kdletters
6bed37542c
统一 gpt-image-2 首发并在符合条件的失败后切换 gpt-image-2-c 保留总尝试预算、worker deadline 与首选失败审计 补齐响应图片校验、脚本兜底和回归测试 同步后端架构、开发运维和项目共享记忆文档
800 lines
26 KiB
Rust
800 lines
26 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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create_vector_engine_image_edit, create_vector_engine_image_edit_with_references,
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create_vector_engine_image_edit_with_references_and_model,
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create_vector_engine_image_generation, create_vector_engine_image_generation_with_model,
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create_vector_engine_nanobanana_generate_content,
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};
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#[cfg(test)]
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use platform_image::{
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build_vector_engine_image_request_body, 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 std::time::Instant;
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use time::OffsetDateTime;
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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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request_context::RequestContext,
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state::AppState,
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tracking::record_external_generation_run_after_success,
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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 request_deadline: Option<Instant>,
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pub external_api_audit_state: Option<AppState>,
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pub external_api_audit_user_id: Option<String>,
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pub external_api_audit_profile_id: Option<String>,
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pub external_api_audit_request_id: Option<String>,
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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("request_deadline_enabled", &self.request_deadline.is_some())
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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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.field(
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"external_api_audit_user_id",
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&self.external_api_audit_user_id,
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)
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.field(
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"external_api_audit_profile_id",
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&self.external_api_audit_profile_id,
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)
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.field(
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"external_api_audit_request_id",
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&self.external_api_audit_request_id,
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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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request_deadline: None,
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external_api_audit_state: Some(state.clone()),
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external_api_audit_user_id: None,
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external_api_audit_profile_id: None,
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external_api_audit_request_id: None,
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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 started_at_micros = current_utc_micros();
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let request_payload = json!({
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"size": size,
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"candidateCount": candidate_count,
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"promptChars": prompt.chars().count(),
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"negativePromptChars": negative_prompt.map(str::chars).map(Iterator::count),
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"referenceImageCount": reference_images.len(),
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});
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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(
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settings,
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result,
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"image_generation",
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failure_context,
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request_payload,
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started_at_micros,
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)
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.await
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}
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#[allow(clippy::too_many_arguments)]
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pub(crate) async fn create_openai_image_generation_with_model(
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http_client: &reqwest::Client,
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settings: &OpenAiImageSettings,
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model: &str,
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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 started_at_micros = current_utc_micros();
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let request_payload = json!({
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"model": model,
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"size": size,
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"candidateCount": candidate_count,
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"promptChars": prompt.chars().count(),
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"negativePromptChars": negative_prompt.map(str::chars).map(Iterator::count),
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"referenceImageCount": reference_images.len(),
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});
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let result = create_vector_engine_image_generation_with_model(
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http_client,
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&settings.provider_settings(),
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model,
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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(
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settings,
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result,
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"image_generation",
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failure_context,
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request_payload,
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started_at_micros,
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)
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.await
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}
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#[allow(clippy::too_many_arguments)]
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pub(crate) async fn create_openai_nanobanana_generate_content(
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http_client: &reqwest::Client,
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settings: &OpenAiImageSettings,
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model: &str,
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prompt: &str,
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negative_prompt: Option<&str>,
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aspect_ratio: &str,
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image_size: &str,
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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 started_at_micros = current_utc_micros();
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let request_payload = json!({
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"model": model,
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"aspectRatio": aspect_ratio,
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"imageSize": image_size,
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"promptChars": prompt.chars().count(),
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"negativePromptChars": negative_prompt.map(str::chars).map(Iterator::count),
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"referenceImageCount": reference_images.len(),
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});
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let result = create_vector_engine_nanobanana_generate_content(
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http_client,
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&settings.provider_settings(),
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model,
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prompt,
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negative_prompt,
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aspect_ratio,
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image_size,
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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(
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settings,
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result,
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"nanobanana_generate_content",
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failure_context,
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request_payload,
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started_at_micros,
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)
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.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 started_at_micros = current_utc_micros();
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let request_payload = json!({
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"size": size,
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"promptChars": prompt.chars().count(),
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"negativePromptChars": negative_prompt.map(str::chars).map(Iterator::count),
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"referenceImageCount": 1,
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});
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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(
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settings,
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result,
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"image_edit",
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failure_context,
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request_payload,
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started_at_micros,
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)
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.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 started_at_micros = current_utc_micros();
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let request_payload = json!({
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"size": size,
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"candidateCount": candidate_count,
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"promptChars": prompt.chars().count(),
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"negativePromptChars": negative_prompt.map(str::chars).map(Iterator::count),
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"referenceImageCount": reference_images.len(),
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});
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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(
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settings,
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result,
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"image_edit_with_references",
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failure_context,
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request_payload,
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started_at_micros,
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)
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.await
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}
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#[allow(clippy::too_many_arguments)]
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pub(crate) async fn create_openai_image_edit_with_references_and_model(
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http_client: &reqwest::Client,
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settings: &OpenAiImageSettings,
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model: &str,
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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 started_at_micros = current_utc_micros();
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let request_payload = json!({
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"model": model,
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"size": size,
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"promptChars": prompt.chars().count(),
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"negativePromptChars": negative_prompt.map(str::chars).map(Iterator::count),
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"referenceImageCount": reference_images.len(),
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});
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let result = create_vector_engine_image_edit_with_references_and_model(
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http_client,
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&settings.provider_settings(),
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model,
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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(
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settings,
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result,
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"image_edit_with_references",
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failure_context,
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request_payload,
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started_at_micros,
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)
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.await
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}
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#[cfg(test)]
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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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pub(crate) fn with_external_api_audit_actor(
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mut self,
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user_id: Option<String>,
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profile_id: Option<String>,
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) -> Self {
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self.external_api_audit_user_id = user_id;
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self.external_api_audit_profile_id = profile_id;
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self
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}
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pub(crate) fn with_external_api_audit_context(
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mut self,
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request_context: &RequestContext,
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user_id: Option<String>,
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profile_id: Option<String>,
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) -> Self {
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self.external_api_audit_user_id = user_id;
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self.external_api_audit_profile_id = profile_id;
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self.external_api_audit_request_id = Some(request_context.request_id().to_string());
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self.request_deadline = request_context.external_call_deadline();
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self
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}
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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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request_deadline: self.request_deadline,
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}
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}
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}
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async fn map_platform_image_result(
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settings: &OpenAiImageSettings,
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result: Result<OpenAiGeneratedImages, PlatformImageError>,
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operation: &'static str,
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failure_context: &str,
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request_payload: Value,
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started_at_micros: i64,
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) -> Result<OpenAiGeneratedImages, AppError> {
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match result {
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Ok(value) => {
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for audit in &value.recovered_failure_audits {
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record_openai_image_failure_audit_if_configured(settings, audit).await;
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}
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if let Some(state) = settings.external_api_audit_state.as_ref() {
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record_external_generation_run_after_success(
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state,
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VECTOR_ENGINE_PROVIDER,
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operation,
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failure_context,
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request_payload,
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started_at_micros,
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true,
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None,
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Some(value.task_id.clone()),
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Some(json!({
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"imageCount": value.images.len(),
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"actualPromptChars": value.actual_prompt.as_ref().map(|prompt| prompt.chars().count()),
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"recoveredFailureCount": value.recovered_failure_audits.len(),
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})),
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)
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.await;
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}
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Ok(value)
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}
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Err(error) => {
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for audit in error.recovered_failure_audits() {
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record_openai_image_failure_audit_if_configured(settings, audit).await;
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}
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if let Some(state) = settings.external_api_audit_state.as_ref() {
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record_external_generation_run_after_success(
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state,
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VECTOR_ENGINE_PROVIDER,
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operation,
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failure_context,
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request_payload,
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started_at_micros,
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false,
|
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Some(error.message().to_string()),
|
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None,
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None,
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)
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.await;
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}
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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(audit) = error.audit() else {
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return;
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};
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record_openai_image_failure_audit_if_configured(settings, audit).await;
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}
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async fn record_openai_image_failure_audit_if_configured(
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settings: &OpenAiImageSettings,
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audit: &platform_image::PlatformImageFailureAudit,
|
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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 draft = build_external_api_failure_draft_from_platform_image_audit(audit)
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.with_user_id(settings.external_api_audit_user_id.clone())
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.with_profile_id(settings.external_api_audit_profile_id.clone())
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.with_request_id(settings.external_api_audit_request_id.clone());
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||
record_external_api_failure(state, draft).await;
|
||
}
|
||
|
||
pub(crate) fn build_openai_image_failure_audit_draft(
|
||
error: &PlatformImageError,
|
||
) -> Option<ExternalApiFailureDraft> {
|
||
error
|
||
.audit()
|
||
.map(build_external_api_failure_draft_from_platform_image_audit)
|
||
}
|
||
|
||
pub(crate) fn map_platform_image_error(error: PlatformImageError) -> AppError {
|
||
let error = error.into_final_error();
|
||
let status = match error.status_hint() {
|
||
PlatformImageStatusHint::BadRequest => StatusCode::BAD_REQUEST,
|
||
PlatformImageStatusHint::ServiceUnavailable => StatusCode::SERVICE_UNAVAILABLE,
|
||
PlatformImageStatusHint::BadGateway => StatusCode::BAD_GATEWAY,
|
||
PlatformImageStatusHint::GatewayTimeout => StatusCode::GATEWAY_TIMEOUT,
|
||
};
|
||
|
||
let mut details = json!({
|
||
"provider": error.provider(),
|
||
"message": error.message(),
|
||
});
|
||
|
||
match &error {
|
||
PlatformImageError::InvalidConfig { .. } | PlatformImageError::InvalidRequest { .. } => {}
|
||
PlatformImageError::Request {
|
||
endpoint,
|
||
timeout,
|
||
connect,
|
||
request,
|
||
body,
|
||
status_code,
|
||
source,
|
||
..
|
||
} => {
|
||
details["endpoint"] = json!(endpoint);
|
||
details["timeout"] = json!(timeout);
|
||
details["connect"] = json!(connect);
|
||
details["request"] = json!(request);
|
||
details["body"] = json!(body);
|
||
details["status"] = json!(status_code);
|
||
details["source"] = json!(source);
|
||
}
|
||
PlatformImageError::Upstream {
|
||
upstream_status,
|
||
raw_excerpt,
|
||
..
|
||
} => {
|
||
details["upstreamStatus"] = json!(upstream_status);
|
||
details["rawExcerpt"] = json!(raw_excerpt);
|
||
}
|
||
PlatformImageError::ResponseParse { raw_excerpt, .. } => {
|
||
details["rawExcerpt"] = json!(raw_excerpt);
|
||
}
|
||
PlatformImageError::MissingImage { .. } => {}
|
||
PlatformImageError::FallbackFailed { .. } => {
|
||
unreachable!("fallback wrapper should be removed before HTTP error mapping")
|
||
}
|
||
}
|
||
|
||
if let Some(audit) = error.audit() {
|
||
details["endpoint"] = json!(audit.endpoint);
|
||
details["failureStage"] = json!(audit.failure_stage);
|
||
details["statusClass"] = json!(audit.status_class);
|
||
details["retryable"] = json!(audit.retryable);
|
||
details["timeout"] = json!(audit.timeout);
|
||
details["latencyMs"] = json!(audit.latency_ms);
|
||
details["promptChars"] = json!(audit.prompt_chars);
|
||
details["referenceImageCount"] = json!(audit.reference_image_count);
|
||
details["imageModel"] = json!(audit.image_model);
|
||
details["rawExcerpt"] = json!(audit.raw_excerpt);
|
||
details["errorSource"] = json!(audit.error_source);
|
||
}
|
||
|
||
AppError::from_status(status).with_details(details)
|
||
}
|
||
|
||
#[cfg(test)]
|
||
fn vector_engine_images_generation_url_for_test(settings: &OpenAiImageSettings) -> String {
|
||
vector_engine_images_generation_url(&settings.provider_settings())
|
||
}
|
||
|
||
#[cfg(test)]
|
||
fn vector_engine_images_edit_url_for_test(settings: &OpenAiImageSettings) -> String {
|
||
vector_engine_images_edit_url(&settings.provider_settings())
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod tests {
|
||
use super::*;
|
||
use base64::{Engine as _, engine::general_purpose::STANDARD as BASE64_STANDARD};
|
||
|
||
#[test]
|
||
fn external_api_audit_context_forwards_external_call_deadline() {
|
||
let deadline = Instant::now() + std::time::Duration::from_secs(30);
|
||
let request_context = RequestContext::new(
|
||
"request-1".to_string(),
|
||
"external-generation-worker editor_image_edit".to_string(),
|
||
std::time::Duration::ZERO,
|
||
false,
|
||
)
|
||
.with_external_call_deadline(deadline);
|
||
let settings = OpenAiImageSettings {
|
||
base_url: "https://vector.example".to_string(),
|
||
api_key: "test-key".to_string(),
|
||
request_timeout_ms: 1_000_000,
|
||
request_deadline: None,
|
||
external_api_audit_state: None,
|
||
external_api_audit_user_id: None,
|
||
external_api_audit_profile_id: None,
|
||
external_api_audit_request_id: None,
|
||
}
|
||
.with_external_api_audit_context(&request_context, None, None);
|
||
|
||
assert_eq!(settings.request_deadline, Some(deadline));
|
||
assert_eq!(
|
||
settings.provider_settings().request_deadline,
|
||
Some(deadline)
|
||
);
|
||
}
|
||
|
||
#[test]
|
||
fn gpt_image_2_generation_request_uses_create_model_without_reference_images() {
|
||
let body = build_openai_image_request_body(
|
||
"雾海神殿",
|
||
Some("文字，水印"),
|
||
"16:9",
|
||
2,
|
||
&["data:image/png;base64,abcd".to_string()],
|
||
);
|
||
|
||
assert_eq!(body["model"], GPT_IMAGE_2_MODEL);
|
||
assert_eq!(body["size"], "1536x1024");
|
||
assert_eq!(body["n"], 2);
|
||
assert!(body.get("official_fallback").is_none());
|
||
assert!(body.get("image").is_none());
|
||
assert!(body["prompt"].as_str().unwrap_or_default().contains("避免"));
|
||
}
|
||
|
||
#[test]
|
||
fn vector_engine_generation_url_normalizes_base_url() {
|
||
let root_settings = OpenAiImageSettings {
|
||
base_url: "https://vector.example".to_string(),
|
||
api_key: "test-key".to_string(),
|
||
request_timeout_ms: 1_000_000,
|
||
request_deadline: None,
|
||
external_api_audit_state: None,
|
||
external_api_audit_user_id: None,
|
||
external_api_audit_profile_id: None,
|
||
external_api_audit_request_id: None,
|
||
};
|
||
let v1_settings = OpenAiImageSettings {
|
||
base_url: "https://vector.example/v1".to_string(),
|
||
api_key: "test-key".to_string(),
|
||
request_timeout_ms: 1_000_000,
|
||
request_deadline: None,
|
||
external_api_audit_state: None,
|
||
external_api_audit_user_id: None,
|
||
external_api_audit_profile_id: None,
|
||
external_api_audit_request_id: None,
|
||
};
|
||
|
||
assert_eq!(
|
||
vector_engine_images_generation_url_for_test(&root_settings),
|
||
"https://vector.example/v1/images/generations"
|
||
);
|
||
assert_eq!(
|
||
vector_engine_images_generation_url_for_test(&v1_settings),
|
||
"https://vector.example/v1/images/generations"
|
||
);
|
||
}
|
||
|
||
#[test]
|
||
fn vector_engine_edit_url_normalizes_base_url() {
|
||
let root_settings = OpenAiImageSettings {
|
||
base_url: "https://vector.example".to_string(),
|
||
api_key: "test-key".to_string(),
|
||
request_timeout_ms: 1_000_000,
|
||
request_deadline: None,
|
||
external_api_audit_state: None,
|
||
external_api_audit_user_id: None,
|
||
external_api_audit_profile_id: None,
|
||
external_api_audit_request_id: None,
|
||
};
|
||
let v1_settings = OpenAiImageSettings {
|
||
base_url: "https://vector.example/v1".to_string(),
|
||
api_key: "test-key".to_string(),
|
||
request_timeout_ms: 1_000_000,
|
||
request_deadline: None,
|
||
external_api_audit_state: None,
|
||
external_api_audit_user_id: None,
|
||
external_api_audit_profile_id: None,
|
||
external_api_audit_request_id: None,
|
||
};
|
||
|
||
assert_eq!(
|
||
vector_engine_images_edit_url_for_test(&root_settings),
|
||
"https://vector.example/v1/images/edits"
|
||
);
|
||
assert_eq!(
|
||
vector_engine_images_edit_url_for_test(&v1_settings),
|
||
"https://vector.example/v1/images/edits"
|
||
);
|
||
}
|
||
|
||
#[tokio::test]
|
||
async fn vector_engine_multi_reference_edit_rejects_empty_references() {
|
||
let settings = OpenAiImageSettings {
|
||
base_url: "https://vector.example".to_string(),
|
||
api_key: "test-key".to_string(),
|
||
request_timeout_ms: 1_000_000,
|
||
request_deadline: None,
|
||
external_api_audit_state: None,
|
||
external_api_audit_user_id: None,
|
||
external_api_audit_profile_id: None,
|
||
external_api_audit_request_id: None,
|
||
};
|
||
let http_client = reqwest::Client::new();
|
||
|
||
let result = create_openai_image_edit_with_references(
|
||
&http_client,
|
||
&settings,
|
||
"提示词",
|
||
None,
|
||
"1:1",
|
||
1,
|
||
&[],
|
||
"测试图片编辑失败",
|
||
)
|
||
.await;
|
||
|
||
let error = result.expect_err("empty references should be rejected locally");
|
||
assert_eq!(error.status_code(), StatusCode::BAD_REQUEST);
|
||
assert!(error.body_text().contains("缺少参考图"));
|
||
}
|
||
|
||
#[test]
|
||
fn reference_data_url_stays_provider_owned() {
|
||
let source = format!(
|
||
"data:image/png;base64,{}",
|
||
BASE64_STANDARD.encode(b"pngbytes")
|
||
);
|
||
let body = build_openai_image_request_body("提示词", None, "1:1", 1, &[source]);
|
||
|
||
assert!(body.get("image").is_none());
|
||
}
|
||
|
||
#[test]
|
||
fn vector_engine_upstream_failure_builds_tracking_ready_audit_event() {
|
||
let audit = platform_image::PlatformImageFailureAudit {
|
||
provider: VECTOR_ENGINE_PROVIDER,
|
||
endpoint: "https://vector.example/v1/images/generations".to_string(),
|
||
operation: "拼图 UI 背景图生成失败".to_string(),
|
||
failure_stage: "upstream_status",
|
||
status_code: Some(429),
|
||
status_class: None,
|
||
timeout: false,
|
||
retryable: true,
|
||
error_message: "上游限流".to_string(),
|
||
error_source: None,
|
||
raw_excerpt: Some("{\"error\":\"rate limited\"}".to_string()),
|
||
latency_ms: Some(321),
|
||
prompt_chars: Some(42),
|
||
reference_image_count: Some(1),
|
||
image_model: Some(VECTOR_ENGINE_GPT_IMAGE_2_MODEL),
|
||
};
|
||
let tracking = crate::external_api_audit::build_external_api_failure_tracking_draft(
|
||
&build_external_api_failure_draft_from_platform_image_audit(&audit),
|
||
);
|
||
|
||
assert_eq!(
|
||
tracking.event_key,
|
||
crate::external_api_audit::EXTERNAL_API_FAILURE_EVENT_KEY
|
||
);
|
||
assert_eq!(tracking.scope_id, VECTOR_ENGINE_PROVIDER);
|
||
assert_eq!(tracking.metadata["provider"], VECTOR_ENGINE_PROVIDER);
|
||
assert_eq!(tracking.metadata["statusCode"], 429);
|
||
assert_eq!(tracking.metadata["statusClass"], "4xx");
|
||
assert_eq!(tracking.metadata["failureStage"], "upstream_status");
|
||
assert_eq!(tracking.metadata["retryable"], true);
|
||
assert_eq!(tracking.metadata["promptChars"], 42);
|
||
assert_eq!(tracking.metadata["referenceImageCount"], 1);
|
||
assert_eq!(
|
||
tracking.metadata["imageModel"],
|
||
VECTOR_ENGINE_GPT_IMAGE_2_MODEL
|
||
);
|
||
}
|
||
}
|
||
|
||
fn current_utc_micros() -> i64 {
|
||
(OffsetDateTime::now_utc().unix_timestamp_nanos() / 1_000) as i64
|
||
}
|