예측 생성

예측을 생성합니다.

POST /v2/{entity}/{project}/predictions

예측 생성

cURL

curl --request POST \
  --url https://api.example.com/v2/{entity}/{project}/predictions \
  --header 'Content-Type: application/json' \
  --data '
{
  "inputs": {},
  "model": "<string>",
  "output": "<unknown>",
  "evaluation_run_id": "<string>",
  "genai_span_ref": [
    {
      "span_id": "<string>",
      "trace_id": "<string>"
    }
  ]
}
'

import requests

url = "https://api.example.com/v2/{entity}/{project}/predictions"

payload = {
    "inputs": {},
    "model": "<string>",
    "output": "<unknown>",
    "evaluation_run_id": "<string>",
    "genai_span_ref": [
        {
            "span_id": "<string>",
            "trace_id": "<string>"
        }
    ]
}
headers = {"Content-Type": "application/json"}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    inputs: {},
    model: '<string>',
    output: '<unknown>',
    evaluation_run_id: '<string>',
    genai_span_ref: [{span_id: '<string>', trace_id: '<string>'}]
  })
};

fetch('https://api.example.com/v2/{entity}/{project}/predictions', 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.example.com/v2/{entity}/{project}/predictions",
  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([
    'inputs' => [
        
    ],
    'model' => '<string>',
    'output' => '<unknown>',
    'evaluation_run_id' => '<string>',
    'genai_span_ref' => [
        [
                'span_id' => '<string>',
                'trace_id' => '<string>'
        ]
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "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.example.com/v2/{entity}/{project}/predictions"

	payload := strings.NewReader("{\n  \"inputs\": {},\n  \"model\": \"<string>\",\n  \"output\": \"<unknown>\",\n  \"evaluation_run_id\": \"<string>\",\n  \"genai_span_ref\": [\n    {\n      \"span_id\": \"<string>\",\n      \"trace_id\": \"<string>\"\n    }\n  ]\n}")

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

	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.example.com/v2/{entity}/{project}/predictions")
  .header("Content-Type", "application/json")
  .body("{\n  \"inputs\": {},\n  \"model\": \"<string>\",\n  \"output\": \"<unknown>\",\n  \"evaluation_run_id\": \"<string>\",\n  \"genai_span_ref\": [\n    {\n      \"span_id\": \"<string>\",\n      \"trace_id\": \"<string>\"\n    }\n  ]\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.example.com/v2/{entity}/{project}/predictions")

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

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"inputs\": {},\n  \"model\": \"<string>\",\n  \"output\": \"<unknown>\",\n  \"evaluation_run_id\": \"<string>\",\n  \"genai_span_ref\": [\n    {\n      \"span_id\": \"<string>\",\n      \"trace_id\": \"<string>\"\n    }\n  ]\n}"

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

200

{
  "prediction_id": "<string>"
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

경로 매개변수

entity · string · 필수

project · string · 필수

본문

application/json

REST API를 통해 Prediction을 생성하기 위한 요청 본문입니다.

이 모델은 project_id를 제외합니다. RESTful 엔드포인트에서는 project_id가 URL 경로에서 제공되기 때문입니다.

inputs · Inputs · object · 필수

예측의 입력값

model · string · 필수

모델 레퍼런스(weave:// URI)

output · any · 필수

예측의 출력값

evaluation_run_id · string | null

이 예측을 하위 call로 연결하기 위한 선택적 Evaluation run ID

genai_span_ref · GenAISpanRef · object[] | null

이 예측으로 생성된 선택 사항인 GenAI span 레퍼런스입니다.

Show child attributes

응답

200

application/json

성공적인 응답

prediction_id · string · 필수

예측 ID