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Split the vector module in submodules

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This commit is contained in:
Louis Dureuil
2025-08-26 16:32:17 +02:00
parent fb7ccc0db3
commit b5f0c19406
19 changed files with 1557 additions and 1563 deletions
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2
crates/milli/src/update/new/extract/vectors/mod.rs
View File

@ -475,7 +475,7 @@ impl<'doc> OnEmbed<'doc> for OnEmbeddingDocumentUpdates<'doc, '_> {
}
fn process_embedding_error(
&mut self,
error: crate::vector::hf::EmbedError,
error: crate::vector::error::EmbedError,
embedder_name: &'doc str,
unused_vectors_distribution: &UnusedVectorsDistributionBump,
metadata: BVec<'doc, Metadata<'doc>>,

50
crates/milli/src/update/settings.rs
View File

@ -33,6 +33,7 @@ use crate::update::index_documents::IndexDocumentsMethod;
use crate::update::new::indexer::reindex;
use crate::update::{IndexDocuments, UpdateIndexingStep};
use crate::vector::db::{FragmentConfigs, IndexEmbeddingConfig};
use crate::vector::embedder::{openai, rest};
use crate::vector::json_template::JsonTemplate;
use crate::vector::settings::{
EmbedderAction, EmbedderSource, EmbeddingSettings, EmbeddingValidationContext, NestingContext,
@ -2208,39 +2209,29 @@ pub fn validate_embedding_settings(
if let Some(request) = request.as_ref().set() {
let request = match with_fragments {
WithFragments::Yes { indexing_fragments, search_fragments } => {
crate::vector::rest::RequestData::new(
request.to_owned(),
indexing_fragments,
search_fragments,
)
.map_err(|error| crate::UserError::VectorEmbeddingError(error.into()))
rest::RequestData::new(request.to_owned(), indexing_fragments, search_fragments)
.map_err(|error| crate::UserError::VectorEmbeddingError(error.into()))
}
WithFragments::No => {
rest::RequestData::new(request.to_owned(), Default::default(), Default::default())
.map_err(|error| crate::UserError::VectorEmbeddingError(error.into()))
}
WithFragments::No => crate::vector::rest::RequestData::new(
request.to_owned(),
Default::default(),
Default::default(),
)
.map_err(|error| crate::UserError::VectorEmbeddingError(error.into())),
WithFragments::Maybe => {
let mut indexing_fragments = BTreeMap::new();
indexing_fragments.insert("test".to_string(), serde_json::json!("test"));
crate::vector::rest::RequestData::new(
request.to_owned(),
indexing_fragments,
Default::default(),
)
.or_else(|_| {
crate::vector::rest::RequestData::new(
request.to_owned(),
Default::default(),
Default::default(),
)
})
.map_err(|error| crate::UserError::VectorEmbeddingError(error.into()))
rest::RequestData::new(request.to_owned(), indexing_fragments, Default::default())
.or_else(|_| {
rest::RequestData::new(
request.to_owned(),
Default::default(),
Default::default(),
)
})
.map_err(|error| crate::UserError::VectorEmbeddingError(error.into()))
}
}?;
if let Some(response) = response.as_ref().set() {
crate::vector::rest::Response::new(response.to_owned(), &request)
rest::Response::new(response.to_owned(), &request)
.map_err(|error| crate::UserError::VectorEmbeddingError(error.into()))?;
}
}
@ -2293,11 +2284,12 @@ pub fn validate_embedding_settings(
match inferred_source {
EmbedderSource::OpenAi => {
if let Setting::Set(model) = &model {
let model = crate::vector::openai::EmbeddingModel::from_name(model.as_str())
.ok_or(crate::error::UserError::InvalidOpenAiModel {
let model = openai::EmbeddingModel::from_name(model.as_str()).ok_or(
crate::error::UserError::InvalidOpenAiModel {
embedder_name: name.to_owned(),
model: model.clone(),
})?;
},
)?;
if let Setting::Set(dimensions) = dimensions {
if !model.supports_overriding_dimensions()
&& dimensions != model.default_dimensions()