MeiliSearch/crates/meilisearch/tests/vector/fragments.rs
2025-07-03 18:18:04 +02:00

1599 lines
40 KiB
Rust

use std::collections::BTreeMap;
use meili_snap::{json_string, snapshot};
use tokio::sync::OnceCell;
use wiremock::matchers::{method, path};
use wiremock::{Mock, MockServer, Request, ResponseTemplate};
use crate::common::index::Index;
use crate::common::{Owned, Shared};
use crate::json;
use crate::vector::{GetAllDocumentsOptions, Server};
async fn shared_index_for_fragments() -> Index<'static, Shared> {
static INDEX: OnceCell<(Server<Shared>, String)> = OnceCell::const_new();
let (server, uid) = INDEX
.get_or_init(|| async {
let (server, uid, _) = init_fragments_index().await;
(server.into_shared(), uid)
})
.await;
server._index(uid).to_shared()
}
pub async fn init_fragments_index() -> (Server<Owned>, String, crate::common::Value) {
let mock_server = MockServer::start().await;
let text_to_embedding: BTreeMap<_, _> = vec![
("kefir", [0.5, -0.5, 0.0]),
("intel", [1.0, 1.0, 0.0]),
("dustin", [-0.5, 0.5, 0.0]),
("bulldog", [0.0, 0.0, 1.0]),
("labrador", [0.0, 0.0, -1.0]),
("{", [-9999.0, -9999.0, -9999.0]), // That wouldn't be nice
]
.into_iter()
.collect();
Mock::given(method("POST"))
.and(path("/"))
.respond_with(move |req: &Request| {
let text = String::from_utf8_lossy(&req.body).to_string();
let mut data = [0.0, 0.0, 0.0];
for (inner_text, inner_data) in &text_to_embedding {
if text.contains(inner_text) {
for (i, &value) in inner_data.iter().enumerate() {
data[i] += value;
}
}
}
ResponseTemplate::new(200).set_body_json(json!({ "data": data }))
})
.mount(&mock_server)
.await;
let url = mock_server.uri();
let server = Server::new().await;
let index = server.unique_index();
let (_response, code) = server.set_features(json!({"multimodal": true})).await;
snapshot!(code, @"200 OK");
// Configure the index to use our mock embedder
let settings = json!({
"embedders": {
"rest": {
"source": "rest",
"url": url,
"dimensions": 3,
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
},
"indexingFragments": {
"withBreed": {"value": "{{ doc.name }} is a {{ doc.breed }}"},
"basic": {"value": "{{ doc.name }} is a dog"},
},
"searchFragments": {
"justBreed": {"value": "It's a {{ media.breed }}"},
"justName": {"value": "{{ media.name }} is a dog"},
"query": {"value": "Some pre-prompt for query {{ q }}"},
}
},
},
});
let (response, code) = index.update_settings(settings.clone()).await;
snapshot!(code, @"202 Accepted");
let task = server.wait_task(response.uid()).await;
snapshot!(task["status"], @r###""succeeded""###);
// Send documents
let documents = json!([
{"id": 0, "name": "kefir"},
{"id": 1, "name": "echo", "_vectors": { "rest": [1, 1, 1] }},
{"id": 2, "name": "intel", "breed": "labrador"},
{"id": 3, "name": "dustin", "breed": "bulldog"},
]);
let (value, code) = index.add_documents(documents, None).await;
snapshot!(code, @"202 Accepted");
let task = index.wait_task(value.uid()).await;
snapshot!(task["status"], @r###""succeeded""###);
let uid = index.uid.clone();
(server, uid, settings)
}
// TODO: Test cannot pass both fragments and document
// TODO: edit fragment
// TODO: document fragment replaced
// TODO: swapping fragments
#[actix_rt::test]
async fn indexing_fragments() {
let index = shared_index_for_fragments().await;
// Make sure the documents have been indexed and their embeddings retrieved
let (documents, code) = index
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
.await;
snapshot!(code, @"200 OK");
snapshot!(json_string!(documents), @r#"
{
"results": [
{
"id": 0,
"name": "kefir",
"_vectors": {
"rest": {
"embeddings": [
[
0.5,
-0.5,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 1,
"name": "echo",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
1.0
]
],
"regenerate": false
}
}
},
{
"id": 2,
"name": "intel",
"breed": "labrador",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
0.0
],
[
1.0,
1.0,
-1.0
]
],
"regenerate": true
}
}
},
{
"id": 3,
"name": "dustin",
"breed": "bulldog",
"_vectors": {
"rest": {
"embeddings": [
[
-0.5,
0.5,
0.0
],
[
-0.5,
0.5,
1.0
]
],
"regenerate": true
}
}
}
],
"offset": 0,
"limit": 20,
"total": 4
}
"#);
}
#[actix_rt::test]
async fn search_with_vector() {
let index = shared_index_for_fragments().await;
let (value, code) = index.search_post(
json!({"vector": [1.0, 1.0, 1.0], "hybrid": {"semanticRatio": 1.0, "embedder": "rest"}, "limit": 1}
)).await;
snapshot!(code, @"200 OK");
snapshot!(value, @r#"
{
"hits": [
{
"id": 1,
"name": "echo"
}
],
"query": "",
"processingTimeMs": "[duration]",
"limit": 1,
"offset": 0,
"estimatedTotalHits": 4,
"semanticHitCount": 1
}
"#);
}
#[actix_rt::test]
async fn search_with_media() {
let index = shared_index_for_fragments().await;
let (value, code) = index
.search_post(json!({
"media": { "breed": "labrador" },
"hybrid": {"semanticRatio": 1.0, "embedder": "rest"},
"limit": 1
}
))
.await;
snapshot!(code, @"200 OK");
snapshot!(value, @r#"
{
"hits": [
{
"id": 2,
"name": "intel",
"breed": "labrador"
}
],
"query": "",
"processingTimeMs": "[duration]",
"limit": 1,
"offset": 0,
"estimatedTotalHits": 4,
"semanticHitCount": 1
}
"#);
}
#[actix_rt::test]
async fn search_with_media_matching_multiple_fragments() {
let index = shared_index_for_fragments().await;
let (value, code) = index
.search_post(json!({
"media": { "name": "dustin", "breed": "labrador" },
"hybrid": {"semanticRatio": 1.0, "embedder": "rest"},
"limit": 1
}
))
.await;
snapshot!(code, @"400 Bad Request");
snapshot!(value, @r#"
{
"message": "Error while generating embeddings: user error: Query matches multiple search fragments.\n - Note: First matched fragment `justBreed`.\n - Note: Second matched fragment `justName`.\n - Note: {\"q\":null,\"media\":{\"name\":\"dustin\",\"breed\":\"labrador\"}}",
"code": "vector_embedding_error",
"type": "invalid_request",
"link": "https://docs.meilisearch.com/errors#vector_embedding_error"
}
"#);
}
#[actix_rt::test]
async fn search_with_media_matching_no_fragment() {
let index = shared_index_for_fragments().await;
let (value, code) = index
.search_post(json!({
"media": { "ticker": "GME", "section": "portfolio" },
"hybrid": {"semanticRatio": 1.0, "embedder": "rest"},
"limit": 1
}
))
.await;
snapshot!(code, @"400 Bad Request");
snapshot!(value, @r#"
{
"message": "Error while generating embeddings: user error: Query matches no search fragment.\n - Note: {\"q\":null,\"media\":{\"ticker\":\"GME\",\"section\":\"portfolio\"}}",
"code": "vector_embedding_error",
"type": "invalid_request",
"link": "https://docs.meilisearch.com/errors#vector_embedding_error"
}
"#);
}
#[actix_rt::test]
async fn search_with_query() {
let index = shared_index_for_fragments().await;
let (value, code) = index
.search_post(json!({
"q": "bulldog",
"hybrid": {"semanticRatio": 1.0, "embedder": "rest"},
"limit": 1
}
))
.await;
snapshot!(code, @"200 OK");
snapshot!(value, @r#"
{
"hits": [
{
"id": 3,
"name": "dustin",
"breed": "bulldog"
}
],
"query": "bulldog",
"processingTimeMs": "[duration]",
"limit": 1,
"offset": 0,
"estimatedTotalHits": 4,
"semanticHitCount": 1
}
"#);
}
#[actix_rt::test]
async fn deleting_fragments_deletes_vectors() {
let (server, uid, mut settings) = init_fragments_index().await;
let index = server.index(uid);
settings["embedders"]["rest"]["indexingFragments"]["basic"] = serde_json::Value::Null;
let (response, code) = index.update_settings(settings).await;
snapshot!(code, @"202 Accepted");
let value = server.wait_task(response.uid()).await.succeeded();
snapshot!(value, @r#"
{
"uid": "[uid]",
"batchUid": "[batch_uid]",
"indexUid": "[uuid]",
"status": "succeeded",
"type": "settingsUpdate",
"canceledBy": null,
"details": {
"embedders": {
"rest": {
"source": "rest",
"dimensions": 3,
"url": "[url]",
"indexingFragments": {
"basic": null,
"withBreed": {
"value": "{{ doc.name }} is a {{ doc.breed }}"
}
},
"searchFragments": {
"justBreed": {
"value": "It's a {{ media.breed }}"
},
"justName": {
"value": "{{ media.name }} is a dog"
},
"query": {
"value": "Some pre-prompt for query {{ q }}"
}
},
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
}
}
}
},
"error": null,
"duration": "[duration]",
"enqueuedAt": "[date]",
"startedAt": "[date]",
"finishedAt": "[date]"
}
"#);
let (value, code) = index.settings().await;
snapshot!(code, @"200 OK");
snapshot!(json_string!(value["embedders"], {
".rest.url" => "[url]",
}), @r#"
{
"rest": {
"source": "rest",
"dimensions": 3,
"url": "[url]",
"indexingFragments": {
"withBreed": {
"value": "{{ doc.name }} is a {{ doc.breed }}"
}
},
"searchFragments": {
"justBreed": {
"value": "It's a {{ media.breed }}"
},
"justName": {
"value": "{{ media.name }} is a dog"
},
"query": {
"value": "Some pre-prompt for query {{ q }}"
}
},
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
},
"headers": {}
}
}
"#);
let (documents, code) = index
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
.await;
snapshot!(code, @"200 OK");
snapshot!(json_string!(documents), @r###"
{
"results": [
{
"id": 0,
"name": "kefir",
"_vectors": {
"rest": {
"embeddings": [],
"regenerate": true
}
}
},
{
"id": 1,
"name": "echo",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
1.0
]
],
"regenerate": false
}
}
},
{
"id": 2,
"name": "intel",
"breed": "labrador",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
-1.0
]
],
"regenerate": true
}
}
},
{
"id": 3,
"name": "dustin",
"breed": "bulldog",
"_vectors": {
"rest": {
"embeddings": [
[
-0.5,
0.5,
1.0
]
],
"regenerate": true
}
}
}
],
"offset": 0,
"limit": 20,
"total": 4
}
"###);
}
#[actix_rt::test]
async fn modifying_fragments_modifies_vectors() {
let (server, uid, mut settings) = init_fragments_index().await;
let index = server.index(uid);
settings["embedders"]["rest"]["indexingFragments"]["basic"]["value"] =
serde_json::Value::String("{{ doc.name }} is a dog (maybe bulldog?)".to_string());
let (response, code) = index.update_settings(settings).await;
snapshot!(code, @"202 Accepted");
let value = server.wait_task(response.uid()).await.succeeded();
snapshot!(value, @r#"
{
"uid": "[uid]",
"batchUid": "[batch_uid]",
"indexUid": "[uuid]",
"status": "succeeded",
"type": "settingsUpdate",
"canceledBy": null,
"details": {
"embedders": {
"rest": {
"source": "rest",
"dimensions": 3,
"url": "[url]",
"indexingFragments": {
"basic": {
"value": "{{ doc.name }} is a dog (maybe bulldog?)"
},
"withBreed": {
"value": "{{ doc.name }} is a {{ doc.breed }}"
}
},
"searchFragments": {
"justBreed": {
"value": "It's a {{ media.breed }}"
},
"justName": {
"value": "{{ media.name }} is a dog"
},
"query": {
"value": "Some pre-prompt for query {{ q }}"
}
},
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
}
}
}
},
"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!(code, @"200 OK");
snapshot!(json_string!(documents), @r#"
{
"results": [
{
"id": 0,
"name": "kefir",
"_vectors": {
"rest": {
"embeddings": [
[
0.5,
-0.5,
1.0
]
],
"regenerate": true
}
}
},
{
"id": 1,
"name": "echo",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
1.0
]
],
"regenerate": false
}
}
},
{
"id": 2,
"name": "intel",
"breed": "labrador",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
1.0
],
[
1.0,
1.0,
-1.0
]
],
"regenerate": true
}
}
},
{
"id": 3,
"name": "dustin",
"breed": "bulldog",
"_vectors": {
"rest": {
"embeddings": [
[
-0.5,
0.5,
1.0
],
[
-0.5,
0.5,
1.0
]
],
"regenerate": true
}
}
}
],
"offset": 0,
"limit": 20,
"total": 4
}
"#);
}
#[actix_rt::test]
async fn ommitted_fragment_isnt_removed() {
let (server, uid, mut settings) = init_fragments_index().await;
let index = server.index(uid);
settings["embedders"]["rest"]["indexingFragments"]["basic"] = serde_json::Value::Null; // basic is removed
settings["embedders"]["rest"]["indexingFragments"].as_object_mut().unwrap().remove("withBreed"); // withBreed isn't specified
let (response, code) = index.update_settings(settings).await;
snapshot!(code, @"202 Accepted");
let value = server.wait_task(response.uid()).await.succeeded();
snapshot!(value, @r#"
{
"uid": "[uid]",
"batchUid": "[batch_uid]",
"indexUid": "[uuid]",
"status": "succeeded",
"type": "settingsUpdate",
"canceledBy": null,
"details": {
"embedders": {
"rest": {
"source": "rest",
"dimensions": 3,
"url": "[url]",
"indexingFragments": {
"basic": null
},
"searchFragments": {
"justBreed": {
"value": "It's a {{ media.breed }}"
},
"justName": {
"value": "{{ media.name }} is a dog"
},
"query": {
"value": "Some pre-prompt for query {{ q }}"
}
},
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
}
}
}
},
"error": null,
"duration": "[duration]",
"enqueuedAt": "[date]",
"startedAt": "[date]",
"finishedAt": "[date]"
}
"#);
let (value, code) = index.settings().await;
snapshot!(code, @"200 OK");
snapshot!(json_string!(value["embedders"], {
".rest.url" => "[url]",
}), @r#"
{
"rest": {
"source": "rest",
"dimensions": 3,
"url": "[url]",
"indexingFragments": {
"withBreed": {
"value": "{{ doc.name }} is a {{ doc.breed }}"
}
},
"searchFragments": {
"justBreed": {
"value": "It's a {{ media.breed }}"
},
"justName": {
"value": "{{ media.name }} is a dog"
},
"query": {
"value": "Some pre-prompt for query {{ q }}"
}
},
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
},
"headers": {}
}
}
"#);
}
#[actix_rt::test]
async fn fragment_insertion() {
let (server, uid, mut settings) = init_fragments_index().await;
let index = server.index(uid);
settings["embedders"]["rest"]["indexingFragments"].as_object_mut().unwrap().insert(
String::from("useless"),
serde_json::json!({
"value": "This fragment is useless"
}),
);
let (response, code) = index.update_settings(settings).await;
snapshot!(code, @"202 Accepted");
let value = server.wait_task(response.uid()).await.succeeded();
snapshot!(value, @r#"
{
"uid": "[uid]",
"batchUid": "[batch_uid]",
"indexUid": "[uuid]",
"status": "succeeded",
"type": "settingsUpdate",
"canceledBy": null,
"details": {
"embedders": {
"rest": {
"source": "rest",
"dimensions": 3,
"url": "[url]",
"indexingFragments": {
"basic": {
"value": "{{ doc.name }} is a dog"
},
"useless": {
"value": "This fragment is useless"
},
"withBreed": {
"value": "{{ doc.name }} is a {{ doc.breed }}"
}
},
"searchFragments": {
"justBreed": {
"value": "It's a {{ media.breed }}"
},
"justName": {
"value": "{{ media.name }} is a dog"
},
"query": {
"value": "Some pre-prompt for query {{ q }}"
}
},
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
}
}
}
},
"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!(code, @"200 OK");
snapshot!(json_string!(documents), @r#"
{
"results": [
{
"id": 0,
"name": "kefir",
"_vectors": {
"rest": {
"embeddings": [
[
0.5,
-0.5,
0.0
],
[
0.0,
0.0,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 1,
"name": "echo",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
1.0
]
],
"regenerate": false
}
}
},
{
"id": 2,
"name": "intel",
"breed": "labrador",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
0.0
],
[
1.0,
1.0,
-1.0
],
[
0.0,
0.0,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 3,
"name": "dustin",
"breed": "bulldog",
"_vectors": {
"rest": {
"embeddings": [
[
-0.5,
0.5,
0.0
],
[
-0.5,
0.5,
1.0
],
[
0.0,
0.0,
0.0
]
],
"regenerate": true
}
}
}
],
"offset": 0,
"limit": 20,
"total": 4
}
"#);
}
#[actix_rt::test]
async fn multiple_embedders() {
let (server, uid, mut settings) = init_fragments_index().await;
let index = server.index(uid);
let url = settings["embedders"]["rest"]["url"].as_str().unwrap();
let settings2 = json!({
"embedders": {
"rest2": {
"source": "rest",
"url": url,
"dimensions": 3,
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
},
"indexingFragments": {
"withBreed": {"value": "{{ doc.name }} is a {{ doc.breed }}"},
"basic": {"value": "{{ doc.name }} is a dog"},
},
"searchFragments": {
"query": {"value": "Some pre-prompt for query {{ q }}"},
}
},
"rest3": {
"source": "rest",
"url": url,
"dimensions": 3,
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
},
"indexingFragments": {
"basic": {"value": "{{ doc.name }} is a dog"},
},
"searchFragments": {
"query": {"value": "Some pre-prompt for query {{ q }}"},
}
},
},
});
let (response, code) = index.update_settings(settings2).await;
snapshot!(code, @"202 Accepted");
let task = server.wait_task(response.uid()).await;
snapshot!(task, @r#"
{
"uid": "[uid]",
"batchUid": "[batch_uid]",
"indexUid": "[uuid]",
"status": "succeeded",
"type": "settingsUpdate",
"canceledBy": null,
"details": {
"embedders": {
"rest2": {
"source": "rest",
"dimensions": 3,
"url": "[url]",
"indexingFragments": {
"basic": {
"value": "{{ doc.name }} is a dog"
},
"withBreed": {
"value": "{{ doc.name }} is a {{ doc.breed }}"
}
},
"searchFragments": {
"query": {
"value": "Some pre-prompt for query {{ q }}"
}
},
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
}
},
"rest3": {
"source": "rest",
"dimensions": 3,
"url": "[url]",
"indexingFragments": {
"basic": {
"value": "{{ doc.name }} is a dog"
}
},
"searchFragments": {
"query": {
"value": "Some pre-prompt for query {{ q }}"
}
},
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
}
}
}
},
"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!(code, @"200 OK");
snapshot!(json_string!(documents), @r#"
{
"results": [
{
"id": 0,
"name": "kefir",
"_vectors": {
"rest": {
"embeddings": [
[
0.5,
-0.5,
0.0
]
],
"regenerate": true
},
"rest2": {
"embeddings": [
[
0.5,
-0.5,
0.0
]
],
"regenerate": true
},
"rest3": {
"embeddings": [
[
0.5,
-0.5,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 1,
"name": "echo",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
1.0
]
],
"regenerate": false
},
"rest2": {
"embeddings": [
[
0.0,
0.0,
0.0
]
],
"regenerate": true
},
"rest3": {
"embeddings": [
[
0.0,
0.0,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 2,
"name": "intel",
"breed": "labrador",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
0.0
],
[
1.0,
1.0,
-1.0
]
],
"regenerate": true
},
"rest2": {
"embeddings": [
[
1.0,
1.0,
0.0
],
[
1.0,
1.0,
-1.0
]
],
"regenerate": true
},
"rest3": {
"embeddings": [
[
1.0,
1.0,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 3,
"name": "dustin",
"breed": "bulldog",
"_vectors": {
"rest": {
"embeddings": [
[
-0.5,
0.5,
0.0
],
[
-0.5,
0.5,
1.0
]
],
"regenerate": true
},
"rest2": {
"embeddings": [
[
-0.5,
0.5,
0.0
],
[
-0.5,
0.5,
1.0
]
],
"regenerate": true
},
"rest3": {
"embeddings": [
[
-0.5,
0.5,
0.0
]
],
"regenerate": true
}
}
}
],
"offset": 0,
"limit": 20,
"total": 4
}
"#);
// Remove Rest2
settings["embedders"]["rest2"] = serde_json::Value::Null;
let (response, code) = index.update_settings(settings.clone()).await;
snapshot!(code, @"202 Accepted");
let value = server.wait_task(response.uid()).await.succeeded();
snapshot!(value["status"], @r###""succeeded""###);
let (documents, code) = index
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
.await;
snapshot!(code, @"200 OK");
snapshot!(json_string!(documents), @r#"
{
"results": [
{
"id": 0,
"name": "kefir",
"_vectors": {
"rest": {
"embeddings": [
[
0.5,
-0.5,
0.0
]
],
"regenerate": true
},
"rest3": {
"embeddings": [
[
0.5,
-0.5,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 1,
"name": "echo",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
1.0
]
],
"regenerate": false
},
"rest3": {
"embeddings": [
[
0.0,
0.0,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 2,
"name": "intel",
"breed": "labrador",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
0.0
],
[
1.0,
1.0,
-1.0
]
],
"regenerate": true
},
"rest3": {
"embeddings": [
[
1.0,
1.0,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 3,
"name": "dustin",
"breed": "bulldog",
"_vectors": {
"rest": {
"embeddings": [
[
-0.5,
0.5,
0.0
],
[
-0.5,
0.5,
1.0
]
],
"regenerate": true
},
"rest3": {
"embeddings": [
[
-0.5,
0.5,
0.0
]
],
"regenerate": true
}
}
}
],
"offset": 0,
"limit": 20,
"total": 4
}
"#);
// Remove rest's basic fragment
settings["embedders"]["rest"]["indexingFragments"]["basic"] = serde_json::Value::Null;
//settings["embedders"].as_object_mut().unwrap().remove("rest2");
let (response, code) = index.update_settings(settings).await;
snapshot!(code, @"202 Accepted");
let value = server.wait_task(response.uid()).await.succeeded();
snapshot!(value["status"], @r###""succeeded""###);
let (documents, code) = index
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
.await;
snapshot!(code, @"200 OK");
snapshot!(json_string!(documents), @r"");
}
#[actix_rt::test]
async fn remove_non_existant_embedder() {
let (server, uid, mut settings) = init_fragments_index().await;
let index = server.index(uid);
settings["embedders"].as_object_mut().unwrap().insert(String::from("non-existant"), serde_json::Value::Null);
let (response, code) = index.update_settings(settings).await;
snapshot!(code, @"202 Accepted");
let task = server.wait_task(response.uid()).await;
snapshot!(task, @r"");
}
#[actix_rt::test]
async fn double_remove_embedder() {
let (server, uid, mut settings) = init_fragments_index().await;
let index = server.index(uid);
settings["embedders"].as_object_mut().unwrap().insert(String::from("rest"), serde_json::Value::Null);
let (response, code) = index.update_settings(settings.clone()).await;
snapshot!(code, @"202 Accepted");
let task = server.wait_task(response.uid()).await;
snapshot!(task, @r#"
{
"uid": "[uid]",
"batchUid": "[batch_uid]",
"indexUid": "[uuid]",
"status": "succeeded",
"type": "settingsUpdate",
"canceledBy": null,
"details": {
"embedders": {
"rest": null
}
},
"error": null,
"duration": "[duration]",
"enqueuedAt": "[date]",
"startedAt": "[date]",
"finishedAt": "[date]"
}
"#);
let (response, code) = index.update_settings(settings.clone()).await;
snapshot!(code, @"202 Accepted");
let task = server.wait_task(response.uid()).await;
snapshot!(task, @r#""#);
}
#[actix_rt::test]
async fn complex_fragment() {
let (server, uid, mut settings) = init_fragments_index().await;
let index = server.index(uid);
settings["embedders"]["rest"]["indexingFragments"].as_object_mut().unwrap().insert(
String::from("complex"),
serde_json::json!({
"value": {
"breed": "{{ doc.breed }}",
"breeds": [
"{{ doc.breed }}",
{
"breed": "{{ doc.breed }}",
}
]
}
}),
);
let (response, code) = index.update_settings(settings).await;
snapshot!(code, @"202 Accepted");
let task = server.wait_task(response.uid()).await;
snapshot!(task, @r#"
{
"uid": "[uid]",
"batchUid": "[batch_uid]",
"indexUid": "[uuid]",
"status": "succeeded",
"type": "settingsUpdate",
"canceledBy": null,
"details": {
"embedders": {
"rest": {
"source": "rest",
"dimensions": 3,
"url": "[url]",
"indexingFragments": {
"basic": {
"value": "{{ doc.name }} is a dog"
},
"complex": {
"value": {
"breed": "{{ doc.breed }}",
"breeds": [
"{{ doc.breed }}",
{
"breed": "{{ doc.breed }}"
}
]
}
},
"withBreed": {
"value": "{{ doc.name }} is a {{ doc.breed }}"
}
},
"searchFragments": {
"justBreed": {
"value": "It's a {{ media.breed }}"
},
"justName": {
"value": "{{ media.name }} is a dog"
},
"query": {
"value": "Some pre-prompt for query {{ q }}"
}
},
"request": "{{fragment}}",
"response": {
"data": "{{embedding}}"
}
}
}
},
"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!(code, @"200 OK");
snapshot!(json_string!(documents), @r#"
{
"results": [
{
"id": 0,
"name": "kefir",
"_vectors": {
"rest": {
"embeddings": [
[
0.5,
-0.5,
0.0
]
],
"regenerate": true
}
}
},
{
"id": 1,
"name": "echo",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
1.0
]
],
"regenerate": false
}
}
},
{
"id": 2,
"name": "intel",
"breed": "labrador",
"_vectors": {
"rest": {
"embeddings": [
[
1.0,
1.0,
0.0
],
[
1.0,
1.0,
-1.0
],
[
0.0,
0.0,
-1.0
]
],
"regenerate": true
}
}
},
{
"id": 3,
"name": "dustin",
"breed": "bulldog",
"_vectors": {
"rest": {
"embeddings": [
[
-0.5,
0.5,
0.0
],
[
-0.5,
0.5,
1.0
],
[
0.0,
0.0,
1.0
]
],
"regenerate": true
}
}
}
],
"offset": 0,
"limit": 20,
"total": 4
}
"#);
}