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Small commit to add hybrid search and autoembedding

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Louis Dureuil 2023-11-15 15:46:37 +01:00
parent 21bcf32109
commit 13c2c6c16b
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42 changed files with 4045 additions and 246 deletions
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Cargo.lock generated
View File

@ -46,7 +46,7 @@ dependencies = [
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@ -120,7 +120,7 @@ dependencies = [
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@ -201,7 +201,7 @@ dependencies = [
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@ -365,6 +365,12 @@ dependencies = [
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@ -2207,7 +2244,7 @@ dependencies = [
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@ -2949,7 +2986,7 @@ version = "8.3.0"
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@ -3483,7 +3587,7 @@ dependencies = [
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version = "1.5.1"
dependencies = [
"base64 0.21.2",
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@ -3544,9 +3648,9 @@ dependencies = [
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@ -3589,6 +3693,7 @@ dependencies = [
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"fst",
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@ -3600,6 +3705,7 @@ dependencies = [
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@ -3607,6 +3713,7 @@ dependencies = [
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"ordered-float",
@ -3614,6 +3721,7 @@ dependencies = [
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@ -3624,8 +3732,10 @@ dependencies = [
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"tiktoken-rs",
"time",
"tokenizers",
"tokio",
"uuid 1.5.0",
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@ -3671,9 +3781,9 @@ dependencies = [
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@ -3725,6 +3835,12 @@ name = "nelson"
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@ -4480,6 +4596,12 @@ dependencies = [
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@ -4488,11 +4610,11 @@ checksum = "e5ea92a5b6195c6ef2a0295ea818b312502c6fc94dde986c5553242e18fd4ce2"
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@ -4514,6 +4636,7 @@ dependencies = [
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@ -4521,7 +4644,7 @@ dependencies = [
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@ -4582,6 +4705,12 @@ version = "0.1.23"
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@ -4648,7 +4777,7 @@ version = "1.0.3"
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@ -5258,11 +5433,10 @@ dependencies = [
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@ -5271,16 +5445,16 @@ dependencies = [
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@ -5508,7 +5682,7 @@ version = "2.7.1"
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@ -5758,6 +5932,12 @@ dependencies = [
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@ -5942,11 +6122,12 @@ dependencies = [
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1
dump/src/lib.rs
View File

@ -276,6 +276,7 @@ pub(crate) mod test {
),
}),
pagination: Setting::NotSet,
embedders: Setting::NotSet,
_kind: std::marker::PhantomData,
};
settings.check()

1
dump/src/reader/compat/v5_to_v6.rs
View File

@ -378,6 +378,7 @@ impl<T> From<v5::Settings<T>> for v6::Settings<v6::Unchecked> {
v5::Setting::Reset => v6::Setting::Reset,
v5::Setting::NotSet => v6::Setting::NotSet,
},
embedders: v6::Setting::NotSet,
_kind: std::marker::PhantomData,
}
}

9
index-scheduler/src/batch.rs
View File

@ -1202,6 +1202,10 @@ impl IndexScheduler {
let config = IndexDocumentsConfig { update_method: method, ..Default::default() };
let embedder_configs = index.embedding_configs(index_wtxn)?;
// TODO: consider Arc'ing the map too (we only need read access + we'll be cloning it multiple times, so really makes sense)
let embedders = self.embedders(embedder_configs)?;
let mut builder = milli::update::IndexDocuments::new(
index_wtxn,
index,
@ -1220,6 +1224,8 @@ impl IndexScheduler {
let (new_builder, user_result) = builder.add_documents(reader)?;
builder = new_builder;
builder = builder.with_embedders(embedders.clone());
let received_documents =
if let Some(Details::DocumentAdditionOrUpdate {
received_documents,
@ -1345,6 +1351,9 @@ impl IndexScheduler {
for (task, (_, settings)) in tasks.iter_mut().zip(settings) {
let checked_settings = settings.clone().check();
if matches!(checked_settings.embedders, milli::update::Setting::Set(_)) {
self.features().check_vector("Passing `embedders` in settings")?
}
if checked_settings.proximity_precision.set().is_some() {
self.features.features().check_proximity_precision()?;
}

4
index-scheduler/src/features.rs
View File

@ -56,12 +56,12 @@ impl RoFeatures {
}
}
pub fn check_vector(&self) -> Result<()> {
pub fn check_vector(&self, disabled_action: &'static str) -> Result<()> {
if self.runtime.vector_store {
Ok(())
} else {
Err(FeatureNotEnabledError {
disabled_action: "Passing `vector` as a query parameter",
disabled_action,
feature: "vector store",
issue_link: "https://github.com/meilisearch/product/discussions/677",
}

1
index-scheduler/src/insta_snapshot.rs
View File

@ -41,6 +41,7 @@ pub fn snapshot_index_scheduler(scheduler: &IndexScheduler) -> String {
planned_failures: _,
run_loop_iteration: _,
currently_updating_index: _,
embedders: _,
} = scheduler;
let rtxn = env.read_txn().unwrap();

41
index-scheduler/src/lib.rs
View File

@ -52,6 +52,7 @@ use meilisearch_types::heed::types::{SerdeBincode, SerdeJson, Str, I128};
use meilisearch_types::heed::{self, Database, Env, PutFlags, RoTxn, RwTxn};
use meilisearch_types::milli::documents::DocumentsBatchBuilder;
use meilisearch_types::milli::update::IndexerConfig;
use meilisearch_types::milli::vector::{Embedder, EmbedderOptions};
use meilisearch_types::milli::{self, CboRoaringBitmapCodec, Index, RoaringBitmapCodec, BEU32};
use meilisearch_types::tasks::{Kind, KindWithContent, Status, Task};
use puffin::FrameView;
@ -341,6 +342,8 @@ pub struct IndexScheduler {
/// so that a handle to the index is available from other threads (search) in an optimized manner.
currently_updating_index: Arc<RwLock<Option<(String, Index)>>>,
embedders: Arc<RwLock<HashMap<EmbedderOptions, std::sync::Arc<Embedder>>>>,
// ================= test
// The next entry is dedicated to the tests.
/// Provide a way to set a breakpoint in multiple part of the scheduler.
@ -386,6 +389,7 @@ impl IndexScheduler {
auth_path: self.auth_path.clone(),
version_file_path: self.version_file_path.clone(),
currently_updating_index: self.currently_updating_index.clone(),
embedders: self.embedders.clone(),
#[cfg(test)]
test_breakpoint_sdr: self.test_breakpoint_sdr.clone(),
#[cfg(test)]
@ -484,6 +488,7 @@ impl IndexScheduler {
auth_path: options.auth_path,
version_file_path: options.version_file_path,
currently_updating_index: Arc::new(RwLock::new(None)),
embedders: Default::default(),
#[cfg(test)]
test_breakpoint_sdr,
@ -1333,6 +1338,42 @@ impl IndexScheduler {
}
}
// TODO: consider using a type alias or a struct embedder/template
#[allow(clippy::type_complexity)]
pub fn embedders(
&self,
embedding_configs: Vec<(String, milli::vector::EmbeddingConfig)>,
) -> Result<HashMap<String, (Arc<milli::vector::Embedder>, Arc<milli::prompt::Prompt>)>> {
let res: Result<_> = embedding_configs
.into_iter()
.map(|(name, milli::vector::EmbeddingConfig { embedder_options, prompt })| {
let prompt =
Arc::new(prompt.try_into().map_err(meilisearch_types::milli::Error::from)?);
// optimistically return existing embedder
{
let embedders = self.embedders.read().unwrap();
if let Some(embedder) = embedders.get(&embedder_options) {
return Ok((name, (embedder.clone(), prompt)));
}
}
// add missing embedder
let embedder = Arc::new(
Embedder::new(embedder_options.clone())
.map_err(meilisearch_types::milli::vector::Error::from)
.map_err(meilisearch_types::milli::UserError::from)
.map_err(meilisearch_types::milli::Error::from)?,
);
{
let mut embedders = self.embedders.write().unwrap();
embedders.insert(embedder_options, embedder.clone());
}
Ok((name, (embedder, prompt)))
})
.collect();
res
}
/// Blocks the thread until the test handle asks to progress to/through this breakpoint.
///
/// Two messages are sent through the channel for each breakpoint.

8
meilisearch-types/src/error.rs
View File

@ -256,6 +256,7 @@ InvalidSettingsProximityPrecision , InvalidRequest , BAD_REQUEST ;
InvalidSettingsFaceting , InvalidRequest , BAD_REQUEST ;
InvalidSettingsFilterableAttributes , InvalidRequest , BAD_REQUEST ;
InvalidSettingsPagination , InvalidRequest , BAD_REQUEST ;
InvalidSettingsEmbedders , InvalidRequest , BAD_REQUEST ;
InvalidSettingsRankingRules , InvalidRequest , BAD_REQUEST ;
InvalidSettingsSearchableAttributes , InvalidRequest , BAD_REQUEST ;
InvalidSettingsSortableAttributes , InvalidRequest , BAD_REQUEST ;
@ -303,7 +304,8 @@ TaskNotFound , InvalidRequest , NOT_FOUND ;
TooManyOpenFiles , System , UNPROCESSABLE_ENTITY ;
UnretrievableDocument , Internal , BAD_REQUEST ;
UnretrievableErrorCode , InvalidRequest , BAD_REQUEST ;
UnsupportedMediaType , InvalidRequest , UNSUPPORTED_MEDIA_TYPE
UnsupportedMediaType , InvalidRequest , UNSUPPORTED_MEDIA_TYPE ;
VectorEmbeddingError , InvalidRequest , BAD_REQUEST
}
impl ErrorCode for JoinError {
@ -336,6 +338,9 @@ impl ErrorCode for milli::Error {
UserError::InvalidDocumentId { .. } | UserError::TooManyDocumentIds { .. } => {
Code::InvalidDocumentId
}
UserError::MissingDocumentField(_) => Code::InvalidDocumentFields,
UserError::InvalidPrompt(_) => Code::InvalidSettingsEmbedders,
UserError::InvalidPromptForEmbeddings(..) => Code::InvalidSettingsEmbedders,
UserError::NoPrimaryKeyCandidateFound => Code::IndexPrimaryKeyNoCandidateFound,
UserError::MultiplePrimaryKeyCandidatesFound { .. } => {
Code::IndexPrimaryKeyMultipleCandidatesFound
@ -358,6 +363,7 @@ impl ErrorCode for milli::Error {
UserError::InvalidMinTypoWordLenSetting(_, _) => {
Code::InvalidSettingsTypoTolerance
}
UserError::VectorEmbeddingError(_) => Code::VectorEmbeddingError,
}
}
}

23
meilisearch-types/src/settings.rs
View File

@ -199,6 +199,10 @@ pub struct Settings<T> {
#[deserr(default, error = DeserrJsonError<InvalidSettingsPagination>)]
pub pagination: Setting<PaginationSettings>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default, error = DeserrJsonError<InvalidSettingsEmbedders>)]
pub embedders: Setting<BTreeMap<String, Setting<milli::vector::settings::EmbeddingSettings>>>,
#[serde(skip)]
#[deserr(skip)]
pub _kind: PhantomData<T>,
@ -222,6 +226,7 @@ impl Settings<Checked> {
typo_tolerance: Setting::Reset,
faceting: Setting::Reset,
pagination: Setting::Reset,
embedders: Setting::Reset,
_kind: PhantomData,
}
}
@ -243,6 +248,7 @@ impl Settings<Checked> {
typo_tolerance,
faceting,
pagination,
embedders,
..
} = self;
@ -262,6 +268,7 @@ impl Settings<Checked> {
typo_tolerance,
faceting,
pagination,
embedders,
_kind: PhantomData,
}
}
@ -307,6 +314,7 @@ impl Settings<Unchecked> {
typo_tolerance: self.typo_tolerance,
faceting: self.faceting,
pagination: self.pagination,
embedders: self.embedders,
_kind: PhantomData,
}
}
@ -490,6 +498,12 @@ pub fn apply_settings_to_builder(
Setting::Reset => builder.reset_pagination_max_total_hits(),
Setting::NotSet => (),
}
match settings.embedders.clone() {
Setting::Set(value) => builder.set_embedder_settings(value),
Setting::Reset => builder.reset_embedder_settings(),
Setting::NotSet => (),
}
}
pub fn settings(
@ -571,6 +585,12 @@ pub fn settings(
),
};
let embedders = index
.embedding_configs(rtxn)?
.into_iter()
.map(|(name, config)| (name, Setting::Set(config.into())))
.collect();
Ok(Settings {
displayed_attributes: match displayed_attributes {
Some(attrs) => Setting::Set(attrs),
@ -599,6 +619,7 @@ pub fn settings(
typo_tolerance: Setting::Set(typo_tolerance),
faceting: Setting::Set(faceting),
pagination: Setting::Set(pagination),
embedders: Setting::Set(embedders),
_kind: PhantomData,
})
}
@ -747,6 +768,7 @@ pub(crate) mod test {
typo_tolerance: Setting::NotSet,
faceting: Setting::NotSet,
pagination: Setting::NotSet,
embedders: Setting::NotSet,
_kind: PhantomData::<Unchecked>,
};
@ -772,6 +794,7 @@ pub(crate) mod test {
typo_tolerance: Setting::NotSet,
faceting: Setting::NotSet,
pagination: Setting::NotSet,
embedders: Setting::NotSet,
_kind: PhantomData::<Unchecked>,
};

2
meilisearch/src/analytics/segment_analytics.rs
View File

@ -686,7 +686,7 @@ impl SearchAggregator {
ret.max_terms_number = q.split_whitespace().count();
}
if let Some(ref vector) = vector {
if let Some(meilisearch_types::milli::VectorQuery::Vector(ref vector)) = vector {
ret.max_vector_size = vector.len();
}

6
meilisearch/src/main.rs
View File

@ -19,7 +19,11 @@ static ALLOC: mimalloc::MiMalloc = mimalloc::MiMalloc;
/// does all the setup before meilisearch is launched
fn setup(opt: &Opt) -> anyhow::Result<()> {
let mut log_builder = env_logger::Builder::new();
log_builder.parse_filters(&opt.log_level.to_string());
let log_filters = format!(
"{},h2=warn,hyper=warn,tokio_util=warn,tracing=warn,rustls=warn,mio=warn,reqwest=warn",
opt.log_level
);
log_builder.parse_filters(&log_filters);
log_builder.init();

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

@ -7,6 +7,7 @@ use meilisearch_types::deserr::DeserrJsonError;
use meilisearch_types::error::deserr_codes::*;
use meilisearch_types::error::ResponseError;
use meilisearch_types::index_uid::IndexUid;
use meilisearch_types::milli::VectorQuery;
use serde_json::Value;
use crate::analytics::{Analytics, FacetSearchAggregator};
@ -117,7 +118,7 @@ impl From<FacetSearchQuery> for SearchQuery {
highlight_post_tag: DEFAULT_HIGHLIGHT_POST_TAG(),
crop_marker: DEFAULT_CROP_MARKER(),
matching_strategy,
vector,
vector: vector.map(VectorQuery::Vector),
attributes_to_search_on,
}
}

40
meilisearch/src/routes/indexes/search.rs
View File

@ -2,12 +2,13 @@ use actix_web::web::Data;
use actix_web::{web, HttpRequest, HttpResponse};
use deserr::actix_web::{AwebJson, AwebQueryParameter};
use index_scheduler::IndexScheduler;
use log::debug;
use log::{debug, warn};
use meilisearch_types::deserr::query_params::Param;
use meilisearch_types::deserr::{DeserrJsonError, DeserrQueryParamError};
use meilisearch_types::error::deserr_codes::*;
use meilisearch_types::error::ResponseError;
use meilisearch_types::index_uid::IndexUid;
use meilisearch_types::milli::VectorQuery;
use meilisearch_types::serde_cs::vec::CS;
use serde_json::Value;
@ -88,7 +89,7 @@ impl From<SearchQueryGet> for SearchQuery {
Self {
q: other.q,
vector: other.vector.map(CS::into_inner),
vector: other.vector.map(CS::into_inner).map(VectorQuery::Vector),
offset: other.offset.0,
limit: other.limit.0,
page: other.page.as_deref().copied(),
@ -193,6 +194,9 @@ pub async fn search_with_post(
let index = index_scheduler.index(&index_uid)?;
let features = index_scheduler.features();
embed(&mut query, index_scheduler.get_ref(), &index).await?;
let search_result =
tokio::task::spawn_blocking(move || perform_search(&index, query, features)).await?;
if let Ok(ref search_result) = search_result {
@ -206,6 +210,38 @@ pub async fn search_with_post(
Ok(HttpResponse::Ok().json(search_result))
}
pub async fn embed(
query: &mut SearchQuery,
index_scheduler: &IndexScheduler,
index: &meilisearch_types::milli::Index,
) -> Result<(), ResponseError> {
if let Some(VectorQuery::String(prompt)) = query.vector.take() {
let embedder_configs = index.embedding_configs(&index.read_txn()?)?;
let embedder = index_scheduler.embedders(embedder_configs)?;
/// FIXME: add error if no embedder, remove unwrap, support multiple embedders
let embeddings = embedder
.get("default")
.unwrap()
.0
.embed(vec![prompt])
.await
.map_err(meilisearch_types::milli::vector::Error::from)
.map_err(meilisearch_types::milli::UserError::from)
.map_err(meilisearch_types::milli::Error::from)?
.pop()
.expect("No vector returned from embedding");
if embeddings.iter().nth(1).is_some() {
warn!("Ignoring embeddings past the first one in long search query");
query.vector = Some(VectorQuery::Vector(embeddings.iter().next().unwrap().to_vec()));
} else {
query.vector = Some(VectorQuery::Vector(embeddings.into_inner()));
}
};
Ok(())
}
#[cfg(test)]
mod test {
use super::*;

3
meilisearch/src/routes/multi_search.rs
View File

@ -13,6 +13,7 @@ use crate::analytics::{Analytics, MultiSearchAggregator};
use crate::extractors::authentication::policies::ActionPolicy;
use crate::extractors::authentication::{AuthenticationError, GuardedData};
use crate::extractors::sequential_extractor::SeqHandler;
use crate::routes::indexes::search::embed;
use crate::search::{
add_search_rules, perform_search, SearchQueryWithIndex, SearchResultWithIndex,
};
@ -74,6 +75,8 @@ pub async fn multi_search_with_post(
})
.with_index(query_index)?;
embed(&mut query, index_scheduler.get_ref(), &index).await.with_index(query_index)?;
let search_result =
tokio::task::spawn_blocking(move || perform_search(&index, query, features))
.await

31
meilisearch/src/search.rs
View File

@ -16,6 +16,7 @@ use meilisearch_types::index_uid::IndexUid;
use meilisearch_types::milli::score_details::{ScoreDetails, ScoringStrategy};
use meilisearch_types::milli::{
dot_product_similarity, FacetValueHit, InternalError, OrderBy, SearchForFacetValues,
VectorQuery,
};
use meilisearch_types::settings::DEFAULT_PAGINATION_MAX_TOTAL_HITS;
use meilisearch_types::{milli, Document};
@ -46,7 +47,7 @@ pub struct SearchQuery {
#[deserr(default, error = DeserrJsonError<InvalidSearchQ>)]
pub q: Option<String>,
#[deserr(default, error = DeserrJsonError<InvalidSearchVector>)]
pub vector: Option<Vec<f32>>,
pub vector: Option<milli::VectorQuery>,
#[deserr(default = DEFAULT_SEARCH_OFFSET(), error = DeserrJsonError<InvalidSearchOffset>)]
pub offset: usize,
#[deserr(default = DEFAULT_SEARCH_LIMIT(), error = DeserrJsonError<InvalidSearchLimit>)]
@ -105,7 +106,7 @@ pub struct SearchQueryWithIndex {
#[deserr(default, error = DeserrJsonError<InvalidSearchQ>)]
pub q: Option<String>,
#[deserr(default, error = DeserrJsonError<InvalidSearchQ>)]
pub vector: Option<Vec<f32>>,
pub vector: Option<VectorQuery>,
#[deserr(default = DEFAULT_SEARCH_OFFSET(), error = DeserrJsonError<InvalidSearchOffset>)]
pub offset: usize,
#[deserr(default = DEFAULT_SEARCH_LIMIT(), error = DeserrJsonError<InvalidSearchLimit>)]
@ -339,11 +340,18 @@ fn prepare_search<'t>(
let mut search = index.search(rtxn);
if query.vector.is_some() && query.q.is_some() {
warn!("Ignoring the query string `q` when used with the `vector` parameter.");
warn!("Attempting hybrid search");
}
if let Some(ref vector) = query.vector {
search.vector(vector.clone());
match vector {
VectorQuery::Vector(vector) => {
search.vector(vector.clone());
}
VectorQuery::String(_) => {
panic!("Failed while preparing search; caller did not generate embedding for query")
}
}
}
if let Some(ref query) = query.q {
@ -375,7 +383,7 @@ fn prepare_search<'t>(
}
if query.vector.is_some() {
features.check_vector()?;
features.check_vector("Passing `vector` as a query parameter")?;
}
// compute the offset on the limit depending on the pagination mode.
@ -429,7 +437,11 @@ pub fn perform_search(
prepare_search(index, &rtxn, &query, features)?;
let milli::SearchResult { documents_ids, matching_words, candidates, document_scores, .. } =
search.execute()?;
if query.q.is_some() && query.vector.is_some() {
search.execute_hybrid()?
} else {
search.execute()?
};
let fields_ids_map = index.fields_ids_map(&rtxn).unwrap();
@ -538,13 +550,13 @@ pub fn perform_search(
insert_geo_distance(sort, &mut document);
}
let semantic_score = match query.vector.as_ref() {
let semantic_score = /*match query.vector.as_ref() {
Some(vector) => match extract_field("_vectors", &fields_ids_map, obkv)? {
Some(vectors) => compute_semantic_score(vector, vectors)?,
None => None,
},
None => None,
};
};*/ None;
let ranking_score =
query.show_ranking_score.then(|| ScoreDetails::global_score(score.iter()));
@ -629,7 +641,8 @@ pub fn perform_search(
hits: documents,
hits_info,
query: query.q.unwrap_or_default(),
vector: query.vector,
// FIXME: display input vector
vector: None,
processing_time_ms: before_search.elapsed().as_millis(),
facet_distribution,
facet_stats,

26
milli/Cargo.toml
View File

@ -27,10 +27,13 @@ fst = "0.4.7"
fxhash = "0.2.1"
geoutils = "0.5.1"
grenad = { version = "0.4.5", default-features = false, features = [
"rayon", "tempfile"
"rayon",
"tempfile",
] }
heed = { version = "0.20.0-alpha.9", default-features = false, features = [
"serde-json", "serde-bincode", "read-txn-no-tls"
"serde-json",
"serde-bincode",
"read-txn-no-tls",
] }
indexmap = { version = "2.0.0", features = ["serde"] }
instant-distance = { version = "0.6.1", features = ["with-serde"] }
@ -77,6 +80,15 @@ candle-transformers = { git = "https://github.com/huggingface/candle.git", versi
candle-nn = { git = "https://github.com/huggingface/candle.git", version = "0.3.1" }
tokenizers = { git = "https://github.com/huggingface/tokenizers.git", tag = "v0.14.1", version = "0.14.1" }
hf-hub = "0.3.2"
tokio = { version = "1.34.0", features = ["rt"] }
futures = "0.3.29"
nolife = { version = "0.3.1" }
reqwest = { version = "0.11.16", features = [
"rustls-tls",
"json",
], default-features = false }
tiktoken-rs = "0.5.7"
liquid = "0.26.4"
[dev-dependencies]
mimalloc = { version = "0.1.37", default-features = false }
@ -88,7 +100,15 @@ meili-snap = { path = "../meili-snap" }
rand = { version = "0.8.5", features = ["small_rng"] }
[features]
all-tokenizations = ["charabia/chinese", "charabia/hebrew", "charabia/japanese", "charabia/thai", "charabia/korean", "charabia/greek", "charabia/khmer"]
all-tokenizations = [
"charabia/chinese",
"charabia/hebrew",
"charabia/japanese",
"charabia/thai",
"charabia/korean",
"charabia/greek",
"charabia/khmer",
]
# Use POSIX semaphores instead of SysV semaphores in LMDB
# For more information on this feature, see heed's Cargo.toml

11
milli/examples/search.rs
View File

@ -5,8 +5,8 @@ use std::time::Instant;
use heed::EnvOpenOptions;
use milli::{
execute_search, DefaultSearchLogger, GeoSortStrategy, Index, SearchContext, SearchLogger,
TermsMatchingStrategy,
execute_search, filtered_universe, DefaultSearchLogger, GeoSortStrategy, Index, SearchContext,
SearchLogger, TermsMatchingStrategy,
};
#[global_allocator]
@ -49,14 +49,15 @@ fn main() -> Result<(), Box<dyn Error>> {
let start = Instant::now();
let mut ctx = SearchContext::new(&index, &txn);
let universe = filtered_universe(&ctx, &None)?;
let docs = execute_search(
&mut ctx,
&(!query.trim().is_empty()).then(|| query.trim().to_owned()),
&None,
(!query.trim().is_empty()).then(|| query.trim()),
TermsMatchingStrategy::Last,
milli::score_details::ScoringStrategy::Skip,
false,
&None,
universe,
&None,
GeoSortStrategy::default(),
0,

28
milli/src/error.rs
View File

@ -180,6 +180,14 @@ only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and undersco
UnknownInternalDocumentId { document_id: DocumentId },
#[error("`minWordSizeForTypos` setting is invalid. `oneTypo` and `twoTypos` fields should be between `0` and `255`, and `twoTypos` should be greater or equals to `oneTypo` but found `oneTypo: {0}` and twoTypos: {1}`.")]
InvalidMinTypoWordLenSetting(u8, u8),
#[error(transparent)]
VectorEmbeddingError(#[from] crate::vector::Error),
#[error(transparent)]
MissingDocumentField(#[from] crate::prompt::error::RenderPromptError),
#[error(transparent)]
InvalidPrompt(#[from] crate::prompt::error::NewPromptError),
#[error("Invalid prompt in for embeddings with name '{0}': {1}")]
InvalidPromptForEmbeddings(String, crate::prompt::error::NewPromptError),
}
#[derive(Error, Debug)]
@ -336,6 +344,26 @@ impl From<HeedError> for Error {
}
}
#[derive(Debug, Clone, Copy)]
pub enum FaultSource {
User,
Runtime,
Bug,
Undecided,
}
impl std::fmt::Display for FaultSource {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let s = match self {
FaultSource::User => "user error",
FaultSource::Runtime => "runtime error",
FaultSource::Bug => "coding error",
FaultSource::Undecided => "error",
};
f.write_str(s)
}
}
#[test]
fn conditionally_lookup_for_error_message() {
let prefix = "Attribute `name` is not sortable.";

29
milli/src/index.rs
View File

@ -23,6 +23,7 @@ use crate::heed_codec::{
};
use crate::proximity::ProximityPrecision;
use crate::readable_slices::ReadableSlices;
use crate::vector::EmbeddingConfig;
use crate::{
default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds,
FacetDistribution, FieldDistribution, FieldId, FieldIdWordCountCodec, GeoPoint, ObkvCodec,
@ -74,6 +75,7 @@ pub mod main_key {
pub const SORT_FACET_VALUES_BY: &str = "sort-facet-values-by";
pub const PAGINATION_MAX_TOTAL_HITS: &str = "pagination-max-total-hits";
pub const PROXIMITY_PRECISION: &str = "proximity-precision";
pub const EMBEDDING_CONFIGS: &str = "embedding_configs";
}
pub mod db_name {
@ -1528,6 +1530,33 @@ impl Index {
Ok(script_language)
}
pub(crate) fn put_embedding_configs(
&self,
wtxn: &mut RwTxn<'_>,
configs: Vec<(String, EmbeddingConfig)>,
) -> heed::Result<()> {
self.main.remap_types::<Str, SerdeJson<Vec<(String, EmbeddingConfig)>>>().put(
wtxn,
main_key::EMBEDDING_CONFIGS,
&configs,
)
}
pub(crate) fn delete_embedding_configs(&self, wtxn: &mut RwTxn<'_>) -> heed::Result<bool> {
self.main.remap_key_type::<Str>().delete(wtxn, main_key::EMBEDDING_CONFIGS)
}
pub fn embedding_configs(
&self,
rtxn: &RoTxn<'_>,
) -> Result<Vec<(String, crate::vector::EmbeddingConfig)>> {
Ok(self
.main
.remap_types::<Str, SerdeJson<Vec<(String, EmbeddingConfig)>>>()
.get(rtxn, main_key::EMBEDDING_CONFIGS)?
.unwrap_or_default())
}
}
#[cfg(test)]

8
milli/src/lib.rs
View File

@ -17,11 +17,13 @@ pub mod facet;
mod fields_ids_map;
pub mod heed_codec;
pub mod index;
pub mod prompt;
pub mod proximity;
mod readable_slices;
pub mod score_details;
mod search;
pub mod update;
pub mod vector;
#[cfg(test)]
#[macro_use]
@ -37,8 +39,8 @@ pub use filter_parser::{Condition, FilterCondition, Span, Token};
use fxhash::{FxHasher32, FxHasher64};
pub use grenad::CompressionType;
pub use search::new::{
execute_search, DefaultSearchLogger, GeoSortStrategy, SearchContext, SearchLogger,
VisualSearchLogger,
execute_search, filtered_universe, DefaultSearchLogger, GeoSortStrategy, SearchContext,
SearchLogger, VisualSearchLogger,
};
use serde_json::Value;
pub use {charabia as tokenizer, heed};
@ -60,7 +62,7 @@ pub use self::index::Index;
pub use self::search::{
FacetDistribution, FacetValueHit, Filter, FormatOptions, MatchBounds, MatcherBuilder,
MatchingWords, OrderBy, Search, SearchForFacetValues, SearchResult, TermsMatchingStrategy,
DEFAULT_VALUES_PER_FACET,
VectorQuery, DEFAULT_VALUES_PER_FACET,
};
pub type Result<T> = std::result::Result<T, error::Error>;

97
milli/src/prompt/context.rs Normal file
View File

@ -0,0 +1,97 @@
use liquid::model::{
ArrayView, DisplayCow, KStringCow, ObjectRender, ObjectSource, State, Value as LiquidValue,
};
use liquid::{ObjectView, ValueView};
use super::document::Document;
use super::fields::Fields;
use crate::FieldsIdsMap;
#[derive(Debug, Clone)]
pub struct Context<'a> {
document: &'a Document<'a>,
fields: Fields<'a>,
}
impl<'a> Context<'a> {
pub fn new(document: &'a Document<'a>, field_id_map: &'a FieldsIdsMap) -> Self {
Self { document, fields: Fields::new(document, field_id_map) }
}
}
impl<'a> ObjectView for Context<'a> {
fn as_value(&self) -> &dyn ValueView {
self
}
fn size(&self) -> i64 {
2
}
fn keys<'k>(&'k self) -> Box<dyn Iterator<Item = KStringCow<'k>> + 'k> {
Box::new(["doc", "fields"].iter().map(|s| KStringCow::from_static(s)))
}
fn values<'k>(&'k self) -> Box<dyn Iterator<Item = &'k dyn ValueView> + 'k> {
Box::new(
std::iter::once(self.document.as_value())
.chain(std::iter::once(self.fields.as_value())),
)
}
fn iter<'k>(&'k self) -> Box<dyn Iterator<Item = (KStringCow<'k>, &'k dyn ValueView)> + 'k> {
Box::new(self.keys().zip(self.values()))
}
fn contains_key(&self, index: &str) -> bool {
index == "doc" || index == "fields"
}
fn get<'s>(&'s self, index: &str) -> Option<&'s dyn ValueView> {
match index {
"doc" => Some(self.document.as_value()),
"fields" => Some(self.fields.as_value()),
_ => None,
}
}
}
impl<'a> ValueView for Context<'a> {
fn as_debug(&self) -> &dyn std::fmt::Debug {
self
}
fn render(&self) -> liquid::model::DisplayCow<'_> {
DisplayCow::Owned(Box::new(ObjectRender::new(self)))
}
fn source(&self) -> liquid::model::DisplayCow<'_> {
DisplayCow::Owned(Box::new(ObjectSource::new(self)))
}
fn type_name(&self) -> &'static str {
"object"
}
fn query_state(&self, state: liquid::model::State) -> bool {
match state {
State::Truthy => true,
State::DefaultValue | State::Empty | State::Blank => false,
}
}
fn to_kstr(&self) -> liquid::model::KStringCow<'_> {
let s = ObjectRender::new(self).to_string();
KStringCow::from_string(s)
}
fn to_value(&self) -> LiquidValue {
LiquidValue::Object(
self.iter().map(|(k, x)| (k.to_string().into(), x.to_value())).collect(),
)
}
fn as_object(&self) -> Option<&dyn ObjectView> {
Some(self)
}
}

131
milli/src/prompt/document.rs Normal file
View File

@ -0,0 +1,131 @@
use std::cell::OnceCell;
use std::collections::BTreeMap;
use liquid::model::{
DisplayCow, KString, KStringCow, ObjectRender, ObjectSource, State, Value as LiquidValue,
};
use liquid::{ObjectView, ValueView};
use crate::update::del_add::{DelAdd, KvReaderDelAdd};
use crate::FieldsIdsMap;
#[derive(Debug, Clone)]
pub struct Document<'a>(BTreeMap<&'a str, (&'a [u8], ParsedValue)>);
#[derive(Debug, Clone)]
struct ParsedValue(std::cell::OnceCell<LiquidValue>);
impl ParsedValue {
fn empty() -> ParsedValue {
ParsedValue(OnceCell::new())
}
fn get(&self, raw: &[u8]) -> &LiquidValue {
self.0.get_or_init(|| {
let value: serde_json::Value = serde_json::from_slice(raw).unwrap();
liquid::model::to_value(&value).unwrap()
})
}
}
impl<'a> Document<'a> {
pub fn new(
data: obkv::KvReaderU16<'a>,
side: DelAdd,
inverted_field_map: &'a FieldsIdsMap,
) -> Self {
let mut out_data = BTreeMap::new();
for (fid, raw) in data {
let obkv = KvReaderDelAdd::new(raw);
let Some(raw) = obkv.get(side) else {
continue;
};
let Some(name) = inverted_field_map.name(fid) else {
continue;
};
out_data.insert(name, (raw, ParsedValue::empty()));
}
Self(out_data)
}
fn is_empty(&self) -> bool {
self.0.is_empty()
}
fn len(&self) -> usize {
self.0.len()
}
fn iter(&self) -> impl Iterator<Item = (KString, LiquidValue)> + '_ {
self.0.iter().map(|(&k, (raw, data))| (k.to_owned().into(), data.get(raw).to_owned()))
}
}
impl<'a> ObjectView for Document<'a> {
fn as_value(&self) -> &dyn ValueView {
self
}
fn size(&self) -> i64 {
self.len() as i64
}
fn keys<'k>(&'k self) -> Box<dyn Iterator<Item = KStringCow<'k>> + 'k> {
let keys = BTreeMap::keys(&self.0).map(|&s| s.into());
Box::new(keys)
}
fn values<'k>(&'k self) -> Box<dyn Iterator<Item = &'k dyn ValueView> + 'k> {
Box::new(self.0.values().map(|(raw, v)| v.get(raw) as &dyn ValueView))
}
fn iter<'k>(&'k self) -> Box<dyn Iterator<Item = (KStringCow<'k>, &'k dyn ValueView)> + 'k> {
Box::new(self.0.iter().map(|(&k, (raw, data))| (k.into(), data.get(raw) as &dyn ValueView)))
}
fn contains_key(&self, index: &str) -> bool {
self.0.contains_key(index)
}
fn get<'s>(&'s self, index: &str) -> Option<&'s dyn ValueView> {
self.0.get(index).map(|(raw, v)| v.get(raw) as &dyn ValueView)
}
}
impl<'a> ValueView for Document<'a> {
fn as_debug(&self) -> &dyn std::fmt::Debug {
self
}
fn render(&self) -> liquid::model::DisplayCow<'_> {
DisplayCow::Owned(Box::new(ObjectRender::new(self)))
}
fn source(&self) -> liquid::model::DisplayCow<'_> {
DisplayCow::Owned(Box::new(ObjectSource::new(self)))
}
fn type_name(&self) -> &'static str {
"object"
}
fn query_state(&self, state: liquid::model::State) -> bool {
match state {
State::Truthy => true,
State::DefaultValue | State::Empty | State::Blank => self.is_empty(),
}
}
fn to_kstr(&self) -> liquid::model::KStringCow<'_> {
let s = ObjectRender::new(self).to_string();
KStringCow::from_string(s)
}
fn to_value(&self) -> LiquidValue {
LiquidValue::Object(self.iter().collect())
}
fn as_object(&self) -> Option<&dyn ObjectView> {
Some(self)
}
}

56
milli/src/prompt/error.rs Normal file
View File

@ -0,0 +1,56 @@
use crate::error::FaultSource;
#[derive(Debug, thiserror::Error)]
#[error("{fault}: {kind}")]
pub struct NewPromptError {
pub kind: NewPromptErrorKind,
pub fault: FaultSource,
}
impl From<NewPromptError> for crate::Error {
fn from(value: NewPromptError) -> Self {
crate::Error::UserError(crate::UserError::InvalidPrompt(value))
}
}
impl NewPromptError {
pub(crate) fn cannot_parse_template(inner: liquid::Error) -> NewPromptError {
Self { kind: NewPromptErrorKind::CannotParseTemplate(inner), fault: FaultSource::User }
}
pub(crate) fn invalid_fields_in_template(inner: liquid::Error) -> NewPromptError {
Self { kind: NewPromptErrorKind::InvalidFieldsInTemplate(inner), fault: FaultSource::User }
}
}
#[derive(Debug, thiserror::Error)]
pub enum NewPromptErrorKind {
#[error("cannot parse template: {0}")]
CannotParseTemplate(liquid::Error),
#[error("template contains invalid fields: {0}. Only `doc.*`, `fields[i].name`, `fields[i].value` are supported")]
InvalidFieldsInTemplate(liquid::Error),
}
#[derive(Debug, thiserror::Error)]
#[error("{fault}: {kind}")]
pub struct RenderPromptError {
pub kind: RenderPromptErrorKind,
pub fault: FaultSource,
}
impl RenderPromptError {
pub(crate) fn missing_context(inner: liquid::Error) -> RenderPromptError {
Self { kind: RenderPromptErrorKind::MissingContext(inner), fault: FaultSource::User }
}
}
#[derive(Debug, thiserror::Error)]
pub enum RenderPromptErrorKind {
#[error("missing field in document: {0}")]
MissingContext(liquid::Error),
}
impl From<RenderPromptError> for crate::Error {
fn from(value: RenderPromptError) -> Self {
crate::Error::UserError(crate::UserError::MissingDocumentField(value))
}
}

172
milli/src/prompt/fields.rs Normal file
View File

@ -0,0 +1,172 @@
use liquid::model::{
ArrayView, DisplayCow, KStringCow, ObjectRender, ObjectSource, State, Value as LiquidValue,
};
use liquid::{ObjectView, ValueView};
use super::document::Document;
use crate::FieldsIdsMap;
#[derive(Debug, Clone)]
pub struct Fields<'a>(Vec<FieldValue<'a>>);
impl<'a> Fields<'a> {
pub fn new(document: &'a Document<'a>, field_id_map: &'a FieldsIdsMap) -> Self {
Self(
std::iter::repeat(document)
.zip(field_id_map.iter())
.map(|(document, (_fid, name))| FieldValue { document, name })
.collect(),
)
}
}
#[derive(Debug, Clone, Copy)]
pub struct FieldValue<'a> {
name: &'a str,
document: &'a Document<'a>,
}
impl<'a> ValueView for FieldValue<'a> {
fn as_debug(&self) -> &dyn std::fmt::Debug {
self
}
fn render(&self) -> liquid::model::DisplayCow<'_> {
DisplayCow::Owned(Box::new(ObjectRender::new(self)))
}
fn source(&self) -> liquid::model::DisplayCow<'_> {
DisplayCow::Owned(Box::new(ObjectSource::new(self)))
}
fn type_name(&self) -> &'static str {
"object"
}
fn query_state(&self, state: liquid::model::State) -> bool {
match state {
State::Truthy => true,
State::DefaultValue | State::Empty | State::Blank => self.is_empty(),
}
}
fn to_kstr(&self) -> liquid::model::KStringCow<'_> {
let s = ObjectRender::new(self).to_string();
KStringCow::from_string(s)
}
fn to_value(&self) -> LiquidValue {
LiquidValue::Object(
self.iter().map(|(k, v)| (k.to_string().into(), v.to_value())).collect(),
)
}
fn as_object(&self) -> Option<&dyn ObjectView> {
Some(self)
}
}
impl<'a> FieldValue<'a> {
pub fn name(&self) -> &&'a str {
&self.name
}
pub fn value(&self) -> &dyn ValueView {
self.document.get(self.name).unwrap_or(&LiquidValue::Nil)
}
pub fn is_empty(&self) -> bool {
self.size() == 0
}
}
impl<'a> ObjectView for FieldValue<'a> {
fn as_value(&self) -> &dyn ValueView {
self
}
fn size(&self) -> i64 {
2
}
fn keys<'k>(&'k self) -> Box<dyn Iterator<Item = KStringCow<'k>> + 'k> {
Box::new(["name", "value"].iter().map(|&x| KStringCow::from_static(x)))
}
fn values<'k>(&'k self) -> Box<dyn Iterator<Item = &'k dyn ValueView> + 'k> {
Box::new(
std::iter::once(self.name() as &dyn ValueView).chain(std::iter::once(self.value())),
)
}
fn iter<'k>(&'k self) -> Box<dyn Iterator<Item = (KStringCow<'k>, &'k dyn ValueView)> + 'k> {
Box::new(self.keys().zip(self.values()))
}
fn contains_key(&self, index: &str) -> bool {
index == "name" || index == "value"
}
fn get<'s>(&'s self, index: &str) -> Option<&'s dyn ValueView> {
match index {
"name" => Some(self.name()),
"value" => Some(self.value()),
_ => None,
}
}
}
impl<'a> ArrayView for Fields<'a> {
fn as_value(&self) -> &dyn ValueView {
self.0.as_value()
}
fn size(&self) -> i64 {
self.0.len() as i64
}
fn values<'k>(&'k self) -> Box<dyn Iterator<Item = &'k dyn ValueView> + 'k> {
self.0.values()
}
fn contains_key(&self, index: i64) -> bool {
self.0.contains_key(index)
}
fn get(&self, index: i64) -> Option<&dyn ValueView> {
ArrayView::get(&self.0, index)
}
}
impl<'a> ValueView for Fields<'a> {
fn as_debug(&self) -> &dyn std::fmt::Debug {
self
}
fn render(&self) -> liquid::model::DisplayCow<'_> {
self.0.render()
}
fn source(&self) -> liquid::model::DisplayCow<'_> {
self.0.source()
}
fn type_name(&self) -> &'static str {
self.0.type_name()
}
fn query_state(&self, state: liquid::model::State) -> bool {
self.0.query_state(state)
}
fn to_kstr(&self) -> liquid::model::KStringCow<'_> {
self.0.to_kstr()
}
fn to_value(&self) -> LiquidValue {
self.0.to_value()
}
fn as_array(&self) -> Option<&dyn ArrayView> {
Some(self)
}
}

144
milli/src/prompt/mod.rs Normal file
View File

@ -0,0 +1,144 @@
mod context;
mod document;
pub(crate) mod error;
mod fields;
mod template_checker;
use std::convert::TryFrom;
use error::{NewPromptError, RenderPromptError};
use self::context::Context;
use self::document::Document;
use crate::update::del_add::DelAdd;
use crate::FieldsIdsMap;
pub struct Prompt {
template: liquid::Template,
template_text: String,
strategy: PromptFallbackStrategy,
fallback: String,
}
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct PromptData {
pub template: String,
pub strategy: PromptFallbackStrategy,
pub fallback: String,
}
impl From<Prompt> for PromptData {
fn from(value: Prompt) -> Self {
Self { template: value.template_text, strategy: value.strategy, fallback: value.fallback }
}
}
impl TryFrom<PromptData> for Prompt {
type Error = NewPromptError;
fn try_from(value: PromptData) -> Result<Self, Self::Error> {
Prompt::new(value.template, Some(value.strategy), Some(value.fallback))
}
}
impl Clone for Prompt {
fn clone(&self) -> Self {
let template_text = self.template_text.clone();
Self {
template: new_template(&template_text).unwrap(),
template_text,
strategy: self.strategy,
fallback: self.fallback.clone(),
}
}
}
fn new_template(text: &str) -> Result<liquid::Template, liquid::Error> {
liquid::ParserBuilder::with_stdlib().build().unwrap().parse(text)
}
fn default_template() -> liquid::Template {
new_template(default_template_text()).unwrap()
}
fn default_template_text() -> &'static str {
"{% for field in fields %} \
{{ field.name }}: {{ field.value }}\n\
{% endfor %}"
}
fn default_fallback() -> &'static str {
"<MISSING>"
}
impl Default for Prompt {
fn default() -> Self {
Self {
template: default_template(),
template_text: default_template_text().into(),
strategy: Default::default(),
fallback: default_fallback().into(),
}
}
}
impl Default for PromptData {
fn default() -> Self {
Self {
template: default_template_text().into(),
strategy: Default::default(),
fallback: default_fallback().into(),
}
}
}
impl Prompt {
pub fn new(
template: String,
strategy: Option<PromptFallbackStrategy>,
fallback: Option<String>,
) -> Result<Self, NewPromptError> {
let this = Self {
template: liquid::ParserBuilder::with_stdlib()
.build()
.unwrap()
.parse(&template)
.map_err(NewPromptError::cannot_parse_template)?,
template_text: template,
strategy: strategy.unwrap_or_default(),
fallback: fallback.unwrap_or_default(),
};
// render template with special object that's OK with `doc.*` and `fields.*`
/// FIXME: doesn't work for nested objects e.g. `doc.a.b`
this.template
.render(&template_checker::TemplateChecker)
.map_err(NewPromptError::invalid_fields_in_template)?;
Ok(this)
}
pub fn render(
&self,
document: obkv::KvReaderU16<'_>,
side: DelAdd,
field_id_map: &FieldsIdsMap,
) -> Result<String, RenderPromptError> {
let document = Document::new(document, side, field_id_map);
let context = Context::new(&document, field_id_map);
self.template.render(&context).map_err(RenderPromptError::missing_context)
}
}
#[derive(
Debug, Default, Clone, PartialEq, Eq, Copy, serde::Serialize, serde::Deserialize, deserr::Deserr,
)]
#[serde(deny_unknown_fields, rename_all = "camelCase")]
#[deserr(rename_all = camelCase, deny_unknown_fields)]
pub enum PromptFallbackStrategy {
Fallback,
Skip,
#[default]
Error,
}

282
milli/src/prompt/template_checker.rs Normal file
View File

@ -0,0 +1,282 @@
use liquid::model::{
ArrayView, DisplayCow, KStringCow, ObjectRender, ObjectSource, State, Value as LiquidValue,
};
use liquid::{ObjectView, ValueView};
#[derive(Debug)]
pub struct TemplateChecker;
#[derive(Debug)]
pub struct DummyDoc;
#[derive(Debug)]
pub struct DummyFields;
#[derive(Debug)]
pub struct DummyField;
const DUMMY_VALUE: &LiquidValue = &LiquidValue::Nil;
impl ObjectView for DummyField {
fn as_value(&self) -> &dyn ValueView {
self
}
fn size(&self) -> i64 {
2
}
fn keys<'k>(&'k self) -> Box<dyn Iterator<Item = KStringCow<'k>> + 'k> {
Box::new(["name", "value"].iter().map(|s| KStringCow::from_static(s)))
}
fn values<'k>(&'k self) -> Box<dyn Iterator<Item = &'k dyn ValueView> + 'k> {
Box::new(std::iter::empty())
}
fn iter<'k>(&'k self) -> Box<dyn Iterator<Item = (KStringCow<'k>, &'k dyn ValueView)> + 'k> {
Box::new(std::iter::empty())
}
fn contains_key(&self, index: &str) -> bool {
index == "name" || index == "value"
}
fn get<'s>(&'s self, index: &str) -> Option<&'s dyn ValueView> {
if self.contains_key(index) {
Some(DUMMY_VALUE.as_view())
} else {
None
}
}
}
impl ValueView for DummyField {
fn as_debug(&self) -> &dyn std::fmt::Debug {
self
}
fn render(&self) -> DisplayCow<'_> {
DUMMY_VALUE.render()
}
fn source(&self) -> DisplayCow<'_> {
DUMMY_VALUE.source()
}
fn type_name(&self) -> &'static str {
"object"
}
fn query_state(&self, state: State) -> bool {
DUMMY_VALUE.query_state(state)
}
fn to_kstr(&self) -> KStringCow<'_> {
DUMMY_VALUE.to_kstr()
}
fn to_value(&self) -> LiquidValue {
LiquidValue::Nil
}
fn as_object(&self) -> Option<&dyn ObjectView> {
Some(self)
}
}
impl ValueView for DummyFields {
fn as_debug(&self) -> &dyn std::fmt::Debug {
self
}
fn render(&self) -> DisplayCow<'_> {
DUMMY_VALUE.render()
}
fn source(&self) -> DisplayCow<'_> {
DUMMY_VALUE.source()
}
fn type_name(&self) -> &'static str {
"array"
}
fn query_state(&self, state: State) -> bool {
DUMMY_VALUE.query_state(state)
}
fn to_kstr(&self) -> KStringCow<'_> {
DUMMY_VALUE.to_kstr()
}
fn to_value(&self) -> LiquidValue {
LiquidValue::Nil
}
fn as_array(&self) -> Option<&dyn ArrayView> {
Some(self)
}
}
impl ArrayView for DummyFields {
fn as_value(&self) -> &dyn ValueView {
self
}
fn size(&self) -> i64 {
i64::MAX
}
fn values<'k>(&'k self) -> Box<dyn Iterator<Item = &'k dyn ValueView> + 'k> {
Box::new(std::iter::empty())
}
fn contains_key(&self, _index: i64) -> bool {
true
}
fn get(&self, _index: i64) -> Option<&dyn ValueView> {
Some(DummyField.as_value())
}
}
impl ObjectView for DummyDoc {
fn as_value(&self) -> &dyn ValueView {
self
}
fn size(&self) -> i64 {
1000
}
fn keys<'k>(&'k self) -> Box<dyn Iterator<Item = KStringCow<'k>> + 'k> {
Box::new(std::iter::empty())
}
fn values<'k>(&'k self) -> Box<dyn Iterator<Item = &'k dyn ValueView> + 'k> {
Box::new(std::iter::empty())
}
fn iter<'k>(&'k self) -> Box<dyn Iterator<Item = (KStringCow<'k>, &'k dyn ValueView)> + 'k> {
Box::new(std::iter::empty())
}
fn contains_key(&self, _index: &str) -> bool {
true
}
fn get<'s>(&'s self, _index: &str) -> Option<&'s dyn ValueView> {
Some(DUMMY_VALUE.as_view())
}
}
impl ValueView for DummyDoc {
fn as_debug(&self) -> &dyn std::fmt::Debug {
self
}
fn render(&self) -> DisplayCow<'_> {
DUMMY_VALUE.render()
}
fn source(&self) -> DisplayCow<'_> {
DUMMY_VALUE.source()
}
fn type_name(&self) -> &'static str {
"object"
}
fn query_state(&self, state: State) -> bool {
DUMMY_VALUE.query_state(state)
}
fn to_kstr(&self) -> KStringCow<'_> {
DUMMY_VALUE.to_kstr()
}
fn to_value(&self) -> LiquidValue {
LiquidValue::Nil
}
fn as_object(&self) -> Option<&dyn ObjectView> {
Some(self)
}
}
impl ObjectView for TemplateChecker {
fn as_value(&self) -> &dyn ValueView {
self
}
fn size(&self) -> i64 {
2
}
fn keys<'k>(&'k self) -> Box<dyn Iterator<Item = KStringCow<'k>> + 'k> {
Box::new(["doc", "fields"].iter().map(|s| KStringCow::from_static(s)))
}
fn values<'k>(&'k self) -> Box<dyn Iterator<Item = &'k dyn ValueView> + 'k> {
Box::new(
std::iter::once(DummyDoc.as_value()).chain(std::iter::once(DummyFields.as_value())),
)
}
fn iter<'k>(&'k self) -> Box<dyn Iterator<Item = (KStringCow<'k>, &'k dyn ValueView)> + 'k> {
Box::new(self.keys().zip(self.values()))
}
fn contains_key(&self, index: &str) -> bool {
index == "doc" || index == "fields"
}
fn get<'s>(&'s self, index: &str) -> Option<&'s dyn ValueView> {
match index {
"doc" => Some(DummyDoc.as_value()),
"fields" => Some(DummyFields.as_value()),
_ => None,
}
}
}
impl ValueView for TemplateChecker {
fn as_debug(&self) -> &dyn std::fmt::Debug {
self
}
fn render(&self) -> liquid::model::DisplayCow<'_> {
DisplayCow::Owned(Box::new(ObjectRender::new(self)))
}
fn source(&self) -> liquid::model::DisplayCow<'_> {
DisplayCow::Owned(Box::new(ObjectSource::new(self)))
}
fn type_name(&self) -> &'static str {
"object"
}
fn query_state(&self, state: liquid::model::State) -> bool {
match state {
State::Truthy => true,
State::DefaultValue | State::Empty | State::Blank => false,
}
}
fn to_kstr(&self) -> liquid::model::KStringCow<'_> {
let s = ObjectRender::new(self).to_string();
KStringCow::from_string(s)
}
fn to_value(&self) -> LiquidValue {
LiquidValue::Object(
self.iter().map(|(k, x)| (k.to_string().into(), x.to_value())).collect(),
)
}
fn as_object(&self) -> Option<&dyn ObjectView> {
Some(self)
}
}

164
milli/src/score_details.rs
View File

@ -1,3 +1,6 @@
use std::cmp::Ordering;
use itertools::Itertools;
use serde::Serialize;
use crate::distance_between_two_points;
@ -12,9 +15,24 @@ pub enum ScoreDetails {
ExactAttribute(ExactAttribute),
ExactWords(ExactWords),
Sort(Sort),
Vector(Vector),
GeoSort(GeoSort),
}
#[derive(Clone, Copy)]
pub enum ScoreValue<'a> {
Score(f64),
Sort(&'a Sort),
GeoSort(&'a GeoSort),
}
enum RankOrValue<'a> {
Rank(Rank),
Sort(&'a Sort),
GeoSort(&'a GeoSort),
Score(f64),
}
impl ScoreDetails {
pub fn local_score(&self) -> Option<f64> {
self.rank().map(Rank::local_score)
@ -31,11 +49,55 @@ impl ScoreDetails {
ScoreDetails::ExactWords(details) => Some(details.rank()),
ScoreDetails::Sort(_) => None,
ScoreDetails::GeoSort(_) => None,
ScoreDetails::Vector(_) => None,
}
}
pub fn global_score<'a>(details: impl Iterator<Item = &'a Self>) -> f64 {
Rank::global_score(details.filter_map(Self::rank))
pub fn global_score<'a>(details: impl Iterator<Item = &'a Self> + 'a) -> f64 {
Self::score_values(details)
.find_map(|x| {
let ScoreValue::Score(score) = x else {
return None;
};
Some(score)
})
.unwrap_or(1.0f64)
}
pub fn score_values<'a>(
details: impl Iterator<Item = &'a Self> + 'a,
) -> impl Iterator<Item = ScoreValue<'a>> + 'a {
details
.map(ScoreDetails::rank_or_value)
.coalesce(|left, right| match (left, right) {
(RankOrValue::Rank(left), RankOrValue::Rank(right)) => {
Ok(RankOrValue::Rank(Rank::merge(left, right)))
}
(left, right) => Err((left, right)),
})
.map(|rank_or_value| match rank_or_value {
RankOrValue::Rank(r) => ScoreValue::Score(r.local_score()),
RankOrValue::Sort(s) => ScoreValue::Sort(s),
RankOrValue::GeoSort(g) => ScoreValue::GeoSort(g),
RankOrValue::Score(s) => ScoreValue::Score(s),
})
}
fn rank_or_value(&self) -> RankOrValue<'_> {
match self {
ScoreDetails::Words(w) => RankOrValue::Rank(w.rank()),
ScoreDetails::Typo(t) => RankOrValue::Rank(t.rank()),
ScoreDetails::Proximity(p) => RankOrValue::Rank(*p),
ScoreDetails::Fid(f) => RankOrValue::Rank(*f),
ScoreDetails::Position(p) => RankOrValue::Rank(*p),
ScoreDetails::ExactAttribute(e) => RankOrValue::Rank(e.rank()),
ScoreDetails::ExactWords(e) => RankOrValue::Rank(e.rank()),
ScoreDetails::Sort(sort) => RankOrValue::Sort(sort),
ScoreDetails::GeoSort(geosort) => RankOrValue::GeoSort(geosort),
ScoreDetails::Vector(vector) => RankOrValue::Score(
vector.value_similarity.as_ref().map(|(_, s)| *s as f64).unwrap_or(0.0f64),
),
}
}
/// Panics
@ -181,6 +243,19 @@ impl ScoreDetails {
details_map.insert(sort, sort_details);
order += 1;
}
ScoreDetails::Vector(s) => {
let vector = format!("vectorSort({:?})", s.target_vector);
let value = s.value_similarity.as_ref().map(|(v, _)| v);
let similarity = s.value_similarity.as_ref().map(|(_, s)| s);
let details = serde_json::json!({
"order": order,
"value": value,
"similarity": similarity,
});
details_map.insert(vector, details);
order += 1;
}
}
}
details_map
@ -297,15 +372,21 @@ impl Rank {
pub fn global_score(details: impl Iterator<Item = Self>) -> f64 {
let mut rank = Rank { rank: 1, max_rank: 1 };
for inner_rank in details {
rank.rank -= 1;
rank.rank *= inner_rank.max_rank;
rank.max_rank *= inner_rank.max_rank;
rank.rank += inner_rank.rank;
rank = Rank::merge(rank, inner_rank);
}
rank.local_score()
}
pub fn merge(mut outer: Rank, inner: Rank) -> Rank {
outer.rank = outer.rank.saturating_sub(1);
outer.rank *= inner.max_rank;
outer.max_rank *= inner.max_rank;
outer.rank += inner.rank;
outer
}
}
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Hash, Serialize)]
@ -335,13 +416,78 @@ pub struct Sort {
pub value: serde_json::Value,
}
#[derive(Debug, Clone, Copy, PartialEq, PartialOrd)]
impl PartialOrd for Sort {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
if self.field_name != other.field_name {
return None;
}
if self.ascending != other.ascending {
return None;
}
match (&self.value, &other.value) {
(serde_json::Value::Null, serde_json::Value::Null) => Some(Ordering::Equal),
(serde_json::Value::Null, _) => Some(Ordering::Less),
(_, serde_json::Value::Null) => Some(Ordering::Greater),
// numbers are always before strings
(serde_json::Value::Number(_), serde_json::Value::String(_)) => Some(Ordering::Greater),
(serde_json::Value::String(_), serde_json::Value::Number(_)) => Some(Ordering::Less),
(serde_json::Value::Number(left), serde_json::Value::Number(right)) => {
// FIXME: unwrap permitted here?
let order = left.as_f64().unwrap().partial_cmp(&right.as_f64().unwrap())?;
// 12 < 42, and when ascending, we want to see 12 first, so the smallest.
// Hence, when ascending, smaller is better
Some(if self.ascending { order.reverse() } else { order })
}
(serde_json::Value::String(left), serde_json::Value::String(right)) => {
let order = left.cmp(right);
// Taking e.g. "a" and "z"
// "a" < "z", and when ascending, we want to see "a" first, so the smallest.
// Hence, when ascending, smaller is better
Some(if self.ascending { order.reverse() } else { order })
}
_ => None,
}
}
}
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct GeoSort {
pub target_point: [f64; 2],
pub ascending: bool,
pub value: Option<[f64; 2]>,
}
impl PartialOrd for GeoSort {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
if self.target_point != other.target_point {
return None;
}
if self.ascending != other.ascending {
return None;
}
Some(match (self.distance(), other.distance()) {
(None, None) => Ordering::Equal,
(None, Some(_)) => Ordering::Less,
(Some(_), None) => Ordering::Greater,
(Some(left), Some(right)) => {
let order = left.partial_cmp(&right)?;
if self.ascending {
// when ascending, the one with the smallest distance has the best score
order.reverse()
} else {
order
}
}
})
}
}
#[derive(Debug, Clone, PartialEq, PartialOrd)]
pub struct Vector {
pub target_vector: Vec<f32>,
pub value_similarity: Option<(Vec<f32>, f32)>,
}
impl GeoSort {
pub fn distance(&self) -> Option<f64> {
self.value.map(|value| distance_between_two_points(&self.target_point, &value))

336
milli/src/search/hybrid.rs Normal file
View File

@ -0,0 +1,336 @@
use std::cmp::Ordering;
use std::collections::HashMap;
use itertools::Itertools;
use roaring::RoaringBitmap;
use super::new::{execute_vector_search, PartialSearchResult};
use crate::score_details::{ScoreDetails, ScoreValue, ScoringStrategy};
use crate::{
execute_search, DefaultSearchLogger, MatchingWords, Result, Search, SearchContext, SearchResult,
};
struct CombinedSearchResult {
matching_words: MatchingWords,
candidates: RoaringBitmap,
document_scores: Vec<(u32, CombinedScore)>,
}
type CombinedScore = (Vec<ScoreDetails>, Option<Vec<ScoreDetails>>);
fn compare_scores(left: &CombinedScore, right: &CombinedScore) -> Ordering {
let mut left_main_it = ScoreDetails::score_values(left.0.iter());
let mut left_sub_it =
ScoreDetails::score_values(left.1.as_ref().map(|x| x.iter()).into_iter().flatten());
let mut right_main_it = ScoreDetails::score_values(right.0.iter());
let mut right_sub_it =
ScoreDetails::score_values(right.1.as_ref().map(|x| x.iter()).into_iter().flatten());
let mut left_main = left_main_it.next();
let mut left_sub = left_sub_it.next();
let mut right_main = right_main_it.next();
let mut right_sub = right_sub_it.next();
loop {
let left =
take_best_score(&mut left_main, &mut left_sub, &mut left_main_it, &mut left_sub_it);
let right =
take_best_score(&mut right_main, &mut right_sub, &mut right_main_it, &mut right_sub_it);
match (left, right) {
(None, None) => return Ordering::Equal,
(None, Some(_)) => return Ordering::Less,
(Some(_), None) => return Ordering::Greater,
(Some(ScoreValue::Score(left)), Some(ScoreValue::Score(right))) => {
if (left - right).abs() <= f64::EPSILON {
continue;
}
return left.partial_cmp(&right).unwrap();
}
(Some(ScoreValue::Sort(left)), Some(ScoreValue::Sort(right))) => {
match left.partial_cmp(right).unwrap() {
Ordering::Equal => continue,
order => return order,
}
}
(Some(ScoreValue::GeoSort(left)), Some(ScoreValue::GeoSort(right))) => {
match left.partial_cmp(right).unwrap() {
Ordering::Equal => continue,
order => return order,
}
}
(Some(ScoreValue::Score(_)), Some(_)) => return Ordering::Greater,
(Some(_), Some(ScoreValue::Score(_))) => return Ordering::Less,
// if we have this, we're bad
(Some(ScoreValue::GeoSort(_)), Some(ScoreValue::Sort(_)))
| (Some(ScoreValue::Sort(_)), Some(ScoreValue::GeoSort(_))) => {
unreachable!("Unexpected geo and sort comparison")
}
}
}
}
fn take_best_score<'a>(
main_score: &mut Option<ScoreValue<'a>>,
sub_score: &mut Option<ScoreValue<'a>>,
main_it: &mut impl Iterator<Item = ScoreValue<'a>>,
sub_it: &mut impl Iterator<Item = ScoreValue<'a>>,
) -> Option<ScoreValue<'a>> {
match (*main_score, *sub_score) {
(Some(main), None) => {
*main_score = main_it.next();
Some(main)
}
(None, Some(sub)) => {
*sub_score = sub_it.next();
Some(sub)
}
(main @ Some(ScoreValue::Score(main_f)), sub @ Some(ScoreValue::Score(sub_v))) => {
// take max, both advance
*main_score = main_it.next();
*sub_score = sub_it.next();
if main_f >= sub_v {
main
} else {
sub
}
}
(main @ Some(ScoreValue::Score(_)), _) => {
*main_score = main_it.next();
main
}
(_, sub @ Some(ScoreValue::Score(_))) => {
*sub_score = sub_it.next();
sub
}
(main @ Some(ScoreValue::GeoSort(main_geo)), sub @ Some(ScoreValue::GeoSort(sub_geo))) => {
// take best advance both
*main_score = main_it.next();
*sub_score = sub_it.next();
if main_geo >= sub_geo {
main
} else {
sub
}
}
(main @ Some(ScoreValue::Sort(main_sort)), sub @ Some(ScoreValue::Sort(sub_sort))) => {
// take best advance both
*main_score = main_it.next();
*sub_score = sub_it.next();
if main_sort >= sub_sort {
main
} else {
sub
}
}
(
Some(ScoreValue::GeoSort(_) | ScoreValue::Sort(_)),
Some(ScoreValue::GeoSort(_) | ScoreValue::Sort(_)),
) => None,
(None, None) => None,
}
}
impl CombinedSearchResult {
fn new(main_results: SearchResult, ancillary_results: PartialSearchResult) -> Self {
let mut docid_scores = HashMap::new();
for (docid, score) in
main_results.documents_ids.iter().zip(main_results.document_scores.into_iter())
{
docid_scores.insert(*docid, (score, None));
}
for (docid, score) in ancillary_results
.documents_ids
.iter()
.zip(ancillary_results.document_scores.into_iter())
{
docid_scores
.entry(*docid)
.and_modify(|(_main_score, ancillary_score)| *ancillary_score = Some(score));
}
let mut document_scores: Vec<_> = docid_scores.into_iter().collect();
document_scores.sort_by(|(_, left), (_, right)| compare_scores(left, right).reverse());
Self {
matching_words: main_results.matching_words,
candidates: main_results.candidates,
document_scores,
}
}
fn merge(left: Self, right: Self, from: usize, length: usize) -> SearchResult {
let mut documents_ids =
Vec::with_capacity(left.document_scores.len() + right.document_scores.len());
let mut document_scores =
Vec::with_capacity(left.document_scores.len() + right.document_scores.len());
let mut documents_seen = RoaringBitmap::new();
for (docid, (main_score, _sub_score)) in left
.document_scores
.into_iter()
.merge_by(right.document_scores.into_iter(), |(_, left), (_, right)| {
// the first value is the one with the greatest score
compare_scores(left, right).is_ge()
})
// remove documents we already saw
.filter(|(docid, _)| documents_seen.insert(*docid))
// start skipping **after** the filter
.skip(from)
// take **after** skipping
.take(length)
{
documents_ids.push(docid);
// TODO: pass both scores to documents_score in some way?
document_scores.push(main_score);
}
SearchResult {
matching_words: left.matching_words,
candidates: left.candidates | right.candidates,
documents_ids,
document_scores,
}
}
}
impl<'a> Search<'a> {
pub fn execute_hybrid(&self) -> Result<SearchResult> {
// TODO: find classier way to achieve that than to reset vector and query params
// create separate keyword and semantic searches
let mut search = Search {
query: self.query.clone(),
vector: self.vector.clone(),
filter: self.filter.clone(),
offset: 0,
limit: self.limit + self.offset,
sort_criteria: self.sort_criteria.clone(),
searchable_attributes: self.searchable_attributes,
geo_strategy: self.geo_strategy,
terms_matching_strategy: self.terms_matching_strategy,
scoring_strategy: ScoringStrategy::Detailed,
words_limit: self.words_limit,
exhaustive_number_hits: self.exhaustive_number_hits,
rtxn: self.rtxn,
index: self.index,
};
let vector_query = search.vector.take();
let keyword_query = self.query.as_deref();
let keyword_results = search.execute()?;
// skip semantic search if we don't have a vector query (placeholder search)
let Some(vector_query) = vector_query else {
return Ok(keyword_results);
};
// completely skip semantic search if the results of the keyword search are good enough
if self.results_good_enough(&keyword_results) {
return Ok(keyword_results);
}
search.vector = Some(vector_query);
search.query = None;
// TODO: would be better to have two distinct functions at this point
let vector_results = search.execute()?;
// Compute keyword scores for vector_results
let keyword_results_for_vector =
self.keyword_results_for_vector(keyword_query, &vector_results)?;
// compute vector scores for keyword_results
let vector_results_for_keyword =
// can unwrap because we returned already if there was no vector query
self.vector_results_for_keyword(search.vector.as_ref().unwrap(), &keyword_results)?;
let keyword_results =
CombinedSearchResult::new(keyword_results, vector_results_for_keyword);
let vector_results = CombinedSearchResult::new(vector_results, keyword_results_for_vector);
let merge_results =
CombinedSearchResult::merge(vector_results, keyword_results, self.offset, self.limit);
assert!(merge_results.documents_ids.len() <= self.limit);
Ok(merge_results)
}
fn vector_results_for_keyword(
&self,
vector: &[f32],
keyword_results: &SearchResult,
) -> Result<PartialSearchResult> {
let mut ctx = SearchContext::new(self.index, self.rtxn);
if let Some(searchable_attributes) = self.searchable_attributes {
ctx.searchable_attributes(searchable_attributes)?;
}
let universe = keyword_results.documents_ids.iter().collect();
execute_vector_search(
&mut ctx,
vector,
ScoringStrategy::Detailed,
universe,
&self.sort_criteria,
self.geo_strategy,
0,
self.limit + self.offset,
)
}
fn keyword_results_for_vector(
&self,
query: Option<&str>,
vector_results: &SearchResult,
) -> Result<PartialSearchResult> {
let mut ctx = SearchContext::new(self.index, self.rtxn);
if let Some(searchable_attributes) = self.searchable_attributes {
ctx.searchable_attributes(searchable_attributes)?;
}
let universe = vector_results.documents_ids.iter().collect();
execute_search(
&mut ctx,
query,
self.terms_matching_strategy,
ScoringStrategy::Detailed,
self.exhaustive_number_hits,
universe,
&self.sort_criteria,
self.geo_strategy,
0,
self.limit + self.offset,
Some(self.words_limit),
&mut DefaultSearchLogger,
&mut DefaultSearchLogger,
)
}
fn results_good_enough(&self, keyword_results: &SearchResult) -> bool {
const GOOD_ENOUGH_SCORE: f64 = 0.9;
// 1. we check that we got a sufficient number of results
if keyword_results.document_scores.len() < self.limit + self.offset {
return false;
}
// 2. and that all results have a good enough score.
// we need to check all results because due to sort like rules, they're not necessarily in relevancy order
for score in &keyword_results.document_scores {
let score = ScoreDetails::global_score(score.iter());
if score < GOOD_ENOUGH_SCORE {
return false;
}
}
true
}
}

102
milli/src/search/mod.rs
View File

@ -3,6 +3,7 @@ use std::ops::ControlFlow;
use charabia::normalizer::NormalizerOption;
use charabia::Normalize;
use deserr::{DeserializeError, Deserr, Sequence};
use fst::automaton::{Automaton, Str};
use fst::{IntoStreamer, Streamer};
use levenshtein_automata::{LevenshteinAutomatonBuilder as LevBuilder, DFA};
@ -12,12 +13,13 @@ use roaring::bitmap::RoaringBitmap;
pub use self::facet::{FacetDistribution, Filter, OrderBy, DEFAULT_VALUES_PER_FACET};
pub use self::new::matches::{FormatOptions, MatchBounds, MatcherBuilder, MatchingWords};
use self::new::PartialSearchResult;
use self::new::{execute_vector_search, PartialSearchResult};
use crate::error::UserError;
use crate::heed_codec::facet::{FacetGroupKey, FacetGroupValue};
use crate::score_details::{ScoreDetails, ScoringStrategy};
use crate::{
execute_search, AscDesc, DefaultSearchLogger, DocumentId, FieldId, Index, Result, SearchContext,
execute_search, filtered_universe, AscDesc, DefaultSearchLogger, DocumentId, FieldId, Index,
Result, SearchContext,
};
// Building these factories is not free.
@ -30,6 +32,7 @@ const MAX_NUMBER_OF_FACETS: usize = 100;
pub mod facet;
mod fst_utils;
pub mod hybrid;
pub mod new;
pub struct Search<'a> {
@ -50,6 +53,53 @@ pub struct Search<'a> {
index: &'a Index,
}
#[derive(Debug, Clone, PartialEq)]
pub enum VectorQuery {
Vector(Vec<f32>),
String(String),
}
impl<E> Deserr<E> for VectorQuery
where
E: DeserializeError,
{
fn deserialize_from_value<V: deserr::IntoValue>(
value: deserr::Value<V>,
location: deserr::ValuePointerRef,
) -> std::result::Result<Self, E> {
match value {
deserr::Value::String(s) => Ok(VectorQuery::String(s)),
deserr::Value::Sequence(seq) => {
let v: std::result::Result<Vec<f32>, _> = seq
.into_iter()
.enumerate()
.map(|(index, v)| match v.into_value() {
deserr::Value::Float(f) => Ok(f as f32),
deserr::Value::Integer(i) => Ok(i as f32),
v => Err(deserr::take_cf_content(E::error::<V>(
None,
deserr::ErrorKind::IncorrectValueKind {
actual: v,
accepted: &[deserr::ValueKind::Float, deserr::ValueKind::Integer],
},
location.push_index(index),
))),
})
.collect();
Ok(VectorQuery::Vector(v?))
}
_ => Err(deserr::take_cf_content(E::error::<V>(
None,
deserr::ErrorKind::IncorrectValueKind {
actual: value,
accepted: &[deserr::ValueKind::String, deserr::ValueKind::Sequence],
},
location,
))),
}
}
}
impl<'a> Search<'a> {
pub fn new(rtxn: &'a heed::RoTxn, index: &'a Index) -> Search<'a> {
Search {
@ -75,8 +125,8 @@ impl<'a> Search<'a> {
self
}
pub fn vector(&mut self, vector: impl Into<Vec<f32>>) -> &mut Search<'a> {
self.vector = Some(vector.into());
pub fn vector(&mut self, vector: Vec<f32>) -> &mut Search<'a> {
self.vector = Some(vector);
self
}
@ -140,23 +190,35 @@ impl<'a> Search<'a> {
ctx.searchable_attributes(searchable_attributes)?;
}
let universe = filtered_universe(&ctx, &self.filter)?;
let PartialSearchResult { located_query_terms, candidates, documents_ids, document_scores } =
execute_search(
&mut ctx,
&self.query,
&self.vector,
self.terms_matching_strategy,
self.scoring_strategy,
self.exhaustive_number_hits,
&self.filter,
&self.sort_criteria,
self.geo_strategy,
self.offset,
self.limit,
Some(self.words_limit),
&mut DefaultSearchLogger,
&mut DefaultSearchLogger,
)?;
match self.vector.as_ref() {
Some(vector) => execute_vector_search(
&mut ctx,
vector,
self.scoring_strategy,
universe,
&self.sort_criteria,
self.geo_strategy,
self.offset,
self.limit,
)?,
None => execute_search(
&mut ctx,
self.query.as_deref(),
self.terms_matching_strategy,
self.scoring_strategy,
self.exhaustive_number_hits,
universe,
&self.sort_criteria,
self.geo_strategy,
self.offset,
self.limit,
Some(self.words_limit),
&mut DefaultSearchLogger,
&mut DefaultSearchLogger,
)?,
};
// consume context and located_query_terms to build MatchingWords.
let matching_words = match located_query_terms {

8
milli/src/search/new/matches/mod.rs
View File

@ -498,19 +498,19 @@ mod tests {
use super::*;
use crate::index::tests::TempIndex;
use crate::{execute_search, SearchContext};
use crate::{execute_search, filtered_universe, SearchContext};
impl<'a> MatcherBuilder<'a> {
fn new_test(rtxn: &'a heed::RoTxn, index: &'a TempIndex, query: &str) -> Self {
let mut ctx = SearchContext::new(index, rtxn);
let universe = filtered_universe(&ctx, &None).unwrap();
let crate::search::PartialSearchResult { located_query_terms, .. } = execute_search(
&mut ctx,
&Some(query.to_string()),
&None,
Some(query),
crate::TermsMatchingStrategy::default(),
crate::score_details::ScoringStrategy::Skip,
false,
&None,
universe,
&None,
crate::search::new::GeoSortStrategy::default(),
0,

175
milli/src/search/new/mod.rs
View File

@ -16,6 +16,7 @@ mod small_bitmap;
mod exact_attribute;
mod sort;
mod vector_sort;
#[cfg(test)]
mod tests;
@ -28,7 +29,6 @@ use db_cache::DatabaseCache;
use exact_attribute::ExactAttribute;
use graph_based_ranking_rule::{Exactness, Fid, Position, Proximity, Typo};
use heed::RoTxn;
use instant_distance::Search;
use interner::{DedupInterner, Interner};
pub use logger::visual::VisualSearchLogger;
pub use logger::{DefaultSearchLogger, SearchLogger};
@ -46,7 +46,7 @@ use self::geo_sort::GeoSort;
pub use self::geo_sort::Strategy as GeoSortStrategy;
use self::graph_based_ranking_rule::Words;
use self::interner::Interned;
use crate::distance::NDotProductPoint;
use self::vector_sort::VectorSort;
use crate::error::FieldIdMapMissingEntry;
use crate::score_details::{ScoreDetails, ScoringStrategy};
use crate::search::new::distinct::apply_distinct_rule;
@ -258,6 +258,70 @@ fn get_ranking_rules_for_placeholder_search<'ctx>(
Ok(ranking_rules)
}
fn get_ranking_rules_for_vector<'ctx>(
ctx: &SearchContext<'ctx>,
sort_criteria: &Option<Vec<AscDesc>>,
geo_strategy: geo_sort::Strategy,
target: &[f32],
) -> Result<Vec<BoxRankingRule<'ctx, PlaceholderQuery>>> {
// query graph search
let mut sort = false;
let mut sorted_fields = HashSet::new();
let mut geo_sorted = false;
let mut vector = false;
let mut ranking_rules: Vec<BoxRankingRule<PlaceholderQuery>> = vec![];
let settings_ranking_rules = ctx.index.criteria(ctx.txn)?;
for rr in settings_ranking_rules {
match rr {
crate::Criterion::Words
| crate::Criterion::Typo
| crate::Criterion::Proximity
| crate::Criterion::Attribute
| crate::Criterion::Exactness => {
if !vector {
let vector_candidates = ctx.index.documents_ids(ctx.txn)?;
let vector_sort = VectorSort::new(ctx, target.to_vec(), vector_candidates)?;
ranking_rules.push(Box::new(vector_sort));
vector = true;
}
}
crate::Criterion::Sort => {
if sort {
continue;
}
resolve_sort_criteria(
sort_criteria,
ctx,
&mut ranking_rules,
&mut sorted_fields,
&mut geo_sorted,
geo_strategy,
)?;
sort = true;
}
crate::Criterion::Asc(field_name) => {
if sorted_fields.contains(&field_name) {
continue;
}
sorted_fields.insert(field_name.clone());
ranking_rules.push(Box::new(Sort::new(ctx.index, ctx.txn, field_name, true)?));
}
crate::Criterion::Desc(field_name) => {
if sorted_fields.contains(&field_name) {
continue;
}
sorted_fields.insert(field_name.clone());
ranking_rules.push(Box::new(Sort::new(ctx.index, ctx.txn, field_name, false)?));
}
}
}
Ok(ranking_rules)
}
/// Return the list of initialised ranking rules to be used for a query graph search.
fn get_ranking_rules_for_query_graph_search<'ctx>(
ctx: &SearchContext<'ctx>,
@ -422,15 +486,62 @@ fn resolve_sort_criteria<'ctx, Query: RankingRuleQueryTrait>(
Ok(())
}
pub fn filtered_universe(ctx: &SearchContext, filters: &Option<Filter>) -> Result<RoaringBitmap> {
Ok(if let Some(filters) = filters {
filters.evaluate(ctx.txn, ctx.index)?
} else {
ctx.index.documents_ids(ctx.txn)?
})
}
#[allow(clippy::too_many_arguments)]
pub fn execute_vector_search(
ctx: &mut SearchContext,
vector: &[f32],
scoring_strategy: ScoringStrategy,
universe: RoaringBitmap,
sort_criteria: &Option<Vec<AscDesc>>,
geo_strategy: geo_sort::Strategy,
from: usize,
length: usize,
) -> Result<PartialSearchResult> {
check_sort_criteria(ctx, sort_criteria.as_ref())?;
/// FIXME: input universe = universe & documents_with_vectors
// for now if we're computing embeddings for ALL documents, we can assume that this is just universe
let ranking_rules = get_ranking_rules_for_vector(ctx, sort_criteria, geo_strategy, vector)?;
let mut placeholder_search_logger = logger::DefaultSearchLogger;
let placeholder_search_logger: &mut dyn SearchLogger<PlaceholderQuery> =
&mut placeholder_search_logger;
let BucketSortOutput { docids, scores, all_candidates } = bucket_sort(
ctx,
ranking_rules,
&PlaceholderQuery,
&universe,
from,
length,
scoring_strategy,
placeholder_search_logger,
)?;
Ok(PartialSearchResult {
candidates: all_candidates,
document_scores: scores,
documents_ids: docids,
located_query_terms: None,
})
}
#[allow(clippy::too_many_arguments)]
pub fn execute_search(
ctx: &mut SearchContext,
query: &Option<String>,
vector: &Option<Vec<f32>>,
query: Option<&str>,
terms_matching_strategy: TermsMatchingStrategy,
scoring_strategy: ScoringStrategy,
exhaustive_number_hits: bool,
filters: &Option<Filter>,
mut universe: RoaringBitmap,
sort_criteria: &Option<Vec<AscDesc>>,
geo_strategy: geo_sort::Strategy,
from: usize,
@ -439,60 +550,8 @@ pub fn execute_search(
placeholder_search_logger: &mut dyn SearchLogger<PlaceholderQuery>,
query_graph_logger: &mut dyn SearchLogger<QueryGraph>,
) -> Result<PartialSearchResult> {
let mut universe = if let Some(filters) = filters {
filters.evaluate(ctx.txn, ctx.index)?
} else {
ctx.index.documents_ids(ctx.txn)?
};
check_sort_criteria(ctx, sort_criteria.as_ref())?;
if let Some(vector) = vector {
let mut search = Search::default();
let docids = match ctx.index.vector_hnsw(ctx.txn)? {
Some(hnsw) => {
if let Some(expected_size) = hnsw.iter().map(|(_, point)| point.len()).next() {
if vector.len() != expected_size {
return Err(UserError::InvalidVectorDimensions {
expected: expected_size,
found: vector.len(),
}
.into());
}
}
let vector = NDotProductPoint::new(vector.clone());
let neighbors = hnsw.search(&vector, &mut search);
let mut docids = Vec::new();
let mut uniq_docids = RoaringBitmap::new();
for instant_distance::Item { distance: _, pid, point: _ } in neighbors {
let index = pid.into_inner();
let docid = ctx.index.vector_id_docid.get(ctx.txn, &index)?.unwrap();
if universe.contains(docid) && uniq_docids.insert(docid) {
docids.push(docid);
if docids.len() == (from + length) {
break;
}
}
}
// return the nearest documents that are also part of the candidates
// along with a dummy list of scores that are useless in this context.
docids.into_iter().skip(from).take(length).collect()
}
None => Vec::new(),
};
return Ok(PartialSearchResult {
candidates: universe,
document_scores: vec![Vec::new(); docids.len()],
documents_ids: docids,
located_query_terms: None,
});
}
let mut located_query_terms = None;
let query_terms = if let Some(query) = query {
// We make sure that the analyzer is aware of the stop words
@ -546,7 +605,7 @@ pub fn execute_search(
terms_matching_strategy,
)?;
universe =
universe &=
resolve_universe(ctx, &universe, &graph, terms_matching_strategy, query_graph_logger)?;
bucket_sort(

150
milli/src/search/new/vector_sort.rs Normal file
View File

@ -0,0 +1,150 @@
use std::future::Future;
use std::iter::FromIterator;
use std::pin::Pin;
use nolife::DynBoxScope;
use roaring::RoaringBitmap;
use super::ranking_rules::{RankingRule, RankingRuleOutput, RankingRuleQueryTrait};
use crate::distance::NDotProductPoint;
use crate::index::Hnsw;
use crate::score_details::{self, ScoreDetails};
use crate::{Result, SearchContext, SearchLogger, UserError};
pub struct VectorSort<Q: RankingRuleQueryTrait> {
query: Option<Q>,
target: Vec<f32>,
vector_candidates: RoaringBitmap,
scope: nolife::DynBoxScope<SearchFamily>,
}
type Item<'a> = instant_distance::Item<'a, NDotProductPoint>;
type SearchFut = Pin<Box<dyn Future<Output = nolife::Never>>>;
struct SearchFamily;
impl<'a> nolife::Family<'a> for SearchFamily {
type Family = Box<dyn Iterator<Item = Item<'a>> + 'a>;
}
async fn search_scope(
mut time_capsule: nolife::TimeCapsule<SearchFamily>,
hnsw: Hnsw,
target: Vec<f32>,
) -> nolife::Never {
let mut search = instant_distance::Search::default();
let it = Box::new(hnsw.search(&NDotProductPoint::new(target), &mut search));
let mut it: Box<dyn Iterator<Item = Item>> = it;
loop {
time_capsule.freeze(&mut it).await;
}
}
impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
pub fn new(
ctx: &SearchContext,
target: Vec<f32>,
vector_candidates: RoaringBitmap,
) -> Result<Self> {
let hnsw =
ctx.index.vector_hnsw(ctx.txn)?.unwrap_or(Hnsw::builder().build_hnsw(Vec::default()).0);
if let Some(expected_size) = hnsw.iter().map(|(_, point)| point.len()).next() {
if target.len() != expected_size {
return Err(UserError::InvalidVectorDimensions {
expected: expected_size,
found: target.len(),
}
.into());
}
}
let target_clone = target.clone();
let producer = move |time_capsule| -> SearchFut {
Box::pin(search_scope(time_capsule, hnsw, target_clone))
};
let scope = DynBoxScope::new(producer);
Ok(Self { query: None, target, vector_candidates, scope })
}
}
impl<'ctx, Q: RankingRuleQueryTrait> RankingRule<'ctx, Q> for VectorSort<Q> {
fn id(&self) -> String {
"vector_sort".to_owned()
}
fn start_iteration(
&mut self,
_ctx: &mut SearchContext<'ctx>,
_logger: &mut dyn SearchLogger<Q>,
universe: &RoaringBitmap,
query: &Q,
) -> Result<()> {
assert!(self.query.is_none());
self.query = Some(query.clone());
self.vector_candidates &= universe;
Ok(())
}
#[allow(clippy::only_used_in_recursion)]
fn next_bucket(
&mut self,
ctx: &mut SearchContext<'ctx>,
_logger: &mut dyn SearchLogger<Q>,
universe: &RoaringBitmap,
) -> Result<Option<RankingRuleOutput<Q>>> {
let query = self.query.as_ref().unwrap().clone();
self.vector_candidates &= universe;
if self.vector_candidates.is_empty() {
return Ok(Some(RankingRuleOutput {
query,
candidates: universe.clone(),
score: ScoreDetails::Vector(score_details::Vector {
target_vector: self.target.clone(),
value_similarity: None,
}),
}));
}
let scope = &mut self.scope;
let target = &self.target;
let vector_candidates = &self.vector_candidates;
scope.enter(|it| {
for item in it.by_ref() {
let item: Item = item;
let index = item.pid.into_inner();
let docid = ctx.index.vector_id_docid.get(ctx.txn, &index)?.unwrap();
if vector_candidates.contains(docid) {
return Ok(Some(RankingRuleOutput {
query,
candidates: RoaringBitmap::from_iter([docid]),
score: ScoreDetails::Vector(score_details::Vector {
target_vector: target.clone(),
value_similarity: Some((
item.point.clone().into_inner(),
1.0 - item.distance,
)),
}),
}));
}
}
Ok(Some(RankingRuleOutput {
query,
candidates: universe.clone(),
score: ScoreDetails::Vector(score_details::Vector {
target_vector: target.clone(),
value_similarity: None,
}),
}))
})
}
fn end_iteration(&mut self, _ctx: &mut SearchContext<'ctx>, _logger: &mut dyn SearchLogger<Q>) {
self.query = None;
}
}

330
milli/src/update/index_documents/extract/extract_vector_points.rs
View File

@ -1,9 +1,10 @@
use std::cmp::Ordering;
use std::convert::TryFrom;
use std::convert::{TryFrom, TryInto};
use std::fs::File;
use std::io::{self, BufReader, BufWriter};
use std::mem::size_of;
use std::str::from_utf8;
use std::sync::Arc;
use bytemuck::cast_slice;
use grenad::Writer;
@ -13,13 +14,56 @@ use serde_json::{from_slice, 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::{DocumentId, FieldId, InternalError, Result, VectorOrArrayOfVectors};
use crate::vector::Embedder;
use crate::{DocumentId, FieldsIdsMap, InternalError, Result, VectorOrArrayOfVectors};
/// The length of the elements that are always in the buffer when inserting new values.
const TRUNCATE_SIZE: usize = size_of::<DocumentId>();
pub struct ExtractedVectorPoints {
// docid, _index -> KvWriterDelAdd -> Vector
pub manual_vectors: grenad::Reader<BufReader<File>>,
// docid -> ()
pub remove_vectors: grenad::Reader<BufReader<File>>,
// docid -> prompt
pub prompts: grenad::Reader<BufReader<File>>,
}
enum VectorStateDelta {
NoChange,
// Remove all vectors, generated or manual, from this document
NowRemoved,
// Add the manually specified vectors, passed in the other grenad
// Remove any previously generated vectors
// Note: changing the value of the manually specified vector **should not record** this delta
WasGeneratedNowManual(Vec<Vec<f32>>),
ManualDelta(Vec<Vec<f32>>, Vec<Vec<f32>>),
// Add the vector computed from the specified prompt
// Remove any previous vector
// Note: changing the value of the prompt **does require** recording this delta
NowGenerated(String),
}
impl VectorStateDelta {
fn into_values(self) -> (bool, String, (Vec<Vec<f32>>, Vec<Vec<f32>>)) {
match self {
VectorStateDelta::NoChange => Default::default(),
VectorStateDelta::NowRemoved => (true, Default::default(), Default::default()),
VectorStateDelta::WasGeneratedNowManual(add) => {
(true, Default::default(), (Default::default(), add))
}
VectorStateDelta::ManualDelta(del, add) => (false, Default::default(), (del, add)),
VectorStateDelta::NowGenerated(prompt) => (true, prompt, Default::default()),
}
}
}
/// 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>
@ -27,16 +71,34 @@ const TRUNCATE_SIZE: usize = size_of::<DocumentId>();
pub fn extract_vector_points<R: io::Read + io::Seek>(
obkv_documents: grenad::Reader<R>,
indexer: GrenadParameters,
vectors_fid: FieldId,
) -> Result<grenad::Reader<BufReader<File>>> {
field_id_map: FieldsIdsMap,
prompt: Option<&Prompt>,
) -> Result<ExtractedVectorPoints> {
puffin::profile_function!();
let mut writer = create_writer(
// (docid, _index) -> KvWriterDelAdd -> Vector
let mut manual_vectors_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
// (docid) -> (prompt)
let mut prompts_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()?,
);
let vectors_fid = field_id_map.id("_vectors");
let mut key_buffer = Vec::new();
let mut cursor = obkv_documents.into_cursor()?;
while let Some((key, value)) = cursor.move_on_next()? {
@ -53,43 +115,148 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
// lazily get it when needed
let document_id = || -> Value { from_utf8(external_id_bytes).unwrap().into() };
// first we retrieve the _vectors field
if let Some(value) = obkv.get(vectors_fid) {
let delta = if let Some(value) = vectors_fid.and_then(|vectors_fid| obkv.get(vectors_fid)) {
let vectors_obkv = KvReaderDelAdd::new(value);
match (vectors_obkv.get(DelAdd::Deletion), vectors_obkv.get(DelAdd::Addition)) {
(Some(old), Some(new)) => {
// no autogeneration
let del_vectors = extract_vectors(old, document_id)?;
let add_vectors = extract_vectors(new, document_id)?;
// then we extract the values
let del_vectors = vectors_obkv
.get(DelAdd::Deletion)
.map(|vectors| extract_vectors(vectors, document_id))
.transpose()?
.flatten();
let add_vectors = vectors_obkv
.get(DelAdd::Addition)
.map(|vectors| extract_vectors(vectors, document_id))
.transpose()?
.flatten();
VectorStateDelta::ManualDelta(
del_vectors.unwrap_or_default(),
add_vectors.unwrap_or_default(),
)
}
(None, Some(new)) => {
// was possibly autogenerated, remove all vectors for that document
let add_vectors = extract_vectors(new, document_id)?;
// and we finally push the unique vectors into the writer
push_vectors_diff(
&mut writer,
&mut key_buffer,
del_vectors.unwrap_or_default(),
add_vectors.unwrap_or_default(),
)?;
}
VectorStateDelta::WasGeneratedNowManual(add_vectors.unwrap_or_default())
}
(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
match prompt {
Some(prompt) => VectorStateDelta::NowGenerated(prompt.render(
obkv,
DelAdd::Addition,
&field_id_map,
)?),
None => VectorStateDelta::NowRemoved,
}
} else {
VectorStateDelta::NowRemoved
}
}
(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 {
match prompt {
Some(prompt) => {
// Don't give up if the old prompt was failing
let old_prompt = prompt
.render(obkv, DelAdd::Deletion, &field_id_map)
.unwrap_or_default();
let new_prompt =
prompt.render(obkv, DelAdd::Addition, &field_id_map)?;
if old_prompt != new_prompt {
log::trace!(
"Changing prompt from\n{old_prompt}\n===\nto\n{new_prompt}"
);
VectorStateDelta::NowGenerated(new_prompt)
} else {
VectorStateDelta::NoChange
}
}
// We no longer have a prompt, so we need to remove any existing vector
None => VectorStateDelta::NowRemoved,
}
} else {
VectorStateDelta::NowRemoved
}
}
}
} else {
// 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 {
match prompt {
Some(prompt) => {
// Don't give up if the old prompt was failing
let old_prompt = prompt
.render(obkv, DelAdd::Deletion, &field_id_map)
.unwrap_or_default();
let new_prompt = prompt.render(obkv, DelAdd::Addition, &field_id_map)?;
if old_prompt != new_prompt {
log::trace!(
"Changing prompt from\n{old_prompt}\n===\nto\n{new_prompt}"
);
VectorStateDelta::NowGenerated(new_prompt)
} else {
VectorStateDelta::NoChange
}
}
None => VectorStateDelta::NowRemoved,
}
} 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,
)?;
}
writer_into_reader(writer)
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)?,
})
}
/// Computes the diff between both Del and Add numbers and
/// only inserts the parts that differ in the sorter.
fn push_vectors_diff(
writer: &mut Writer<BufWriter<File>>,
remove_vectors_writer: &mut Writer<BufWriter<File>>,
prompts_writer: &mut Writer<BufWriter<File>>,
manual_vectors_writer: &mut Writer<BufWriter<File>>,
key_buffer: &mut Vec<u8>,
mut del_vectors: Vec<Vec<f32>>,
mut add_vectors: Vec<Vec<f32>>,
delta: VectorStateDelta,
) -> Result<()> {
let (must_remove, prompt, (mut del_vectors, mut add_vectors)) = delta.into_values();
if must_remove {
key_buffer.truncate(TRUNCATE_SIZE);
remove_vectors_writer.insert(&key_buffer, [])?;
}
if !prompt.is_empty() {
key_buffer.truncate(TRUNCATE_SIZE);
prompts_writer.insert(&key_buffer, prompt.as_bytes())?;
}
// We sort and dedup the vectors
del_vectors.sort_unstable_by(|a, b| compare_vectors(a, b));
add_vectors.sort_unstable_by(|a, b| compare_vectors(a, b));
@ -114,7 +281,7 @@ fn push_vectors_diff(
let mut obkv = KvWriterDelAdd::memory();
obkv.insert(DelAdd::Deletion, cast_slice(&vector))?;
let bytes = obkv.into_inner()?;
writer.insert(&key_buffer, bytes)?;
manual_vectors_writer.insert(&key_buffer, bytes)?;
}
EitherOrBoth::Right(vector) => {
// We insert only the Add part of the Obkv to inform
@ -122,7 +289,7 @@ fn push_vectors_diff(
let mut obkv = KvWriterDelAdd::memory();
obkv.insert(DelAdd::Addition, cast_slice(&vector))?;
let bytes = obkv.into_inner()?;
writer.insert(&key_buffer, bytes)?;
manual_vectors_writer.insert(&key_buffer, bytes)?;
}
}
}
@ -146,3 +313,102 @@ fn extract_vectors(value: &[u8], document_id: impl Fn() -> Value) -> Result<Opti
.into()),
}
}
#[logging_timer::time]
pub fn extract_embeddings<R: io::Read + io::Seek>(
// docid, prompt
prompt_reader: grenad::Reader<R>,
indexer: GrenadParameters,
embedder: Arc<Embedder>,
) -> Result<(grenad::Reader<BufReader<File>>, Option<usize>)> {
let rt = tokio::runtime::Builder::new_current_thread().enable_io().enable_time().build()?;
let n_chunks = embedder.chunk_count_hint(); // chunk level parellelism
let n_vectors_per_chunk = embedder.prompt_count_in_chunk_hint(); // number of vectors in a single chunk
// docid, state with embedding
let mut state_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
let mut chunks = Vec::with_capacity(n_chunks);
let mut current_chunk = Vec::with_capacity(n_vectors_per_chunk);
let mut current_chunk_ids = Vec::with_capacity(n_vectors_per_chunk);
let mut chunks_ids = Vec::with_capacity(n_chunks);
let mut cursor = prompt_reader.into_cursor()?;
let mut expected_dimension = None;
while let Some((key, value)) = cursor.move_on_next()? {
let docid = key.try_into().map(DocumentId::from_be_bytes).unwrap();
// SAFETY: precondition, the grenad value was saved from a string
let prompt = unsafe { std::str::from_utf8_unchecked(value) };
if current_chunk.len() == current_chunk.capacity() {
chunks.push(std::mem::replace(
&mut current_chunk,
Vec::with_capacity(n_vectors_per_chunk),
));
chunks_ids.push(std::mem::replace(
&mut current_chunk_ids,
Vec::with_capacity(n_vectors_per_chunk),
));
};
current_chunk.push(prompt.to_owned());
current_chunk_ids.push(docid);
if chunks.len() == chunks.capacity() {
let chunked_embeds = rt
.block_on(
embedder
.embed_chunks(std::mem::replace(&mut chunks, Vec::with_capacity(n_chunks))),
)
.map_err(crate::vector::Error::from)
.map_err(crate::UserError::from)
.map_err(crate::Error::from)?;
for (docid, embeddings) in chunks_ids
.iter()
.flat_map(|docids| docids.iter())
.zip(chunked_embeds.iter().flat_map(|embeds| embeds.iter()))
{
state_writer.insert(docid.to_be_bytes(), cast_slice(embeddings.as_inner()))?;
expected_dimension = Some(embeddings.dimension());
}
chunks_ids.clear();
}
}
// send last chunk
if !chunks.is_empty() {
let chunked_embeds = rt
.block_on(embedder.embed_chunks(std::mem::take(&mut chunks)))
.map_err(crate::vector::Error::from)
.map_err(crate::UserError::from)
.map_err(crate::Error::from)?;
for (docid, embeddings) in chunks_ids
.iter()
.flat_map(|docids| docids.iter())
.zip(chunked_embeds.iter().flat_map(|embeds| embeds.iter()))
{
state_writer.insert(docid.to_be_bytes(), cast_slice(embeddings.as_inner()))?;
expected_dimension = Some(embeddings.dimension());
}
}
if !current_chunk.is_empty() {
let embeds = rt
.block_on(embedder.embed(std::mem::take(&mut current_chunk)))
.map_err(crate::vector::Error::from)
.map_err(crate::UserError::from)
.map_err(crate::Error::from)?;
for (docid, embeddings) in current_chunk_ids.iter().zip(embeds.iter()) {
state_writer.insert(docid.to_be_bytes(), cast_slice(embeddings.as_inner()))?;
expected_dimension = Some(embeddings.dimension());
}
}
Ok((writer_into_reader(state_writer)?, expected_dimension))
}

63
milli/src/update/index_documents/extract/mod.rs
View File

@ -9,9 +9,10 @@ mod extract_word_docids;
mod extract_word_pair_proximity_docids;
mod extract_word_position_docids;
use std::collections::HashSet;
use std::collections::{HashMap, HashSet};
use std::fs::File;
use std::io::BufReader;
use std::sync::Arc;
use crossbeam_channel::Sender;
use log::debug;
@ -23,7 +24,9 @@ use self::extract_facet_string_docids::extract_facet_string_docids;
use self::extract_fid_docid_facet_values::{extract_fid_docid_facet_values, ExtractedFacetValues};
use self::extract_fid_word_count_docids::extract_fid_word_count_docids;
use self::extract_geo_points::extract_geo_points;
use self::extract_vector_points::extract_vector_points;
use self::extract_vector_points::{
extract_embeddings, extract_vector_points, ExtractedVectorPoints,
};
use self::extract_word_docids::extract_word_docids;
use self::extract_word_pair_proximity_docids::extract_word_pair_proximity_docids;
use self::extract_word_position_docids::extract_word_position_docids;
@ -32,8 +35,10 @@ use super::helpers::{
MergeFn, MergeableReader,
};
use super::{helpers, TypedChunk};
use crate::prompt::Prompt;
use crate::proximity::ProximityPrecision;
use crate::{FieldId, Result};
use crate::vector::Embedder;
use crate::{FieldId, FieldsIdsMap, Result};
/// Extract data for each databases from obkv documents in parallel.
/// Send data in grenad file over provided Sender.
@ -47,13 +52,14 @@ pub(crate) fn data_from_obkv_documents(
faceted_fields: HashSet<FieldId>,
primary_key_id: FieldId,
geo_fields_ids: Option<(FieldId, FieldId)>,
vectors_field_id: Option<FieldId>,
field_id_map: FieldsIdsMap,
stop_words: Option<fst::Set<&[u8]>>,
allowed_separators: Option<&[&str]>,
dictionary: Option<&[&str]>,
max_positions_per_attributes: Option<u32>,
exact_attributes: HashSet<FieldId>,
proximity_precision: ProximityPrecision,
embedders: HashMap<String, (Arc<Embedder>, Arc<Prompt>)>,
) -> Result<()> {
puffin::profile_function!();
@ -64,7 +70,8 @@ pub(crate) fn data_from_obkv_documents(
original_documents_chunk,
indexer,
lmdb_writer_sx.clone(),
vectors_field_id,
field_id_map.clone(),
embedders.clone(),
)
})
.collect::<Result<()>>()?;
@ -276,24 +283,42 @@ fn send_original_documents_data(
original_documents_chunk: Result<grenad::Reader<BufReader<File>>>,
indexer: GrenadParameters,
lmdb_writer_sx: Sender<Result<TypedChunk>>,
vectors_field_id: Option<FieldId>,
field_id_map: FieldsIdsMap,
embedders: HashMap<String, (Arc<Embedder>, Arc<Prompt>)>,
) -> Result<()> {
let original_documents_chunk =
original_documents_chunk.and_then(|c| unsafe { as_cloneable_grenad(&c) })?;
if let Some(vectors_field_id) = vectors_field_id {
let documents_chunk_cloned = original_documents_chunk.clone();
let lmdb_writer_sx_cloned = lmdb_writer_sx.clone();
rayon::spawn(move || {
let result = extract_vector_points(documents_chunk_cloned, indexer, vectors_field_id);
let _ = match result {
Ok(vector_points) => {
lmdb_writer_sx_cloned.send(Ok(TypedChunk::VectorPoints(vector_points)))
}
Err(error) => lmdb_writer_sx_cloned.send(Err(error)),
};
});
}
let documents_chunk_cloned = original_documents_chunk.clone();
let lmdb_writer_sx_cloned = lmdb_writer_sx.clone();
rayon::spawn(move || {
let (embedder, prompt) = embedders.get("default").cloned().unzip();
let result =
extract_vector_points(documents_chunk_cloned, indexer, field_id_map, prompt.as_deref());
let _ = match result {
Ok(ExtractedVectorPoints { manual_vectors, remove_vectors, prompts }) => {
/// FIXME: support multiple embedders
let results = embedder.and_then(|embedder| {
match extract_embeddings(prompts, indexer, embedder.clone()) {
Ok(results) => Some(results),
Err(error) => {
let _ = lmdb_writer_sx_cloned.send(Err(error));
None
}
}
});
let (embeddings, expected_dimension) = results.unzip();
let expected_dimension = expected_dimension.flatten();
lmdb_writer_sx_cloned.send(Ok(TypedChunk::VectorPoints {
remove_vectors,
embeddings,
expected_dimension,
manual_vectors,
}))
}
Err(error) => lmdb_writer_sx_cloned.send(Err(error)),
};
});
// TODO: create a custom internal error
lmdb_writer_sx.send(Ok(TypedChunk::Documents(original_documents_chunk))).unwrap();

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

@ -4,11 +4,12 @@ mod helpers;
mod transform;
mod typed_chunk;
use std::collections::HashSet;
use std::collections::{HashMap, HashSet};
use std::io::{Cursor, Read, Seek};
use std::iter::FromIterator;
use std::num::NonZeroU32;
use std::result::Result as StdResult;
use std::sync::Arc;
use crossbeam_channel::{Receiver, Sender};
use heed::types::Str;
@ -32,10 +33,12 @@ use self::helpers::{grenad_obkv_into_chunks, GrenadParameters};
pub use self::transform::{Transform, TransformOutput};
use crate::documents::{obkv_to_object, DocumentsBatchReader};
use crate::error::{Error, InternalError, UserError};
use crate::prompt::Prompt;
pub use crate::update::index_documents::helpers::CursorClonableMmap;
use crate::update::{
IndexerConfig, UpdateIndexingStep, WordPrefixDocids, WordPrefixIntegerDocids, WordsPrefixesFst,
};
use crate::vector::Embedder;
use crate::{CboRoaringBitmapCodec, Index, Result};
static MERGED_DATABASE_COUNT: usize = 7;
@ -78,6 +81,7 @@ pub struct IndexDocuments<'t, 'i, 'a, FP, FA> {
should_abort: FA,
added_documents: u64,
deleted_documents: u64,
embedders: HashMap<String, (Arc<Embedder>, Arc<Prompt>)>,
}
#[derive(Default, Debug, Clone)]
@ -121,6 +125,7 @@ where
index,
added_documents: 0,
deleted_documents: 0,
embedders: Default::default(),
})
}
@ -167,6 +172,14 @@ where
Ok((self, Ok(indexed_documents)))
}
pub fn with_embedders(
mut self,
embedders: HashMap<String, (Arc<Embedder>, Arc<Prompt>)>,
) -> Self {
self.embedders = embedders;
self
}
/// Remove a batch of documents from the current builder.
///
/// Returns the number of documents deleted from the builder.
@ -322,17 +335,18 @@ where
// get filterable fields for facet databases
let faceted_fields = self.index.faceted_fields_ids(self.wtxn)?;
// get the fid of the `_geo.lat` and `_geo.lng` fields.
let geo_fields_ids = match self.index.fields_ids_map(self.wtxn)?.id("_geo") {
let mut field_id_map = self.index.fields_ids_map(self.wtxn)?;
// self.index.fields_ids_map($a)? ==>> field_id_map
let geo_fields_ids = match field_id_map.id("_geo") {
Some(gfid) => {
let is_sortable = self.index.sortable_fields_ids(self.wtxn)?.contains(&gfid);
let is_filterable = self.index.filterable_fields_ids(self.wtxn)?.contains(&gfid);
// if `_geo` is faceted then we get the `lat` and `lng`
if is_sortable || is_filterable {
let field_ids = self
.index
.fields_ids_map(self.wtxn)?
let field_ids = field_id_map
.insert("_geo.lat")
.zip(self.index.fields_ids_map(self.wtxn)?.insert("_geo.lng"))
.zip(field_id_map.insert("_geo.lng"))
.ok_or(UserError::AttributeLimitReached)?;
Some(field_ids)
} else {
@ -341,8 +355,6 @@ where
}
None => None,
};
// get the fid of the `_vectors` field.
let vectors_field_id = self.index.fields_ids_map(self.wtxn)?.id("_vectors");
let stop_words = self.index.stop_words(self.wtxn)?;
let separators = self.index.allowed_separators(self.wtxn)?;
@ -364,6 +376,8 @@ where
self.indexer_config.documents_chunk_size.unwrap_or(1024 * 1024 * 4); // 4MiB
let max_positions_per_attributes = self.indexer_config.max_positions_per_attributes;
let cloned_embedder = self.embedders.clone();
// Run extraction pipeline in parallel.
pool.install(|| {
puffin::profile_scope!("extract_and_send_grenad_chunks");
@ -387,13 +401,14 @@ where
faceted_fields,
primary_key_id,
geo_fields_ids,
vectors_field_id,
field_id_map,
stop_words,
separators.as_deref(),
dictionary.as_deref(),
max_positions_per_attributes,
exact_attributes,
proximity_precision,
cloned_embedder,
)
});
@ -2505,7 +2520,7 @@ mod tests {
.unwrap();
let rtxn = index.read_txn().unwrap();
let res = index.search(&rtxn).vector([0.0, 1.0, 2.0]).execute().unwrap();
let res = index.search(&rtxn).vector([0.0, 1.0, 2.0].to_vec()).execute().unwrap();
assert_eq!(res.documents_ids.len(), 3);
}

90
milli/src/update/index_documents/typed_chunk.rs
View File

@ -47,7 +47,12 @@ pub(crate) enum TypedChunk {
FieldIdFacetIsNullDocids(grenad::Reader<BufReader<File>>),
FieldIdFacetIsEmptyDocids(grenad::Reader<BufReader<File>>),
GeoPoints(grenad::Reader<BufReader<File>>),
VectorPoints(grenad::Reader<BufReader<File>>),
VectorPoints {
remove_vectors: grenad::Reader<BufReader<File>>,
embeddings: Option<grenad::Reader<BufReader<File>>>,
expected_dimension: Option<usize>,
manual_vectors: grenad::Reader<BufReader<File>>,
},
ScriptLanguageDocids(HashMap<(Script, Language), (RoaringBitmap, RoaringBitmap)>),
}
@ -100,8 +105,8 @@ impl TypedChunk {
TypedChunk::GeoPoints(grenad) => {
format!("GeoPoints {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::VectorPoints(grenad) => {
format!("VectorPoints {{ number_of_entries: {} }}", grenad.len())
TypedChunk::VectorPoints{ remove_vectors, manual_vectors, embeddings, expected_dimension } => {
format!("VectorPoints {{ remove_vectors: {}, manual_vectors: {}, embeddings: {}, dimension: {} }}", remove_vectors.len(), manual_vectors.len(), embeddings.as_ref().map(|e| e.len()).unwrap_or_default(), expected_dimension.unwrap_or_default())
}
TypedChunk::ScriptLanguageDocids(sl_map) => {
format!("ScriptLanguageDocids {{ number_of_entries: {} }}", sl_map.len())
@ -355,19 +360,64 @@ pub(crate) fn write_typed_chunk_into_index(
index.put_geo_rtree(wtxn, &rtree)?;
index.put_geo_faceted_documents_ids(wtxn, &geo_faceted_docids)?;
}
TypedChunk::VectorPoints(vector_points) => {
let mut vectors_set = HashSet::new();
TypedChunk::VectorPoints {
remove_vectors,
manual_vectors,
embeddings,
expected_dimension,
} => {
if remove_vectors.is_empty()
&& manual_vectors.is_empty()
&& embeddings.as_ref().map_or(true, |e| e.is_empty())
{
return Ok((RoaringBitmap::new(), is_merged_database));
}
let mut docid_vectors_map: HashMap<DocumentId, HashSet<Vec<OrderedFloat<f32>>>> =
HashMap::new();
// We extract and store the previous vectors
if let Some(hnsw) = index.vector_hnsw(wtxn)? {
for (pid, point) in hnsw.iter() {
let pid_key = pid.into_inner();
let docid = index.vector_id_docid.get(wtxn, &pid_key)?.unwrap();
let vector: Vec<_> = point.iter().copied().map(OrderedFloat).collect();
vectors_set.insert((docid, vector));
docid_vectors_map.entry(docid).or_default().insert(vector);
}
}
let mut cursor = vector_points.into_cursor()?;
// remove vectors for docids we want them removed
let mut cursor = remove_vectors.into_cursor()?;
while let Some((key, _)) = cursor.move_on_next()? {
let docid = key.try_into().map(DocumentId::from_be_bytes).unwrap();
docid_vectors_map.remove(&docid);
}
// add generated embeddings
if let Some((embeddings, expected_dimension)) = embeddings.zip(expected_dimension) {
let mut cursor = embeddings.into_cursor()?;
while let Some((key, value)) = cursor.move_on_next()? {
let docid = key.try_into().map(DocumentId::from_be_bytes).unwrap();
let data: Vec<OrderedFloat<_>> =
pod_collect_to_vec(value).into_iter().map(OrderedFloat).collect();
// it is a code error to have embeddings and not expected_dimension
let embeddings =
crate::vector::Embeddings::from_inner(data, expected_dimension)
// code error if we somehow got the wrong dimension
.unwrap();
let mut set = HashSet::new();
for embedding in embeddings.iter() {
set.insert(embedding.to_vec());
}
docid_vectors_map.insert(docid, set);
}
}
// perform the manual diff
let mut cursor = manual_vectors.into_cursor()?;
while let Some((key, value)) = cursor.move_on_next()? {
// convert the key back to a u32 (4 bytes)
let (left, _index) = try_split_array_at(key).unwrap();
@ -376,23 +426,30 @@ pub(crate) fn write_typed_chunk_into_index(
let vector_deladd_obkv = KvReaderDelAdd::new(value);
if let Some(value) = vector_deladd_obkv.get(DelAdd::Deletion) {
// convert the vector back to a Vec<f32>
let vector = pod_collect_to_vec(value).into_iter().map(OrderedFloat).collect();
let key = (docid, vector);
if !vectors_set.remove(&key) {
error!("Unable to delete the vector: {:?}", key.1);
}
let vector: Vec<OrderedFloat<_>> =
pod_collect_to_vec(value).into_iter().map(OrderedFloat).collect();
docid_vectors_map.entry(docid).and_modify(|v| {
if !v.remove(&vector) {
error!("Unable to delete the vector: {:?}", vector);
}
});
}
if let Some(value) = vector_deladd_obkv.get(DelAdd::Addition) {
// convert the vector back to a Vec<f32>
let vector = pod_collect_to_vec(value).into_iter().map(OrderedFloat).collect();
vectors_set.insert((docid, vector));
docid_vectors_map.entry(docid).and_modify(|v| {
v.insert(vector);
});
}
}
// Extract the most common vector dimension
let expected_dimension_size = {
let mut dims = HashMap::new();
vectors_set.iter().for_each(|(_, v)| *dims.entry(v.len()).or_insert(0) += 1);
docid_vectors_map
.values()
.flat_map(|v| v.iter())
.for_each(|v| *dims.entry(v.len()).or_insert(0) += 1);
dims.into_iter().max_by_key(|(_, count)| *count).map(|(len, _)| len)
};
@ -400,7 +457,10 @@ pub(crate) fn write_typed_chunk_into_index(
// prepare the vectors before inserting them in the HNSW.
let mut points = Vec::new();
let mut docids = Vec::new();
for (docid, vector) in vectors_set {
for (docid, vector) in docid_vectors_map
.into_iter()
.flat_map(|(docid, vectors)| std::iter::repeat(docid).zip(vectors))
{
if expected_dimension_size.map_or(false, |expected| expected != vector.len()) {
return Err(UserError::InvalidVectorDimensions {
expected: expected_dimension_size.unwrap_or(vector.len()),

113
milli/src/update/settings.rs
View File

@ -3,7 +3,7 @@ use std::result::Result as StdResult;
use charabia::{Normalize, Tokenizer, TokenizerBuilder};
use deserr::{DeserializeError, Deserr};
use itertools::Itertools;
use itertools::{EitherOrBoth, Itertools};
use serde::{Deserialize, Deserializer, Serialize, Serializer};
use time::OffsetDateTime;
@ -15,6 +15,8 @@ use crate::index::{DEFAULT_MIN_WORD_LEN_ONE_TYPO, DEFAULT_MIN_WORD_LEN_TWO_TYPOS
use crate::proximity::ProximityPrecision;
use crate::update::index_documents::IndexDocumentsMethod;
use crate::update::{IndexDocuments, UpdateIndexingStep};
use crate::vector::settings::{EmbeddingSettings, PromptSettings};
use crate::vector::EmbeddingConfig;
use crate::{FieldsIdsMap, Index, OrderBy, Result};
#[derive(Debug, Clone, PartialEq, Eq, Copy)]
@ -73,6 +75,13 @@ impl<T> Setting<T> {
otherwise => otherwise,
}
}
pub fn apply(&mut self, new: Self) {
if let Setting::NotSet = new {
return;
}
*self = new;
}
}
impl<T: Serialize> Serialize for Setting<T> {
@ -129,6 +138,7 @@ pub struct Settings<'a, 't, 'i> {
sort_facet_values_by: Setting<HashMap<String, OrderBy>>,
pagination_max_total_hits: Setting<usize>,
proximity_precision: Setting<ProximityPrecision>,
embedder_settings: Setting<BTreeMap<String, Setting<EmbeddingSettings>>>,
}
impl<'a, 't, 'i> Settings<'a, 't, 'i> {
@ -161,6 +171,7 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
sort_facet_values_by: Setting::NotSet,
pagination_max_total_hits: Setting::NotSet,
proximity_precision: Setting::NotSet,
embedder_settings: Setting::NotSet,
indexer_config,
}
}
@ -343,6 +354,14 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
self.proximity_precision = Setting::Reset;
}
pub fn set_embedder_settings(&mut self, value: BTreeMap<String, Setting<EmbeddingSettings>>) {
self.embedder_settings = Setting::Set(value);
}
pub fn reset_embedder_settings(&mut self) {
self.embedder_settings = Setting::Reset;
}
fn reindex<FP, FA>(
&mut self,
progress_callback: &FP,
@ -890,6 +909,60 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
Ok(changed)
}
fn update_embedding_configs(&mut self) -> Result<bool> {
let update = match std::mem::take(&mut self.embedder_settings) {
Setting::Set(configs) => {
let mut changed = false;
let old_configs = self.index.embedding_configs(self.wtxn)?;
let old_configs: BTreeMap<String, Setting<EmbeddingSettings>> =
old_configs.into_iter().map(|(k, v)| (k, Setting::Set(v.into()))).collect();
let mut new_configs = BTreeMap::new();
for joined in old_configs
.into_iter()
.merge_join_by(configs.into_iter(), |(left, _), (right, _)| left.cmp(right))
{
match joined {
EitherOrBoth::Both((name, mut old), (_, new)) => {
old.apply(new);
let new = validate_prompt(&name, old)?;
changed = true;
new_configs.insert(name, new);
}
EitherOrBoth::Left((name, setting)) => {
new_configs.insert(name, setting);
}
EitherOrBoth::Right((name, setting)) => {
let setting = validate_prompt(&name, setting)?;
changed = true;
new_configs.insert(name, setting);
}
}
}
let new_configs: Vec<(String, EmbeddingConfig)> = new_configs
.into_iter()
.filter_map(|(name, setting)| match setting {
Setting::Set(value) => Some((name, value.into())),
Setting::Reset => None,
Setting::NotSet => Some((name, EmbeddingSettings::default().into())),
})
.collect();
if new_configs.is_empty() {
self.index.delete_embedding_configs(self.wtxn)?;
} else {
self.index.put_embedding_configs(self.wtxn, new_configs)?;
}
changed
}
Setting::Reset => {
self.index.delete_embedding_configs(self.wtxn)?;
true
}
Setting::NotSet => false,
};
Ok(update)
}
pub fn execute<FP, FA>(mut self, progress_callback: FP, should_abort: FA) -> Result<()>
where
FP: Fn(UpdateIndexingStep) + Sync,
@ -927,6 +1000,13 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
let searchable_updated = self.update_searchable()?;
let exact_attributes_updated = self.update_exact_attributes()?;
let proximity_precision = self.update_proximity_precision()?;
// TODO: very rough approximation of the needs for reindexing where any change will result in
// a full reindexing.
// What can be done instead:
// 1. Only change the distance on a distance change
// 2. Only change the name -> embedder mapping on a name change
// 3. Keep the old vectors but reattempt indexing on a prompt change: only actually changed prompt will need embedding + storage
let embedding_configs_updated = self.update_embedding_configs()?;
if stop_words_updated
|| non_separator_tokens_updated
@ -937,6 +1017,7 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
|| searchable_updated
|| exact_attributes_updated
|| proximity_precision
|| embedding_configs_updated
{
self.reindex(&progress_callback, &should_abort, old_fields_ids_map)?;
}
@ -945,6 +1026,34 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
}
}
fn validate_prompt(
name: &str,
new: Setting<EmbeddingSettings>,
) -> Result<Setting<EmbeddingSettings>> {
match new {
Setting::Set(EmbeddingSettings {
embedder_options,
prompt:
Setting::Set(PromptSettings { template: Setting::Set(template), strategy, fallback }),
}) => {
// validate
let template = crate::prompt::Prompt::new(template, None, None)
.map(|prompt| crate::prompt::PromptData::from(prompt).template)
.map_err(|inner| UserError::InvalidPromptForEmbeddings(name.to_owned(), inner))?;
Ok(Setting::Set(EmbeddingSettings {
embedder_options,
prompt: Setting::Set(PromptSettings {
template: Setting::Set(template),
strategy,
fallback,
}),
}))
}
new => Ok(new),
}
}
#[cfg(test)]
mod tests {
use big_s::S;
@ -1763,6 +1872,7 @@ mod tests {
sort_facet_values_by,
pagination_max_total_hits,
proximity_precision,
embedder_settings,
} = settings;
assert!(matches!(searchable_fields, Setting::NotSet));
assert!(matches!(displayed_fields, Setting::NotSet));
@ -1785,6 +1895,7 @@ mod tests {
assert!(matches!(sort_facet_values_by, Setting::NotSet));
assert!(matches!(pagination_max_total_hits, Setting::NotSet));
assert!(matches!(proximity_precision, Setting::NotSet));
assert!(matches!(embedder_settings, Setting::NotSet));
})
.unwrap();
}

229
milli/src/vector/error.rs Normal file
View File

@ -0,0 +1,229 @@
use std::path::PathBuf;
use hf_hub::api::sync::ApiError;
use crate::error::FaultSource;
use crate::vector::openai::OpenAiError;
#[derive(Debug, thiserror::Error)]
#[error("Error while generating embeddings: {inner}")]
pub struct Error {
pub inner: Box<ErrorKind>,
}
impl<I: Into<ErrorKind>> From<I> for Error {
fn from(value: I) -> Self {
Self { inner: Box::new(value.into()) }
}
}
impl Error {
pub fn fault(&self) -> FaultSource {
match &*self.inner {
ErrorKind::NewEmbedderError(inner) => inner.fault,
ErrorKind::EmbedError(inner) => inner.fault,
}
}
}
#[derive(Debug, thiserror::Error)]
pub enum ErrorKind {
#[error(transparent)]
NewEmbedderError(#[from] NewEmbedderError),
#[error(transparent)]
EmbedError(#[from] EmbedError),
}
#[derive(Debug, thiserror::Error)]
#[error("{fault}: {kind}")]
pub struct EmbedError {
pub kind: EmbedErrorKind,
pub fault: FaultSource,
}
#[derive(Debug, thiserror::Error)]
pub enum EmbedErrorKind {
#[error("could not tokenize: {0}")]
Tokenize(Box<dyn std::error::Error + Send + Sync>),
#[error("unexpected tensor shape: {0}")]
TensorShape(candle_core::Error),
#[error("unexpected tensor value: {0}")]
TensorValue(candle_core::Error),
#[error("could not run model: {0}")]
ModelForward(candle_core::Error),
#[error("could not reach OpenAI: {0}")]
OpenAiNetwork(reqwest::Error),
#[error("unexpected response from OpenAI: {0}")]
OpenAiUnexpected(reqwest::Error),
#[error("could not authenticate against OpenAI: {0}")]
OpenAiAuth(OpenAiError),
#[error("sent too many requests to OpenAI: {0}")]
OpenAiTooManyRequests(OpenAiError),
#[error("received internal error from OpenAI: {0}")]
OpenAiInternalServerError(OpenAiError),
#[error("sent too many tokens in a request to OpenAI: {0}")]
OpenAiTooManyTokens(OpenAiError),
#[error("received unhandled HTTP status code {0} from OpenAI")]
OpenAiUnhandledStatusCode(u16),
}
impl EmbedError {
pub fn tokenize(inner: Box<dyn std::error::Error + Send + Sync>) -> Self {
Self { kind: EmbedErrorKind::Tokenize(inner), fault: FaultSource::Runtime }
}
pub fn tensor_shape(inner: candle_core::Error) -> Self {
Self { kind: EmbedErrorKind::TensorShape(inner), fault: FaultSource::Bug }
}
pub fn tensor_value(inner: candle_core::Error) -> Self {
Self { kind: EmbedErrorKind::TensorValue(inner), fault: FaultSource::Bug }
}
pub fn model_forward(inner: candle_core::Error) -> Self {
Self { kind: EmbedErrorKind::ModelForward(inner), fault: FaultSource::Runtime }
}
pub fn openai_network(inner: reqwest::Error) -> Self {
Self { kind: EmbedErrorKind::OpenAiNetwork(inner), fault: FaultSource::Runtime }
}
pub fn openai_unexpected(inner: reqwest::Error) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiUnexpected(inner), fault: FaultSource::Bug }
}
pub(crate) fn openai_auth_error(inner: OpenAiError) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiAuth(inner), fault: FaultSource::User }
}
pub(crate) fn openai_too_many_requests(inner: OpenAiError) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiTooManyRequests(inner), fault: FaultSource::Runtime }
}
pub(crate) fn openai_internal_server_error(inner: OpenAiError) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiInternalServerError(inner), fault: FaultSource::Runtime }
}
pub(crate) fn openai_too_many_tokens(inner: OpenAiError) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiTooManyTokens(inner), fault: FaultSource::Bug }
}
pub(crate) fn openai_unhandled_status_code(code: u16) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiUnhandledStatusCode(code), fault: FaultSource::Bug }
}
}
#[derive(Debug, thiserror::Error)]
#[error("{fault}: {kind}")]
pub struct NewEmbedderError {
pub kind: NewEmbedderErrorKind,
pub fault: FaultSource,
}
impl NewEmbedderError {
pub fn open_config(config_filename: PathBuf, inner: std::io::Error) -> NewEmbedderError {
let open_config = OpenConfig { filename: config_filename, inner };
Self { kind: NewEmbedderErrorKind::OpenConfig(open_config), fault: FaultSource::Runtime }
}
pub fn deserialize_config(
config: String,
config_filename: PathBuf,
inner: serde_json::Error,
) -> NewEmbedderError {
let deserialize_config = DeserializeConfig { config, filename: config_filename, inner };
Self {
kind: NewEmbedderErrorKind::DeserializeConfig(deserialize_config),
fault: FaultSource::Runtime,
}
}
pub fn open_tokenizer(
tokenizer_filename: PathBuf,
inner: Box<dyn std::error::Error + Send + Sync>,
) -> NewEmbedderError {
let open_tokenizer = OpenTokenizer { filename: tokenizer_filename, inner };
Self {
kind: NewEmbedderErrorKind::OpenTokenizer(open_tokenizer),
fault: FaultSource::Runtime,
}
}
pub fn new_api_fail(inner: ApiError) -> Self {
Self { kind: NewEmbedderErrorKind::NewApiFail(inner), fault: FaultSource::Bug }
}
pub fn api_get(inner: ApiError) -> Self {
Self { kind: NewEmbedderErrorKind::ApiGet(inner), fault: FaultSource::Undecided }
}
pub fn pytorch_weight(inner: candle_core::Error) -> Self {
Self { kind: NewEmbedderErrorKind::PytorchWeight(inner), fault: FaultSource::Runtime }
}
pub fn safetensor_weight(inner: candle_core::Error) -> Self {
Self { kind: NewEmbedderErrorKind::PytorchWeight(inner), fault: FaultSource::Runtime }
}
pub fn load_model(inner: candle_core::Error) -> Self {
Self { kind: NewEmbedderErrorKind::LoadModel(inner), fault: FaultSource::Runtime }
}
pub fn openai_initialize_web_client(inner: reqwest::Error) -> Self {
Self { kind: NewEmbedderErrorKind::InitWebClient(inner), fault: FaultSource::Runtime }
}
pub fn openai_invalid_api_key_format(inner: reqwest::header::InvalidHeaderValue) -> Self {
Self { kind: NewEmbedderErrorKind::InvalidApiKeyFormat(inner), fault: FaultSource::User }
}
}
#[derive(Debug, thiserror::Error)]
#[error("could not open config at {filename:?}: {inner}")]
pub struct OpenConfig {
pub filename: PathBuf,
pub inner: std::io::Error,
}
#[derive(Debug, thiserror::Error)]
#[error("could not deserialize config at {filename}: {inner}. Config follows:\n{config}")]
pub struct DeserializeConfig {
pub config: String,
pub filename: PathBuf,
pub inner: serde_json::Error,
}
#[derive(Debug, thiserror::Error)]
#[error("could not open tokenizer at {filename}: {inner}")]
pub struct OpenTokenizer {
pub filename: PathBuf,
#[source]
pub inner: Box<dyn std::error::Error + Send + Sync>,
}
#[derive(Debug, thiserror::Error)]
pub enum NewEmbedderErrorKind {
// hf
#[error(transparent)]
OpenConfig(OpenConfig),
#[error(transparent)]
DeserializeConfig(DeserializeConfig),
#[error(transparent)]
OpenTokenizer(OpenTokenizer),
#[error("could not build weights from Pytorch weights: {0}")]
PytorchWeight(candle_core::Error),
#[error("could not build weights from Safetensor weights: {0}")]
SafetensorWeight(candle_core::Error),
#[error("could not spawn HG_HUB API client: {0}")]
NewApiFail(ApiError),
#[error("fetching file from HG_HUB failed: {0}")]
ApiGet(ApiError),
#[error("loading model failed: {0}")]
LoadModel(candle_core::Error),
// openai
#[error("initializing web client for sending embedding requests failed: {0}")]
InitWebClient(reqwest::Error),
#[error("The API key passed to Authorization error was in an invalid format: {0}")]
InvalidApiKeyFormat(reqwest::header::InvalidHeaderValue),
}

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use candle_core::Tensor;
use candle_nn::VarBuilder;
use candle_transformers::models::bert::{BertModel, Config, DTYPE};
// FIXME: currently we'll be using the hub to retrieve model, in the future we might want to embed it into Meilisearch itself
use hf_hub::api::sync::Api;
use hf_hub::{Repo, RepoType};
use tokenizers::{PaddingParams, Tokenizer};
pub use super::error::{EmbedError, Error, NewEmbedderError};
use super::{Embedding, Embeddings};
#[derive(
Debug,
Clone,
Copy,
Default,
Hash,
PartialEq,
Eq,
serde::Deserialize,
serde::Serialize,
deserr::Deserr,
)]
#[serde(deny_unknown_fields, rename_all = "camelCase")]
#[deserr(rename_all = camelCase, deny_unknown_fields)]
pub enum WeightSource {
#[default]
Safetensors,
Pytorch,
}
#[derive(Debug, Clone, Hash, PartialEq, Eq, serde::Deserialize, serde::Serialize)]
pub struct EmbedderOptions {
pub model: String,
pub revision: Option<String>,
pub weight_source: WeightSource,
pub normalize_embeddings: bool,
}
impl EmbedderOptions {
pub fn new() -> Self {
Self {
//model: "sentence-transformers/all-MiniLM-L6-v2".to_string(),
model: "BAAI/bge-base-en-v1.5".to_string(),
//revision: Some("refs/pr/21".to_string()),
revision: None,
//weight_source: Default::default(),
weight_source: WeightSource::Pytorch,
normalize_embeddings: true,
}
}
}
impl Default for EmbedderOptions {
fn default() -> Self {
Self::new()
}
}
/// Perform embedding of documents and queries
pub struct Embedder {
model: BertModel,
tokenizer: Tokenizer,
options: EmbedderOptions,
}
impl std::fmt::Debug for Embedder {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("Embedder")
.field("model", &self.options.model)
.field("tokenizer", &self.tokenizer)
.field("options", &self.options)
.finish()
}
}
impl Embedder {
pub fn new(options: EmbedderOptions) -> std::result::Result<Self, NewEmbedderError> {
let device = candle_core::Device::Cpu;
let repo = match options.revision.clone() {
Some(revision) => Repo::with_revision(options.model.clone(), RepoType::Model, revision),
None => Repo::model(options.model.clone()),
};
let (config_filename, tokenizer_filename, weights_filename) = {
let api = Api::new().map_err(NewEmbedderError::new_api_fail)?;
let api = api.repo(repo);
let config = api.get("config.json").map_err(NewEmbedderError::api_get)?;
let tokenizer = api.get("tokenizer.json").map_err(NewEmbedderError::api_get)?;
let weights = match options.weight_source {
WeightSource::Pytorch => {
api.get("pytorch_model.bin").map_err(NewEmbedderError::api_get)?
}
WeightSource::Safetensors => {
api.get("model.safetensors").map_err(NewEmbedderError::api_get)?
}
};
(config, tokenizer, weights)
};
let config = std::fs::read_to_string(&config_filename)
.map_err(|inner| NewEmbedderError::open_config(config_filename.clone(), inner))?;
let config: Config = serde_json::from_str(&config).map_err(|inner| {
NewEmbedderError::deserialize_config(config, config_filename, inner)
})?;
let mut tokenizer = Tokenizer::from_file(&tokenizer_filename)
.map_err(|inner| NewEmbedderError::open_tokenizer(tokenizer_filename, inner))?;
let vb = match options.weight_source {
WeightSource::Pytorch => VarBuilder::from_pth(&weights_filename, DTYPE, &device)
.map_err(NewEmbedderError::pytorch_weight)?,
WeightSource::Safetensors => unsafe {
VarBuilder::from_mmaped_safetensors(&[weights_filename], DTYPE, &device)
.map_err(NewEmbedderError::safetensor_weight)?
},
};
let model = BertModel::load(vb, &config).map_err(NewEmbedderError::load_model)?;
if let Some(pp) = tokenizer.get_padding_mut() {
pp.strategy = tokenizers::PaddingStrategy::BatchLongest
} else {
let pp = PaddingParams {
strategy: tokenizers::PaddingStrategy::BatchLongest,
..Default::default()
};
tokenizer.with_padding(Some(pp));
}
Ok(Self { model, tokenizer, options })
}
pub async fn embed(
&self,
mut texts: Vec<String>,
) -> std::result::Result<Vec<Embeddings<f32>>, EmbedError> {
let tokens = match texts.len() {
1 => vec![self
.tokenizer
.encode(texts.pop().unwrap(), true)
.map_err(EmbedError::tokenize)?],
_ => self.tokenizer.encode_batch(texts, true).map_err(EmbedError::tokenize)?,
};
let token_ids = tokens
.iter()
.map(|tokens| {
let tokens = tokens.get_ids().to_vec();
Tensor::new(tokens.as_slice(), &self.model.device).map_err(EmbedError::tensor_shape)
})
.collect::<Result<Vec<_>, EmbedError>>()?;
let token_ids = Tensor::stack(&token_ids, 0).map_err(EmbedError::tensor_shape)?;
let token_type_ids = token_ids.zeros_like().map_err(EmbedError::tensor_shape)?;
let embeddings =
self.model.forward(&token_ids, &token_type_ids).map_err(EmbedError::model_forward)?;
// Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
let (_n_sentence, n_tokens, _hidden_size) =
embeddings.dims3().map_err(EmbedError::tensor_shape)?;
let embeddings = (embeddings.sum(1).map_err(EmbedError::tensor_value)? / (n_tokens as f64))
.map_err(EmbedError::tensor_shape)?;
let embeddings: Tensor = if self.options.normalize_embeddings {
normalize_l2(&embeddings).map_err(EmbedError::tensor_value)?
} else {
embeddings
};
let embeddings: Vec<Embedding> = embeddings.to_vec2().map_err(EmbedError::tensor_shape)?;
Ok(embeddings.into_iter().map(Embeddings::from_single_embedding).collect())
}
pub async fn embed_chunks(
&self,
text_chunks: Vec<Vec<String>>,
) -> std::result::Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
futures::future::try_join_all(text_chunks.into_iter().map(|prompts| self.embed(prompts)))
.await
}
pub fn chunk_count_hint(&self) -> usize {
1
}
pub fn prompt_count_in_chunk_hint(&self) -> usize {
std::thread::available_parallelism().map(|x| x.get()).unwrap_or(8)
}
}
fn normalize_l2(v: &Tensor) -> Result<Tensor, candle_core::Error> {
v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)
}

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use self::error::{EmbedError, NewEmbedderError};
use crate::prompt::PromptData;
pub mod error;
pub mod hf;
pub mod openai;
pub mod settings;
pub use self::error::Error;
pub type Embedding = Vec<f32>;
pub struct Embeddings<F> {
data: Vec<F>,
dimension: usize,
}
impl<F> Embeddings<F> {
pub fn new(dimension: usize) -> Self {
Self { data: Default::default(), dimension }
}
pub fn from_single_embedding(embedding: Vec<F>) -> Self {
Self { dimension: embedding.len(), data: embedding }
}
pub fn from_inner(data: Vec<F>, dimension: usize) -> Result<Self, Vec<F>> {
let mut this = Self::new(dimension);
this.append(data)?;
Ok(this)
}
pub fn dimension(&self) -> usize {
self.dimension
}
pub fn into_inner(self) -> Vec<F> {
self.data
}
pub fn as_inner(&self) -> &[F] {
&self.data
}
pub fn iter(&self) -> impl Iterator<Item = &'_ [F]> + '_ {
self.data.as_slice().chunks_exact(self.dimension)
}
pub fn push(&mut self, mut embedding: Vec<F>) -> Result<(), Vec<F>> {
if embedding.len() != self.dimension {
return Err(embedding);
}
self.data.append(&mut embedding);
Ok(())
}
pub fn append(&mut self, mut embeddings: Vec<F>) -> Result<(), Vec<F>> {
if embeddings.len() % self.dimension != 0 {
return Err(embeddings);
}
self.data.append(&mut embeddings);
Ok(())
}
}
#[derive(Debug)]
pub enum Embedder {
HuggingFace(hf::Embedder),
OpenAi(openai::Embedder),
}
#[derive(Debug, Clone, Default, serde::Deserialize, serde::Serialize)]
pub struct EmbeddingConfig {
pub embedder_options: EmbedderOptions,
pub prompt: PromptData,
// TODO: add metrics and anything needed
}
#[derive(Debug, Clone, Hash, PartialEq, Eq, serde::Deserialize, serde::Serialize)]
pub enum EmbedderOptions {
HuggingFace(hf::EmbedderOptions),
OpenAi(openai::EmbedderOptions),
}
impl Default for EmbedderOptions {
fn default() -> Self {
Self::HuggingFace(Default::default())
}
}
impl EmbedderOptions {
pub fn huggingface() -> Self {
Self::HuggingFace(hf::EmbedderOptions::new())
}
pub fn openai(api_key: String) -> Self {
Self::OpenAi(openai::EmbedderOptions::with_default_model(api_key))
}
}
impl Embedder {
pub fn new(options: EmbedderOptions) -> std::result::Result<Self, NewEmbedderError> {
Ok(match options {
EmbedderOptions::HuggingFace(options) => Self::HuggingFace(hf::Embedder::new(options)?),
EmbedderOptions::OpenAi(options) => Self::OpenAi(openai::Embedder::new(options)?),
})
}
pub async fn embed(
&self,
texts: Vec<String>,
) -> std::result::Result<Vec<Embeddings<f32>>, EmbedError> {
match self {
Embedder::HuggingFace(embedder) => embedder.embed(texts).await,
Embedder::OpenAi(embedder) => embedder.embed(texts).await,
}
}
pub async fn embed_chunks(
&self,
text_chunks: Vec<Vec<String>>,
) -> std::result::Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
match self {
Embedder::HuggingFace(embedder) => embedder.embed_chunks(text_chunks).await,
Embedder::OpenAi(embedder) => embedder.embed_chunks(text_chunks).await,
}
}
pub fn chunk_count_hint(&self) -> usize {
match self {
Embedder::HuggingFace(embedder) => embedder.chunk_count_hint(),
Embedder::OpenAi(embedder) => embedder.chunk_count_hint(),
}
}
pub fn prompt_count_in_chunk_hint(&self) -> usize {
match self {
Embedder::HuggingFace(embedder) => embedder.prompt_count_in_chunk_hint(),
Embedder::OpenAi(embedder) => embedder.prompt_count_in_chunk_hint(),
}
}
}

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use std::fmt::Display;
use reqwest::StatusCode;
use serde::{Deserialize, Serialize};
use super::error::{EmbedError, NewEmbedderError};
use super::{Embedding, Embeddings};
#[derive(Debug)]
pub struct Embedder {
client: reqwest::Client,
tokenizer: tiktoken_rs::CoreBPE,
options: EmbedderOptions,
}
#[derive(Debug, Clone, Hash, PartialEq, Eq, serde::Deserialize, serde::Serialize)]
pub struct EmbedderOptions {
pub api_key: String,
pub embedding_model: EmbeddingModel,
}
#[derive(
Debug,
Clone,
Copy,
Default,
Hash,
PartialEq,
Eq,
serde::Serialize,
serde::Deserialize,
deserr::Deserr,
)]
#[serde(deny_unknown_fields, rename_all = "camelCase")]
#[deserr(rename_all = camelCase, deny_unknown_fields)]
pub enum EmbeddingModel {
#[default]
TextEmbeddingAda002,
}
impl EmbeddingModel {
pub fn max_token(&self) -> usize {
match self {
EmbeddingModel::TextEmbeddingAda002 => 8191,
}
}
pub fn dimensions(&self) -> usize {
match self {
EmbeddingModel::TextEmbeddingAda002 => 1536,
}
}
pub fn name(&self) -> &'static str {
match self {
EmbeddingModel::TextEmbeddingAda002 => "text-embedding-ada-002",
}
}
pub fn from_name(name: &'static str) -> Option<Self> {
match name {
"text-embedding-ada-002" => Some(EmbeddingModel::TextEmbeddingAda002),
_ => None,
}
}
}
pub const OPENAI_EMBEDDINGS_URL: &str = "https://api.openai.com/v1/embeddings";
impl EmbedderOptions {
pub fn with_default_model(api_key: String) -> Self {
Self { api_key, embedding_model: Default::default() }
}
pub fn with_embedding_model(api_key: String, embedding_model: EmbeddingModel) -> Self {
Self { api_key, embedding_model }
}
}
impl Embedder {
pub fn new(options: EmbedderOptions) -> Result<Self, NewEmbedderError> {
let mut headers = reqwest::header::HeaderMap::new();
headers.insert(
reqwest::header::AUTHORIZATION,
reqwest::header::HeaderValue::from_str(&format!("Bearer {}", &options.api_key))
.map_err(NewEmbedderError::openai_invalid_api_key_format)?,
);
headers.insert(
reqwest::header::CONTENT_TYPE,
reqwest::header::HeaderValue::from_static("application/json"),
);
let client = reqwest::ClientBuilder::new()
.default_headers(headers)
.build()
.map_err(NewEmbedderError::openai_initialize_web_client)?;
// looking at the code it is very unclear that this can actually fail.
let tokenizer = tiktoken_rs::cl100k_base().unwrap();
Ok(Self { options, client, tokenizer })
}
pub async fn embed(&self, texts: Vec<String>) -> Result<Vec<Embeddings<f32>>, EmbedError> {
let mut tokenized = false;
for attempt in 0..7 {
let result = if tokenized {
self.try_embed_tokenized(&texts).await
} else {
self.try_embed(&texts).await
};
let retry_duration = match result {
Ok(embeddings) => return Ok(embeddings),
Err(retry) => {
log::warn!("Failed: {}", retry.error);
tokenized |= retry.must_tokenize();
retry.into_duration(attempt)
}
}?;
log::warn!("Attempt #{}, retrying after {}ms.", attempt, retry_duration.as_millis());
tokio::time::sleep(retry_duration).await;
}
let result = if tokenized {
self.try_embed_tokenized(&texts).await
} else {
self.try_embed(&texts).await
};
result.map_err(Retry::into_error)
}
async fn check_response(response: reqwest::Response) -> Result<reqwest::Response, Retry> {
if !response.status().is_success() {
match response.status() {
StatusCode::UNAUTHORIZED => {
let error_response: OpenAiErrorResponse = response
.json()
.await
.map_err(EmbedError::openai_unexpected)
.map_err(Retry::retry_later)?;
return Err(Retry::give_up(EmbedError::openai_auth_error(
error_response.error,
)));
}
StatusCode::TOO_MANY_REQUESTS => {
let error_response: OpenAiErrorResponse = response
.json()
.await
.map_err(EmbedError::openai_unexpected)
.map_err(Retry::retry_later)?;
return Err(Retry::rate_limited(EmbedError::openai_too_many_requests(
error_response.error,
)));
}
StatusCode::INTERNAL_SERVER_ERROR => {
let error_response: OpenAiErrorResponse = response
.json()
.await
.map_err(EmbedError::openai_unexpected)
.map_err(Retry::retry_later)?;
return Err(Retry::retry_later(EmbedError::openai_internal_server_error(
error_response.error,
)));
}
StatusCode::SERVICE_UNAVAILABLE => {
let error_response: OpenAiErrorResponse = response
.json()
.await
.map_err(EmbedError::openai_unexpected)
.map_err(Retry::retry_later)?;
return Err(Retry::retry_later(EmbedError::openai_internal_server_error(
error_response.error,
)));
}
StatusCode::BAD_REQUEST => {
// Most probably, one text contained too many tokens
let error_response: OpenAiErrorResponse = response
.json()
.await
.map_err(EmbedError::openai_unexpected)
.map_err(Retry::retry_later)?;
log::warn!("OpenAI: input was too long, retrying on tokenized version. For best performance, limit the size of your prompt.");
return Err(Retry::retry_tokenized(EmbedError::openai_too_many_tokens(
error_response.error,
)));
}
code => {
return Err(Retry::give_up(EmbedError::openai_unhandled_status_code(
code.as_u16(),
)));
}
}
}
Ok(response)
}
async fn try_embed<S: AsRef<str> + serde::Serialize>(
&self,
texts: &[S],
) -> Result<Vec<Embeddings<f32>>, Retry> {
for text in texts {
log::trace!("Received prompt: {}", text.as_ref())
}
let request = OpenAiRequest { model: self.options.embedding_model.name(), input: texts };
let response = self
.client
.post(OPENAI_EMBEDDINGS_URL)
.json(&request)
.send()
.await
.map_err(EmbedError::openai_network)
.map_err(Retry::retry_later)?;
let response = Self::check_response(response).await?;
let response: OpenAiResponse = response
.json()
.await
.map_err(EmbedError::openai_unexpected)
.map_err(Retry::retry_later)?;
log::trace!("response: {:?}", response.data);
Ok(response
.data
.into_iter()
.map(|data| Embeddings::from_single_embedding(data.embedding))
.collect())
}
async fn try_embed_tokenized(&self, text: &[String]) -> Result<Vec<Embeddings<f32>>, Retry> {
pub const OVERLAP_SIZE: usize = 200;
let mut all_embeddings = Vec::with_capacity(text.len());
for text in text {
let max_token_count = self.options.embedding_model.max_token();
let encoded = self.tokenizer.encode_ordinary(text.as_str());
let len = encoded.len();
if len < max_token_count {
all_embeddings.append(&mut self.try_embed(&[text]).await?);
continue;
}
let mut tokens = encoded.as_slice();
let mut embeddings_for_prompt =
Embeddings::new(self.options.embedding_model.dimensions());
while tokens.len() > max_token_count {
let window = &tokens[..max_token_count];
embeddings_for_prompt.push(self.embed_tokens(window).await?).unwrap();
tokens = &tokens[max_token_count - OVERLAP_SIZE..];
}
// end of text
embeddings_for_prompt.push(self.embed_tokens(tokens).await?).unwrap();
all_embeddings.push(embeddings_for_prompt);
}
Ok(all_embeddings)
}
async fn embed_tokens(&self, tokens: &[usize]) -> Result<Embedding, Retry> {
for attempt in 0..9 {
let duration = match self.try_embed_tokens(tokens).await {
Ok(embedding) => return Ok(embedding),
Err(retry) => retry.into_duration(attempt),
}
.map_err(Retry::retry_later)?;
tokio::time::sleep(duration).await;
}
self.try_embed_tokens(tokens).await.map_err(|retry| Retry::give_up(retry.into_error()))
}
async fn try_embed_tokens(&self, tokens: &[usize]) -> Result<Embedding, Retry> {
let request =
OpenAiTokensRequest { model: self.options.embedding_model.name(), input: tokens };
let response = self
.client
.post(OPENAI_EMBEDDINGS_URL)
.json(&request)
.send()
.await
.map_err(EmbedError::openai_network)
.map_err(Retry::retry_later)?;
let response = Self::check_response(response).await?;
let mut response: OpenAiResponse = response
.json()
.await
.map_err(EmbedError::openai_unexpected)
.map_err(Retry::retry_later)?;
Ok(response.data.pop().map(|data| data.embedding).unwrap_or_default())
}
pub async fn embed_chunks(
&self,
text_chunks: Vec<Vec<String>>,
) -> Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
futures::future::try_join_all(text_chunks.into_iter().map(|prompts| self.embed(prompts)))
.await
}
pub fn chunk_count_hint(&self) -> usize {
10
}
pub fn prompt_count_in_chunk_hint(&self) -> usize {
10
}
}
// retrying in case of failure
struct Retry {
error: EmbedError,
strategy: RetryStrategy,
}
enum RetryStrategy {
GiveUp,
Retry,
RetryTokenized,
RetryAfterRateLimit,
}
impl Retry {
fn give_up(error: EmbedError) -> Self {
Self { error, strategy: RetryStrategy::GiveUp }
}
fn retry_later(error: EmbedError) -> Self {
Self { error, strategy: RetryStrategy::Retry }
}
fn retry_tokenized(error: EmbedError) -> Self {
Self { error, strategy: RetryStrategy::RetryTokenized }
}
fn rate_limited(error: EmbedError) -> Self {
Self { error, strategy: RetryStrategy::RetryAfterRateLimit }
}
fn into_duration(self, attempt: u32) -> Result<tokio::time::Duration, EmbedError> {
match self.strategy {
RetryStrategy::GiveUp => Err(self.error),
RetryStrategy::Retry => Ok(tokio::time::Duration::from_millis((10u64).pow(attempt))),
RetryStrategy::RetryTokenized => Ok(tokio::time::Duration::from_millis(1)),
RetryStrategy::RetryAfterRateLimit => {
Ok(tokio::time::Duration::from_millis(100 + 10u64.pow(attempt)))
}
}
}
fn must_tokenize(&self) -> bool {
matches!(self.strategy, RetryStrategy::RetryTokenized)
}
fn into_error(self) -> EmbedError {
self.error
}
}
// openai api structs
#[derive(Debug, Serialize)]
struct OpenAiRequest<'a, S: AsRef<str> + serde::Serialize> {
model: &'a str,
input: &'a [S],
}
#[derive(Debug, Serialize)]
struct OpenAiTokensRequest<'a> {
model: &'a str,
input: &'a [usize],
}
#[derive(Debug, Deserialize)]
struct OpenAiResponse {
data: Vec<OpenAiEmbedding>,
}
#[derive(Debug, Deserialize)]
struct OpenAiErrorResponse {
error: OpenAiError,
}
#[derive(Debug, Deserialize)]
pub struct OpenAiError {
message: String,
// type: String,
code: Option<String>,
}
impl Display for OpenAiError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
match &self.code {
Some(code) => write!(f, "{} ({})", self.message, code),
None => write!(f, "{}", self.message),
}
}
}
#[derive(Debug, Deserialize)]
struct OpenAiEmbedding {
embedding: Embedding,
// object: String,
// index: usize,
}

308
milli/src/vector/settings.rs Normal file
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@ -0,0 +1,308 @@
use deserr::Deserr;
use serde::{Deserialize, Serialize};
use crate::prompt::{PromptData, PromptFallbackStrategy};
use crate::update::Setting;
use crate::vector::hf::WeightSource;
use crate::vector::EmbeddingConfig;
#[derive(Debug, Clone, Default, Serialize, Deserialize, PartialEq, Eq, Deserr)]
#[serde(deny_unknown_fields, rename_all = "camelCase")]
#[deserr(rename_all = camelCase, deny_unknown_fields)]
pub struct EmbeddingSettings {
#[serde(default, skip_serializing_if = "Setting::is_not_set", rename = "source")]
#[deserr(default, rename = "source")]
pub embedder_options: Setting<EmbedderSettings>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub prompt: Setting<PromptSettings>,
}
impl EmbeddingSettings {
pub fn apply(&mut self, new: Self) {
let EmbeddingSettings { embedder_options, prompt } = new;
self.embedder_options.apply(embedder_options);
self.prompt.apply(prompt);
}
}
impl From<EmbeddingConfig> for EmbeddingSettings {
fn from(value: EmbeddingConfig) -> Self {
Self {
embedder_options: Setting::Set(value.embedder_options.into()),
prompt: Setting::Set(value.prompt.into()),
}
}
}
impl From<EmbeddingSettings> for EmbeddingConfig {
fn from(value: EmbeddingSettings) -> Self {
let mut this = Self::default();
let EmbeddingSettings { embedder_options, prompt } = value;
if let Some(embedder_options) = embedder_options.set() {
this.embedder_options = embedder_options.into();
}
if let Some(prompt) = prompt.set() {
this.prompt = prompt.into();
}
this
}
}
#[derive(Debug, Clone, Default, Serialize, Deserialize, PartialEq, Eq, Deserr)]
#[serde(deny_unknown_fields, rename_all = "camelCase")]
#[deserr(rename_all = camelCase, deny_unknown_fields)]
pub struct PromptSettings {
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub template: Setting<String>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub strategy: Setting<PromptFallbackStrategy>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub fallback: Setting<String>,
}
impl PromptSettings {
pub fn apply(&mut self, new: Self) {
let PromptSettings { template, strategy, fallback } = new;
self.template.apply(template);
self.strategy.apply(strategy);
self.fallback.apply(fallback);
}
}
impl From<PromptData> for PromptSettings {
fn from(value: PromptData) -> Self {
Self {
template: Setting::Set(value.template),
strategy: Setting::Set(value.strategy),
fallback: Setting::Set(value.fallback),
}
}
}
impl From<PromptSettings> for PromptData {
fn from(value: PromptSettings) -> Self {
let mut this = PromptData::default();
let PromptSettings { template, strategy, fallback } = value;
if let Some(template) = template.set() {
this.template = template;
}
if let Some(strategy) = strategy.set() {
this.strategy = strategy;
}
if let Some(fallback) = fallback.set() {
this.fallback = fallback;
}
this
}
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
#[serde(deny_unknown_fields, rename_all = "camelCase")]
pub enum EmbedderSettings {
HuggingFace(Setting<HfEmbedderSettings>),
OpenAi(Setting<OpenAiEmbedderSettings>),
}
impl<E> Deserr<E> for EmbedderSettings
where
E: deserr::DeserializeError,
{
fn deserialize_from_value<V: deserr::IntoValue>(
value: deserr::Value<V>,
location: deserr::ValuePointerRef,
) -> Result<Self, E> {
match value {
deserr::Value::Map(map) => {
if deserr::Map::len(&map) != 1 {
return Err(deserr::take_cf_content(E::error::<V>(
None,
deserr::ErrorKind::Unexpected {
msg: format!(
"Expected a single field, got {} fields",
deserr::Map::len(&map)
),
},
location,
)));
}
let mut it = deserr::Map::into_iter(map);
let (k, v) = it.next().unwrap();
match k.as_str() {
"huggingFace" => Ok(EmbedderSettings::HuggingFace(Setting::Set(
HfEmbedderSettings::deserialize_from_value(
v.into_value(),
location.push_key(&k),
)?,
))),
"openAi" => Ok(EmbedderSettings::OpenAi(Setting::Set(
OpenAiEmbedderSettings::deserialize_from_value(
v.into_value(),
location.push_key(&k),
)?,
))),
other => Err(deserr::take_cf_content(E::error::<V>(
None,
deserr::ErrorKind::UnknownKey {
key: other,
accepted: &["huggingFace", "openAi"],
},
location,
))),
}
}
_ => Err(deserr::take_cf_content(E::error::<V>(
None,
deserr::ErrorKind::IncorrectValueKind {
actual: value,
accepted: &[deserr::ValueKind::Map],
},
location,
))),
}
}
}
impl Default for EmbedderSettings {
fn default() -> Self {
Self::HuggingFace(Default::default())
}
}
impl From<crate::vector::EmbedderOptions> for EmbedderSettings {
fn from(value: crate::vector::EmbedderOptions) -> Self {
match value {
crate::vector::EmbedderOptions::HuggingFace(hf) => {
Self::HuggingFace(Setting::Set(hf.into()))
}
crate::vector::EmbedderOptions::OpenAi(openai) => {
Self::OpenAi(Setting::Set(openai.into()))
}
}
}
}
impl From<EmbedderSettings> for crate::vector::EmbedderOptions {
fn from(value: EmbedderSettings) -> Self {
match value {
EmbedderSettings::HuggingFace(Setting::Set(hf)) => Self::HuggingFace(hf.into()),
EmbedderSettings::HuggingFace(_setting) => Self::HuggingFace(Default::default()),
EmbedderSettings::OpenAi(Setting::Set(ai)) => Self::OpenAi(ai.into()),
EmbedderSettings::OpenAi(_setting) => Self::OpenAi(
crate::vector::openai::EmbedderOptions::with_default_model(infer_api_key()),
),
}
}
}
#[derive(Debug, Clone, Default, Serialize, Deserialize, PartialEq, Eq, Deserr)]
#[serde(deny_unknown_fields, rename_all = "camelCase")]
#[deserr(rename_all = camelCase, deny_unknown_fields)]
pub struct HfEmbedderSettings {
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub model: Setting<String>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub revision: Setting<String>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub weight_source: Setting<WeightSource>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub normalize_embeddings: Setting<bool>,
}
impl HfEmbedderSettings {
pub fn apply(&mut self, new: Self) {
let HfEmbedderSettings {
model,
revision,
weight_source,
normalize_embeddings: normalize_embedding,
} = new;
self.model.apply(model);
self.revision.apply(revision);
self.weight_source.apply(weight_source);
self.normalize_embeddings.apply(normalize_embedding);
}
}
impl From<crate::vector::hf::EmbedderOptions> for HfEmbedderSettings {
fn from(value: crate::vector::hf::EmbedderOptions) -> Self {
Self {
model: Setting::Set(value.model),
revision: value.revision.map(Setting::Set).unwrap_or(Setting::NotSet),
weight_source: Setting::Set(value.weight_source),
normalize_embeddings: Setting::Set(value.normalize_embeddings),
}
}
}
impl From<HfEmbedderSettings> for crate::vector::hf::EmbedderOptions {
fn from(value: HfEmbedderSettings) -> Self {
let HfEmbedderSettings { model, revision, weight_source, normalize_embeddings } = value;
let mut this = Self::default();
if let Some(model) = model.set() {
this.model = model;
}
if let Some(revision) = revision.set() {
this.revision = Some(revision);
}
if let Some(weight_source) = weight_source.set() {
this.weight_source = weight_source;
}
if let Some(normalize_embeddings) = normalize_embeddings.set() {
this.normalize_embeddings = normalize_embeddings;
}
this
}
}
#[derive(Debug, Clone, Default, Serialize, Deserialize, PartialEq, Eq, Deserr)]
#[serde(deny_unknown_fields, rename_all = "camelCase")]
#[deserr(rename_all = camelCase, deny_unknown_fields)]
pub struct OpenAiEmbedderSettings {
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub api_key: Setting<String>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub embedding_model: Setting<crate::vector::openai::EmbeddingModel>,
}
impl OpenAiEmbedderSettings {
pub fn apply(&mut self, new: Self) {
let Self { api_key, embedding_model: embedding_mode } = new;
self.api_key.apply(api_key);
self.embedding_model.apply(embedding_mode);
}
}
impl From<crate::vector::openai::EmbedderOptions> for OpenAiEmbedderSettings {
fn from(value: crate::vector::openai::EmbedderOptions) -> Self {
Self {
api_key: Setting::Set(value.api_key),
embedding_model: Setting::Set(value.embedding_model),
}
}
}
impl From<OpenAiEmbedderSettings> for crate::vector::openai::EmbedderOptions {
fn from(value: OpenAiEmbedderSettings) -> Self {
let OpenAiEmbedderSettings { api_key, embedding_model } = value;
Self {
api_key: api_key.set().unwrap_or_else(infer_api_key),
embedding_model: embedding_model.set().unwrap_or_default(),
}
}
}
fn infer_api_key() -> String {
/// FIXME: get key from instance options?
std::env::var("MEILI_OPENAI_API_KEY").unwrap_or_default()
}