Preview a structure change against past feedback
curl --request POST \
--url https://api.moonbounce.ai/v1/decisions/{decision_id}/previews \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"structure": {
"questions": {
"item_used": {
"type": "noul",
"instructions": "Has the customer used the item beyond trying it on or setting it up once?",
"reads": [
"state.message"
]
},
"defective": {
"type": "noul",
"instructions": "Does the customer describe a defect, damage on arrival, or the item failing under normal use?",
"reads": [
"state.message"
]
},
"shipping_failure": {
"type": "noul",
"instructions": "Did the order arrive later than promised, or get lost in transit?",
"reads": [
"state.message"
]
}
},
"attributes": {
"days_since_delivery": {
"type": "number",
"minimum": 0
}
},
"aggregations": {
"grant_score": {
"type": "weighted_mean",
"inputs": [
{
"name": "questions.item_used",
"invert": true
},
{
"name": "questions.defective"
},
{
"name": "questions.shipping_failure"
}
]
},
"handling": {
"type": "rules",
"rules": [
{
"when": {
"name": "attributes.days_since_delivery",
"gt": 30
},
"outcome": "escalate"
},
{
"when": {
"name": "aggregations.grant_score",
"gte": 0.72
},
"outcome": "grant"
},
{
"when": {
"name": "aggregations.grant_score",
"lt": 0.38
},
"outcome": "deny"
}
],
"else": "escalate"
}
},
"result": "aggregations.handling"
}
}
'import requests
url = "https://api.moonbounce.ai/v1/decisions/{decision_id}/previews"
payload = { "structure": {
"questions": {
"item_used": {
"type": "noul",
"instructions": "Has the customer used the item beyond trying it on or setting it up once?",
"reads": ["state.message"]
},
"defective": {
"type": "noul",
"instructions": "Does the customer describe a defect, damage on arrival, or the item failing under normal use?",
"reads": ["state.message"]
},
"shipping_failure": {
"type": "noul",
"instructions": "Did the order arrive later than promised, or get lost in transit?",
"reads": ["state.message"]
}
},
"attributes": { "days_since_delivery": {
"type": "number",
"minimum": 0
} },
"aggregations": {
"grant_score": {
"type": "weighted_mean",
"inputs": [{
"name": "questions.item_used",
"invert": True
}, { "name": "questions.defective" }, { "name": "questions.shipping_failure" }]
},
"handling": {
"type": "rules",
"rules": [
{
"when": {
"name": "attributes.days_since_delivery",
"gt": 30
},
"outcome": "escalate"
},
{
"when": {
"name": "aggregations.grant_score",
"gte": 0.72
},
"outcome": "grant"
},
{
"when": {
"name": "aggregations.grant_score",
"lt": 0.38
},
"outcome": "deny"
}
],
"else": "escalate"
}
},
"result": "aggregations.handling"
} }
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({
structure: {
questions: {
item_used: {
type: 'noul',
instructions: 'Has the customer used the item beyond trying it on or setting it up once?',
reads: ['state.message']
},
defective: {
type: 'noul',
instructions: 'Does the customer describe a defect, damage on arrival, or the item failing under normal use?',
reads: ['state.message']
},
shipping_failure: {
type: 'noul',
instructions: 'Did the order arrive later than promised, or get lost in transit?',
reads: ['state.message']
}
},
attributes: {days_since_delivery: {type: 'number', minimum: 0}},
aggregations: {
grant_score: {
type: 'weighted_mean',
inputs: [
{name: 'questions.item_used', invert: true},
{name: 'questions.defective'},
{name: 'questions.shipping_failure'}
]
},
handling: {
type: 'rules',
rules: [
{when: {name: 'attributes.days_since_delivery', gt: 30}, outcome: 'escalate'},
{when: {name: 'aggregations.grant_score', gte: 0.72}, outcome: 'grant'},
{when: {name: 'aggregations.grant_score', lt: 0.38}, outcome: 'deny'}
],
else: 'escalate'
}
},
result: 'aggregations.handling'
}
})
};
fetch('https://api.moonbounce.ai/v1/decisions/{decision_id}/previews', 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.moonbounce.ai/v1/decisions/{decision_id}/previews",
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([
'structure' => [
'questions' => [
'item_used' => [
'type' => 'noul',
'instructions' => 'Has the customer used the item beyond trying it on or setting it up once?',
'reads' => [
'state.message'
]
],
'defective' => [
'type' => 'noul',
'instructions' => 'Does the customer describe a defect, damage on arrival, or the item failing under normal use?',
'reads' => [
'state.message'
]
],
'shipping_failure' => [
'type' => 'noul',
'instructions' => 'Did the order arrive later than promised, or get lost in transit?',
'reads' => [
'state.message'
]
]
],
'attributes' => [
'days_since_delivery' => [
'type' => 'number',
'minimum' => 0
]
],
'aggregations' => [
'grant_score' => [
'type' => 'weighted_mean',
'inputs' => [
[
'name' => 'questions.item_used',
'invert' => true
],
[
'name' => 'questions.defective'
],
[
'name' => 'questions.shipping_failure'
]
]
],
'handling' => [
'type' => 'rules',
'rules' => [
[
'when' => [
'name' => 'attributes.days_since_delivery',
'gt' => 30
],
'outcome' => 'escalate'
],
[
'when' => [
'name' => 'aggregations.grant_score',
'gte' => 0.72
],
'outcome' => 'grant'
],
[
'when' => [
'name' => 'aggregations.grant_score',
'lt' => 0.38
],
'outcome' => 'deny'
]
],
'else' => 'escalate'
]
],
'result' => 'aggregations.handling'
]
]),
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.moonbounce.ai/v1/decisions/{decision_id}/previews"
payload := strings.NewReader("{\n \"structure\": {\n \"questions\": {\n \"item_used\": {\n \"type\": \"noul\",\n \"instructions\": \"Has the customer used the item beyond trying it on or setting it up once?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"defective\": {\n \"type\": \"noul\",\n \"instructions\": \"Does the customer describe a defect, damage on arrival, or the item failing under normal use?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"shipping_failure\": {\n \"type\": \"noul\",\n \"instructions\": \"Did the order arrive later than promised, or get lost in transit?\",\n \"reads\": [\n \"state.message\"\n ]\n }\n },\n \"attributes\": {\n \"days_since_delivery\": {\n \"type\": \"number\",\n \"minimum\": 0\n }\n },\n \"aggregations\": {\n \"grant_score\": {\n \"type\": \"weighted_mean\",\n \"inputs\": [\n {\n \"name\": \"questions.item_used\",\n \"invert\": true\n },\n {\n \"name\": \"questions.defective\"\n },\n {\n \"name\": \"questions.shipping_failure\"\n }\n ]\n },\n \"handling\": {\n \"type\": \"rules\",\n \"rules\": [\n {\n \"when\": {\n \"name\": \"attributes.days_since_delivery\",\n \"gt\": 30\n },\n \"outcome\": \"escalate\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"gte\": 0.72\n },\n \"outcome\": \"grant\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"lt\": 0.38\n },\n \"outcome\": \"deny\"\n }\n ],\n \"else\": \"escalate\"\n }\n },\n \"result\": \"aggregations.handling\"\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.moonbounce.ai/v1/decisions/{decision_id}/previews")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"structure\": {\n \"questions\": {\n \"item_used\": {\n \"type\": \"noul\",\n \"instructions\": \"Has the customer used the item beyond trying it on or setting it up once?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"defective\": {\n \"type\": \"noul\",\n \"instructions\": \"Does the customer describe a defect, damage on arrival, or the item failing under normal use?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"shipping_failure\": {\n \"type\": \"noul\",\n \"instructions\": \"Did the order arrive later than promised, or get lost in transit?\",\n \"reads\": [\n \"state.message\"\n ]\n }\n },\n \"attributes\": {\n \"days_since_delivery\": {\n \"type\": \"number\",\n \"minimum\": 0\n }\n },\n \"aggregations\": {\n \"grant_score\": {\n \"type\": \"weighted_mean\",\n \"inputs\": [\n {\n \"name\": \"questions.item_used\",\n \"invert\": true\n },\n {\n \"name\": \"questions.defective\"\n },\n {\n \"name\": \"questions.shipping_failure\"\n }\n ]\n },\n \"handling\": {\n \"type\": \"rules\",\n \"rules\": [\n {\n \"when\": {\n \"name\": \"attributes.days_since_delivery\",\n \"gt\": 30\n },\n \"outcome\": \"escalate\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"gte\": 0.72\n },\n \"outcome\": \"grant\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"lt\": 0.38\n },\n \"outcome\": \"deny\"\n }\n ],\n \"else\": \"escalate\"\n }\n },\n \"result\": \"aggregations.handling\"\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.moonbounce.ai/v1/decisions/{decision_id}/previews")
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 \"structure\": {\n \"questions\": {\n \"item_used\": {\n \"type\": \"noul\",\n \"instructions\": \"Has the customer used the item beyond trying it on or setting it up once?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"defective\": {\n \"type\": \"noul\",\n \"instructions\": \"Does the customer describe a defect, damage on arrival, or the item failing under normal use?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"shipping_failure\": {\n \"type\": \"noul\",\n \"instructions\": \"Did the order arrive later than promised, or get lost in transit?\",\n \"reads\": [\n \"state.message\"\n ]\n }\n },\n \"attributes\": {\n \"days_since_delivery\": {\n \"type\": \"number\",\n \"minimum\": 0\n }\n },\n \"aggregations\": {\n \"grant_score\": {\n \"type\": \"weighted_mean\",\n \"inputs\": [\n {\n \"name\": \"questions.item_used\",\n \"invert\": true\n },\n {\n \"name\": \"questions.defective\"\n },\n {\n \"name\": \"questions.shipping_failure\"\n }\n ]\n },\n \"handling\": {\n \"type\": \"rules\",\n \"rules\": [\n {\n \"when\": {\n \"name\": \"attributes.days_since_delivery\",\n \"gt\": 30\n },\n \"outcome\": \"escalate\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"gte\": 0.72\n },\n \"outcome\": \"grant\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"lt\": 0.38\n },\n \"outcome\": \"deny\"\n }\n ],\n \"else\": \"escalate\"\n }\n },\n \"result\": \"aggregations.handling\"\n }\n}"
response = http.request(request)
puts response.read_body{
"preview_id": "<string>",
"decision_id": "<string>",
"environment": "production",
"base": {
"version": {
"structure_hash": "<string>",
"number": 2
},
"model": {
"profile": "<string>"
}
},
"candidate": {
"version": {
"structure_hash": "<string>",
"number": 2
},
"model": {
"profile": "<string>"
}
},
"hashes_match": {
"structure": true
},
"evidence": {
"label": "imported_examples",
"frozen_at": "2023-11-07T05:31:56Z",
"by_split": {
"example": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"train": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
}
},
"noise_rate": 0.5,
"action_mix": {
"before": {},
"after": {},
"on": "<string>",
"n": 1
},
"changed_items": [
{
"evaluation_id": "<string>",
"feedback_id": "<string>",
"split": "example",
"change": "fix",
"before": {
"answers": {},
"outcome": "<string>",
"value": 123
},
"after": {
"answers": {},
"outcome": "<string>",
"value": 123
}
}
],
"credited_feedback": [
"<string>"
],
"by_source": {
"app_native": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"app_resolution": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"reviewer": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"owner": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"import": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"synthetic_review": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
}
}
},
"cost": {
"model_calls": 1,
"questions": 1,
"input_tokens": 1,
"output_tokens": 1,
"usd_micros": 1,
"cache_hits": 1
},
"objective": {
"revision": 2,
"objective_hash": "<string>"
},
"loss_report": {
"objective": {
"source": "stored",
"revision": 2,
"objective_hash": "<string>",
"effective_hash": "<string>",
"unknown_outcomes": [
"<string>"
]
},
"base": {
"n": 1,
"loss": 1,
"loss_per_case": 1,
"target_met": true,
"macro_f1": 0.5,
"outcomes": [
{
"outcome": "<string>",
"expected": 1,
"given": 1,
"precision": 0.5,
"recall": 0.5,
"f1": 0.5
}
],
"by_mistake": [
{
"expected": "<string>",
"got": "<string>",
"n": 2,
"loss": 1,
"examples": [
"<string>"
]
}
],
"by_mistake_total": 1,
"by_question": [
{
"question": "<string>",
"loss": 1,
"share": 0.5,
"examples": [
"<string>"
]
}
],
"missing_criterion": {
"n": 1,
"loss": 1,
"examples": [
"<string>"
]
},
"failed": {
"n": 1,
"loss": 1
},
"near_cuts": [
{
"aggregation": "<string>",
"at": 123,
"lower_it": {
"n": 1,
"loss": 1
},
"raise_it": {
"n": 1,
"loss": 1
},
"examples": [
"<string>"
]
}
],
"severe": {
"n": 1,
"human": 1
},
"cases": [
{
"expected": "<string>",
"got": "<string>",
"failed": true,
"loss": 1,
"severe": true,
"human": true,
"margin": {
"value": 123,
"cut": 123,
"distance": 1,
"adjacent": true
},
"attributed": true,
"fixing_flips": [
"<string>"
],
"flips_total": 1,
"answer_conflicts": [
"<string>"
],
"suspects": [
"<string>"
],
"answers": {},
"evaluation_id": "<string>",
"item_id": "<string>",
"flip_effects": [
{
"question": "<string>",
"from": 0,
"fixes": 1,
"breaks": 1,
"loss_change": 123,
"broken": [
"<string>"
]
}
]
}
],
"cases_total": 1,
"attributed": 1,
"accuracy": 0.5,
"confusion": [
{
"expected": "<string>",
"got": "<string>",
"n": 2
}
],
"confusion_total": 1
},
"candidate": {
"n": 1,
"loss": 1,
"loss_per_case": 1,
"target_met": true,
"macro_f1": 0.5,
"outcomes": [
{
"outcome": "<string>",
"expected": 1,
"given": 1,
"precision": 0.5,
"recall": 0.5,
"f1": 0.5
}
],
"by_mistake": [
{
"expected": "<string>",
"got": "<string>",
"n": 2,
"loss": 1,
"examples": [
"<string>"
]
}
],
"by_mistake_total": 1,
"by_question": [
{
"question": "<string>",
"loss": 1,
"share": 0.5,
"examples": [
"<string>"
]
}
],
"missing_criterion": {
"n": 1,
"loss": 1,
"examples": [
"<string>"
]
},
"failed": {
"n": 1,
"loss": 1
},
"near_cuts": [
{
"aggregation": "<string>",
"at": 123,
"lower_it": {
"n": 1,
"loss": 1
},
"raise_it": {
"n": 1,
"loss": 1
},
"examples": [
"<string>"
]
}
],
"severe": {
"n": 1,
"human": 1
},
"cases": [
{
"expected": "<string>",
"got": "<string>",
"failed": true,
"loss": 1,
"severe": true,
"human": true,
"margin": {
"value": 123,
"cut": 123,
"distance": 1,
"adjacent": true
},
"attributed": true,
"fixing_flips": [
"<string>"
],
"flips_total": 1,
"answer_conflicts": [
"<string>"
],
"suspects": [
"<string>"
],
"answers": {},
"evaluation_id": "<string>",
"item_id": "<string>",
"flip_effects": [
{
"question": "<string>",
"from": 0,
"fixes": 1,
"breaks": 1,
"loss_change": 123,
"broken": [
"<string>"
]
}
]
}
],
"cases_total": 1,
"attributed": 1,
"accuracy": 0.5,
"confusion": [
{
"expected": "<string>",
"got": "<string>",
"n": 2
}
],
"confusion_total": 1
},
"accepted": true,
"reasons": [
"<string>"
],
"new_severe_human": [
"<string>"
]
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}Previews & tests
Preview a structure change against past feedback
Replays a structure change over past cases with feedback: it spends model calls and stores a run. Not a dry run of evaluate. Replays a local structure and its base over the decision’s reviewed and imported cases in the key’s environment (a test key previews against test cases only), and returns fixes, breaks, the action mix, the loss_report (base and candidate judged by the decision’s objective, or balanced losses when none is set, with loss per case, macro-F1, where the cost sits, the costliest mistakes, and whether the candidate is accepted) and an evidence label: training_cases when a scored case is a training case (its deciding feedback is not an import), else imported_examples (every scored case is an imported example); either way the cases are ones a suggestion may have learned from. The result must decide outcomes (bands, rules or match): a value result is 400 OUTCOMES_INVALID. Each case reuses the answers its evaluation stored for questions whose hash is unchanged; changed and new questions are asked the way evaluate asks them (each question’s reads, grouped into System One calls), so a preview costs what evaluating the changed questions would. Writes an eval run only: never an evaluation, never traffic, never acceptance. A preview does not register the candidate: before evaluating by its structure hash, register it with POST /v1/decisions (or send the structure inline on evaluate once). The base, in order: the base of suggestion_id when it is sent (then base is ignored), else base, else the version serving primary traffic in the key’s environment, else the newest one examples were imported under there, else none. With suggestion_id, hashes_match says whether the structure is that suggestion’s candidate; another environment’s suggestion is 404. The evidence replays only cases with an outcome from the owner, reviewers or imported examples; the action mix replays a sample of the environment’s recent primary traffic. For questions the candidate changes, before is a fresh re-run of the base question (the fair baseline), so model nondeterminism can move it between previews. noise_rate measures outcome flips from that re-run against the stored answers, not value drift. A test key’s preview neither reads nor fills the shared cache of model answers. A decision nothing was registered, evaluated or imported under is 404.
POST
/
v1
/
decisions
/
{decision_id}
/
previews
Preview a structure change against past feedback
curl --request POST \
--url https://api.moonbounce.ai/v1/decisions/{decision_id}/previews \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"structure": {
"questions": {
"item_used": {
"type": "noul",
"instructions": "Has the customer used the item beyond trying it on or setting it up once?",
"reads": [
"state.message"
]
},
"defective": {
"type": "noul",
"instructions": "Does the customer describe a defect, damage on arrival, or the item failing under normal use?",
"reads": [
"state.message"
]
},
"shipping_failure": {
"type": "noul",
"instructions": "Did the order arrive later than promised, or get lost in transit?",
"reads": [
"state.message"
]
}
},
"attributes": {
"days_since_delivery": {
"type": "number",
"minimum": 0
}
},
"aggregations": {
"grant_score": {
"type": "weighted_mean",
"inputs": [
{
"name": "questions.item_used",
"invert": true
},
{
"name": "questions.defective"
},
{
"name": "questions.shipping_failure"
}
]
},
"handling": {
"type": "rules",
"rules": [
{
"when": {
"name": "attributes.days_since_delivery",
"gt": 30
},
"outcome": "escalate"
},
{
"when": {
"name": "aggregations.grant_score",
"gte": 0.72
},
"outcome": "grant"
},
{
"when": {
"name": "aggregations.grant_score",
"lt": 0.38
},
"outcome": "deny"
}
],
"else": "escalate"
}
},
"result": "aggregations.handling"
}
}
'import requests
url = "https://api.moonbounce.ai/v1/decisions/{decision_id}/previews"
payload = { "structure": {
"questions": {
"item_used": {
"type": "noul",
"instructions": "Has the customer used the item beyond trying it on or setting it up once?",
"reads": ["state.message"]
},
"defective": {
"type": "noul",
"instructions": "Does the customer describe a defect, damage on arrival, or the item failing under normal use?",
"reads": ["state.message"]
},
"shipping_failure": {
"type": "noul",
"instructions": "Did the order arrive later than promised, or get lost in transit?",
"reads": ["state.message"]
}
},
"attributes": { "days_since_delivery": {
"type": "number",
"minimum": 0
} },
"aggregations": {
"grant_score": {
"type": "weighted_mean",
"inputs": [{
"name": "questions.item_used",
"invert": True
}, { "name": "questions.defective" }, { "name": "questions.shipping_failure" }]
},
"handling": {
"type": "rules",
"rules": [
{
"when": {
"name": "attributes.days_since_delivery",
"gt": 30
},
"outcome": "escalate"
},
{
"when": {
"name": "aggregations.grant_score",
"gte": 0.72
},
"outcome": "grant"
},
{
"when": {
"name": "aggregations.grant_score",
"lt": 0.38
},
"outcome": "deny"
}
],
"else": "escalate"
}
},
"result": "aggregations.handling"
} }
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({
structure: {
questions: {
item_used: {
type: 'noul',
instructions: 'Has the customer used the item beyond trying it on or setting it up once?',
reads: ['state.message']
},
defective: {
type: 'noul',
instructions: 'Does the customer describe a defect, damage on arrival, or the item failing under normal use?',
reads: ['state.message']
},
shipping_failure: {
type: 'noul',
instructions: 'Did the order arrive later than promised, or get lost in transit?',
reads: ['state.message']
}
},
attributes: {days_since_delivery: {type: 'number', minimum: 0}},
aggregations: {
grant_score: {
type: 'weighted_mean',
inputs: [
{name: 'questions.item_used', invert: true},
{name: 'questions.defective'},
{name: 'questions.shipping_failure'}
]
},
handling: {
type: 'rules',
rules: [
{when: {name: 'attributes.days_since_delivery', gt: 30}, outcome: 'escalate'},
{when: {name: 'aggregations.grant_score', gte: 0.72}, outcome: 'grant'},
{when: {name: 'aggregations.grant_score', lt: 0.38}, outcome: 'deny'}
],
else: 'escalate'
}
},
result: 'aggregations.handling'
}
})
};
fetch('https://api.moonbounce.ai/v1/decisions/{decision_id}/previews', 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.moonbounce.ai/v1/decisions/{decision_id}/previews",
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([
'structure' => [
'questions' => [
'item_used' => [
'type' => 'noul',
'instructions' => 'Has the customer used the item beyond trying it on or setting it up once?',
'reads' => [
'state.message'
]
],
'defective' => [
'type' => 'noul',
'instructions' => 'Does the customer describe a defect, damage on arrival, or the item failing under normal use?',
'reads' => [
'state.message'
]
],
'shipping_failure' => [
'type' => 'noul',
'instructions' => 'Did the order arrive later than promised, or get lost in transit?',
'reads' => [
'state.message'
]
]
],
'attributes' => [
'days_since_delivery' => [
'type' => 'number',
'minimum' => 0
]
],
'aggregations' => [
'grant_score' => [
'type' => 'weighted_mean',
'inputs' => [
[
'name' => 'questions.item_used',
'invert' => true
],
[
'name' => 'questions.defective'
],
[
'name' => 'questions.shipping_failure'
]
]
],
'handling' => [
'type' => 'rules',
'rules' => [
[
'when' => [
'name' => 'attributes.days_since_delivery',
'gt' => 30
],
'outcome' => 'escalate'
],
[
'when' => [
'name' => 'aggregations.grant_score',
'gte' => 0.72
],
'outcome' => 'grant'
],
[
'when' => [
'name' => 'aggregations.grant_score',
'lt' => 0.38
],
'outcome' => 'deny'
]
],
'else' => 'escalate'
]
],
'result' => 'aggregations.handling'
]
]),
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.moonbounce.ai/v1/decisions/{decision_id}/previews"
payload := strings.NewReader("{\n \"structure\": {\n \"questions\": {\n \"item_used\": {\n \"type\": \"noul\",\n \"instructions\": \"Has the customer used the item beyond trying it on or setting it up once?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"defective\": {\n \"type\": \"noul\",\n \"instructions\": \"Does the customer describe a defect, damage on arrival, or the item failing under normal use?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"shipping_failure\": {\n \"type\": \"noul\",\n \"instructions\": \"Did the order arrive later than promised, or get lost in transit?\",\n \"reads\": [\n \"state.message\"\n ]\n }\n },\n \"attributes\": {\n \"days_since_delivery\": {\n \"type\": \"number\",\n \"minimum\": 0\n }\n },\n \"aggregations\": {\n \"grant_score\": {\n \"type\": \"weighted_mean\",\n \"inputs\": [\n {\n \"name\": \"questions.item_used\",\n \"invert\": true\n },\n {\n \"name\": \"questions.defective\"\n },\n {\n \"name\": \"questions.shipping_failure\"\n }\n ]\n },\n \"handling\": {\n \"type\": \"rules\",\n \"rules\": [\n {\n \"when\": {\n \"name\": \"attributes.days_since_delivery\",\n \"gt\": 30\n },\n \"outcome\": \"escalate\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"gte\": 0.72\n },\n \"outcome\": \"grant\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"lt\": 0.38\n },\n \"outcome\": \"deny\"\n }\n ],\n \"else\": \"escalate\"\n }\n },\n \"result\": \"aggregations.handling\"\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.moonbounce.ai/v1/decisions/{decision_id}/previews")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"structure\": {\n \"questions\": {\n \"item_used\": {\n \"type\": \"noul\",\n \"instructions\": \"Has the customer used the item beyond trying it on or setting it up once?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"defective\": {\n \"type\": \"noul\",\n \"instructions\": \"Does the customer describe a defect, damage on arrival, or the item failing under normal use?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"shipping_failure\": {\n \"type\": \"noul\",\n \"instructions\": \"Did the order arrive later than promised, or get lost in transit?\",\n \"reads\": [\n \"state.message\"\n ]\n }\n },\n \"attributes\": {\n \"days_since_delivery\": {\n \"type\": \"number\",\n \"minimum\": 0\n }\n },\n \"aggregations\": {\n \"grant_score\": {\n \"type\": \"weighted_mean\",\n \"inputs\": [\n {\n \"name\": \"questions.item_used\",\n \"invert\": true\n },\n {\n \"name\": \"questions.defective\"\n },\n {\n \"name\": \"questions.shipping_failure\"\n }\n ]\n },\n \"handling\": {\n \"type\": \"rules\",\n \"rules\": [\n {\n \"when\": {\n \"name\": \"attributes.days_since_delivery\",\n \"gt\": 30\n },\n \"outcome\": \"escalate\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"gte\": 0.72\n },\n \"outcome\": \"grant\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"lt\": 0.38\n },\n \"outcome\": \"deny\"\n }\n ],\n \"else\": \"escalate\"\n }\n },\n \"result\": \"aggregations.handling\"\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.moonbounce.ai/v1/decisions/{decision_id}/previews")
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 \"structure\": {\n \"questions\": {\n \"item_used\": {\n \"type\": \"noul\",\n \"instructions\": \"Has the customer used the item beyond trying it on or setting it up once?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"defective\": {\n \"type\": \"noul\",\n \"instructions\": \"Does the customer describe a defect, damage on arrival, or the item failing under normal use?\",\n \"reads\": [\n \"state.message\"\n ]\n },\n \"shipping_failure\": {\n \"type\": \"noul\",\n \"instructions\": \"Did the order arrive later than promised, or get lost in transit?\",\n \"reads\": [\n \"state.message\"\n ]\n }\n },\n \"attributes\": {\n \"days_since_delivery\": {\n \"type\": \"number\",\n \"minimum\": 0\n }\n },\n \"aggregations\": {\n \"grant_score\": {\n \"type\": \"weighted_mean\",\n \"inputs\": [\n {\n \"name\": \"questions.item_used\",\n \"invert\": true\n },\n {\n \"name\": \"questions.defective\"\n },\n {\n \"name\": \"questions.shipping_failure\"\n }\n ]\n },\n \"handling\": {\n \"type\": \"rules\",\n \"rules\": [\n {\n \"when\": {\n \"name\": \"attributes.days_since_delivery\",\n \"gt\": 30\n },\n \"outcome\": \"escalate\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"gte\": 0.72\n },\n \"outcome\": \"grant\"\n },\n {\n \"when\": {\n \"name\": \"aggregations.grant_score\",\n \"lt\": 0.38\n },\n \"outcome\": \"deny\"\n }\n ],\n \"else\": \"escalate\"\n }\n },\n \"result\": \"aggregations.handling\"\n }\n}"
response = http.request(request)
puts response.read_body{
"preview_id": "<string>",
"decision_id": "<string>",
"environment": "production",
"base": {
"version": {
"structure_hash": "<string>",
"number": 2
},
"model": {
"profile": "<string>"
}
},
"candidate": {
"version": {
"structure_hash": "<string>",
"number": 2
},
"model": {
"profile": "<string>"
}
},
"hashes_match": {
"structure": true
},
"evidence": {
"label": "imported_examples",
"frozen_at": "2023-11-07T05:31:56Z",
"by_split": {
"example": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"train": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
}
},
"noise_rate": 0.5,
"action_mix": {
"before": {},
"after": {},
"on": "<string>",
"n": 1
},
"changed_items": [
{
"evaluation_id": "<string>",
"feedback_id": "<string>",
"split": "example",
"change": "fix",
"before": {
"answers": {},
"outcome": "<string>",
"value": 123
},
"after": {
"answers": {},
"outcome": "<string>",
"value": 123
}
}
],
"credited_feedback": [
"<string>"
],
"by_source": {
"app_native": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"app_resolution": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"reviewer": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"owner": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"import": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
},
"synthetic_review": {
"n": 1,
"fixes": 1,
"breaks": 1,
"base_correct": 1,
"candidate_correct": 1
}
}
},
"cost": {
"model_calls": 1,
"questions": 1,
"input_tokens": 1,
"output_tokens": 1,
"usd_micros": 1,
"cache_hits": 1
},
"objective": {
"revision": 2,
"objective_hash": "<string>"
},
"loss_report": {
"objective": {
"source": "stored",
"revision": 2,
"objective_hash": "<string>",
"effective_hash": "<string>",
"unknown_outcomes": [
"<string>"
]
},
"base": {
"n": 1,
"loss": 1,
"loss_per_case": 1,
"target_met": true,
"macro_f1": 0.5,
"outcomes": [
{
"outcome": "<string>",
"expected": 1,
"given": 1,
"precision": 0.5,
"recall": 0.5,
"f1": 0.5
}
],
"by_mistake": [
{
"expected": "<string>",
"got": "<string>",
"n": 2,
"loss": 1,
"examples": [
"<string>"
]
}
],
"by_mistake_total": 1,
"by_question": [
{
"question": "<string>",
"loss": 1,
"share": 0.5,
"examples": [
"<string>"
]
}
],
"missing_criterion": {
"n": 1,
"loss": 1,
"examples": [
"<string>"
]
},
"failed": {
"n": 1,
"loss": 1
},
"near_cuts": [
{
"aggregation": "<string>",
"at": 123,
"lower_it": {
"n": 1,
"loss": 1
},
"raise_it": {
"n": 1,
"loss": 1
},
"examples": [
"<string>"
]
}
],
"severe": {
"n": 1,
"human": 1
},
"cases": [
{
"expected": "<string>",
"got": "<string>",
"failed": true,
"loss": 1,
"severe": true,
"human": true,
"margin": {
"value": 123,
"cut": 123,
"distance": 1,
"adjacent": true
},
"attributed": true,
"fixing_flips": [
"<string>"
],
"flips_total": 1,
"answer_conflicts": [
"<string>"
],
"suspects": [
"<string>"
],
"answers": {},
"evaluation_id": "<string>",
"item_id": "<string>",
"flip_effects": [
{
"question": "<string>",
"from": 0,
"fixes": 1,
"breaks": 1,
"loss_change": 123,
"broken": [
"<string>"
]
}
]
}
],
"cases_total": 1,
"attributed": 1,
"accuracy": 0.5,
"confusion": [
{
"expected": "<string>",
"got": "<string>",
"n": 2
}
],
"confusion_total": 1
},
"candidate": {
"n": 1,
"loss": 1,
"loss_per_case": 1,
"target_met": true,
"macro_f1": 0.5,
"outcomes": [
{
"outcome": "<string>",
"expected": 1,
"given": 1,
"precision": 0.5,
"recall": 0.5,
"f1": 0.5
}
],
"by_mistake": [
{
"expected": "<string>",
"got": "<string>",
"n": 2,
"loss": 1,
"examples": [
"<string>"
]
}
],
"by_mistake_total": 1,
"by_question": [
{
"question": "<string>",
"loss": 1,
"share": 0.5,
"examples": [
"<string>"
]
}
],
"missing_criterion": {
"n": 1,
"loss": 1,
"examples": [
"<string>"
]
},
"failed": {
"n": 1,
"loss": 1
},
"near_cuts": [
{
"aggregation": "<string>",
"at": 123,
"lower_it": {
"n": 1,
"loss": 1
},
"raise_it": {
"n": 1,
"loss": 1
},
"examples": [
"<string>"
]
}
],
"severe": {
"n": 1,
"human": 1
},
"cases": [
{
"expected": "<string>",
"got": "<string>",
"failed": true,
"loss": 1,
"severe": true,
"human": true,
"margin": {
"value": 123,
"cut": 123,
"distance": 1,
"adjacent": true
},
"attributed": true,
"fixing_flips": [
"<string>"
],
"flips_total": 1,
"answer_conflicts": [
"<string>"
],
"suspects": [
"<string>"
],
"answers": {},
"evaluation_id": "<string>",
"item_id": "<string>",
"flip_effects": [
{
"question": "<string>",
"from": 0,
"fixes": 1,
"breaks": 1,
"loss_change": 123,
"broken": [
"<string>"
]
}
]
}
],
"cases_total": 1,
"attributed": 1,
"accuracy": 0.5,
"confusion": [
{
"expected": "<string>",
"got": "<string>",
"n": 2
}
],
"confusion_total": 1
},
"accepted": true,
"reasons": [
"<string>"
],
"new_severe_human": [
"<string>"
]
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}{
"error": {
"type": "invalid_request",
"message": "<string>",
"reason": "<string>",
"param": "<string>",
"details": {
"issues": [
{
"param": "<string>",
"code": "<string>",
"message": "<string>"
}
]
}
}
}Authorizations
An API key, sent as Authorization: Bearer <key>. Each operation lists the scope its key needs: decisions:evaluate (evaluate and read evaluations), decisions:feedback (your app's feedback) or decisions:develop (everything else, including owner feedback and the review queue). An operation listing no scope takes any scoped key. A key with no scope, or without the operation's, gets 403 INSUFFICIENT_SCOPE.
Headers
Makes a retry safe. For 24 hours, the same key with the same request returns the first stored result (with Idempotent-Replayed: true); the same key with a different request is a 422 IDEMPOTENCY_KEY_REUSED. Keys are scoped to the organization and environment across every operation, so use a fresh key per request. A request that failed before its result was stored runs again when retried with its key.
Required string length:
1 - 255Pattern:
^[\x20-\x7e]+$Path Parameters
A name you choose for this decision, such as refund-exception: a lowercase letter, then up to 127 of a-z 0-9 _ . -. Unique in your organization and shared by its test and production keys. The first registration or evaluation under a new one creates the decision.
Pattern:
^[a-z][a-z0-9_.-]{0,127}$Body
application/json
Everything that defines a decision: questions, attribute declarations, aggregations and the result. Names are unique across all three maps.
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Pattern:
^sug_[0-9a-f]{8}-[0-9a-f]{4}-7[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$Show child attributes
Show child attributes
Response
The preview
Pattern:
^run_[0-9a-f]{8}-[0-9a-f]{4}-7[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$A name you choose for this decision, such as refund-exception: a lowercase letter, then up to 127 of a-z 0-9 _ . -. Unique in your organization and shared by its test and production keys. The first registration or evaluation under a new one creates the decision.
Pattern:
^[a-z][a-z0-9_.-]{0,127}$The environment of the key that made the call: production or test. Evaluations, feedback, reviews and suggestions are separate per environment; decisions and their versions belong to the whole organization.
Available options:
production, test Show child attributes
Show child attributes
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Show child attributes
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The decision's stored objective revision this preview was scored with, or null when none is set (balanced losses). Absent only on a replay of a response stored before it was recorded.
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Base and candidate judged by the decision's objective, on the same scored cases (each mistake's loss is its severity, every mistake normal without an objective, weighted so each expected outcome counts equally), and whether the candidate is accepted: lower loss per case and no new severe mistake on a human-labeled case. Absent only on a replay of a response stored before previews were judged.
Show child attributes
Show child attributes