use std::collections::BTreeMap; use std::io::Write; use std::sync::atomic::{AtomicU32, Ordering}; use std::sync::OnceLock; use meili_snap::{json_string, snapshot}; use wiremock::matchers::{method, path}; use wiremock::{Mock, MockServer, Request, ResponseTemplate}; use crate::common::{GetAllDocumentsOptions, Value}; use crate::json; use crate::vector::get_server_vector; #[derive(serde::Deserialize)] struct OpenAiResponses(BTreeMap); #[derive(serde::Deserialize)] struct OpenAiResponse { large: Option>, small: Option>, ada: Option>, large_512: Option>, } #[derive(serde::Deserialize)] struct OpenAiTokenizedResponses { tokens: Vec, embedding: Vec, } impl OpenAiResponses { fn get(&self, text: &str, model_dimensions: ModelDimensions) -> Option<&[f32]> { let entry = self.0.get(text)?; match model_dimensions { ModelDimensions::Large => entry.large.as_deref(), ModelDimensions::Small => entry.small.as_deref(), ModelDimensions::Ada => entry.ada.as_deref(), ModelDimensions::Large512 => entry.large_512.as_deref(), } } } #[derive(Debug, Clone, Copy, PartialEq, Eq)] enum ModelDimensions { Large, Small, Ada, Large512, } impl ModelDimensions { fn add_to_settings(&self, settings: &mut Value) { settings["model"] = serde_json::json!(self.model()); if let ModelDimensions::Large512 = self { settings["dimensions"] = serde_json::json!(512); } } fn model(&self) -> &'static str { match self { ModelDimensions::Large | ModelDimensions::Large512 => "text-embedding-3-large", ModelDimensions::Small => "text-embedding-3-small", ModelDimensions::Ada => "text-embedding-ada-002", } } fn from_request(request: &serde_json::Value) -> Self { let has_dimensions_512 = if let Some(dimensions) = request.get("dimensions") { if dimensions != 512 { panic!("unsupported dimensions values") } true } else { false }; let serde_json::Value::String(model) = &request["model"] else { panic!("unsupported non string model") }; match (model.as_str(), has_dimensions_512) { ("text-embedding-3-large", true) => Self::Large512, (_, true) => panic!("unsupported dimensions with non-large model"), ("text-embedding-3-large", false) => Self::Large, ("text-embedding-3-small", false) => Self::Small, ("text-embedding-ada-002", false) => Self::Ada, (_, false) => panic!("unsupported model"), } } } fn openai_responses() -> &'static OpenAiResponses { static OPENAI_RESPONSES: OnceLock = OnceLock::new(); OPENAI_RESPONSES.get_or_init(|| { // json file that was compressed with gzip // decompress with `gzip --keep -d openai_responses.json.gz` // recompress with `gzip --keep -c openai_responses.json > openai_responses.json.gz` let compressed_responses = include_bytes!("openai_responses.json.gz"); let mut responses = Vec::new(); let mut decoder = flate2::write::GzDecoder::new(&mut responses); decoder.write_all(compressed_responses).unwrap(); drop(decoder); serde_json::from_slice(&responses).unwrap() }) } fn openai_tokenized_responses() -> &'static OpenAiTokenizedResponses { static OPENAI_TOKENIZED_RESPONSES: OnceLock = OnceLock::new(); OPENAI_TOKENIZED_RESPONSES.get_or_init(|| { // json file that was compressed with gzip // decompress with `gzip --keep -d openai_tokenized_responses.json.gz` // recompress with `gzip --keep -c openai_tokenized_responses.json > openai_tokenized_responses.json.gz` let compressed_responses = include_bytes!("openai_tokenized_responses.json.gz"); let mut responses = Vec::new(); let mut decoder = flate2::write::GzDecoder::new(&mut responses); decoder.write_all(compressed_responses).unwrap(); drop(decoder); serde_json::from_slice(&responses).unwrap() }) } fn long_text() -> &'static str { static LONG_TEXT: OnceLock = OnceLock::new(); LONG_TEXT.get_or_init(|| { // decompress with `gzip --keep -d intel_gen.txt.gz` // recompress with `gzip --keep -c intel_gen.txt > intel_gen.txt.gz` let compressed_long_text = include_bytes!("intel_gen.txt.gz"); let mut long_text = Vec::new(); let mut decoder = flate2::write::GzDecoder::new(&mut long_text); decoder.write_all(compressed_long_text).unwrap(); drop(decoder); let long_text = std::str::from_utf8(&long_text).unwrap(); long_text.repeat(3) }) } async fn create_mock_tokenized() -> (MockServer, Value) { create_mock_with_template("{{doc.text}}", ModelDimensions::Large, false).await } async fn create_mock_with_template( document_template: &str, model_dimensions: ModelDimensions, fallible: bool, ) -> (MockServer, Value) { let mock_server = MockServer::start().await; const API_KEY: &str = "my-api-key"; const API_KEY_BEARER: &str = "Bearer my-api-key"; let attempt = AtomicU32::new(0); Mock::given(method("POST")) .and(path("/")) .respond_with(move |req: &Request| { // 0. maybe return 500 if fallible { let attempt = attempt.fetch_add(1, Ordering::Relaxed); let failed = matches!(attempt % 4, 0 | 1 | 3); if failed { return ResponseTemplate::new(503).set_body_json(json!({ "error": { "message": "come back later", "type": "come_back_later" } })) } } // 1. check API key match req.headers.get("Authorization") { Some(api_key) if api_key == API_KEY_BEARER => { {} } Some(api_key) => { let api_key = api_key.to_str().unwrap(); return ResponseTemplate::new(401).set_body_json( json!( { "error": { "message": format!("Incorrect API key provided: {api_key}. You can find your API key at https://platform.openai.com/account/api-keys."), "type": "invalid_request_error", "param": serde_json::Value::Null, "code": "invalid_api_key" } } ), ) } None => { return ResponseTemplate::new(401).set_body_json( json!( { "error": { "message": "You didn't provide an API key. You need to provide your API key in an Authorization header using Bearer auth (i.e. Authorization: Bearer YOUR_KEY), or as the password field (with blank username) if you're accessing the API from your browser and are prompted for a username and password. You can obtain an API key from https://platform.openai.com/account/api-keys.", "type": "invalid_request_error", "param": serde_json::Value::Null, "code": serde_json::Value::Null } } ), ) } } // 2. parse text inputs let query: serde_json::Value = match req.body_json() { Ok(query) => query, Err(_error) => return ResponseTemplate::new(400).set_body_json( json!( { "error": { "message": "We could not parse the JSON body of your request. (HINT: This likely means you aren't using your HTTP library correctly. The OpenAI API expects a JSON payload, but what was sent was not valid JSON. If you have trouble figuring out how to fix this, please contact us through our help center at help.openai.com.)", "type": "invalid_request_error", "param": serde_json::Value::Null, "code": serde_json::Value::Null } } ) ) }; let query_model_dimensions = ModelDimensions::from_request(&query); if query_model_dimensions != model_dimensions { panic!("Expected {model_dimensions:?}, got {query_model_dimensions:?}") } // 3. for each text, find embedding in responses let serde_json::Value::Array(inputs) = &query["input"] else { panic!("Unexpected `input` value") }; let openai_tokenized_responses = openai_tokenized_responses(); let embeddings = if inputs == openai_tokenized_responses.tokens.as_slice() { vec![openai_tokenized_responses.embedding.clone()] } else { let mut embeddings = Vec::new(); for input in inputs { let serde_json::Value::String(input) = input else { return ResponseTemplate::new(400).set_body_json(json!({ "error": { "message": "Unexpected `input` value", "type": "test_response", "query": query } })) }; if input == long_text() { return ResponseTemplate::new(400).set_body_json(json!( { "error": { "message": "This model's maximum context length is 8192 tokens, however you requested 10554 tokens (10554 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.", "type": "invalid_request_error", "param": null, "code": null, } } )); } let Some(embedding) = openai_responses().get(input, model_dimensions) else { return ResponseTemplate::new(404).set_body_json(json!( { "error": { "message": "Could not find embedding for text", "text": input, "model_dimensions": format!("{model_dimensions:?}"), "type": "add_to_openai_responses_json_please", "query": query, } } )) }; embeddings.push(embedding.to_vec()); } embeddings }; let data : Vec<_> = embeddings.into_iter().enumerate().map(|(index, embedding)| json!({ "object": "embedding", "index": index, "embedding": embedding, })).collect(); // 4. produce output from embeddings ResponseTemplate::new(200).set_body_json(json!({ "object": "list", "data": data, "model": model_dimensions.model(), "usage": { "prompt_tokens": "[prompt_tokens]", "total_tokens": "[total_tokens]" } })) }) .mount(&mock_server) .await; let url = mock_server.uri(); let mut embedder_settings = json!({ "source": "openAi", "url": url, "apiKey": API_KEY, "documentTemplate": document_template }); model_dimensions.add_to_settings(&mut embedder_settings); (mock_server, embedder_settings) } const DOGGO_TEMPLATE: &str = r#"{%- if doc.gender == "F" -%}Une chienne nommée {{doc.name}}, née en {{doc.birthyear}} {%- else -%} Un chien nommé {{doc.name}}, né en {{doc.birthyear}} {%- endif %}, de race {{doc.breed}}."#; async fn create_mock() -> (MockServer, Value) { create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, false).await } async fn create_mock_dimensions() -> (MockServer, Value) { create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large512, false).await } async fn create_mock_small_embedding_model() -> (MockServer, Value) { create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Small, false).await } async fn create_mock_legacy_embedding_model() -> (MockServer, Value) { create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Ada, false).await } async fn create_fallible_mock() -> (MockServer, Value) { create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true).await } // basic test "it works" #[actix_rt::test] async fn it_works() { let (_mock, setting) = create_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"}, {"id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle"}, {"id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier"}, {"id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever"}, ]); 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": 4, "indexedDocuments": 4 }, "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 } } }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } } ], "offset": 0, "limit": 20, "total": 4 } "###); let (response, code) = index .search_post(json!({ "q": "chien de chasse", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" } ] "###); let (response, code) = index .search_post(json!({ "q": "petit chien", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" } ] "###); let (response, code) = index .search_post(json!({ "q": "grand chien de berger des montagnes", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" } ] "###); } // tokenize long text // basic test "it works" #[actix_rt::test] async fn tokenize_long_text() { let (_mock, setting) = create_mock_tokenized().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, "text": long_text()} ]); 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 (response, code) = index .search_post(json!({ "q": "grand chien de berger des montagnes", "showRankingScore": true, "attributesToRetrieve": ["id"], "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 0, "_rankingScore": 0.07944583892822266 } ] "###); } // "wrong parameters" #[actix_rt::test] async fn bad_api_key() { let (_mock, mut setting) = create_mock().await; let server = get_server_vector().await; let index = server.index("doggo"); let documents = json!([ {"id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou"}, {"id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle"}, {"id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier"}, {"id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever"}, ]); 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": 4, "indexedDocuments": 4 }, "error": null, "duration": "[duration]", "enqueuedAt": "[date]", "startedAt": "[date]", "finishedAt": "[date]" } "###); // wrong API key setting["apiKey"] = "doggo".into(); 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, @r###" { "uid": "[uid]", "indexUid": "doggo", "status": "failed", "type": "settingsUpdate", "canceledBy": null, "details": { "embedders": { "default": { "source": "openAi", "model": "text-embedding-3-large", "apiKey": "XXX...", "documentTemplate": "{%- if doc.gender == \"F\" -%}Une chienne nommée {{doc.name}}, née en {{doc.birthyear}}\n {%- else -%}\n Un chien nommé {{doc.name}}, né en {{doc.birthyear}}\n {%- endif %}, de race {{doc.breed}}.", "url": "[url]" } } }, "error": { "message": "While embedding documents for embedder `default`: user error: could not authenticate against OpenAI server\n - server replied with `{\"error\":{\"message\":\"Incorrect API key provided: Bearer doggo. You can find your API key at https://platform.openai.com/account/api-keys.\",\"type\":\"invalid_request_error\",\"param\":null,\"code\":\"invalid_api_key\"}}`\n - Hint: Check the `apiKey` parameter in the embedder configuration, and the `MEILI_OPENAI_API_KEY` and `OPENAI_API_KEY` environment variables", "code": "vector_embedding_error", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#vector_embedding_error" }, "duration": "[duration]", "enqueuedAt": "[date]", "startedAt": "[date]", "finishedAt": "[date]" } "###); // no API key setting.as_object_mut().unwrap().remove("apiKey"); 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, @r###" { "uid": "[uid]", "indexUid": "doggo", "status": "failed", "type": "settingsUpdate", "canceledBy": null, "details": { "embedders": { "default": { "source": "openAi", "model": "text-embedding-3-large", "documentTemplate": "{%- if doc.gender == \"F\" -%}Une chienne nommée {{doc.name}}, née en {{doc.birthyear}}\n {%- else -%}\n Un chien nommé {{doc.name}}, né en {{doc.birthyear}}\n {%- endif %}, de race {{doc.breed}}.", "url": "[url]" } } }, "error": { "message": "While embedding documents for embedder `default`: user error: could not authenticate against OpenAI server\n - server replied with `{\"error\":{\"message\":\"You didn't provide an API key. You need to provide your API key in an Authorization header using Bearer auth (i.e. Authorization: Bearer YOUR_KEY), or as the password field (with blank username) if you're accessing the API from your browser and are prompted for a username and password. You can obtain an API key from https://platform.openai.com/account/api-keys.\",\"type\":\"invalid_request_error\",\"param\":null,\"code\":null}}`\n - Hint: Check the `apiKey` parameter in the embedder configuration, and the `MEILI_OPENAI_API_KEY` and `OPENAI_API_KEY` environment variables", "code": "vector_embedding_error", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#vector_embedding_error" }, "duration": "[duration]", "enqueuedAt": "[date]", "startedAt": "[date]", "finishedAt": "[date]" } "###); // not a string API key setting["apiKey"] = 42.into(); let (response, code) = index .update_settings(json!({ "embedders": { "default": setting, }, })) .await; snapshot!(code, @"400 Bad Request"); snapshot!(response, @r###" { "message": "Invalid value type at `.embedders.default.apiKey`: expected a string, but found a positive integer: `42`", "code": "invalid_settings_embedders", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#invalid_settings_embedders" } "###); } // one test with wrong model #[actix_rt::test] async fn bad_model() { let (_mock, mut setting) = create_mock().await; let server = get_server_vector().await; let index = server.index("doggo"); let documents = json!([ {"id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou"}, {"id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle"}, {"id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier"}, {"id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever"}, ]); 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": 4, "indexedDocuments": 4 }, "error": null, "duration": "[duration]", "enqueuedAt": "[date]", "startedAt": "[date]", "finishedAt": "[date]" } "###); // wrong model setting["model"] = "doggo".into(); let (response, code) = index .update_settings(json!({ "embedders": { "default": setting, }, })) .await; snapshot!(code, @"400 Bad Request"); snapshot!(response, @r###" { "message": "`.embedders.default.model`: Invalid model `doggo` for OpenAI. Supported models: [\"text-embedding-ada-002\", \"text-embedding-3-small\", \"text-embedding-3-large\"]", "code": "invalid_settings_embedders", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#invalid_settings_embedders" } "###); // not a string model setting["model"] = 42.into(); let (response, code) = index .update_settings(json!({ "embedders": { "default": setting, }, })) .await; snapshot!(code, @"400 Bad Request"); snapshot!(response, @r###" { "message": "Invalid value type at `.embedders.default.model`: expected a string, but found a positive integer: `42`", "code": "invalid_settings_embedders", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#invalid_settings_embedders" } "###); } #[actix_rt::test] async fn bad_dimensions() { let (_mock, mut setting) = create_mock().await; let server = get_server_vector().await; let index = server.index("doggo"); let documents = json!([ {"id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou"}, {"id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle"}, {"id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier"}, {"id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever"}, ]); 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": 4, "indexedDocuments": 4 }, "error": null, "duration": "[duration]", "enqueuedAt": "[date]", "startedAt": "[date]", "finishedAt": "[date]" } "###); // null dimensions setting["dimensions"] = 0.into(); let (response, code) = index .update_settings(json!({ "embedders": { "default": setting, }, })) .await; snapshot!(code, @"400 Bad Request"); snapshot!(response, @r###" { "message": "`.embedders.default.dimensions`: `dimensions` cannot be zero", "code": "invalid_settings_embedders", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#invalid_settings_embedders" } "###); // negative dimensions setting["dimensions"] = (-42).into(); let (response, code) = index .update_settings(json!({ "embedders": { "default": setting, }, })) .await; snapshot!(code, @"400 Bad Request"); snapshot!(response, @r###" { "message": "Invalid value type at `.embedders.default.dimensions`: expected a positive integer, but found a negative integer: `-42`", "code": "invalid_settings_embedders", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#invalid_settings_embedders" } "###); // huge dimensions setting["dimensions"] = (42_000_000).into(); let (response, code) = index .update_settings(json!({ "embedders": { "default": setting, }, })) .await; snapshot!(code, @"400 Bad Request"); snapshot!(response, @r###" { "message": "`.embedders.default.dimensions`: Model `text-embedding-3-large` does not support overriding its dimensions to a value higher than 3072. Found 42000000", "code": "invalid_settings_embedders", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#invalid_settings_embedders" } "###); } // one test with changed dimensions #[actix_rt::test] async fn smaller_dimensions() { let (_mock, setting) = create_mock_dimensions().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"}, {"id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle"}, {"id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier"}, {"id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever"}, ]); 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": 4, "indexedDocuments": 4 }, "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 } } }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } } ], "offset": 0, "limit": 20, "total": 4 } "###); let (response, code) = index .search_post(json!({ "q": "chien de chasse", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" } ] "###); let (response, code) = index .search_post(json!({ "q": "petit chien", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" } ] "###); let (response, code) = index .search_post(json!({ "q": "grand chien de berger des montagnes", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" } ] "###); } // one test with different models #[actix_rt::test] async fn small_embedding_model() { let (_mock, setting) = create_mock_small_embedding_model().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"}, {"id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle"}, {"id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier"}, {"id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever"}, ]); 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": 4, "indexedDocuments": 4 }, "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 } } }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } } ], "offset": 0, "limit": 20, "total": 4 } "###); let (response, code) = index .search_post(json!({ "q": "chien de chasse", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" } ] "###); let (response, code) = index .search_post(json!({ "q": "petit chien", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" } ] "###); let (response, code) = index .search_post(json!({ "q": "grand chien de berger des montagnes", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" } ] "###); } #[actix_rt::test] async fn legacy_embedding_model() { let (_mock, setting) = create_mock_legacy_embedding_model().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"}, {"id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle"}, {"id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier"}, {"id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever"}, ]); 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": 4, "indexedDocuments": 4 }, "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 } } }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } } ], "offset": 0, "limit": 20, "total": 4 } "###); let (response, code) = index .search_post(json!({ "q": "chien de chasse", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" } ] "###); let (response, code) = index .search_post(json!({ "q": "petit chien", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "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": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" } ] "###); } // test with a server that responds 500 on 3 out of 4 calls #[actix_rt::test] async fn it_still_works() { let (_mock, setting) = create_fallible_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"}, {"id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle"}, {"id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier"}, {"id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever"}, ]); 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": 4, "indexedDocuments": 4 }, "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 } } }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever", "_vectors": { "default": { "embeddings": "[vector]", "regenerate": true } } } ], "offset": 0, "limit": 20, "total": 4 } "###); let (response, code) = index .search_post(json!({ "q": "chien de chasse", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" } ] "###); let (response, code) = index .search_post(json!({ "q": "petit chien", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" }, { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" } ] "###); let (response, code) = index .search_post(json!({ "q": "grand chien de berger des montagnes", "hybrid": {"semanticRatio": 1.0} })) .await; snapshot!(code, @"200 OK"); snapshot!(json_string!(response["hits"]), @r###" [ { "id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou" }, { "id": 1, "name": "Intel", "gender": "M", "birthyear": 2011, "breed": "Beagle" }, { "id": 3, "name": "Max", "gender": "M", "birthyear": 1995, "breed": "Labrador Retriever" }, { "id": 2, "name": "Vénus", "gender": "F", "birthyear": 2003, "breed": "Jack Russel Terrier" } ] "###); } // test with a server that wrongly responds 400