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https://github.com/meilisearch/meilisearch.git
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No commits in common. "a01bc7b454558fbc9d30ae9ebf3cc53149a37ce6" and "68bbf674c9fe3641b33b867a6f43abf95c7fbe07" have entirely different histories.
a01bc7b454
...
68bbf674c9
@ -1335,6 +1335,7 @@ async fn error_add_documents_missing_document_id() {
|
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}
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#[actix_rt::test]
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#[should_panic]
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async fn error_document_field_limit_reached_in_one_document() {
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let server = Server::new().await;
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let index = server.index("test");
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@ -1351,7 +1352,7 @@ async fn error_document_field_limit_reached_in_one_document() {
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let documents = json!([big_object]);
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let (response, code) = index.update_documents(documents, Some("id")).await;
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snapshot!(code, @"202 Accepted");
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snapshot!(code, @"500 Internal Server Error");
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let response = index.wait_task(response.uid()).await;
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snapshot!(code, @"202 Accepted");
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@ -1359,21 +1360,16 @@ async fn error_document_field_limit_reached_in_one_document() {
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snapshot!(response,
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@r###"
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{
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"uid": "[uid]",
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"uid": 1,
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"indexUid": "test",
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"status": "failed",
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"status": "succeeded",
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"type": "documentAdditionOrUpdate",
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"canceledBy": null,
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"details": {
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"receivedDocuments": 1,
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"indexedDocuments": 0
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},
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"error": {
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"message": "A document cannot contain more than 65,535 fields.",
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"code": "max_fields_limit_exceeded",
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"type": "invalid_request",
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"link": "https://docs.meilisearch.com/errors#max_fields_limit_exceeded"
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"indexedDocuments": 1
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},
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"error": null,
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"duration": "[duration]",
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"enqueuedAt": "[date]",
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"startedAt": "[date]",
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@ -4346,10 +4346,10 @@ async fn federation_vector_two_indexes() {
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let (response, code) = server
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.multi_search(json!({"federation": {}, "queries": [
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{"indexUid" : "vectors-animal", "vector": [1.0, 0.0, 0.5], "hybrid": {"semanticRatio": 1.0, "embedder": "animal"}, "retrieveVectors": true},
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{"indexUid" : "vectors-animal", "vector": [1.0, 0.0, 0.5], "hybrid": {"semanticRatio": 1.0, "embedder": "animal"}},
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// joyful and energetic first
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{"indexUid": "vectors-sentiment", "vector": [0.8, 0.6], "hybrid": {"semanticRatio": 1.0, "embedder": "sentiment"}, "retrieveVectors": true},
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{"indexUid": "vectors-sentiment", "q": "dog", "retrieveVectors": true},
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{"indexUid": "vectors-sentiment", "vector": [0.8, 0.6], "hybrid": {"semanticRatio": 1.0, "embedder": "sentiment"}},
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{"indexUid": "vectors-sentiment", "q": "dog"},
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]}))
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.await;
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snapshot!(code, @"200 OK");
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@ -4364,16 +4364,7 @@ async fn federation_vector_two_indexes() {
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0.8,
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0.09,
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0.8
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],
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"sentiment": {
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"embeddings": [
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[
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0.800000011920929,
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0.30000001192092896
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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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"_federation": {
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"indexUid": "vectors-sentiment",
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@ -4388,17 +4379,7 @@ async fn federation_vector_two_indexes() {
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"sentiment": [
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0.8,
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0.3
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],
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"animal": {
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"embeddings": [
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[
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0.800000011920929,
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0.09000000357627869,
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0.800000011920929
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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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"_federation": {
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"indexUid": "vectors-animal",
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@ -4413,17 +4394,7 @@ async fn federation_vector_two_indexes() {
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"sentiment": [
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-1.0,
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0.1
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],
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"animal": {
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"embeddings": [
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[
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0.8500000238418579,
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0.019999999552965164,
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0.10000000149011612
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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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"_federation": {
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"indexUid": "vectors-animal",
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@ -4439,16 +4410,7 @@ async fn federation_vector_two_indexes() {
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0.9,
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0.8,
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0.05
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],
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"sentiment": {
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"embeddings": [
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[
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-0.10000000149011612,
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0.550000011920929
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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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"_federation": {
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"indexUid": "vectors-sentiment",
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@ -4464,16 +4426,7 @@ async fn federation_vector_two_indexes() {
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0.85,
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0.02,
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0.1
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],
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"sentiment": {
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"embeddings": [
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[
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-1.0,
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0.10000000149011612
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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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"_federation": {
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"indexUid": "vectors-sentiment",
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@ -4488,17 +4441,7 @@ async fn federation_vector_two_indexes() {
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"sentiment": [
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-0.2,
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0.65
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],
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"animal": {
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"embeddings": [
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[
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0.800000011920929,
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0.8999999761581421,
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0.5
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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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"_federation": {
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"indexUid": "vectors-animal",
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@ -4513,17 +4456,7 @@ async fn federation_vector_two_indexes() {
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"sentiment": [
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-0.1,
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0.55
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],
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"animal": {
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"embeddings": [
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[
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0.8999999761581421,
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0.800000011920929,
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0.05000000074505806
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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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"_federation": {
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"indexUid": "vectors-animal",
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@ -4539,16 +4472,7 @@ async fn federation_vector_two_indexes() {
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0.8,
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0.9,
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0.5
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],
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"sentiment": {
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"embeddings": [
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[
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-0.20000000298023224,
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0.6499999761581421
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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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"_federation": {
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"indexUid": "vectors-sentiment",
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@ -4568,8 +4492,8 @@ async fn federation_vector_two_indexes() {
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// hybrid search, distinct embedder
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let (response, code) = server
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.multi_search(json!({"federation": {}, "queries": [
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{"indexUid" : "vectors-animal", "vector": [1.0, 0.0, 0.5], "hybrid": {"semanticRatio": 1.0, "embedder": "animal"}, "showRankingScore": true, "retrieveVectors": true},
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{"indexUid": "vectors-sentiment", "vector": [-1, 0.6], "q": "beagle", "hybrid": {"semanticRatio": 1.0, "embedder": "sentiment"}, "showRankingScore": true, "retrieveVectors": true,},
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{"indexUid" : "vectors-animal", "vector": [1.0, 0.0, 0.5], "hybrid": {"semanticRatio": 1.0, "embedder": "animal"}, "showRankingScore": true},
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{"indexUid": "vectors-sentiment", "vector": [-1, 0.6], "q": "beagle", "hybrid": {"semanticRatio": 1.0, "embedder": "sentiment"}, "showRankingScore": true},
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]}))
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.await;
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snapshot!(code, @"200 OK");
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@ -4583,17 +4507,7 @@ async fn federation_vector_two_indexes() {
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"sentiment": [
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0.8,
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0.3
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],
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"animal": {
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"embeddings": [
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[
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0.800000011920929,
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||||
0.09000000357627869,
|
||||
0.800000011920929
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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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"_federation": {
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"indexUid": "vectors-animal",
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@ -4609,17 +4523,7 @@ async fn federation_vector_two_indexes() {
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"sentiment": [
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-1.0,
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0.1
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],
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"animal": {
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"embeddings": [
|
||||
[
|
||||
0.8500000238418579,
|
||||
0.019999999552965164,
|
||||
0.10000000149011612
|
||||
]
|
||||
],
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"regenerate": false
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}
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},
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"_federation": {
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"indexUid": "vectors-animal",
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@ -4636,16 +4540,7 @@ async fn federation_vector_two_indexes() {
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0.85,
|
||||
0.02,
|
||||
0.1
|
||||
],
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||||
"sentiment": {
|
||||
"embeddings": [
|
||||
[
|
||||
-1.0,
|
||||
0.10000000149011612
|
||||
]
|
||||
],
|
||||
"regenerate": false
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||||
}
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},
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"_federation": {
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||||
"indexUid": "vectors-sentiment",
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@ -4662,16 +4557,7 @@ async fn federation_vector_two_indexes() {
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0.8,
|
||||
0.9,
|
||||
0.5
|
||||
],
|
||||
"sentiment": {
|
||||
"embeddings": [
|
||||
[
|
||||
-0.20000000298023224,
|
||||
0.6499999761581421
|
||||
]
|
||||
],
|
||||
"regenerate": false
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||||
}
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},
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"_federation": {
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"indexUid": "vectors-sentiment",
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@ -4687,17 +4573,7 @@ async fn federation_vector_two_indexes() {
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"sentiment": [
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-0.2,
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0.65
|
||||
],
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||||
"animal": {
|
||||
"embeddings": [
|
||||
[
|
||||
0.800000011920929,
|
||||
0.8999999761581421,
|
||||
0.5
|
||||
]
|
||||
],
|
||||
"regenerate": false
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||||
}
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},
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||||
"_federation": {
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"indexUid": "vectors-animal",
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@ -4713,17 +4589,7 @@ async fn federation_vector_two_indexes() {
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"sentiment": [
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-0.1,
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||||
0.55
|
||||
],
|
||||
"animal": {
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||||
"embeddings": [
|
||||
[
|
||||
0.8999999761581421,
|
||||
0.800000011920929,
|
||||
0.05000000074505806
|
||||
]
|
||||
],
|
||||
"regenerate": false
|
||||
}
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||||
},
|
||||
"_federation": {
|
||||
"indexUid": "vectors-animal",
|
||||
@ -4740,16 +4606,7 @@ async fn federation_vector_two_indexes() {
|
||||
0.9,
|
||||
0.8,
|
||||
0.05
|
||||
],
|
||||
"sentiment": {
|
||||
"embeddings": [
|
||||
[
|
||||
-0.10000000149011612,
|
||||
0.550000011920929
|
||||
]
|
||||
],
|
||||
"regenerate": false
|
||||
}
|
||||
},
|
||||
"_federation": {
|
||||
"indexUid": "vectors-sentiment",
|
||||
@ -4766,16 +4623,7 @@ async fn federation_vector_two_indexes() {
|
||||
0.8,
|
||||
0.09,
|
||||
0.8
|
||||
],
|
||||
"sentiment": {
|
||||
"embeddings": [
|
||||
[
|
||||
0.800000011920929,
|
||||
0.30000001192092896
|
||||
]
|
||||
],
|
||||
"regenerate": false
|
||||
}
|
||||
},
|
||||
"_federation": {
|
||||
"indexUid": "vectors-sentiment",
|
||||
|
||||
@ -249,7 +249,7 @@ async fn user_provided_embeddings_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "Bad embedder configuration in the document with id: `0`. Missing field `._vectors.manual.regenerate`\n - note: `._vectors.manual` must be an array of floats, an array of arrays of floats, or an object with field `regenerate`",
|
||||
"message": "Bad embedder configuration in the document with id: `\"0\"`. Missing field `regenerate` inside `.manual`",
|
||||
"code": "invalid_vectors_type",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_vectors_type"
|
||||
@ -278,7 +278,7 @@ async fn user_provided_embeddings_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "Bad embedder configuration in the document with id: `0`. Missing field `._vectors.manual.regenerate`\n - note: `._vectors.manual` must be an array of floats, an array of arrays of floats, or an object with field `regenerate`",
|
||||
"message": "Bad embedder configuration in the document with id: `\"0\"`. Missing field `regenerate` inside `.manual`",
|
||||
"code": "invalid_vectors_type",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_vectors_type"
|
||||
@ -308,7 +308,7 @@ async fn user_provided_embeddings_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "Bad embedder configuration in the document with id: `0`. Could not parse `._vectors.manual.regenerate`: invalid type: string \"yes please\", expected a boolean at line 1 column 26",
|
||||
"message": "Bad embedder configuration in the document with id: `\"0\"`. Invalid value type at `.manual.regenerate`: expected a boolean, but found a string: `\"yes please\"`",
|
||||
"code": "invalid_vectors_type",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_vectors_type"
|
||||
@ -320,7 +320,8 @@ async fn user_provided_embeddings_error() {
|
||||
}
|
||||
"###);
|
||||
|
||||
let documents = json!({"id": 0, "name": "kefir", "_vectors": { "manual": { "embeddings": true, "regenerate": true }}});
|
||||
let documents =
|
||||
json!({"id": 0, "name": "kefir", "_vectors": { "manual": { "embeddings": true }}});
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
let task = index.wait_task(value.uid()).await;
|
||||
@ -336,7 +337,7 @@ async fn user_provided_embeddings_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "Bad embedder configuration in the document with id: `0`. Invalid value type at `._vectors.manual.embeddings`: expected null or an array, but found a boolean: `true`",
|
||||
"message": "Bad embedder configuration in the document with id: `\"0\"`. Invalid value type at `.manual.embeddings`: expected null or an array, but found a boolean: `true`",
|
||||
"code": "invalid_vectors_type",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_vectors_type"
|
||||
@ -348,7 +349,8 @@ async fn user_provided_embeddings_error() {
|
||||
}
|
||||
"###);
|
||||
|
||||
let documents = json!({"id": 0, "name": "kefir", "_vectors": { "manual": { "embeddings": [true], "regenerate": true }}});
|
||||
let documents =
|
||||
json!({"id": 0, "name": "kefir", "_vectors": { "manual": { "embeddings": [true] }}});
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
let task = index.wait_task(value.uid()).await;
|
||||
@ -364,7 +366,7 @@ async fn user_provided_embeddings_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "Bad embedder configuration in the document with id: `0`. Invalid value type at `._vectors.manual.embeddings[0]`: expected a number or an array, but found a boolean: `true`",
|
||||
"message": "Bad embedder configuration in the document with id: `\"0\"`. Invalid value type at `.manual.embeddings[0]`: expected a number or an array, but found a boolean: `true`",
|
||||
"code": "invalid_vectors_type",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_vectors_type"
|
||||
@ -376,7 +378,8 @@ async fn user_provided_embeddings_error() {
|
||||
}
|
||||
"###);
|
||||
|
||||
let documents = json!({"id": 0, "name": "kefir", "_vectors": { "manual": { "embeddings": [[true]], "regenerate": false }}});
|
||||
let documents =
|
||||
json!({"id": 0, "name": "kefir", "_vectors": { "manual": { "embeddings": [[true]] }}});
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
let task = index.wait_task(value.uid()).await;
|
||||
@ -392,7 +395,7 @@ async fn user_provided_embeddings_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "Bad embedder configuration in the document with id: `0`. Invalid value type at `._vectors.manual.embeddings[0][0]`: expected a number, but found a boolean: `true`",
|
||||
"message": "Bad embedder configuration in the document with id: `\"0\"`. Invalid value type at `.manual.embeddings[0][0]`: expected a number, but found a boolean: `true`",
|
||||
"code": "invalid_vectors_type",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_vectors_type"
|
||||
@ -433,7 +436,7 @@ async fn user_provided_embeddings_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "Bad embedder configuration in the document with id: `0`. Invalid value type at `._vectors.manual.embeddings[1]`: expected a number, but found an array: `[0.2,0.3]`",
|
||||
"message": "Bad embedder configuration in the document with id: `\"0\"`. Invalid value type at `.manual.embeddings[1]`: expected a number, but found an array: `[0.2,0.3]`",
|
||||
"code": "invalid_vectors_type",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_vectors_type"
|
||||
@ -461,7 +464,7 @@ async fn user_provided_embeddings_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "Bad embedder configuration in the document with id: `0`. Invalid value type at `._vectors.manual.embeddings[1]`: expected an array, but found a number: `0.3`",
|
||||
"message": "Bad embedder configuration in the document with id: `\"0\"`. Invalid value type at `.manual.embeddings[1]`: expected an array, but found a number: `0.3`",
|
||||
"code": "invalid_vectors_type",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_vectors_type"
|
||||
@ -489,7 +492,7 @@ async fn user_provided_embeddings_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "Bad embedder configuration in the document with id: `0`. Invalid value type at `._vectors.manual.embeddings[0][1]`: expected a number, but found a boolean: `true`",
|
||||
"message": "Bad embedder configuration in the document with id: `\"0\"`. Invalid value type at `.manual.embeddings[0][1]`: expected a number, but found a boolean: `true`",
|
||||
"code": "invalid_vectors_type",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_vectors_type"
|
||||
@ -529,7 +532,7 @@ async fn user_provided_vectors_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "While embedding documents for embedder `manual`: no vectors provided for document `40` and at least 4 other document(s)\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: opt-out for a document with `_vectors.manual: null`",
|
||||
"message": "While embedding documents for embedder `manual`: no vectors provided for document \"40\" and at least 4 other document(s)\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: opt-out for a document with `_vectors.manual: null`",
|
||||
"code": "vector_embedding_error",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#vector_embedding_error"
|
||||
@ -558,7 +561,7 @@ async fn user_provided_vectors_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "While embedding documents for embedder `manual`: no vectors provided for document `42`\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: try replacing `_vector` by `_vectors` in 1 document(s).",
|
||||
"message": "While embedding documents for embedder `manual`: no vectors provided for document \"42\"\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: try replacing `_vector` by `_vectors` in 1 document(s).",
|
||||
"code": "vector_embedding_error",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#vector_embedding_error"
|
||||
@ -587,7 +590,7 @@ async fn user_provided_vectors_error() {
|
||||
"indexedDocuments": 0
|
||||
},
|
||||
"error": {
|
||||
"message": "While embedding documents for embedder `manual`: no vectors provided for document `42`\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: try replacing `_vectors.manaul` by `_vectors.manual` in 1 document(s).",
|
||||
"message": "While embedding documents for embedder `manual`: no vectors provided for document \"42\"\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: try replacing `_vectors.manaul` by `_vectors.manual` in 1 document(s).",
|
||||
"code": "vector_embedding_error",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#vector_embedding_error"
|
||||
|
||||
@ -122,7 +122,7 @@ and can not be more than 512 bytes.", .document_id.to_string()
|
||||
#[error("The `_vectors` field in the document with id: `{document_id}` is not an object. Was expecting an object with a key for each embedder with manually provided vectors, but instead got `{value}`")]
|
||||
InvalidVectorsMapType { document_id: String, value: Value },
|
||||
#[error("Bad embedder configuration in the document with id: `{document_id}`. {error}")]
|
||||
InvalidVectorsEmbedderConf { document_id: String, error: String },
|
||||
InvalidVectorsEmbedderConf { document_id: String, error: deserr::errors::JsonError },
|
||||
#[error("{0}")]
|
||||
InvalidFilter(String),
|
||||
#[error("Invalid type for filter subexpression: expected: {}, found: {1}.", .0.join(", "))]
|
||||
|
||||
@ -97,7 +97,7 @@ impl<'doc> Insertion<'doc> {
|
||||
doc_alloc: &'doc Bump,
|
||||
embedders: &'doc EmbeddingConfigs,
|
||||
) -> Result<Option<VectorDocumentFromVersions<'doc>>> {
|
||||
VectorDocumentFromVersions::new(self.external_document_id, &self.new, doc_alloc, embedders)
|
||||
VectorDocumentFromVersions::new(&self.new, doc_alloc, embedders)
|
||||
}
|
||||
}
|
||||
|
||||
@ -169,7 +169,7 @@ impl<'doc> Update<'doc> {
|
||||
doc_alloc: &'doc Bump,
|
||||
embedders: &'doc EmbeddingConfigs,
|
||||
) -> Result<Option<VectorDocumentFromVersions<'doc>>> {
|
||||
VectorDocumentFromVersions::new(self.external_document_id, &self.new, doc_alloc, embedders)
|
||||
VectorDocumentFromVersions::new(&self.new, doc_alloc, embedders)
|
||||
}
|
||||
|
||||
pub fn merged_vectors<Mapper: FieldIdMapper>(
|
||||
@ -181,22 +181,10 @@ impl<'doc> Update<'doc> {
|
||||
embedders: &'doc EmbeddingConfigs,
|
||||
) -> Result<Option<MergedVectorDocument<'doc>>> {
|
||||
if self.has_deletion {
|
||||
MergedVectorDocument::without_db(
|
||||
self.external_document_id,
|
||||
&self.new,
|
||||
doc_alloc,
|
||||
embedders,
|
||||
)
|
||||
MergedVectorDocument::without_db(&self.new, doc_alloc, embedders)
|
||||
} else {
|
||||
MergedVectorDocument::with_db(
|
||||
self.docid,
|
||||
self.external_document_id,
|
||||
index,
|
||||
rtxn,
|
||||
mapper,
|
||||
&self.new,
|
||||
doc_alloc,
|
||||
embedders,
|
||||
self.docid, index, rtxn, mapper, &self.new, doc_alloc, embedders,
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
@ -126,7 +126,7 @@ impl<'a, 'extractor> Extractor<'extractor> for EmbeddingExtractor<'a> {
|
||||
.into_vec(&context.doc_alloc, embedder_name)
|
||||
.map_err(|error| UserError::InvalidVectorsEmbedderConf {
|
||||
document_id: update.external_document_id().to_string(),
|
||||
error: error.to_string(),
|
||||
error,
|
||||
})?,
|
||||
);
|
||||
} else if new_vectors.regenerate {
|
||||
@ -151,7 +151,6 @@ impl<'a, 'extractor> Extractor<'extractor> for EmbeddingExtractor<'a> {
|
||||
if new_rendered != old_rendered {
|
||||
chunks.set_autogenerated(
|
||||
update.docid(),
|
||||
update.external_document_id(),
|
||||
new_rendered,
|
||||
&unused_vectors_distribution,
|
||||
)?;
|
||||
@ -179,7 +178,6 @@ impl<'a, 'extractor> Extractor<'extractor> for EmbeddingExtractor<'a> {
|
||||
if new_rendered != old_rendered {
|
||||
chunks.set_autogenerated(
|
||||
update.docid(),
|
||||
update.external_document_id(),
|
||||
new_rendered,
|
||||
&unused_vectors_distribution,
|
||||
)?;
|
||||
@ -212,7 +210,7 @@ impl<'a, 'extractor> Extractor<'extractor> for EmbeddingExtractor<'a> {
|
||||
document_id: insertion
|
||||
.external_document_id()
|
||||
.to_string(),
|
||||
error: error.to_string(),
|
||||
error,
|
||||
})?,
|
||||
);
|
||||
} else if new_vectors.regenerate {
|
||||
@ -223,7 +221,6 @@ impl<'a, 'extractor> Extractor<'extractor> for EmbeddingExtractor<'a> {
|
||||
)?;
|
||||
chunks.set_autogenerated(
|
||||
insertion.docid(),
|
||||
insertion.external_document_id(),
|
||||
rendered,
|
||||
&unused_vectors_distribution,
|
||||
)?;
|
||||
@ -236,7 +233,6 @@ impl<'a, 'extractor> Extractor<'extractor> for EmbeddingExtractor<'a> {
|
||||
)?;
|
||||
chunks.set_autogenerated(
|
||||
insertion.docid(),
|
||||
insertion.external_document_id(),
|
||||
rendered,
|
||||
&unused_vectors_distribution,
|
||||
)?;
|
||||
@ -272,7 +268,6 @@ struct Chunks<'a, 'extractor> {
|
||||
user_provided: &'a RefCell<EmbeddingExtractorData<'extractor>>,
|
||||
threads: &'a ThreadPoolNoAbort,
|
||||
sender: &'a EmbeddingSender<'a>,
|
||||
has_manual_generation: Option<&'a str>,
|
||||
}
|
||||
|
||||
impl<'a, 'extractor> Chunks<'a, 'extractor> {
|
||||
@ -302,22 +297,15 @@ impl<'a, 'extractor> Chunks<'a, 'extractor> {
|
||||
embedder_id,
|
||||
embedder_name,
|
||||
user_provided,
|
||||
has_manual_generation: None,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn set_autogenerated(
|
||||
&mut self,
|
||||
docid: DocumentId,
|
||||
external_docid: &'a str,
|
||||
rendered: &'a str,
|
||||
unused_vectors_distribution: &UnusedVectorsDistributionBump,
|
||||
) -> Result<()> {
|
||||
let is_manual = matches!(&self.embedder, &Embedder::UserProvided(_));
|
||||
if is_manual {
|
||||
self.has_manual_generation.get_or_insert(external_docid);
|
||||
}
|
||||
|
||||
if self.texts.len() < self.texts.capacity() {
|
||||
self.texts.push(rendered);
|
||||
self.ids.push(docid);
|
||||
@ -334,7 +322,6 @@ impl<'a, 'extractor> Chunks<'a, 'extractor> {
|
||||
unused_vectors_distribution,
|
||||
self.threads,
|
||||
self.sender,
|
||||
self.has_manual_generation.take(),
|
||||
)
|
||||
}
|
||||
|
||||
@ -352,7 +339,6 @@ impl<'a, 'extractor> Chunks<'a, 'extractor> {
|
||||
unused_vectors_distribution,
|
||||
self.threads,
|
||||
self.sender,
|
||||
self.has_manual_generation,
|
||||
);
|
||||
// optimization: don't run bvec dtors as they only contain bumpalo allocated stuff
|
||||
std::mem::forget(self);
|
||||
@ -370,46 +356,7 @@ impl<'a, 'extractor> Chunks<'a, 'extractor> {
|
||||
unused_vectors_distribution: &UnusedVectorsDistributionBump,
|
||||
threads: &ThreadPoolNoAbort,
|
||||
sender: &EmbeddingSender<'a>,
|
||||
has_manual_generation: Option<&'a str>,
|
||||
) -> Result<()> {
|
||||
if let Some(external_docid) = has_manual_generation {
|
||||
let mut msg = format!(
|
||||
r"While embedding documents for embedder `{embedder_name}`: no vectors provided for document `{}`{}",
|
||||
external_docid,
|
||||
if ids.len() > 1 {
|
||||
format!(" and at least {} other document(s)", ids.len() - 1)
|
||||
} else {
|
||||
"".to_string()
|
||||
}
|
||||
);
|
||||
|
||||
msg += &format!("\n- Note: `{embedder_name}` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.{embedder_name}`.");
|
||||
|
||||
let mut hint_count = 0;
|
||||
|
||||
for (vector_misspelling, count) in possible_embedding_mistakes.vector_mistakes().take(2)
|
||||
{
|
||||
msg += &format!("\n- Hint: try replacing `{vector_misspelling}` by `_vectors` in {count} document(s).");
|
||||
hint_count += 1;
|
||||
}
|
||||
|
||||
for (embedder_misspelling, count) in possible_embedding_mistakes
|
||||
.embedder_mistakes_bump(embedder_name, unused_vectors_distribution)
|
||||
.take(2)
|
||||
{
|
||||
msg += &format!("\n- Hint: try replacing `_vectors.{embedder_misspelling}` by `_vectors.{embedder_name}` in {count} document(s).");
|
||||
hint_count += 1;
|
||||
}
|
||||
|
||||
if hint_count == 0 {
|
||||
msg += &format!(
|
||||
"\n- Hint: opt-out for a document with `_vectors.{embedder_name}: null`"
|
||||
);
|
||||
}
|
||||
|
||||
return Err(crate::Error::UserError(crate::UserError::DocumentEmbeddingError(msg)));
|
||||
}
|
||||
|
||||
let res = match embedder.embed_chunks_ref(texts.as_slice(), threads) {
|
||||
Ok(embeddings) => {
|
||||
for (docid, embedding) in ids.into_iter().zip(embeddings) {
|
||||
|
||||
@ -41,11 +41,6 @@ impl<'de, 'p, 'indexer: 'de, Mapper: MutFieldIdMapper> Visitor<'de>
|
||||
where
|
||||
A: serde::de::MapAccess<'de>,
|
||||
{
|
||||
// We need to remember if we encountered a semantic error, because raw values don't like to be parsed partially
|
||||
// (trying to do so results in parsing errors).
|
||||
// So we'll exhaust all keys and values even if we encounter an error, and we'll then return any error we detected.
|
||||
let mut attribute_limit_reached = false;
|
||||
let mut document_id_extraction_error = None;
|
||||
let mut docid = None;
|
||||
|
||||
while let Some(((level_name, right), (fid, fields_ids_map))) =
|
||||
@ -54,35 +49,19 @@ impl<'de, 'p, 'indexer: 'de, Mapper: MutFieldIdMapper> Visitor<'de>
|
||||
visitor: MutFieldIdMapVisitor(self.fields_ids_map),
|
||||
})?
|
||||
{
|
||||
let Some(_fid) = fid else {
|
||||
return Ok(Err(crate::UserError::AttributeLimitReached));
|
||||
};
|
||||
self.fields_ids_map = fields_ids_map;
|
||||
|
||||
let value: &'de RawValue = map.next_value()?;
|
||||
if attribute_limit_reached || document_id_extraction_error.is_some() {
|
||||
continue;
|
||||
}
|
||||
|
||||
let Some(_fid) = fid else {
|
||||
attribute_limit_reached = true;
|
||||
continue;
|
||||
};
|
||||
|
||||
match match_component(level_name, right, value, self.indexer, &mut docid) {
|
||||
ControlFlow::Continue(()) => continue,
|
||||
ControlFlow::Break(Err(err)) => return Err(serde::de::Error::custom(err)),
|
||||
ControlFlow::Break(Ok(err)) => {
|
||||
document_id_extraction_error = Some(err);
|
||||
continue;
|
||||
ControlFlow::Break(Ok(err)) => return Ok(Ok(Err(err))),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// return previously detected errors
|
||||
if attribute_limit_reached {
|
||||
return Ok(Err(UserError::AttributeLimitReached));
|
||||
}
|
||||
if let Some(document_id_extraction_error) = document_id_extraction_error {
|
||||
return Ok(Ok(Err(document_id_extraction_error)));
|
||||
}
|
||||
|
||||
Ok(Ok(match docid {
|
||||
Some(docid) => Ok(docid),
|
||||
|
||||
@ -12,7 +12,7 @@ use super::indexer::de::DeserrRawValue;
|
||||
use crate::documents::FieldIdMapper;
|
||||
use crate::index::IndexEmbeddingConfig;
|
||||
use crate::vector::parsed_vectors::{
|
||||
RawVectors, RawVectorsError, VectorOrArrayOfVectors, RESERVED_VECTORS_FIELD_NAME,
|
||||
RawVectors, VectorOrArrayOfVectors, RESERVED_VECTORS_FIELD_NAME,
|
||||
};
|
||||
use crate::vector::{ArroyWrapper, Embedding, EmbeddingConfigs};
|
||||
use crate::{DocumentId, Index, InternalError, Result, UserError};
|
||||
@ -143,14 +143,7 @@ impl<'t> VectorDocument<'t> for VectorDocumentFromDb<'t> {
|
||||
Ok((&*config_name, entry))
|
||||
})
|
||||
.chain(self.vectors_field.iter().flat_map(|map| map.iter()).map(|(name, value)| {
|
||||
Ok((
|
||||
name,
|
||||
entry_from_raw_value(value, false).map_err(|_| {
|
||||
InternalError::Serialization(crate::SerializationError::Decoding {
|
||||
db_name: Some(crate::index::db_name::VECTOR_ARROY),
|
||||
})
|
||||
})?,
|
||||
))
|
||||
Ok((name, entry_from_raw_value(value, false).map_err(InternalError::SerdeJson)?))
|
||||
}))
|
||||
}
|
||||
|
||||
@ -162,38 +155,20 @@ impl<'t> VectorDocument<'t> for VectorDocumentFromDb<'t> {
|
||||
Some(self.entry_from_db(embedder_id, config)?)
|
||||
}
|
||||
None => match self.vectors_field.as_ref().and_then(|obkv| obkv.get(key)) {
|
||||
Some(embedding_from_doc) => {
|
||||
Some(entry_from_raw_value(embedding_from_doc, false).map_err(|_| {
|
||||
InternalError::Serialization(crate::SerializationError::Decoding {
|
||||
db_name: Some(crate::index::db_name::VECTOR_ARROY),
|
||||
})
|
||||
})?)
|
||||
}
|
||||
Some(embedding_from_doc) => Some(
|
||||
entry_from_raw_value(embedding_from_doc, false)
|
||||
.map_err(InternalError::SerdeJson)?,
|
||||
),
|
||||
None => None,
|
||||
},
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
fn entry_from_raw_value_user<'doc>(
|
||||
external_docid: &str,
|
||||
embedder_name: &str,
|
||||
value: &'doc RawValue,
|
||||
has_configured_embedder: bool,
|
||||
) -> Result<VectorEntry<'doc>> {
|
||||
entry_from_raw_value(value, has_configured_embedder).map_err(|error| {
|
||||
UserError::InvalidVectorsEmbedderConf {
|
||||
document_id: external_docid.to_string(),
|
||||
error: error.msg(embedder_name),
|
||||
}
|
||||
.into()
|
||||
})
|
||||
}
|
||||
|
||||
fn entry_from_raw_value(
|
||||
value: &RawValue,
|
||||
has_configured_embedder: bool,
|
||||
) -> std::result::Result<VectorEntry<'_>, RawVectorsError> {
|
||||
) -> std::result::Result<VectorEntry<'_>, serde_json::Error> {
|
||||
let value: RawVectors = RawVectors::from_raw_value(value)?;
|
||||
|
||||
Ok(match value {
|
||||
@ -219,14 +194,12 @@ fn entry_from_raw_value(
|
||||
}
|
||||
|
||||
pub struct VectorDocumentFromVersions<'doc> {
|
||||
external_document_id: &'doc str,
|
||||
vectors: RawMap<'doc>,
|
||||
embedders: &'doc EmbeddingConfigs,
|
||||
}
|
||||
|
||||
impl<'doc> VectorDocumentFromVersions<'doc> {
|
||||
pub fn new(
|
||||
external_document_id: &'doc str,
|
||||
versions: &Versions<'doc>,
|
||||
bump: &'doc Bump,
|
||||
embedders: &'doc EmbeddingConfigs,
|
||||
@ -235,7 +208,7 @@ impl<'doc> VectorDocumentFromVersions<'doc> {
|
||||
if let Some(vectors_field) = document.vectors_field()? {
|
||||
let vectors =
|
||||
RawMap::from_raw_value(vectors_field, bump).map_err(UserError::SerdeJson)?;
|
||||
Ok(Some(Self { external_document_id, vectors, embedders }))
|
||||
Ok(Some(Self { vectors, embedders }))
|
||||
} else {
|
||||
Ok(None)
|
||||
}
|
||||
@ -245,24 +218,16 @@ impl<'doc> VectorDocumentFromVersions<'doc> {
|
||||
impl<'doc> VectorDocument<'doc> for VectorDocumentFromVersions<'doc> {
|
||||
fn iter_vectors(&self) -> impl Iterator<Item = Result<(&'doc str, VectorEntry<'doc>)>> {
|
||||
self.vectors.iter().map(|(embedder, vectors)| {
|
||||
let vectors = entry_from_raw_value_user(
|
||||
self.external_document_id,
|
||||
embedder,
|
||||
vectors,
|
||||
self.embedders.contains(embedder),
|
||||
)?;
|
||||
let vectors = entry_from_raw_value(vectors, self.embedders.contains(embedder))
|
||||
.map_err(UserError::SerdeJson)?;
|
||||
Ok((embedder, vectors))
|
||||
})
|
||||
}
|
||||
|
||||
fn vectors_for_key(&self, key: &str) -> Result<Option<VectorEntry<'doc>>> {
|
||||
let Some(vectors) = self.vectors.get(key) else { return Ok(None) };
|
||||
let vectors = entry_from_raw_value_user(
|
||||
self.external_document_id,
|
||||
key,
|
||||
vectors,
|
||||
self.embedders.contains(key),
|
||||
)?;
|
||||
let vectors = entry_from_raw_value(vectors, self.embedders.contains(key))
|
||||
.map_err(UserError::SerdeJson)?;
|
||||
Ok(Some(vectors))
|
||||
}
|
||||
}
|
||||
@ -273,10 +238,8 @@ pub struct MergedVectorDocument<'doc> {
|
||||
}
|
||||
|
||||
impl<'doc> MergedVectorDocument<'doc> {
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub fn with_db<Mapper: FieldIdMapper>(
|
||||
docid: DocumentId,
|
||||
external_document_id: &'doc str,
|
||||
index: &'doc Index,
|
||||
rtxn: &'doc RoTxn,
|
||||
db_fields_ids_map: &'doc Mapper,
|
||||
@ -285,20 +248,16 @@ impl<'doc> MergedVectorDocument<'doc> {
|
||||
embedders: &'doc EmbeddingConfigs,
|
||||
) -> Result<Option<Self>> {
|
||||
let db = VectorDocumentFromDb::new(docid, index, rtxn, db_fields_ids_map, doc_alloc)?;
|
||||
let new_doc =
|
||||
VectorDocumentFromVersions::new(&external_document_id, versions, doc_alloc, embedders)?;
|
||||
let new_doc = VectorDocumentFromVersions::new(versions, doc_alloc, embedders)?;
|
||||
Ok(if db.is_none() && new_doc.is_none() { None } else { Some(Self { new_doc, db }) })
|
||||
}
|
||||
|
||||
pub fn without_db(
|
||||
external_document_id: &'doc str,
|
||||
versions: &Versions<'doc>,
|
||||
doc_alloc: &'doc Bump,
|
||||
embedders: &'doc EmbeddingConfigs,
|
||||
) -> Result<Option<Self>> {
|
||||
let Some(new_doc) =
|
||||
VectorDocumentFromVersions::new(external_document_id, versions, doc_alloc, embedders)?
|
||||
else {
|
||||
let Some(new_doc) = VectorDocumentFromVersions::new(versions, doc_alloc, embedders)? else {
|
||||
return Ok(None);
|
||||
};
|
||||
Ok(Some(Self { new_doc: Some(new_doc), db: None }))
|
||||
|
||||
@ -648,7 +648,7 @@ impl Embedder {
|
||||
Embedder::HuggingFace(embedder) => embedder.chunk_count_hint(),
|
||||
Embedder::OpenAi(embedder) => embedder.chunk_count_hint(),
|
||||
Embedder::Ollama(embedder) => embedder.chunk_count_hint(),
|
||||
Embedder::UserProvided(_) => 100,
|
||||
Embedder::UserProvided(_) => 1,
|
||||
Embedder::Rest(embedder) => embedder.chunk_count_hint(),
|
||||
}
|
||||
}
|
||||
|
||||
@ -19,54 +19,10 @@ pub enum RawVectors<'doc> {
|
||||
ImplicitlyUserProvided(#[serde(borrow)] Option<&'doc RawValue>),
|
||||
}
|
||||
|
||||
pub enum RawVectorsError {
|
||||
DeserializeSeq { index: usize, error: String },
|
||||
DeserializeKey { error: String },
|
||||
DeserializeRegenerate { error: String },
|
||||
DeserializeEmbeddings { error: String },
|
||||
UnknownField { field: String },
|
||||
MissingRegenerate,
|
||||
WrongKind { kind: &'static str, value: String },
|
||||
Parsing(serde_json::Error),
|
||||
}
|
||||
|
||||
impl RawVectorsError {
|
||||
pub fn msg(self, embedder_name: &str) -> String {
|
||||
match self {
|
||||
RawVectorsError::DeserializeSeq { index, error } => format!(
|
||||
"Could not parse `._vectors.{embedder_name}[{index}]`: {error}"
|
||||
),
|
||||
RawVectorsError::DeserializeKey { error } => format!(
|
||||
"Could not parse a field at `._vectors.{embedder_name}`: {error}"
|
||||
),
|
||||
RawVectorsError::DeserializeRegenerate { error } => format!(
|
||||
"Could not parse `._vectors.{embedder_name}.regenerate`: {error}"
|
||||
),
|
||||
RawVectorsError::DeserializeEmbeddings { error } => format!(
|
||||
"Could not parse `._vectors.{embedder_name}.embeddings`: {error}"
|
||||
),
|
||||
RawVectorsError::UnknownField { field } => format!(
|
||||
"Unexpected field `._vectors.{embedder_name}.{field}`\n \
|
||||
- note: the allowed fields are `regenerate` and `embeddings`"
|
||||
),
|
||||
RawVectorsError::MissingRegenerate => format!(
|
||||
"Missing field `._vectors.{embedder_name}.regenerate`\n \
|
||||
- note: `._vectors.{embedder_name}` must be an array of floats, an array of arrays of floats, or an object with field `regenerate`"
|
||||
),
|
||||
RawVectorsError::WrongKind { kind, value } => format!(
|
||||
"Expected `._vectors.{embedder_name}` to be an array of floats, an array of arrays of floats, or an object with at least the field `regenerate`, but got the {kind} `{value}`"
|
||||
),
|
||||
RawVectorsError::Parsing(error) => format!(
|
||||
"Could not parse `._vectors.{embedder_name}`: {error}"
|
||||
),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'doc> RawVectors<'doc> {
|
||||
pub fn from_raw_value(raw: &'doc RawValue) -> Result<Self, RawVectorsError> {
|
||||
pub fn from_raw_value(raw: &'doc RawValue) -> Result<Self, serde_json::Error> {
|
||||
use serde::de::Deserializer as _;
|
||||
Ok(match raw.deserialize_any(RawVectorsVisitor).map_err(RawVectorsError::Parsing)?? {
|
||||
Ok(match raw.deserialize_any(RawVectorsVisitor)? {
|
||||
RawVectorsVisitorValue::ImplicitNone => RawVectors::ImplicitlyUserProvided(None),
|
||||
RawVectorsVisitorValue::Implicit => RawVectors::ImplicitlyUserProvided(Some(raw)),
|
||||
RawVectorsVisitorValue::Explicit { regenerate, embeddings } => {
|
||||
@ -85,7 +41,7 @@ enum RawVectorsVisitorValue<'doc> {
|
||||
}
|
||||
|
||||
impl<'doc> serde::de::Visitor<'doc> for RawVectorsVisitor {
|
||||
type Value = std::result::Result<RawVectorsVisitorValue<'doc>, RawVectorsError>;
|
||||
type Value = RawVectorsVisitorValue<'doc>;
|
||||
|
||||
fn expecting(&self, formatter: &mut std::fmt::Formatter) -> std::fmt::Result {
|
||||
write!(formatter, "a map containing at least `regenerate`, or an array of floats`")
|
||||
@ -95,7 +51,7 @@ impl<'doc> serde::de::Visitor<'doc> for RawVectorsVisitor {
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Ok(RawVectorsVisitorValue::ImplicitNone))
|
||||
Ok(RawVectorsVisitorValue::ImplicitNone)
|
||||
}
|
||||
|
||||
fn visit_some<D>(self, deserializer: D) -> Result<Self::Value, D::Error>
|
||||
@ -109,150 +65,42 @@ impl<'doc> serde::de::Visitor<'doc> for RawVectorsVisitor {
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Ok(RawVectorsVisitorValue::ImplicitNone))
|
||||
Ok(RawVectorsVisitorValue::ImplicitNone)
|
||||
}
|
||||
|
||||
fn visit_seq<A>(self, mut seq: A) -> Result<Self::Value, A::Error>
|
||||
where
|
||||
A: serde::de::SeqAccess<'doc>,
|
||||
{
|
||||
let mut index = 0;
|
||||
// must consume all elements or parsing fails
|
||||
loop {
|
||||
match seq.next_element::<&RawValue>() {
|
||||
Ok(Some(_)) => index += 1,
|
||||
Err(error) => {
|
||||
return Ok(Err(RawVectorsError::DeserializeSeq {
|
||||
index,
|
||||
error: error.to_string(),
|
||||
}))
|
||||
}
|
||||
Ok(None) => break,
|
||||
};
|
||||
}
|
||||
Ok(Ok(RawVectorsVisitorValue::Implicit))
|
||||
while let Some(_) = seq.next_element::<&RawValue>()? {}
|
||||
Ok(RawVectorsVisitorValue::Implicit)
|
||||
}
|
||||
|
||||
fn visit_map<A>(self, mut map: A) -> Result<Self::Value, A::Error>
|
||||
where
|
||||
A: serde::de::MapAccess<'doc>,
|
||||
{
|
||||
use serde::de::Error as _;
|
||||
let mut regenerate = None;
|
||||
let mut embeddings = None;
|
||||
loop {
|
||||
match map.next_key::<&str>() {
|
||||
Ok(Some("regenerate")) => {
|
||||
let value: bool = match map.next_value() {
|
||||
Ok(value) => value,
|
||||
Err(error) => {
|
||||
return Ok(Err(RawVectorsError::DeserializeRegenerate {
|
||||
error: error.to_string(),
|
||||
}))
|
||||
}
|
||||
};
|
||||
while let Some(s) = map.next_key()? {
|
||||
match s {
|
||||
"regenerate" => {
|
||||
let value: bool = map.next_value()?;
|
||||
regenerate = Some(value);
|
||||
}
|
||||
Ok(Some("embeddings")) => {
|
||||
let value: &RawValue = match map.next_value() {
|
||||
Ok(value) => value,
|
||||
Err(error) => {
|
||||
return Ok(Err(RawVectorsError::DeserializeEmbeddings {
|
||||
error: error.to_string(),
|
||||
}))
|
||||
}
|
||||
};
|
||||
"embeddings" => {
|
||||
let value: &RawValue = map.next_value()?;
|
||||
embeddings = Some(value);
|
||||
}
|
||||
Ok(Some(other)) => {
|
||||
return Ok(Err(RawVectorsError::UnknownField { field: other.to_string() }))
|
||||
}
|
||||
Ok(None) => break,
|
||||
Err(error) => {
|
||||
return Ok(Err(RawVectorsError::DeserializeKey { error: error.to_string() }))
|
||||
}
|
||||
other => return Err(A::Error::unknown_field(other, &["regenerate", "embeddings"])),
|
||||
}
|
||||
}
|
||||
let Some(regenerate) = regenerate else {
|
||||
return Ok(Err(RawVectorsError::MissingRegenerate));
|
||||
return Err(A::Error::missing_field("regenerate"));
|
||||
};
|
||||
Ok(Ok(RawVectorsVisitorValue::Explicit { regenerate, embeddings }))
|
||||
}
|
||||
|
||||
fn visit_bool<E>(self, v: bool) -> Result<Self::Value, E>
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "boolean", value: v.to_string() }))
|
||||
}
|
||||
|
||||
fn visit_i64<E>(self, v: i64) -> Result<Self::Value, E>
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "integer", value: v.to_string() }))
|
||||
}
|
||||
|
||||
fn visit_i128<E>(self, v: i128) -> Result<Self::Value, E>
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "integer", value: v.to_string() }))
|
||||
}
|
||||
|
||||
fn visit_u64<E>(self, v: u64) -> Result<Self::Value, E>
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "integer", value: v.to_string() }))
|
||||
}
|
||||
|
||||
fn visit_u128<E>(self, v: u128) -> Result<Self::Value, E>
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "integer", value: v.to_string() }))
|
||||
}
|
||||
|
||||
fn visit_f64<E>(self, v: f64) -> Result<Self::Value, E>
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "number", value: v.to_string() }))
|
||||
}
|
||||
|
||||
fn visit_str<E>(self, v: &str) -> Result<Self::Value, E>
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "string", value: v.to_string() }))
|
||||
}
|
||||
|
||||
fn visit_string<E>(self, v: String) -> Result<Self::Value, E>
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "string", value: v }))
|
||||
}
|
||||
|
||||
fn visit_bytes<E>(self, v: &[u8]) -> Result<Self::Value, E>
|
||||
where
|
||||
E: serde::de::Error,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "bytes", value: format!("{v:?}") }))
|
||||
}
|
||||
|
||||
fn visit_newtype_struct<D>(self, deserializer: D) -> Result<Self::Value, D::Error>
|
||||
where
|
||||
D: serde::Deserializer<'doc>,
|
||||
{
|
||||
deserializer.deserialize_any(self)
|
||||
}
|
||||
|
||||
fn visit_enum<A>(self, _data: A) -> Result<Self::Value, A::Error>
|
||||
where
|
||||
A: serde::de::EnumAccess<'doc>,
|
||||
{
|
||||
Ok(Err(RawVectorsError::WrongKind { kind: "enum", value: "a variant".to_string() }))
|
||||
Ok(RawVectorsVisitorValue::Explicit { regenerate, embeddings })
|
||||
}
|
||||
}
|
||||
|
||||
@ -495,7 +343,7 @@ impl Error {
|
||||
Error::InvalidEmbedderConf { error } => {
|
||||
crate::Error::UserError(UserError::InvalidVectorsEmbedderConf {
|
||||
document_id,
|
||||
error: error.to_string(),
|
||||
error,
|
||||
})
|
||||
}
|
||||
Error::InternalSerdeJson(error) => {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user