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Consistent error codes

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This commit is contained in:
Louis Dureuil 2025-01-14 13:08:27 +01:00
parent cb8f033130
commit de6cd3ac01
No known key found for this signature in database
8 changed files with 51 additions and 26 deletions
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8
crates/meilisearch-types/src/error.rs
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@ -243,8 +243,9 @@ InvalidVectorsType , InvalidRequest , BAD_REQUEST ;
InvalidDocumentId , InvalidRequest , BAD_REQUEST ;
InvalidDocumentLimit , InvalidRequest , BAD_REQUEST ;
InvalidDocumentOffset , InvalidRequest , BAD_REQUEST ;
InvalidEmbedder , InvalidRequest , BAD_REQUEST ;
InvalidHybridQuery , InvalidRequest , BAD_REQUEST ;
InvalidSearchEmbedder , InvalidRequest , BAD_REQUEST ;
InvalidSimilarEmbedder , InvalidRequest , BAD_REQUEST ;
InvalidSearchHybridQuery , InvalidRequest , BAD_REQUEST ;
InvalidIndexLimit , InvalidRequest , BAD_REQUEST ;
InvalidIndexOffset , InvalidRequest , BAD_REQUEST ;
InvalidIndexPrimaryKey , InvalidRequest , BAD_REQUEST ;
@ -443,7 +444,8 @@ impl ErrorCode for milli::Error {
UserError::InvalidMinTypoWordLenSetting(_, _) => {
Code::InvalidSettingsTypoTolerance
}
UserError::InvalidEmbedder(_) => Code::InvalidEmbedder,
UserError::InvalidSearchEmbedder(_) => Code::InvalidSearchEmbedder,
UserError::InvalidSimilarEmbedder(_) => Code::InvalidSimilarEmbedder,
UserError::VectorEmbeddingError(_) | UserError::DocumentEmbeddingError(_) => {
Code::VectorEmbeddingError
}

2
crates/meilisearch/src/routes/indexes/facet_search.rs
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@ -56,7 +56,7 @@ pub struct FacetSearchQuery {
pub q: Option<String>,
#[deserr(default, error = DeserrJsonError<InvalidSearchVector>)]
pub vector: Option<Vec<f32>>,
#[deserr(default, error = DeserrJsonError<InvalidHybridQuery>)]
#[deserr(default, error = DeserrJsonError<InvalidSearchHybridQuery>)]
pub hybrid: Option<HybridQuery>,
#[deserr(default, error = DeserrJsonError<InvalidSearchFilter>)]
pub filter: Option<Value>,

4
crates/meilisearch/src/routes/indexes/search.rs
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@ -121,7 +121,7 @@ pub struct SearchQueryGet {
#[deserr(default, error = DeserrQueryParamError<InvalidSearchAttributesToSearchOn>)]
#[param(value_type = Vec<String>, explode = false)]
pub attributes_to_search_on: Option<CS<String>>,
#[deserr(default, error = DeserrQueryParamError<InvalidEmbedder>)]
#[deserr(default, error = DeserrQueryParamError<InvalidSearchEmbedder>)]
pub hybrid_embedder: Option<String>,
#[deserr(default, error = DeserrQueryParamError<InvalidSearchSemanticRatio>)]
#[param(value_type = f32)]
@ -185,7 +185,7 @@ impl TryFrom<SearchQueryGet> for SearchQuery {
(None, Some(_)) => {
return Err(ResponseError::from_msg(
"`hybridEmbedder` is mandatory when `hybridSemanticRatio` is present".into(),
meilisearch_types::error::Code::InvalidHybridQuery,
meilisearch_types::error::Code::InvalidSearchHybridQuery,
));
}
(Some(embedder), None) => {

7
crates/meilisearch/src/routes/indexes/similar.rs
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@ -19,8 +19,8 @@ use crate::extractors::authentication::GuardedData;
use crate::extractors::sequential_extractor::SeqHandler;
use crate::routes::indexes::similar_analytics::{SimilarAggregator, SimilarGET, SimilarPOST};
use crate::search::{
add_search_rules, perform_similar, RankingScoreThresholdSimilar, RetrieveVectors, SearchKind,
SimilarQuery, SimilarResult, DEFAULT_SEARCH_LIMIT, DEFAULT_SEARCH_OFFSET,
add_search_rules, perform_similar, RankingScoreThresholdSimilar, RetrieveVectors, Route,
SearchKind, SimilarQuery, SimilarResult, DEFAULT_SEARCH_LIMIT, DEFAULT_SEARCH_OFFSET,
};
#[derive(OpenApi)]
@ -235,6 +235,7 @@ async fn similar(
&index,
&query.embedder,
None,
Route::Similar,
)?;
tokio::task::spawn_blocking(move || {
@ -281,7 +282,7 @@ pub struct SimilarQueryGet {
#[deserr(default, error = DeserrQueryParamError<InvalidSimilarRankingScoreThreshold>, default)]
#[param(value_type = Option<f32>)]
pub ranking_score_threshold: Option<RankingScoreThresholdGet>,
#[deserr(error = DeserrQueryParamError<InvalidEmbedder>)]
#[deserr(error = DeserrQueryParamError<InvalidSimilarEmbedder>)]
pub embedder: String,
}

43
crates/meilisearch/src/search/mod.rs
View File

@ -63,7 +63,7 @@ pub struct SearchQuery {
pub q: Option<String>,
#[deserr(default, error = DeserrJsonError<InvalidSearchVector>)]
pub vector: Option<Vec<f32>>,
#[deserr(default, error = DeserrJsonError<InvalidHybridQuery>)]
#[deserr(default, error = DeserrJsonError<InvalidSearchHybridQuery>)]
pub hybrid: Option<HybridQuery>,
#[deserr(default = DEFAULT_SEARCH_OFFSET(), error = DeserrJsonError<InvalidSearchOffset>)]
#[schema(default = DEFAULT_SEARCH_OFFSET)]
@ -276,12 +276,12 @@ impl fmt::Debug for SearchQuery {
}
#[derive(Debug, Clone, Default, PartialEq, Deserr, ToSchema)]
#[deserr(error = DeserrJsonError<InvalidHybridQuery>, rename_all = camelCase, deny_unknown_fields)]
#[deserr(error = DeserrJsonError<InvalidSearchHybridQuery>, rename_all = camelCase, deny_unknown_fields)]
pub struct HybridQuery {
#[deserr(default, error = DeserrJsonError<InvalidSearchSemanticRatio>, default)]
#[schema(value_type = f32, default)]
pub semantic_ratio: SemanticRatio,
#[deserr(error = DeserrJsonError<InvalidEmbedder>)]
#[deserr(error = DeserrJsonError<InvalidSearchEmbedder>)]
pub embedder: String,
}
@ -300,8 +300,14 @@ impl SearchKind {
embedder_name: &str,
vector_len: Option<usize>,
) -> Result<Self, ResponseError> {
let (embedder_name, embedder, quantized) =
Self::embedder(index_scheduler, index_uid, index, embedder_name, vector_len)?;
let (embedder_name, embedder, quantized) = Self::embedder(
index_scheduler,
index_uid,
index,
embedder_name,
vector_len,
Route::Search,
)?;
Ok(Self::SemanticOnly { embedder_name, embedder, quantized })
}
@ -313,8 +319,14 @@ impl SearchKind {
semantic_ratio: f32,
vector_len: Option<usize>,
) -> Result<Self, ResponseError> {
let (embedder_name, embedder, quantized) =
Self::embedder(index_scheduler, index_uid, index, embedder_name, vector_len)?;
let (embedder_name, embedder, quantized) = Self::embedder(
index_scheduler,
index_uid,
index,
embedder_name,
vector_len,
Route::Search,
)?;
Ok(Self::Hybrid { embedder_name, embedder, quantized, semantic_ratio })
}
@ -324,13 +336,21 @@ impl SearchKind {
index: &Index,
embedder_name: &str,
vector_len: Option<usize>,
route: Route,
) -> Result<(String, Arc<Embedder>, bool), ResponseError> {
let embedder_configs = index.embedding_configs(&index.read_txn()?)?;
let embedders = index_scheduler.embedders(index_uid, embedder_configs)?;
let (embedder, _, quantized) = embedders
.get(embedder_name)
.ok_or(milli::UserError::InvalidEmbedder(embedder_name.to_owned()))
.ok_or(match route {
Route::Search | Route::MultiSearch => {
milli::UserError::InvalidSearchEmbedder(embedder_name.to_owned())
}
Route::Similar => {
milli::UserError::InvalidSimilarEmbedder(embedder_name.to_owned())
}
})
.map_err(milli::Error::from)?;
if let Some(vector_len) = vector_len {
@ -401,7 +421,7 @@ pub struct SearchQueryWithIndex {
pub q: Option<String>,
#[deserr(default, error = DeserrJsonError<InvalidSearchQ>)]
pub vector: Option<Vec<f32>>,
#[deserr(default, error = DeserrJsonError<InvalidHybridQuery>)]
#[deserr(default, error = DeserrJsonError<InvalidSearchHybridQuery>)]
pub hybrid: Option<HybridQuery>,
#[deserr(default, error = DeserrJsonError<InvalidSearchOffset>)]
pub offset: Option<usize>,
@ -553,7 +573,7 @@ pub struct SimilarQuery {
pub limit: usize,
#[deserr(default, error = DeserrJsonError<InvalidSimilarFilter>)]
pub filter: Option<Value>,
#[deserr(error = DeserrJsonError<InvalidEmbedder>)]
#[deserr(error = DeserrJsonError<InvalidSimilarEmbedder>)]
pub embedder: String,
#[deserr(default, error = DeserrJsonError<InvalidSimilarAttributesToRetrieve>)]
pub attributes_to_retrieve: Option<BTreeSet<String>>,
@ -1048,9 +1068,10 @@ pub struct ComputedFacets {
pub stats: BTreeMap<String, FacetStats>,
}
enum Route {
pub enum Route {
Search,
MultiSearch,
Similar,
}
fn compute_facet_distribution_stats<S: AsRef<str>>(

4
crates/milli/src/error.rs
View File

@ -222,7 +222,9 @@ and can not be more than 511 bytes.", .document_id.to_string()
#[error("Too many embedders in the configuration. Found {0}, but limited to 256.")]
TooManyEmbedders(usize),
#[error("Cannot find embedder with name `{0}`.")]
InvalidEmbedder(String),
InvalidSearchEmbedder(String),
#[error("Cannot find embedder with name `{0}`.")]
InvalidSimilarEmbedder(String),
#[error("Too many vectors for document with id {0}: found {1}, but limited to 256.")]
TooManyVectors(String, usize),
#[error("`.embedders.{embedder_name}`: Field `{field}` unavailable for source `{source_}` (only available for sources: {}). Available fields: {}",

2
crates/milli/src/search/new/vector_sort.rs
View File

@ -32,7 +32,7 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
.index
.embedder_category_id
.get(ctx.txn, embedder_name)?
.ok_or_else(|| crate::UserError::InvalidEmbedder(embedder_name.to_owned()))?;
.ok_or_else(|| crate::UserError::InvalidSearchEmbedder(embedder_name.to_owned()))?;
Ok(Self {
query: None,

7
crates/milli/src/search/similar.rs
View File

@ -65,10 +65,9 @@ impl<'a> Similar<'a> {
let universe = universe;
let embedder_index =
self.index
.embedder_category_id
.get(self.rtxn, &self.embedder_name)?
.ok_or_else(|| crate::UserError::InvalidEmbedder(self.embedder_name.to_owned()))?;
self.index.embedder_category_id.get(self.rtxn, &self.embedder_name)?.ok_or_else(
|| crate::UserError::InvalidSimilarEmbedder(self.embedder_name.to_owned()),
)?;
let reader = ArroyWrapper::new(self.index.vector_arroy, embedder_index, self.quantized);
let results = reader.nns_by_item(