Send a message to the AI assistant
curl --request POST \
--url https://hiimpria.ai/api/ai/personal/qanda \
--header 'Content-Type: application/json' \
--header 'x-access-token: <api-key>' \
--data '
{
"id": 1750660464754,
"requestArgs": {
"userISODate": "2025-06-23T06:34:24.754Z",
"userTimezone": "America/New_York",
"socketId": "DhXE7OVjCtfUmDFTAAAB",
"institutionPublicId": "f831501f-b645-481a-9cbb-331509aaf8c1",
"assistantId": "6856fa89cbafcff8d98680f5",
"selectedCourse": {
"course_id": 1750532703472,
"course_name": "Research Discussion",
"assistant": {
"_id": "6856fa89cbafcff8d98680f5"
}
},
"ragOnly": false,
"ragIgnore": false
},
"inputs": [
"What is machine learning?"
],
"outputs": []
}
'import requests
url = "https://hiimpria.ai/api/ai/personal/qanda"
payload = {
"id": 1750660464754,
"requestArgs": {
"userISODate": "2025-06-23T06:34:24.754Z",
"userTimezone": "America/New_York",
"socketId": "DhXE7OVjCtfUmDFTAAAB",
"institutionPublicId": "f831501f-b645-481a-9cbb-331509aaf8c1",
"assistantId": "6856fa89cbafcff8d98680f5",
"selectedCourse": {
"course_id": 1750532703472,
"course_name": "Research Discussion",
"assistant": { "_id": "6856fa89cbafcff8d98680f5" }
},
"ragOnly": False,
"ragIgnore": False
},
"inputs": ["What is machine learning?"],
"outputs": []
}
headers = {
"x-access-token": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'x-access-token': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
id: 1750660464754,
requestArgs: {
userISODate: '2025-06-23T06:34:24.754Z',
userTimezone: 'America/New_York',
socketId: 'DhXE7OVjCtfUmDFTAAAB',
institutionPublicId: 'f831501f-b645-481a-9cbb-331509aaf8c1',
assistantId: '6856fa89cbafcff8d98680f5',
selectedCourse: {
course_id: 1750532703472,
course_name: 'Research Discussion',
assistant: {_id: '6856fa89cbafcff8d98680f5'}
},
ragOnly: false,
ragIgnore: false
},
inputs: ['What is machine learning?'],
outputs: []
})
};
fetch('https://hiimpria.ai/api/ai/personal/qanda', 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://hiimpria.ai/api/ai/personal/qanda",
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([
'id' => 1750660464754,
'requestArgs' => [
'userISODate' => '2025-06-23T06:34:24.754Z',
'userTimezone' => 'America/New_York',
'socketId' => 'DhXE7OVjCtfUmDFTAAAB',
'institutionPublicId' => 'f831501f-b645-481a-9cbb-331509aaf8c1',
'assistantId' => '6856fa89cbafcff8d98680f5',
'selectedCourse' => [
'course_id' => 1750532703472,
'course_name' => 'Research Discussion',
'assistant' => [
'_id' => '6856fa89cbafcff8d98680f5'
]
],
'ragOnly' => false,
'ragIgnore' => false
],
'inputs' => [
'What is machine learning?'
],
'outputs' => [
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"x-access-token: <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"
"strings"
"net/http"
"io"
)
func main() {
url := "https://hiimpria.ai/api/ai/personal/qanda"
payload := strings.NewReader("{\n \"id\": 1750660464754,\n \"requestArgs\": {\n \"userISODate\": \"2025-06-23T06:34:24.754Z\",\n \"userTimezone\": \"America/New_York\",\n \"socketId\": \"DhXE7OVjCtfUmDFTAAAB\",\n \"institutionPublicId\": \"f831501f-b645-481a-9cbb-331509aaf8c1\",\n \"assistantId\": \"6856fa89cbafcff8d98680f5\",\n \"selectedCourse\": {\n \"course_id\": 1750532703472,\n \"course_name\": \"Research Discussion\",\n \"assistant\": {\n \"_id\": \"6856fa89cbafcff8d98680f5\"\n }\n },\n \"ragOnly\": false,\n \"ragIgnore\": false\n },\n \"inputs\": [\n \"What is machine learning?\"\n ],\n \"outputs\": []\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-access-token", "<api-key>")
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://hiimpria.ai/api/ai/personal/qanda")
.header("x-access-token", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"id\": 1750660464754,\n \"requestArgs\": {\n \"userISODate\": \"2025-06-23T06:34:24.754Z\",\n \"userTimezone\": \"America/New_York\",\n \"socketId\": \"DhXE7OVjCtfUmDFTAAAB\",\n \"institutionPublicId\": \"f831501f-b645-481a-9cbb-331509aaf8c1\",\n \"assistantId\": \"6856fa89cbafcff8d98680f5\",\n \"selectedCourse\": {\n \"course_id\": 1750532703472,\n \"course_name\": \"Research Discussion\",\n \"assistant\": {\n \"_id\": \"6856fa89cbafcff8d98680f5\"\n }\n },\n \"ragOnly\": false,\n \"ragIgnore\": false\n },\n \"inputs\": [\n \"What is machine learning?\"\n ],\n \"outputs\": []\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://hiimpria.ai/api/ai/personal/qanda")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-access-token"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"id\": 1750660464754,\n \"requestArgs\": {\n \"userISODate\": \"2025-06-23T06:34:24.754Z\",\n \"userTimezone\": \"America/New_York\",\n \"socketId\": \"DhXE7OVjCtfUmDFTAAAB\",\n \"institutionPublicId\": \"f831501f-b645-481a-9cbb-331509aaf8c1\",\n \"assistantId\": \"6856fa89cbafcff8d98680f5\",\n \"selectedCourse\": {\n \"course_id\": 1750532703472,\n \"course_name\": \"Research Discussion\",\n \"assistant\": {\n \"_id\": \"6856fa89cbafcff8d98680f5\"\n }\n },\n \"ragOnly\": false,\n \"ragIgnore\": false\n },\n \"inputs\": [\n \"What is machine learning?\"\n ],\n \"outputs\": []\n}"
response = http.request(request)
puts response.read_body{
"success": true,
"streamingFailed": false,
"outputs": [
"Machine learning is a subset of artificial intelligence..."
],
"usage": 1234,
"credits": 5,
"query_duration_ms": 2500,
"model": "gpt-4o"
}{
"success": false,
"message": "<string>",
"error": "<string>"
}{
"success": false,
"message": "<string>",
"error": "<string>"
}{
"success": false,
"credits": 0,
"code": "insufficient_credits",
"message": "No credits left. Refill your Praxis Unified Credits to continue.",
"error": "<string>"
}{
"success": false,
"error": "TEACHING_WEEK_UNAVAILABLE",
"code": "TEACHING_WEEK_UNAVAILABLE",
"message": "Teaching weeks aren't available on this Digital Twin, so your message wasn't sent. It's back in the message box."
}{
"success": false,
"message": "<string>",
"error": "<string>"
}Conversation
Send a message to the AI assistant
Processes conversational AI requests with full context awareness including:
- User location and timezone
- Conversation history (via selectedCourse)
- Assistant personality and instructions
- RAG (Retrieval-Augmented Generation) from uploaded documents
Streaming: For real-time response streaming, include a valid socketId in requestArgs.
The AI will stream chunks via Socket.IO to the RECEIVE_STREAM event.
Without Streaming: If socketId is omitted, the full response is returned synchronously.
POST
/
api
/
ai
/
personal
/
qanda
Send a message to the AI assistant
curl --request POST \
--url https://hiimpria.ai/api/ai/personal/qanda \
--header 'Content-Type: application/json' \
--header 'x-access-token: <api-key>' \
--data '
{
"id": 1750660464754,
"requestArgs": {
"userISODate": "2025-06-23T06:34:24.754Z",
"userTimezone": "America/New_York",
"socketId": "DhXE7OVjCtfUmDFTAAAB",
"institutionPublicId": "f831501f-b645-481a-9cbb-331509aaf8c1",
"assistantId": "6856fa89cbafcff8d98680f5",
"selectedCourse": {
"course_id": 1750532703472,
"course_name": "Research Discussion",
"assistant": {
"_id": "6856fa89cbafcff8d98680f5"
}
},
"ragOnly": false,
"ragIgnore": false
},
"inputs": [
"What is machine learning?"
],
"outputs": []
}
'import requests
url = "https://hiimpria.ai/api/ai/personal/qanda"
payload = {
"id": 1750660464754,
"requestArgs": {
"userISODate": "2025-06-23T06:34:24.754Z",
"userTimezone": "America/New_York",
"socketId": "DhXE7OVjCtfUmDFTAAAB",
"institutionPublicId": "f831501f-b645-481a-9cbb-331509aaf8c1",
"assistantId": "6856fa89cbafcff8d98680f5",
"selectedCourse": {
"course_id": 1750532703472,
"course_name": "Research Discussion",
"assistant": { "_id": "6856fa89cbafcff8d98680f5" }
},
"ragOnly": False,
"ragIgnore": False
},
"inputs": ["What is machine learning?"],
"outputs": []
}
headers = {
"x-access-token": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'x-access-token': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
id: 1750660464754,
requestArgs: {
userISODate: '2025-06-23T06:34:24.754Z',
userTimezone: 'America/New_York',
socketId: 'DhXE7OVjCtfUmDFTAAAB',
institutionPublicId: 'f831501f-b645-481a-9cbb-331509aaf8c1',
assistantId: '6856fa89cbafcff8d98680f5',
selectedCourse: {
course_id: 1750532703472,
course_name: 'Research Discussion',
assistant: {_id: '6856fa89cbafcff8d98680f5'}
},
ragOnly: false,
ragIgnore: false
},
inputs: ['What is machine learning?'],
outputs: []
})
};
fetch('https://hiimpria.ai/api/ai/personal/qanda', 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://hiimpria.ai/api/ai/personal/qanda",
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([
'id' => 1750660464754,
'requestArgs' => [
'userISODate' => '2025-06-23T06:34:24.754Z',
'userTimezone' => 'America/New_York',
'socketId' => 'DhXE7OVjCtfUmDFTAAAB',
'institutionPublicId' => 'f831501f-b645-481a-9cbb-331509aaf8c1',
'assistantId' => '6856fa89cbafcff8d98680f5',
'selectedCourse' => [
'course_id' => 1750532703472,
'course_name' => 'Research Discussion',
'assistant' => [
'_id' => '6856fa89cbafcff8d98680f5'
]
],
'ragOnly' => false,
'ragIgnore' => false
],
'inputs' => [
'What is machine learning?'
],
'outputs' => [
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"x-access-token: <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"
"strings"
"net/http"
"io"
)
func main() {
url := "https://hiimpria.ai/api/ai/personal/qanda"
payload := strings.NewReader("{\n \"id\": 1750660464754,\n \"requestArgs\": {\n \"userISODate\": \"2025-06-23T06:34:24.754Z\",\n \"userTimezone\": \"America/New_York\",\n \"socketId\": \"DhXE7OVjCtfUmDFTAAAB\",\n \"institutionPublicId\": \"f831501f-b645-481a-9cbb-331509aaf8c1\",\n \"assistantId\": \"6856fa89cbafcff8d98680f5\",\n \"selectedCourse\": {\n \"course_id\": 1750532703472,\n \"course_name\": \"Research Discussion\",\n \"assistant\": {\n \"_id\": \"6856fa89cbafcff8d98680f5\"\n }\n },\n \"ragOnly\": false,\n \"ragIgnore\": false\n },\n \"inputs\": [\n \"What is machine learning?\"\n ],\n \"outputs\": []\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-access-token", "<api-key>")
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://hiimpria.ai/api/ai/personal/qanda")
.header("x-access-token", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"id\": 1750660464754,\n \"requestArgs\": {\n \"userISODate\": \"2025-06-23T06:34:24.754Z\",\n \"userTimezone\": \"America/New_York\",\n \"socketId\": \"DhXE7OVjCtfUmDFTAAAB\",\n \"institutionPublicId\": \"f831501f-b645-481a-9cbb-331509aaf8c1\",\n \"assistantId\": \"6856fa89cbafcff8d98680f5\",\n \"selectedCourse\": {\n \"course_id\": 1750532703472,\n \"course_name\": \"Research Discussion\",\n \"assistant\": {\n \"_id\": \"6856fa89cbafcff8d98680f5\"\n }\n },\n \"ragOnly\": false,\n \"ragIgnore\": false\n },\n \"inputs\": [\n \"What is machine learning?\"\n ],\n \"outputs\": []\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://hiimpria.ai/api/ai/personal/qanda")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-access-token"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"id\": 1750660464754,\n \"requestArgs\": {\n \"userISODate\": \"2025-06-23T06:34:24.754Z\",\n \"userTimezone\": \"America/New_York\",\n \"socketId\": \"DhXE7OVjCtfUmDFTAAAB\",\n \"institutionPublicId\": \"f831501f-b645-481a-9cbb-331509aaf8c1\",\n \"assistantId\": \"6856fa89cbafcff8d98680f5\",\n \"selectedCourse\": {\n \"course_id\": 1750532703472,\n \"course_name\": \"Research Discussion\",\n \"assistant\": {\n \"_id\": \"6856fa89cbafcff8d98680f5\"\n }\n },\n \"ragOnly\": false,\n \"ragIgnore\": false\n },\n \"inputs\": [\n \"What is machine learning?\"\n ],\n \"outputs\": []\n}"
response = http.request(request)
puts response.read_body{
"success": true,
"streamingFailed": false,
"outputs": [
"Machine learning is a subset of artificial intelligence..."
],
"usage": 1234,
"credits": 5,
"query_duration_ms": 2500,
"model": "gpt-4o"
}{
"success": false,
"message": "<string>",
"error": "<string>"
}{
"success": false,
"message": "<string>",
"error": "<string>"
}{
"success": false,
"credits": 0,
"code": "insufficient_credits",
"message": "No credits left. Refill your Praxis Unified Credits to continue.",
"error": "<string>"
}{
"success": false,
"error": "TEACHING_WEEK_UNAVAILABLE",
"code": "TEACHING_WEEK_UNAVAILABLE",
"message": "Teaching weeks aren't available on this Digital Twin, so your message wasn't sent. It's back in the message box."
}{
"success": false,
"message": "<string>",
"error": "<string>"
}Authorizations
JWT token passed in x-access-token header
Body
application/json
Request payload for AI Q&A conversation
User messages to send to the AI
Example:
["What is machine learning?"]
Client-generated request identifier (epoch timestamp)
Example:
1750660464754
Context arguments for AI conversation requests
Show child attributes
Show child attributes
Previous AI responses (for context continuity)
Example:
[]
Response
AI response generated successfully
Response from AI Q&A conversation
Whether the request completed successfully
True if Socket.IO streaming failed (response still contains full output)
Error message if streaming failed
AI-generated response messages
Total tokens consumed (input + output)
Credits consumed for this request
User's total credits consumed
Present on a personal account's turn (no current Digital Twin, no monthly plan) when its credits are running
low or out, computed from the balance after this turn's debit. Also saved on the history record.
low: the balance is at or under the threshold and above zero. grace: the balance is at or under zero and
the next message in this conversation will still be answered. out: the next message will be refused until
credits are added.
Show child attributes
Show child attributes
User's remaining credit balance
Total processing time in milliseconds
AI model identifier used for generation
Example:
"gpt-4o"
Pricing modality — true when the turn was billed on the subagent-modality lanes (conversation + orchestrator + workers at true model cost × margin), false/absent for flat single-shot pricing (10,000 tokens = 1 credit)
Per-lane fractional credits (post-margin) summed into the billed integer — voice (conversation), conductor (orchestrator), subagents (workers), and voiceLayer (GPT-Live voice minutes, only when that lane billed). Present on billed turns only.
Show child attributes
Show child attributes
Orchestrator-lane fractional credits (subagent-modality turns only)
The cost margin applied to the subagent-modality lanes on this turn (e.g. 1.5)
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