use meili_snap::{json_string, snapshot}; use crate::common::{GetAllDocumentsOptions, Server}; use crate::json; use crate::vector::generate_default_user_provided_documents; #[actix_rt::test] async fn update_embedder() { let server = Server::new().await; let index = server.index("doggo"); let (value, code) = server.set_features(json!({"vectorStore": true})).await; snapshot!(code, @"200 OK"); snapshot!(value, @r###" { "vectorStore": true, "metrics": false, "logsRoute": false } "###); let (response, code) = index .update_settings(json!({ "embedders": { "manual": {}}, })) .await; snapshot!(code, @"202 Accepted"); server.wait_task(response.uid()).await; let (response, code) = index .update_settings(json!({ "embedders": { "manual": { "source": "userProvided", "dimensions": 2, } }, })) .await; snapshot!(code, @"202 Accepted"); let ret = server.wait_task(response.uid()).await; snapshot!(ret, @r###" { "uid": 1, "indexUid": "doggo", "status": "failed", "type": "settingsUpdate", "canceledBy": null, "details": { "embedders": { "manual": { "source": "userProvided", "dimensions": 2 } } }, "error": { "message": "`.embedders.manual`: Field `model` unavailable for source `userProvided` (only available for sources: `huggingFace`, `openAi`, `ollama`). Available fields: `source`, `dimensions`, `distribution`", "code": "invalid_settings_embedders", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#invalid_settings_embedders" }, "duration": "[duration]", "enqueuedAt": "[date]", "startedAt": "[date]", "finishedAt": "[date]" } "###); } #[actix_rt::test] async fn reset_embedder_documents() { let server = Server::new().await; let index = generate_default_user_provided_documents(&server).await; let (response, code) = index.delete_settings().await; snapshot!(code, @"202 Accepted"); server.wait_task(response.uid()).await; // Make sure the documents are still present let (documents, _code) = index.get_all_documents(Default::default()).await; snapshot!(json_string!(documents), @r###" { "results": [ { "id": 0, "name": "kefir" }, { "id": 1, "name": "echo" }, { "id": 2, "name": "billou" }, { "id": 3, "name": "intel" }, { "id": 4, "name": "max" } ], "offset": 0, "limit": 20, "total": 5 } "###); // Make sure we are still able to retrieve their vectors let (documents, _code) = index .get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() }) .await; snapshot!(json_string!(documents), @r###" { "results": [ { "id": 0, "name": "kefir", "_vectors": {} }, { "id": 1, "name": "echo", "_vectors": {} }, { "id": 2, "name": "billou", "_vectors": {} }, { "id": 3, "name": "intel", "_vectors": {} }, { "id": 4, "name": "max", "_vectors": {} } ], "offset": 0, "limit": 20, "total": 5 } "###); // Make sure the arroy DB has been cleared let (documents, _code) = index.search_post(json!({ "vector": [1, 1, 1] })).await; snapshot!(json_string!(documents), @r###" { "message": "Cannot find embedder with name `default`.", "code": "invalid_embedder", "type": "invalid_request", "link": "https://docs.meilisearch.com/errors#invalid_embedder" } "###); }