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Merge #4633

Browse Source 
4633: Allow to mark vectors as "userProvided" r=Kerollmops a=dureuill

# Pull Request

## Related issue
Fixes #4606 

## What does this PR do?

[See usage in PRD](https://meilisearch.notion.site/v1-9-AI-search-changes-e90d6803eca8417aa70a1ac5d0225697#deb96fb0595947bda7d4a371100326eb)

- Extends the shape of the special `_vectors` field in documents.
    - previously, the `_vectors` field had to be an object, with each field the name of a configured embedder, and each value either `null`, an embedding (array of numbers), or an array of embeddings.
    - In this PR, the value of an embedder in the `_vectors` field can additionally be an object. The object has two fields:
      1. `embeddings`: `null`, an embedding (array of numbers), or an array of embeddings.
      2. `userProvided`: a boolean indicating if the vector was provided by the user.
    - The previous form `embedder_or_array_of_embedders` is semantically equivalent to:
    ```json
    {
        "embeddings": embedder_or_array_of_embedders,
        "userProvided": true
    }
    ```
- During the indexing step, the subfields and values of the `_vectors` field that have `userProvided` set to **false** are added in the vector DB, but not in the documents DB: that means that future modifications of the documents will trigger a regeneration of that particular vector using the document template.
- This allows **importing** embeddings as a one-shot process, while still retaining the ability to regenerate embeddings on document change.
- The dump process now uses this ability: it enriches the `_vectors` fields of documents with the embeddings that were autogenerated, marking them as not `userProvided`. This allows importing the vectors from a dump without regenerating them.

### Tests

This PR adds the following tests

- Long-needed hybrid search tests of a simple hf embedder
- Dump test that imports vectors. Due to the difficulty of actually importing a dump in tests, we just read the dump and check it contains the expected content.
- Tests in the index-scheduler: this tests that documents containing the same kind of instructions as in the dump indexes as expected


Co-authored-by: Louis Dureuil <louis@meilisearch.com>
This commit is contained in:
meili-bors[bot] 2024-05-23 08:17:54 +00:00 committed by GitHub
commit 14bc80e3df
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GPG Key ID: B5690EEEBB952194
32 changed files with 4581 additions and 297 deletions
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134
dump/src/reader/mod.rs
View File

@ -197,6 +197,140 @@ pub(crate) mod test {
use super::*;
use crate::reader::v6::RuntimeTogglableFeatures;
#[test]
fn import_dump_v6_with_vectors() {
// dump containing two indexes
//
// "vector", configured with an embedder
// contains:
// - one document with an overriden vector,
// - one document with a natural vector
// - one document with a _vectors map containing one additional embedder name and a natural vector
// - one document with a _vectors map containing one additional embedder name and an overriden vector
//
// "novector", no embedder
// contains:
// - a document without vector
// - a document with a random _vectors field
let dump = File::open("tests/assets/v6-with-vectors.dump").unwrap();
let mut dump = DumpReader::open(dump).unwrap();
// top level infos
insta::assert_display_snapshot!(dump.date().unwrap(), @"2024-05-16 15:51:34.151044 +00:00:00");
insta::assert_debug_snapshot!(dump.instance_uid().unwrap(), @"None");
// tasks
let tasks = dump.tasks().unwrap().collect::<Result<Vec<_>>>().unwrap();
let (tasks, update_files): (Vec<_>, Vec<_>) = tasks.into_iter().unzip();
meili_snap::snapshot_hash!(meili_snap::json_string!(tasks), @"278f63325ef06ca04d01df98d8207b94");
assert_eq!(update_files.len(), 10);
assert!(update_files[0].is_none()); // the dump creation
assert!(update_files[1].is_none());
assert!(update_files[2].is_none());
assert!(update_files[3].is_none());
assert!(update_files[4].is_none());
assert!(update_files[5].is_none());
assert!(update_files[6].is_none());
assert!(update_files[7].is_none());
assert!(update_files[8].is_none());
assert!(update_files[9].is_none());
// indexes
let mut indexes = dump.indexes().unwrap().collect::<Result<Vec<_>>>().unwrap();
// the index are not ordered in any way by default
indexes.sort_by_key(|index| index.metadata().uid.to_string());
let mut vector_index = indexes.pop().unwrap();
let mut novector_index = indexes.pop().unwrap();
assert!(indexes.is_empty());
// vector
insta::assert_json_snapshot!(vector_index.metadata(), @r###"
{
"uid": "vector",
"primaryKey": "id",
"createdAt": "2024-05-16T15:33:17.240962Z",
"updatedAt": "2024-05-16T15:40:55.723052Z"
}
"###);
{
let documents: Result<Vec<_>> = vector_index.documents().unwrap().collect();
let mut documents = documents.unwrap();
assert_eq!(documents.len(), 4);
documents.sort_by_key(|doc| doc.get("id").unwrap().to_string());
{
let document = documents.pop().unwrap();
insta::assert_json_snapshot!(document);
}
{
let document = documents.pop().unwrap();
insta::assert_json_snapshot!(document);
}
{
let document = documents.pop().unwrap();
insta::assert_json_snapshot!(document);
}
{
let document = documents.pop().unwrap();
insta::assert_json_snapshot!(document);
}
}
// novector
insta::assert_json_snapshot!(novector_index.metadata(), @r###"
{
"uid": "novector",
"primaryKey": "id",
"createdAt": "2024-05-16T15:33:03.568055Z",
"updatedAt": "2024-05-16T15:33:07.530217Z"
}
"###);
insta::assert_json_snapshot!(novector_index.settings().unwrap().embedders, @"null");
{
let documents: Result<Vec<_>> = novector_index.documents().unwrap().collect();
let mut documents = documents.unwrap();
assert_eq!(documents.len(), 2);
documents.sort_by_key(|doc| doc.get("id").unwrap().to_string());
{
let document = documents.pop().unwrap();
insta::assert_json_snapshot!(document, @r###"
{
"id": "e1",
"other": "random1",
"_vectors": "toto"
}
"###);
}
{
let document = documents.pop().unwrap();
insta::assert_json_snapshot!(document, @r###"
{
"id": "e0",
"other": "random0"
}
"###);
}
}
assert_eq!(
dump.features().unwrap().unwrap(),
RuntimeTogglableFeatures { vector_store: true, ..Default::default() }
);
}
#[test]
fn import_dump_v6_experimental() {
let dump = File::open("tests/assets/v6-with-experimental.dump").unwrap();

783
dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-5.snap Normal file
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@ -0,0 +1,783 @@
---
source: dump/src/reader/mod.rs
expression: document
---
{
"id": "e3",
"desc": "overriden vector + map",
"_vectors": {
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"toto": [
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]
}
}

786
dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-6.snap Normal file
View File

@ -0,0 +1,786 @@
---
source: dump/src/reader/mod.rs
expression: document
---
{
"id": "e2",
"desc": "natural vector + map",
"_vectors": {
"toto": [],
"default": {
"embeddings": [
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785
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expression: document
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"userProvided": false
}
}
}

780
dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-8.snap Normal file
View File

@ -0,0 +1,780 @@
---
source: dump/src/reader/mod.rs
expression: document
---
{
"id": "e0",
"desc": "overriden vector",
"_vectors": {
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}
}

BIN
dump/tests/assets/v6-with-vectors.dump Normal file
View File

Binary file not shown.

54
index-scheduler/src/batch.rs
View File

@ -31,6 +31,9 @@ use meilisearch_types::milli::heed::CompactionOption;
use meilisearch_types::milli::update::{
IndexDocumentsConfig, IndexDocumentsMethod, IndexerConfig, Settings as MilliSettings,
};
use meilisearch_types::milli::vector::parsed_vectors::{
ExplicitVectors, VectorOrArrayOfVectors, RESERVED_VECTORS_FIELD_NAME,
};
use meilisearch_types::milli::{self, Filter};
use meilisearch_types::settings::{apply_settings_to_builder, Settings, Unchecked};
use meilisearch_types::tasks::{Details, IndexSwap, Kind, KindWithContent, Status, Task};
@ -916,8 +919,55 @@ impl IndexScheduler {
if self.must_stop_processing.get() {
return Err(Error::AbortedTask);
}
let (_id, doc) = ret?;
let document = milli::obkv_to_json(&all_fields, &fields_ids_map, doc)?;
let (id, doc) = ret?;
let mut document = milli::obkv_to_json(&all_fields, &fields_ids_map, doc)?;
'inject_vectors: {
let embeddings = index.embeddings(&rtxn, id)?;
if embeddings.is_empty() {
break 'inject_vectors;
}
let vectors = document
.entry(RESERVED_VECTORS_FIELD_NAME.to_owned())
.or_insert(serde_json::Value::Object(Default::default()));
let serde_json::Value::Object(vectors) = vectors else {
return Err(milli::Error::UserError(
milli::UserError::InvalidVectorsMapType {
document_id: {
if let Ok(Some(Ok(index))) = index
.external_id_of(&rtxn, std::iter::once(id))
.map(|it| it.into_iter().next())
{
index
} else {
format!("internal docid={id}")
}
},
value: vectors.clone(),
},
)
.into());
};
for (embedder_name, embeddings) in embeddings {
// don't change the entry if it already exists, because it was user-provided
vectors.entry(embedder_name).or_insert_with(|| {
let embeddings = ExplicitVectors {
embeddings: VectorOrArrayOfVectors::from_array_of_vectors(
embeddings,
),
user_provided: false,
};
serde_json::to_value(embeddings).unwrap()
});
}
}
index_dumper.push_document(&document)?;
}

236
index-scheduler/src/lib.rs
View File

@ -1774,6 +1774,7 @@ mod tests {
use big_s::S;
use crossbeam::channel::RecvTimeoutError;
use file_store::File;
use insta::assert_json_snapshot;
use meili_snap::{json_string, snapshot};
use meilisearch_auth::AuthFilter;
use meilisearch_types::document_formats::DocumentFormatError;
@ -1851,7 +1852,7 @@ mod tests {
// To be 100% consistent between all test we're going to start the scheduler right now
// and ensure it's in the expected starting state.
let breakpoint = match receiver.recv_timeout(std::time::Duration::from_secs(1)) {
let breakpoint = match receiver.recv_timeout(std::time::Duration::from_secs(10)) {
Ok(b) => b,
Err(RecvTimeoutError::Timeout) => {
panic!("The scheduler seems to be waiting for a new task while your test is waiting for a breakpoint.")
@ -1962,7 +1963,7 @@ mod tests {
fn advance(&mut self) -> Breakpoint {
let (breakpoint_1, b) = match self
.test_breakpoint_rcv
.recv_timeout(std::time::Duration::from_secs(5))
.recv_timeout(std::time::Duration::from_secs(50))
{
Ok(b) => b,
Err(RecvTimeoutError::Timeout) => {
@ -1983,7 +1984,7 @@ mod tests {
let (breakpoint_2, b) = match self
.test_breakpoint_rcv
.recv_timeout(std::time::Duration::from_secs(5))
.recv_timeout(std::time::Duration::from_secs(50))
{
Ok(b) => b,
Err(RecvTimeoutError::Timeout) => {
@ -4982,4 +4983,233 @@ mod tests {
----------------------------------------------------------------------
"###);
}
#[test]
fn import_vectors() {
use meilisearch_types::settings::{Settings, Unchecked};
use milli::update::Setting;
let (index_scheduler, mut handle) = IndexScheduler::test(true, vec![]);
let mut new_settings: Box<Settings<Unchecked>> = Box::default();
let mut embedders = BTreeMap::default();
let embedding_settings = milli::vector::settings::EmbeddingSettings {
source: Setting::Set(milli::vector::settings::EmbedderSource::Rest),
api_key: Setting::Set(S("My super secret")),
url: Setting::Set(S("http://localhost:7777")),
dimensions: Setting::Set(384),
..Default::default()
};
embedders.insert(S("A_fakerest"), Setting::Set(embedding_settings));
let embedding_settings = milli::vector::settings::EmbeddingSettings {
source: Setting::Set(milli::vector::settings::EmbedderSource::HuggingFace),
model: Setting::Set(S("sentence-transformers/all-MiniLM-L6-v2")),
revision: Setting::Set(S("e4ce9877abf3edfe10b0d82785e83bdcb973e22e")),
document_template: Setting::Set(S("{{doc.doggo}} the {{doc.breed}} best doggo")),
..Default::default()
};
embedders.insert(S("B_small_hf"), Setting::Set(embedding_settings));
new_settings.embedders = Setting::Set(embedders);
index_scheduler
.register(
KindWithContent::SettingsUpdate {
index_uid: S("doggos"),
new_settings,
is_deletion: false,
allow_index_creation: true,
},
None,
false,
)
.unwrap();
index_scheduler.assert_internally_consistent();
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "after_registering_settings_task_vectors");
{
let rtxn = index_scheduler.read_txn().unwrap();
let task = index_scheduler.get_task(&rtxn, 0).unwrap().unwrap();
let task = meilisearch_types::task_view::TaskView::from_task(&task);
insta::assert_json_snapshot!(task.details);
}
handle.advance_n_successful_batches(1);
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "settings_update_processed_vectors");
{
let rtxn = index_scheduler.read_txn().unwrap();
let task = index_scheduler.get_task(&rtxn, 0).unwrap().unwrap();
let task = meilisearch_types::task_view::TaskView::from_task(&task);
insta::assert_json_snapshot!(task.details);
}
let (fakerest_name, simple_hf_name, beagle_embed, lab_embed, patou_embed) = {
let index = index_scheduler.index("doggos").unwrap();
let rtxn = index.read_txn().unwrap();
let configs = index.embedding_configs(&rtxn).unwrap();
// for consistency with the below
#[allow(clippy::get_first)]
let (name, fakerest_config) = configs.get(0).unwrap();
insta::assert_json_snapshot!(name, @r###""A_fakerest""###);
insta::assert_json_snapshot!(fakerest_config.embedder_options);
let fakerest_name = name.clone();
let (name, simple_hf_config) = configs.get(1).unwrap();
insta::assert_json_snapshot!(name, @r###""B_small_hf""###);
insta::assert_json_snapshot!(simple_hf_config.embedder_options);
let simple_hf_name = name.clone();
let configs = index_scheduler.embedders(configs).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 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();
(fakerest_name, simple_hf_name, beagle_embed, lab_embed, patou_embed)
};
// add one doc, specifying vectors
let doc = serde_json::json!(
{
"id": 0,
"doggo": "Intel",
"breed": "beagle",
"_vectors": {
&fakerest_name: {
// this will never trigger regeneration, which is good because we can't actually generate with
// this embedder
"userProvided": true,
"embeddings": beagle_embed,
},
&simple_hf_name: {
// this will be regenerated on updates
"userProvided": false,
"embeddings": lab_embed,
},
"noise": [0.1, 0.2, 0.3]
}
}
);
let (uuid, mut file) = index_scheduler.create_update_file_with_uuid(0u128).unwrap();
let documents_count = read_json(doc.to_string().as_bytes(), &mut file).unwrap();
assert_eq!(documents_count, 1);
file.persist().unwrap();
index_scheduler
.register(
KindWithContent::DocumentAdditionOrUpdate {
index_uid: S("doggos"),
primary_key: Some(S("id")),
method: UpdateDocuments,
content_file: uuid,
documents_count,
allow_index_creation: true,
},
None,
false,
)
.unwrap();
index_scheduler.assert_internally_consistent();
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "after adding Intel");
handle.advance_one_successful_batch();
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "adding Intel succeeds");
// check embeddings
{
let index = index_scheduler.index("doggos").unwrap();
let rtxn = index.read_txn().unwrap();
let embeddings = index.embeddings(&rtxn, 0).unwrap();
assert_json_snapshot!(embeddings[&simple_hf_name][0] == lab_embed, @"true");
assert_json_snapshot!(embeddings[&fakerest_name][0] == beagle_embed, @"true");
let doc = index.documents(&rtxn, std::iter::once(0)).unwrap()[0].1;
let fields_ids_map = index.fields_ids_map(&rtxn).unwrap();
let doc = obkv_to_json(
&[
fields_ids_map.id("doggo").unwrap(),
fields_ids_map.id("breed").unwrap(),
fields_ids_map.id("_vectors").unwrap(),
],
&fields_ids_map,
doc,
)
.unwrap();
assert_json_snapshot!(doc, {"._vectors.A_fakerest.embeddings" => "[vector]"});
}
// update the doc, specifying vectors
let doc = serde_json::json!(
{
"id": 0,
"doggo": "kefir",
"breed": "patou",
}
);
let (uuid, mut file) = index_scheduler.create_update_file_with_uuid(1u128).unwrap();
let documents_count = read_json(doc.to_string().as_bytes(), &mut file).unwrap();
assert_eq!(documents_count, 1);
file.persist().unwrap();
index_scheduler
.register(
KindWithContent::DocumentAdditionOrUpdate {
index_uid: S("doggos"),
primary_key: None,
method: UpdateDocuments,
content_file: uuid,
documents_count,
allow_index_creation: true,
},
None,
false,
)
.unwrap();
index_scheduler.assert_internally_consistent();
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "Intel to kefir");
handle.advance_one_successful_batch();
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "Intel to kefir succeeds");
{
// check embeddings
{
let index = index_scheduler.index("doggos").unwrap();
let rtxn = index.read_txn().unwrap();
let embeddings = index.embeddings(&rtxn, 0).unwrap();
// automatically changed to patou
assert_json_snapshot!(embeddings[&simple_hf_name][0] == patou_embed, @"true");
// remained beagle because set to userProvided
assert_json_snapshot!(embeddings[&fakerest_name][0] == beagle_embed, @"true");
let doc = index.documents(&rtxn, std::iter::once(0)).unwrap()[0].1;
let fields_ids_map = index.fields_ids_map(&rtxn).unwrap();
let doc = obkv_to_json(
&[
fields_ids_map.id("doggo").unwrap(),
fields_ids_map.id("breed").unwrap(),
fields_ids_map.id("_vectors").unwrap(),
],
&fields_ids_map,
doc,
)
.unwrap();
assert_json_snapshot!(doc, {"._vectors.A_fakerest.embeddings" => "[vector]"});
}
}
}
}

19
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@ -0,0 +1,19 @@
---
source: index-scheduler/src/lib.rs
expression: doc
---
{
"doggo": "kefir",
"breed": "patou",
"_vectors": {
"A_fakerest": {
"embeddings": "[vector]",
"userProvided": true
},
"noise": [
0.1,
0.2,
0.3
]
}
}

20
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@ -0,0 +1,20 @@
---
source: index-scheduler/src/lib.rs
expression: task.details
---
{
"embedders": {
"A_fakerest": {
"source": "rest",
"apiKey": "MyXXXX...",
"dimensions": 384,
"url": "http://localhost:7777"
},
"B_small_hf": {
"source": "huggingFace",
"model": "sentence-transformers/all-MiniLM-L6-v2",
"revision": "e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
"documentTemplate": "{{doc.doggo}} the {{doc.breed}} best doggo"
}
}
}

23
index-scheduler/src/snapshots/index_scheduler__tests__import_vectors-4.snap Normal file
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@ -0,0 +1,23 @@
---
source: index-scheduler/src/lib.rs
expression: fakerest_config.embedder_options
---
{
"Rest": {
"api_key": "My super secret",
"distribution": null,
"dimensions": 384,
"url": "http://localhost:7777",
"query": null,
"input_field": [
"input"
],
"path_to_embeddings": [
"data"
],
"embedding_object": [
"embedding"
],
"input_type": "text"
}
}

11
index-scheduler/src/snapshots/index_scheduler__tests__import_vectors-6.snap Normal file
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@ -0,0 +1,11 @@
---
source: index-scheduler/src/lib.rs
expression: simple_hf_config.embedder_options
---
{
"HuggingFace": {
"model": "sentence-transformers/all-MiniLM-L6-v2",
"revision": "e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
"distribution": null
}
}

19
index-scheduler/src/snapshots/index_scheduler__tests__import_vectors-9.snap Normal file
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@ -0,0 +1,19 @@
---
source: index-scheduler/src/lib.rs
expression: doc
---
{
"doggo": "Intel",
"breed": "beagle",
"_vectors": {
"A_fakerest": {
"embeddings": "[vector]",
"userProvided": true
},
"noise": [
0.1,
0.2,
0.3
]
}
}

20
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@ -0,0 +1,20 @@
---
source: index-scheduler/src/lib.rs
expression: task.details
---
{
"embedders": {
"A_fakerest": {
"source": "rest",
"apiKey": "MyXXXX...",
"dimensions": 384,
"url": "http://localhost:7777"
},
"B_small_hf": {
"source": "huggingFace",
"model": "sentence-transformers/all-MiniLM-L6-v2",
"revision": "e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
"documentTemplate": "{{doc.doggo}} the {{doc.breed}} best doggo"
}
}
}

49
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@ -0,0 +1,49 @@
---
source: index-scheduler/src/lib.rs
---
### Autobatching Enabled = true
### Processing 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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 }}
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 }}
----------------------------------------------------------------------
### Status:
enqueued []
succeeded [0,1,2,]
----------------------------------------------------------------------
### Kind:
"documentAdditionOrUpdate" [1,2,]
"settingsUpdate" [0,]
----------------------------------------------------------------------
### Index Tasks:
doggos [0,1,2,]
----------------------------------------------------------------------
### Index Mapper:
doggos: { number_of_documents: 1, field_distribution: {"_vectors": 1, "breed": 1, "doggo": 1, "id": 1} }
----------------------------------------------------------------------
### Canceled By:
----------------------------------------------------------------------
### Enqueued At:
[timestamp] [0,]
[timestamp] [1,]
[timestamp] [2,]
----------------------------------------------------------------------
### Started At:
[timestamp] [0,]
[timestamp] [1,]
[timestamp] [2,]
----------------------------------------------------------------------
### Finished At:
[timestamp] [0,]
[timestamp] [1,]
[timestamp] [2,]
----------------------------------------------------------------------
### File Store:
----------------------------------------------------------------------

48
index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir.snap Normal file
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@ -0,0 +1,48 @@
---
source: index-scheduler/src/lib.rs
---
### Autobatching Enabled = true
### Processing 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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 }}
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 }}
----------------------------------------------------------------------
### Status:
enqueued [2,]
succeeded [0,1,]
----------------------------------------------------------------------
### Kind:
"documentAdditionOrUpdate" [1,2,]
"settingsUpdate" [0,]
----------------------------------------------------------------------
### Index Tasks:
doggos [0,1,2,]
----------------------------------------------------------------------
### Index Mapper:
doggos: { number_of_documents: 1, field_distribution: {"_vectors": 1, "breed": 1, "doggo": 1, "id": 1} }
----------------------------------------------------------------------
### Canceled By:
----------------------------------------------------------------------
### Enqueued At:
[timestamp] [0,]
[timestamp] [1,]
[timestamp] [2,]
----------------------------------------------------------------------
### Started At:
[timestamp] [0,]
[timestamp] [1,]
----------------------------------------------------------------------
### Finished At:
[timestamp] [0,]
[timestamp] [1,]
----------------------------------------------------------------------
### File Store:
00000000-0000-0000-0000-000000000001
----------------------------------------------------------------------

45
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View File

@ -0,0 +1,45 @@
---
source: index-scheduler/src/lib.rs
---
### Autobatching Enabled = true
### Processing 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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 }}
----------------------------------------------------------------------
### Status:
enqueued []
succeeded [0,1,]
----------------------------------------------------------------------
### Kind:
"documentAdditionOrUpdate" [1,]
"settingsUpdate" [0,]
----------------------------------------------------------------------
### Index Tasks:
doggos [0,1,]
----------------------------------------------------------------------
### Index Mapper:
doggos: { number_of_documents: 1, field_distribution: {"_vectors": 1, "breed": 1, "doggo": 1, "id": 1} }
----------------------------------------------------------------------
### Canceled By:
----------------------------------------------------------------------
### Enqueued At:
[timestamp] [0,]
[timestamp] [1,]
----------------------------------------------------------------------
### Started At:
[timestamp] [0,]
[timestamp] [1,]
----------------------------------------------------------------------
### Finished At:
[timestamp] [0,]
[timestamp] [1,]
----------------------------------------------------------------------
### File Store:
----------------------------------------------------------------------

44
index-scheduler/src/snapshots/lib.rs/import_vectors/after adding Intel.snap Normal file
View File

@ -0,0 +1,44 @@
---
source: index-scheduler/src/lib.rs
---
### Autobatching Enabled = true
### Processing 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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 }}
----------------------------------------------------------------------
### Status:
enqueued [1,]
succeeded [0,]
----------------------------------------------------------------------
### Kind:
"documentAdditionOrUpdate" [1,]
"settingsUpdate" [0,]
----------------------------------------------------------------------
### Index Tasks:
doggos [0,1,]
----------------------------------------------------------------------
### Index Mapper:
doggos: { number_of_documents: 0, field_distribution: {} }
----------------------------------------------------------------------
### Canceled By:
----------------------------------------------------------------------
### Enqueued At:
[timestamp] [0,]
[timestamp] [1,]
----------------------------------------------------------------------
### Started At:
[timestamp] [0,]
----------------------------------------------------------------------
### Finished At:
[timestamp] [0,]
----------------------------------------------------------------------
### File Store:
00000000-0000-0000-0000-000000000000
----------------------------------------------------------------------

36
index-scheduler/src/snapshots/lib.rs/import_vectors/after_registering_settings_task_vectors.snap Normal file
View File

@ -0,0 +1,36 @@
---
source: index-scheduler/src/lib.rs
---
### Autobatching Enabled = true
### Processing 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
----------------------------------------------------------------------
### Status:
enqueued [0,]
----------------------------------------------------------------------
### Kind:
"settingsUpdate" [0,]
----------------------------------------------------------------------
### Index Tasks:
doggos [0,]
----------------------------------------------------------------------
### Index Mapper:
----------------------------------------------------------------------
### Canceled By:
----------------------------------------------------------------------
### Enqueued At:
[timestamp] [0,]
----------------------------------------------------------------------
### Started At:
----------------------------------------------------------------------
### Finished At:
----------------------------------------------------------------------
### File Store:
----------------------------------------------------------------------

40
index-scheduler/src/snapshots/lib.rs/import_vectors/settings_update_processed_vectors.snap Normal file
View File

@ -0,0 +1,40 @@
---
source: index-scheduler/src/lib.rs
---
### Autobatching Enabled = true
### Processing 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
----------------------------------------------------------------------
### Status:
enqueued []
succeeded [0,]
----------------------------------------------------------------------
### Kind:
"settingsUpdate" [0,]
----------------------------------------------------------------------
### Index Tasks:
doggos [0,]
----------------------------------------------------------------------
### Index Mapper:
doggos: { number_of_documents: 0, field_distribution: {} }
----------------------------------------------------------------------
### Canceled By:
----------------------------------------------------------------------
### Enqueued At:
[timestamp] [0,]
----------------------------------------------------------------------
### Started At:
[timestamp] [0,]
----------------------------------------------------------------------
### Finished At:
[timestamp] [0,]
----------------------------------------------------------------------
### File Store:
----------------------------------------------------------------------

1
meilisearch-types/src/error.rs
View File

@ -384,7 +384,6 @@ impl ErrorCode for milli::Error {
UserError::InvalidGeoField { .. } => Code::InvalidDocumentGeoField,
UserError::InvalidVectorDimensions { .. } => Code::InvalidVectorDimensions,
UserError::InvalidVectorsMapType { .. } => Code::InvalidVectorsType,
UserError::InvalidVectorsType { .. } => Code::InvalidVectorsType,
UserError::TooManyVectors(_, _) => Code::TooManyVectors,
UserError::SortError(_) => Code::InvalidSearchSort,
UserError::InvalidMinTypoWordLenSetting(_, _) => {

121
meilisearch/tests/search/hybrid.rs
View File

@ -5,7 +5,10 @@ use crate::common::index::Index;
use crate::common::{Server, Value};
use crate::json;
async fn index_with_documents<'a>(server: &'a Server, documents: &Value) -> Index<'a> {
async fn index_with_documents_user_provided<'a>(
server: &'a Server,
documents: &Value,
) -> Index<'a> {
let index = server.index("test");
let (response, code) = server.set_features(json!({"vectorStore": true})).await;
@ -34,7 +37,39 @@ async fn index_with_documents<'a>(server: &'a Server, documents: &Value) -> Inde
index
}
static SIMPLE_SEARCH_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
async fn index_with_documents_hf<'a>(server: &'a Server, documents: &Value) -> Index<'a> {
let index = server.index("test");
let (response, code) = server.set_features(json!({"vectorStore": true})).await;
meili_snap::snapshot!(code, @"200 OK");
meili_snap::snapshot!(meili_snap::json_string!(response), @r###"
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"exportPuffinReports": false
}
"###);
let (response, code) = index
.update_settings(json!({ "embedders": {"default": {
"source": "huggingFace",
"model": "sentence-transformers/all-MiniLM-L6-v2",
"revision": "e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
"documentTemplate": "{{doc.title}}, {{doc.desc}}"
}}} ))
.await;
assert_eq!(202, code, "{:?}", response);
index.wait_task(response.uid()).await;
let (response, code) = index.add_documents(documents.clone(), None).await;
assert_eq!(202, code, "{:?}", response);
index.wait_task(response.uid()).await;
index
}
static SIMPLE_SEARCH_DOCUMENTS_VEC: Lazy<Value> = Lazy::new(|| {
json!([
{
"title": "Shazam!",
@ -56,7 +91,7 @@ static SIMPLE_SEARCH_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
}])
});
static SINGLE_DOCUMENT: Lazy<Value> = Lazy::new(|| {
static SINGLE_DOCUMENT_VEC: Lazy<Value> = Lazy::new(|| {
json!([{
"title": "Shazam!",
"desc": "a Captain Marvel ersatz",
@ -65,10 +100,29 @@ static SINGLE_DOCUMENT: Lazy<Value> = Lazy::new(|| {
}])
});
static SIMPLE_SEARCH_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
json!([
{
"title": "Shazam!",
"desc": "a Captain Marvel ersatz",
"id": "1",
},
{
"title": "Captain Planet",
"desc": "He's not part of the Marvel Cinematic Universe",
"id": "2",
},
{
"title": "Captain Marvel",
"desc": "a Shazam ersatz",
"id": "3",
}])
});
#[actix_rt::test]
async fn simple_search() {
let server = Server::new().await;
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
let (response, code) = index
.search_post(
@ -98,10 +152,59 @@ async fn simple_search() {
snapshot!(response["semanticHitCount"], @"3");
}
#[actix_rt::test]
async fn simple_search_hf() {
let server = Server::new().await;
let index = index_with_documents_hf(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let (response, code) =
index.search_post(json!({"q": "Captain", "hybrid": {"semanticRatio": 0.2}})).await;
snapshot!(code, @"200 OK");
snapshot!(response["hits"], @r###"[{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2"},{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3"},{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1"}]"###);
snapshot!(response["semanticHitCount"], @"0");
let (response, code) = index
.search_post(
// disable ranking score as the vectors between architectures are not equal
json!({"q": "Captain", "hybrid": {"semanticRatio": 0.55}, "showRankingScore": false}),
)
.await;
snapshot!(code, @"200 OK");
snapshot!(response["hits"], @r###"[{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2"},{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3"},{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1"}]"###);
snapshot!(response["semanticHitCount"], @"1");
let (response, code) = index
.search_post(
json!({"q": "Captain", "hybrid": {"semanticRatio": 0.8}, "showRankingScore": false}),
)
.await;
snapshot!(code, @"200 OK");
snapshot!(response["hits"], @r###"[{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1"},{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3"},{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2"}]"###);
snapshot!(response["semanticHitCount"], @"3");
let (response, code) = index
.search_post(
json!({"q": "Movie World", "hybrid": {"semanticRatio": 0.2}, "showRankingScore": false}),
)
.await;
snapshot!(code, @"200 OK");
snapshot!(response["hits"], @r###"[{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2"},{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1"},{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3"}]"###);
snapshot!(response["semanticHitCount"], @"3");
let (response, code) = index
.search_post(
json!({"q": "Wonder replacement", "hybrid": {"semanticRatio": 0.2}, "showRankingScore": false}),
)
.await;
snapshot!(code, @"200 OK");
snapshot!(response["hits"], @r###"[{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3"},{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1"},{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2"}]"###);
snapshot!(response["semanticHitCount"], @"3");
}
#[actix_rt::test]
async fn distribution_shift() {
let server = Server::new().await;
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
let search = json!({"q": "Captain", "vector": [1.0, 1.0], "showRankingScore": true, "hybrid": {"semanticRatio": 1.0}});
let (response, code) = index.search_post(search.clone()).await;
@ -133,7 +236,7 @@ async fn distribution_shift() {
#[actix_rt::test]
async fn highlighter() {
let server = Server::new().await;
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
let (response, code) = index
.search_post(json!({"q": "Captain Marvel", "vector": [1.0, 1.0],
@ -184,7 +287,7 @@ async fn highlighter() {
#[actix_rt::test]
async fn invalid_semantic_ratio() {
let server = Server::new().await;
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
let (response, code) = index
.search_post(
@ -256,7 +359,7 @@ async fn invalid_semantic_ratio() {
#[actix_rt::test]
async fn single_document() {
let server = Server::new().await;
let index = index_with_documents(&server, &SINGLE_DOCUMENT).await;
let index = index_with_documents_user_provided(&server, &SINGLE_DOCUMENT_VEC).await;
let (response, code) = index
.search_post(
@ -272,7 +375,7 @@ async fn single_document() {
#[actix_rt::test]
async fn query_combination() {
let server = Server::new().await;
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
// search without query and vector, but with hybrid => still placeholder
let (response, code) = index

54
meilisearch/tests/search/mod.rs
View File

@ -895,9 +895,9 @@ async fn test_score_details() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
},
"_rankingScoreDetails": {
@ -1096,9 +1096,9 @@ async fn experimental_feature_vector_store() {
"id": "287947",
"_vectors": {
"manual": [
1,
2,
3
1.0,
2.0,
3.0
]
},
"_rankingScore": 1.0
@ -1108,9 +1108,9 @@ async fn experimental_feature_vector_store() {
"id": "299537",
"_vectors": {
"manual": [
1,
2,
54
1.0,
2.0,
54.0
]
},
"_rankingScore": 0.9129111766815186
@ -1120,9 +1120,9 @@ async fn experimental_feature_vector_store() {
"id": "450465",
"_vectors": {
"manual": [
-100,
340,
90
-100.0,
340.0,
90.0
]
},
"_rankingScore": 0.8106412887573242
@ -1132,9 +1132,9 @@ async fn experimental_feature_vector_store() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
},
"_rankingScore": 0.7412010431289673
@ -1144,9 +1144,9 @@ async fn experimental_feature_vector_store() {
"id": "522681",
"_vectors": {
"manual": [
10,
-23,
32
10.0,
-23.0,
32.0
]
},
"_rankingScore": 0.6972063183784485
@ -1405,9 +1405,9 @@ async fn simple_search_with_strange_synonyms() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
}
}
@ -1426,9 +1426,9 @@ async fn simple_search_with_strange_synonyms() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
}
}
@ -1447,9 +1447,9 @@ async fn simple_search_with_strange_synonyms() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
}
}

30
meilisearch/tests/search/multi.rs
View File

@ -75,9 +75,9 @@ async fn simple_search_single_index() {
"id": "450465",
"_vectors": {
"manual": [
-100,
340,
90
-100.0,
340.0,
90.0
]
}
}
@ -96,9 +96,9 @@ async fn simple_search_single_index() {
"id": "299537",
"_vectors": {
"manual": [
1,
2,
54
1.0,
2.0,
54.0
]
}
}
@ -194,9 +194,9 @@ async fn simple_search_two_indexes() {
"id": "450465",
"_vectors": {
"manual": [
-100,
340,
90
-100.0,
340.0,
90.0
]
}
}
@ -227,9 +227,9 @@ async fn simple_search_two_indexes() {
"cattos": "pésti",
"_vectors": {
"manual": [
1,
2,
3
1.0,
2.0,
3.0
]
}
},
@ -249,9 +249,9 @@ async fn simple_search_two_indexes() {
],
"_vectors": {
"manual": [
1,
2,
54
1.0,
2.0,
54.0
]
}
}

4
milli/src/error.rs
View File

@ -117,10 +117,8 @@ only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and undersco
InvalidGeoField(#[from] GeoError),
#[error("Invalid vector dimensions: expected: `{}`, found: `{}`.", .expected, .found)]
InvalidVectorDimensions { expected: usize, found: usize },
#[error("The `_vectors.{subfield}` field in the document with id: `{document_id}` is not an array. Was expecting an array of floats or an array of arrays of floats but instead got `{value}`.")]
InvalidVectorsType { document_id: Value, value: Value, subfield: String },
#[error("The `_vectors` field in the document with id: `{document_id}` is not an object. Was expecting an object with a key for each embedder with manually provided vectors, but instead got `{value}`")]
InvalidVectorsMapType { document_id: Value, value: Value },
InvalidVectorsMapType { document_id: String, value: Value },
#[error("{0}")]
InvalidFilter(String),
#[error("Invalid type for filter subexpression: expected: {}, found: {1}.", .0.join(", "))]

40
milli/src/index.rs
View File

@ -23,7 +23,7 @@ use crate::heed_codec::{
};
use crate::order_by_map::OrderByMap;
use crate::proximity::ProximityPrecision;
use crate::vector::EmbeddingConfig;
use crate::vector::{Embedding, EmbeddingConfig};
use crate::{
default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds,
FacetDistribution, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldIdWordCountCodec,
@ -1606,6 +1606,44 @@ impl Index {
pub(crate) fn delete_search_cutoff(&self, wtxn: &mut RwTxn<'_>) -> heed::Result<bool> {
self.main.remap_key_type::<Str>().delete(wtxn, main_key::SEARCH_CUTOFF)
}
pub fn embeddings(
&self,
rtxn: &RoTxn<'_>,
docid: DocumentId,
) -> Result<BTreeMap<String, Vec<Embedding>>> {
let mut res = BTreeMap::new();
for row in self.embedder_category_id.iter(rtxn)? {
let (embedder_name, embedder_id) = row?;
let embedder_id = (embedder_id as u16) << 8;
let mut embeddings = Vec::new();
'vectors: for i in 0..=u8::MAX {
let reader = arroy::Reader::open(rtxn, embedder_id | (i as u16), self.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?.item_vector(rtxn, docid)?;
if let Some(embedding) = embedding {
embeddings.push(embedding)
} else {
break 'vectors;
}
}
if !embeddings.is_empty() {
res.insert(embedder_name.to_owned(), embeddings);
}
}
Ok(res)
}
}
#[cfg(test)]

29
milli/src/lib.rs
View File

@ -362,35 +362,6 @@ pub fn normalize_facet(original: &str) -> String {
CompatibilityDecompositionNormalizer.normalize_str(original.trim()).to_lowercase()
}
/// Represents either a vector or an array of multiple vectors.
#[derive(serde::Serialize, serde::Deserialize, Debug)]
#[serde(transparent)]
pub struct VectorOrArrayOfVectors {
#[serde(with = "either::serde_untagged_optional")]
inner: Option<either::Either<Vec<f32>, Vec<Vec<f32>>>>,
}
impl VectorOrArrayOfVectors {
pub fn into_array_of_vectors(self) -> Option<Vec<Vec<f32>>> {
match self.inner? {
either::Either::Left(vector) => Some(vec![vector]),
either::Either::Right(vectors) => Some(vectors),
}
}
}
/// Normalize a vector by dividing the dimensions by the length of it.
pub fn normalize_vector(mut vector: Vec<f32>) -> Vec<f32> {
let squared: f32 = vector.iter().map(|x| x * x).sum();
let length = squared.sqrt();
if length <= f32::EPSILON {
vector
} else {
vector.iter_mut().for_each(|x| *x /= length);
vector
}
}
#[cfg(test)]
mod tests {
use serde_json::json;

369
milli/src/update/index_documents/extract/extract_vector_points.rs
View File

@ -10,16 +10,16 @@ use bytemuck::cast_slice;
use grenad::Writer;
use itertools::EitherOrBoth;
use ordered_float::OrderedFloat;
use serde_json::{from_slice, Value};
use serde_json::Value;
use super::helpers::{create_writer, writer_into_reader, GrenadParameters};
use crate::error::UserError;
use crate::prompt::Prompt;
use crate::update::del_add::{DelAdd, KvReaderDelAdd, KvWriterDelAdd};
use crate::update::index_documents::helpers::try_split_at;
use crate::update::settings::InnerIndexSettingsDiff;
use crate::vector::parsed_vectors::{ParsedVectorsDiff, RESERVED_VECTORS_FIELD_NAME};
use crate::vector::Embedder;
use crate::{DocumentId, InternalError, Result, ThreadPoolNoAbort, VectorOrArrayOfVectors};
use crate::{DocumentId, Result, ThreadPoolNoAbort};
/// The length of the elements that are always in the buffer when inserting new values.
const TRUNCATE_SIZE: usize = size_of::<DocumentId>();
@ -31,6 +31,10 @@ pub struct ExtractedVectorPoints {
pub remove_vectors: grenad::Reader<BufReader<File>>,
// docid -> prompt
pub prompts: grenad::Reader<BufReader<File>>,
// embedder
pub embedder_name: String,
pub embedder: Arc<Embedder>,
}
enum VectorStateDelta {
@ -65,6 +69,19 @@ impl VectorStateDelta {
}
}
struct EmbedderVectorExtractor {
embedder_name: String,
embedder: Arc<Embedder>,
prompt: Arc<Prompt>,
// (docid, _index) -> KvWriterDelAdd -> Vector
manual_vectors_writer: Writer<BufWriter<File>>,
// (docid) -> (prompt)
prompts_writer: Writer<BufWriter<File>>,
// (docid) -> ()
remove_vectors_writer: Writer<BufWriter<File>>,
}
/// Extracts the embedding vector contained in each document under the `_vectors` field.
///
/// Returns the generated grenad reader containing the docid as key associated to the Vec<f32>
@ -73,34 +90,54 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
obkv_documents: grenad::Reader<R>,
indexer: GrenadParameters,
settings_diff: &InnerIndexSettingsDiff,
prompt: &Prompt,
embedder_name: &str,
) -> Result<ExtractedVectorPoints> {
) -> Result<Vec<ExtractedVectorPoints>> {
puffin::profile_function!();
let reindex_vectors = settings_diff.reindex_vectors();
let old_fields_ids_map = &settings_diff.old.fields_ids_map;
let new_fields_ids_map = &settings_diff.new.fields_ids_map;
// the vector field id may have changed
let old_vectors_fid = old_fields_ids_map.id(RESERVED_VECTORS_FIELD_NAME);
// filter the old vector fid if the settings has been changed forcing reindexing.
let old_vectors_fid = old_vectors_fid.filter(|_| !reindex_vectors);
// (docid, _index) -> KvWriterDelAdd -> Vector
let mut manual_vectors_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
let new_vectors_fid = new_fields_ids_map.id(RESERVED_VECTORS_FIELD_NAME);
// (docid) -> (prompt)
let mut prompts_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
let mut extractors = Vec::new();
for (embedder_name, (embedder, prompt)) in
settings_diff.new.embedding_configs.clone().into_iter()
{
// (docid, _index) -> KvWriterDelAdd -> Vector
let manual_vectors_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
// (docid) -> ()
let mut remove_vectors_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
// (docid) -> (prompt)
let prompts_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
// (docid) -> ()
let remove_vectors_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
extractors.push(EmbedderVectorExtractor {
embedder_name,
embedder,
prompt,
manual_vectors_writer,
prompts_writer,
remove_vectors_writer,
});
}
let mut key_buffer = Vec::new();
let mut cursor = obkv_documents.into_cursor()?;
@ -114,152 +151,138 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
key_buffer.clear();
key_buffer.extend_from_slice(docid_bytes);
// since we only needs the primary key when we throw an error we create this getter to
// since we only need the primary key when we throw an error we create this getter to
// lazily get it when needed
let document_id = || -> Value { from_utf8(external_id_bytes).unwrap().into() };
// the vector field id may have changed
let old_vectors_fid = old_fields_ids_map.id("_vectors");
// filter the old vector fid if the settings has been changed forcing reindexing.
let old_vectors_fid = old_vectors_fid.filter(|_| !settings_diff.reindex_vectors());
let mut parsed_vectors = ParsedVectorsDiff::new(obkv, old_vectors_fid, new_vectors_fid)
.map_err(|error| error.to_crate_error(document_id().to_string()))?;
let new_vectors_fid = new_fields_ids_map.id("_vectors");
let vectors_field = {
let del = old_vectors_fid
.and_then(|vectors_fid| obkv.get(vectors_fid))
.map(KvReaderDelAdd::new)
.map(|obkv| to_vector_map(obkv, DelAdd::Deletion, &document_id))
.transpose()?
.flatten();
let add = new_vectors_fid
.and_then(|vectors_fid| obkv.get(vectors_fid))
.map(KvReaderDelAdd::new)
.map(|obkv| to_vector_map(obkv, DelAdd::Addition, &document_id))
.transpose()?
.flatten();
(del, add)
};
for EmbedderVectorExtractor {
embedder_name,
embedder: _,
prompt,
manual_vectors_writer,
prompts_writer,
remove_vectors_writer,
} in extractors.iter_mut()
{
let delta = match parsed_vectors.remove(embedder_name) {
(Some(old), Some(new)) => {
// no autogeneration
let del_vectors = old.into_array_of_vectors();
let add_vectors = new.into_array_of_vectors();
let (del_map, add_map) = vectors_field;
let del_value = del_map.and_then(|mut map| map.remove(embedder_name));
let add_value = add_map.and_then(|mut map| map.remove(embedder_name));
let delta = match (del_value, add_value) {
(Some(old), Some(new)) => {
// no autogeneration
let del_vectors = extract_vectors(old, document_id, embedder_name)?;
let add_vectors = extract_vectors(new, document_id, embedder_name)?;
if add_vectors.len() > usize::from(u8::MAX) {
return Err(crate::Error::UserError(crate::UserError::TooManyVectors(
document_id().to_string(),
add_vectors.len(),
)));
}
VectorStateDelta::ManualDelta(del_vectors, add_vectors)
}
(Some(_old), None) => {
// Do we keep this document?
let document_is_kept = obkv
.iter()
.map(|(_, deladd)| KvReaderDelAdd::new(deladd))
.any(|deladd| deladd.get(DelAdd::Addition).is_some());
if document_is_kept {
// becomes autogenerated
VectorStateDelta::NowGenerated(prompt.render(
obkv,
DelAdd::Addition,
new_fields_ids_map,
)?)
} else {
VectorStateDelta::NowRemoved
}
}
(None, Some(new)) => {
// was possibly autogenerated, remove all vectors for that document
let add_vectors = extract_vectors(new, document_id, embedder_name)?;
if add_vectors.len() > usize::from(u8::MAX) {
return Err(crate::Error::UserError(crate::UserError::TooManyVectors(
document_id().to_string(),
add_vectors.len(),
)));
}
VectorStateDelta::WasGeneratedNowManual(add_vectors)
}
(None, None) => {
// Do we keep this document?
let document_is_kept = obkv
.iter()
.map(|(_, deladd)| KvReaderDelAdd::new(deladd))
.any(|deladd| deladd.get(DelAdd::Addition).is_some());
if document_is_kept {
// Don't give up if the old prompt was failing
let old_prompt = Some(prompt)
// TODO: this filter works because we erase the vec database when a embedding setting changes.
// When vector pipeline will be optimized, this should be removed.
.filter(|_| !settings_diff.reindex_vectors())
.map(|p| {
p.render(obkv, DelAdd::Deletion, old_fields_ids_map).unwrap_or_default()
});
let new_prompt = prompt.render(obkv, DelAdd::Addition, new_fields_ids_map)?;
if old_prompt.as_ref() != Some(&new_prompt) {
let old_prompt = old_prompt.unwrap_or_default();
tracing::trace!(
"🚀 Changing prompt from\n{old_prompt}\n===to===\n{new_prompt}"
);
VectorStateDelta::NowGenerated(new_prompt)
} else {
tracing::trace!("⏭️ Prompt unmodified, skipping");
VectorStateDelta::NoChange
if add_vectors.len() > usize::from(u8::MAX) {
return Err(crate::Error::UserError(crate::UserError::TooManyVectors(
document_id().to_string(),
add_vectors.len(),
)));
}
} else {
VectorStateDelta::NowRemoved
}
}
};
// and we finally push the unique vectors into the writer
push_vectors_diff(
&mut remove_vectors_writer,
&mut prompts_writer,
&mut manual_vectors_writer,
&mut key_buffer,
delta,
settings_diff,
)?;
VectorStateDelta::ManualDelta(del_vectors, add_vectors)
}
(Some(_old), None) => {
// Do we keep this document?
let document_is_kept = obkv
.iter()
.map(|(_, deladd)| KvReaderDelAdd::new(deladd))
.any(|deladd| deladd.get(DelAdd::Addition).is_some());
if document_is_kept {
// becomes autogenerated
VectorStateDelta::NowGenerated(prompt.render(
obkv,
DelAdd::Addition,
new_fields_ids_map,
)?)
} else {
VectorStateDelta::NowRemoved
}
}
(None, Some(new)) => {
// was possibly autogenerated, remove all vectors for that document
let add_vectors = new.into_array_of_vectors();
if add_vectors.len() > usize::from(u8::MAX) {
return Err(crate::Error::UserError(crate::UserError::TooManyVectors(
document_id().to_string(),
add_vectors.len(),
)));
}
VectorStateDelta::WasGeneratedNowManual(add_vectors)
}
(None, None) => {
// Do we keep this document?
let document_is_kept = obkv
.iter()
.map(|(_, deladd)| KvReaderDelAdd::new(deladd))
.any(|deladd| deladd.get(DelAdd::Addition).is_some());
if document_is_kept {
// Don't give up if the old prompt was failing
let old_prompt = Some(&prompt)
// TODO: this filter works because we erase the vec database when a embedding setting changes.
// When vector pipeline will be optimized, this should be removed.
.filter(|_| !settings_diff.reindex_vectors())
.map(|p| {
p.render(obkv, DelAdd::Deletion, old_fields_ids_map)
.unwrap_or_default()
});
let new_prompt =
prompt.render(obkv, DelAdd::Addition, new_fields_ids_map)?;
if old_prompt.as_ref() != Some(&new_prompt) {
let old_prompt = old_prompt.unwrap_or_default();
tracing::trace!(
"🚀 Changing prompt from\n{old_prompt}\n===to===\n{new_prompt}"
);
VectorStateDelta::NowGenerated(new_prompt)
} else {
tracing::trace!("⏭️ Prompt unmodified, skipping");
VectorStateDelta::NoChange
}
} else {
VectorStateDelta::NowRemoved
}
}
};
// and we finally push the unique vectors into the writer
push_vectors_diff(
remove_vectors_writer,
prompts_writer,
manual_vectors_writer,
&mut key_buffer,
delta,
reindex_vectors,
)?;
}
}
Ok(ExtractedVectorPoints {
// docid, _index -> KvWriterDelAdd -> Vector
manual_vectors: writer_into_reader(manual_vectors_writer)?,
// docid -> ()
remove_vectors: writer_into_reader(remove_vectors_writer)?,
// docid -> prompt
prompts: writer_into_reader(prompts_writer)?,
})
}
let mut results = Vec::new();
fn to_vector_map(
obkv: KvReaderDelAdd,
side: DelAdd,
document_id: &impl Fn() -> Value,
) -> Result<Option<serde_json::Map<String, Value>>> {
Ok(if let Some(value) = obkv.get(side) {
let Ok(value) = from_slice(value) else {
let value = from_slice(value).map_err(InternalError::SerdeJson)?;
return Err(crate::Error::UserError(UserError::InvalidVectorsMapType {
document_id: document_id(),
value,
}));
};
Some(value)
} else {
None
})
for EmbedderVectorExtractor {
embedder_name,
embedder,
prompt: _,
manual_vectors_writer,
prompts_writer,
remove_vectors_writer,
} in extractors
{
results.push(ExtractedVectorPoints {
// docid, _index -> KvWriterDelAdd -> Vector
manual_vectors: writer_into_reader(manual_vectors_writer)?,
// docid -> ()
remove_vectors: writer_into_reader(remove_vectors_writer)?,
// docid -> prompt
prompts: writer_into_reader(prompts_writer)?,
embedder,
embedder_name,
})
}
Ok(results)
}
/// Computes the diff between both Del and Add numbers and
@ -270,14 +293,14 @@ fn push_vectors_diff(
manual_vectors_writer: &mut Writer<BufWriter<File>>,
key_buffer: &mut Vec<u8>,
delta: VectorStateDelta,
settings_diff: &InnerIndexSettingsDiff,
reindex_vectors: bool,
) -> Result<()> {
puffin::profile_function!();
let (must_remove, prompt, (mut del_vectors, mut add_vectors)) = delta.into_values();
if must_remove
// TODO: the below condition works because we erase the vec database when a embedding setting changes.
// When vector pipeline will be optimized, this should be removed.
&& !settings_diff.reindex_vectors()
&& !reindex_vectors
{
key_buffer.truncate(TRUNCATE_SIZE);
remove_vectors_writer.insert(&key_buffer, [])?;
@ -308,7 +331,7 @@ fn push_vectors_diff(
EitherOrBoth::Left(vector) => {
// TODO: the below condition works because we erase the vec database when a embedding setting changes.
// When vector pipeline will be optimized, this should be removed.
if !settings_diff.reindex_vectors() {
if !reindex_vectors {
// We insert only the Del part of the Obkv to inform
// that we only want to remove all those vectors.
let mut obkv = KvWriterDelAdd::memory();
@ -336,26 +359,6 @@ fn compare_vectors(a: &[f32], b: &[f32]) -> Ordering {
a.iter().copied().map(OrderedFloat).cmp(b.iter().copied().map(OrderedFloat))
}
/// Extracts the vectors from a JSON value.
fn extract_vectors(
value: Value,
document_id: impl Fn() -> Value,
name: &str,
) -> Result<Vec<Vec<f32>>> {
// FIXME: ugly clone of the vectors here
match serde_json::from_value(value.clone()) {
Ok(vectors) => {
Ok(VectorOrArrayOfVectors::into_array_of_vectors(vectors).unwrap_or_default())
}
Err(_) => Err(UserError::InvalidVectorsType {
document_id: document_id(),
value,
subfield: name.to_owned(),
}
.into()),
}
}
#[tracing::instrument(level = "trace", skip_all, target = "indexing::extract")]
pub fn extract_embeddings<R: io::Read + io::Seek>(
// docid, prompt

46
milli/src/update/index_documents/extract/mod.rs
View File

@ -226,27 +226,31 @@ fn send_original_documents_data(
let original_documents_chunk =
original_documents_chunk.and_then(|c| unsafe { as_cloneable_grenad(&c) })?;
let documents_chunk_cloned = original_documents_chunk.clone();
let lmdb_writer_sx_cloned = lmdb_writer_sx.clone();
let request_threads = ThreadPoolNoAbortBuilder::new()
.num_threads(crate::vector::REQUEST_PARALLELISM)
.thread_name(|index| format!("embedding-request-{index}"))
.build()?;
if settings_diff.reindex_vectors() || !settings_diff.settings_update_only() {
let index_vectors = (settings_diff.reindex_vectors() || !settings_diff.settings_update_only())
// no point in indexing vectors without embedders
&& (!settings_diff.new.embedding_configs.inner_as_ref().is_empty());
if index_vectors {
let settings_diff = settings_diff.clone();
let original_documents_chunk = original_documents_chunk.clone();
let lmdb_writer_sx = lmdb_writer_sx.clone();
rayon::spawn(move || {
for (name, (embedder, prompt)) in settings_diff.new.embedding_configs.clone() {
let result = extract_vector_points(
documents_chunk_cloned.clone(),
indexer,
&settings_diff,
&prompt,
&name,
);
match result {
Ok(ExtractedVectorPoints { manual_vectors, remove_vectors, prompts }) => {
match extract_vector_points(original_documents_chunk.clone(), indexer, &settings_diff) {
Ok(extracted_vectors) => {
for ExtractedVectorPoints {
manual_vectors,
remove_vectors,
prompts,
embedder_name,
embedder,
} in extracted_vectors
{
let embeddings = match extract_embeddings(
prompts,
indexer,
@ -255,28 +259,26 @@ fn send_original_documents_data(
) {
Ok(results) => Some(results),
Err(error) => {
let _ = lmdb_writer_sx_cloned.send(Err(error));
let _ = lmdb_writer_sx.send(Err(error));
None
}
};
if !(remove_vectors.is_empty()
&& manual_vectors.is_empty()
&& embeddings.as_ref().map_or(true, |e| e.is_empty()))
{
let _ = lmdb_writer_sx_cloned.send(Ok(TypedChunk::VectorPoints {
let _ = lmdb_writer_sx.send(Ok(TypedChunk::VectorPoints {
remove_vectors,
embeddings,
expected_dimension: embedder.dimensions(),
manual_vectors,
embedder_name: name,
embedder_name,
}));
}
}
Err(error) => {
let _ = lmdb_writer_sx_cloned.send(Err(error));
}
}
Err(error) => {
let _ = lmdb_writer_sx.send(Err(error));
}
}
});

40
milli/src/update/index_documents/typed_chunk.rs
View File

@ -1,4 +1,4 @@
use std::collections::HashMap;
use std::collections::{BTreeSet, HashMap};
use std::convert::TryInto;
use std::fs::File;
use std::io::{self, BufReader};
@ -193,6 +193,10 @@ pub(crate) fn write_typed_chunk_into_index(
let span = tracing::trace_span!(target: "indexing::write_db", "documents");
let _entered = span.enter();
let fields_ids_map = index.fields_ids_map(wtxn)?;
let vectors_fid =
fields_ids_map.id(crate::vector::parsed_vectors::RESERVED_VECTORS_FIELD_NAME);
let mut builder = MergerBuilder::new(keep_latest_obkv as MergeFn);
for typed_chunk in typed_chunks {
let TypedChunk::Documents(chunk) = typed_chunk else {
@ -206,6 +210,10 @@ pub(crate) fn write_typed_chunk_into_index(
let mut docids = index.documents_ids(wtxn)?;
let mut iter = merger.into_stream_merger_iter()?;
let embedders: BTreeSet<_> =
index.embedding_configs(wtxn)?.into_iter().map(|(k, _v)| k).collect();
let mut vectors_buffer = Vec::new();
while let Some((key, reader)) = iter.next()? {
let mut writer: KvWriter<_, FieldId> = KvWriter::memory();
let reader: KvReader<FieldId> = KvReader::new(reader);
@ -219,7 +227,35 @@ pub(crate) fn write_typed_chunk_into_index(
let del_add_reader = KvReaderDelAdd::new(value);
if let Some(addition) = del_add_reader.get(DelAdd::Addition) {
writer.insert(field_id, addition)?;
let addition = if vectors_fid == Some(field_id) {
'vectors: {
vectors_buffer.clear();
let Ok(mut vectors) =
crate::vector::parsed_vectors::ParsedVectors::from_bytes(
addition,
)
else {
// if the `_vectors` field cannot be parsed as map of vectors, just write it as-is
break 'vectors Some(addition);
};
vectors.retain_user_provided_vectors(&embedders);
let crate::vector::parsed_vectors::ParsedVectors(vectors) = vectors;
if vectors.is_empty() {
// skip writing empty `_vectors` map
break 'vectors None;
}
serde_json::to_writer(&mut vectors_buffer, &vectors)
.map_err(InternalError::SerdeJson)?;
Some(vectors_buffer.as_slice())
}
} else {
Some(addition)
};
if let Some(addition) = addition {
writer.insert(field_id, addition)?;
}
}
}

5
milli/src/vector/mod.rs
View File

@ -13,6 +13,7 @@ pub mod error;
pub mod hf;
pub mod manual;
pub mod openai;
pub mod parsed_vectors;
pub mod settings;
pub mod ollama;
@ -147,6 +148,10 @@ impl EmbeddingConfigs {
self.get(self.get_default_embedder_name())
}
pub fn inner_as_ref(&self) -> &HashMap<String, (Arc<Embedder>, Arc<Prompt>)> {
&self.0
}
/// Get the name of the default embedder configuration.
///
/// The default embedder is determined as follows:

207
milli/src/vector/parsed_vectors.rs Normal file
View File

@ -0,0 +1,207 @@
use std::collections::{BTreeMap, BTreeSet};
use obkv::KvReader;
use serde_json::{from_slice, Value};
use super::Embedding;
use crate::update::del_add::{DelAdd, KvReaderDelAdd};
use crate::{FieldId, InternalError, UserError};
pub const RESERVED_VECTORS_FIELD_NAME: &str = "_vectors";
#[derive(serde::Serialize, serde::Deserialize, Debug)]
#[serde(untagged)]
pub enum Vectors {
ImplicitlyUserProvided(VectorOrArrayOfVectors),
Explicit(ExplicitVectors),
}
impl Vectors {
pub fn into_array_of_vectors(self) -> Vec<Embedding> {
match self {
Vectors::ImplicitlyUserProvided(embeddings)
| Vectors::Explicit(ExplicitVectors { embeddings, user_provided: _ }) => {
embeddings.into_array_of_vectors().unwrap_or_default()
}
}
}
}
#[derive(serde::Serialize, serde::Deserialize, Debug)]
#[serde(rename_all = "camelCase")]
pub struct ExplicitVectors {
pub embeddings: VectorOrArrayOfVectors,
pub user_provided: bool,
}
pub struct ParsedVectorsDiff {
pub old: Option<BTreeMap<String, Vectors>>,
pub new: Option<BTreeMap<String, Vectors>>,
}
impl ParsedVectorsDiff {
pub fn new(
documents_diff: KvReader<'_, FieldId>,
old_vectors_fid: Option<FieldId>,
new_vectors_fid: Option<FieldId>,
) -> Result<Self, Error> {
let old = match old_vectors_fid
.and_then(|vectors_fid| documents_diff.get(vectors_fid))
.map(KvReaderDelAdd::new)
.map(|obkv| to_vector_map(obkv, DelAdd::Deletion))
.transpose()
{
Ok(del) => del,
// ignore wrong shape for old version of documents, use an empty map in this case
Err(Error::InvalidMap(value)) => {
tracing::warn!(%value, "Previous version of the `_vectors` field had a wrong shape");
Default::default()
}
Err(error) => {
return Err(error);
}
}
.flatten();
let new = new_vectors_fid
.and_then(|vectors_fid| documents_diff.get(vectors_fid))
.map(KvReaderDelAdd::new)
.map(|obkv| to_vector_map(obkv, DelAdd::Addition))
.transpose()?
.flatten();
Ok(Self { old, new })
}
pub fn remove(&mut self, embedder_name: &str) -> (Option<Vectors>, Option<Vectors>) {
let old = self.old.as_mut().and_then(|old| old.remove(embedder_name));
let new = self.new.as_mut().and_then(|new| new.remove(embedder_name));
(old, new)
}
}
pub struct ParsedVectors(pub BTreeMap<String, Vectors>);
impl ParsedVectors {
pub fn from_bytes(value: &[u8]) -> Result<Self, Error> {
let Ok(value) = from_slice(value) else {
let value = from_slice(value).map_err(Error::InternalSerdeJson)?;
return Err(Error::InvalidMap(value));
};
Ok(ParsedVectors(value))
}
pub fn retain_user_provided_vectors(&mut self, embedders: &BTreeSet<String>) {
self.0.retain(|k, v| match v {
Vectors::ImplicitlyUserProvided(_) => true,
Vectors::Explicit(ExplicitVectors { embeddings: _, user_provided }) => {
*user_provided
// if the embedder is not in the config, then never touch it
|| !embedders.contains(k)
}
});
}
}
pub enum Error {
InvalidMap(Value),
InternalSerdeJson(serde_json::Error),
}
impl Error {
pub fn to_crate_error(self, document_id: String) -> crate::Error {
match self {
Error::InvalidMap(value) => {
crate::Error::UserError(UserError::InvalidVectorsMapType { document_id, value })
}
Error::InternalSerdeJson(error) => {
crate::Error::InternalError(InternalError::SerdeJson(error))
}
}
}
}
fn to_vector_map(
obkv: KvReaderDelAdd,
side: DelAdd,
) -> Result<Option<BTreeMap<String, Vectors>>, Error> {
Ok(if let Some(value) = obkv.get(side) {
let ParsedVectors(parsed_vectors) = ParsedVectors::from_bytes(value)?;
Some(parsed_vectors)
} else {
None
})
}
/// Represents either a vector or an array of multiple vectors.
#[derive(serde::Serialize, serde::Deserialize, Debug)]
#[serde(transparent)]
pub struct VectorOrArrayOfVectors {
#[serde(with = "either::serde_untagged_optional")]
inner: Option<either::Either<Vec<Embedding>, Embedding>>,
}
impl VectorOrArrayOfVectors {
pub fn into_array_of_vectors(self) -> Option<Vec<Embedding>> {
match self.inner? {
either::Either::Left(vectors) => Some(vectors),
either::Either::Right(vector) => Some(vec![vector]),
}
}
pub fn from_array_of_vectors(array_of_vec: Vec<Embedding>) -> Self {
Self { inner: Some(either::Either::Left(array_of_vec)) }
}
}
#[cfg(test)]
mod test {
use super::VectorOrArrayOfVectors;
#[test]
fn array_of_vectors() {
let null: VectorOrArrayOfVectors = serde_json::from_str("null").unwrap();
let empty: VectorOrArrayOfVectors = serde_json::from_str("[]").unwrap();
let one: VectorOrArrayOfVectors = serde_json::from_str("[0.1]").unwrap();
let two: VectorOrArrayOfVectors = serde_json::from_str("[0.1, 0.2]").unwrap();
let one_vec: VectorOrArrayOfVectors = serde_json::from_str("[[0.1, 0.2]]").unwrap();
let two_vecs: VectorOrArrayOfVectors =
serde_json::from_str("[[0.1, 0.2], [0.3, 0.4]]").unwrap();
insta::assert_json_snapshot!(null.into_array_of_vectors(), @"null");
insta::assert_json_snapshot!(empty.into_array_of_vectors(), @"[]");
insta::assert_json_snapshot!(one.into_array_of_vectors(), @r###"
[
[
0.1
]
]
"###);
insta::assert_json_snapshot!(two.into_array_of_vectors(), @r###"
[
[
0.1,
0.2
]
]
"###);
insta::assert_json_snapshot!(one_vec.into_array_of_vectors(), @r###"
[
[
0.1,
0.2
]
]
"###);
insta::assert_json_snapshot!(two_vecs.into_array_of_vectors(), @r###"
[
[
0.1,
0.2
],
[
0.3,
0.4
]
]
"###);
}
}