Files
cursor-byok/server_backup/tests/model_configuration.rs
T
2026-08-29 22:05:04 +08:00

199 lines
6.9 KiB
Rust

use cursor_server::{
model::{
ConversationId, ModelConfigInput, ModelSpec, ModelType, NewLlmCall, PreparedRun,
PromptSpec, ProviderType, RunAction, RunId, RunKind, Usage, OPENAI_CHAT_ENDPOINT,
},
store::{RunStatus, Store},
};
async fn store() -> (tempfile::TempDir, Store) {
let directory = tempfile::tempdir().unwrap();
let url = format!("sqlite://{}", directory.path().join("test.db").display());
let store = Store::connect(&url).await.unwrap();
(directory, store)
}
fn model_input() -> ModelConfigInput {
ModelConfigInput {
sort_order: 0,
display_name: "Model A".into(),
model_type: ModelType::OpenAi,
base_url: "https://example.com/v1/chat/completions".into(),
use_full_url: true,
api_key: "secret".into(),
tooltip_data: "Model A".into(),
model_id: "model-a".into(),
reasoning_effort: None,
openai_endpoint: OPENAI_CHAT_ENDPOINT.into(),
openai_extra_params_enabled: true,
openai_extra_params: serde_json::json!({"temperature":0}),
custom_headers_enabled: true,
custom_headers: serde_json::json!({"x-route":"one"}),
anthropic_extra_params_enabled: false,
anthropic_extra_params: serde_json::json!({}),
context_window_tokens: None,
max_completion_tokens: None,
anthropic_max_tokens: None,
anthropic_thinking_effort: None,
thinking_budget_tokens: None,
}
}
#[tokio::test]
async fn model_configuration_round_trips_and_hash_uses_v0049_identity() {
let (_directory, store) = store().await;
let model = store.create_model(&model_input()).await.unwrap();
assert_eq!(model.model_hash.len(), 16);
assert_eq!(model.api_key, "secret");
assert_eq!(model.custom_headers["x-route"], "one");
let original_hash = model.model_hash.clone();
let mut input = model_input();
input.base_url = "https://example.com/v1/chat/completions".into();
input.sort_order = 3;
input.tooltip_data = "Updated tooltip".into();
let updated = store.update_model(&original_hash, &input).await.unwrap();
assert_eq!(updated.model_hash, original_hash);
assert_eq!(updated.sort_order, 3);
assert_eq!(updated.tooltip_data, "Updated tooltip");
}
#[tokio::test]
async fn arbitrary_request_url_is_independent_from_openai_protocol() {
let (_directory, store) = store().await;
let mut input = model_input();
input.base_url = "https://proxy.example.com/arbitrary/generate?api-version=2026-01-01".into();
let chat = store.create_model(&input).await.unwrap();
assert_eq!(chat.provider_type(), ProviderType::OpenAiChat);
assert_eq!(chat.request_url().unwrap(), input.base_url);
input.openai_endpoint = "/v1/responses".into();
let responses = store.update_model(&chat.model_hash, &input).await.unwrap();
assert_eq!(responses.provider_type(), ProviderType::OpenAiResponses);
assert_eq!(responses.request_url().unwrap(), input.base_url);
}
#[tokio::test]
async fn standard_server_address_resolves_to_the_same_model_identity_as_a_complete_url() {
let (_directory, store) = store().await;
let complete = model_input();
let mut standard = complete.clone();
standard.base_url = "https://example.com/v1".into();
standard.use_full_url = false;
let model = store.create_model(&standard).await.unwrap();
assert!(!model.use_full_url);
assert_eq!(
model.request_url().unwrap(),
"https://example.com/v1/chat/completions"
);
assert_eq!(
model.model_hash,
cursor_server::model::model_hash(&complete).unwrap()
);
}
#[tokio::test]
async fn call_summary_is_always_stored_and_payloads_follow_detailed_setting() {
let (_directory, store) = store().await;
let model = store.create_model(&model_input()).await.unwrap();
let call = NewLlmCall {
call_id: "call-1".into(),
run_id: "run-1".into(),
conversation_id: "conversation-1".into(),
provider_call_index: 0,
model_hash: model.model_hash,
provider_type: ProviderType::OpenAiChat,
provider_url: model.base_url.clone(),
request_type: ProviderType::OpenAiChat,
request_url: "https://example.com/v1/chat/completions".into(),
model_id: model.model_id,
display_name: model.display_name,
reasoning_effort: Some("high".into()),
fast: true,
message_count: 2,
tool_count: 3,
detailed: false,
};
let conversation_id = ConversationId::new("conversation-1");
let base_revision_id = store.ensure_conversation(&conversation_id).await.unwrap();
store
.claim_run(&PreparedRun {
run_id: RunId::new("run-1"),
cursor_request_id: None,
conversation_id,
kind: RunKind::Root,
model: ModelSpec::new(call.model_hash.clone()),
prompt: PromptSpec {
instructions: String::new(),
tools: Vec::new(),
},
initial_messages: Vec::new(),
action: RunAction::Resume {
pending_tool_round: None,
},
base_revision_id,
})
.await
.unwrap();
store.start_llm_call(&call).await.unwrap();
store
.record_llm_request(
"call-1",
&serde_json::json!({}),
&serde_json::json!({"model":"model-a"}),
false,
)
.await
.unwrap();
store
.record_llm_chunk("call-1", 0, 4, b"data", false)
.await
.unwrap();
store
.record_llm_usage(
"call-1",
Usage {
input_tokens: Some(10),
output_tokens: Some(5),
total_tokens: Some(15),
..Default::default()
},
)
.await
.unwrap();
store
.finish_llm_call("call-1", "completed", Some("stop"), 9, None, None)
.await
.unwrap();
let summary = store.llm_call("call-1").await.unwrap().unwrap();
assert_eq!(summary.total_tokens, Some(15));
assert_eq!(summary.reasoning_effort.as_deref(), Some("high"));
assert_eq!(summary.fast, Some(true));
assert_eq!(summary.request_bytes, Some(19));
assert_eq!(summary.response_bytes, 4);
assert!(store.llm_call_request("call-1").await.unwrap().is_none());
assert!(store.llm_call_chunks("call-1").await.unwrap().is_empty());
let abandoned = NewLlmCall {
call_id: "call-2".into(),
provider_call_index: 1,
..call
};
store.start_llm_call(&abandoned).await.unwrap();
store
.finish_run(&RunId::new("run-1"), RunStatus::Cancelled, None, None)
.await
.unwrap();
let abandoned = store.llm_call("call-2").await.unwrap().unwrap();
assert_eq!(abandoned.status, "cancelled");
assert!(abandoned.finished_at_ms.is_some());
assert!(abandoned.duration_ms.is_some());
assert_eq!(
store.llm_call("call-1").await.unwrap().unwrap().status,
"completed"
);
}