mirror of
https://github.com/meilisearch/MeiliSearch
synced 2024-11-22 21:04:27 +01:00
382 lines
10 KiB
Rust
382 lines
10 KiB
Rust
use meili_snap::{json_string, snapshot};
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use crate::common::{GetAllDocumentsOptions, Server};
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use crate::json;
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use crate::vector::generate_default_user_provided_documents;
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#[actix_rt::test]
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async fn retrieve_binary_quantize_status_in_the_settings() {
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let server = Server::new().await;
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let index = server.index("doggo");
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let (value, code) = server.set_features(json!({"vectorStore": true})).await;
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snapshot!(code, @"200 OK");
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snapshot!(value, @r###"
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{
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"vectorStore": true,
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"metrics": false,
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"logsRoute": false,
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"editDocumentsByFunction": false,
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"containsFilter": false
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}
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"###);
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"manual": {
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"source": "userProvided",
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"dimensions": 3,
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}
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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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server.wait_task(response.uid()).await.succeeded();
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let (settings, code) = index.settings().await;
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snapshot!(code, @"200 OK");
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snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3}"###);
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"manual": {
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"source": "userProvided",
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"dimensions": 3,
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"binaryQuantized": false,
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}
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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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server.wait_task(response.uid()).await.succeeded();
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let (settings, code) = index.settings().await;
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snapshot!(code, @"200 OK");
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snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3,"binaryQuantized":false}"###);
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"manual": {
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"source": "userProvided",
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"dimensions": 3,
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"binaryQuantized": true,
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}
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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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server.wait_task(response.uid()).await.succeeded();
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let (settings, code) = index.settings().await;
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snapshot!(code, @"200 OK");
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snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3,"binaryQuantized":true}"###);
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}
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#[actix_rt::test]
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async fn binary_quantize_before_sending_documents() {
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let server = Server::new().await;
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let index = server.index("doggo");
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let (value, code) = server.set_features(json!({"vectorStore": true})).await;
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snapshot!(code, @"200 OK");
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snapshot!(value, @r###"
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{
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"vectorStore": true,
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"metrics": false,
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"logsRoute": false,
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"editDocumentsByFunction": false,
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"containsFilter": false
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}
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"###);
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"manual": {
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"source": "userProvided",
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"dimensions": 3,
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"binaryQuantized": true,
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}
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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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server.wait_task(response.uid()).await.succeeded();
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let documents = json!([
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{"id": 0, "name": "kefir", "_vectors": { "manual": [-1.2, -2.3, 3.2] }},
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{"id": 1, "name": "echo", "_vectors": { "manual": [2.5, 1.5, -130] }},
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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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index.wait_task(value.uid()).await.succeeded();
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// Make sure the documents are binary quantized
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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), @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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"_vectors": {
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"manual": {
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"embeddings": [
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[
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-1.0,
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-1.0,
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1.0
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]
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],
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"regenerate": false
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}
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}
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},
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{
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"id": 1,
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"name": "echo",
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"_vectors": {
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"manual": {
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"embeddings": [
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[
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1.0,
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1.0,
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-1.0
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]
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],
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"regenerate": false
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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": 2
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}
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"###);
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}
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#[actix_rt::test]
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async fn binary_quantize_after_sending_documents() {
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let server = Server::new().await;
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let index = server.index("doggo");
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let (value, code) = server.set_features(json!({"vectorStore": true})).await;
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snapshot!(code, @"200 OK");
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snapshot!(value, @r###"
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{
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"vectorStore": true,
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"metrics": false,
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"logsRoute": false,
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"editDocumentsByFunction": false,
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"containsFilter": false
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}
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"###);
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"manual": {
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"source": "userProvided",
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"dimensions": 3,
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}
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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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server.wait_task(response.uid()).await.succeeded();
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let documents = json!([
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{"id": 0, "name": "kefir", "_vectors": { "manual": [-1.2, -2.3, 3.2] }},
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{"id": 1, "name": "echo", "_vectors": { "manual": [2.5, 1.5, -130] }},
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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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index.wait_task(value.uid()).await.succeeded();
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"manual": {
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"source": "userProvided",
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"dimensions": 3,
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"binaryQuantized": true,
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}
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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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server.wait_task(response.uid()).await.succeeded();
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// Make sure the documents are binary quantized
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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), @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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"_vectors": {
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"manual": {
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"embeddings": [
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[
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-1.0,
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-1.0,
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1.0
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]
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],
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"regenerate": false
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}
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}
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},
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{
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"id": 1,
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"name": "echo",
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"_vectors": {
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"manual": {
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"embeddings": [
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[
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1.0,
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1.0,
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-1.0
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]
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],
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"regenerate": false
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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": 2
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}
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"###);
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}
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#[actix_rt::test]
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async fn try_to_disable_binary_quantization() {
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let server = Server::new().await;
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let index = server.index("doggo");
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let (value, code) = server.set_features(json!({"vectorStore": true})).await;
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snapshot!(code, @"200 OK");
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snapshot!(value, @r###"
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{
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"vectorStore": true,
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"metrics": false,
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"logsRoute": false,
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"editDocumentsByFunction": false,
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"containsFilter": false
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}
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"###);
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"manual": {
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"source": "userProvided",
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"dimensions": 3,
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"binaryQuantized": true,
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}
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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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server.wait_task(response.uid()).await.succeeded();
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"manual": {
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"source": "userProvided",
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"dimensions": 3,
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"binaryQuantized": false,
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}
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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 ret = server.wait_task(response.uid()).await;
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snapshot!(ret, @r###"
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{
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"uid": "[uid]",
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"batchUid": "[batch_uid]",
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"indexUid": "doggo",
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"status": "failed",
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"type": "settingsUpdate",
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"canceledBy": null,
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"details": {
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"embedders": {
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"manual": {
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"source": "userProvided",
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"dimensions": 3,
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"binaryQuantized": false
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}
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}
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},
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"error": {
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"message": "`.embedders.manual.binaryQuantized`: Cannot disable the binary quantization.\n - Note: Binary quantization is a lossy operation that cannot be reverted.\n - Hint: Add a new embedder that is non-quantized and regenerate the vectors.",
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"code": "invalid_settings_embedders",
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"type": "invalid_request",
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"link": "https://docs.meilisearch.com/errors#invalid_settings_embedders"
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},
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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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}
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#[actix_rt::test]
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async fn binary_quantize_clear_documents() {
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let server = Server::new().await;
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let index = generate_default_user_provided_documents(&server).await;
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let (response, code) = index
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.update_settings(json!({
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"embedders": {
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"manual": {
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"binaryQuantized": true,
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}
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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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server.wait_task(response.uid()).await.succeeded();
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let (value, _code) = index.clear_all_documents().await;
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index.wait_task(value.uid()).await.succeeded();
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// Make sure the documents DB has been cleared
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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), @r###"
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{
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"results": [],
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"offset": 0,
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"limit": 20,
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"total": 0
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}
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"###);
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// Make sure the arroy DB has been cleared
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let (documents, _code) =
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index.search_post(json!({ "hybrid": { "embedder": "manual" }, "vector": [1, 1, 1] })).await;
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snapshot!(documents, @r###"
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{
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"hits": [],
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"query": "",
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"processingTimeMs": "[duration]",
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"limit": 20,
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"offset": 0,
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"estimatedTotalHits": 0,
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"semanticHitCount": 0
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}
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"###);
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}
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