d8ea688481
3825: Accept semantic vectors and allow users to query nearest neighbors r=Kerollmops a=Kerollmops This Pull Request brings a new feature to the current API. The engine accepts a new `_vectors` field akin to the `_geo` one. This vector is stored in Meilisearch and can be retrieved via search. This work is the first step toward hybrid search, bringing the best of both worlds: keyword and semantic search ❤️🔥 ## ToDo - [x] Make it possible to get the `limit` nearest neighbors from a user-generated vector by using the `vector` field of search route. - [x] Delete the documents and vectors from the HNSW-related data structures. - [x] Do it the slow and ugly way (we need to be able to iterate over all the values). - [ ] Do it the efficient way (Wait for a new method or implement it myself). - [ ] ~~Move from the `hnsw` crate to the hgg one~~ The hgg crate is too slow. Meilisearch takes approximately 88s to answer a query. It is related to the time it takes to deserialize the `Hgg` data structure or search in it. I didn't take the time to measure precisely. We moved back to the hnsw crate which takes approximately 40ms to answer. - [ ] ~~Wait for a fix for https://github.com/rust-cv/hgg/issues/4.~~ - [x] Fix the current dot product function. - [x] Fill in the other `SearchResult` fields. - [x] Remove the `hnsw` dependency of the meilisearch crate. - [x] Fix the pages by taking the offset into account. - [x] Release a first prototype https://github.com/meilisearch/product/discussions/621#discussioncomment-6183647 - [x] Make the pagination and filtering faster and more correct. - [x] Return the original vector in the output search results (like `query`). - [x] Return an `_semanticSimilarity` field in the documents (it's a dot product) - [x] Return this score even if the `_vectors` field is not displayed - [x] Rename the field `_semanticScore`. - [ ] Return the `_geoDistance` value even if the `_geo` field is not displayed - [x] Store the HNSW on possibly multiple LMDB values. - [ ] Measure it and make it faster if needed - [ ] Export the `ReadableSlices` type into a small external crate - [x] Accept an `_vectors` field instead of the `_vector` one. - [x] Normalize all vectors. - [ ] Remove the `_vectors` field from the default searchable attributes (as we do with `_geo`?). - [ ] Correctly compute the candidates by remembering the documents having a valid `_vectors` field. - [ ] Return the right errors: - [ ] Return an error when the query vector is not the same length as the vectors in the HNSW. - [ ] We must return the user document id that triggered the vector dimension issue. - [x] If an indexation error occurs. - [ ] Fix the error codes when using the search route. - [ ] ~~Introduce some settings:~~ We currently ensure that the vector length is consistent over the whole set of documents and return an error for when a vector dimension doesn't follow the current number of dimensions. - [ ] The length of the vector the user will provide. - [ ] The distance function (we only support dot as of now). - [ ] Introduce other distance functions - [ ] Euclidean - [ ] Dot Product - [ ] Cosine - [ ] Make them SIMD optimized - [ ] Give credit to qdrant - [ ] Add tests. - [ ] Write a mini spec. - [ ] Release it in v1.3 as an experimental feature. Co-authored-by: Clément Renault <clement@meilisearch.com> Co-authored-by: Kerollmops <clement@meilisearch.com> |
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⚡ A lightning-fast search engine that fits effortlessly into your apps, websites, and workflow 🔍
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