460 lines
15 KiB
Rust
460 lines
15 KiB
Rust
use axum::http::StatusCode;
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use platform_image::{
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DownloadedImage, GeneratedImages, PlatformImageError, PlatformImageStatusHint, ReferenceImage,
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VECTOR_ENGINE_PROVIDER, VectorEngineImageSettings, build_vector_engine_image_http_client,
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build_vector_engine_image_request_body, create_vector_engine_image_edit,
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create_vector_engine_image_edit_with_references, create_vector_engine_image_generation,
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download_remote_image as download_platform_image_remote_image, vector_engine_images_edit_url,
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vector_engine_images_generation_url,
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};
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use serde_json::{Value, json};
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use crate::{
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external_api_audit::{
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ExternalApiFailureDraft, build_external_api_failure_draft_from_platform_image_audit,
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record_external_api_failure,
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},
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http_error::AppError,
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state::AppState,
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};
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pub(crate) use platform_image::GPT_IMAGE_2_MODEL;
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#[cfg(test)]
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use platform_image::VECTOR_ENGINE_GPT_IMAGE_2_MODEL;
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pub(crate) type OpenAiGeneratedImages = GeneratedImages;
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pub(crate) type DownloadedOpenAiImage = DownloadedImage;
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pub(crate) type OpenAiReferenceImage = ReferenceImage;
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#[derive(Clone)]
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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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pub external_api_audit_state: Option<AppState>,
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}
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impl std::fmt::Debug for OpenAiImageSettings {
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fn fmt(&self, formatter: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
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formatter
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.debug_struct("OpenAiImageSettings")
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.field("base_url", &self.base_url)
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.field("api_key", &"<redacted>")
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.field("request_timeout_ms", &self.request_timeout_ms)
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.field(
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"external_api_audit_enabled",
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&self.external_api_audit_state.is_some(),
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)
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.finish()
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}
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}
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// 中文注释：api-server 只负责配置、审计和 HTTP envelope，VectorEngine 协议细节统一由 platform-image provider 承接。
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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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external_api_audit_state: Some(state.clone()),
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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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build_vector_engine_image_http_client(&settings.provider_settings())
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.map_err(map_platform_image_error)
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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 result = create_vector_engine_image_generation(
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http_client,
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&settings.provider_settings(),
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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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failure_context,
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)
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.await;
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map_platform_image_result(settings, result).await
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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 result = create_vector_engine_image_edit(
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http_client,
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&settings.provider_settings(),
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prompt,
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negative_prompt,
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size,
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reference_image,
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failure_context,
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)
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.await;
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map_platform_image_result(settings, result).await
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}
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pub(crate) async fn create_openai_image_edit_with_references(
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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: &[OpenAiReferenceImage],
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failure_context: &str,
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) -> Result<OpenAiGeneratedImages, AppError> {
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let result = create_vector_engine_image_edit_with_references(
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http_client,
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&settings.provider_settings(),
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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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failure_context,
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)
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.await;
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map_platform_image_result(settings, result).await
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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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download_platform_image_remote_image(http_client, image_url)
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.await
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.map_err(map_platform_image_error)
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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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build_vector_engine_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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}
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impl OpenAiImageSettings {
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fn provider_settings(&self) -> VectorEngineImageSettings {
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VectorEngineImageSettings {
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base_url: self.base_url.clone(),
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api_key: self.api_key.clone(),
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request_timeout_ms: self.request_timeout_ms.max(1),
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}
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}
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}
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async fn map_platform_image_result<T>(
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settings: &OpenAiImageSettings,
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result: Result<T, PlatformImageError>,
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) -> Result<T, AppError> {
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match result {
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Ok(value) => Ok(value),
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Err(error) => {
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record_openai_image_failure_if_configured(settings, &error).await;
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Err(map_platform_image_error(error))
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}
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}
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}
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pub(crate) async fn record_openai_image_failure_if_configured(
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settings: &OpenAiImageSettings,
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error: &PlatformImageError,
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) {
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let Some(state) = settings.external_api_audit_state.as_ref() else {
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return;
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};
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let Some(draft) = build_openai_image_failure_audit_draft(error) else {
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return;
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};
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record_external_api_failure(state, draft).await;
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}
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pub(crate) fn build_openai_image_failure_audit_draft(
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error: &PlatformImageError,
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) -> Option<ExternalApiFailureDraft> {
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error
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.audit()
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.map(build_external_api_failure_draft_from_platform_image_audit)
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}
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pub(crate) fn map_platform_image_error(error: PlatformImageError) -> AppError {
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let status = match error.status_hint() {
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PlatformImageStatusHint::BadRequest => StatusCode::BAD_REQUEST,
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PlatformImageStatusHint::ServiceUnavailable => StatusCode::SERVICE_UNAVAILABLE,
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PlatformImageStatusHint::BadGateway => StatusCode::BAD_GATEWAY,
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PlatformImageStatusHint::GatewayTimeout => StatusCode::GATEWAY_TIMEOUT,
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};
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let mut details = json!({
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"provider": error.provider(),
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"message": error.message(),
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});
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match &error {
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PlatformImageError::InvalidConfig { .. } | PlatformImageError::InvalidRequest { .. } => {}
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PlatformImageError::Request {
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endpoint,
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timeout,
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connect,
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request,
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body,
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status_code,
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source,
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..
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} => {
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details["endpoint"] = json!(endpoint);
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details["timeout"] = json!(timeout);
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details["connect"] = json!(connect);
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details["request"] = json!(request);
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details["body"] = json!(body);
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details["status"] = json!(status_code);
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details["source"] = json!(source);
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}
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PlatformImageError::Upstream {
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upstream_status,
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raw_excerpt,
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..
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} => {
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details["upstreamStatus"] = json!(upstream_status);
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details["rawExcerpt"] = json!(raw_excerpt);
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}
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PlatformImageError::ResponseParse { raw_excerpt, .. } => {
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details["rawExcerpt"] = json!(raw_excerpt);
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}
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PlatformImageError::MissingImage { .. } => {}
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}
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if let Some(audit) = error.audit() {
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details["endpoint"] = json!(audit.endpoint);
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details["failureStage"] = json!(audit.failure_stage);
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details["statusClass"] = json!(audit.status_class);
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details["retryable"] = json!(audit.retryable);
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details["timeout"] = json!(audit.timeout);
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details["latencyMs"] = json!(audit.latency_ms);
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details["promptChars"] = json!(audit.prompt_chars);
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details["referenceImageCount"] = json!(audit.reference_image_count);
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details["imageModel"] = json!(audit.image_model);
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details["rawExcerpt"] = json!(audit.raw_excerpt);
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}
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AppError::from_status(status).with_details(details)
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}
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fn vector_engine_images_generation_url_for_test(settings: &OpenAiImageSettings) -> String {
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vector_engine_images_generation_url(&settings.provider_settings())
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}
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fn vector_engine_images_edit_url_for_test(settings: &OpenAiImageSettings) -> String {
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vector_engine_images_edit_url(&settings.provider_settings())
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use base64::{Engine as _, engine::general_purpose::STANDARD as BASE64_STANDARD};
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#[test]
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fn gpt_image_2_generation_request_uses_create_model_without_reference_images() {
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let body = build_openai_image_request_body(
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"雾海神殿",
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Some("文字，水印"),
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"1280*720",
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2,
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&["data:image/png;base64,abcd".to_string()],
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);
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assert_eq!(body["model"], GPT_IMAGE_2_MODEL);
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assert_eq!(body["size"], "1536x1024");
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assert_eq!(body["n"], 2);
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assert!(body.get("official_fallback").is_none());
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assert!(body.get("image").is_none());
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assert!(body["prompt"].as_str().unwrap_or_default().contains("避免"));
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}
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#[test]
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fn vector_engine_generation_url_normalizes_base_url() {
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let root_settings = OpenAiImageSettings {
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base_url: "https://vector.example".to_string(),
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api_key: "test-key".to_string(),
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request_timeout_ms: 1_000_000,
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external_api_audit_state: None,
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};
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let v1_settings = OpenAiImageSettings {
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base_url: "https://vector.example/v1".to_string(),
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api_key: "test-key".to_string(),
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request_timeout_ms: 1_000_000,
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external_api_audit_state: None,
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};
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assert_eq!(
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vector_engine_images_generation_url_for_test(&root_settings),
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"https://vector.example/v1/images/generations"
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);
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assert_eq!(
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vector_engine_images_generation_url_for_test(&v1_settings),
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"https://vector.example/v1/images/generations"
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);
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}
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#[test]
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fn vector_engine_edit_url_normalizes_base_url() {
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let root_settings = OpenAiImageSettings {
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base_url: "https://vector.example".to_string(),
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api_key: "test-key".to_string(),
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request_timeout_ms: 1_000_000,
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external_api_audit_state: None,
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};
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let v1_settings = OpenAiImageSettings {
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base_url: "https://vector.example/v1".to_string(),
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api_key: "test-key".to_string(),
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request_timeout_ms: 1_000_000,
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external_api_audit_state: None,
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};
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assert_eq!(
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vector_engine_images_edit_url_for_test(&root_settings),
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"https://vector.example/v1/images/edits"
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);
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assert_eq!(
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vector_engine_images_edit_url_for_test(&v1_settings),
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"https://vector.example/v1/images/edits"
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);
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}
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#[tokio::test]
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async fn vector_engine_multi_reference_edit_rejects_empty_references() {
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let settings = OpenAiImageSettings {
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base_url: "https://vector.example".to_string(),
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api_key: "test-key".to_string(),
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request_timeout_ms: 1_000_000,
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external_api_audit_state: None,
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};
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let http_client = reqwest::Client::new();
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let result = create_openai_image_edit_with_references(
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&http_client,
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&settings,
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"提示词",
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None,
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"1:1",
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1,
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&[],
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"测试图片编辑失败",
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)
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.await;
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let error = result.expect_err("empty references should be rejected locally");
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assert_eq!(error.status_code(), StatusCode::BAD_REQUEST);
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assert!(error.body_text().contains("缺少参考图"));
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}
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#[test]
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fn reference_data_url_stays_provider_owned() {
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let source = format!(
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"data:image/png;base64,{}",
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BASE64_STANDARD.encode(b"pngbytes")
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);
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let body = build_openai_image_request_body("提示词", None, "1:1", 1, &[source]);
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assert!(body.get("image").is_none());
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}
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#[test]
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fn vector_engine_upstream_failure_builds_tracking_ready_audit_event() {
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let audit = platform_image::PlatformImageFailureAudit {
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provider: VECTOR_ENGINE_PROVIDER,
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endpoint: "https://vector.example/v1/images/generations".to_string(),
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operation: "拼图 UI 背景图生成失败".to_string(),
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failure_stage: "upstream_status",
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status_code: Some(429),
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status_class: None,
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timeout: false,
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retryable: true,
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error_message: "上游限流".to_string(),
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error_source: None,
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raw_excerpt: Some("{\"error\":\"rate limited\"}".to_string()),
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latency_ms: Some(321),
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prompt_chars: Some(42),
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reference_image_count: Some(1),
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image_model: Some(VECTOR_ENGINE_GPT_IMAGE_2_MODEL),
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};
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let tracking = crate::external_api_audit::build_external_api_failure_tracking_draft(
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&build_external_api_failure_draft_from_platform_image_audit(&audit),
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);
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assert_eq!(
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tracking.event_key,
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crate::external_api_audit::EXTERNAL_API_FAILURE_EVENT_KEY
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);
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assert_eq!(tracking.scope_id, VECTOR_ENGINE_PROVIDER);
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assert_eq!(tracking.metadata["provider"], VECTOR_ENGINE_PROVIDER);
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assert_eq!(tracking.metadata["statusCode"], 429);
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assert_eq!(tracking.metadata["statusClass"], "4xx");
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assert_eq!(tracking.metadata["failureStage"], "upstream_status");
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assert_eq!(tracking.metadata["retryable"], true);
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assert_eq!(tracking.metadata["promptChars"], 42);
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assert_eq!(tracking.metadata["referenceImageCount"], 1);
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assert_eq!(
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tracking.metadata["imageModel"],
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VECTOR_ENGINE_GPT_IMAGE_2_MODEL
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);
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}
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}
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