高物
548db78ca7
Adds/updates documentation, assets and implementation for Match3D and puzzle image generation workflows. Key changes: decision logs and pitfalls updated to prefer VectorEngine Gemini for Match3D material sheets and to require edits (multipart) for 1:1 container reference images; guidance added for when to use APIMart vs VectorEngine. .env.example clarified APIMart/Responses config. Many new public assets and PPT visuals added. Code changes across frontend and backend: updated shared contracts, server-rs match3d/puzzle/image-generation handlers, VectorEngine/OpenAI image generation clients, and multiple React components/tests to handle UI/background/container image signing, edits workflow, and puzzle UI background resolution. Added src/services/puzzle-runtime/puzzleUiBackgroundSource.ts and related test updates. Includes notes about multipart HTTP/1.1 requirement and test/verification commands in docs.
662 lines
22 KiB
Rust
662 lines
22 KiB
Rust
use std::time::Duration;
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use axum::http::StatusCode;
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use base64::{Engine as _, engine::general_purpose::STANDARD as BASE64_STANDARD};
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use reqwest::header;
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use serde_json::{Map, Value, json};
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use crate::{http_error::AppError, state::AppState};
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pub(crate) const GPT_IMAGE_2_MODEL: &str = "gpt-image-2";
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pub(crate) const VECTOR_ENGINE_GPT_IMAGE_2_MODEL: &str = "gpt-image-2-all";
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const VECTOR_ENGINE_PROVIDER: &str = "vector-engine";
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#[derive(Clone, Debug)]
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pub(crate) struct OpenAiImageSettings {
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pub base_url: String,
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pub api_key: String,
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pub request_timeout_ms: u64,
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}
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#[derive(Clone, Debug)]
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pub(crate) struct OpenAiGeneratedImages {
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pub task_id: String,
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pub actual_prompt: Option<String>,
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pub images: Vec<DownloadedOpenAiImage>,
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}
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#[derive(Clone, Debug)]
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pub(crate) struct DownloadedOpenAiImage {
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pub bytes: Vec<u8>,
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pub mime_type: String,
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pub extension: String,
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}
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#[derive(Clone, Debug)]
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pub(crate) struct OpenAiReferenceImage {
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pub bytes: Vec<u8>,
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pub mime_type: String,
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pub file_name: String,
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}
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// 中文注释：RPG、方洞等图片资产统一走 VectorEngine GPT-image-2-all，避免把密钥或供应商协议暴露到前端。
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pub(crate) fn require_openai_image_settings(
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state: &AppState,
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) -> Result<OpenAiImageSettings, AppError> {
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let base_url = state
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.config
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.vector_engine_base_url
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.trim()
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.trim_end_matches('/');
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if base_url.is_empty() {
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return Err(
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AppError::from_status(StatusCode::SERVICE_UNAVAILABLE).with_details(json!({
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"provider": VECTOR_ENGINE_PROVIDER,
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"reason": "VECTOR_ENGINE_BASE_URL 未配置",
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})),
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);
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}
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let api_key = state
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.config
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.vector_engine_api_key
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.as_deref()
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.map(str::trim)
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.filter(|value| !value.is_empty())
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.ok_or_else(|| {
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AppError::from_status(StatusCode::SERVICE_UNAVAILABLE).with_details(json!({
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"provider": VECTOR_ENGINE_PROVIDER,
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"reason": "VECTOR_ENGINE_API_KEY 未配置",
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}))
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})?;
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Ok(OpenAiImageSettings {
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base_url: base_url.to_string(),
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api_key: api_key.to_string(),
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request_timeout_ms: state.config.vector_engine_image_request_timeout_ms.max(1),
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})
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}
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pub(crate) fn build_openai_image_http_client(
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settings: &OpenAiImageSettings,
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) -> Result<reqwest::Client, AppError> {
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reqwest::Client::builder()
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.timeout(Duration::from_millis(settings.request_timeout_ms))
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// 中文注释：同一客户端也会承载 `/v1/images/edits` multipart 图生图请求，强制 HTTP/1.1 可避开部分网关对 HTTP/2 multipart 流的兼容问题。
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.http1_only()
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.build()
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.map_err(|error| {
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AppError::from_status(StatusCode::INTERNAL_SERVER_ERROR).with_details(json!({
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"provider": VECTOR_ENGINE_PROVIDER,
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"message": format!("构造 VectorEngine 图片生成 HTTP 客户端失败：{error}"),
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}))
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})
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}
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pub(crate) async fn create_openai_image_generation(
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http_client: &reqwest::Client,
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settings: &OpenAiImageSettings,
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prompt: &str,
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negative_prompt: Option<&str>,
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size: &str,
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candidate_count: u32,
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reference_images: &[String],
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failure_context: &str,
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) -> Result<OpenAiGeneratedImages, AppError> {
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let request_body = build_openai_image_request_body(
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prompt,
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negative_prompt,
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size,
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candidate_count,
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reference_images,
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);
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let response = http_client
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.post(vector_engine_images_generation_url(settings))
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.header(
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header::AUTHORIZATION,
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format!("Bearer {}", settings.api_key),
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)
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.header(header::ACCEPT, "application/json")
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.header(header::CONTENT_TYPE, "application/json")
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.json(&request_body)
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.send()
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.await
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.map_err(|error| {
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map_openai_image_request_error(format!(
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"{failure_context}：创建图片生成任务失败：{error}"
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))
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})?;
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let response_status = response.status();
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let response_text = response.text().await.map_err(|error| {
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map_openai_image_request_error(format!("{failure_context}：读取图片生成响应失败：{error}"))
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})?;
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if !response_status.is_success() {
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return Err(map_openai_image_upstream_error(
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response_status.as_u16(),
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response_text.as_str(),
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failure_context,
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));
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}
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let response_json = parse_json_payload(response_text.as_str(), failure_context)?;
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let generation_id = extract_generation_id(&response_json.payload)
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.unwrap_or_else(|| format!("vector-engine-{}", current_utc_micros()));
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let actual_prompt = find_first_string_by_key(&response_json.payload, "revised_prompt")
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.or_else(|| find_first_string_by_key(&response_json.payload, "actual_prompt"));
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let image_urls = extract_image_urls(&response_json.payload);
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if !image_urls.is_empty() {
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let mut generated =
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download_images_from_urls(http_client, generation_id, image_urls, candidate_count)
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.await?;
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generated.actual_prompt = actual_prompt;
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return Ok(generated);
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}
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let b64_images = extract_b64_images(&response_json.payload);
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if !b64_images.is_empty() {
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let mut generated = images_from_base64(generation_id, b64_images, candidate_count);
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generated.actual_prompt = actual_prompt;
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return Ok(generated);
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}
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Err(
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AppError::from_status(StatusCode::BAD_GATEWAY).with_details(json!({
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"provider": VECTOR_ENGINE_PROVIDER,
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"message": format!("{failure_context}：VectorEngine 未返回图片地址"),
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})),
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)
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}
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pub(crate) async fn create_openai_image_edit(
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http_client: &reqwest::Client,
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settings: &OpenAiImageSettings,
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prompt: &str,
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negative_prompt: Option<&str>,
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size: &str,
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reference_image: &OpenAiReferenceImage,
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failure_context: &str,
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) -> Result<OpenAiGeneratedImages, AppError> {
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let task_id = format!("vector-engine-edit-{}", current_utc_micros());
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let image_part = reqwest::multipart::Part::bytes(reference_image.bytes.clone())
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.file_name(reference_image.file_name.clone())
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.mime_str(reference_image.mime_type.as_str())
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.map_err(|error| {
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map_openai_image_request_error(format!("{failure_context}：构造参考图失败：{error}"))
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})?;
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let form = reqwest::multipart::Form::new()
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.part("image", image_part)
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.text("model", GPT_IMAGE_2_MODEL.to_string())
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.text(
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"prompt",
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build_prompt_with_negative(prompt, negative_prompt),
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)
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.text("n", "1")
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.text("size", normalize_image_size(size));
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let response = http_client
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.post(vector_engine_images_edit_url(settings).as_str())
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.header(
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header::AUTHORIZATION,
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format!("Bearer {}", settings.api_key),
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)
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.header(header::ACCEPT, "application/json")
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.multipart(form)
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.send()
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.await
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.map_err(|error| {
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map_openai_image_request_error(format!(
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"{failure_context}：创建图片编辑任务失败：{error}"
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))
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})?;
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let response_status = response.status();
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let response_text = response.text().await.map_err(|error| {
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map_openai_image_request_error(format!("{failure_context}：读取图片编辑响应失败：{error}"))
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})?;
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if !response_status.is_success() {
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return Err(map_openai_image_upstream_error(
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response_status.as_u16(),
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response_text.as_str(),
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failure_context,
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));
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}
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let response_json = parse_json_payload(response_text.as_str(), failure_context)?;
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let actual_prompt = find_first_string_by_key(&response_json.payload, "revised_prompt")
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.or_else(|| find_first_string_by_key(&response_json.payload, "actual_prompt"));
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let image_urls = extract_image_urls(&response_json.payload);
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if !image_urls.is_empty() {
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let mut generated = download_images_from_urls(http_client, task_id, image_urls, 1).await?;
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generated.actual_prompt = actual_prompt;
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return Ok(generated);
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}
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let b64_images = extract_b64_images(&response_json.payload);
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if !b64_images.is_empty() {
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let mut generated = images_from_base64(task_id, b64_images, 1);
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generated.actual_prompt = actual_prompt;
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return Ok(generated);
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}
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Err(
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AppError::from_status(StatusCode::BAD_GATEWAY).with_details(json!({
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"provider": VECTOR_ENGINE_PROVIDER,
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"message": format!("{failure_context}：VectorEngine 未返回编辑图片"),
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})),
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)
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}
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pub(crate) fn build_openai_image_request_body(
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prompt: &str,
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negative_prompt: Option<&str>,
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size: &str,
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candidate_count: u32,
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reference_images: &[String],
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) -> Value {
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let mut body = Map::from_iter([
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(
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"model".to_string(),
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Value::String(VECTOR_ENGINE_GPT_IMAGE_2_MODEL.to_string()),
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),
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(
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"prompt".to_string(),
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Value::String(build_prompt_with_negative(prompt, negative_prompt)),
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),
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("n".to_string(), json!(candidate_count.clamp(1, 4))),
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(
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"size".to_string(),
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Value::String(normalize_image_size(size)),
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),
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]);
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if !reference_images.is_empty() {
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body.insert("image".to_string(), json!(reference_images));
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}
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Value::Object(body)
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}
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fn build_prompt_with_negative(prompt: &str, negative_prompt: Option<&str>) -> String {
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let prompt = prompt.trim();
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let Some(negative_prompt) = negative_prompt
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.map(str::trim)
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.filter(|value| !value.is_empty())
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else {
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return prompt.to_string();
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};
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format!("{prompt}\n避免：{negative_prompt}")
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}
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fn normalize_image_size(size: &str) -> String {
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match size.trim() {
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"1024*1024" | "1024x1024" | "1:1" => "1024x1024",
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"1280*720" | "1280x720" | "1600*900" | "1600x900" | "16:9" | "1536x1024" | "2048x1152"
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| "2k" => "1536x1024",
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"1024*1536" | "1024x1536" | "9:16" => "1024x1536",
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value if !value.is_empty() => value,
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_ => "1024x1024",
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}
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.to_string()
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}
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async fn download_images_from_urls(
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http_client: &reqwest::Client,
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task_id: String,
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image_urls: Vec<String>,
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candidate_count: u32,
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) -> Result<OpenAiGeneratedImages, AppError> {
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let mut images = Vec::with_capacity(candidate_count.clamp(1, 4) as usize);
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for image_url in image_urls
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.into_iter()
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.take(candidate_count.clamp(1, 4) as usize)
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{
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images.push(download_remote_image(http_client, image_url.as_str()).await?);
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}
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Ok(OpenAiGeneratedImages {
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task_id,
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actual_prompt: None,
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images,
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})
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}
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fn images_from_base64(
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task_id: String,
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b64_images: Vec<String>,
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candidate_count: u32,
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) -> OpenAiGeneratedImages {
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let images = b64_images
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.into_iter()
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.take(candidate_count.clamp(1, 4) as usize)
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.filter_map(|raw| decode_generated_image_base64(raw.as_str()))
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.collect();
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OpenAiGeneratedImages {
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task_id,
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actual_prompt: None,
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images,
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}
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}
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fn decode_generated_image_base64(raw: &str) -> Option<DownloadedOpenAiImage> {
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let bytes = BASE64_STANDARD.decode(raw.trim()).ok()?;
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let mime_type = infer_image_mime_type(bytes.as_slice());
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Some(DownloadedOpenAiImage {
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extension: mime_to_extension(mime_type.as_str()).to_string(),
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mime_type,
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bytes,
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})
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}
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pub(crate) async fn download_remote_image(
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http_client: &reqwest::Client,
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image_url: &str,
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) -> Result<DownloadedOpenAiImage, AppError> {
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let response =
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http_client.get(image_url).send().await.map_err(|error| {
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map_openai_image_request_error(format!("下载生成图片失败：{error}"))
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})?;
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let status = response.status();
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let content_type = response
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.headers()
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.get(header::CONTENT_TYPE)
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.and_then(|value| value.to_str().ok())
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.unwrap_or("image/jpeg")
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.to_string();
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let body = response.bytes().await.map_err(|error| {
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map_openai_image_request_error(format!("读取生成图片内容失败：{error}"))
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})?;
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if !status.is_success() {
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return Err(
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AppError::from_status(StatusCode::BAD_GATEWAY).with_details(json!({
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"provider": VECTOR_ENGINE_PROVIDER,
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"message": "下载生成图片失败",
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"status": status.as_u16(),
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})),
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);
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}
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let normalized_mime_type = normalize_downloaded_image_mime_type(content_type.as_str());
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Ok(DownloadedOpenAiImage {
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extension: mime_to_extension(normalized_mime_type.as_str()).to_string(),
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mime_type: normalized_mime_type,
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bytes: body.to_vec(),
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})
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}
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fn parse_json_payload(
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raw_text: &str,
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failure_context: &str,
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) -> Result<ParsedJsonPayload, AppError> {
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serde_json::from_str::<Value>(raw_text)
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.map(|payload| ParsedJsonPayload { payload })
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.map_err(|error| {
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AppError::from_status(StatusCode::BAD_GATEWAY).with_details(json!({
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"provider": VECTOR_ENGINE_PROVIDER,
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"message": format!("{failure_context}：解析响应失败：{error}"),
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"rawExcerpt": truncate_raw(raw_text),
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}))
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})
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}
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fn map_openai_image_request_error(message: String) -> AppError {
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AppError::from_status(StatusCode::BAD_GATEWAY).with_details(json!({
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"provider": VECTOR_ENGINE_PROVIDER,
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"message": message,
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}))
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}
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fn map_openai_image_upstream_error(
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upstream_status: u16,
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raw_text: &str,
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failure_context: &str,
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) -> AppError {
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let message = parse_api_error_message(raw_text, failure_context);
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tracing::warn!(
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provider = VECTOR_ENGINE_PROVIDER,
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upstream_status,
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raw_excerpt = %truncate_raw(raw_text),
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message,
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"VectorEngine 图片生成上游错误"
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);
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AppError::from_status(StatusCode::BAD_GATEWAY).with_details(json!({
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"provider": VECTOR_ENGINE_PROVIDER,
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"message": message,
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"upstreamStatus": upstream_status,
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"rawExcerpt": truncate_raw(raw_text),
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}))
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}
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fn parse_api_error_message(raw_text: &str, fallback_message: &str) -> String {
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if raw_text.trim().is_empty() {
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return fallback_message.to_string();
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}
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if let Ok(parsed) = serde_json::from_str::<Value>(raw_text) {
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for pointer in [
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"/error/message",
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"/message",
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"/output/message",
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"/data/message",
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] {
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if let Some(message) = parsed
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.pointer(pointer)
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.and_then(Value::as_str)
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.map(str::trim)
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.filter(|value| !value.is_empty())
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{
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return message.to_string();
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}
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}
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for pointer in ["/error/code", "/code", "/output/code", "/data/code"] {
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if let Some(code) = parsed
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.pointer(pointer)
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.and_then(Value::as_str)
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.map(str::trim)
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.filter(|value| !value.is_empty())
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{
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return format!("{fallback_message}（{code}）");
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}
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||
}
|
||
}
|
||
|
||
raw_text.trim().to_string()
|
||
}
|
||
|
||
fn collect_strings_by_key(value: &Value, target_key: &str, results: &mut Vec<String>) {
|
||
match value {
|
||
Value::Array(entries) => {
|
||
for entry in entries {
|
||
collect_strings_by_key(entry, target_key, results);
|
||
}
|
||
}
|
||
Value::Object(object) => {
|
||
for (key, nested_value) in object {
|
||
if key == target_key {
|
||
match nested_value {
|
||
Value::String(text) => {
|
||
let text = text.trim();
|
||
if !text.is_empty() {
|
||
results.push(text.to_string());
|
||
continue;
|
||
}
|
||
}
|
||
Value::Array(entries) => {
|
||
for entry in entries {
|
||
if let Some(text) = entry
|
||
.as_str()
|
||
.map(str::trim)
|
||
.filter(|value| !value.is_empty())
|
||
{
|
||
results.push(text.to_string());
|
||
}
|
||
}
|
||
}
|
||
_ => {}
|
||
}
|
||
}
|
||
collect_strings_by_key(nested_value, target_key, results);
|
||
}
|
||
}
|
||
_ => {}
|
||
}
|
||
}
|
||
|
||
fn find_first_string_by_key(value: &Value, target_key: &str) -> Option<String> {
|
||
let mut results = Vec::new();
|
||
collect_strings_by_key(value, target_key, &mut results);
|
||
results.into_iter().next()
|
||
}
|
||
|
||
fn extract_generation_id(payload: &Value) -> Option<String> {
|
||
find_first_string_by_key(payload, "id")
|
||
.or_else(|| find_first_string_by_key(payload, "created"))
|
||
.or_else(|| find_first_string_by_key(payload, "request_id"))
|
||
}
|
||
|
||
fn extract_image_urls(payload: &Value) -> Vec<String> {
|
||
let mut urls = Vec::new();
|
||
collect_strings_by_key(payload, "url", &mut urls);
|
||
collect_strings_by_key(payload, "image", &mut urls);
|
||
collect_strings_by_key(payload, "image_url", &mut urls);
|
||
let mut deduped = Vec::new();
|
||
for url in urls {
|
||
if (url.starts_with("http://") || url.starts_with("https://")) && !deduped.contains(&url) {
|
||
deduped.push(url);
|
||
}
|
||
}
|
||
deduped
|
||
}
|
||
|
||
fn extract_b64_images(payload: &Value) -> Vec<String> {
|
||
let mut values = Vec::new();
|
||
collect_strings_by_key(payload, "b64_json", &mut values);
|
||
values
|
||
}
|
||
|
||
fn vector_engine_images_generation_url(settings: &OpenAiImageSettings) -> String {
|
||
if settings.base_url.ends_with("/v1") {
|
||
format!("{}/images/generations", settings.base_url)
|
||
} else {
|
||
format!("{}/v1/images/generations", settings.base_url)
|
||
}
|
||
}
|
||
|
||
fn vector_engine_images_edit_url(settings: &OpenAiImageSettings) -> String {
|
||
if settings.base_url.ends_with("/v1") {
|
||
format!("{}/images/edits", settings.base_url)
|
||
} else {
|
||
format!("{}/v1/images/edits", settings.base_url)
|
||
}
|
||
}
|
||
|
||
fn normalize_downloaded_image_mime_type(content_type: &str) -> String {
|
||
let mime_type = content_type
|
||
.split(';')
|
||
.next()
|
||
.map(str::trim)
|
||
.unwrap_or("image/jpeg");
|
||
match mime_type {
|
||
"image/png" | "image/webp" | "image/jpeg" | "image/jpg" | "image/gif" => {
|
||
mime_type.to_string()
|
||
}
|
||
_ => "image/jpeg".to_string(),
|
||
}
|
||
}
|
||
|
||
fn mime_to_extension(mime_type: &str) -> &str {
|
||
match mime_type {
|
||
"image/png" => "png",
|
||
"image/webp" => "webp",
|
||
"image/gif" => "gif",
|
||
_ => "jpg",
|
||
}
|
||
}
|
||
|
||
fn infer_image_mime_type(bytes: &[u8]) -> String {
|
||
if bytes.starts_with(b"\x89PNG\r\n\x1A\n") {
|
||
return "image/png".to_string();
|
||
}
|
||
if bytes.starts_with(b"\xFF\xD8\xFF") {
|
||
return "image/jpeg".to_string();
|
||
}
|
||
if bytes.starts_with(b"RIFF") && bytes.get(8..12) == Some(b"WEBP") {
|
||
return "image/webp".to_string();
|
||
}
|
||
if bytes.starts_with(b"GIF87a") || bytes.starts_with(b"GIF89a") {
|
||
return "image/gif".to_string();
|
||
}
|
||
"image/png".to_string()
|
||
}
|
||
|
||
fn truncate_raw(raw_text: &str) -> String {
|
||
raw_text.chars().take(800).collect()
|
||
}
|
||
|
||
fn current_utc_micros() -> i64 {
|
||
use std::time::{SystemTime, UNIX_EPOCH};
|
||
let duration = SystemTime::now()
|
||
.duration_since(UNIX_EPOCH)
|
||
.expect("system time should be after unix epoch");
|
||
i64::try_from(duration.as_micros()).expect("current unix micros should fit in i64")
|
||
}
|
||
|
||
struct ParsedJsonPayload {
|
||
payload: Value,
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod tests {
|
||
use super::*;
|
||
|
||
#[test]
|
||
fn gpt_image_2_request_uses_vector_engine_contract() {
|
||
let body = build_openai_image_request_body(
|
||
"雾海神殿",
|
||
Some("文字，水印"),
|
||
"1280*720",
|
||
2,
|
||
&["data:image/png;base64,abcd".to_string()],
|
||
);
|
||
|
||
assert_eq!(body["model"], VECTOR_ENGINE_GPT_IMAGE_2_MODEL);
|
||
assert_eq!(body["size"], "1536x1024");
|
||
assert_eq!(body["n"], 2);
|
||
assert!(body.get("official_fallback").is_none());
|
||
assert_eq!(body["image"][0], "data:image/png;base64,abcd");
|
||
assert!(body["prompt"].as_str().unwrap_or_default().contains("避免"));
|
||
}
|
||
|
||
#[test]
|
||
fn vector_engine_edit_url_uses_images_edits_endpoint() {
|
||
let root_settings = OpenAiImageSettings {
|
||
base_url: "https://vector.example".to_string(),
|
||
api_key: "test-key".to_string(),
|
||
request_timeout_ms: 180_000,
|
||
};
|
||
let v1_settings = OpenAiImageSettings {
|
||
base_url: "https://vector.example/v1".to_string(),
|
||
api_key: "test-key".to_string(),
|
||
request_timeout_ms: 180_000,
|
||
};
|
||
|
||
assert_eq!(
|
||
vector_engine_images_edit_url(&root_settings),
|
||
"https://vector.example/v1/images/edits"
|
||
);
|
||
assert_eq!(
|
||
vector_engine_images_edit_url(&v1_settings),
|
||
"https://vector.example/v1/images/edits"
|
||
);
|
||
}
|
||
|
||
#[test]
|
||
fn b64_json_response_decodes_png_image() {
|
||
let images = images_from_base64(
|
||
"task-1".to_string(),
|
||
vec![BASE64_STANDARD.encode(b"\x89PNG\r\n\x1A\nrest")],
|
||
1,
|
||
);
|
||
|
||
assert_eq!(images.images.len(), 1);
|
||
assert_eq!(images.images[0].mime_type, "image/png");
|
||
assert_eq!(images.images[0].extension, "png");
|
||
}
|
||
}
|