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implement the binary quantization in meilisearch

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This commit is contained in:
Tamo 2024-09-18 18:13:37 +02:00
parent 5f474a640d
commit cc45e264ca
20 changed files with 559 additions and 223 deletions
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30
Cargo.lock generated
View File

@ -384,6 +384,24 @@ version = "0.7.4"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "96d30a06541fbafbc7f82ed10c06164cfbd2c401138f6addd8404629c4b16711"
[[package]]
name = "arroy"
version = "0.4.0"
dependencies = [
"bytemuck",
"byteorder",
"heed",
"log",
"memmap2",
"nohash",
"ordered-float",
"rand",
"rayon",
"roaring",
"tempfile",
"thiserror",
]
[[package]]
name = "arroy"
version = "0.4.0"
@ -2555,7 +2573,7 @@ name = "index-scheduler"
version = "1.11.0"
dependencies = [
"anyhow",
"arroy",
"arroy 0.4.0 (registry+https://github.com/rust-lang/crates.io-index)",
"big_s",
"bincode",
"crossbeam",
@ -2838,7 +2856,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "e310b3a6b5907f99202fcdb4960ff45b93735d7c7d96b760fcff8db2dc0e103d"
dependencies = [
"cfg-if",
"windows-targets 0.48.1",
"windows-targets 0.52.4",
]
[[package]]
@ -3545,7 +3563,7 @@ dependencies = [
name = "milli"
version = "1.11.0"
dependencies = [
"arroy",
"arroy 0.4.0",
"big_s",
"bimap",
"bincode",
@ -3686,6 +3704,12 @@ version = "0.0.3"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "6d02c0b00610773bb7fc61d85e13d86c7858cbdf00e1a120bfc41bc055dbaa0e"
[[package]]
name = "nohash"
version = "0.2.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a0f889fb66f7acdf83442c35775764b51fed3c606ab9cee51500dbde2cf528ca"
[[package]]
name = "nom"
version = "7.1.3"

11
index-scheduler/src/lib.rs
View File

@ -1477,7 +1477,7 @@ impl IndexScheduler {
.map(
|IndexEmbeddingConfig {
name,
config: milli::vector::EmbeddingConfig { embedder_options, prompt },
config: milli::vector::EmbeddingConfig { embedder_options, prompt, quantized },
..
}| {
let prompt =
@ -1486,7 +1486,10 @@ impl IndexScheduler {
{
let embedders = self.embedders.read().unwrap();
if let Some(embedder) = embedders.get(&embedder_options) {
return Ok((name, (embedder.clone(), prompt)));
return Ok((
name,
(embedder.clone(), prompt, quantized.unwrap_or_default()),
));
}
}
@ -1500,7 +1503,7 @@ impl IndexScheduler {
let mut embedders = self.embedders.write().unwrap();
embedders.insert(embedder_options, embedder.clone());
}
Ok((name, (embedder, prompt)))
Ok((name, (embedder, prompt, quantized.unwrap_or_default())))
},
)
.collect();
@ -5197,7 +5200,7 @@ mod tests {
let simple_hf_name = name.clone();
let configs = index_scheduler.embedders(configs).unwrap();
let (hf_embedder, _) = configs.get(&simple_hf_name).unwrap();
let (hf_embedder, _, _) = configs.get(&simple_hf_name).unwrap();
let beagle_embed = hf_embedder.embed_one(S("Intel the beagle best doggo")).unwrap();
let 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();

5
meilisearch-types/src/error.rs
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@ -395,7 +395,10 @@ impl ErrorCode for milli::Error {
| UserError::InvalidSettingsDimensions { .. }
| UserError::InvalidUrl { .. }
| UserError::InvalidSettingsDocumentTemplateMaxBytes { .. }
| UserError::InvalidPrompt(_) => Code::InvalidSettingsEmbedders,
| UserError::InvalidPrompt(_)
| UserError::InvalidDisableBinaryQuantization { .. } => {
Code::InvalidSettingsEmbedders
}
UserError::TooManyEmbedders(_) => Code::InvalidSettingsEmbedders,
UserError::InvalidPromptForEmbeddings(..) => Code::InvalidSettingsEmbedders,
UserError::NoPrimaryKeyCandidateFound => Code::IndexPrimaryKeyNoCandidateFound,

5
meilisearch/src/routes/indexes/similar.rs
View File

@ -102,8 +102,8 @@ async fn similar(
let index = index_scheduler.index(&index_uid)?;
let (embedder_name, embedder) =
SearchKind::embedder(&index_scheduler, &index, &query.embedder, None)?;
let (embedder_name, embedder, quantized) =
SearchKind::embedder(&index_scheduler, &index, query.embedder.as_deref(), None)?;
tokio::task::spawn_blocking(move || {
perform_similar(
@ -111,6 +111,7 @@ async fn similar(
query,
embedder_name,
embedder,
quantized,
retrieve_vectors,
index_scheduler.features(),
)

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

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

3
milli/Cargo.toml
View File

@ -80,7 +80,8 @@ hf-hub = { git = "https://github.com/dureuill/hf-hub.git", branch = "rust_tls",
tiktoken-rs = "0.5.9"
liquid = "0.26.6"
rhai = { version = "1.19.0", features = ["serde", "no_module", "no_custom_syntax", "no_time", "sync"] }
arroy = "0.4.0"
# arroy = "0.4.0"
arroy = { path = "../../arroy" }
rand = "0.8.5"
tracing = "0.1.40"
ureq = { version = "2.10.0", features = ["json"] }

4
milli/src/error.rs
View File

@ -258,6 +258,10 @@ only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and undersco
},
#[error("`.embedders.{embedder_name}.dimensions`: `dimensions` cannot be zero")]
InvalidSettingsDimensions { embedder_name: String },
#[error(
"`.embedders.{embedder_name}.binaryQuantized`: Cannot disable the binary quantization"
)]
InvalidDisableBinaryQuantization { embedder_name: String },
#[error("`.embedders.{embedder_name}.documentTemplateMaxBytes`: `documentTemplateMaxBytes` cannot be zero")]
InvalidSettingsDocumentTemplateMaxBytes { embedder_name: String },
#[error("`.embedders.{embedder_name}.url`: could not parse `{url}`: {inner_error}")]

55
milli/src/index.rs
View File

@ -21,7 +21,7 @@ use crate::heed_codec::{BEU16StrCodec, FstSetCodec, StrBEU16Codec, StrRefCodec};
use crate::order_by_map::OrderByMap;
use crate::proximity::ProximityPrecision;
use crate::vector::parsed_vectors::RESERVED_VECTORS_FIELD_NAME;
use crate::vector::{Embedding, EmbeddingConfig};
use crate::vector::{ArroyReader, Embedding, EmbeddingConfig};
use crate::{
default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds,
FacetDistribution, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldIdWordCountCodec,
@ -162,7 +162,7 @@ pub struct Index {
/// Maps an embedder name to its id in the arroy store.
pub embedder_category_id: Database<Str, U8>,
/// Vector store based on arroy™.
pub vector_arroy: arroy::Database<arroy::distances::Angular>,
pub vector_arroy: arroy::Database<Unspecified>,
/// Maps the document id to the document as an obkv store.
pub(crate) documents: Database<BEU32, ObkvCodec>,
@ -1612,18 +1612,11 @@ impl Index {
pub fn arroy_readers<'a>(
&'a self,
rtxn: &'a RoTxn<'a>,
embedder_id: u8,
) -> impl Iterator<Item = Result<arroy::Reader<'a, arroy::distances::Angular>>> + 'a {
crate::vector::arroy_db_range_for_embedder(embedder_id).map_while(move |k| {
arroy::Reader::open(rtxn, k, self.vector_arroy)
.map(Some)
.or_else(|e| match e {
arroy::Error::MissingMetadata(_) => Ok(None),
e => Err(e.into()),
})
.transpose()
})
quantized: bool,
) -> impl Iterator<Item = ArroyReader> + 'a {
crate::vector::arroy_db_range_for_embedder(embedder_id)
.map_while(move |k| Some(ArroyReader::new(self.vector_arroy, k, quantized)))
}
pub(crate) fn put_search_cutoff(&self, wtxn: &mut RwTxn<'_>, cutoff: u64) -> heed::Result<()> {
@ -1644,32 +1637,28 @@ impl Index {
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 embedding_configs = self.embedding_configs(rtxn)?;
for config in embedding_configs {
// TODO: return internal error instead
let embedder_id = self.embedder_category_id.get(rtxn, &config.name)?.unwrap();
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 reader = ArroyReader::new(
self.vector_arroy,
embedder_id | (i as u16),
config.config.quantized(),
);
match reader.item_vector(rtxn, docid) {
Err(arroy::Error::MissingMetadata(_)) => break 'vectors,
Err(err) => return Err(err.into()),
Ok(None) => break 'vectors,
Ok(Some(embedding)) => embeddings.push(embedding),
};
let embedding = reader?.item_vector(rtxn, docid)?;
if let Some(embedding) = embedding {
embeddings.push(embedding)
} else {
break 'vectors;
}
}
res.insert(embedder_name.to_owned(), embeddings);
res.insert(config.name.to_owned(), embeddings);
}
Ok(res)
}

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

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

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

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

4
milli/src/search/new/mod.rs
View File

@ -320,6 +320,7 @@ fn get_ranking_rules_for_vector<'ctx>(
target: &[f32],
embedder_name: &str,
embedder: &Embedder,
quantized: bool,
) -> Result<Vec<BoxRankingRule<'ctx, PlaceholderQuery>>> {
// query graph search
@ -347,6 +348,7 @@ fn get_ranking_rules_for_vector<'ctx>(
limit_plus_offset,
embedder_name,
embedder,
quantized,
)?;
ranking_rules.push(Box::new(vector_sort));
vector = true;
@ -576,6 +578,7 @@ pub fn execute_vector_search(
length: usize,
embedder_name: &str,
embedder: &Embedder,
quantized: bool,
time_budget: TimeBudget,
ranking_score_threshold: Option<f64>,
) -> Result<PartialSearchResult> {
@ -591,6 +594,7 @@ pub fn execute_vector_search(
vector,
embedder_name,
embedder,
quantized,
)?;
let mut placeholder_search_logger = logger::DefaultSearchLogger;

10
milli/src/search/new/vector_sort.rs
View File

@ -16,6 +16,7 @@ pub struct VectorSort<Q: RankingRuleQueryTrait> {
limit: usize,
distribution_shift: Option<DistributionShift>,
embedder_index: u8,
quantized: bool,
}
impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
@ -26,6 +27,7 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
limit: usize,
embedder_name: &str,
embedder: &Embedder,
quantized: bool,
) -> Result<Self> {
let embedder_index = ctx
.index
@ -41,6 +43,7 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
limit,
distribution_shift: embedder.distribution(),
embedder_index,
quantized,
})
}
@ -49,16 +52,15 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
ctx: &mut SearchContext<'_>,
vector_candidates: &RoaringBitmap,
) -> Result<()> {
let readers: std::result::Result<Vec<_>, _> =
ctx.index.arroy_readers(ctx.txn, self.embedder_index).collect();
let readers = readers?;
let readers: Vec<_> =
ctx.index.arroy_readers(self.embedder_index, self.quantized).collect();
let target = &self.target;
let mut results = Vec::new();
for reader in readers.iter() {
let nns_by_vector =
reader.nns_by_vector(ctx.txn, target, self.limit, None, Some(vector_candidates))?;
reader.nns_by_vector(ctx.txn, target, self.limit, Some(vector_candidates))?;
results.extend(nns_by_vector.into_iter());
}
results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));

9
milli/src/search/similar.rs
View File

@ -18,6 +18,7 @@ pub struct Similar<'a> {
embedder_name: String,
embedder: Arc<Embedder>,
ranking_score_threshold: Option<f64>,
quantized: bool,
}
impl<'a> Similar<'a> {
@ -29,6 +30,7 @@ impl<'a> Similar<'a> {
rtxn: &'a heed::RoTxn<'a>,
embedder_name: String,
embedder: Arc<Embedder>,
quantized: bool,
) -> Self {
Self {
id,
@ -40,6 +42,7 @@ impl<'a> Similar<'a> {
embedder_name,
embedder,
ranking_score_threshold: None,
quantized,
}
}
@ -67,10 +70,7 @@ impl<'a> Similar<'a> {
.get(self.rtxn, &self.embedder_name)?
.ok_or_else(|| crate::UserError::InvalidEmbedder(self.embedder_name.to_owned()))?;
let readers: std::result::Result<Vec<_>, _> =
self.index.arroy_readers(self.rtxn, embedder_index).collect();
let readers = readers?;
let readers: Vec<_> = self.index.arroy_readers(embedder_index, self.quantized).collect();
let mut results = Vec::new();
@ -79,7 +79,6 @@ impl<'a> Similar<'a> {
self.rtxn,
self.id,
self.limit + self.offset + 1,
None,
Some(&universe),
)?;
if let Some(mut nns_by_item) = nns_by_item {

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

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

37
milli/src/update/index_documents/mod.rs
View File

@ -43,7 +43,7 @@ use crate::update::index_documents::parallel::ImmutableObkvs;
use crate::update::{
IndexerConfig, UpdateIndexingStep, WordPrefixDocids, WordPrefixIntegerDocids, WordsPrefixesFst,
};
use crate::vector::EmbeddingConfigs;
use crate::vector::{ArroyReader, EmbeddingConfigs};
use crate::{CboRoaringBitmapCodec, Index, Object, Result};
static MERGED_DATABASE_COUNT: usize = 7;
@ -679,6 +679,24 @@ where
let number_of_documents = self.index.number_of_documents(self.wtxn)?;
let mut rng = rand::rngs::StdRng::seed_from_u64(42);
// If an embedder wasn't used in the typedchunk but must be binary quantized
// we should insert it in `dimension`
for (name, action) in settings_diff.embedding_config_updates.iter() {
if action.is_being_quantized && !dimension.contains_key(name.as_str()) {
let index = self.index.embedder_category_id.get(self.wtxn, name)?.ok_or(
InternalError::DatabaseMissingEntry {
db_name: "embedder_category_id",
key: None,
},
)?;
let first_id = crate::vector::arroy_db_range_for_embedder(index).next().unwrap();
let reader =
ArroyReader::new(self.index.vector_arroy, first_id, action.was_quantized);
let dim = reader.dimensions(self.wtxn)?;
dimension.insert(name.to_string(), dim);
}
}
for (embedder_name, dimension) in dimension {
let wtxn = &mut *self.wtxn;
let vector_arroy = self.index.vector_arroy;
@ -686,13 +704,19 @@ where
let embedder_index = self.index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or(
InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None },
)?;
let embedder_config = settings_diff.embedding_config_updates.get(&embedder_name);
let was_quantized = embedder_config.map_or(false, |action| action.was_quantized);
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 writer = arroy::Writer::new(vector_arroy, k, dimension);
if writer.need_build(wtxn)? {
writer.build(wtxn, &mut rng, None)?;
} else if writer.is_empty(wtxn)? {
let mut writer = ArroyReader::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;
}
}
@ -2746,6 +2770,7 @@ mod tests {
response: Setting::NotSet,
distribution: Setting::NotSet,
headers: Setting::NotSet,
binary_quantized: Setting::NotSet,
}),
);
settings.set_embedder_settings(embedders);
@ -2774,7 +2799,7 @@ mod tests {
std::sync::Arc::new(crate::vector::Embedder::new(embedder.embedder_options).unwrap());
let res = index
.search(&rtxn)
.semantic(embedder_name, embedder, Some([0.0, 1.0, 2.0].to_vec()))
.semantic(embedder_name, embedder, false, Some([0.0, 1.0, 2.0].to_vec()))
.execute()
.unwrap();
assert_eq!(res.documents_ids.len(), 3);

35
milli/src/update/index_documents/transform.rs
View File

@ -28,7 +28,8 @@ use crate::update::index_documents::GrenadParameters;
use crate::update::settings::{InnerIndexSettings, InnerIndexSettingsDiff};
use crate::update::{AvailableDocumentsIds, UpdateIndexingStep};
use crate::vector::parsed_vectors::{ExplicitVectors, VectorOrArrayOfVectors};
use crate::vector::settings::{EmbedderAction, WriteBackToDocuments};
use crate::vector::settings::WriteBackToDocuments;
use crate::vector::ArroyReader;
use crate::{
is_faceted_by, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldsIdsMap, Index, Result,
};
@ -989,23 +990,16 @@ impl<'a, 'i> Transform<'a, 'i> {
None
};
let readers: Result<
BTreeMap<&str, (Vec<arroy::Reader<'_, arroy::distances::Angular>>, &RoaringBitmap)>,
> = settings_diff
let readers: Result<BTreeMap<&str, (Vec<ArroyReader>, &RoaringBitmap)>> = settings_diff
.embedding_config_updates
.iter()
.filter_map(|(name, action)| {
if let EmbedderAction::WriteBackToDocuments(WriteBackToDocuments {
embedder_id,
user_provided,
}) = action
if let Some(WriteBackToDocuments { embedder_id, user_provided }) =
action.write_back()
{
let readers: Result<Vec<_>> =
self.index.arroy_readers(wtxn, *embedder_id).collect();
match readers {
Ok(readers) => Some(Ok((name.as_str(), (readers, user_provided)))),
Err(error) => Some(Err(error)),
}
let readers: Vec<_> =
self.index.arroy_readers(*embedder_id, action.was_quantized).collect();
Some(Ok((name.as_str(), (readers, user_provided))))
} else {
None
}
@ -1104,23 +1098,14 @@ impl<'a, 'i> Transform<'a, 'i> {
}
}
let mut writers = Vec::new();
// delete all vectors from the embedders that need removal
for (_, (readers, _)) in readers {
for reader in readers {
let dimensions = reader.dimensions();
let arroy_index = reader.index();
drop(reader);
let writer = arroy::Writer::new(self.index.vector_arroy, arroy_index, dimensions);
writers.push(writer);
let dimensions = reader.dimensions(wtxn)?;
reader.clear(wtxn, dimensions)?;
}
}
for writer in writers {
writer.clear(wtxn)?;
}
let grenad_params = GrenadParameters {
chunk_compression_type: self.indexer_settings.chunk_compression_type,
chunk_compression_level: self.indexer_settings.chunk_compression_level,

26
milli/src/update/index_documents/typed_chunk.rs
View File

@ -27,6 +27,7 @@ use crate::update::index_documents::helpers::{
as_cloneable_grenad, keep_latest_obkv, try_split_array_at,
};
use crate::update::settings::InnerIndexSettingsDiff;
use crate::vector::ArroyReader;
use crate::{
lat_lng_to_xyz, CboRoaringBitmapCodec, DocumentId, FieldId, GeoPoint, Index, InternalError,
Result, SerializationError, U8StrStrCodec,
@ -666,9 +667,13 @@ pub(crate) fn write_typed_chunk_into_index(
let embedder_index = index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or(
InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None },
)?;
let binary_quantized = settings_diff
.embedding_config_updates
.get(&embedder_name)
.map_or(false, |conf| conf.was_quantized);
// FIXME: allow customizing distance
let writers: Vec<_> = crate::vector::arroy_db_range_for_embedder(embedder_index)
.map(|k| arroy::Writer::new(index.vector_arroy, k, expected_dimension))
.map(|k| ArroyReader::new(index.vector_arroy, k, binary_quantized))
.collect();
// remove vectors for docids we want them removed
@ -679,7 +684,7 @@ pub(crate) fn write_typed_chunk_into_index(
for writer in &writers {
// Uses invariant: vectors are packed in the first writers.
if !writer.del_item(wtxn, docid)? {
if !writer.del_item(wtxn, expected_dimension, docid)? {
break;
}
}
@ -711,7 +716,7 @@ pub(crate) fn write_typed_chunk_into_index(
)));
}
for (embedding, writer) in embeddings.iter().zip(&writers) {
writer.add_item(wtxn, docid, embedding)?;
writer.add_item(wtxn, expected_dimension, docid, embedding)?;
}
}
@ -734,7 +739,7 @@ pub(crate) fn write_typed_chunk_into_index(
break;
};
if candidate == vector {
writer.del_item(wtxn, docid)?;
writer.del_item(wtxn, expected_dimension, docid)?;
deleted_index = Some(index);
}
}
@ -751,8 +756,13 @@ pub(crate) fn write_typed_chunk_into_index(
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, docid)?;
writers.get(deleted_index).unwrap().add_item(wtxn, docid, &vector)?;
writer.del_item(wtxn, expected_dimension, docid)?;
writers.get(deleted_index).unwrap().add_item(
wtxn,
expected_dimension,
docid,
&vector,
)?;
}
}
}
@ -762,8 +772,8 @@ pub(crate) fn write_typed_chunk_into_index(
// overflow was detected during vector extraction.
for writer in &writers {
if !writer.contains_item(wtxn, docid)? {
writer.add_item(wtxn, docid, &vector)?;
if !writer.contains_item(wtxn, expected_dimension, docid)? {
writer.add_item(wtxn, expected_dimension, docid, &vector)?;
break;
}
}

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

@ -425,11 +425,13 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
FP: Fn(UpdateIndexingStep) + Sync,
FA: Fn() -> bool + Sync,
{
println!("inside reindex");
// if the settings are set before any document update, we don't need to do anything, and
// will set the primary key during the first document addition.
if self.index.number_of_documents(self.wtxn)? == 0 {
return Ok(());
}
println!("didnt early exit");
let transform = Transform::new(
self.wtxn,
@ -954,7 +956,7 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
let old_configs = self.index.embedding_configs(self.wtxn)?;
let remove_all: Result<BTreeMap<String, EmbedderAction>> = old_configs
.into_iter()
.map(|IndexEmbeddingConfig { name, config: _, user_provided }| -> Result<_> {
.map(|IndexEmbeddingConfig { name, config, user_provided }| -> Result<_> {
let embedder_id =
self.index.embedder_category_id.get(self.wtxn, &name)?.ok_or(
crate::InternalError::DatabaseMissingEntry {
@ -964,10 +966,10 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
)?;
Ok((
name,
EmbedderAction::WriteBackToDocuments(WriteBackToDocuments {
embedder_id,
user_provided,
}),
EmbedderAction::with_write_back(
WriteBackToDocuments { embedder_id, user_provided },
config.quantized(),
),
))
})
.collect();
@ -1004,7 +1006,8 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
match joined {
// updated config
EitherOrBoth::Both((name, (old, user_provided)), (_, new)) => {
let settings_diff = SettingsDiff::from_settings(old, new);
let was_quantized = old.binary_quantized.set().unwrap_or_default();
let settings_diff = SettingsDiff::from_settings(old, new)?;
match settings_diff {
SettingsDiff::Remove => {
tracing::debug!(
@ -1023,25 +1026,29 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
self.index.embedder_category_id.delete(self.wtxn, &name)?;
embedder_actions.insert(
name,
EmbedderAction::WriteBackToDocuments(WriteBackToDocuments {
embedder_id,
user_provided,
}),
EmbedderAction::with_write_back(
WriteBackToDocuments { embedder_id, user_provided },
was_quantized,
),
);
}
SettingsDiff::Reindex { action, updated_settings } => {
SettingsDiff::Reindex { action, updated_settings, quantize } => {
tracing::debug!(
embedder = name,
user_provided = user_provided.len(),
?action,
"reindex embedder"
);
embedder_actions.insert(name.clone(), EmbedderAction::Reindex(action));
embedder_actions.insert(
name.clone(),
EmbedderAction::with_reindex(action, was_quantized)
.with_is_being_quantized(quantize),
);
let new =
validate_embedding_settings(Setting::Set(updated_settings), &name)?;
updated_configs.insert(name, (new, user_provided));
}
SettingsDiff::UpdateWithoutReindex { updated_settings } => {
SettingsDiff::UpdateWithoutReindex { updated_settings, quantize } => {
tracing::debug!(
embedder = name,
user_provided = user_provided.len(),
@ -1049,6 +1056,12 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
);
let new =
validate_embedding_settings(Setting::Set(updated_settings), &name)?;
if quantize {
embedder_actions.insert(
name.clone(),
EmbedderAction::default().with_is_being_quantized(true),
);
}
updated_configs.insert(name, (new, user_provided));
}
}
@ -1067,8 +1080,10 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
&mut setting,
);
let setting = validate_embedding_settings(setting, &name)?;
embedder_actions
.insert(name.clone(), EmbedderAction::Reindex(ReindexAction::FullReindex));
embedder_actions.insert(
name.clone(),
EmbedderAction::with_reindex(ReindexAction::FullReindex, false),
);
updated_configs.insert(name, (setting, RoaringBitmap::new()));
}
}
@ -1082,21 +1097,15 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
let mut find_free_index =
move || free_indices.find(|(_, free)| **free).map(|(index, _)| index as u8);
for (name, action) in embedder_actions.iter() {
match action {
EmbedderAction::Reindex(ReindexAction::RegeneratePrompts) => {
/* cannot be a new embedder, so has to have an id already */
}
EmbedderAction::Reindex(ReindexAction::FullReindex) => {
if self.index.embedder_category_id.get(self.wtxn, name)?.is_none() {
let id = find_free_index()
.ok_or(UserError::TooManyEmbedders(updated_configs.len()))?;
if matches!(action.reindex(), Some(ReindexAction::FullReindex))
&& self.index.embedder_category_id.get(self.wtxn, name)?.is_none()
{
let id =
find_free_index().ok_or(UserError::TooManyEmbedders(updated_configs.len()))?;
tracing::debug!(embedder = name, id, "assigning free id to new embedder");
self.index.embedder_category_id.put(self.wtxn, name, &id)?;
}
}
EmbedderAction::WriteBackToDocuments(_) => { /* already removed */ }
}
}
let updated_configs: Vec<IndexEmbeddingConfig> = updated_configs
.into_iter()
.filter_map(|(name, (config, user_provided))| match config {
@ -1277,7 +1286,11 @@ impl InnerIndexSettingsDiff {
// if the user-defined searchables changed, then we need to reindex prompts.
if cache_user_defined_searchables {
for (embedder_name, (config, _)) in new_settings.embedding_configs.inner_as_ref() {
for (embedder_name, (config, _, _quantized)) in
new_settings.embedding_configs.inner_as_ref()
{
let was_quantized =
old_settings.embedding_configs.get(&embedder_name).map_or(false, |conf| conf.2);
// skip embedders that don't use document templates
if !config.uses_document_template() {
continue;
@ -1287,16 +1300,19 @@ impl InnerIndexSettingsDiff {
// this always makes the code clearer by explicitly handling the cases
match embedding_config_updates.entry(embedder_name.clone()) {
std::collections::btree_map::Entry::Vacant(entry) => {
entry.insert(EmbedderAction::Reindex(ReindexAction::RegeneratePrompts));
entry.insert(EmbedderAction::with_reindex(
ReindexAction::RegeneratePrompts,
was_quantized,
));
}
std::collections::btree_map::Entry::Occupied(entry) => match entry.get() {
EmbedderAction::WriteBackToDocuments(_) => { /* we are deleting this embedder, so no point in regeneration */
std::collections::btree_map::Entry::Occupied(entry) => {
let EmbedderAction {
was_quantized: _,
is_being_quantized: _, // We are deleting this embedder, so no point in regeneration
write_back: _, // We are already fully reindexing
reindex: _, // We are already regenerating prompts
} = entry.get();
}
EmbedderAction::Reindex(ReindexAction::FullReindex) => { /* we are already fully reindexing */
}
EmbedderAction::Reindex(ReindexAction::RegeneratePrompts) => { /* we are already regenerating prompts */
}
},
};
}
}
@ -1546,7 +1562,7 @@ fn embedders(embedding_configs: Vec<IndexEmbeddingConfig>) -> Result<EmbeddingCo
.map(
|IndexEmbeddingConfig {
name,
config: EmbeddingConfig { embedder_options, prompt },
config: EmbeddingConfig { embedder_options, prompt, quantized },
..
}| {
let prompt = Arc::new(prompt.try_into().map_err(crate::Error::from)?);
@ -1556,7 +1572,7 @@ fn embedders(embedding_configs: Vec<IndexEmbeddingConfig>) -> Result<EmbeddingCo
.map_err(crate::vector::Error::from)
.map_err(crate::Error::from)?,
);
Ok((name, (embedder, prompt)))
Ok((name, (embedder, prompt, quantized.unwrap_or_default())))
},
)
.collect();
@ -1581,6 +1597,7 @@ fn validate_prompt(
response,
distribution,
headers,
binary_quantized: binary_quantize,
}) => {
let max_bytes = match document_template_max_bytes.set() {
Some(max_bytes) => NonZeroUsize::new(max_bytes).ok_or_else(|| {
@ -1613,6 +1630,7 @@ fn validate_prompt(
response,
distribution,
headers,
binary_quantized: binary_quantize,
}))
}
new => Ok(new),
@ -1638,6 +1656,7 @@ pub fn validate_embedding_settings(
response,
distribution,
headers,
binary_quantized: binary_quantize,
} = settings;
if let Some(0) = dimensions.set() {
@ -1678,6 +1697,7 @@ pub fn validate_embedding_settings(
response,
distribution,
headers,
binary_quantized: binary_quantize,
}));
};
match inferred_source {
@ -1779,6 +1799,7 @@ pub fn validate_embedding_settings(
response,
distribution,
headers,
binary_quantized: binary_quantize,
}))
}

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

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

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

@ -32,6 +32,9 @@ pub struct EmbeddingSettings {
pub dimensions: Setting<usize>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub binary_quantized: Setting<bool>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub document_template: Setting<String>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
@ -85,23 +88,62 @@ pub enum ReindexAction {
pub enum SettingsDiff {
Remove,
Reindex { action: ReindexAction, updated_settings: EmbeddingSettings },
UpdateWithoutReindex { updated_settings: EmbeddingSettings },
Reindex { action: ReindexAction, updated_settings: EmbeddingSettings, quantize: bool },
UpdateWithoutReindex { updated_settings: EmbeddingSettings, quantize: bool },
}
pub enum EmbedderAction {
WriteBackToDocuments(WriteBackToDocuments),
Reindex(ReindexAction),
#[derive(Default, Debug)]
pub struct EmbedderAction {
pub was_quantized: bool,
pub is_being_quantized: bool,
pub write_back: Option<WriteBackToDocuments>,
pub reindex: Option<ReindexAction>,
}
impl EmbedderAction {
pub fn is_being_quantized(&self) -> bool {
self.is_being_quantized
}
pub fn write_back(&self) -> Option<&WriteBackToDocuments> {
self.write_back.as_ref()
}
pub fn reindex(&self) -> Option<&ReindexAction> {
self.reindex.as_ref()
}
pub fn with_is_being_quantized(mut self, quantize: bool) -> Self {
self.is_being_quantized = quantize;
self
}
pub fn with_write_back(write_back: WriteBackToDocuments, was_quantized: bool) -> Self {
Self {
was_quantized,
is_being_quantized: false,
write_back: Some(write_back),
reindex: None,
}
}
pub fn with_reindex(reindex: ReindexAction, was_quantized: bool) -> Self {
Self { was_quantized, is_being_quantized: false, write_back: None, reindex: Some(reindex) }
}
}
#[derive(Debug)]
pub struct WriteBackToDocuments {
pub embedder_id: u8,
pub user_provided: RoaringBitmap,
}
impl SettingsDiff {
pub fn from_settings(old: EmbeddingSettings, new: Setting<EmbeddingSettings>) -> Self {
match new {
pub fn from_settings(
old: EmbeddingSettings,
new: Setting<EmbeddingSettings>,
) -> Result<Self, UserError> {
let ret = match new {
Setting::Set(new) => {
let EmbeddingSettings {
mut source,
@ -116,6 +158,7 @@ impl SettingsDiff {
mut distribution,
mut headers,
mut document_template_max_bytes,
binary_quantized: mut binary_quantize,
} = old;
let EmbeddingSettings {
@ -131,8 +174,17 @@ impl SettingsDiff {
distribution: new_distribution,
headers: new_headers,
document_template_max_bytes: new_document_template_max_bytes,
binary_quantized: new_binary_quantize,
} = new;
if matches!(binary_quantize, Setting::Set(true))
&& matches!(new_binary_quantize, Setting::Set(false))
{
return Err(UserError::InvalidDisableBinaryQuantization {
embedder_name: String::from("todo"),
});
}
let mut reindex_action = None;
// **Warning**: do not use short-circuiting || here, we want all these operations applied
@ -172,6 +224,7 @@ impl SettingsDiff {
_ => {}
}
}
let binary_quantize_changed = binary_quantize.apply(new_binary_quantize);
if url.apply(new_url) {
match source {
// do not regenerate on an url change in OpenAI
@ -231,16 +284,27 @@ impl SettingsDiff {
distribution,
headers,
document_template_max_bytes,
binary_quantized: binary_quantize,
};
match reindex_action {
Some(action) => Self::Reindex { action, updated_settings },
None => Self::UpdateWithoutReindex { updated_settings },
Some(action) => Self::Reindex {
action,
updated_settings,
quantize: binary_quantize_changed,
},
None => Self::UpdateWithoutReindex {
updated_settings,
quantize: binary_quantize_changed,
},
}
}
Setting::Reset => Self::Remove,
Setting::NotSet => Self::UpdateWithoutReindex { updated_settings: old },
Setting::NotSet => {
Self::UpdateWithoutReindex { updated_settings: old, quantize: false }
}
};
Ok(ret)
}
}
@ -486,7 +550,7 @@ impl std::fmt::Display for EmbedderSource {
impl From<EmbeddingConfig> for EmbeddingSettings {
fn from(value: EmbeddingConfig) -> Self {
let EmbeddingConfig { embedder_options, prompt } = value;
let EmbeddingConfig { embedder_options, prompt, quantized } = value;
let document_template_max_bytes =
Setting::Set(prompt.max_bytes.unwrap_or(default_max_bytes()).get());
match embedder_options {
@ -507,6 +571,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::NotSet,
headers: Setting::NotSet,
distribution: Setting::some_or_not_set(distribution),
binary_quantized: Setting::some_or_not_set(quantized),
},
super::EmbedderOptions::OpenAi(super::openai::EmbedderOptions {
url,
@ -527,6 +592,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::NotSet,
headers: Setting::NotSet,
distribution: Setting::some_or_not_set(distribution),
binary_quantized: Setting::some_or_not_set(quantized),
},
super::EmbedderOptions::Ollama(super::ollama::EmbedderOptions {
embedding_model,
@ -547,6 +613,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::NotSet,
headers: Setting::NotSet,
distribution: Setting::some_or_not_set(distribution),
binary_quantized: Setting::some_or_not_set(quantized),
},
super::EmbedderOptions::UserProvided(super::manual::EmbedderOptions {
dimensions,
@ -564,6 +631,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::NotSet,
headers: Setting::NotSet,
distribution: Setting::some_or_not_set(distribution),
binary_quantized: Setting::some_or_not_set(quantized),
},
super::EmbedderOptions::Rest(super::rest::EmbedderOptions {
api_key,
@ -586,6 +654,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
response: Setting::Set(response),
distribution: Setting::some_or_not_set(distribution),
headers: Setting::Set(headers),
binary_quantized: Setting::some_or_not_set(quantized),
},
}
}
@ -607,8 +676,11 @@ impl From<EmbeddingSettings> for EmbeddingConfig {
response,
distribution,
headers,
binary_quantized,
} = value;
this.quantized = binary_quantized.set();
if let Some(source) = source.set() {
match source {
EmbedderSource::OpenAi => {