

Page number in the source document

created_at · number

Unix timestamp when the workflow was created

Authorizations

Authorization · string · header · required

Basic authentication header of the form Basic <encoded-value>, where <encoded-value> is the base64-encoded string username:password.

Path Parameters

id · string · required

Knowledge engine ID

Body

application/json

query · string · required

The natural language query

name · string · required

Name of the workflow

advanced_reasoning · boolean

Enable advanced reasoning

model · enum<string>

LLM to use for the query

Available options: auto, claude-4.5-sonnet, llama-v3-70b, llama-4-scout, llama-4-maverick, gemini-2.5, gpt-5.4, claude-4.6-haiku, gpt-4.1

Example:

"llama-v3-70b"

context_id · string

Context ID for maintaining conversation history in chat mode

Response

200

text/event-stream

Streaming response using Server-Sent Events

The response is of type string.