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Merge #1835
1835: Changes related to the next Meilisearch release (v1.13) r=brunoocasali a=meili-bot Related to this issue: meilisearch/integration-guides#314 This PR: - gathers the changes related to the next Meilisearch release (v1.13) so that this package is ready when the official release is out. - should pass the tests against the [latest pre-release of Meilisearch](https://github.com/meilisearch/meilisearch/releases). - might eventually contain test failures until the Meilisearch v1.13 is out. ⚠️ This PR should NOT be merged until the next release of Meilisearch (v1.13) is out. _This PR is auto-generated for the [pre-release week](https://github.com/meilisearch/integration-guides/blob/main/resources/pre-release-week.md) purpose._ Co-authored-by: Laurent Cazanove <[email protected]> Co-authored-by: Strift <[email protected]> Co-authored-by: Clémentine <[email protected]>
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‎.code-samples.meilisearch.yaml

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@@ -821,3 +821,13 @@ get_all_batches_1: |-
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client.getBatches();
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get_batch_1: |-
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client.getBatch(BATCH_UID);
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# Vector search
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update_embedders_1: |-
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client.index('INDEX_NAME').updateEmbedders({
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default: {
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source: 'openAi',
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apiKey: 'OPEN_AI_API_KEY',
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model: 'text-embedding-3-small',
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documentTemplate: 'A document titled '{{doc.title}}' whose description starts with {{doc.overview|truncatewords: 20}}'
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}
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});

‎README.md

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@@ -1066,8 +1066,6 @@ client.index('myIndex').resetPrefixSearch(): Promise<EnqueuedTask>
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### Embedders <!-- omit in toc -->
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⚠️ This feature is experimental. Activate the [`vectorStore` experimental feature to use it](https://www.meilisearch.com/docs/reference/api/experimental_features#configure-experimental-features)
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#### Get embedders
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```ts

‎tests/__snapshots__/settings.test.ts.snap

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@@ -7,6 +7,7 @@ exports[`Test on settings > Admin key: Get default settings of an index 1`] = `
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 100,
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 100,
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 100,
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 100,
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 100,
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": false,
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"faceting": {
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"maxValuesPerFacet": 100,
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 100,
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 100,
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"title",
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],
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"distinctAttribute": "title",
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 50,
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"*",
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],
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"distinctAttribute": "title",
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 100,
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"*",
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],
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"distinctAttribute": null,
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"embedders": {},
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"facetSearch": true,
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"faceting": {
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"maxValuesPerFacet": 100,
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"distinctAttribute": null,
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"embedders": {},
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"faceting": {
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"distinctAttribute": null,
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"embedders": {},
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"distinctAttribute": null,
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"embedders": {},
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"embedders": {},
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"embedders": {},
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"embedders": {},
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"embedders": {},
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"embedders": {},
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"embedders": {},
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"embedders": {},
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‎tests/documents.test.ts

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const client = await getClient(permission);
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const adminKey = await getKey("Admin");
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await fetch(`${HOST}/experimental-features`, {
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body: JSON.stringify({ vectorStore: true }),
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headers: {
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Authorization: `Bearer ${adminKey}`,
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"Content-Type": "application/json",
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},
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method: "PATCH",
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});
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.index(indexPk.uid)
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.addDocuments(dataset);
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await fetch(`${HOST}/experimental-features`, {
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body: JSON.stringify({ vectorStore: true }),
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headers: {
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Authorization: `Bearer ${adminKey}`,
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"Content-Type": "application/json",
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},
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method: "PATCH",
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});
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const { taskUid } = await client
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.addDocuments(dataset);

‎tests/embedders.test.ts

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import {
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config,
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HOST,
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BAD_HOST,
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MeiliSearch,
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getClient,
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getKey,
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} from "./utils/meilisearch-test-utils.js";
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const index = {
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beforeEach(async () => {
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await clearAllIndexes(config);
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const key = await getKey(permission);
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await fetch(`${HOST}/experimental-features`, {
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body: JSON.stringify({ vectorStore: true }),
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headers: {
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Authorization: `Bearer ${key}`,
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"Content-Type": "application/json",
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},
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method: "PATCH",
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});
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expect(response).toEqual(null);
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expect(response).toEqual({});
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test(`${permission} key: Update embedders with 'userProvided' source`, async () => {
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expect(response).toEqual({});
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test(`${permission} key: search (POST) with vectors`, async () => {

‎tests/get_search.test.ts

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test(`${permission} key: search with retrieveVectors to true`, async () => {
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await fetch(`${HOST}/experimental-features`, {
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"Content-Type": "application/json",
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test(`${permission} key: search without retrieveVectors`, async () => {
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const response = await client.index(index.uid).searchGet("prince");
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‎tests/search.test.ts

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test(`${permission} key: search with retrieveVectors to true`, async () => {
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const adminKey = await getKey("Admin");
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await fetch(`${HOST}/experimental-features`, {
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body: JSON.stringify({ vectorStore: true }),
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headers: {
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"Content-Type": "application/json",
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},
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const response = await client.index(index.uid).search("prince", {
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},
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method: "PATCH",
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const response = await client.index(index.uid).search("prince");
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