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https://github.com/meilisearch/meilisearch.git
synced 2024-11-25 19:45:05 +08:00
Add test
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parent
37a4fd7f99
commit
1d574bd443
@ -137,13 +137,14 @@ fn long_text() -> &'static str {
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}
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async fn create_mock_tokenized() -> (MockServer, Value) {
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create_mock_with_template("{{doc.text}}", ModelDimensions::Large, false).await
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create_mock_with_template("{{doc.text}}", ModelDimensions::Large, false, false).await
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}
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async fn create_mock_with_template(
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document_template: &str,
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model_dimensions: ModelDimensions,
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fallible: bool,
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slow: bool,
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) -> (MockServer, Value) {
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let mock_server = MockServer::start().await;
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const API_KEY: &str = "my-api-key";
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@ -154,7 +155,11 @@ async fn create_mock_with_template(
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Mock::given(method("POST"))
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.and(path("/"))
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.respond_with(move |req: &Request| {
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// 0. maybe return 500
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// 0. wait for a long time
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if slow {
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std::thread::sleep(std::time::Duration::from_secs(1));
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}
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// 1. maybe return 500
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if fallible {
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let attempt = attempt.fetch_add(1, Ordering::Relaxed);
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let failed = matches!(attempt % 4, 0 | 1 | 3);
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@ -167,7 +172,7 @@ async fn create_mock_with_template(
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}))
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}
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}
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// 1. check API key
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// 3. check API key
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match req.headers.get("Authorization") {
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Some(api_key) if api_key == API_KEY_BEARER => {
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{}
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@ -202,7 +207,7 @@ async fn create_mock_with_template(
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)
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}
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}
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// 2. parse text inputs
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// 3. parse text inputs
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let query: serde_json::Value = match req.body_json() {
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Ok(query) => query,
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Err(_error) => return ResponseTemplate::new(400).set_body_json(
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@ -223,7 +228,7 @@ async fn create_mock_with_template(
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panic!("Expected {model_dimensions:?}, got {query_model_dimensions:?}")
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}
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// 3. for each text, find embedding in responses
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// 4. for each text, find embedding in responses
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let serde_json::Value::Array(inputs) = &query["input"] else {
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panic!("Unexpected `input` value")
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};
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@ -283,7 +288,7 @@ async fn create_mock_with_template(
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"embedding": embedding,
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})).collect();
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// 4. produce output from embeddings
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// 5. produce output from embeddings
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ResponseTemplate::new(200).set_body_json(json!({
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"object": "list",
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"data": data,
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@ -317,23 +322,27 @@ const DOGGO_TEMPLATE: &str = r#"{%- if doc.gender == "F" -%}Une chienne nommée
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{%- endif %}, de race {{doc.breed}}."#;
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async fn create_mock() -> (MockServer, Value) {
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, false).await
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, false, false).await
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}
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async fn create_mock_dimensions() -> (MockServer, Value) {
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large512, false).await
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large512, false, false).await
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}
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async fn create_mock_small_embedding_model() -> (MockServer, Value) {
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Small, false).await
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Small, false, false).await
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}
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async fn create_mock_legacy_embedding_model() -> (MockServer, Value) {
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Ada, false).await
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Ada, false, false).await
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}
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async fn create_fallible_mock() -> (MockServer, Value) {
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true).await
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true, false).await
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}
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async fn create_slow_mock() -> (MockServer, Value) {
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create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true, true).await
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}
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// basic test "it works"
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@ -1873,4 +1882,114 @@ async fn it_still_works() {
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]
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"###);
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}
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// test with a server that responds 500 on 3 out of 4 calls
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#[actix_rt::test]
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async fn timeout() {
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let (_mock, setting) = create_slow_mock().await;
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let server = get_server_vector().await;
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let index = server.index("doggo");
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"default": setting,
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},
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}))
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.await;
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snapshot!(code, @"202 Accepted");
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let task = server.wait_task(response.uid()).await;
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snapshot!(task["status"], @r###""succeeded""###);
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let documents = json!([
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{"id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou"},
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]);
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let (value, code) = index.add_documents(documents, None).await;
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snapshot!(code, @"202 Accepted");
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let task = index.wait_task(value.uid()).await;
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snapshot!(task, @r###"
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{
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"uid": "[uid]",
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"indexUid": "doggo",
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"status": "succeeded",
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"type": "documentAdditionOrUpdate",
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"canceledBy": null,
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"details": {
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"receivedDocuments": 1,
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"indexedDocuments": 1
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},
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"error": null,
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"duration": "[duration]",
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"enqueuedAt": "[date]",
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"startedAt": "[date]",
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"finishedAt": "[date]"
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}
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"###);
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let (documents, _code) = index
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.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
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.await;
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snapshot!(json_string!(documents, {".results.*._vectors.default.embeddings" => "[vector]"}), @r###"
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{
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"results": [
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{
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"id": 0,
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"name": "kefir",
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"gender": "M",
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"birthyear": 2023,
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"breed": "Patou",
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"_vectors": {
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"default": {
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"embeddings": "[vector]",
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"regenerate": true
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}
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}
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}
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],
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"offset": 0,
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"limit": 20,
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"total": 1
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}
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"###);
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let (response, code) = index
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.search_post(json!({
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"q": "grand chien de berger des montagnes",
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"hybrid": {"semanticRatio": 0.99, "embedder": "default"}
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}))
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.await;
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snapshot!(code, @"200 OK");
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snapshot!(json_string!(response["semanticHitCount"]), @"0");
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snapshot!(json_string!(response["hits"]), @"[]");
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let (response, code) = index
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.search_post(json!({
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"q": "grand chien de berger des montagnes",
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"hybrid": {"semanticRatio": 0.99, "embedder": "default"}
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}))
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.await;
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snapshot!(code, @"200 OK");
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snapshot!(json_string!(response["semanticHitCount"]), @"1");
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snapshot!(json_string!(response["hits"]), @r###"
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[
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{
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"id": 0,
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"name": "kefir",
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"gender": "M",
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"birthyear": 2023,
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"breed": "Patou"
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}
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]
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"###);
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let (response, code) = index
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.search_post(json!({
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"q": "grand chien de berger des montagnes",
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"hybrid": {"semanticRatio": 0.99, "embedder": "default"}
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}))
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.await;
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snapshot!(code, @"200 OK");
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snapshot!(json_string!(response["semanticHitCount"]), @"0");
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snapshot!(json_string!(response["hits"]), @"[]");
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}
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// test with a server that wrongly responds 400
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