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fix(gateway): normalize Gemini Vertex embedding transport

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This commit is contained in:
MMEXA
2026-05-18 15:39:29 +00:00
parent 66f154a251
commit b480f3aaff
24 changed files with 1046 additions and 107 deletions
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apps/aether-gateway/src/tests/control/admin/provider_query.rs
+140
View File
@@ -1256,6 +1256,146 @@ async fn gateway_handles_admin_provider_query_gemini_embedding_model_test() {
execution_runtime_handle.abort();
}
#[tokio::test]
async fn gateway_handles_admin_provider_query_vertex_gemini_embedding_model_test() {
let execution_runtime = Router::new().route(
"/v1/execute/sync",
any(move |Json(plan): Json<ExecutionPlan>| async move {
assert_eq!(plan.provider_id, "provider-vertex-ai");
assert_eq!(plan.endpoint_id, "endpoint-vertex-gemini-embedding");
assert_eq!(plan.key_id, "key-vertex-gemini-embedding");
assert_eq!(plan.client_api_format, "openai:embedding");
assert_eq!(plan.provider_api_format, "gemini:embedding");
assert_eq!(
plan.url,
"https://aiplatform.googleapis.com/v1/publishers/google/models/gemini-embedding-2:predict?key=sk-vertex-gemini-embedding"
);
assert_eq!(plan.model_name.as_deref(), Some("gemini-embedding-2"));
assert!(!plan.stream);
let body = plan.body.json_body.as_ref().expect("json body");
assert!(
body.get("model").is_none(),
"Vertex predict carries the model in the URL path; the test body must not repeat it"
);
assert_eq!(
body["instances"][0]["content"],
json!("This is a test embedding input.")
);
assert!(body.get("content").is_none());
assert!(body.get("requests").is_none());
assert!(
body.get("stream").is_none(),
"gemini embedding provider body must not carry stream"
);
Json(json!({
"request_id": plan.request_id,
"candidate_id": plan.candidate_id,
"status_code": 200,
"headers": {
"content-type": "application/json"
},
"body": {
"json_body": {
"predictions": [
{
"embeddings": {
"values": [0.1, 0.2, 0.3]
}
}
],
"deployedModelId": "gemini-embedding-2"
}
},
"telemetry": {
"elapsed_ms": 27
}
}))
}),
);
let (execution_runtime_url, execution_runtime_handle) = start_server(execution_runtime).await;
let mut provider = sample_provider("provider-vertex-ai", "Vertex AI", 10);
provider.provider_type = "vertex_ai".to_string();
let mut key = sample_key(
"key-vertex-gemini-embedding",
"provider-vertex-ai",
"gemini:embedding",
"sk-vertex-gemini-embedding",
);
key.allowed_models = Some(json!(["gemini-embedding-2"]));
let provider_catalog_repository = Arc::new(InMemoryProviderCatalogReadRepository::seed(
vec![provider],
vec![sample_endpoint(
"endpoint-vertex-gemini-embedding",
"provider-vertex-ai",
"gemini:embedding",
"https://aiplatform.googleapis.com",
)],
vec![key],
));
let gateway = build_router_with_state(
build_state_with_execution_runtime_override(execution_runtime_url)
.with_data_state_for_tests(GatewayDataState::with_provider_transport_reader_for_tests(
provider_catalog_repository,
DEVELOPMENT_ENCRYPTION_KEY.to_string(),
)),
);
let (gateway_url, gateway_handle) = start_server(gateway).await;
let response = reqwest::Client::new()
.post(format!("{gateway_url}/api/admin/provider-query/test-model"))
.header(GATEWAY_HEADER, "rust-phase3b")
.header(TRUSTED_ADMIN_USER_ID_HEADER, "admin-user-123")
.header(TRUSTED_ADMIN_USER_ROLE_HEADER, "admin")
.header(TRUSTED_ADMIN_SESSION_ID_HEADER, "session-123")
.json(&json!({
"provider_id": "provider-vertex-ai",
"model": "gemini-embedding-2",
"api_format": "gemini:embedding",
"endpoint_id": "endpoint-vertex-gemini-embedding",
"request_body": {
"model": "gemini-embedding-2",
"input": "This is a test embedding input."
}
}))
.send()
.await
.expect("request should succeed");
assert_eq!(response.status(), StatusCode::OK);
let payload: serde_json::Value = response.json().await.expect("json body should parse");
assert_eq!(payload["success"], json!(true));
assert_eq!(payload["error"], serde_json::Value::Null);
assert_eq!(payload["attempts"][0]["status"], json!("success"));
assert_eq!(
payload["attempts"][0]["request_body"]["instances"][0]["content"],
json!("This is a test embedding input.")
);
assert_eq!(
payload["attempts"][0]["endpoint_product"],
json!("Vertex AI")
);
assert_eq!(
payload["attempts"][0]["endpoint_variant"],
json!("vertex_native")
);
assert_eq!(payload["attempts"][0]["endpoint_action"], json!("predict"));
assert_eq!(
payload["attempts"][0]["endpoint_batch_strategy"],
json!("single_instance")
);
assert!(
payload["attempts"][0]["request_body"]
.get("model")
.is_none(),
"attempt debug payload must expose the exact Vertex body without a duplicate model"
);
gateway_handle.abort();
execution_runtime_handle.abort();
}
#[tokio::test]
async fn gateway_handles_admin_provider_query_jina_embedding_model_test() {
let execution_runtime = Router::new().route(
apps/aether-gateway/src/tests/control/admin/system.rs
+8
View File
@@ -1152,6 +1152,14 @@ async fn gateway_handles_admin_system_api_formats_locally_with_trusted_admin_pri
.expect("formats should be an array");
assert_eq!(formats[0]["value"], "openai:chat");
assert_eq!(formats[0]["default_path"], "/v1/chat/completions");
let gemini_embedding = formats
.iter()
.find(|item| item["value"] == "gemini:embedding")
.expect("gemini embedding format should exist");
assert_eq!(
gemini_embedding["default_path"],
"/v1beta/models/{model}:{action}"
);
assert!(formats
.iter()
.any(|item| item["value"] == "openai:embedding"));
apps/aether-gateway/src/tests/control/proxy/embeddings.rs
+30 -5
View File
@@ -194,7 +194,7 @@ fn vertex_gemini_embedding_conversion_execution_runtime() -> Router {
"/v1/execute/sync",
any(|Json(plan): Json<ExecutionPlan>| async move {
assert_openai_to_vertex_gemini_embedding_execution_plan(&plan);
Json(gemini_embedding_execution_result(&plan))
Json(vertex_gemini_embedding_execution_result(&plan))
}),
)
}
@@ -342,7 +342,7 @@ fn assert_openai_to_vertex_gemini_embedding_execution_plan(plan: &ExecutionPlan)
assert_eq!(plan.method, "POST");
assert_eq!(
plan.url,
"https://aiplatform.googleapis.com/v1/publishers/google/models/gemini-embedding-2:embedContent?key=sk-upstream-vertex-gemini-embedding"
"https://aiplatform.googleapis.com/v1/publishers/google/models/gemini-embedding-2:predict?key=sk-upstream-vertex-gemini-embedding"
);
assert_eq!(
plan.model_name.as_deref(),
@@ -352,9 +352,10 @@ fn assert_openai_to_vertex_gemini_embedding_execution_plan(plan: &ExecutionPlan)
let body = plan.body.json_body.as_ref().expect("json request body");
assert!(
body.get("model").is_none(),
"Vertex embedContent carries the model in the path; the body must not repeat it"
"Vertex predict carries the model in the path; the body must not repeat it"
);
assert_eq!(body["content"]["parts"][0]["text"], "hello");
assert_eq!(body["instances"][0]["content"], "hello");
assert!(body.get("content").is_none());
assert!(body.get("input").is_none());
assert!(body.get("messages").is_none());
}
@@ -453,6 +454,30 @@ fn gemini_embedding_execution_result(plan: &ExecutionPlan) -> ExecutionResult {
}
}
fn vertex_gemini_embedding_execution_result(plan: &ExecutionPlan) -> ExecutionResult {
ExecutionResult {
request_id: plan.request_id.clone(),
candidate_id: plan.candidate_id.clone(),
status_code: 200,
headers: BTreeMap::from([("content-type".to_string(), "application/json".to_string())]),
body: Some(ResponseBody {
json_body: Some(json!({
"predictions": [
{
"embeddings": {
"values": [0.1, 0.2, 0.3]
}
}
],
"deployedModelId": "gemini-embedding-2"
})),
body_bytes_b64: None,
}),
telemetry: None,
error: None,
}
}
fn gemini_batch_embedding_execution_result(plan: &ExecutionPlan) -> ExecutionResult {
ExecutionResult {
request_id: plan.request_id.clone(),
@@ -636,7 +661,7 @@ async fn embeddings_route_converts_openai_payload_to_vertex_gemini_embedding_pro
assert_eq!(endpoint_signature.as_deref(), Some("openai:embedding"));
let payload: serde_json::Value = serde_json::from_str(&body_text).expect("body should parse");
assert_eq!(payload["object"], "list");
assert_eq!(payload["model"], "gemini-embedding-2-preview");
assert_eq!(payload["model"], "gemini-embedding-2");
assert_eq!(payload["data"][0]["embedding"], json!([0.1, 0.2, 0.3]));
gateway_handle.abort();