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Merge #4953

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4953: Move the multi arroy index logic to the arroy wrapper r=irevoire a=irevoire

# Pull Request

## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/4948

## What does this PR do?
- Make the `ArroyWrapper` we introduced in the last PR handle all the embedded for a specific docid itself.


Co-authored-by: Tamo <tamo@meilisearch.com>
This commit is contained in:
meili-bors[bot] 2024-09-24 15:02:24 +00:00 committed by GitHub
commit efdc5739d7
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GPG Key ID: B5690EEEBB952194
7 changed files with 319 additions and 226 deletions
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29
milli/src/index.rs
View File

@ -1610,24 +1610,6 @@ impl Index {
.unwrap_or_default())
}
pub fn arroy_readers<'a>(
&'a self,
rtxn: &'a RoTxn<'a>,
embedder_id: u8,
quantized: bool,
) -> impl Iterator<Item = Result<ArroyWrapper>> + 'a {
crate::vector::arroy_db_range_for_embedder(embedder_id).map_while(move |k| {
let reader = ArroyWrapper::new(self.vector_arroy, k, quantized);
// Here we don't care about the dimensions, but we want to know if we can read
// in the database or if its metadata are missing because there is no document with that many vectors.
match reader.dimensions(rtxn) {
Ok(_) => Some(Ok(reader)),
Err(arroy::Error::MissingMetadata(_)) => None,
Err(e) => Some(Err(e.into())),
}
})
}
pub(crate) fn put_search_cutoff(&self, wtxn: &mut RwTxn<'_>, cutoff: u64) -> heed::Result<()> {
self.main.remap_types::<Str, BEU64>().put(wtxn, main_key::SEARCH_CUTOFF, &cutoff)
}
@ -1649,14 +1631,9 @@ impl Index {
let embedding_configs = self.embedding_configs(rtxn)?;
for config in embedding_configs {
let embedder_id = self.embedder_category_id.get(rtxn, &config.name)?.unwrap();
let embeddings = self
.arroy_readers(rtxn, embedder_id, config.config.quantized())
.map_while(|reader| {
reader
.and_then(|r| r.item_vector(rtxn, docid).map_err(|e| e.into()))
.transpose()
})
.collect::<Result<Vec<_>>>()?;
let reader =
ArroyWrapper::new(self.vector_arroy, embedder_id, config.config.quantized());
let embeddings = reader.item_vectors(rtxn, docid)?;
res.insert(config.name.to_owned(), embeddings);
}
Ok(res)

12
milli/src/search/new/vector_sort.rs
View File

@ -1,11 +1,10 @@
use std::iter::FromIterator;
use ordered_float::OrderedFloat;
use roaring::RoaringBitmap;
use super::ranking_rules::{RankingRule, RankingRuleOutput, RankingRuleQueryTrait};
use crate::score_details::{self, ScoreDetails};
use crate::vector::{DistributionShift, Embedder};
use crate::vector::{ArroyWrapper, DistributionShift, Embedder};
use crate::{DocumentId, Result, SearchContext, SearchLogger};
pub struct VectorSort<Q: RankingRuleQueryTrait> {
@ -53,14 +52,9 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
vector_candidates: &RoaringBitmap,
) -> Result<()> {
let target = &self.target;
let mut results = Vec::new();
for reader in ctx.index.arroy_readers(ctx.txn, self.embedder_index, self.quantized) {
let nns_by_vector =
reader?.nns_by_vector(ctx.txn, target, self.limit, Some(vector_candidates))?;
results.extend(nns_by_vector.into_iter());
}
results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));
let reader = ArroyWrapper::new(ctx.index.vector_arroy, self.embedder_index, self.quantized);
let results = reader.nns_by_vector(ctx.txn, target, self.limit, Some(vector_candidates))?;
self.cached_sorted_docids = results.into_iter();
Ok(())

27
milli/src/search/similar.rs
View File

@ -1,10 +1,9 @@
use std::sync::Arc;
use ordered_float::OrderedFloat;
use roaring::RoaringBitmap;
use crate::score_details::{self, ScoreDetails};
use crate::vector::Embedder;
use crate::vector::{ArroyWrapper, Embedder};
use crate::{filtered_universe, DocumentId, Filter, Index, Result, SearchResult};
pub struct Similar<'a> {
@ -71,23 +70,13 @@ impl<'a> Similar<'a> {
.get(self.rtxn, &self.embedder_name)?
.ok_or_else(|| crate::UserError::InvalidEmbedder(self.embedder_name.to_owned()))?;
let mut results = Vec::new();
for reader in self.index.arroy_readers(self.rtxn, embedder_index, self.quantized) {
let nns_by_item = reader?.nns_by_item(
self.rtxn,
self.id,
self.limit + self.offset + 1,
Some(&universe),
)?;
if let Some(mut nns_by_item) = nns_by_item {
results.append(&mut nns_by_item);
} else {
break;
}
}
results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));
let reader = ArroyWrapper::new(self.index.vector_arroy, embedder_index, self.quantized);
let results = reader.nns_by_item(
self.rtxn,
self.id,
self.limit + self.offset + 1,
Some(&universe),
)?;
let mut documents_ids = Vec::with_capacity(self.limit);
let mut document_scores = Vec::with_capacity(self.limit);

16
milli/src/update/index_documents/mod.rs
View File

@ -689,9 +689,8 @@ where
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);
ArroyWrapper::new(self.index.vector_arroy, index, action.was_quantized);
let dim = reader.dimensions(self.wtxn)?;
dimension.insert(name.to_string(), dim);
}
@ -713,17 +712,8 @@ where
let is_quantizing = embedder_config.map_or(false, |action| action.is_being_quantized);
pool.install(|| {
for k in crate::vector::arroy_db_range_for_embedder(embedder_index) {
let mut writer = ArroyWrapper::new(vector_arroy, k, was_quantized);
if is_quantizing {
writer.quantize(wtxn, k, dimension)?;
}
if writer.need_build(wtxn, dimension)? {
writer.build(wtxn, &mut rng, dimension)?;
} else if writer.is_empty(wtxn, dimension)? {
break;
}
}
let mut writer = ArroyWrapper::new(vector_arroy, embedder_index, was_quantized);
writer.build_and_quantize(wtxn, &mut rng, dimension, is_quantizing)?;
Result::Ok(())
})
.map_err(InternalError::from)??;

63
milli/src/update/index_documents/transform.rs
View File

@ -990,27 +990,24 @@ impl<'a, 'i> Transform<'a, 'i> {
None
};
let readers: Result<BTreeMap<&str, (Vec<ArroyWrapper>, &RoaringBitmap)>> = settings_diff
let readers: BTreeMap<&str, (ArroyWrapper, &RoaringBitmap)> = settings_diff
.embedding_config_updates
.iter()
.filter_map(|(name, action)| {
if let Some(WriteBackToDocuments { embedder_id, user_provided }) =
action.write_back()
{
let readers: Result<Vec<_>> = self
.index
.arroy_readers(wtxn, *embedder_id, action.was_quantized)
.collect();
match readers {
Ok(readers) => Some(Ok((name.as_str(), (readers, user_provided)))),
Err(error) => Some(Err(error)),
}
let reader = ArroyWrapper::new(
self.index.vector_arroy,
*embedder_id,
action.was_quantized,
);
Some((name.as_str(), (reader, user_provided)))
} else {
None
}
})
.collect();
let readers = readers?;
let old_vectors_fid = settings_diff
.old
@ -1048,34 +1045,24 @@ impl<'a, 'i> Transform<'a, 'i> {
arroy::Error,
> = readers
.iter()
.filter_map(|(name, (readers, user_provided))| {
.filter_map(|(name, (reader, user_provided))| {
if !user_provided.contains(docid) {
return None;
}
let mut vectors = Vec::new();
for reader in readers {
let Some(vector) = reader.item_vector(wtxn, docid).transpose() else {
break;
};
match vector {
Ok(vector) => vectors.push(vector),
Err(error) => return Some(Err(error)),
}
match reader.item_vectors(wtxn, docid) {
Ok(vectors) if vectors.is_empty() => None,
Ok(vectors) => Some(Ok((
name.to_string(),
serde_json::to_value(ExplicitVectors {
embeddings: Some(
VectorOrArrayOfVectors::from_array_of_vectors(vectors),
),
regenerate: false,
})
.unwrap(),
))),
Err(e) => Some(Err(e)),
}
if vectors.is_empty() {
return None;
}
Some(Ok((
name.to_string(),
serde_json::to_value(ExplicitVectors {
embeddings: Some(VectorOrArrayOfVectors::from_array_of_vectors(
vectors,
)),
regenerate: false,
})
.unwrap(),
)))
})
.collect();
@ -1104,11 +1091,9 @@ impl<'a, 'i> Transform<'a, 'i> {
}
// delete all vectors from the embedders that need removal
for (_, (readers, _)) in readers {
for reader in readers {
let dimensions = reader.dimensions(wtxn)?;
reader.clear(wtxn, dimensions)?;
}
for (_, (reader, _)) in readers {
let dimensions = reader.dimensions(wtxn)?;
reader.clear(wtxn, dimensions)?;
}
let grenad_params = GrenadParameters {

57
milli/src/update/index_documents/typed_chunk.rs
View File

@ -673,22 +673,14 @@ pub(crate) fn write_typed_chunk_into_index(
.get(&embedder_name)
.map_or(false, |conf| conf.2);
// FIXME: allow customizing distance
let writers: Vec<_> = crate::vector::arroy_db_range_for_embedder(embedder_index)
.map(|k| ArroyWrapper::new(index.vector_arroy, k, binary_quantized))
.collect();
let writer = ArroyWrapper::new(index.vector_arroy, embedder_index, binary_quantized);
// remove vectors for docids we want them removed
let merger = remove_vectors_builder.build();
let mut iter = merger.into_stream_merger_iter()?;
while let Some((key, _)) = iter.next()? {
let docid = key.try_into().map(DocumentId::from_be_bytes).unwrap();
for writer in &writers {
// Uses invariant: vectors are packed in the first writers.
if !writer.del_item(wtxn, expected_dimension, docid)? {
break;
}
}
writer.del_items(wtxn, expected_dimension, docid)?;
}
// add generated embeddings
@ -716,9 +708,7 @@ pub(crate) fn write_typed_chunk_into_index(
embeddings.embedding_count(),
)));
}
for (embedding, writer) in embeddings.iter().zip(&writers) {
writer.add_item(wtxn, expected_dimension, docid, embedding)?;
}
writer.add_items(wtxn, docid, &embeddings)?;
}
// perform the manual diff
@ -733,51 +723,14 @@ pub(crate) fn write_typed_chunk_into_index(
if let Some(value) = vector_deladd_obkv.get(DelAdd::Deletion) {
let vector: Vec<f32> = pod_collect_to_vec(value);
let mut deleted_index = None;
for (index, writer) in writers.iter().enumerate() {
let Some(candidate) = writer.item_vector(wtxn, docid)? else {
// uses invariant: vectors are packed in the first writers.
break;
};
if candidate == vector {
writer.del_item(wtxn, expected_dimension, docid)?;
deleted_index = Some(index);
}
}
// 🥲 enforce invariant: vectors are packed in the first writers.
if let Some(deleted_index) = deleted_index {
let mut last_index_with_a_vector = None;
for (index, writer) in writers.iter().enumerate().skip(deleted_index) {
let Some(candidate) = writer.item_vector(wtxn, docid)? else {
break;
};
last_index_with_a_vector = Some((index, candidate));
}
if let Some((last_index, vector)) = last_index_with_a_vector {
// unwrap: computed the index from the list of writers
let writer = writers.get(last_index).unwrap();
writer.del_item(wtxn, expected_dimension, docid)?;
writers.get(deleted_index).unwrap().add_item(
wtxn,
expected_dimension,
docid,
&vector,
)?;
}
}
writer.del_item(wtxn, docid, &vector)?;
}
if let Some(value) = vector_deladd_obkv.get(DelAdd::Addition) {
let vector = pod_collect_to_vec(value);
// overflow was detected during vector extraction.
for writer in &writers {
if !writer.contains_item(wtxn, expected_dimension, docid)? {
writer.add_item(wtxn, expected_dimension, docid, &vector)?;
break;
}
}
writer.add_item(wtxn, docid, &vector)?;
}
}

341
milli/src/vector/mod.rs
View File

@ -32,105 +32,243 @@ pub const REQUEST_PARALLELISM: usize = 40;
pub struct ArroyWrapper {
quantized: bool,
index: u16,
embedder_index: u8,
database: arroy::Database<Unspecified>,
}
impl ArroyWrapper {
pub fn new(database: arroy::Database<Unspecified>, index: u16, quantized: bool) -> Self {
Self { database, index, quantized }
pub fn new(
database: arroy::Database<Unspecified>,
embedder_index: u8,
quantized: bool,
) -> Self {
Self { database, embedder_index, quantized }
}
pub fn index(&self) -> u16 {
self.index
pub fn embedder_index(&self) -> u8 {
self.embedder_index
}
fn readers<'a, D: arroy::Distance>(
&'a self,
rtxn: &'a RoTxn<'a>,
db: arroy::Database<D>,
) -> impl Iterator<Item = Result<arroy::Reader<D>, arroy::Error>> + 'a {
arroy_db_range_for_embedder(self.embedder_index).map_while(move |index| {
match arroy::Reader::open(rtxn, index, db) {
Ok(reader) => match reader.is_empty(rtxn) {
Ok(false) => Some(Ok(reader)),
Ok(true) => None,
Err(e) => Some(Err(e)),
},
Err(arroy::Error::MissingMetadata(_)) => None,
Err(e) => Some(Err(e)),
}
})
}
pub fn dimensions(&self, rtxn: &RoTxn) -> Result<usize, arroy::Error> {
let first_id = arroy_db_range_for_embedder(self.embedder_index).next().unwrap();
if self.quantized {
Ok(arroy::Reader::open(rtxn, self.index, self.quantized_db())?.dimensions())
Ok(arroy::Reader::open(rtxn, first_id, self.quantized_db())?.dimensions())
} else {
Ok(arroy::Reader::open(rtxn, self.index, self.angular_db())?.dimensions())
Ok(arroy::Reader::open(rtxn, first_id, self.angular_db())?.dimensions())
}
}
pub fn quantize(
pub fn build_and_quantize<R: rand::Rng + rand::SeedableRng>(
&mut self,
wtxn: &mut RwTxn,
index: u16,
rng: &mut R,
dimension: usize,
quantizing: bool,
) -> 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;
for index in arroy_db_range_for_embedder(self.embedder_index) {
if self.quantized {
let writer = arroy::Writer::new(self.quantized_db(), index, dimension);
if writer.need_build(wtxn)? {
writer.build(wtxn, rng, None)?
} else if writer.is_empty(wtxn)? {
break;
}
} else {
let writer = arroy::Writer::new(self.angular_db(), index, dimension);
// If we are quantizing the databases, we can't know from meilisearch
// if the db was empty but still contained the wrong metadata, thus we need
// to quantize everything and can't stop early. Since this operation can
// only happens once in the life of an embedder, it's not very performances
// sensitive.
if quantizing && !self.quantized {
let writer =
writer.prepare_changing_distance::<BinaryQuantizedAngular>(wtxn)?;
writer.build(wtxn, rng, None)?
} else if writer.need_build(wtxn)? {
writer.build(wtxn, rng, None)?
} else if writer.is_empty(wtxn)? {
break;
}
}
}
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>(
/// Overwrite all the embeddings associated with the index and item ID.
/// /!\ It won't remove embeddings after the last passed embedding, which can leave stale embeddings.
/// You should call `del_items` on the `item_id` before calling this method.
/// /!\ Cannot insert more than u8::MAX embeddings; after inserting u8::MAX embeddings, all the remaining ones will be silently ignored.
pub fn add_items(
&self,
wtxn: &mut RwTxn,
rng: &mut R,
dimension: usize,
item_id: arroy::ItemId,
embeddings: &Embeddings<f32>,
) -> 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)
let dimension = embeddings.dimension();
for (index, vector) in
arroy_db_range_for_embedder(self.embedder_index).zip(embeddings.iter())
{
if self.quantized {
arroy::Writer::new(self.quantized_db(), index, dimension)
.add_item(wtxn, item_id, vector)?
} else {
arroy::Writer::new(self.angular_db(), index, dimension)
.add_item(wtxn, item_id, vector)?
}
}
Ok(())
}
/// Add one document int for this index where we can find an empty spot.
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)
self._add_item(wtxn, self.quantized_db(), item_id, vector)
} else {
arroy::Writer::new(self.angular_db(), self.index, dimension)
.add_item(wtxn, item_id, vector)
self._add_item(wtxn, self.angular_db(), item_id, vector)
}
}
pub fn del_item(
fn _add_item<D: arroy::Distance>(
&self,
wtxn: &mut RwTxn,
db: arroy::Database<D>,
item_id: arroy::ItemId,
vector: &[f32],
) -> Result<(), arroy::Error> {
let dimension = vector.len();
for index in arroy_db_range_for_embedder(self.embedder_index) {
let writer = arroy::Writer::new(db, index, dimension);
if !writer.contains_item(wtxn, item_id)? {
writer.add_item(wtxn, item_id, vector)?;
break;
}
}
Ok(())
}
/// Delete all embeddings from a specific `item_id`
pub fn del_items(
&self,
wtxn: &mut RwTxn,
dimension: usize,
item_id: arroy::ItemId,
) -> Result<(), arroy::Error> {
for index in arroy_db_range_for_embedder(self.embedder_index) {
if self.quantized {
let writer = arroy::Writer::new(self.quantized_db(), index, dimension);
if !writer.del_item(wtxn, item_id)? {
break;
}
} else {
let writer = arroy::Writer::new(self.angular_db(), index, dimension);
if !writer.del_item(wtxn, item_id)? {
break;
}
}
}
Ok(())
}
/// Delete one item.
pub fn del_item(
&self,
wtxn: &mut RwTxn,
item_id: arroy::ItemId,
vector: &[f32],
) -> Result<bool, arroy::Error> {
if self.quantized {
arroy::Writer::new(self.quantized_db(), self.index, dimension).del_item(wtxn, item_id)
self._del_item(wtxn, self.quantized_db(), item_id, vector)
} else {
arroy::Writer::new(self.angular_db(), self.index, dimension).del_item(wtxn, item_id)
self._del_item(wtxn, self.angular_db(), item_id, vector)
}
}
fn _del_item<D: arroy::Distance>(
&self,
wtxn: &mut RwTxn,
db: arroy::Database<D>,
item_id: arroy::ItemId,
vector: &[f32],
) -> Result<bool, arroy::Error> {
let dimension = vector.len();
let mut deleted_index = None;
for index in arroy_db_range_for_embedder(self.embedder_index) {
let writer = arroy::Writer::new(db, index, dimension);
let Some(candidate) = writer.item_vector(wtxn, item_id)? else {
// uses invariant: vectors are packed in the first writers.
break;
};
if candidate == vector {
writer.del_item(wtxn, item_id)?;
deleted_index = Some(index);
}
}
// 🥲 enforce invariant: vectors are packed in the first writers.
if let Some(deleted_index) = deleted_index {
let mut last_index_with_a_vector = None;
for index in
arroy_db_range_for_embedder(self.embedder_index).skip(deleted_index as usize)
{
let writer = arroy::Writer::new(db, index, dimension);
let Some(candidate) = writer.item_vector(wtxn, item_id)? else {
break;
};
last_index_with_a_vector = Some((index, candidate));
}
if let Some((last_index, vector)) = last_index_with_a_vector {
let writer = arroy::Writer::new(db, last_index, dimension);
writer.del_item(wtxn, item_id)?;
let writer = arroy::Writer::new(db, deleted_index, dimension);
writer.add_item(wtxn, item_id, &vector)?;
}
}
Ok(deleted_index.is_some())
}
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)
for index in arroy_db_range_for_embedder(self.embedder_index) {
if self.quantized {
let writer = arroy::Writer::new(self.quantized_db(), index, dimension);
if writer.is_empty(wtxn)? {
break;
}
writer.clear(wtxn)?;
} else {
let writer = arroy::Writer::new(self.angular_db(), index, dimension);
if writer.is_empty(wtxn)? {
break;
}
writer.clear(wtxn)?;
}
}
Ok(())
}
pub fn contains_item(
@ -139,11 +277,25 @@ impl ArroyWrapper {
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)
for index in arroy_db_range_for_embedder(self.embedder_index) {
let contains = if self.quantized {
let writer = arroy::Writer::new(self.quantized_db(), index, dimension);
if writer.is_empty(rtxn)? {
break;
}
writer.contains_item(rtxn, item)?
} else {
let writer = arroy::Writer::new(self.angular_db(), index, dimension);
if writer.is_empty(rtxn)? {
break;
}
writer.contains_item(rtxn, item)?
};
if contains {
return Ok(contains);
}
}
Ok(false)
}
pub fn nns_by_item(
@ -152,38 +304,91 @@ impl ArroyWrapper {
item: ItemId,
limit: usize,
filter: Option<&RoaringBitmap>,
) -> Result<Option<Vec<(ItemId, f32)>>, arroy::Error> {
) -> Result<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)
self._nns_by_item(rtxn, self.quantized_db(), item, limit, filter)
} else {
arroy::Reader::open(rtxn, self.index, self.angular_db())?
.nns_by_item(rtxn, item, limit, None, None, filter)
self._nns_by_item(rtxn, self.angular_db(), item, limit, filter)
}
}
fn _nns_by_item<D: arroy::Distance>(
&self,
rtxn: &RoTxn,
db: arroy::Database<D>,
item: ItemId,
limit: usize,
filter: Option<&RoaringBitmap>,
) -> Result<Vec<(ItemId, f32)>, arroy::Error> {
let mut results = Vec::new();
for reader in self.readers(rtxn, db) {
let ret = reader?.nns_by_item(rtxn, item, limit, None, None, filter)?;
if let Some(mut ret) = ret {
results.append(&mut ret);
} else {
break;
}
}
results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));
Ok(results)
}
pub fn nns_by_vector(
&self,
txn: &RoTxn,
item: &[f32],
rtxn: &RoTxn,
vector: &[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)
self._nns_by_vector(rtxn, self.quantized_db(), vector, limit, filter)
} else {
arroy::Reader::open(txn, self.index, self.angular_db())?
.nns_by_vector(txn, item, limit, None, None, filter)
self._nns_by_vector(rtxn, self.angular_db(), vector, limit, 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 _nns_by_vector<D: arroy::Distance>(
&self,
rtxn: &RoTxn,
db: arroy::Database<D>,
vector: &[f32],
limit: usize,
filter: Option<&RoaringBitmap>,
) -> Result<Vec<(ItemId, f32)>, arroy::Error> {
let mut results = Vec::new();
for reader in self.readers(rtxn, db) {
let mut ret = reader?.nns_by_vector(rtxn, vector, limit, None, None, filter)?;
results.append(&mut ret);
}
results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));
Ok(results)
}
pub fn item_vectors(&self, rtxn: &RoTxn, item_id: u32) -> Result<Vec<Vec<f32>>, arroy::Error> {
let mut vectors = Vec::new();
if self.quantized {
for reader in self.readers(rtxn, self.quantized_db()) {
if let Some(vec) = reader?.item_vector(rtxn, item_id)? {
vectors.push(vec);
} else {
break;
}
}
} else {
for reader in self.readers(rtxn, self.angular_db()) {
if let Some(vec) = reader?.item_vector(rtxn, item_id)? {
vectors.push(vec);
} else {
break;
}
}
}
Ok(vectors)
}
fn angular_db(&self) -> arroy::Database<Angular> {