- when the feature is disabled, documents are never modified
- when the feature is enabled and `retrieveVectors` is disabled, `_vectors` is removed from documents
- when the feature is enabled and `retrieveVectors` is enabled, vectors from the vectors DB are merged with `_vectors` in documents
Additionally `_vectors` is never displayed when the `displayedAttributes` list does not contain either `*` or `_vectors`
- fixed an issue where `_vectors` was not injected when all vectors in the dataset where always generated
4646: Reduce `Transform`'s disk usage r=Kerollmops a=Kerollmops
This PR implements what is described in #4485. It reduces the number of disk writes and disk usage.
Co-authored-by: Clément Renault <clement@meilisearch.com>
4633: Allow to mark vectors as "userProvided" r=Kerollmops a=dureuill
# Pull Request
## Related issue
Fixes#4606
## What does this PR do?
[See usage in PRD](https://meilisearch.notion.site/v1-9-AI-search-changes-e90d6803eca8417aa70a1ac5d0225697#deb96fb0595947bda7d4a371100326eb)
- Extends the shape of the special `_vectors` field in documents.
- previously, the `_vectors` field had to be an object, with each field the name of a configured embedder, and each value either `null`, an embedding (array of numbers), or an array of embeddings.
- In this PR, the value of an embedder in the `_vectors` field can additionally be an object. The object has two fields:
1. `embeddings`: `null`, an embedding (array of numbers), or an array of embeddings.
2. `userProvided`: a boolean indicating if the vector was provided by the user.
- The previous form `embedder_or_array_of_embedders` is semantically equivalent to:
```json
{
"embeddings": embedder_or_array_of_embedders,
"userProvided": true
}
```
- During the indexing step, the subfields and values of the `_vectors` field that have `userProvided` set to **false** are added in the vector DB, but not in the documents DB: that means that future modifications of the documents will trigger a regeneration of that particular vector using the document template.
- This allows **importing** embeddings as a one-shot process, while still retaining the ability to regenerate embeddings on document change.
- The dump process now uses this ability: it enriches the `_vectors` fields of documents with the embeddings that were autogenerated, marking them as not `userProvided`. This allows importing the vectors from a dump without regenerating them.
### Tests
This PR adds the following tests
- Long-needed hybrid search tests of a simple hf embedder
- Dump test that imports vectors. Due to the difficulty of actually importing a dump in tests, we just read the dump and check it contains the expected content.
- Tests in the index-scheduler: this tests that documents containing the same kind of instructions as in the dump indexes as expected
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
