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Merge #4941

Browse Source 
4941: Implement the binary quantization in meilisearch r=irevoire a=irevoire

# Pull Request

## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/4873

## What does this PR do?
- Add a settings for the binary quantization
- Once enabled, the bq cannot be disabled

TODO:
- [ ] Missing a bunch of tests

Co-authored-by: Tamo <tamo@meilisearch.com>
This commit is contained in:
meili-bors[bot] 2024-09-19 15:50:24 +00:00 committed by GitHub
commit 462a2329f1
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
38 changed files with 4107 additions and 3355 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
Cargo.lock generated
View File

@ -387,14 +387,14 @@ checksum = "96d30a06541fbafbc7f82ed10c06164cfbd2c401138f6addd8404629c4b16711"
[[package]] [[package]]
name = "arroy" name = "arroy"
version = "0.4.0" version = "0.4.0"
source = "registry+https://github.com/rust-lang/crates.io-index" source = "git+https://github.com/meilisearch/arroy/?rev=2386594dfb009ce08821a925ccc89fb8e30bf73d#2386594dfb009ce08821a925ccc89fb8e30bf73d"
checksum = "2ece9e5347e7fdaaea3181dec7f916677ad5f3fcbac183648ce1924eb4aeef9a"
dependencies = [ dependencies = [
"bytemuck", "bytemuck",
"byteorder", "byteorder",
"heed", "heed",
"log", "log",
"memmap2", "memmap2",
"nohash",
"ordered-float", "ordered-float",
"rand", "rand",
"rayon", "rayon",
@ -3686,6 +3686,12 @@ version = "0.0.3"
source = "registry+https://github.com/rust-lang/crates.io-index" source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "6d02c0b00610773bb7fc61d85e13d86c7858cbdf00e1a120bfc41bc055dbaa0e" checksum = "6d02c0b00610773bb7fc61d85e13d86c7858cbdf00e1a120bfc41bc055dbaa0e"
[[package]]
name = "nohash"
version = "0.2.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a0f889fb66f7acdf83442c35775764b51fed3c606ab9cee51500dbde2cf528ca"
[[package]] [[package]]
name = "nom" name = "nom"
version = "7.1.3" version = "7.1.3"

2
dump/src/reader/mod.rs
View File

@ -255,6 +255,8 @@ pub(crate) mod test {
} }
"###); "###);
insta::assert_json_snapshot!(vector_index.settings().unwrap());
{ {
let documents: Result<Vec<_>> = vector_index.documents().unwrap().collect(); let documents: Result<Vec<_>> = vector_index.documents().unwrap().collect();
let mut documents = documents.unwrap(); let mut documents = documents.unwrap();

825
dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-5.snap
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@ -1,783 +1,56 @@
--- ---
source: dump/src/reader/mod.rs source: dump/src/reader/mod.rs
expression: document expression: vector_index.settings().unwrap()
--- ---
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"rankingRules": [
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"synonyms": {},
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"proximityPrecision": "byWord",
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},
"faceting": {
"maxValuesPerFacet": 100,
"sortFacetValuesBy": {
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},
"pagination": {
"maxTotalHits": 1000
},
"embedders": {
"default": {
"source": "huggingFace",
"model": "BAAI/bge-base-en-v1.5",
"revision": "617ca489d9e86b49b8167676d8220688b99db36e",
"documentTemplate": "{% for field in fields %} {{ field.name }}: {{ field.value }}\n{% endfor %}"
}
},
"searchCutoffMs": null
} }

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dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-6.snap
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@ -0,0 +1,780 @@
---
source: dump/src/reader/mod.rs
expression: document
---
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}
}

2
index-scheduler/Cargo.toml
View File

@ -40,7 +40,7 @@ ureq = "2.10.0"
uuid = { version = "1.10.0", features = ["serde", "v4"] } uuid = { version = "1.10.0", features = ["serde", "v4"] }
[dev-dependencies] [dev-dependencies]
arroy = "0.4.0" arroy = { git = "https://github.com/meilisearch/arroy/", rev = "2386594dfb009ce08821a925ccc89fb8e30bf73d" }
big_s = "1.0.2" big_s = "1.0.2"
crossbeam = "0.8.4" crossbeam = "0.8.4"
insta = { version = "1.39.0", features = ["json", "redactions"] } insta = { version = "1.39.0", features = ["json", "redactions"] }

38
index-scheduler/src/lib.rs
View File

@ -1477,7 +1477,7 @@ impl IndexScheduler {
.map( .map(
|IndexEmbeddingConfig { |IndexEmbeddingConfig {
name, name,
config: milli::vector::EmbeddingConfig { embedder_options, prompt }, config: milli::vector::EmbeddingConfig { embedder_options, prompt, quantized },
.. ..
}| { }| {
let prompt = let prompt =
@ -1486,7 +1486,10 @@ impl IndexScheduler {
{ {
let embedders = self.embedders.read().unwrap(); let embedders = self.embedders.read().unwrap();
if let Some(embedder) = embedders.get(&embedder_options) { if let Some(embedder) = embedders.get(&embedder_options) {
return Ok((name, (embedder.clone(), prompt))); return Ok((
name,
(embedder.clone(), prompt, quantized.unwrap_or_default()),
));
} }
} }
@ -1500,7 +1503,7 @@ impl IndexScheduler {
let mut embedders = self.embedders.write().unwrap(); let mut embedders = self.embedders.write().unwrap();
embedders.insert(embedder_options, embedder.clone()); embedders.insert(embedder_options, embedder.clone());
} }
Ok((name, (embedder, prompt))) Ok((name, (embedder, prompt, quantized.unwrap_or_default())))
}, },
) )
.collect(); .collect();
@ -5197,7 +5200,7 @@ mod tests {
let simple_hf_name = name.clone(); let simple_hf_name = name.clone();
let configs = index_scheduler.embedders(configs).unwrap(); let configs = index_scheduler.embedders(configs).unwrap();
let (hf_embedder, _) = configs.get(&simple_hf_name).unwrap(); let (hf_embedder, _, _) = configs.get(&simple_hf_name).unwrap();
let beagle_embed = hf_embedder.embed_one(S("Intel the beagle best doggo")).unwrap(); let beagle_embed = hf_embedder.embed_one(S("Intel the beagle best doggo")).unwrap();
let lab_embed = hf_embedder.embed_one(S("Max the lab best doggo")).unwrap(); let lab_embed = hf_embedder.embed_one(S("Max the lab best doggo")).unwrap();
let patou_embed = hf_embedder.embed_one(S("kefir the patou best doggo")).unwrap(); let patou_embed = hf_embedder.embed_one(S("kefir the patou best doggo")).unwrap();
@ -5519,6 +5522,7 @@ mod tests {
400, 400,
), ),
}, },
quantized: None,
}, },
user_provided: RoaringBitmap<[1, 2]>, user_provided: RoaringBitmap<[1, 2]>,
}, },
@ -5531,28 +5535,8 @@ mod tests {
// the document with the id 3 should keep its original embedding // the document with the id 3 should keep its original embedding
let docid = index.external_documents_ids.get(&rtxn, "3").unwrap().unwrap(); let docid = index.external_documents_ids.get(&rtxn, "3").unwrap().unwrap();
let mut embeddings = Vec::new(); let embeddings = index.embeddings(&rtxn, docid).unwrap();
let embeddings = &embeddings["my_doggo_embedder"];
'vectors: for i in 0..=u8::MAX {
let reader = arroy::Reader::open(&rtxn, i as u16, index.vector_arroy)
.map(Some)
.or_else(|e| match e {
arroy::Error::MissingMetadata(_) => Ok(None),
e => Err(e),
})
.transpose();
let Some(reader) = reader else {
break 'vectors;
};
let embedding = reader.unwrap().item_vector(&rtxn, docid).unwrap();
if let Some(embedding) = embedding {
embeddings.push(embedding)
} else {
break 'vectors;
}
}
snapshot!(embeddings.len(), @"1"); snapshot!(embeddings.len(), @"1");
assert!(embeddings[0].iter().all(|i| *i == 3.0), "{:?}", embeddings[0]); assert!(embeddings[0].iter().all(|i| *i == 3.0), "{:?}", embeddings[0]);
@ -5737,6 +5721,7 @@ mod tests {
400, 400,
), ),
}, },
quantized: None,
}, },
user_provided: RoaringBitmap<[0]>, user_provided: RoaringBitmap<[0]>,
}, },
@ -5780,6 +5765,7 @@ mod tests {
400, 400,
), ),
}, },
quantized: None,
}, },
user_provided: RoaringBitmap<[]>, user_provided: RoaringBitmap<[]>,
}, },

2
index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir succeeds.snap
View File

@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
[] []
---------------------------------------------------------------------- ----------------------------------------------------------------------
### All Tasks: ### All Tasks:
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }} 0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }} 1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
2 {uid: 2, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }} 2 {uid: 2, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }}
---------------------------------------------------------------------- ----------------------------------------------------------------------

2
index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir.snap
View File

@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
[] []
---------------------------------------------------------------------- ----------------------------------------------------------------------
### All Tasks: ### All Tasks:
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }} 0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }} 1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
2 {uid: 2, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }} 2 {uid: 2, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }}
---------------------------------------------------------------------- ----------------------------------------------------------------------

2
index-scheduler/src/snapshots/lib.rs/import_vectors/adding Intel succeeds.snap
View File

@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
[] []
---------------------------------------------------------------------- ----------------------------------------------------------------------
### All Tasks: ### All Tasks:
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }} 0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }} 1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
---------------------------------------------------------------------- ----------------------------------------------------------------------
### Status: ### Status:

2
index-scheduler/src/snapshots/lib.rs/import_vectors/after adding Intel.snap
View File

@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
[] []
---------------------------------------------------------------------- ----------------------------------------------------------------------
### All Tasks: ### All Tasks:
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }} 0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
1 {uid: 1, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }} 1 {uid: 1, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
---------------------------------------------------------------------- ----------------------------------------------------------------------
### Status: ### Status:

2
index-scheduler/src/snapshots/lib.rs/import_vectors/after_registering_settings_task_vectors.snap
View File

@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
[] []
---------------------------------------------------------------------- ----------------------------------------------------------------------
### All Tasks: ### All Tasks:
0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }} 0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
---------------------------------------------------------------------- ----------------------------------------------------------------------
### Status: ### Status:
enqueued [0,] enqueued [0,]

2
index-scheduler/src/snapshots/lib.rs/import_vectors/settings_update_processed_vectors.snap
View File

@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
[] []
---------------------------------------------------------------------- ----------------------------------------------------------------------
### All Tasks: ### All Tasks:
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }} 0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
---------------------------------------------------------------------- ----------------------------------------------------------------------
### Status: ### Status:
enqueued [] enqueued []

2
index-scheduler/src/snapshots/lib.rs/test_settings_update/after_registering_settings_task.snap
View File

@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
[] []
---------------------------------------------------------------------- ----------------------------------------------------------------------
### All Tasks: ### All Tasks:
0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }} 0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
---------------------------------------------------------------------- ----------------------------------------------------------------------
### Status: ### Status:
enqueued [0,] enqueued [0,]

2
index-scheduler/src/snapshots/lib.rs/test_settings_update/settings_update_processed.snap
View File

@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
[] []
---------------------------------------------------------------------- ----------------------------------------------------------------------
### All Tasks: ### All Tasks:
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }} 0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
---------------------------------------------------------------------- ----------------------------------------------------------------------
### Status: ### Status:
enqueued [] enqueued []

5
meilisearch-types/src/error.rs
View File

@ -395,7 +395,10 @@ impl ErrorCode for milli::Error {
| UserError::InvalidSettingsDimensions { .. } | UserError::InvalidSettingsDimensions { .. }
| UserError::InvalidUrl { .. } | UserError::InvalidUrl { .. }
| UserError::InvalidSettingsDocumentTemplateMaxBytes { .. } | UserError::InvalidSettingsDocumentTemplateMaxBytes { .. }
| UserError::InvalidPrompt(_) => Code::InvalidSettingsEmbedders, | UserError::InvalidPrompt(_)
| UserError::InvalidDisableBinaryQuantization { .. } => {
Code::InvalidSettingsEmbedders
}
UserError::TooManyEmbedders(_) => Code::InvalidSettingsEmbedders, UserError::TooManyEmbedders(_) => Code::InvalidSettingsEmbedders,
UserError::InvalidPromptForEmbeddings(..) => Code::InvalidSettingsEmbedders, UserError::InvalidPromptForEmbeddings(..) => Code::InvalidSettingsEmbedders,
UserError::NoPrimaryKeyCandidateFound => Code::IndexPrimaryKeyNoCandidateFound, UserError::NoPrimaryKeyCandidateFound => Code::IndexPrimaryKeyNoCandidateFound,

9
meilisearch/src/routes/indexes/settings.rs
View File

@ -643,12 +643,19 @@ fn embedder_analytics(
.max() .max()
}); });
let binary_quantization_used = setting.as_ref().map(|map| {
map.values()
.filter_map(|config| config.clone().set())
.any(|config| config.binary_quantized.set().is_some())
});
json!( json!(
{ {
"total": setting.as_ref().map(|s| s.len()), "total": setting.as_ref().map(|s| s.len()),
"sources": sources, "sources": sources,
"document_template_used": document_template_used, "document_template_used": document_template_used,
"document_template_max_bytes": document_template_max_bytes "document_template_max_bytes": document_template_max_bytes,
"binary_quantization_used": binary_quantization_used,
} }
) )
} }

3
meilisearch/src/routes/indexes/similar.rs
View File

@ -102,7 +102,7 @@ async fn similar(
let index = index_scheduler.index(&index_uid)?; let index = index_scheduler.index(&index_uid)?;
let (embedder_name, embedder) = let (embedder_name, embedder, quantized) =
SearchKind::embedder(&index_scheduler, &index, &query.embedder, None)?; SearchKind::embedder(&index_scheduler, &index, &query.embedder, None)?;
tokio::task::spawn_blocking(move || { tokio::task::spawn_blocking(move || {
@ -111,6 +111,7 @@ async fn similar(
query, query,
embedder_name, embedder_name,
embedder, embedder,
quantized,
retrieve_vectors, retrieve_vectors,
index_scheduler.features(), index_scheduler.features(),
) )

50
meilisearch/src/search/mod.rs
View File

@ -274,8 +274,8 @@ pub struct HybridQuery {
#[derive(Clone)] #[derive(Clone)]
pub enum SearchKind { pub enum SearchKind {
KeywordOnly, KeywordOnly,
SemanticOnly { embedder_name: String, embedder: Arc<Embedder> }, SemanticOnly { embedder_name: String, embedder: Arc<Embedder>, quantized: bool },
Hybrid { embedder_name: String, embedder: Arc<Embedder>, semantic_ratio: f32 }, Hybrid { embedder_name: String, embedder: Arc<Embedder>, quantized: bool, semantic_ratio: f32 },
} }
impl SearchKind { impl SearchKind {
@ -285,9 +285,9 @@ impl SearchKind {
embedder_name: &str, embedder_name: &str,
vector_len: Option<usize>, vector_len: Option<usize>,
) -> Result<Self, ResponseError> { ) -> Result<Self, ResponseError> {
let (embedder_name, embedder) = let (embedder_name, embedder, quantized) =
Self::embedder(index_scheduler, index, embedder_name, vector_len)?; Self::embedder(index_scheduler, index, embedder_name, vector_len)?;
Ok(Self::SemanticOnly { embedder_name, embedder }) Ok(Self::SemanticOnly { embedder_name, embedder, quantized })
} }
pub(crate) fn hybrid( pub(crate) fn hybrid(
@ -297,9 +297,9 @@ impl SearchKind {
semantic_ratio: f32, semantic_ratio: f32,
vector_len: Option<usize>, vector_len: Option<usize>,
) -> Result<Self, ResponseError> { ) -> Result<Self, ResponseError> {
let (embedder_name, embedder) = let (embedder_name, embedder, quantized) =
Self::embedder(index_scheduler, index, embedder_name, vector_len)?; Self::embedder(index_scheduler, index, embedder_name, vector_len)?;
Ok(Self::Hybrid { embedder_name, embedder, semantic_ratio }) Ok(Self::Hybrid { embedder_name, embedder, quantized, semantic_ratio })
} }
pub(crate) fn embedder( pub(crate) fn embedder(
@ -307,16 +307,14 @@ impl SearchKind {
index: &Index, index: &Index,
embedder_name: &str, embedder_name: &str,
vector_len: Option<usize>, vector_len: Option<usize>,
) -> Result<(String, Arc<Embedder>), ResponseError> { ) -> Result<(String, Arc<Embedder>, bool), ResponseError> {
let embedder_configs = index.embedding_configs(&index.read_txn()?)?; let embedder_configs = index.embedding_configs(&index.read_txn()?)?;
let embedders = index_scheduler.embedders(embedder_configs)?; let embedders = index_scheduler.embedders(embedder_configs)?;
let embedder = embedders.get(embedder_name); let (embedder, _, quantized) = embedders
.get(embedder_name)
let embedder = embedder
.ok_or(milli::UserError::InvalidEmbedder(embedder_name.to_owned())) .ok_or(milli::UserError::InvalidEmbedder(embedder_name.to_owned()))
.map_err(milli::Error::from)? .map_err(milli::Error::from)?;
.0;
if let Some(vector_len) = vector_len { if let Some(vector_len) = vector_len {
if vector_len != embedder.dimensions() { if vector_len != embedder.dimensions() {
@ -330,7 +328,7 @@ impl SearchKind {
} }
} }
Ok((embedder_name.to_owned(), embedder)) Ok((embedder_name.to_owned(), embedder, quantized))
} }
} }
@ -791,7 +789,7 @@ fn prepare_search<'t>(
search.query(q); search.query(q);
} }
} }
SearchKind::SemanticOnly { embedder_name, embedder } => { SearchKind::SemanticOnly { embedder_name, embedder, quantized } => {
let vector = match query.vector.clone() { let vector = match query.vector.clone() {
Some(vector) => vector, Some(vector) => vector,
None => { None => {
@ -805,14 +803,19 @@ fn prepare_search<'t>(
} }
}; };
search.semantic(embedder_name.clone(), embedder.clone(), Some(vector)); search.semantic(embedder_name.clone(), embedder.clone(), *quantized, Some(vector));
} }
SearchKind::Hybrid { embedder_name, embedder, semantic_ratio: _ } => { SearchKind::Hybrid { embedder_name, embedder, quantized, semantic_ratio: _ } => {
if let Some(q) = &query.q { if let Some(q) = &query.q {
search.query(q); search.query(q);
} }
// will be embedded in hybrid search if necessary // will be embedded in hybrid search if necessary
search.semantic(embedder_name.clone(), embedder.clone(), query.vector.clone()); search.semantic(
embedder_name.clone(),
embedder.clone(),
*quantized,
query.vector.clone(),
);
} }
} }
@ -1441,6 +1444,7 @@ pub fn perform_similar(
query: SimilarQuery, query: SimilarQuery,
embedder_name: String, embedder_name: String,
embedder: Arc<Embedder>, embedder: Arc<Embedder>,
quantized: bool,
retrieve_vectors: RetrieveVectors, retrieve_vectors: RetrieveVectors,
features: RoFeatures, features: RoFeatures,
) -> Result<SimilarResult, ResponseError> { ) -> Result<SimilarResult, ResponseError> {
@ -1469,8 +1473,16 @@ pub fn perform_similar(
)); ));
}; };
let mut similar = let mut similar = milli::Similar::new(
milli::Similar::new(internal_id, offset, limit, index, &rtxn, embedder_name, embedder); internal_id,
offset,
limit,
index,
&rtxn,
embedder_name,
embedder,
quantized,
);
if let Some(ref filter) = query.filter { if let Some(ref filter) = query.filter {
if let Some(facets) = parse_filter(filter, Code::InvalidSimilarFilter, features)? { if let Some(facets) = parse_filter(filter, Code::InvalidSimilarFilter, features)? {

380
meilisearch/tests/vector/binary_quantized.rs Normal file
View File

@ -0,0 +1,380 @@
use meili_snap::{json_string, snapshot};
use crate::common::{GetAllDocumentsOptions, Server};
use crate::json;
use crate::vector::generate_default_user_provided_documents;
#[actix_rt::test]
async fn retrieve_binary_quantize_status_in_the_settings() {
let server = Server::new().await;
let index = server.index("doggo");
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
snapshot!(code, @"200 OK");
snapshot!(value, @r###"
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"editDocumentsByFunction": false,
"containsFilter": false
}
"###);
let (response, code) = index
.update_settings(json!({
"embedders": {
"manual": {
"source": "userProvided",
"dimensions": 3,
}
},
}))
.await;
snapshot!(code, @"202 Accepted");
server.wait_task(response.uid()).await.succeeded();
let (settings, code) = index.settings().await;
snapshot!(code, @"200 OK");
snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3}"###);
let (response, code) = index
.update_settings(json!({
"embedders": {
"manual": {
"source": "userProvided",
"dimensions": 3,
"binaryQuantized": false,
}
},
}))
.await;
snapshot!(code, @"202 Accepted");
server.wait_task(response.uid()).await.succeeded();
let (settings, code) = index.settings().await;
snapshot!(code, @"200 OK");
snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3,"binaryQuantized":false}"###);
let (response, code) = index
.update_settings(json!({
"embedders": {
"manual": {
"source": "userProvided",
"dimensions": 3,
"binaryQuantized": true,
}
},
}))
.await;
snapshot!(code, @"202 Accepted");
server.wait_task(response.uid()).await.succeeded();
let (settings, code) = index.settings().await;
snapshot!(code, @"200 OK");
snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3,"binaryQuantized":true}"###);
}
#[actix_rt::test]
async fn binary_quantize_before_sending_documents() {
let server = Server::new().await;
let index = server.index("doggo");
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
snapshot!(code, @"200 OK");
snapshot!(value, @r###"
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"editDocumentsByFunction": false,
"containsFilter": false
}
"###);
let (response, code) = index
.update_settings(json!({
"embedders": {
"manual": {
"source": "userProvided",
"dimensions": 3,
"binaryQuantized": true,
}
},
}))
.await;
snapshot!(code, @"202 Accepted");
server.wait_task(response.uid()).await.succeeded();
let documents = json!([
{"id": 0, "name": "kefir", "_vectors": { "manual": [-1.2, -2.3, 3.2] }},
{"id": 1, "name": "echo", "_vectors": { "manual": [2.5, 1.5, -130] }},
]);
let (value, code) = index.add_documents(documents, None).await;
snapshot!(code, @"202 Accepted");
index.wait_task(value.uid()).await.succeeded();
// Make sure the documents are binary quantized
let (documents, _code) = index
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
.await;
snapshot!(json_string!(documents), @r###"
{
"results": [
{
"id": 0,
"name": "kefir",
"_vectors": {
"manual": {
"embeddings": [
[
-1.0,
-1.0,
1.0
]
],
"regenerate": false
}
}
},
{
"id": 1,
"name": "echo",
"_vectors": {
"manual": {
"embeddings": [
[
1.0,
1.0,
-1.0
]
],
"regenerate": false
}
}
}
],
"offset": 0,
"limit": 20,
"total": 2
}
"###);
}
#[actix_rt::test]
async fn binary_quantize_after_sending_documents() {
let server = Server::new().await;
let index = server.index("doggo");
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
snapshot!(code, @"200 OK");
snapshot!(value, @r###"
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"editDocumentsByFunction": false,
"containsFilter": false
}
"###);
let (response, code) = index
.update_settings(json!({
"embedders": {
"manual": {
"source": "userProvided",
"dimensions": 3,
}
},
}))
.await;
snapshot!(code, @"202 Accepted");
server.wait_task(response.uid()).await.succeeded();
let documents = json!([
{"id": 0, "name": "kefir", "_vectors": { "manual": [-1.2, -2.3, 3.2] }},
{"id": 1, "name": "echo", "_vectors": { "manual": [2.5, 1.5, -130] }},
]);
let (value, code) = index.add_documents(documents, None).await;
snapshot!(code, @"202 Accepted");
index.wait_task(value.uid()).await.succeeded();
let (response, code) = index
.update_settings(json!({
"embedders": {
"manual": {
"source": "userProvided",
"dimensions": 3,
"binaryQuantized": true,
}
},
}))
.await;
snapshot!(code, @"202 Accepted");
server.wait_task(response.uid()).await.succeeded();
// Make sure the documents are binary quantized
let (documents, _code) = index
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
.await;
snapshot!(json_string!(documents), @r###"
{
"results": [
{
"id": 0,
"name": "kefir",
"_vectors": {
"manual": {
"embeddings": [
[
-1.0,
-1.0,
1.0
]
],
"regenerate": false
}
}
},
{
"id": 1,
"name": "echo",
"_vectors": {
"manual": {
"embeddings": [
[
1.0,
1.0,
-1.0
]
],
"regenerate": false
}
}
}
],
"offset": 0,
"limit": 20,
"total": 2
}
"###);
}
#[actix_rt::test]
async fn try_to_disable_binary_quantization() {
let server = Server::new().await;
let index = server.index("doggo");
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
snapshot!(code, @"200 OK");
snapshot!(value, @r###"
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"editDocumentsByFunction": false,
"containsFilter": false
}
"###);
let (response, code) = index
.update_settings(json!({
"embedders": {
"manual": {
"source": "userProvided",
"dimensions": 3,
"binaryQuantized": true,
}
},
}))
.await;
snapshot!(code, @"202 Accepted");
server.wait_task(response.uid()).await.succeeded();
let (response, code) = index
.update_settings(json!({
"embedders": {
"manual": {
"source": "userProvided",
"dimensions": 3,
"binaryQuantized": false,
}
},
}))
.await;
snapshot!(code, @"202 Accepted");
let ret = server.wait_task(response.uid()).await;
snapshot!(ret, @r###"
{
"uid": "[uid]",
"indexUid": "doggo",
"status": "failed",
"type": "settingsUpdate",
"canceledBy": null,
"details": {
"embedders": {
"manual": {
"source": "userProvided",
"dimensions": 3,
"binaryQuantized": false
}
}
},
"error": {
"message": "`.embedders.manual.binaryQuantized`: Cannot disable the binary quantization.\n - Note: Binary quantization is a lossy operation that cannot be reverted.\n - Hint: Add a new embedder that is non-quantized and regenerate the vectors.",
"code": "invalid_settings_embedders",
"type": "invalid_request",
"link": "https://docs.meilisearch.com/errors#invalid_settings_embedders"
},
"duration": "[duration]",
"enqueuedAt": "[date]",
"startedAt": "[date]",
"finishedAt": "[date]"
}
"###);
}
#[actix_rt::test]
async fn binary_quantize_clear_documents() {
let server = Server::new().await;
let index = generate_default_user_provided_documents(&server).await;
let (response, code) = index
.update_settings(json!({
"embedders": {
"manual": {
"binaryQuantized": true,
}
},
}))
.await;
snapshot!(code, @"202 Accepted");
server.wait_task(response.uid()).await.succeeded();
let (value, _code) = index.clear_all_documents().await;
index.wait_task(value.uid()).await.succeeded();
// Make sure the documents DB has been cleared
let (documents, _code) = index
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
.await;
snapshot!(json_string!(documents), @r###"
{
"results": [],
"offset": 0,
"limit": 20,
"total": 0
}
"###);
// Make sure the arroy DB has been cleared
let (documents, _code) =
index.search_post(json!({ "hybrid": { "embedder": "manual" }, "vector": [1, 1, 1] })).await;
snapshot!(documents, @r###"
{
"hits": [],
"query": "",
"processingTimeMs": "[duration]",
"limit": 20,
"offset": 0,
"estimatedTotalHits": 0,
"semanticHitCount": 0
}
"###);
}

1
meilisearch/tests/vector/mod.rs
View File

@ -1,3 +1,4 @@
mod binary_quantized;
mod openai; mod openai;
mod rest; mod rest;
mod settings; mod settings;

2
milli/Cargo.toml
View File

@ -80,7 +80,7 @@ hf-hub = { git = "https://github.com/dureuill/hf-hub.git", branch = "rust_tls",
tiktoken-rs = "0.5.9" tiktoken-rs = "0.5.9"
liquid = "0.26.6" liquid = "0.26.6"
rhai = { version = "1.19.0", features = ["serde", "no_module", "no_custom_syntax", "no_time", "sync"] } rhai = { version = "1.19.0", features = ["serde", "no_module", "no_custom_syntax", "no_time", "sync"] }
arroy = "0.4.0" arroy = { git = "https://github.com/meilisearch/arroy/", rev = "2386594dfb009ce08821a925ccc89fb8e30bf73d" }
rand = "0.8.5" rand = "0.8.5"
tracing = "0.1.40" tracing = "0.1.40"
ureq = { version = "2.10.0", features = ["json"] } ureq = { version = "2.10.0", features = ["json"] }

4
milli/src/error.rs
View File

@ -258,6 +258,10 @@ only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and undersco
}, },
#[error("`.embedders.{embedder_name}.dimensions`: `dimensions` cannot be zero")] #[error("`.embedders.{embedder_name}.dimensions`: `dimensions` cannot be zero")]
InvalidSettingsDimensions { embedder_name: String }, InvalidSettingsDimensions { embedder_name: String },
#[error(
"`.embedders.{embedder_name}.binaryQuantized`: Cannot disable the binary quantization.\n - Note: Binary quantization is a lossy operation that cannot be reverted.\n - Hint: Add a new embedder that is non-quantized and regenerate the vectors."
)]
InvalidDisableBinaryQuantization { embedder_name: String },
#[error("`.embedders.{embedder_name}.documentTemplateMaxBytes`: `documentTemplateMaxBytes` cannot be zero")] #[error("`.embedders.{embedder_name}.documentTemplateMaxBytes`: `documentTemplateMaxBytes` cannot be zero")]
InvalidSettingsDocumentTemplateMaxBytes { embedder_name: String }, InvalidSettingsDocumentTemplateMaxBytes { embedder_name: String },
#[error("`.embedders.{embedder_name}.url`: could not parse `{url}`: {inner_error}")] #[error("`.embedders.{embedder_name}.url`: could not parse `{url}`: {inner_error}")]

58
milli/src/index.rs
View File

@ -21,7 +21,7 @@ use crate::heed_codec::{BEU16StrCodec, FstSetCodec, StrBEU16Codec, StrRefCodec};
use crate::order_by_map::OrderByMap; use crate::order_by_map::OrderByMap;
use crate::proximity::ProximityPrecision; use crate::proximity::ProximityPrecision;
use crate::vector::parsed_vectors::RESERVED_VECTORS_FIELD_NAME; use crate::vector::parsed_vectors::RESERVED_VECTORS_FIELD_NAME;
use crate::vector::{Embedding, EmbeddingConfig}; use crate::vector::{ArroyWrapper, Embedding, EmbeddingConfig};
use crate::{ use crate::{
default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds, default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds,
FacetDistribution, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldIdWordCountCodec, FacetDistribution, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldIdWordCountCodec,
@ -162,7 +162,7 @@ pub struct Index {
/// Maps an embedder name to its id in the arroy store. /// Maps an embedder name to its id in the arroy store.
pub embedder_category_id: Database<Str, U8>, pub embedder_category_id: Database<Str, U8>,
/// Vector store based on arroy™. /// Vector store based on arroy™.
pub vector_arroy: arroy::Database<arroy::distances::Angular>, pub vector_arroy: arroy::Database<Unspecified>,
/// Maps the document id to the document as an obkv store. /// Maps the document id to the document as an obkv store.
pub(crate) documents: Database<BEU32, ObkvCodec>, pub(crate) documents: Database<BEU32, ObkvCodec>,
@ -1614,15 +1614,17 @@ impl Index {
&'a self, &'a self,
rtxn: &'a RoTxn<'a>, rtxn: &'a RoTxn<'a>,
embedder_id: u8, embedder_id: u8,
) -> impl Iterator<Item = Result<arroy::Reader<'a, arroy::distances::Angular>>> + 'a { quantized: bool,
) -> impl Iterator<Item = Result<ArroyWrapper>> + 'a {
crate::vector::arroy_db_range_for_embedder(embedder_id).map_while(move |k| { crate::vector::arroy_db_range_for_embedder(embedder_id).map_while(move |k| {
arroy::Reader::open(rtxn, k, self.vector_arroy) let reader = ArroyWrapper::new(self.vector_arroy, k, quantized);
.map(Some) // Here we don't care about the dimensions, but we want to know if we can read
.or_else(|e| match e { // in the database or if its metadata are missing because there is no document with that many vectors.
arroy::Error::MissingMetadata(_) => Ok(None), match reader.dimensions(rtxn) {
e => Err(e.into()), Ok(_) => Some(Ok(reader)),
}) Err(arroy::Error::MissingMetadata(_)) => None,
.transpose() Err(e) => Some(Err(e.into())),
}
}) })
} }
@ -1644,32 +1646,18 @@ impl Index {
docid: DocumentId, docid: DocumentId,
) -> Result<BTreeMap<String, Vec<Embedding>>> { ) -> Result<BTreeMap<String, Vec<Embedding>>> {
let mut res = BTreeMap::new(); let mut res = BTreeMap::new();
for row in self.embedder_category_id.iter(rtxn)? { let embedding_configs = self.embedding_configs(rtxn)?;
let (embedder_name, embedder_id) = row?; for config in embedding_configs {
let embedder_id = (embedder_id as u16) << 8; let embedder_id = self.embedder_category_id.get(rtxn, &config.name)?.unwrap();
let mut embeddings = Vec::new(); let embeddings = self
'vectors: for i in 0..=u8::MAX { .arroy_readers(rtxn, embedder_id, config.config.quantized())
let reader = arroy::Reader::open(rtxn, embedder_id | (i as u16), self.vector_arroy) .map_while(|reader| {
.map(Some) reader
.or_else(|e| match e { .and_then(|r| r.item_vector(rtxn, docid).map_err(|e| e.into()))
arroy::Error::MissingMetadata(_) => Ok(None), .transpose()
e => Err(e),
}) })
.transpose(); .collect::<Result<Vec<_>>>()?;
res.insert(config.name.to_owned(), embeddings);
let Some(reader) = reader else {
break 'vectors;
};
let embedding = reader?.item_vector(rtxn, docid)?;
if let Some(embedding) = embedding {
embeddings.push(embedding)
} else {
break 'vectors;
}
}
res.insert(embedder_name.to_owned(), embeddings);
} }
Ok(res) Ok(res)
} }

4
milli/src/search/hybrid.rs
View File

@ -190,7 +190,7 @@ impl<'a> Search<'a> {
return Ok(return_keyword_results(self.limit, self.offset, keyword_results)); return Ok(return_keyword_results(self.limit, self.offset, keyword_results));
}; };
// no embedder, no semantic search // no embedder, no semantic search
let Some(SemanticSearch { vector, embedder_name, embedder }) = semantic else { let Some(SemanticSearch { vector, embedder_name, embedder, quantized }) = semantic else {
return Ok(return_keyword_results(self.limit, self.offset, keyword_results)); return Ok(return_keyword_results(self.limit, self.offset, keyword_results));
}; };
@ -212,7 +212,7 @@ impl<'a> Search<'a> {
}; };
search.semantic = search.semantic =
Some(SemanticSearch { vector: Some(vector_query), embedder_name, embedder }); Some(SemanticSearch { vector: Some(vector_query), embedder_name, embedder, quantized });
// TODO: would be better to have two distinct functions at this point // TODO: would be better to have two distinct functions at this point
let vector_results = search.execute()?; let vector_results = search.execute()?;

7
milli/src/search/mod.rs
View File

@ -32,6 +32,7 @@ pub struct SemanticSearch {
vector: Option<Vec<f32>>, vector: Option<Vec<f32>>,
embedder_name: String, embedder_name: String,
embedder: Arc<Embedder>, embedder: Arc<Embedder>,
quantized: bool,
} }
pub struct Search<'a> { pub struct Search<'a> {
@ -89,9 +90,10 @@ impl<'a> Search<'a> {
&mut self, &mut self,
embedder_name: String, embedder_name: String,
embedder: Arc<Embedder>, embedder: Arc<Embedder>,
quantized: bool,
vector: Option<Vec<f32>>, vector: Option<Vec<f32>>,
) -> &mut Search<'a> { ) -> &mut Search<'a> {
self.semantic = Some(SemanticSearch { embedder_name, embedder, vector }); self.semantic = Some(SemanticSearch { embedder_name, embedder, quantized, vector });
self self
} }
@ -206,7 +208,7 @@ impl<'a> Search<'a> {
degraded, degraded,
used_negative_operator, used_negative_operator,
} = match self.semantic.as_ref() { } = match self.semantic.as_ref() {
Some(SemanticSearch { vector: Some(vector), embedder_name, embedder }) => { Some(SemanticSearch { vector: Some(vector), embedder_name, embedder, quantized }) => {
execute_vector_search( execute_vector_search(
&mut ctx, &mut ctx,
vector, vector,
@ -219,6 +221,7 @@ impl<'a> Search<'a> {
self.limit, self.limit,
embedder_name, embedder_name,
embedder, embedder,
*quantized,
self.time_budget.clone(), self.time_budget.clone(),
self.ranking_score_threshold, self.ranking_score_threshold,
)? )?

5
milli/src/search/new/mod.rs
View File

@ -312,6 +312,7 @@ fn get_ranking_rules_for_placeholder_search<'ctx>(
Ok(ranking_rules) Ok(ranking_rules)
} }
#[allow(clippy::too_many_arguments)]
fn get_ranking_rules_for_vector<'ctx>( fn get_ranking_rules_for_vector<'ctx>(
ctx: &SearchContext<'ctx>, ctx: &SearchContext<'ctx>,
sort_criteria: &Option<Vec<AscDesc>>, sort_criteria: &Option<Vec<AscDesc>>,
@ -320,6 +321,7 @@ fn get_ranking_rules_for_vector<'ctx>(
target: &[f32], target: &[f32],
embedder_name: &str, embedder_name: &str,
embedder: &Embedder, embedder: &Embedder,
quantized: bool,
) -> Result<Vec<BoxRankingRule<'ctx, PlaceholderQuery>>> { ) -> Result<Vec<BoxRankingRule<'ctx, PlaceholderQuery>>> {
// query graph search // query graph search
@ -347,6 +349,7 @@ fn get_ranking_rules_for_vector<'ctx>(
limit_plus_offset, limit_plus_offset,
embedder_name, embedder_name,
embedder, embedder,
quantized,
)?; )?;
ranking_rules.push(Box::new(vector_sort)); ranking_rules.push(Box::new(vector_sort));
vector = true; vector = true;
@ -576,6 +579,7 @@ pub fn execute_vector_search(
length: usize, length: usize,
embedder_name: &str, embedder_name: &str,
embedder: &Embedder, embedder: &Embedder,
quantized: bool,
time_budget: TimeBudget, time_budget: TimeBudget,
ranking_score_threshold: Option<f64>, ranking_score_threshold: Option<f64>,
) -> Result<PartialSearchResult> { ) -> Result<PartialSearchResult> {
@ -591,6 +595,7 @@ pub fn execute_vector_search(
vector, vector,
embedder_name, embedder_name,
embedder, embedder,
quantized,
)?; )?;
let mut placeholder_search_logger = logger::DefaultSearchLogger; let mut placeholder_search_logger = logger::DefaultSearchLogger;

11
milli/src/search/new/vector_sort.rs
View File

@ -16,6 +16,7 @@ pub struct VectorSort<Q: RankingRuleQueryTrait> {
limit: usize, limit: usize,
distribution_shift: Option<DistributionShift>, distribution_shift: Option<DistributionShift>,
embedder_index: u8, embedder_index: u8,
quantized: bool,
} }
impl<Q: RankingRuleQueryTrait> VectorSort<Q> { impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
@ -26,6 +27,7 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
limit: usize, limit: usize,
embedder_name: &str, embedder_name: &str,
embedder: &Embedder, embedder: &Embedder,
quantized: bool,
) -> Result<Self> { ) -> Result<Self> {
let embedder_index = ctx let embedder_index = ctx
.index .index
@ -41,6 +43,7 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
limit, limit,
distribution_shift: embedder.distribution(), distribution_shift: embedder.distribution(),
embedder_index, embedder_index,
quantized,
}) })
} }
@ -49,16 +52,12 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
ctx: &mut SearchContext<'_>, ctx: &mut SearchContext<'_>,
vector_candidates: &RoaringBitmap, vector_candidates: &RoaringBitmap,
) -> Result<()> { ) -> Result<()> {
let readers: std::result::Result<Vec<_>, _> =
ctx.index.arroy_readers(ctx.txn, self.embedder_index).collect();
let readers = readers?;
let target = &self.target; let target = &self.target;
let mut results = Vec::new(); let mut results = Vec::new();
for reader in readers.iter() { for reader in ctx.index.arroy_readers(ctx.txn, self.embedder_index, self.quantized) {
let nns_by_vector = let nns_by_vector =
reader.nns_by_vector(ctx.txn, target, self.limit, None, Some(vector_candidates))?; reader?.nns_by_vector(ctx.txn, target, self.limit, Some(vector_candidates))?;
results.extend(nns_by_vector.into_iter()); results.extend(nns_by_vector.into_iter());
} }
results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance)); results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));

14
milli/src/search/similar.rs
View File

@ -18,9 +18,11 @@ pub struct Similar<'a> {
embedder_name: String, embedder_name: String,
embedder: Arc<Embedder>, embedder: Arc<Embedder>,
ranking_score_threshold: Option<f64>, ranking_score_threshold: Option<f64>,
quantized: bool,
} }
impl<'a> Similar<'a> { impl<'a> Similar<'a> {
#[allow(clippy::too_many_arguments)]
pub fn new( pub fn new(
id: DocumentId, id: DocumentId,
offset: usize, offset: usize,
@ -29,6 +31,7 @@ impl<'a> Similar<'a> {
rtxn: &'a heed::RoTxn<'a>, rtxn: &'a heed::RoTxn<'a>,
embedder_name: String, embedder_name: String,
embedder: Arc<Embedder>, embedder: Arc<Embedder>,
quantized: bool,
) -> Self { ) -> Self {
Self { Self {
id, id,
@ -40,6 +43,7 @@ impl<'a> Similar<'a> {
embedder_name, embedder_name,
embedder, embedder,
ranking_score_threshold: None, ranking_score_threshold: None,
quantized,
} }
} }
@ -67,19 +71,13 @@ impl<'a> Similar<'a> {
.get(self.rtxn, &self.embedder_name)? .get(self.rtxn, &self.embedder_name)?
.ok_or_else(|| crate::UserError::InvalidEmbedder(self.embedder_name.to_owned()))?; .ok_or_else(|| crate::UserError::InvalidEmbedder(self.embedder_name.to_owned()))?;
let readers: std::result::Result<Vec<_>, _> =
self.index.arroy_readers(self.rtxn, embedder_index).collect();
let readers = readers?;
let mut results = Vec::new(); let mut results = Vec::new();
for reader in readers.iter() { for reader in self.index.arroy_readers(self.rtxn, embedder_index, self.quantized) {
let nns_by_item = reader.nns_by_item( let nns_by_item = reader?.nns_by_item(
self.rtxn, self.rtxn,
self.id, self.id,
self.limit + self.offset + 1, self.limit + self.offset + 1,
None,
Some(&universe), Some(&universe),
)?; )?;
if let Some(mut nns_by_item) = nns_by_item { if let Some(mut nns_by_item) = nns_by_item {

16
milli/src/update/index_documents/extract/extract_vector_points.rs
View File

@ -20,7 +20,7 @@ use crate::update::del_add::{DelAdd, KvReaderDelAdd, KvWriterDelAdd};
use crate::update::settings::InnerIndexSettingsDiff; use crate::update::settings::InnerIndexSettingsDiff;
use crate::vector::error::{EmbedErrorKind, PossibleEmbeddingMistakes, UnusedVectorsDistribution}; use crate::vector::error::{EmbedErrorKind, PossibleEmbeddingMistakes, UnusedVectorsDistribution};
use crate::vector::parsed_vectors::{ParsedVectorsDiff, VectorState, RESERVED_VECTORS_FIELD_NAME}; use crate::vector::parsed_vectors::{ParsedVectorsDiff, VectorState, RESERVED_VECTORS_FIELD_NAME};
use crate::vector::settings::{EmbedderAction, ReindexAction}; use crate::vector::settings::ReindexAction;
use crate::vector::{Embedder, Embeddings}; use crate::vector::{Embedder, Embeddings};
use crate::{try_split_array_at, DocumentId, FieldId, Result, ThreadPoolNoAbort}; use crate::{try_split_array_at, DocumentId, FieldId, Result, ThreadPoolNoAbort};
@ -208,10 +208,9 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
if reindex_vectors { if reindex_vectors {
for (name, action) in settings_diff.embedding_config_updates.iter() { for (name, action) in settings_diff.embedding_config_updates.iter() {
match action { if let Some(action) = action.reindex() {
EmbedderAction::WriteBackToDocuments(_) => continue, // already deleted let Some((embedder_name, (embedder, prompt, _quantized))) =
EmbedderAction::Reindex(action) => { configs.remove_entry(name)
let Some((embedder_name, (embedder, prompt))) = configs.remove_entry(name)
else { else {
tracing::error!(embedder = name, "Requested embedder config not found"); tracing::error!(embedder = name, "Requested embedder config not found");
continue; continue;
@ -241,7 +240,7 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
let action = match action { let action = match action {
ReindexAction::FullReindex => ExtractionAction::SettingsFullReindex, ReindexAction::FullReindex => ExtractionAction::SettingsFullReindex,
ReindexAction::RegeneratePrompts => { ReindexAction::RegeneratePrompts => {
let Some((_, old_prompt)) = old_configs.get(name) else { let Some((_, old_prompt, _quantized)) = old_configs.get(name) else {
tracing::error!(embedder = name, "Old embedder config not found"); tracing::error!(embedder = name, "Old embedder config not found");
continue; continue;
}; };
@ -260,13 +259,14 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
add_to_user_provided: RoaringBitmap::new(), add_to_user_provided: RoaringBitmap::new(),
action, action,
}); });
} } else {
continue;
} }
} }
} else { } else {
// document operation // document operation
for (embedder_name, (embedder, prompt)) in configs.into_iter() { for (embedder_name, (embedder, prompt, _quantized)) in configs.into_iter() {
// (docid, _index) -> KvWriterDelAdd -> Vector // (docid, _index) -> KvWriterDelAdd -> Vector
let manual_vectors_writer = create_writer( let manual_vectors_writer = create_writer(
indexer.chunk_compression_type, indexer.chunk_compression_type,

41
milli/src/update/index_documents/mod.rs
View File

@ -43,7 +43,7 @@ use crate::update::index_documents::parallel::ImmutableObkvs;
use crate::update::{ use crate::update::{
IndexerConfig, UpdateIndexingStep, WordPrefixDocids, WordPrefixIntegerDocids, WordsPrefixesFst, IndexerConfig, UpdateIndexingStep, WordPrefixDocids, WordPrefixIntegerDocids, WordsPrefixesFst,
}; };
use crate::vector::EmbeddingConfigs; use crate::vector::{ArroyWrapper, EmbeddingConfigs};
use crate::{CboRoaringBitmapCodec, Index, Object, Result}; use crate::{CboRoaringBitmapCodec, Index, Object, Result};
static MERGED_DATABASE_COUNT: usize = 7; static MERGED_DATABASE_COUNT: usize = 7;
@ -679,6 +679,24 @@ where
let number_of_documents = self.index.number_of_documents(self.wtxn)?; let number_of_documents = self.index.number_of_documents(self.wtxn)?;
let mut rng = rand::rngs::StdRng::seed_from_u64(42); let mut rng = rand::rngs::StdRng::seed_from_u64(42);
// If an embedder wasn't used in the typedchunk but must be binary quantized
// we should insert it in `dimension`
for (name, action) in settings_diff.embedding_config_updates.iter() {
if action.is_being_quantized && !dimension.contains_key(name.as_str()) {
let index = self.index.embedder_category_id.get(self.wtxn, name)?.ok_or(
InternalError::DatabaseMissingEntry {
db_name: "embedder_category_id",
key: None,
},
)?;
let first_id = crate::vector::arroy_db_range_for_embedder(index).next().unwrap();
let reader =
ArroyWrapper::new(self.index.vector_arroy, first_id, action.was_quantized);
let dim = reader.dimensions(self.wtxn)?;
dimension.insert(name.to_string(), dim);
}
}
for (embedder_name, dimension) in dimension { for (embedder_name, dimension) in dimension {
let wtxn = &mut *self.wtxn; let wtxn = &mut *self.wtxn;
let vector_arroy = self.index.vector_arroy; let vector_arroy = self.index.vector_arroy;
@ -686,13 +704,23 @@ where
let embedder_index = self.index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or( let embedder_index = self.index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or(
InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None }, InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None },
)?; )?;
let embedder_config = settings_diff.embedding_config_updates.get(&embedder_name);
let was_quantized = settings_diff
.old
.embedding_configs
.get(&embedder_name)
.map_or(false, |conf| conf.2);
let is_quantizing = embedder_config.map_or(false, |action| action.is_being_quantized);
pool.install(|| { pool.install(|| {
for k in crate::vector::arroy_db_range_for_embedder(embedder_index) { for k in crate::vector::arroy_db_range_for_embedder(embedder_index) {
let writer = arroy::Writer::new(vector_arroy, k, dimension); let mut writer = ArroyWrapper::new(vector_arroy, k, was_quantized);
if writer.need_build(wtxn)? { if is_quantizing {
writer.build(wtxn, &mut rng, None)?; writer.quantize(wtxn, k, dimension)?;
} else if writer.is_empty(wtxn)? { }
if writer.need_build(wtxn, dimension)? {
writer.build(wtxn, &mut rng, dimension)?;
} else if writer.is_empty(wtxn, dimension)? {
break; break;
} }
} }
@ -2746,6 +2774,7 @@ mod tests {
response: Setting::NotSet, response: Setting::NotSet,
distribution: Setting::NotSet, distribution: Setting::NotSet,
headers: Setting::NotSet, headers: Setting::NotSet,
binary_quantized: Setting::NotSet,
}), }),
); );
settings.set_embedder_settings(embedders); settings.set_embedder_settings(embedders);
@ -2774,7 +2803,7 @@ mod tests {
std::sync::Arc::new(crate::vector::Embedder::new(embedder.embedder_options).unwrap()); std::sync::Arc::new(crate::vector::Embedder::new(embedder.embedder_options).unwrap());
let res = index let res = index
.search(&rtxn) .search(&rtxn)
.semantic(embedder_name, embedder, Some([0.0, 1.0, 2.0].to_vec())) .semantic(embedder_name, embedder, false, Some([0.0, 1.0, 2.0].to_vec()))
.execute() .execute()
.unwrap(); .unwrap();
assert_eq!(res.documents_ids.len(), 3); assert_eq!(res.documents_ids.len(), 3);

32
milli/src/update/index_documents/transform.rs
View File

@ -28,7 +28,8 @@ use crate::update::index_documents::GrenadParameters;
use crate::update::settings::{InnerIndexSettings, InnerIndexSettingsDiff}; use crate::update::settings::{InnerIndexSettings, InnerIndexSettingsDiff};
use crate::update::{AvailableDocumentsIds, UpdateIndexingStep}; use crate::update::{AvailableDocumentsIds, UpdateIndexingStep};
use crate::vector::parsed_vectors::{ExplicitVectors, VectorOrArrayOfVectors}; use crate::vector::parsed_vectors::{ExplicitVectors, VectorOrArrayOfVectors};
use crate::vector::settings::{EmbedderAction, WriteBackToDocuments}; use crate::vector::settings::WriteBackToDocuments;
use crate::vector::ArroyWrapper;
use crate::{ use crate::{
is_faceted_by, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldsIdsMap, Index, Result, is_faceted_by, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldsIdsMap, Index, Result,
}; };
@ -989,19 +990,17 @@ impl<'a, 'i> Transform<'a, 'i> {
None None
}; };
let readers: Result< let readers: Result<BTreeMap<&str, (Vec<ArroyWrapper>, &RoaringBitmap)>> = settings_diff
BTreeMap<&str, (Vec<arroy::Reader<'_, arroy::distances::Angular>>, &RoaringBitmap)>,
> = settings_diff
.embedding_config_updates .embedding_config_updates
.iter() .iter()
.filter_map(|(name, action)| { .filter_map(|(name, action)| {
if let EmbedderAction::WriteBackToDocuments(WriteBackToDocuments { if let Some(WriteBackToDocuments { embedder_id, user_provided }) =
embedder_id, action.write_back()
user_provided,
}) = action
{ {
let readers: Result<Vec<_>> = let readers: Result<Vec<_>> = self
self.index.arroy_readers(wtxn, *embedder_id).collect(); .index
.arroy_readers(wtxn, *embedder_id, action.was_quantized)
.collect();
match readers { match readers {
Ok(readers) => Some(Ok((name.as_str(), (readers, user_provided)))), Ok(readers) => Some(Ok((name.as_str(), (readers, user_provided)))),
Err(error) => Some(Err(error)), Err(error) => Some(Err(error)),
@ -1104,23 +1103,14 @@ impl<'a, 'i> Transform<'a, 'i> {
} }
} }
let mut writers = Vec::new();
// delete all vectors from the embedders that need removal // delete all vectors from the embedders that need removal
for (_, (readers, _)) in readers { for (_, (readers, _)) in readers {
for reader in readers { for reader in readers {
let dimensions = reader.dimensions(); let dimensions = reader.dimensions(wtxn)?;
let arroy_index = reader.index(); reader.clear(wtxn, dimensions)?;
drop(reader);
let writer = arroy::Writer::new(self.index.vector_arroy, arroy_index, dimensions);
writers.push(writer);
} }
} }
for writer in writers {
writer.clear(wtxn)?;
}
let grenad_params = GrenadParameters { let grenad_params = GrenadParameters {
chunk_compression_type: self.indexer_settings.chunk_compression_type, chunk_compression_type: self.indexer_settings.chunk_compression_type,
chunk_compression_level: self.indexer_settings.chunk_compression_level, chunk_compression_level: self.indexer_settings.chunk_compression_level,

27
milli/src/update/index_documents/typed_chunk.rs
View File

@ -27,6 +27,7 @@ use crate::update::index_documents::helpers::{
as_cloneable_grenad, keep_latest_obkv, try_split_array_at, as_cloneable_grenad, keep_latest_obkv, try_split_array_at,
}; };
use crate::update::settings::InnerIndexSettingsDiff; use crate::update::settings::InnerIndexSettingsDiff;
use crate::vector::ArroyWrapper;
use crate::{ use crate::{
lat_lng_to_xyz, CboRoaringBitmapCodec, DocumentId, FieldId, GeoPoint, Index, InternalError, lat_lng_to_xyz, CboRoaringBitmapCodec, DocumentId, FieldId, GeoPoint, Index, InternalError,
Result, SerializationError, U8StrStrCodec, Result, SerializationError, U8StrStrCodec,
@ -666,9 +667,14 @@ pub(crate) fn write_typed_chunk_into_index(
let embedder_index = index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or( let embedder_index = index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or(
InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None }, InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None },
)?; )?;
let binary_quantized = settings_diff
.old
.embedding_configs
.get(&embedder_name)
.map_or(false, |conf| conf.2);
// FIXME: allow customizing distance // FIXME: allow customizing distance
let writers: Vec<_> = crate::vector::arroy_db_range_for_embedder(embedder_index) let writers: Vec<_> = crate::vector::arroy_db_range_for_embedder(embedder_index)
.map(|k| arroy::Writer::new(index.vector_arroy, k, expected_dimension)) .map(|k| ArroyWrapper::new(index.vector_arroy, k, binary_quantized))
.collect(); .collect();
// remove vectors for docids we want them removed // remove vectors for docids we want them removed
@ -679,7 +685,7 @@ pub(crate) fn write_typed_chunk_into_index(
for writer in &writers { for writer in &writers {
// Uses invariant: vectors are packed in the first writers. // Uses invariant: vectors are packed in the first writers.
if !writer.del_item(wtxn, docid)? { if !writer.del_item(wtxn, expected_dimension, docid)? {
break; break;
} }
} }
@ -711,7 +717,7 @@ pub(crate) fn write_typed_chunk_into_index(
))); )));
} }
for (embedding, writer) in embeddings.iter().zip(&writers) { for (embedding, writer) in embeddings.iter().zip(&writers) {
writer.add_item(wtxn, docid, embedding)?; writer.add_item(wtxn, expected_dimension, docid, embedding)?;
} }
} }
@ -734,7 +740,7 @@ pub(crate) fn write_typed_chunk_into_index(
break; break;
}; };
if candidate == vector { if candidate == vector {
writer.del_item(wtxn, docid)?; writer.del_item(wtxn, expected_dimension, docid)?;
deleted_index = Some(index); deleted_index = Some(index);
} }
} }
@ -751,8 +757,13 @@ pub(crate) fn write_typed_chunk_into_index(
if let Some((last_index, vector)) = last_index_with_a_vector { if let Some((last_index, vector)) = last_index_with_a_vector {
// unwrap: computed the index from the list of writers // unwrap: computed the index from the list of writers
let writer = writers.get(last_index).unwrap(); let writer = writers.get(last_index).unwrap();
writer.del_item(wtxn, docid)?; writer.del_item(wtxn, expected_dimension, docid)?;
writers.get(deleted_index).unwrap().add_item(wtxn, docid, &vector)?; writers.get(deleted_index).unwrap().add_item(
wtxn,
expected_dimension,
docid,
&vector,
)?;
} }
} }
} }
@ -762,8 +773,8 @@ pub(crate) fn write_typed_chunk_into_index(
// overflow was detected during vector extraction. // overflow was detected during vector extraction.
for writer in &writers { for writer in &writers {
if !writer.contains_item(wtxn, docid)? { if !writer.contains_item(wtxn, expected_dimension, docid)? {
writer.add_item(wtxn, docid, &vector)?; writer.add_item(wtxn, expected_dimension, docid, &vector)?;
break; break;
} }
} }

94
milli/src/update/settings.rs
View File

@ -954,7 +954,7 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
let old_configs = self.index.embedding_configs(self.wtxn)?; let old_configs = self.index.embedding_configs(self.wtxn)?;
let remove_all: Result<BTreeMap<String, EmbedderAction>> = old_configs let remove_all: Result<BTreeMap<String, EmbedderAction>> = old_configs
.into_iter() .into_iter()
.map(|IndexEmbeddingConfig { name, config: _, user_provided }| -> Result<_> { .map(|IndexEmbeddingConfig { name, config, user_provided }| -> Result<_> {
let embedder_id = let embedder_id =
self.index.embedder_category_id.get(self.wtxn, &name)?.ok_or( self.index.embedder_category_id.get(self.wtxn, &name)?.ok_or(
crate::InternalError::DatabaseMissingEntry { crate::InternalError::DatabaseMissingEntry {
@ -964,10 +964,10 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
)?; )?;
Ok(( Ok((
name, name,
EmbedderAction::WriteBackToDocuments(WriteBackToDocuments { EmbedderAction::with_write_back(
embedder_id, WriteBackToDocuments { embedder_id, user_provided },
user_provided, config.quantized(),
}), ),
)) ))
}) })
.collect(); .collect();
@ -1004,7 +1004,8 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
match joined { match joined {
// updated config // updated config
EitherOrBoth::Both((name, (old, user_provided)), (_, new)) => { EitherOrBoth::Both((name, (old, user_provided)), (_, new)) => {
let settings_diff = SettingsDiff::from_settings(old, new); let was_quantized = old.binary_quantized.set().unwrap_or_default();
let settings_diff = SettingsDiff::from_settings(&name, old, new)?;
match settings_diff { match settings_diff {
SettingsDiff::Remove => { SettingsDiff::Remove => {
tracing::debug!( tracing::debug!(
@ -1023,25 +1024,29 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
self.index.embedder_category_id.delete(self.wtxn, &name)?; self.index.embedder_category_id.delete(self.wtxn, &name)?;
embedder_actions.insert( embedder_actions.insert(
name, name,
EmbedderAction::WriteBackToDocuments(WriteBackToDocuments { EmbedderAction::with_write_back(
embedder_id, WriteBackToDocuments { embedder_id, user_provided },
user_provided, was_quantized,
}), ),
); );
} }
SettingsDiff::Reindex { action, updated_settings } => { SettingsDiff::Reindex { action, updated_settings, quantize } => {
tracing::debug!( tracing::debug!(
embedder = name, embedder = name,
user_provided = user_provided.len(), user_provided = user_provided.len(),
?action, ?action,
"reindex embedder" "reindex embedder"
); );
embedder_actions.insert(name.clone(), EmbedderAction::Reindex(action)); embedder_actions.insert(
name.clone(),
EmbedderAction::with_reindex(action, was_quantized)
.with_is_being_quantized(quantize),
);
let new = let new =
validate_embedding_settings(Setting::Set(updated_settings), &name)?; validate_embedding_settings(Setting::Set(updated_settings), &name)?;
updated_configs.insert(name, (new, user_provided)); updated_configs.insert(name, (new, user_provided));
} }
SettingsDiff::UpdateWithoutReindex { updated_settings } => { SettingsDiff::UpdateWithoutReindex { updated_settings, quantize } => {
tracing::debug!( tracing::debug!(
embedder = name, embedder = name,
user_provided = user_provided.len(), user_provided = user_provided.len(),
@ -1049,6 +1054,12 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
); );
let new = let new =
validate_embedding_settings(Setting::Set(updated_settings), &name)?; validate_embedding_settings(Setting::Set(updated_settings), &name)?;
if quantize {
embedder_actions.insert(
name.clone(),
EmbedderAction::default().with_is_being_quantized(true),
);
}
updated_configs.insert(name, (new, user_provided)); updated_configs.insert(name, (new, user_provided));
} }
} }
@ -1067,8 +1078,10 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
&mut setting, &mut setting,
); );
let setting = validate_embedding_settings(setting, &name)?; let setting = validate_embedding_settings(setting, &name)?;
embedder_actions embedder_actions.insert(
.insert(name.clone(), EmbedderAction::Reindex(ReindexAction::FullReindex)); name.clone(),
EmbedderAction::with_reindex(ReindexAction::FullReindex, false),
);
updated_configs.insert(name, (setting, RoaringBitmap::new())); updated_configs.insert(name, (setting, RoaringBitmap::new()));
} }
} }
@ -1082,21 +1095,16 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
let mut find_free_index = let mut find_free_index =
move || free_indices.find(|(_, free)| **free).map(|(index, _)| index as u8); move || free_indices.find(|(_, free)| **free).map(|(index, _)| index as u8);
for (name, action) in embedder_actions.iter() { for (name, action) in embedder_actions.iter() {
match action { // ignore actions that are not possible for a new embedder
EmbedderAction::Reindex(ReindexAction::RegeneratePrompts) => { if matches!(action.reindex(), Some(ReindexAction::FullReindex))
/* cannot be a new embedder, so has to have an id already */ && self.index.embedder_category_id.get(self.wtxn, name)?.is_none()
} {
EmbedderAction::Reindex(ReindexAction::FullReindex) => { let id =
if self.index.embedder_category_id.get(self.wtxn, name)?.is_none() { find_free_index().ok_or(UserError::TooManyEmbedders(updated_configs.len()))?;
let id = find_free_index()
.ok_or(UserError::TooManyEmbedders(updated_configs.len()))?;
tracing::debug!(embedder = name, id, "assigning free id to new embedder"); tracing::debug!(embedder = name, id, "assigning free id to new embedder");
self.index.embedder_category_id.put(self.wtxn, name, &id)?; self.index.embedder_category_id.put(self.wtxn, name, &id)?;
} }
} }
EmbedderAction::WriteBackToDocuments(_) => { /* already removed */ }
}
}
let updated_configs: Vec<IndexEmbeddingConfig> = updated_configs let updated_configs: Vec<IndexEmbeddingConfig> = updated_configs
.into_iter() .into_iter()
.filter_map(|(name, (config, user_provided))| match config { .filter_map(|(name, (config, user_provided))| match config {
@ -1277,7 +1285,11 @@ impl InnerIndexSettingsDiff {
// if the user-defined searchables changed, then we need to reindex prompts. // if the user-defined searchables changed, then we need to reindex prompts.
if cache_user_defined_searchables { if cache_user_defined_searchables {
for (embedder_name, (config, _)) in new_settings.embedding_configs.inner_as_ref() { for (embedder_name, (config, _, _quantized)) in
new_settings.embedding_configs.inner_as_ref()
{
let was_quantized =
old_settings.embedding_configs.get(embedder_name).map_or(false, |conf| conf.2);
// skip embedders that don't use document templates // skip embedders that don't use document templates
if !config.uses_document_template() { if !config.uses_document_template() {
continue; continue;
@ -1287,16 +1299,19 @@ impl InnerIndexSettingsDiff {
// this always makes the code clearer by explicitly handling the cases // this always makes the code clearer by explicitly handling the cases
match embedding_config_updates.entry(embedder_name.clone()) { match embedding_config_updates.entry(embedder_name.clone()) {
std::collections::btree_map::Entry::Vacant(entry) => { std::collections::btree_map::Entry::Vacant(entry) => {
entry.insert(EmbedderAction::Reindex(ReindexAction::RegeneratePrompts)); entry.insert(EmbedderAction::with_reindex(
ReindexAction::RegeneratePrompts,
was_quantized,
));
} }
std::collections::btree_map::Entry::Occupied(entry) => match entry.get() { std::collections::btree_map::Entry::Occupied(entry) => {
EmbedderAction::WriteBackToDocuments(_) => { /* we are deleting this embedder, so no point in regeneration */ let EmbedderAction {
was_quantized: _,
is_being_quantized: _,
write_back: _, // We are deleting this embedder, so no point in regeneration
reindex: _, // We are already fully reindexing
} = entry.get();
} }
EmbedderAction::Reindex(ReindexAction::FullReindex) => { /* we are already fully reindexing */
}
EmbedderAction::Reindex(ReindexAction::RegeneratePrompts) => { /* we are already regenerating prompts */
}
},
}; };
} }
} }
@ -1546,7 +1561,7 @@ fn embedders(embedding_configs: Vec<IndexEmbeddingConfig>) -> Result<EmbeddingCo
.map( .map(
|IndexEmbeddingConfig { |IndexEmbeddingConfig {
name, name,
config: EmbeddingConfig { embedder_options, prompt }, config: EmbeddingConfig { embedder_options, prompt, quantized },
.. ..
}| { }| {
let prompt = Arc::new(prompt.try_into().map_err(crate::Error::from)?); let prompt = Arc::new(prompt.try_into().map_err(crate::Error::from)?);
@ -1556,7 +1571,7 @@ fn embedders(embedding_configs: Vec<IndexEmbeddingConfig>) -> Result<EmbeddingCo
.map_err(crate::vector::Error::from) .map_err(crate::vector::Error::from)
.map_err(crate::Error::from)?, .map_err(crate::Error::from)?,
); );
Ok((name, (embedder, prompt))) Ok((name, (embedder, prompt, quantized.unwrap_or_default())))
}, },
) )
.collect(); .collect();
@ -1581,6 +1596,7 @@ fn validate_prompt(
response, response,
distribution, distribution,
headers, headers,
binary_quantized: binary_quantize,
}) => { }) => {
let max_bytes = match document_template_max_bytes.set() { let max_bytes = match document_template_max_bytes.set() {
Some(max_bytes) => NonZeroUsize::new(max_bytes).ok_or_else(|| { Some(max_bytes) => NonZeroUsize::new(max_bytes).ok_or_else(|| {
@ -1613,6 +1629,7 @@ fn validate_prompt(
response, response,
distribution, distribution,
headers, headers,
binary_quantized: binary_quantize,
})) }))
} }
new => Ok(new), new => Ok(new),
@ -1638,6 +1655,7 @@ pub fn validate_embedding_settings(
response, response,
distribution, distribution,
headers, headers,
binary_quantized: binary_quantize,
} = settings; } = settings;
if let Some(0) = dimensions.set() { if let Some(0) = dimensions.set() {
@ -1678,6 +1696,7 @@ pub fn validate_embedding_settings(
response, response,
distribution, distribution,
headers, headers,
binary_quantized: binary_quantize,
})); }));
}; };
match inferred_source { match inferred_source {
@ -1779,6 +1798,7 @@ pub fn validate_embedding_settings(
response, response,
distribution, distribution,
headers, headers,
binary_quantized: binary_quantize,
})) }))
} }

192
milli/src/vector/mod.rs
View File

@ -1,8 +1,12 @@
use std::collections::HashMap; use std::collections::HashMap;
use std::sync::Arc; use std::sync::Arc;
use arroy::distances::{Angular, BinaryQuantizedAngular};
use arroy::ItemId;
use deserr::{DeserializeError, Deserr}; use deserr::{DeserializeError, Deserr};
use heed::{RoTxn, RwTxn, Unspecified};
use ordered_float::OrderedFloat; use ordered_float::OrderedFloat;
use roaring::RoaringBitmap;
use serde::{Deserialize, Serialize}; use serde::{Deserialize, Serialize};
use self::error::{EmbedError, NewEmbedderError}; use self::error::{EmbedError, NewEmbedderError};
@ -26,6 +30,171 @@ pub type Embedding = Vec<f32>;
pub const REQUEST_PARALLELISM: usize = 40; pub const REQUEST_PARALLELISM: usize = 40;
pub struct ArroyWrapper {
quantized: bool,
index: u16,
database: arroy::Database<Unspecified>,
}
impl ArroyWrapper {
pub fn new(database: arroy::Database<Unspecified>, index: u16, quantized: bool) -> Self {
Self { database, index, quantized }
}
pub fn index(&self) -> u16 {
self.index
}
pub fn dimensions(&self, rtxn: &RoTxn) -> Result<usize, arroy::Error> {
if self.quantized {
Ok(arroy::Reader::open(rtxn, self.index, self.quantized_db())?.dimensions())
} else {
Ok(arroy::Reader::open(rtxn, self.index, self.angular_db())?.dimensions())
}
}
pub fn quantize(
&mut self,
wtxn: &mut RwTxn,
index: u16,
dimension: usize,
) -> Result<(), arroy::Error> {
if !self.quantized {
let writer = arroy::Writer::new(self.angular_db(), index, dimension);
writer.prepare_changing_distance::<BinaryQuantizedAngular>(wtxn)?;
self.quantized = true;
}
Ok(())
}
pub fn need_build(&self, rtxn: &RoTxn, dimension: usize) -> Result<bool, arroy::Error> {
if self.quantized {
arroy::Writer::new(self.quantized_db(), self.index, dimension).need_build(rtxn)
} else {
arroy::Writer::new(self.angular_db(), self.index, dimension).need_build(rtxn)
}
}
pub fn build<R: rand::Rng + rand::SeedableRng>(
&self,
wtxn: &mut RwTxn,
rng: &mut R,
dimension: usize,
) -> Result<(), arroy::Error> {
if self.quantized {
arroy::Writer::new(self.quantized_db(), self.index, dimension).build(wtxn, rng, None)
} else {
arroy::Writer::new(self.angular_db(), self.index, dimension).build(wtxn, rng, None)
}
}
pub fn add_item(
&self,
wtxn: &mut RwTxn,
dimension: usize,
item_id: arroy::ItemId,
vector: &[f32],
) -> Result<(), arroy::Error> {
if self.quantized {
arroy::Writer::new(self.quantized_db(), self.index, dimension)
.add_item(wtxn, item_id, vector)
} else {
arroy::Writer::new(self.angular_db(), self.index, dimension)
.add_item(wtxn, item_id, vector)
}
}
pub fn del_item(
&self,
wtxn: &mut RwTxn,
dimension: usize,
item_id: arroy::ItemId,
) -> Result<bool, arroy::Error> {
if self.quantized {
arroy::Writer::new(self.quantized_db(), self.index, dimension).del_item(wtxn, item_id)
} else {
arroy::Writer::new(self.angular_db(), self.index, dimension).del_item(wtxn, item_id)
}
}
pub fn clear(&self, wtxn: &mut RwTxn, dimension: usize) -> Result<(), arroy::Error> {
if self.quantized {
arroy::Writer::new(self.quantized_db(), self.index, dimension).clear(wtxn)
} else {
arroy::Writer::new(self.angular_db(), self.index, dimension).clear(wtxn)
}
}
pub fn is_empty(&self, rtxn: &RoTxn, dimension: usize) -> Result<bool, arroy::Error> {
if self.quantized {
arroy::Writer::new(self.quantized_db(), self.index, dimension).is_empty(rtxn)
} else {
arroy::Writer::new(self.angular_db(), self.index, dimension).is_empty(rtxn)
}
}
pub fn contains_item(
&self,
rtxn: &RoTxn,
dimension: usize,
item: arroy::ItemId,
) -> Result<bool, arroy::Error> {
if self.quantized {
arroy::Writer::new(self.quantized_db(), self.index, dimension).contains_item(rtxn, item)
} else {
arroy::Writer::new(self.angular_db(), self.index, dimension).contains_item(rtxn, item)
}
}
pub fn nns_by_item(
&self,
rtxn: &RoTxn,
item: ItemId,
limit: usize,
filter: Option<&RoaringBitmap>,
) -> Result<Option<Vec<(ItemId, f32)>>, arroy::Error> {
if self.quantized {
arroy::Reader::open(rtxn, self.index, self.quantized_db())?
.nns_by_item(rtxn, item, limit, None, None, filter)
} else {
arroy::Reader::open(rtxn, self.index, self.angular_db())?
.nns_by_item(rtxn, item, limit, None, None, filter)
}
}
pub fn nns_by_vector(
&self,
txn: &RoTxn,
item: &[f32],
limit: usize,
filter: Option<&RoaringBitmap>,
) -> Result<Vec<(ItemId, f32)>, arroy::Error> {
if self.quantized {
arroy::Reader::open(txn, self.index, self.quantized_db())?
.nns_by_vector(txn, item, limit, None, None, filter)
} else {
arroy::Reader::open(txn, self.index, self.angular_db())?
.nns_by_vector(txn, item, limit, None, None, filter)
}
}
pub fn item_vector(&self, rtxn: &RoTxn, docid: u32) -> Result<Option<Vec<f32>>, arroy::Error> {
if self.quantized {
arroy::Reader::open(rtxn, self.index, self.quantized_db())?.item_vector(rtxn, docid)
} else {
arroy::Reader::open(rtxn, self.index, self.angular_db())?.item_vector(rtxn, docid)
}
}
fn angular_db(&self) -> arroy::Database<Angular> {
self.database.remap_data_type()
}
fn quantized_db(&self) -> arroy::Database<BinaryQuantizedAngular> {
self.database.remap_data_type()
}
}
/// One or multiple embeddings stored consecutively in a flat vector. /// One or multiple embeddings stored consecutively in a flat vector.
pub struct Embeddings<F> { pub struct Embeddings<F> {
data: Vec<F>, data: Vec<F>,
@ -124,39 +293,48 @@ pub struct EmbeddingConfig {
pub embedder_options: EmbedderOptions, pub embedder_options: EmbedderOptions,
/// Document template /// Document template
pub prompt: PromptData, pub prompt: PromptData,
/// If this embedder is binary quantized
pub quantized: Option<bool>,
// TODO: add metrics and anything needed // TODO: add metrics and anything needed
} }
impl EmbeddingConfig {
pub fn quantized(&self) -> bool {
self.quantized.unwrap_or_default()
}
}
/// Map of embedder configurations. /// Map of embedder configurations.
/// ///
/// Each configuration is mapped to a name. /// Each configuration is mapped to a name.
#[derive(Clone, Default)] #[derive(Clone, Default)]
pub struct EmbeddingConfigs(HashMap<String, (Arc<Embedder>, Arc<Prompt>)>); pub struct EmbeddingConfigs(HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)>);
impl EmbeddingConfigs { impl EmbeddingConfigs {
/// Create the map from its internal component.s /// Create the map from its internal component.s
pub fn new(data: HashMap<String, (Arc<Embedder>, Arc<Prompt>)>) -> Self { pub fn new(data: HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)>) -> Self {
Self(data) Self(data)
} }
/// Get an embedder configuration and template from its name. /// Get an embedder configuration and template from its name.
pub fn get(&self, name: &str) -> Option<(Arc<Embedder>, Arc<Prompt>)> { pub fn get(&self, name: &str) -> Option<(Arc<Embedder>, Arc<Prompt>, bool)> {
self.0.get(name).cloned() self.0.get(name).cloned()
} }
pub fn inner_as_ref(&self) -> &HashMap<String, (Arc<Embedder>, Arc<Prompt>)> { pub fn inner_as_ref(&self) -> &HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)> {
&self.0 &self.0
} }
pub fn into_inner(self) -> HashMap<String, (Arc<Embedder>, Arc<Prompt>)> { pub fn into_inner(self) -> HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)> {
self.0 self.0
} }
} }
impl IntoIterator for EmbeddingConfigs { impl IntoIterator for EmbeddingConfigs {
type Item = (String, (Arc<Embedder>, Arc<Prompt>)); type Item = (String, (Arc<Embedder>, Arc<Prompt>, bool));
type IntoIter = std::collections::hash_map::IntoIter<String, (Arc<Embedder>, Arc<Prompt>)>; type IntoIter =
std::collections::hash_map::IntoIter<String, (Arc<Embedder>, Arc<Prompt>, bool)>;
fn into_iter(self) -> Self::IntoIter { fn into_iter(self) -> Self::IntoIter {
self.0.into_iter() self.0.into_iter()

95
milli/src/vector/settings.rs
View File

@ -32,6 +32,9 @@ pub struct EmbeddingSettings {
pub dimensions: Setting<usize>, pub dimensions: Setting<usize>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")] #[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)] #[deserr(default)]
pub binary_quantized: Setting<bool>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub document_template: Setting<String>, pub document_template: Setting<String>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")] #[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)] #[deserr(default)]
@ -85,23 +88,63 @@ pub enum ReindexAction {
pub enum SettingsDiff { pub enum SettingsDiff {
Remove, Remove,
Reindex { action: ReindexAction, updated_settings: EmbeddingSettings }, Reindex { action: ReindexAction, updated_settings: EmbeddingSettings, quantize: bool },
UpdateWithoutReindex { updated_settings: EmbeddingSettings }, UpdateWithoutReindex { updated_settings: EmbeddingSettings, quantize: bool },
} }
pub enum EmbedderAction { #[derive(Default, Debug)]
WriteBackToDocuments(WriteBackToDocuments), pub struct EmbedderAction {
Reindex(ReindexAction), pub was_quantized: bool,
pub is_being_quantized: bool,
pub write_back: Option<WriteBackToDocuments>,
pub reindex: Option<ReindexAction>,
} }
impl EmbedderAction {
pub fn is_being_quantized(&self) -> bool {
self.is_being_quantized
}
pub fn write_back(&self) -> Option<&WriteBackToDocuments> {
self.write_back.as_ref()
}
pub fn reindex(&self) -> Option<&ReindexAction> {
self.reindex.as_ref()
}
pub fn with_is_being_quantized(mut self, quantize: bool) -> Self {
self.is_being_quantized = quantize;
self
}
pub fn with_write_back(write_back: WriteBackToDocuments, was_quantized: bool) -> Self {
Self {
was_quantized,
is_being_quantized: false,
write_back: Some(write_back),
reindex: None,
}
}
pub fn with_reindex(reindex: ReindexAction, was_quantized: bool) -> Self {
Self { was_quantized, is_being_quantized: false, write_back: None, reindex: Some(reindex) }
}
}
#[derive(Debug)]
pub struct WriteBackToDocuments { pub struct WriteBackToDocuments {
pub embedder_id: u8, pub embedder_id: u8,
pub user_provided: RoaringBitmap, pub user_provided: RoaringBitmap,
} }
impl SettingsDiff { impl SettingsDiff {
pub fn from_settings(old: EmbeddingSettings, new: Setting<EmbeddingSettings>) -> Self { pub fn from_settings(
match new { embedder_name: &str,
old: EmbeddingSettings,
new: Setting<EmbeddingSettings>,
) -> Result<Self, UserError> {
let ret = match new {
Setting::Set(new) => { Setting::Set(new) => {
let EmbeddingSettings { let EmbeddingSettings {
mut source, mut source,
@ -116,6 +159,7 @@ impl SettingsDiff {
mut distribution, mut distribution,
mut headers, mut headers,
mut document_template_max_bytes, mut document_template_max_bytes,
binary_quantized: mut binary_quantize,
} = old; } = old;
let EmbeddingSettings { let EmbeddingSettings {
@ -131,8 +175,17 @@ impl SettingsDiff {
distribution: new_distribution, distribution: new_distribution,
headers: new_headers, headers: new_headers,
document_template_max_bytes: new_document_template_max_bytes, document_template_max_bytes: new_document_template_max_bytes,
binary_quantized: new_binary_quantize,
} = new; } = new;
if matches!(binary_quantize, Setting::Set(true))
&& matches!(new_binary_quantize, Setting::Set(false))
{
return Err(UserError::InvalidDisableBinaryQuantization {
embedder_name: embedder_name.to_string(),
});
}
let mut reindex_action = None; let mut reindex_action = None;
// **Warning**: do not use short-circuiting || here, we want all these operations applied // **Warning**: do not use short-circuiting || here, we want all these operations applied
@ -172,6 +225,7 @@ impl SettingsDiff {
_ => {} _ => {}
} }
} }
let binary_quantize_changed = binary_quantize.apply(new_binary_quantize);
if url.apply(new_url) { if url.apply(new_url) {
match source { match source {
// do not regenerate on an url change in OpenAI // do not regenerate on an url change in OpenAI
@ -231,16 +285,27 @@ impl SettingsDiff {
distribution, distribution,
headers, headers,
document_template_max_bytes, document_template_max_bytes,
binary_quantized: binary_quantize,
}; };
match reindex_action { match reindex_action {
Some(action) => Self::Reindex { action, updated_settings }, Some(action) => Self::Reindex {
None => Self::UpdateWithoutReindex { updated_settings }, action,
updated_settings,
quantize: binary_quantize_changed,
},
None => Self::UpdateWithoutReindex {
updated_settings,
quantize: binary_quantize_changed,
},
} }
} }
Setting::Reset => Self::Remove, Setting::Reset => Self::Remove,
Setting::NotSet => Self::UpdateWithoutReindex { updated_settings: old }, Setting::NotSet => {
Self::UpdateWithoutReindex { updated_settings: old, quantize: false }
} }
};
Ok(ret)
} }
} }
@ -486,7 +551,7 @@ impl std::fmt::Display for EmbedderSource {
impl From<EmbeddingConfig> for EmbeddingSettings { impl From<EmbeddingConfig> for EmbeddingSettings {
fn from(value: EmbeddingConfig) -> Self { fn from(value: EmbeddingConfig) -> Self {
let EmbeddingConfig { embedder_options, prompt } = value; let EmbeddingConfig { embedder_options, prompt, quantized } = value;
let document_template_max_bytes = let document_template_max_bytes =
Setting::Set(prompt.max_bytes.unwrap_or(default_max_bytes()).get()); Setting::Set(prompt.max_bytes.unwrap_or(default_max_bytes()).get());
match embedder_options { match embedder_options {
@ -507,6 +572,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::NotSet, response: Setting::NotSet,
headers: Setting::NotSet, headers: Setting::NotSet,
distribution: Setting::some_or_not_set(distribution), distribution: Setting::some_or_not_set(distribution),
binary_quantized: Setting::some_or_not_set(quantized),
}, },
super::EmbedderOptions::OpenAi(super::openai::EmbedderOptions { super::EmbedderOptions::OpenAi(super::openai::EmbedderOptions {
url, url,
@ -527,6 +593,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::NotSet, response: Setting::NotSet,
headers: Setting::NotSet, headers: Setting::NotSet,
distribution: Setting::some_or_not_set(distribution), distribution: Setting::some_or_not_set(distribution),
binary_quantized: Setting::some_or_not_set(quantized),
}, },
super::EmbedderOptions::Ollama(super::ollama::EmbedderOptions { super::EmbedderOptions::Ollama(super::ollama::EmbedderOptions {
embedding_model, embedding_model,
@ -547,6 +614,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::NotSet, response: Setting::NotSet,
headers: Setting::NotSet, headers: Setting::NotSet,
distribution: Setting::some_or_not_set(distribution), distribution: Setting::some_or_not_set(distribution),
binary_quantized: Setting::some_or_not_set(quantized),
}, },
super::EmbedderOptions::UserProvided(super::manual::EmbedderOptions { super::EmbedderOptions::UserProvided(super::manual::EmbedderOptions {
dimensions, dimensions,
@ -564,6 +632,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::NotSet, response: Setting::NotSet,
headers: Setting::NotSet, headers: Setting::NotSet,
distribution: Setting::some_or_not_set(distribution), distribution: Setting::some_or_not_set(distribution),
binary_quantized: Setting::some_or_not_set(quantized),
}, },
super::EmbedderOptions::Rest(super::rest::EmbedderOptions { super::EmbedderOptions::Rest(super::rest::EmbedderOptions {
api_key, api_key,
@ -586,6 +655,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::Set(response), response: Setting::Set(response),
distribution: Setting::some_or_not_set(distribution), distribution: Setting::some_or_not_set(distribution),
headers: Setting::Set(headers), headers: Setting::Set(headers),
binary_quantized: Setting::some_or_not_set(quantized),
}, },
} }
} }
@ -607,8 +677,11 @@ impl From<EmbeddingSettings> for EmbeddingConfig {
response, response,
distribution, distribution,
headers, headers,
binary_quantized,
} = value; } = value;
this.quantized = binary_quantized.set();
if let Some(source) = source.set() { if let Some(source) = source.set() {
match source { match source {
EmbedderSource::OpenAi => { EmbedderSource::OpenAi => {