Merge #5232
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5232: Stabilize vector store feature r=Kerollmops a=dureuill

# Pull Request

## Related issue
Fixes #4733 

## What does this PR do?
- `vectorStore` feature can no longer be set or get from `/experimental-features`
- That feature has been removed, and there is no longer any check for its activation
- Always display `embedders` in the settings, even if empty
- Always hide `_vectors` in documents, unless `retrieveVectors: true`
- Make error codes consistent with the usual nomenclature
- Update tests as needed


Co-authored-by: Louis Dureuil <louis@meilisearch.com>
This commit is contained in:
meili-bors[bot]
2025-01-16 11:50:21 +00:00
committed by GitHub
32 changed files with 214 additions and 1010 deletions

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: {}",

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,

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(