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Add test

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Louis Dureuil 2024-11-06 09:25:41 +01:00
parent 6570da3bcb
commit a05e448cf8
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1 changed files with 130 additions and 11 deletions
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141
crates/meilisearch/tests/vector/openai.rs
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@ -137,13 +137,14 @@ fn long_text() -> &'static str {
}
async fn create_mock_tokenized() -> (MockServer, Value) {
create_mock_with_template("{{doc.text}}", ModelDimensions::Large, false).await
create_mock_with_template("{{doc.text}}", ModelDimensions::Large, false, false).await
}
async fn create_mock_with_template(
document_template: &str,
model_dimensions: ModelDimensions,
fallible: bool,
slow: bool,
) -> (MockServer, Value) {
let mock_server = MockServer::start().await;
const API_KEY: &str = "my-api-key";
@ -154,7 +155,11 @@ async fn create_mock_with_template(
Mock::given(method("POST"))
.and(path("/"))
.respond_with(move |req: &Request| {
// 0. maybe return 500
// 0. wait for a long time
if slow {
std::thread::sleep(std::time::Duration::from_secs(1));
}
// 1. maybe return 500
if fallible {
let attempt = attempt.fetch_add(1, Ordering::Relaxed);
let failed = matches!(attempt % 4, 0 | 1 | 3);
@ -167,7 +172,7 @@ async fn create_mock_with_template(
}))
}
}
// 1. check API key
// 3. check API key
match req.headers.get("Authorization") {
Some(api_key) if api_key == API_KEY_BEARER => {
{}
@ -202,7 +207,7 @@ async fn create_mock_with_template(
)
}
}
// 2. parse text inputs
// 3. parse text inputs
let query: serde_json::Value = match req.body_json() {
Ok(query) => query,
Err(_error) => return ResponseTemplate::new(400).set_body_json(
@ -223,7 +228,7 @@ async fn create_mock_with_template(
panic!("Expected {model_dimensions:?}, got {query_model_dimensions:?}")
}
// 3. for each text, find embedding in responses
// 4. for each text, find embedding in responses
let serde_json::Value::Array(inputs) = &query["input"] else {
panic!("Unexpected `input` value")
};
@ -283,7 +288,7 @@ async fn create_mock_with_template(
"embedding": embedding,
})).collect();
// 4. produce output from embeddings
// 5. produce output from embeddings
ResponseTemplate::new(200).set_body_json(json!({
"object": "list",
"data": data,
@ -317,23 +322,27 @@ const DOGGO_TEMPLATE: &str = r#"{%- if doc.gender == "F" -%}Une chienne nommée
{%- endif %}, de race {{doc.breed}}."#;
async fn create_mock() -> (MockServer, Value) {
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, false).await
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, false, false).await
}
async fn create_mock_dimensions() -> (MockServer, Value) {
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large512, false).await
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large512, false, false).await
}
async fn create_mock_small_embedding_model() -> (MockServer, Value) {
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Small, false).await
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Small, false, false).await
}
async fn create_mock_legacy_embedding_model() -> (MockServer, Value) {
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Ada, false).await
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Ada, false, false).await
}
async fn create_fallible_mock() -> (MockServer, Value) {
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true).await
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true, false).await
}
async fn create_slow_mock() -> (MockServer, Value) {
create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true, true).await
}
// basic test "it works"
@ -1873,4 +1882,114 @@ async fn it_still_works() {
]
"###);
}
// test with a server that responds 500 on 3 out of 4 calls
#[actix_rt::test]
async fn timeout() {
let (_mock, setting) = create_slow_mock().await;
let server = get_server_vector().await;
let index = server.index("doggo");
let (response, code) = index
.update_settings(json!({
"embedders": {
"default": setting,
},
}))
.await;
snapshot!(code, @"202 Accepted");
let task = server.wait_task(response.uid()).await;
snapshot!(task["status"], @r###""succeeded""###);
let documents = json!([
{"id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou"},
]);
let (value, code) = index.add_documents(documents, None).await;
snapshot!(code, @"202 Accepted");
let task = index.wait_task(value.uid()).await;
snapshot!(task, @r###"
{
"uid": "[uid]",
"indexUid": "doggo",
"status": "succeeded",
"type": "documentAdditionOrUpdate",
"canceledBy": null,
"details": {
"receivedDocuments": 1,
"indexedDocuments": 1
},
"error": null,
"duration": "[duration]",
"enqueuedAt": "[date]",
"startedAt": "[date]",
"finishedAt": "[date]"
}
"###);
let (documents, _code) = index
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
.await;
snapshot!(json_string!(documents, {".results.*._vectors.default.embeddings" => "[vector]"}), @r###"
{
"results": [
{
"id": 0,
"name": "kefir",
"gender": "M",
"birthyear": 2023,
"breed": "Patou",
"_vectors": {
"default": {
"embeddings": "[vector]",
"regenerate": true
}
}
}
],
"offset": 0,
"limit": 20,
"total": 1
}
"###);
let (response, code) = index
.search_post(json!({
"q": "grand chien de berger des montagnes",
"hybrid": {"semanticRatio": 0.99, "embedder": "default"}
}))
.await;
snapshot!(code, @"200 OK");
snapshot!(json_string!(response["semanticHitCount"]), @"0");
snapshot!(json_string!(response["hits"]), @"[]");
let (response, code) = index
.search_post(json!({
"q": "grand chien de berger des montagnes",
"hybrid": {"semanticRatio": 0.99, "embedder": "default"}
}))
.await;
snapshot!(code, @"200 OK");
snapshot!(json_string!(response["semanticHitCount"]), @"1");
snapshot!(json_string!(response["hits"]), @r###"
[
{
"id": 0,
"name": "kefir",
"gender": "M",
"birthyear": 2023,
"breed": "Patou"
}
]
"###);
let (response, code) = index
.search_post(json!({
"q": "grand chien de berger des montagnes",
"hybrid": {"semanticRatio": 0.99, "embedder": "default"}
}))
.await;
snapshot!(code, @"200 OK");
snapshot!(json_string!(response["semanticHitCount"]), @"0");
snapshot!(json_string!(response["hits"]), @"[]");
}
// test with a server that wrongly responds 400