Decisions API is in public beta

來源原標題

Decisions API is in public beta

作者

chiefstorm

原始標籤

hackernews

來源訊號

HN points 381　HN 留言 221

原始連結

https://developers.openai.com/api/docs/guides/decisions

摘要原文

The Decisions API evaluates text, images, or both and returns typed answers about 10x faster than the Responses API. Get the probability that a condition is true, a choice from a fixed set, or a score against a rubric. Use those answers to classify content, route requests, and prioritize work in your application. Try the Decisions API in the Playground to experiment with questions and inputs before writing code. The Decisions API is in public beta, and we expect to GA in the coming weeks. gpt-6-luna is the only model currently available. Use the dedicated POST /v1/decisions endpoint. To run the SDK examples below, use these OpenAI SDK versions or later: Python 3.26.0, JavaScript 7.30.0, Go 3.73.0, Ruby 0.101.0, and Java 4.78.0. See OpenAI SDK for installation instructions. How decisions work A request has three parts: Field Purpose model The model that evaluates the request. Currently, only gpt-6-luna is supported. input Shared evidence for the questions: a text string or user messages containing text and images. questions What to evaluate, including each question’s type, instructions, and any allowed choices or score levels. The response contains an answers array. Give each question a unique name to identify its answer; the API echoes that name in the response. Choose a question type Type Use it to Main result predicate Check a condition, such as visible damage or passage relevance. probability : an estimate from 0 to 1 that the condition is true. choice Select one option, such as a department or content category. choice : one of your supplied values. score Rate an input against ordered levels, such as issue severity. score : the probability-weighted average of the level indices. Both choice and score return probabilities over discrete options. Use choice for categories without an order, such as departments. Use score for ordered levels, such as severity; it takes the probability-weighted average of their numeric indices to produce a score that can fall between levels. Use Decisions when your application needs one of these answer types. Use Structured Outputs with the Responses API when you need to generate an object that follows your own JSON schema, such as extracted fields or a written explanation, or function calling when you need a model to request a tool call with arguments. Check an image for visible damage Use a predicate question to check a product photo for visible damage. This request combines the image with instructions to look for a crack, tear, or dent. Dent Cancellation Answer relevance Does this item have a dent? probability 1.00 “ Please cancel my subscription before the next renewal. Can I switch to annual billing and keep my current plan? I don’t need this anymore. How do I stop future charges? probability 1.00 Question Can I return an item after 30 days? Answer Returns are accepted within 30 days of delivery. After that, items aren’t eligible for a refund. Start a return from the Orders page in your account. Standard shipping takes three to five business days. probability 0.90 Check an image for visible damage curl 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 IMAGE_BASE64 = "$( base64 < product.png | tr -d '\r\n')" curl https://api.openai.com/v1/decisions \ -H "Authorization: Bearer $OPENAI_API_KEY " \ -H "Content-Type: application/json" \ --data-binary @- << JSON { "model": "gpt-6-luna", "input": [{ "role": "user", "content": [ {"type": "input_text", "text": "Inspect the product in this photo."}, {"type": "input_image", "image_url": "data:image/png;base64, $IMAGE_BASE64 "} ] }], "questions": [{ "type": "predicate", "name": "visible_damage", "instructions": "Does the product have visible damage, such as a crack, tear, or dent? Ignore shadows and damage to the packaging." }] } JSON 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 import { readFile } from "node:fs/promises"; import OpenAI from "openai"; const client = new OpenAI(); const imageBase64 = (await readFile("product.png")).toString("base64"); const decision = await client.decisions.create({ model: "gpt-6-luna", input: [ { role: "user", content: [ { type: "input_text", text: "Inspect the product in this photo." }, { type: "input_image", image_url: `data:image/png;base64,${imageBase64}`, }, ], }, ], questions: [ { type: "predicate", name: "visible_damage", instructions: "Does the product have visible damage, such as a crack, tear, or dent? Ignore shadows and damage to the packaging.", }, ], }); const answer = decision.answers[0]; if (answer.type === "refusal") { console.log(`Refused: ${answer.name}`); } else if (answer.type === "predicate") { console.log(`Visible damage probability: ${answer.probability}`); } 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 import base64 from pathlib import Path from openai import OpenAI client = OpenAI() image_base64 = base64.b64encode(Path("product.png").read_bytes()).decode("ascii") decision = client.decisions.create( model="gpt-6-luna", input=[ { "role": "user", "content": [ {"type": "input_text", "text": "Inspect the product in this photo."}, { "type": "input_image", "image_url": f"data:image/png;base64,{image_base64}", }, ], } ], questions=[ { "type": "predicate", "name": "visible_damage", "instructions": ( "Does the product have visible damage, such as a crack, tear, or dent? " "Ignore shadows and damage to the packaging." ), } ], ) answer = decision.answers[0] if answer.type == "refusal": print(f"Refused: {answer.name}") elif answer.type == "predicate": print(f"Visible damage probability: {answer.probability}") 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 package main import ( "context" "encoding/base64" "fmt" "os" "github.com/openai/openai-go/v3" ) func main() { image, err := os.ReadFile("product.png") if err != nil { panic(err) } client := openai.NewClient() decision, err := client.Decisions.New(context.Background(), openai.DecisionNewParams{ Model: "gpt-6-luna", Input: openai.DecisionNewParamsInputUnion{ OfDecisionInputMessageArray: []openai.DecisionInputMessageParam{{ Content: openai.DecisionInputMessageContentUnionParam{ OfParts: []openai.DecisionInputPartUnionParam{ {OfInputText: &openai.DecisionInputTextParam{Text: "Inspect the product in this photo."}}, {OfInputImage: &openai.DecisionInputImageParam{ ImageURL: "data:image/png;base64," + base64.StdEncoding.EncodeToString(image), }}, }, }, }}, }, Questions: []openai.DecisionNewParamsQuestionUnion{{ OfPredicate: &openai.DecisionNewParamsQuestionPredicate{ Name: openai.String("visible_damage"), Instructions: "Does the product have visible damage, such as a crack, tear, or dent? Ignore shadows and damage to the packaging.", }, }}, }) if err != nil { panic(err) } switch answer := decision.Answers[0].AsAny().(type) { case openai.DecisionAnswerPredicate: fmt.Println(answer.Probability) case openai.DecisionAnswerRefusal: fmt.Printf("Decision refused for %s\n", answer.Name) default: panic("unexpected answer type") } } 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 import com.openai.models.decisions.DecisionCreateParams; import com.openai.models.decisions.DecisionInputImage; import com.openai.models.decisions.DecisionInputMessage; import com.openai.models.decisions.DecisionInputPart; import com.openai.models.decisions.DecisionInputText; import java.nio.file.Files; import java.nio.file.Path; import java.util.Base64; import java.util.List; String imageBase64 = Base64.getEncoder().encodeToString(Files.readAllBytes(Path.of("product.png"))); var message = DecisionInputMessage.builder() .contentOfParts( List.of( DecisionInputPart.ofInputText( DecisionInputText.builder() .text("Inspect the product in this photo.") .build()), DecisionInputPart.ofInputImage( DecisionInputImage.builder() . 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