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https://github.com/meilisearch/MeiliSearch
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Add test for vector writeback
This commit is contained in:
Louis Dureuil
parent
fca9fe39b3
commit
34fabed214
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@ -5639,4 +5639,171 @@ mod tests {
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]
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"###);
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}
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#[test]
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fn delete_embedder_with_user_provided_vectors() {
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// 1. Add two embedders
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// 2. Push two documents containing a simple vector
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// 3. The documents must not contain the vectors after the update as they are in the vectors db
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// 3. Delete the embedders
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// 4. The documents contain the vectors again
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let (index_scheduler, mut handle) = IndexScheduler::test(true, vec![]);
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let setting = meilisearch_types::settings::Settings::<Unchecked> {
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embedders: Setting::Set(maplit::btreemap! {
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S("manual") => Setting::Set(EmbeddingSettings {
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source: Setting::Set(milli::vector::settings::EmbedderSource::UserProvided),
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dimensions: Setting::Set(3),
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..Default::default()
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}),
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S("my_doggo_embedder") => Setting::Set(EmbeddingSettings {
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source: Setting::Set(milli::vector::settings::EmbedderSource::HuggingFace),
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model: Setting::Set(S("sentence-transformers/all-MiniLM-L6-v2")),
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revision: Setting::Set(S("e4ce9877abf3edfe10b0d82785e83bdcb973e22e")),
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document_template: Setting::Set(S("{{doc.doggo}}")),
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..Default::default()
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}),
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}),
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..Default::default()
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};
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index_scheduler
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.register(
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KindWithContent::SettingsUpdate {
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index_uid: S("doggos"),
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new_settings: Box::new(setting),
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is_deletion: false,
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allow_index_creation: true,
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},
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None,
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false,
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)
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.unwrap();
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handle.advance_one_successful_batch();
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let content = serde_json::json!(
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[
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{
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"id": 0,
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"doggo": "kefir",
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"_vectors": {
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"manual": vec![0, 0, 0],
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"my_doggo_embedder": vec![1; 384],
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}
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},
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{
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"id": 1,
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"doggo": "intel",
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"_vectors": {
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"manual": vec![1, 1, 1],
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}
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},
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]
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);
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let (uuid, mut file) = index_scheduler.create_update_file_with_uuid(0_u128).unwrap();
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let documents_count =
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read_json(serde_json::to_string_pretty(&content).unwrap().as_bytes(), &mut file)
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.unwrap();
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snapshot!(documents_count, @"2");
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file.persist().unwrap();
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index_scheduler
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.register(
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KindWithContent::DocumentAdditionOrUpdate {
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index_uid: S("doggos"),
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primary_key: None,
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method: ReplaceDocuments,
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content_file: uuid,
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documents_count,
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allow_index_creation: false,
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},
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None,
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false,
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)
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.unwrap();
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handle.advance_one_successful_batch();
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{
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let index = index_scheduler.index("doggos").unwrap();
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let rtxn = index.read_txn().unwrap();
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let field_ids_map = index.fields_ids_map(&rtxn).unwrap();
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let field_ids = field_ids_map.ids().collect::<Vec<_>>();
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let documents = index
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.all_documents(&rtxn)
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.unwrap()
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.map(|ret| obkv_to_json(&field_ids, &field_ids_map, ret.unwrap().1).unwrap())
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.collect::<Vec<_>>();
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snapshot!(serde_json::to_string(&documents).unwrap(), @r###"[{"id":0,"doggo":"kefir"},{"id":1,"doggo":"intel"}]"###);
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}
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{
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let setting = meilisearch_types::settings::Settings::<Unchecked> {
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embedders: Setting::Set(maplit::btreemap! {
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S("manual") => Setting::Reset,
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}),
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..Default::default()
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};
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index_scheduler
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.register(
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KindWithContent::SettingsUpdate {
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index_uid: S("doggos"),
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new_settings: Box::new(setting),
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is_deletion: false,
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allow_index_creation: true,
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},
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None,
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false,
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)
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.unwrap();
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handle.advance_one_successful_batch();
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}
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{
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let index = index_scheduler.index("doggos").unwrap();
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let rtxn = index.read_txn().unwrap();
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let field_ids_map = index.fields_ids_map(&rtxn).unwrap();
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let field_ids = field_ids_map.ids().collect::<Vec<_>>();
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let documents = index
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.all_documents(&rtxn)
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.unwrap()
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.map(|ret| obkv_to_json(&field_ids, &field_ids_map, ret.unwrap().1).unwrap())
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.collect::<Vec<_>>();
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snapshot!(serde_json::to_string(&documents).unwrap(), @r###"[{"id":0,"doggo":"kefir","_vectors":{"manual":{"embeddings":[[0.0,0.0,0.0]],"userProvided":true}}},{"id":1,"doggo":"intel","_vectors":{"manual":{"embeddings":[[1.0,1.0,1.0]],"userProvided":true}}}]"###);
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}
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{
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let setting = meilisearch_types::settings::Settings::<Unchecked> {
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embedders: Setting::Reset,
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..Default::default()
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};
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index_scheduler
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.register(
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KindWithContent::SettingsUpdate {
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index_uid: S("doggos"),
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new_settings: Box::new(setting),
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is_deletion: false,
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allow_index_creation: true,
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},
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None,
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false,
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)
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.unwrap();
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handle.advance_one_successful_batch();
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}
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{
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let index = index_scheduler.index("doggos").unwrap();
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let rtxn = index.read_txn().unwrap();
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let field_ids_map = index.fields_ids_map(&rtxn).unwrap();
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let field_ids = field_ids_map.ids().collect::<Vec<_>>();
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let documents = index
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.all_documents(&rtxn)
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.unwrap()
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.map(|ret| obkv_to_json(&field_ids, &field_ids_map, ret.unwrap().1).unwrap())
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.collect::<Vec<_>>();
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/// FIXME: redaction
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snapshot!(json_string!(serde_json::to_string(&documents).unwrap(), { "[]._vectors.doggo_embedder.embeddings" => "[vector]" }), @r###""[{\"id\":0,\"doggo\":\"kefir\",\"_vectors\":{\"manual\":{\"embeddings\":[[0.0,0.0,0.0]],\"userProvided\":true},\"my_doggo_embedder\":{\"embeddings\":[[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0]],\"userProvided\":true}}},{\"id\":1,\"doggo\":\"intel\",\"_vectors\":{\"manual\":{\"embeddings\":[[1.0,1.0,1.0]],\"userProvided\":true}}}]""###);
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}
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}
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}
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