mirror of
https://github.com/meilisearch/MeiliSearch
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Merge #4941
4941: Implement the binary quantization in meilisearch r=irevoire a=irevoire # Pull Request ## Related issue Fixes https://github.com/meilisearch/meilisearch/issues/4873 ## What does this PR do? - Add a settings for the binary quantization - Once enabled, the bq cannot be disabled TODO: - [ ] Missing a bunch of tests Co-authored-by: Tamo <tamo@meilisearch.com>
This commit is contained in:
meili-bors[bot]
GitHub
commit
462a2329f1
10
Cargo.lock
generated
10
Cargo.lock
generated
@ -387,14 +387,14 @@ checksum = "96d30a06541fbafbc7f82ed10c06164cfbd2c401138f6addd8404629c4b16711"
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[[package]]
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name = "arroy"
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version = "0.4.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "2ece9e5347e7fdaaea3181dec7f916677ad5f3fcbac183648ce1924eb4aeef9a"
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source = "git+https://github.com/meilisearch/arroy/?rev=2386594dfb009ce08821a925ccc89fb8e30bf73d#2386594dfb009ce08821a925ccc89fb8e30bf73d"
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dependencies = [
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"bytemuck",
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"byteorder",
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"heed",
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"log",
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"memmap2",
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"nohash",
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"ordered-float",
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"rand",
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"rayon",
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@ -3686,6 +3686,12 @@ version = "0.0.3"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "6d02c0b00610773bb7fc61d85e13d86c7858cbdf00e1a120bfc41bc055dbaa0e"
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[[package]]
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name = "nohash"
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version = "0.2.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "a0f889fb66f7acdf83442c35775764b51fed3c606ab9cee51500dbde2cf528ca"
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[[package]]
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name = "nom"
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version = "7.1.3"
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@ -255,6 +255,8 @@ pub(crate) mod test {
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}
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"###);
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insta::assert_json_snapshot!(vector_index.settings().unwrap());
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{
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let documents: Result<Vec<_>> = vector_index.documents().unwrap().collect();
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let mut documents = documents.unwrap();
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@ -1,783 +1,56 @@
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---
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source: dump/src/reader/mod.rs
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expression: document
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expression: vector_index.settings().unwrap()
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---
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{
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File diff suppressed because it is too large
Load Diff
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Load Diff
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Load Diff
@ -0,0 +1,780 @@
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---
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
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0.1,
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0.1,
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0.1,
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0.1,
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0.1,
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0.1,
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
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0.1,
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0.1,
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0.1,
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0.1,
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0.1,
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0.1,
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0.1,
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
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0.1,
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0.1,
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0.1,
|
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0.1,
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
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0.1,
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0.1,
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0.1,
|
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0.1,
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1
|
||||
]
|
||||
}
|
||||
}
|
||||
@ -40,7 +40,7 @@ ureq = "2.10.0"
|
||||
uuid = { version = "1.10.0", features = ["serde", "v4"] }
|
||||
|
||||
[dev-dependencies]
|
||||
arroy = "0.4.0"
|
||||
arroy = { git = "https://github.com/meilisearch/arroy/", rev = "2386594dfb009ce08821a925ccc89fb8e30bf73d" }
|
||||
big_s = "1.0.2"
|
||||
crossbeam = "0.8.4"
|
||||
insta = { version = "1.39.0", features = ["json", "redactions"] }
|
||||
|
||||
@ -1477,7 +1477,7 @@ impl IndexScheduler {
|
||||
.map(
|
||||
|IndexEmbeddingConfig {
|
||||
name,
|
||||
config: milli::vector::EmbeddingConfig { embedder_options, prompt },
|
||||
config: milli::vector::EmbeddingConfig { embedder_options, prompt, quantized },
|
||||
..
|
||||
}| {
|
||||
let prompt =
|
||||
@ -1486,7 +1486,10 @@ impl IndexScheduler {
|
||||
{
|
||||
let embedders = self.embedders.read().unwrap();
|
||||
if let Some(embedder) = embedders.get(&embedder_options) {
|
||||
return Ok((name, (embedder.clone(), prompt)));
|
||||
return Ok((
|
||||
name,
|
||||
(embedder.clone(), prompt, quantized.unwrap_or_default()),
|
||||
));
|
||||
}
|
||||
}
|
||||
|
||||
@ -1500,7 +1503,7 @@ impl IndexScheduler {
|
||||
let mut embedders = self.embedders.write().unwrap();
|
||||
embedders.insert(embedder_options, embedder.clone());
|
||||
}
|
||||
Ok((name, (embedder, prompt)))
|
||||
Ok((name, (embedder, prompt, quantized.unwrap_or_default())))
|
||||
},
|
||||
)
|
||||
.collect();
|
||||
@ -5197,7 +5200,7 @@ mod tests {
|
||||
let simple_hf_name = name.clone();
|
||||
|
||||
let configs = index_scheduler.embedders(configs).unwrap();
|
||||
let (hf_embedder, _) = configs.get(&simple_hf_name).unwrap();
|
||||
let (hf_embedder, _, _) = configs.get(&simple_hf_name).unwrap();
|
||||
let beagle_embed = hf_embedder.embed_one(S("Intel the beagle best doggo")).unwrap();
|
||||
let lab_embed = hf_embedder.embed_one(S("Max the lab best doggo")).unwrap();
|
||||
let patou_embed = hf_embedder.embed_one(S("kefir the patou best doggo")).unwrap();
|
||||
@ -5519,6 +5522,7 @@ mod tests {
|
||||
400,
|
||||
),
|
||||
},
|
||||
quantized: None,
|
||||
},
|
||||
user_provided: RoaringBitmap<[1, 2]>,
|
||||
},
|
||||
@ -5531,28 +5535,8 @@ mod tests {
|
||||
|
||||
// the document with the id 3 should keep its original embedding
|
||||
let docid = index.external_documents_ids.get(&rtxn, "3").unwrap().unwrap();
|
||||
let mut embeddings = Vec::new();
|
||||
|
||||
'vectors: for i in 0..=u8::MAX {
|
||||
let reader = arroy::Reader::open(&rtxn, i as u16, index.vector_arroy)
|
||||
.map(Some)
|
||||
.or_else(|e| match e {
|
||||
arroy::Error::MissingMetadata(_) => Ok(None),
|
||||
e => Err(e),
|
||||
})
|
||||
.transpose();
|
||||
|
||||
let Some(reader) = reader else {
|
||||
break 'vectors;
|
||||
};
|
||||
|
||||
let embedding = reader.unwrap().item_vector(&rtxn, docid).unwrap();
|
||||
if let Some(embedding) = embedding {
|
||||
embeddings.push(embedding)
|
||||
} else {
|
||||
break 'vectors;
|
||||
}
|
||||
}
|
||||
let embeddings = index.embeddings(&rtxn, docid).unwrap();
|
||||
let embeddings = &embeddings["my_doggo_embedder"];
|
||||
|
||||
snapshot!(embeddings.len(), @"1");
|
||||
assert!(embeddings[0].iter().all(|i| *i == 3.0), "{:?}", embeddings[0]);
|
||||
@ -5737,6 +5721,7 @@ mod tests {
|
||||
400,
|
||||
),
|
||||
},
|
||||
quantized: None,
|
||||
},
|
||||
user_provided: RoaringBitmap<[0]>,
|
||||
},
|
||||
@ -5780,6 +5765,7 @@ mod tests {
|
||||
400,
|
||||
),
|
||||
},
|
||||
quantized: None,
|
||||
},
|
||||
user_provided: RoaringBitmap<[]>,
|
||||
},
|
||||
|
||||
@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### All Tasks:
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
|
||||
2 {uid: 2, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
|
||||
@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### All Tasks:
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
|
||||
2 {uid: 2, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
|
||||
@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### All Tasks:
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
|
||||
@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### All Tasks:
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
1 {uid: 1, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
|
||||
@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### All Tasks:
|
||||
0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
enqueued [0,]
|
||||
|
||||
@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### All Tasks:
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
enqueued []
|
||||
|
||||
@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### All Tasks:
|
||||
0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
enqueued [0,]
|
||||
|
||||
@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### All Tasks:
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
enqueued []
|
||||
|
||||
@ -395,7 +395,10 @@ impl ErrorCode for milli::Error {
|
||||
| UserError::InvalidSettingsDimensions { .. }
|
||||
| UserError::InvalidUrl { .. }
|
||||
| UserError::InvalidSettingsDocumentTemplateMaxBytes { .. }
|
||||
| UserError::InvalidPrompt(_) => Code::InvalidSettingsEmbedders,
|
||||
| UserError::InvalidPrompt(_)
|
||||
| UserError::InvalidDisableBinaryQuantization { .. } => {
|
||||
Code::InvalidSettingsEmbedders
|
||||
}
|
||||
UserError::TooManyEmbedders(_) => Code::InvalidSettingsEmbedders,
|
||||
UserError::InvalidPromptForEmbeddings(..) => Code::InvalidSettingsEmbedders,
|
||||
UserError::NoPrimaryKeyCandidateFound => Code::IndexPrimaryKeyNoCandidateFound,
|
||||
|
||||
@ -643,12 +643,19 @@ fn embedder_analytics(
|
||||
.max()
|
||||
});
|
||||
|
||||
let binary_quantization_used = setting.as_ref().map(|map| {
|
||||
map.values()
|
||||
.filter_map(|config| config.clone().set())
|
||||
.any(|config| config.binary_quantized.set().is_some())
|
||||
});
|
||||
|
||||
json!(
|
||||
{
|
||||
"total": setting.as_ref().map(|s| s.len()),
|
||||
"sources": sources,
|
||||
"document_template_used": document_template_used,
|
||||
"document_template_max_bytes": document_template_max_bytes
|
||||
"document_template_max_bytes": document_template_max_bytes,
|
||||
"binary_quantization_used": binary_quantization_used,
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
@ -102,7 +102,7 @@ async fn similar(
|
||||
|
||||
let index = index_scheduler.index(&index_uid)?;
|
||||
|
||||
let (embedder_name, embedder) =
|
||||
let (embedder_name, embedder, quantized) =
|
||||
SearchKind::embedder(&index_scheduler, &index, &query.embedder, None)?;
|
||||
|
||||
tokio::task::spawn_blocking(move || {
|
||||
@ -111,6 +111,7 @@ async fn similar(
|
||||
query,
|
||||
embedder_name,
|
||||
embedder,
|
||||
quantized,
|
||||
retrieve_vectors,
|
||||
index_scheduler.features(),
|
||||
)
|
||||
|
||||
@ -274,8 +274,8 @@ pub struct HybridQuery {
|
||||
#[derive(Clone)]
|
||||
pub enum SearchKind {
|
||||
KeywordOnly,
|
||||
SemanticOnly { embedder_name: String, embedder: Arc<Embedder> },
|
||||
Hybrid { embedder_name: String, embedder: Arc<Embedder>, semantic_ratio: f32 },
|
||||
SemanticOnly { embedder_name: String, embedder: Arc<Embedder>, quantized: bool },
|
||||
Hybrid { embedder_name: String, embedder: Arc<Embedder>, quantized: bool, semantic_ratio: f32 },
|
||||
}
|
||||
|
||||
impl SearchKind {
|
||||
@ -285,9 +285,9 @@ impl SearchKind {
|
||||
embedder_name: &str,
|
||||
vector_len: Option<usize>,
|
||||
) -> Result<Self, ResponseError> {
|
||||
let (embedder_name, embedder) =
|
||||
let (embedder_name, embedder, quantized) =
|
||||
Self::embedder(index_scheduler, index, embedder_name, vector_len)?;
|
||||
Ok(Self::SemanticOnly { embedder_name, embedder })
|
||||
Ok(Self::SemanticOnly { embedder_name, embedder, quantized })
|
||||
}
|
||||
|
||||
pub(crate) fn hybrid(
|
||||
@ -297,9 +297,9 @@ impl SearchKind {
|
||||
semantic_ratio: f32,
|
||||
vector_len: Option<usize>,
|
||||
) -> Result<Self, ResponseError> {
|
||||
let (embedder_name, embedder) =
|
||||
let (embedder_name, embedder, quantized) =
|
||||
Self::embedder(index_scheduler, index, embedder_name, vector_len)?;
|
||||
Ok(Self::Hybrid { embedder_name, embedder, semantic_ratio })
|
||||
Ok(Self::Hybrid { embedder_name, embedder, quantized, semantic_ratio })
|
||||
}
|
||||
|
||||
pub(crate) fn embedder(
|
||||
@ -307,16 +307,14 @@ impl SearchKind {
|
||||
index: &Index,
|
||||
embedder_name: &str,
|
||||
vector_len: Option<usize>,
|
||||
) -> Result<(String, Arc<Embedder>), ResponseError> {
|
||||
) -> Result<(String, Arc<Embedder>, bool), ResponseError> {
|
||||
let embedder_configs = index.embedding_configs(&index.read_txn()?)?;
|
||||
let embedders = index_scheduler.embedders(embedder_configs)?;
|
||||
|
||||
let embedder = embedders.get(embedder_name);
|
||||
|
||||
let embedder = embedder
|
||||
let (embedder, _, quantized) = embedders
|
||||
.get(embedder_name)
|
||||
.ok_or(milli::UserError::InvalidEmbedder(embedder_name.to_owned()))
|
||||
.map_err(milli::Error::from)?
|
||||
.0;
|
||||
.map_err(milli::Error::from)?;
|
||||
|
||||
if let Some(vector_len) = vector_len {
|
||||
if vector_len != embedder.dimensions() {
|
||||
@ -330,7 +328,7 @@ impl SearchKind {
|
||||
}
|
||||
}
|
||||
|
||||
Ok((embedder_name.to_owned(), embedder))
|
||||
Ok((embedder_name.to_owned(), embedder, quantized))
|
||||
}
|
||||
}
|
||||
|
||||
@ -791,7 +789,7 @@ fn prepare_search<'t>(
|
||||
search.query(q);
|
||||
}
|
||||
}
|
||||
SearchKind::SemanticOnly { embedder_name, embedder } => {
|
||||
SearchKind::SemanticOnly { embedder_name, embedder, quantized } => {
|
||||
let vector = match query.vector.clone() {
|
||||
Some(vector) => vector,
|
||||
None => {
|
||||
@ -805,14 +803,19 @@ fn prepare_search<'t>(
|
||||
}
|
||||
};
|
||||
|
||||
search.semantic(embedder_name.clone(), embedder.clone(), Some(vector));
|
||||
search.semantic(embedder_name.clone(), embedder.clone(), *quantized, Some(vector));
|
||||
}
|
||||
SearchKind::Hybrid { embedder_name, embedder, semantic_ratio: _ } => {
|
||||
SearchKind::Hybrid { embedder_name, embedder, quantized, semantic_ratio: _ } => {
|
||||
if let Some(q) = &query.q {
|
||||
search.query(q);
|
||||
}
|
||||
// will be embedded in hybrid search if necessary
|
||||
search.semantic(embedder_name.clone(), embedder.clone(), query.vector.clone());
|
||||
search.semantic(
|
||||
embedder_name.clone(),
|
||||
embedder.clone(),
|
||||
*quantized,
|
||||
query.vector.clone(),
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@ -1441,6 +1444,7 @@ pub fn perform_similar(
|
||||
query: SimilarQuery,
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
quantized: bool,
|
||||
retrieve_vectors: RetrieveVectors,
|
||||
features: RoFeatures,
|
||||
) -> Result<SimilarResult, ResponseError> {
|
||||
@ -1469,8 +1473,16 @@ pub fn perform_similar(
|
||||
));
|
||||
};
|
||||
|
||||
let mut similar =
|
||||
milli::Similar::new(internal_id, offset, limit, index, &rtxn, embedder_name, embedder);
|
||||
let mut similar = milli::Similar::new(
|
||||
internal_id,
|
||||
offset,
|
||||
limit,
|
||||
index,
|
||||
&rtxn,
|
||||
embedder_name,
|
||||
embedder,
|
||||
quantized,
|
||||
);
|
||||
|
||||
if let Some(ref filter) = query.filter {
|
||||
if let Some(facets) = parse_filter(filter, Code::InvalidSimilarFilter, features)? {
|
||||
|
||||
380
meilisearch/tests/vector/binary_quantized.rs
Normal file
380
meilisearch/tests/vector/binary_quantized.rs
Normal file
@ -0,0 +1,380 @@
|
||||
use meili_snap::{json_string, snapshot};
|
||||
|
||||
use crate::common::{GetAllDocumentsOptions, Server};
|
||||
use crate::json;
|
||||
use crate::vector::generate_default_user_provided_documents;
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn retrieve_binary_quantize_status_in_the_settings() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("doggo");
|
||||
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(value, @r###"
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false,
|
||||
"editDocumentsByFunction": false,
|
||||
"containsFilter": false
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await.succeeded();
|
||||
|
||||
let (settings, code) = index.settings().await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3}"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
"binaryQuantized": false,
|
||||
}
|
||||
},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await.succeeded();
|
||||
|
||||
let (settings, code) = index.settings().await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3,"binaryQuantized":false}"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
"binaryQuantized": true,
|
||||
}
|
||||
},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await.succeeded();
|
||||
|
||||
let (settings, code) = index.settings().await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3,"binaryQuantized":true}"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn binary_quantize_before_sending_documents() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("doggo");
|
||||
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(value, @r###"
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false,
|
||||
"editDocumentsByFunction": false,
|
||||
"containsFilter": false
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
"binaryQuantized": true,
|
||||
}
|
||||
},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await.succeeded();
|
||||
|
||||
let documents = json!([
|
||||
{"id": 0, "name": "kefir", "_vectors": { "manual": [-1.2, -2.3, 3.2] }},
|
||||
{"id": 1, "name": "echo", "_vectors": { "manual": [2.5, 1.5, -130] }},
|
||||
]);
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await.succeeded();
|
||||
|
||||
// Make sure the documents are binary quantized
|
||||
let (documents, _code) = index
|
||||
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
|
||||
.await;
|
||||
snapshot!(json_string!(documents), @r###"
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"id": 0,
|
||||
"name": "kefir",
|
||||
"_vectors": {
|
||||
"manual": {
|
||||
"embeddings": [
|
||||
[
|
||||
-1.0,
|
||||
-1.0,
|
||||
1.0
|
||||
]
|
||||
],
|
||||
"regenerate": false
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"name": "echo",
|
||||
"_vectors": {
|
||||
"manual": {
|
||||
"embeddings": [
|
||||
[
|
||||
1.0,
|
||||
1.0,
|
||||
-1.0
|
||||
]
|
||||
],
|
||||
"regenerate": false
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"offset": 0,
|
||||
"limit": 20,
|
||||
"total": 2
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn binary_quantize_after_sending_documents() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("doggo");
|
||||
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(value, @r###"
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false,
|
||||
"editDocumentsByFunction": false,
|
||||
"containsFilter": false
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await.succeeded();
|
||||
|
||||
let documents = json!([
|
||||
{"id": 0, "name": "kefir", "_vectors": { "manual": [-1.2, -2.3, 3.2] }},
|
||||
{"id": 1, "name": "echo", "_vectors": { "manual": [2.5, 1.5, -130] }},
|
||||
]);
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await.succeeded();
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
"binaryQuantized": true,
|
||||
}
|
||||
},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await.succeeded();
|
||||
|
||||
// Make sure the documents are binary quantized
|
||||
let (documents, _code) = index
|
||||
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
|
||||
.await;
|
||||
snapshot!(json_string!(documents), @r###"
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"id": 0,
|
||||
"name": "kefir",
|
||||
"_vectors": {
|
||||
"manual": {
|
||||
"embeddings": [
|
||||
[
|
||||
-1.0,
|
||||
-1.0,
|
||||
1.0
|
||||
]
|
||||
],
|
||||
"regenerate": false
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"name": "echo",
|
||||
"_vectors": {
|
||||
"manual": {
|
||||
"embeddings": [
|
||||
[
|
||||
1.0,
|
||||
1.0,
|
||||
-1.0
|
||||
]
|
||||
],
|
||||
"regenerate": false
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"offset": 0,
|
||||
"limit": 20,
|
||||
"total": 2
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn try_to_disable_binary_quantization() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("doggo");
|
||||
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(value, @r###"
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false,
|
||||
"editDocumentsByFunction": false,
|
||||
"containsFilter": false
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
"binaryQuantized": true,
|
||||
}
|
||||
},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await.succeeded();
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
"binaryQuantized": false,
|
||||
}
|
||||
},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
let ret = server.wait_task(response.uid()).await;
|
||||
snapshot!(ret, @r###"
|
||||
{
|
||||
"uid": "[uid]",
|
||||
"indexUid": "doggo",
|
||||
"status": "failed",
|
||||
"type": "settingsUpdate",
|
||||
"canceledBy": null,
|
||||
"details": {
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
"binaryQuantized": false
|
||||
}
|
||||
}
|
||||
},
|
||||
"error": {
|
||||
"message": "`.embedders.manual.binaryQuantized`: Cannot disable the binary quantization.\n - Note: Binary quantization is a lossy operation that cannot be reverted.\n - Hint: Add a new embedder that is non-quantized and regenerate the vectors.",
|
||||
"code": "invalid_settings_embedders",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_settings_embedders"
|
||||
},
|
||||
"duration": "[duration]",
|
||||
"enqueuedAt": "[date]",
|
||||
"startedAt": "[date]",
|
||||
"finishedAt": "[date]"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn binary_quantize_clear_documents() {
|
||||
let server = Server::new().await;
|
||||
let index = generate_default_user_provided_documents(&server).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"binaryQuantized": true,
|
||||
}
|
||||
},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await.succeeded();
|
||||
|
||||
let (value, _code) = index.clear_all_documents().await;
|
||||
index.wait_task(value.uid()).await.succeeded();
|
||||
|
||||
// Make sure the documents DB has been cleared
|
||||
let (documents, _code) = index
|
||||
.get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
|
||||
.await;
|
||||
snapshot!(json_string!(documents), @r###"
|
||||
{
|
||||
"results": [],
|
||||
"offset": 0,
|
||||
"limit": 20,
|
||||
"total": 0
|
||||
}
|
||||
"###);
|
||||
|
||||
// Make sure the arroy DB has been cleared
|
||||
let (documents, _code) =
|
||||
index.search_post(json!({ "hybrid": { "embedder": "manual" }, "vector": [1, 1, 1] })).await;
|
||||
snapshot!(documents, @r###"
|
||||
{
|
||||
"hits": [],
|
||||
"query": "",
|
||||
"processingTimeMs": "[duration]",
|
||||
"limit": 20,
|
||||
"offset": 0,
|
||||
"estimatedTotalHits": 0,
|
||||
"semanticHitCount": 0
|
||||
}
|
||||
"###);
|
||||
}
|
||||
@ -1,3 +1,4 @@
|
||||
mod binary_quantized;
|
||||
mod openai;
|
||||
mod rest;
|
||||
mod settings;
|
||||
|
||||
@ -80,7 +80,7 @@ hf-hub = { git = "https://github.com/dureuill/hf-hub.git", branch = "rust_tls",
|
||||
tiktoken-rs = "0.5.9"
|
||||
liquid = "0.26.6"
|
||||
rhai = { version = "1.19.0", features = ["serde", "no_module", "no_custom_syntax", "no_time", "sync"] }
|
||||
arroy = "0.4.0"
|
||||
arroy = { git = "https://github.com/meilisearch/arroy/", rev = "2386594dfb009ce08821a925ccc89fb8e30bf73d" }
|
||||
rand = "0.8.5"
|
||||
tracing = "0.1.40"
|
||||
ureq = { version = "2.10.0", features = ["json"] }
|
||||
|
||||
@ -258,6 +258,10 @@ only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and undersco
|
||||
},
|
||||
#[error("`.embedders.{embedder_name}.dimensions`: `dimensions` cannot be zero")]
|
||||
InvalidSettingsDimensions { embedder_name: String },
|
||||
#[error(
|
||||
"`.embedders.{embedder_name}.binaryQuantized`: Cannot disable the binary quantization.\n - Note: Binary quantization is a lossy operation that cannot be reverted.\n - Hint: Add a new embedder that is non-quantized and regenerate the vectors."
|
||||
)]
|
||||
InvalidDisableBinaryQuantization { embedder_name: String },
|
||||
#[error("`.embedders.{embedder_name}.documentTemplateMaxBytes`: `documentTemplateMaxBytes` cannot be zero")]
|
||||
InvalidSettingsDocumentTemplateMaxBytes { embedder_name: String },
|
||||
#[error("`.embedders.{embedder_name}.url`: could not parse `{url}`: {inner_error}")]
|
||||
|
||||
@ -21,7 +21,7 @@ use crate::heed_codec::{BEU16StrCodec, FstSetCodec, StrBEU16Codec, StrRefCodec};
|
||||
use crate::order_by_map::OrderByMap;
|
||||
use crate::proximity::ProximityPrecision;
|
||||
use crate::vector::parsed_vectors::RESERVED_VECTORS_FIELD_NAME;
|
||||
use crate::vector::{Embedding, EmbeddingConfig};
|
||||
use crate::vector::{ArroyWrapper, Embedding, EmbeddingConfig};
|
||||
use crate::{
|
||||
default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds,
|
||||
FacetDistribution, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldIdWordCountCodec,
|
||||
@ -162,7 +162,7 @@ pub struct Index {
|
||||
/// Maps an embedder name to its id in the arroy store.
|
||||
pub embedder_category_id: Database<Str, U8>,
|
||||
/// Vector store based on arroy™.
|
||||
pub vector_arroy: arroy::Database<arroy::distances::Angular>,
|
||||
pub vector_arroy: arroy::Database<Unspecified>,
|
||||
|
||||
/// Maps the document id to the document as an obkv store.
|
||||
pub(crate) documents: Database<BEU32, ObkvCodec>,
|
||||
@ -1614,15 +1614,17 @@ impl Index {
|
||||
&'a self,
|
||||
rtxn: &'a RoTxn<'a>,
|
||||
embedder_id: u8,
|
||||
) -> impl Iterator<Item = Result<arroy::Reader<'a, arroy::distances::Angular>>> + 'a {
|
||||
quantized: bool,
|
||||
) -> impl Iterator<Item = Result<ArroyWrapper>> + 'a {
|
||||
crate::vector::arroy_db_range_for_embedder(embedder_id).map_while(move |k| {
|
||||
arroy::Reader::open(rtxn, k, self.vector_arroy)
|
||||
.map(Some)
|
||||
.or_else(|e| match e {
|
||||
arroy::Error::MissingMetadata(_) => Ok(None),
|
||||
e => Err(e.into()),
|
||||
})
|
||||
.transpose()
|
||||
let reader = ArroyWrapper::new(self.vector_arroy, k, quantized);
|
||||
// Here we don't care about the dimensions, but we want to know if we can read
|
||||
// in the database or if its metadata are missing because there is no document with that many vectors.
|
||||
match reader.dimensions(rtxn) {
|
||||
Ok(_) => Some(Ok(reader)),
|
||||
Err(arroy::Error::MissingMetadata(_)) => None,
|
||||
Err(e) => Some(Err(e.into())),
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
@ -1644,32 +1646,18 @@ impl Index {
|
||||
docid: DocumentId,
|
||||
) -> Result<BTreeMap<String, Vec<Embedding>>> {
|
||||
let mut res = BTreeMap::new();
|
||||
for row in self.embedder_category_id.iter(rtxn)? {
|
||||
let (embedder_name, embedder_id) = row?;
|
||||
let embedder_id = (embedder_id as u16) << 8;
|
||||
let mut embeddings = Vec::new();
|
||||
'vectors: for i in 0..=u8::MAX {
|
||||
let reader = arroy::Reader::open(rtxn, embedder_id | (i as u16), self.vector_arroy)
|
||||
.map(Some)
|
||||
.or_else(|e| match e {
|
||||
arroy::Error::MissingMetadata(_) => Ok(None),
|
||||
e => Err(e),
|
||||
})
|
||||
.transpose();
|
||||
|
||||
let Some(reader) = reader else {
|
||||
break 'vectors;
|
||||
};
|
||||
|
||||
let embedding = reader?.item_vector(rtxn, docid)?;
|
||||
if let Some(embedding) = embedding {
|
||||
embeddings.push(embedding)
|
||||
} else {
|
||||
break 'vectors;
|
||||
}
|
||||
}
|
||||
|
||||
res.insert(embedder_name.to_owned(), embeddings);
|
||||
let embedding_configs = self.embedding_configs(rtxn)?;
|
||||
for config in embedding_configs {
|
||||
let embedder_id = self.embedder_category_id.get(rtxn, &config.name)?.unwrap();
|
||||
let embeddings = self
|
||||
.arroy_readers(rtxn, embedder_id, config.config.quantized())
|
||||
.map_while(|reader| {
|
||||
reader
|
||||
.and_then(|r| r.item_vector(rtxn, docid).map_err(|e| e.into()))
|
||||
.transpose()
|
||||
})
|
||||
.collect::<Result<Vec<_>>>()?;
|
||||
res.insert(config.name.to_owned(), embeddings);
|
||||
}
|
||||
Ok(res)
|
||||
}
|
||||
|
||||
@ -190,7 +190,7 @@ impl<'a> Search<'a> {
|
||||
return Ok(return_keyword_results(self.limit, self.offset, keyword_results));
|
||||
};
|
||||
// no embedder, no semantic search
|
||||
let Some(SemanticSearch { vector, embedder_name, embedder }) = semantic else {
|
||||
let Some(SemanticSearch { vector, embedder_name, embedder, quantized }) = semantic else {
|
||||
return Ok(return_keyword_results(self.limit, self.offset, keyword_results));
|
||||
};
|
||||
|
||||
@ -212,7 +212,7 @@ impl<'a> Search<'a> {
|
||||
};
|
||||
|
||||
search.semantic =
|
||||
Some(SemanticSearch { vector: Some(vector_query), embedder_name, embedder });
|
||||
Some(SemanticSearch { vector: Some(vector_query), embedder_name, embedder, quantized });
|
||||
|
||||
// TODO: would be better to have two distinct functions at this point
|
||||
let vector_results = search.execute()?;
|
||||
|
||||
@ -32,6 +32,7 @@ pub struct SemanticSearch {
|
||||
vector: Option<Vec<f32>>,
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
quantized: bool,
|
||||
}
|
||||
|
||||
pub struct Search<'a> {
|
||||
@ -89,9 +90,10 @@ impl<'a> Search<'a> {
|
||||
&mut self,
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
quantized: bool,
|
||||
vector: Option<Vec<f32>>,
|
||||
) -> &mut Search<'a> {
|
||||
self.semantic = Some(SemanticSearch { embedder_name, embedder, vector });
|
||||
self.semantic = Some(SemanticSearch { embedder_name, embedder, quantized, vector });
|
||||
self
|
||||
}
|
||||
|
||||
@ -206,7 +208,7 @@ impl<'a> Search<'a> {
|
||||
degraded,
|
||||
used_negative_operator,
|
||||
} = match self.semantic.as_ref() {
|
||||
Some(SemanticSearch { vector: Some(vector), embedder_name, embedder }) => {
|
||||
Some(SemanticSearch { vector: Some(vector), embedder_name, embedder, quantized }) => {
|
||||
execute_vector_search(
|
||||
&mut ctx,
|
||||
vector,
|
||||
@ -219,6 +221,7 @@ impl<'a> Search<'a> {
|
||||
self.limit,
|
||||
embedder_name,
|
||||
embedder,
|
||||
*quantized,
|
||||
self.time_budget.clone(),
|
||||
self.ranking_score_threshold,
|
||||
)?
|
||||
|
||||
@ -312,6 +312,7 @@ fn get_ranking_rules_for_placeholder_search<'ctx>(
|
||||
Ok(ranking_rules)
|
||||
}
|
||||
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
fn get_ranking_rules_for_vector<'ctx>(
|
||||
ctx: &SearchContext<'ctx>,
|
||||
sort_criteria: &Option<Vec<AscDesc>>,
|
||||
@ -320,6 +321,7 @@ fn get_ranking_rules_for_vector<'ctx>(
|
||||
target: &[f32],
|
||||
embedder_name: &str,
|
||||
embedder: &Embedder,
|
||||
quantized: bool,
|
||||
) -> Result<Vec<BoxRankingRule<'ctx, PlaceholderQuery>>> {
|
||||
// query graph search
|
||||
|
||||
@ -347,6 +349,7 @@ fn get_ranking_rules_for_vector<'ctx>(
|
||||
limit_plus_offset,
|
||||
embedder_name,
|
||||
embedder,
|
||||
quantized,
|
||||
)?;
|
||||
ranking_rules.push(Box::new(vector_sort));
|
||||
vector = true;
|
||||
@ -576,6 +579,7 @@ pub fn execute_vector_search(
|
||||
length: usize,
|
||||
embedder_name: &str,
|
||||
embedder: &Embedder,
|
||||
quantized: bool,
|
||||
time_budget: TimeBudget,
|
||||
ranking_score_threshold: Option<f64>,
|
||||
) -> Result<PartialSearchResult> {
|
||||
@ -591,6 +595,7 @@ pub fn execute_vector_search(
|
||||
vector,
|
||||
embedder_name,
|
||||
embedder,
|
||||
quantized,
|
||||
)?;
|
||||
|
||||
let mut placeholder_search_logger = logger::DefaultSearchLogger;
|
||||
|
||||
@ -16,6 +16,7 @@ pub struct VectorSort<Q: RankingRuleQueryTrait> {
|
||||
limit: usize,
|
||||
distribution_shift: Option<DistributionShift>,
|
||||
embedder_index: u8,
|
||||
quantized: bool,
|
||||
}
|
||||
|
||||
impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
|
||||
@ -26,6 +27,7 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
|
||||
limit: usize,
|
||||
embedder_name: &str,
|
||||
embedder: &Embedder,
|
||||
quantized: bool,
|
||||
) -> Result<Self> {
|
||||
let embedder_index = ctx
|
||||
.index
|
||||
@ -41,6 +43,7 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
|
||||
limit,
|
||||
distribution_shift: embedder.distribution(),
|
||||
embedder_index,
|
||||
quantized,
|
||||
})
|
||||
}
|
||||
|
||||
@ -49,16 +52,12 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
|
||||
ctx: &mut SearchContext<'_>,
|
||||
vector_candidates: &RoaringBitmap,
|
||||
) -> Result<()> {
|
||||
let readers: std::result::Result<Vec<_>, _> =
|
||||
ctx.index.arroy_readers(ctx.txn, self.embedder_index).collect();
|
||||
let readers = readers?;
|
||||
|
||||
let target = &self.target;
|
||||
let mut results = Vec::new();
|
||||
|
||||
for reader in readers.iter() {
|
||||
for reader in ctx.index.arroy_readers(ctx.txn, self.embedder_index, self.quantized) {
|
||||
let nns_by_vector =
|
||||
reader.nns_by_vector(ctx.txn, target, self.limit, None, Some(vector_candidates))?;
|
||||
reader?.nns_by_vector(ctx.txn, target, self.limit, Some(vector_candidates))?;
|
||||
results.extend(nns_by_vector.into_iter());
|
||||
}
|
||||
results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));
|
||||
|
||||
@ -18,9 +18,11 @@ pub struct Similar<'a> {
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
ranking_score_threshold: Option<f64>,
|
||||
quantized: bool,
|
||||
}
|
||||
|
||||
impl<'a> Similar<'a> {
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub fn new(
|
||||
id: DocumentId,
|
||||
offset: usize,
|
||||
@ -29,6 +31,7 @@ impl<'a> Similar<'a> {
|
||||
rtxn: &'a heed::RoTxn<'a>,
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
quantized: bool,
|
||||
) -> Self {
|
||||
Self {
|
||||
id,
|
||||
@ -40,6 +43,7 @@ impl<'a> Similar<'a> {
|
||||
embedder_name,
|
||||
embedder,
|
||||
ranking_score_threshold: None,
|
||||
quantized,
|
||||
}
|
||||
}
|
||||
|
||||
@ -67,19 +71,13 @@ impl<'a> Similar<'a> {
|
||||
.get(self.rtxn, &self.embedder_name)?
|
||||
.ok_or_else(|| crate::UserError::InvalidEmbedder(self.embedder_name.to_owned()))?;
|
||||
|
||||
let readers: std::result::Result<Vec<_>, _> =
|
||||
self.index.arroy_readers(self.rtxn, embedder_index).collect();
|
||||
|
||||
let readers = readers?;
|
||||
|
||||
let mut results = Vec::new();
|
||||
|
||||
for reader in readers.iter() {
|
||||
let nns_by_item = reader.nns_by_item(
|
||||
for reader in self.index.arroy_readers(self.rtxn, embedder_index, self.quantized) {
|
||||
let nns_by_item = reader?.nns_by_item(
|
||||
self.rtxn,
|
||||
self.id,
|
||||
self.limit + self.offset + 1,
|
||||
None,
|
||||
Some(&universe),
|
||||
)?;
|
||||
if let Some(mut nns_by_item) = nns_by_item {
|
||||
|
||||
@ -20,7 +20,7 @@ use crate::update::del_add::{DelAdd, KvReaderDelAdd, KvWriterDelAdd};
|
||||
use crate::update::settings::InnerIndexSettingsDiff;
|
||||
use crate::vector::error::{EmbedErrorKind, PossibleEmbeddingMistakes, UnusedVectorsDistribution};
|
||||
use crate::vector::parsed_vectors::{ParsedVectorsDiff, VectorState, RESERVED_VECTORS_FIELD_NAME};
|
||||
use crate::vector::settings::{EmbedderAction, ReindexAction};
|
||||
use crate::vector::settings::ReindexAction;
|
||||
use crate::vector::{Embedder, Embeddings};
|
||||
use crate::{try_split_array_at, DocumentId, FieldId, Result, ThreadPoolNoAbort};
|
||||
|
||||
@ -208,65 +208,65 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
|
||||
|
||||
if reindex_vectors {
|
||||
for (name, action) in settings_diff.embedding_config_updates.iter() {
|
||||
match action {
|
||||
EmbedderAction::WriteBackToDocuments(_) => continue, // already deleted
|
||||
EmbedderAction::Reindex(action) => {
|
||||
let Some((embedder_name, (embedder, prompt))) = configs.remove_entry(name)
|
||||
else {
|
||||
tracing::error!(embedder = name, "Requested embedder config not found");
|
||||
continue;
|
||||
};
|
||||
if let Some(action) = action.reindex() {
|
||||
let Some((embedder_name, (embedder, prompt, _quantized))) =
|
||||
configs.remove_entry(name)
|
||||
else {
|
||||
tracing::error!(embedder = name, "Requested embedder config not found");
|
||||
continue;
|
||||
};
|
||||
|
||||
// (docid, _index) -> KvWriterDelAdd -> Vector
|
||||
let manual_vectors_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
// (docid, _index) -> KvWriterDelAdd -> Vector
|
||||
let manual_vectors_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
// (docid) -> (prompt)
|
||||
let prompts_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
// (docid) -> (prompt)
|
||||
let prompts_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
// (docid) -> ()
|
||||
let remove_vectors_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
// (docid) -> ()
|
||||
let remove_vectors_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
let action = match action {
|
||||
ReindexAction::FullReindex => ExtractionAction::SettingsFullReindex,
|
||||
ReindexAction::RegeneratePrompts => {
|
||||
let Some((_, old_prompt)) = old_configs.get(name) else {
|
||||
tracing::error!(embedder = name, "Old embedder config not found");
|
||||
continue;
|
||||
};
|
||||
let action = match action {
|
||||
ReindexAction::FullReindex => ExtractionAction::SettingsFullReindex,
|
||||
ReindexAction::RegeneratePrompts => {
|
||||
let Some((_, old_prompt, _quantized)) = old_configs.get(name) else {
|
||||
tracing::error!(embedder = name, "Old embedder config not found");
|
||||
continue;
|
||||
};
|
||||
|
||||
ExtractionAction::SettingsRegeneratePrompts { old_prompt }
|
||||
}
|
||||
};
|
||||
ExtractionAction::SettingsRegeneratePrompts { old_prompt }
|
||||
}
|
||||
};
|
||||
|
||||
extractors.push(EmbedderVectorExtractor {
|
||||
embedder_name,
|
||||
embedder,
|
||||
prompt,
|
||||
prompts_writer,
|
||||
remove_vectors_writer,
|
||||
manual_vectors_writer,
|
||||
add_to_user_provided: RoaringBitmap::new(),
|
||||
action,
|
||||
});
|
||||
}
|
||||
extractors.push(EmbedderVectorExtractor {
|
||||
embedder_name,
|
||||
embedder,
|
||||
prompt,
|
||||
prompts_writer,
|
||||
remove_vectors_writer,
|
||||
manual_vectors_writer,
|
||||
add_to_user_provided: RoaringBitmap::new(),
|
||||
action,
|
||||
});
|
||||
} else {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// document operation
|
||||
|
||||
for (embedder_name, (embedder, prompt)) in configs.into_iter() {
|
||||
for (embedder_name, (embedder, prompt, _quantized)) in configs.into_iter() {
|
||||
// (docid, _index) -> KvWriterDelAdd -> Vector
|
||||
let manual_vectors_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
|
||||
@ -43,7 +43,7 @@ use crate::update::index_documents::parallel::ImmutableObkvs;
|
||||
use crate::update::{
|
||||
IndexerConfig, UpdateIndexingStep, WordPrefixDocids, WordPrefixIntegerDocids, WordsPrefixesFst,
|
||||
};
|
||||
use crate::vector::EmbeddingConfigs;
|
||||
use crate::vector::{ArroyWrapper, EmbeddingConfigs};
|
||||
use crate::{CboRoaringBitmapCodec, Index, Object, Result};
|
||||
|
||||
static MERGED_DATABASE_COUNT: usize = 7;
|
||||
@ -679,6 +679,24 @@ where
|
||||
let number_of_documents = self.index.number_of_documents(self.wtxn)?;
|
||||
let mut rng = rand::rngs::StdRng::seed_from_u64(42);
|
||||
|
||||
// If an embedder wasn't used in the typedchunk but must be binary quantized
|
||||
// we should insert it in `dimension`
|
||||
for (name, action) in settings_diff.embedding_config_updates.iter() {
|
||||
if action.is_being_quantized && !dimension.contains_key(name.as_str()) {
|
||||
let index = self.index.embedder_category_id.get(self.wtxn, name)?.ok_or(
|
||||
InternalError::DatabaseMissingEntry {
|
||||
db_name: "embedder_category_id",
|
||||
key: None,
|
||||
},
|
||||
)?;
|
||||
let first_id = crate::vector::arroy_db_range_for_embedder(index).next().unwrap();
|
||||
let reader =
|
||||
ArroyWrapper::new(self.index.vector_arroy, first_id, action.was_quantized);
|
||||
let dim = reader.dimensions(self.wtxn)?;
|
||||
dimension.insert(name.to_string(), dim);
|
||||
}
|
||||
}
|
||||
|
||||
for (embedder_name, dimension) in dimension {
|
||||
let wtxn = &mut *self.wtxn;
|
||||
let vector_arroy = self.index.vector_arroy;
|
||||
@ -686,13 +704,23 @@ where
|
||||
let embedder_index = self.index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or(
|
||||
InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None },
|
||||
)?;
|
||||
let embedder_config = settings_diff.embedding_config_updates.get(&embedder_name);
|
||||
let was_quantized = settings_diff
|
||||
.old
|
||||
.embedding_configs
|
||||
.get(&embedder_name)
|
||||
.map_or(false, |conf| conf.2);
|
||||
let is_quantizing = embedder_config.map_or(false, |action| action.is_being_quantized);
|
||||
|
||||
pool.install(|| {
|
||||
for k in crate::vector::arroy_db_range_for_embedder(embedder_index) {
|
||||
let writer = arroy::Writer::new(vector_arroy, k, dimension);
|
||||
if writer.need_build(wtxn)? {
|
||||
writer.build(wtxn, &mut rng, None)?;
|
||||
} else if writer.is_empty(wtxn)? {
|
||||
let mut writer = ArroyWrapper::new(vector_arroy, k, was_quantized);
|
||||
if is_quantizing {
|
||||
writer.quantize(wtxn, k, dimension)?;
|
||||
}
|
||||
if writer.need_build(wtxn, dimension)? {
|
||||
writer.build(wtxn, &mut rng, dimension)?;
|
||||
} else if writer.is_empty(wtxn, dimension)? {
|
||||
break;
|
||||
}
|
||||
}
|
||||
@ -2746,6 +2774,7 @@ mod tests {
|
||||
response: Setting::NotSet,
|
||||
distribution: Setting::NotSet,
|
||||
headers: Setting::NotSet,
|
||||
binary_quantized: Setting::NotSet,
|
||||
}),
|
||||
);
|
||||
settings.set_embedder_settings(embedders);
|
||||
@ -2774,7 +2803,7 @@ mod tests {
|
||||
std::sync::Arc::new(crate::vector::Embedder::new(embedder.embedder_options).unwrap());
|
||||
let res = index
|
||||
.search(&rtxn)
|
||||
.semantic(embedder_name, embedder, Some([0.0, 1.0, 2.0].to_vec()))
|
||||
.semantic(embedder_name, embedder, false, Some([0.0, 1.0, 2.0].to_vec()))
|
||||
.execute()
|
||||
.unwrap();
|
||||
assert_eq!(res.documents_ids.len(), 3);
|
||||
|
||||
@ -28,7 +28,8 @@ use crate::update::index_documents::GrenadParameters;
|
||||
use crate::update::settings::{InnerIndexSettings, InnerIndexSettingsDiff};
|
||||
use crate::update::{AvailableDocumentsIds, UpdateIndexingStep};
|
||||
use crate::vector::parsed_vectors::{ExplicitVectors, VectorOrArrayOfVectors};
|
||||
use crate::vector::settings::{EmbedderAction, WriteBackToDocuments};
|
||||
use crate::vector::settings::WriteBackToDocuments;
|
||||
use crate::vector::ArroyWrapper;
|
||||
use crate::{
|
||||
is_faceted_by, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldsIdsMap, Index, Result,
|
||||
};
|
||||
@ -989,19 +990,17 @@ impl<'a, 'i> Transform<'a, 'i> {
|
||||
None
|
||||
};
|
||||
|
||||
let readers: Result<
|
||||
BTreeMap<&str, (Vec<arroy::Reader<'_, arroy::distances::Angular>>, &RoaringBitmap)>,
|
||||
> = settings_diff
|
||||
let readers: Result<BTreeMap<&str, (Vec<ArroyWrapper>, &RoaringBitmap)>> = settings_diff
|
||||
.embedding_config_updates
|
||||
.iter()
|
||||
.filter_map(|(name, action)| {
|
||||
if let EmbedderAction::WriteBackToDocuments(WriteBackToDocuments {
|
||||
embedder_id,
|
||||
user_provided,
|
||||
}) = action
|
||||
if let Some(WriteBackToDocuments { embedder_id, user_provided }) =
|
||||
action.write_back()
|
||||
{
|
||||
let readers: Result<Vec<_>> =
|
||||
self.index.arroy_readers(wtxn, *embedder_id).collect();
|
||||
let readers: Result<Vec<_>> = self
|
||||
.index
|
||||
.arroy_readers(wtxn, *embedder_id, action.was_quantized)
|
||||
.collect();
|
||||
match readers {
|
||||
Ok(readers) => Some(Ok((name.as_str(), (readers, user_provided)))),
|
||||
Err(error) => Some(Err(error)),
|
||||
@ -1104,23 +1103,14 @@ impl<'a, 'i> Transform<'a, 'i> {
|
||||
}
|
||||
}
|
||||
|
||||
let mut writers = Vec::new();
|
||||
|
||||
// delete all vectors from the embedders that need removal
|
||||
for (_, (readers, _)) in readers {
|
||||
for reader in readers {
|
||||
let dimensions = reader.dimensions();
|
||||
let arroy_index = reader.index();
|
||||
drop(reader);
|
||||
let writer = arroy::Writer::new(self.index.vector_arroy, arroy_index, dimensions);
|
||||
writers.push(writer);
|
||||
let dimensions = reader.dimensions(wtxn)?;
|
||||
reader.clear(wtxn, dimensions)?;
|
||||
}
|
||||
}
|
||||
|
||||
for writer in writers {
|
||||
writer.clear(wtxn)?;
|
||||
}
|
||||
|
||||
let grenad_params = GrenadParameters {
|
||||
chunk_compression_type: self.indexer_settings.chunk_compression_type,
|
||||
chunk_compression_level: self.indexer_settings.chunk_compression_level,
|
||||
|
||||
@ -27,6 +27,7 @@ use crate::update::index_documents::helpers::{
|
||||
as_cloneable_grenad, keep_latest_obkv, try_split_array_at,
|
||||
};
|
||||
use crate::update::settings::InnerIndexSettingsDiff;
|
||||
use crate::vector::ArroyWrapper;
|
||||
use crate::{
|
||||
lat_lng_to_xyz, CboRoaringBitmapCodec, DocumentId, FieldId, GeoPoint, Index, InternalError,
|
||||
Result, SerializationError, U8StrStrCodec,
|
||||
@ -666,9 +667,14 @@ pub(crate) fn write_typed_chunk_into_index(
|
||||
let embedder_index = index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or(
|
||||
InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None },
|
||||
)?;
|
||||
let binary_quantized = settings_diff
|
||||
.old
|
||||
.embedding_configs
|
||||
.get(&embedder_name)
|
||||
.map_or(false, |conf| conf.2);
|
||||
// FIXME: allow customizing distance
|
||||
let writers: Vec<_> = crate::vector::arroy_db_range_for_embedder(embedder_index)
|
||||
.map(|k| arroy::Writer::new(index.vector_arroy, k, expected_dimension))
|
||||
.map(|k| ArroyWrapper::new(index.vector_arroy, k, binary_quantized))
|
||||
.collect();
|
||||
|
||||
// remove vectors for docids we want them removed
|
||||
@ -679,7 +685,7 @@ pub(crate) fn write_typed_chunk_into_index(
|
||||
|
||||
for writer in &writers {
|
||||
// Uses invariant: vectors are packed in the first writers.
|
||||
if !writer.del_item(wtxn, docid)? {
|
||||
if !writer.del_item(wtxn, expected_dimension, docid)? {
|
||||
break;
|
||||
}
|
||||
}
|
||||
@ -711,7 +717,7 @@ pub(crate) fn write_typed_chunk_into_index(
|
||||
)));
|
||||
}
|
||||
for (embedding, writer) in embeddings.iter().zip(&writers) {
|
||||
writer.add_item(wtxn, docid, embedding)?;
|
||||
writer.add_item(wtxn, expected_dimension, docid, embedding)?;
|
||||
}
|
||||
}
|
||||
|
||||
@ -734,7 +740,7 @@ pub(crate) fn write_typed_chunk_into_index(
|
||||
break;
|
||||
};
|
||||
if candidate == vector {
|
||||
writer.del_item(wtxn, docid)?;
|
||||
writer.del_item(wtxn, expected_dimension, docid)?;
|
||||
deleted_index = Some(index);
|
||||
}
|
||||
}
|
||||
@ -751,8 +757,13 @@ pub(crate) fn write_typed_chunk_into_index(
|
||||
if let Some((last_index, vector)) = last_index_with_a_vector {
|
||||
// unwrap: computed the index from the list of writers
|
||||
let writer = writers.get(last_index).unwrap();
|
||||
writer.del_item(wtxn, docid)?;
|
||||
writers.get(deleted_index).unwrap().add_item(wtxn, docid, &vector)?;
|
||||
writer.del_item(wtxn, expected_dimension, docid)?;
|
||||
writers.get(deleted_index).unwrap().add_item(
|
||||
wtxn,
|
||||
expected_dimension,
|
||||
docid,
|
||||
&vector,
|
||||
)?;
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -762,8 +773,8 @@ pub(crate) fn write_typed_chunk_into_index(
|
||||
|
||||
// overflow was detected during vector extraction.
|
||||
for writer in &writers {
|
||||
if !writer.contains_item(wtxn, docid)? {
|
||||
writer.add_item(wtxn, docid, &vector)?;
|
||||
if !writer.contains_item(wtxn, expected_dimension, docid)? {
|
||||
writer.add_item(wtxn, expected_dimension, docid, &vector)?;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
@ -954,7 +954,7 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
|
||||
let old_configs = self.index.embedding_configs(self.wtxn)?;
|
||||
let remove_all: Result<BTreeMap<String, EmbedderAction>> = old_configs
|
||||
.into_iter()
|
||||
.map(|IndexEmbeddingConfig { name, config: _, user_provided }| -> Result<_> {
|
||||
.map(|IndexEmbeddingConfig { name, config, user_provided }| -> Result<_> {
|
||||
let embedder_id =
|
||||
self.index.embedder_category_id.get(self.wtxn, &name)?.ok_or(
|
||||
crate::InternalError::DatabaseMissingEntry {
|
||||
@ -964,10 +964,10 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
|
||||
)?;
|
||||
Ok((
|
||||
name,
|
||||
EmbedderAction::WriteBackToDocuments(WriteBackToDocuments {
|
||||
embedder_id,
|
||||
user_provided,
|
||||
}),
|
||||
EmbedderAction::with_write_back(
|
||||
WriteBackToDocuments { embedder_id, user_provided },
|
||||
config.quantized(),
|
||||
),
|
||||
))
|
||||
})
|
||||
.collect();
|
||||
@ -1004,7 +1004,8 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
|
||||
match joined {
|
||||
// updated config
|
||||
EitherOrBoth::Both((name, (old, user_provided)), (_, new)) => {
|
||||
let settings_diff = SettingsDiff::from_settings(old, new);
|
||||
let was_quantized = old.binary_quantized.set().unwrap_or_default();
|
||||
let settings_diff = SettingsDiff::from_settings(&name, old, new)?;
|
||||
match settings_diff {
|
||||
SettingsDiff::Remove => {
|
||||
tracing::debug!(
|
||||
@ -1023,25 +1024,29 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
|
||||
self.index.embedder_category_id.delete(self.wtxn, &name)?;
|
||||
embedder_actions.insert(
|
||||
name,
|
||||
EmbedderAction::WriteBackToDocuments(WriteBackToDocuments {
|
||||
embedder_id,
|
||||
user_provided,
|
||||
}),
|
||||
EmbedderAction::with_write_back(
|
||||
WriteBackToDocuments { embedder_id, user_provided },
|
||||
was_quantized,
|
||||
),
|
||||
);
|
||||
}
|
||||
SettingsDiff::Reindex { action, updated_settings } => {
|
||||
SettingsDiff::Reindex { action, updated_settings, quantize } => {
|
||||
tracing::debug!(
|
||||
embedder = name,
|
||||
user_provided = user_provided.len(),
|
||||
?action,
|
||||
"reindex embedder"
|
||||
);
|
||||
embedder_actions.insert(name.clone(), EmbedderAction::Reindex(action));
|
||||
embedder_actions.insert(
|
||||
name.clone(),
|
||||
EmbedderAction::with_reindex(action, was_quantized)
|
||||
.with_is_being_quantized(quantize),
|
||||
);
|
||||
let new =
|
||||
validate_embedding_settings(Setting::Set(updated_settings), &name)?;
|
||||
updated_configs.insert(name, (new, user_provided));
|
||||
}
|
||||
SettingsDiff::UpdateWithoutReindex { updated_settings } => {
|
||||
SettingsDiff::UpdateWithoutReindex { updated_settings, quantize } => {
|
||||
tracing::debug!(
|
||||
embedder = name,
|
||||
user_provided = user_provided.len(),
|
||||
@ -1049,6 +1054,12 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
|
||||
);
|
||||
let new =
|
||||
validate_embedding_settings(Setting::Set(updated_settings), &name)?;
|
||||
if quantize {
|
||||
embedder_actions.insert(
|
||||
name.clone(),
|
||||
EmbedderAction::default().with_is_being_quantized(true),
|
||||
);
|
||||
}
|
||||
updated_configs.insert(name, (new, user_provided));
|
||||
}
|
||||
}
|
||||
@ -1067,8 +1078,10 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
|
||||
&mut setting,
|
||||
);
|
||||
let setting = validate_embedding_settings(setting, &name)?;
|
||||
embedder_actions
|
||||
.insert(name.clone(), EmbedderAction::Reindex(ReindexAction::FullReindex));
|
||||
embedder_actions.insert(
|
||||
name.clone(),
|
||||
EmbedderAction::with_reindex(ReindexAction::FullReindex, false),
|
||||
);
|
||||
updated_configs.insert(name, (setting, RoaringBitmap::new()));
|
||||
}
|
||||
}
|
||||
@ -1082,19 +1095,14 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
|
||||
let mut find_free_index =
|
||||
move || free_indices.find(|(_, free)| **free).map(|(index, _)| index as u8);
|
||||
for (name, action) in embedder_actions.iter() {
|
||||
match action {
|
||||
EmbedderAction::Reindex(ReindexAction::RegeneratePrompts) => {
|
||||
/* cannot be a new embedder, so has to have an id already */
|
||||
}
|
||||
EmbedderAction::Reindex(ReindexAction::FullReindex) => {
|
||||
if self.index.embedder_category_id.get(self.wtxn, name)?.is_none() {
|
||||
let id = find_free_index()
|
||||
.ok_or(UserError::TooManyEmbedders(updated_configs.len()))?;
|
||||
tracing::debug!(embedder = name, id, "assigning free id to new embedder");
|
||||
self.index.embedder_category_id.put(self.wtxn, name, &id)?;
|
||||
}
|
||||
}
|
||||
EmbedderAction::WriteBackToDocuments(_) => { /* already removed */ }
|
||||
// ignore actions that are not possible for a new embedder
|
||||
if matches!(action.reindex(), Some(ReindexAction::FullReindex))
|
||||
&& self.index.embedder_category_id.get(self.wtxn, name)?.is_none()
|
||||
{
|
||||
let id =
|
||||
find_free_index().ok_or(UserError::TooManyEmbedders(updated_configs.len()))?;
|
||||
tracing::debug!(embedder = name, id, "assigning free id to new embedder");
|
||||
self.index.embedder_category_id.put(self.wtxn, name, &id)?;
|
||||
}
|
||||
}
|
||||
let updated_configs: Vec<IndexEmbeddingConfig> = updated_configs
|
||||
@ -1277,7 +1285,11 @@ impl InnerIndexSettingsDiff {
|
||||
|
||||
// if the user-defined searchables changed, then we need to reindex prompts.
|
||||
if cache_user_defined_searchables {
|
||||
for (embedder_name, (config, _)) in new_settings.embedding_configs.inner_as_ref() {
|
||||
for (embedder_name, (config, _, _quantized)) in
|
||||
new_settings.embedding_configs.inner_as_ref()
|
||||
{
|
||||
let was_quantized =
|
||||
old_settings.embedding_configs.get(embedder_name).map_or(false, |conf| conf.2);
|
||||
// skip embedders that don't use document templates
|
||||
if !config.uses_document_template() {
|
||||
continue;
|
||||
@ -1287,16 +1299,19 @@ impl InnerIndexSettingsDiff {
|
||||
// this always makes the code clearer by explicitly handling the cases
|
||||
match embedding_config_updates.entry(embedder_name.clone()) {
|
||||
std::collections::btree_map::Entry::Vacant(entry) => {
|
||||
entry.insert(EmbedderAction::Reindex(ReindexAction::RegeneratePrompts));
|
||||
entry.insert(EmbedderAction::with_reindex(
|
||||
ReindexAction::RegeneratePrompts,
|
||||
was_quantized,
|
||||
));
|
||||
}
|
||||
std::collections::btree_map::Entry::Occupied(entry) => {
|
||||
let EmbedderAction {
|
||||
was_quantized: _,
|
||||
is_being_quantized: _,
|
||||
write_back: _, // We are deleting this embedder, so no point in regeneration
|
||||
reindex: _, // We are already fully reindexing
|
||||
} = entry.get();
|
||||
}
|
||||
std::collections::btree_map::Entry::Occupied(entry) => match entry.get() {
|
||||
EmbedderAction::WriteBackToDocuments(_) => { /* we are deleting this embedder, so no point in regeneration */
|
||||
}
|
||||
EmbedderAction::Reindex(ReindexAction::FullReindex) => { /* we are already fully reindexing */
|
||||
}
|
||||
EmbedderAction::Reindex(ReindexAction::RegeneratePrompts) => { /* we are already regenerating prompts */
|
||||
}
|
||||
},
|
||||
};
|
||||
}
|
||||
}
|
||||
@ -1546,7 +1561,7 @@ fn embedders(embedding_configs: Vec<IndexEmbeddingConfig>) -> Result<EmbeddingCo
|
||||
.map(
|
||||
|IndexEmbeddingConfig {
|
||||
name,
|
||||
config: EmbeddingConfig { embedder_options, prompt },
|
||||
config: EmbeddingConfig { embedder_options, prompt, quantized },
|
||||
..
|
||||
}| {
|
||||
let prompt = Arc::new(prompt.try_into().map_err(crate::Error::from)?);
|
||||
@ -1556,7 +1571,7 @@ fn embedders(embedding_configs: Vec<IndexEmbeddingConfig>) -> Result<EmbeddingCo
|
||||
.map_err(crate::vector::Error::from)
|
||||
.map_err(crate::Error::from)?,
|
||||
);
|
||||
Ok((name, (embedder, prompt)))
|
||||
Ok((name, (embedder, prompt, quantized.unwrap_or_default())))
|
||||
},
|
||||
)
|
||||
.collect();
|
||||
@ -1581,6 +1596,7 @@ fn validate_prompt(
|
||||
response,
|
||||
distribution,
|
||||
headers,
|
||||
binary_quantized: binary_quantize,
|
||||
}) => {
|
||||
let max_bytes = match document_template_max_bytes.set() {
|
||||
Some(max_bytes) => NonZeroUsize::new(max_bytes).ok_or_else(|| {
|
||||
@ -1613,6 +1629,7 @@ fn validate_prompt(
|
||||
response,
|
||||
distribution,
|
||||
headers,
|
||||
binary_quantized: binary_quantize,
|
||||
}))
|
||||
}
|
||||
new => Ok(new),
|
||||
@ -1638,6 +1655,7 @@ pub fn validate_embedding_settings(
|
||||
response,
|
||||
distribution,
|
||||
headers,
|
||||
binary_quantized: binary_quantize,
|
||||
} = settings;
|
||||
|
||||
if let Some(0) = dimensions.set() {
|
||||
@ -1678,6 +1696,7 @@ pub fn validate_embedding_settings(
|
||||
response,
|
||||
distribution,
|
||||
headers,
|
||||
binary_quantized: binary_quantize,
|
||||
}));
|
||||
};
|
||||
match inferred_source {
|
||||
@ -1779,6 +1798,7 @@ pub fn validate_embedding_settings(
|
||||
response,
|
||||
distribution,
|
||||
headers,
|
||||
binary_quantized: binary_quantize,
|
||||
}))
|
||||
}
|
||||
|
||||
|
||||
@ -1,8 +1,12 @@
|
||||
use std::collections::HashMap;
|
||||
use std::sync::Arc;
|
||||
|
||||
use arroy::distances::{Angular, BinaryQuantizedAngular};
|
||||
use arroy::ItemId;
|
||||
use deserr::{DeserializeError, Deserr};
|
||||
use heed::{RoTxn, RwTxn, Unspecified};
|
||||
use ordered_float::OrderedFloat;
|
||||
use roaring::RoaringBitmap;
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use self::error::{EmbedError, NewEmbedderError};
|
||||
@ -26,6 +30,171 @@ pub type Embedding = Vec<f32>;
|
||||
|
||||
pub const REQUEST_PARALLELISM: usize = 40;
|
||||
|
||||
pub struct ArroyWrapper {
|
||||
quantized: bool,
|
||||
index: u16,
|
||||
database: arroy::Database<Unspecified>,
|
||||
}
|
||||
|
||||
impl ArroyWrapper {
|
||||
pub fn new(database: arroy::Database<Unspecified>, index: u16, quantized: bool) -> Self {
|
||||
Self { database, index, quantized }
|
||||
}
|
||||
|
||||
pub fn index(&self) -> u16 {
|
||||
self.index
|
||||
}
|
||||
|
||||
pub fn dimensions(&self, rtxn: &RoTxn) -> Result<usize, arroy::Error> {
|
||||
if self.quantized {
|
||||
Ok(arroy::Reader::open(rtxn, self.index, self.quantized_db())?.dimensions())
|
||||
} else {
|
||||
Ok(arroy::Reader::open(rtxn, self.index, self.angular_db())?.dimensions())
|
||||
}
|
||||
}
|
||||
|
||||
pub fn quantize(
|
||||
&mut self,
|
||||
wtxn: &mut RwTxn,
|
||||
index: u16,
|
||||
dimension: usize,
|
||||
) -> Result<(), arroy::Error> {
|
||||
if !self.quantized {
|
||||
let writer = arroy::Writer::new(self.angular_db(), index, dimension);
|
||||
writer.prepare_changing_distance::<BinaryQuantizedAngular>(wtxn)?;
|
||||
self.quantized = true;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn need_build(&self, rtxn: &RoTxn, dimension: usize) -> Result<bool, arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Writer::new(self.quantized_db(), self.index, dimension).need_build(rtxn)
|
||||
} else {
|
||||
arroy::Writer::new(self.angular_db(), self.index, dimension).need_build(rtxn)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn build<R: rand::Rng + rand::SeedableRng>(
|
||||
&self,
|
||||
wtxn: &mut RwTxn,
|
||||
rng: &mut R,
|
||||
dimension: usize,
|
||||
) -> Result<(), arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Writer::new(self.quantized_db(), self.index, dimension).build(wtxn, rng, None)
|
||||
} else {
|
||||
arroy::Writer::new(self.angular_db(), self.index, dimension).build(wtxn, rng, None)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn add_item(
|
||||
&self,
|
||||
wtxn: &mut RwTxn,
|
||||
dimension: usize,
|
||||
item_id: arroy::ItemId,
|
||||
vector: &[f32],
|
||||
) -> Result<(), arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Writer::new(self.quantized_db(), self.index, dimension)
|
||||
.add_item(wtxn, item_id, vector)
|
||||
} else {
|
||||
arroy::Writer::new(self.angular_db(), self.index, dimension)
|
||||
.add_item(wtxn, item_id, vector)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn del_item(
|
||||
&self,
|
||||
wtxn: &mut RwTxn,
|
||||
dimension: usize,
|
||||
item_id: arroy::ItemId,
|
||||
) -> Result<bool, arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Writer::new(self.quantized_db(), self.index, dimension).del_item(wtxn, item_id)
|
||||
} else {
|
||||
arroy::Writer::new(self.angular_db(), self.index, dimension).del_item(wtxn, item_id)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn clear(&self, wtxn: &mut RwTxn, dimension: usize) -> Result<(), arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Writer::new(self.quantized_db(), self.index, dimension).clear(wtxn)
|
||||
} else {
|
||||
arroy::Writer::new(self.angular_db(), self.index, dimension).clear(wtxn)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn is_empty(&self, rtxn: &RoTxn, dimension: usize) -> Result<bool, arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Writer::new(self.quantized_db(), self.index, dimension).is_empty(rtxn)
|
||||
} else {
|
||||
arroy::Writer::new(self.angular_db(), self.index, dimension).is_empty(rtxn)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn contains_item(
|
||||
&self,
|
||||
rtxn: &RoTxn,
|
||||
dimension: usize,
|
||||
item: arroy::ItemId,
|
||||
) -> Result<bool, arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Writer::new(self.quantized_db(), self.index, dimension).contains_item(rtxn, item)
|
||||
} else {
|
||||
arroy::Writer::new(self.angular_db(), self.index, dimension).contains_item(rtxn, item)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn nns_by_item(
|
||||
&self,
|
||||
rtxn: &RoTxn,
|
||||
item: ItemId,
|
||||
limit: usize,
|
||||
filter: Option<&RoaringBitmap>,
|
||||
) -> Result<Option<Vec<(ItemId, f32)>>, arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Reader::open(rtxn, self.index, self.quantized_db())?
|
||||
.nns_by_item(rtxn, item, limit, None, None, filter)
|
||||
} else {
|
||||
arroy::Reader::open(rtxn, self.index, self.angular_db())?
|
||||
.nns_by_item(rtxn, item, limit, None, None, filter)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn nns_by_vector(
|
||||
&self,
|
||||
txn: &RoTxn,
|
||||
item: &[f32],
|
||||
limit: usize,
|
||||
filter: Option<&RoaringBitmap>,
|
||||
) -> Result<Vec<(ItemId, f32)>, arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Reader::open(txn, self.index, self.quantized_db())?
|
||||
.nns_by_vector(txn, item, limit, None, None, filter)
|
||||
} else {
|
||||
arroy::Reader::open(txn, self.index, self.angular_db())?
|
||||
.nns_by_vector(txn, item, limit, None, None, filter)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn item_vector(&self, rtxn: &RoTxn, docid: u32) -> Result<Option<Vec<f32>>, arroy::Error> {
|
||||
if self.quantized {
|
||||
arroy::Reader::open(rtxn, self.index, self.quantized_db())?.item_vector(rtxn, docid)
|
||||
} else {
|
||||
arroy::Reader::open(rtxn, self.index, self.angular_db())?.item_vector(rtxn, docid)
|
||||
}
|
||||
}
|
||||
|
||||
fn angular_db(&self) -> arroy::Database<Angular> {
|
||||
self.database.remap_data_type()
|
||||
}
|
||||
|
||||
fn quantized_db(&self) -> arroy::Database<BinaryQuantizedAngular> {
|
||||
self.database.remap_data_type()
|
||||
}
|
||||
}
|
||||
|
||||
/// One or multiple embeddings stored consecutively in a flat vector.
|
||||
pub struct Embeddings<F> {
|
||||
data: Vec<F>,
|
||||
@ -124,39 +293,48 @@ pub struct EmbeddingConfig {
|
||||
pub embedder_options: EmbedderOptions,
|
||||
/// Document template
|
||||
pub prompt: PromptData,
|
||||
/// If this embedder is binary quantized
|
||||
pub quantized: Option<bool>,
|
||||
// TODO: add metrics and anything needed
|
||||
}
|
||||
|
||||
impl EmbeddingConfig {
|
||||
pub fn quantized(&self) -> bool {
|
||||
self.quantized.unwrap_or_default()
|
||||
}
|
||||
}
|
||||
|
||||
/// Map of embedder configurations.
|
||||
///
|
||||
/// Each configuration is mapped to a name.
|
||||
#[derive(Clone, Default)]
|
||||
pub struct EmbeddingConfigs(HashMap<String, (Arc<Embedder>, Arc<Prompt>)>);
|
||||
pub struct EmbeddingConfigs(HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)>);
|
||||
|
||||
impl EmbeddingConfigs {
|
||||
/// Create the map from its internal component.s
|
||||
pub fn new(data: HashMap<String, (Arc<Embedder>, Arc<Prompt>)>) -> Self {
|
||||
pub fn new(data: HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)>) -> Self {
|
||||
Self(data)
|
||||
}
|
||||
|
||||
/// Get an embedder configuration and template from its name.
|
||||
pub fn get(&self, name: &str) -> Option<(Arc<Embedder>, Arc<Prompt>)> {
|
||||
pub fn get(&self, name: &str) -> Option<(Arc<Embedder>, Arc<Prompt>, bool)> {
|
||||
self.0.get(name).cloned()
|
||||
}
|
||||
|
||||
pub fn inner_as_ref(&self) -> &HashMap<String, (Arc<Embedder>, Arc<Prompt>)> {
|
||||
pub fn inner_as_ref(&self) -> &HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)> {
|
||||
&self.0
|
||||
}
|
||||
|
||||
pub fn into_inner(self) -> HashMap<String, (Arc<Embedder>, Arc<Prompt>)> {
|
||||
pub fn into_inner(self) -> HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)> {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
impl IntoIterator for EmbeddingConfigs {
|
||||
type Item = (String, (Arc<Embedder>, Arc<Prompt>));
|
||||
type Item = (String, (Arc<Embedder>, Arc<Prompt>, bool));
|
||||
|
||||
type IntoIter = std::collections::hash_map::IntoIter<String, (Arc<Embedder>, Arc<Prompt>)>;
|
||||
type IntoIter =
|
||||
std::collections::hash_map::IntoIter<String, (Arc<Embedder>, Arc<Prompt>, bool)>;
|
||||
|
||||
fn into_iter(self) -> Self::IntoIter {
|
||||
self.0.into_iter()
|
||||
|
||||
@ -32,6 +32,9 @@ pub struct EmbeddingSettings {
|
||||
pub dimensions: Setting<usize>,
|
||||
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
|
||||
#[deserr(default)]
|
||||
pub binary_quantized: Setting<bool>,
|
||||
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
|
||||
#[deserr(default)]
|
||||
pub document_template: Setting<String>,
|
||||
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
|
||||
#[deserr(default)]
|
||||
@ -85,23 +88,63 @@ pub enum ReindexAction {
|
||||
|
||||
pub enum SettingsDiff {
|
||||
Remove,
|
||||
Reindex { action: ReindexAction, updated_settings: EmbeddingSettings },
|
||||
UpdateWithoutReindex { updated_settings: EmbeddingSettings },
|
||||
Reindex { action: ReindexAction, updated_settings: EmbeddingSettings, quantize: bool },
|
||||
UpdateWithoutReindex { updated_settings: EmbeddingSettings, quantize: bool },
|
||||
}
|
||||
|
||||
pub enum EmbedderAction {
|
||||
WriteBackToDocuments(WriteBackToDocuments),
|
||||
Reindex(ReindexAction),
|
||||
#[derive(Default, Debug)]
|
||||
pub struct EmbedderAction {
|
||||
pub was_quantized: bool,
|
||||
pub is_being_quantized: bool,
|
||||
pub write_back: Option<WriteBackToDocuments>,
|
||||
pub reindex: Option<ReindexAction>,
|
||||
}
|
||||
|
||||
impl EmbedderAction {
|
||||
pub fn is_being_quantized(&self) -> bool {
|
||||
self.is_being_quantized
|
||||
}
|
||||
|
||||
pub fn write_back(&self) -> Option<&WriteBackToDocuments> {
|
||||
self.write_back.as_ref()
|
||||
}
|
||||
|
||||
pub fn reindex(&self) -> Option<&ReindexAction> {
|
||||
self.reindex.as_ref()
|
||||
}
|
||||
|
||||
pub fn with_is_being_quantized(mut self, quantize: bool) -> Self {
|
||||
self.is_being_quantized = quantize;
|
||||
self
|
||||
}
|
||||
|
||||
pub fn with_write_back(write_back: WriteBackToDocuments, was_quantized: bool) -> Self {
|
||||
Self {
|
||||
was_quantized,
|
||||
is_being_quantized: false,
|
||||
write_back: Some(write_back),
|
||||
reindex: None,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn with_reindex(reindex: ReindexAction, was_quantized: bool) -> Self {
|
||||
Self { was_quantized, is_being_quantized: false, write_back: None, reindex: Some(reindex) }
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
pub struct WriteBackToDocuments {
|
||||
pub embedder_id: u8,
|
||||
pub user_provided: RoaringBitmap,
|
||||
}
|
||||
|
||||
impl SettingsDiff {
|
||||
pub fn from_settings(old: EmbeddingSettings, new: Setting<EmbeddingSettings>) -> Self {
|
||||
match new {
|
||||
pub fn from_settings(
|
||||
embedder_name: &str,
|
||||
old: EmbeddingSettings,
|
||||
new: Setting<EmbeddingSettings>,
|
||||
) -> Result<Self, UserError> {
|
||||
let ret = match new {
|
||||
Setting::Set(new) => {
|
||||
let EmbeddingSettings {
|
||||
mut source,
|
||||
@ -116,6 +159,7 @@ impl SettingsDiff {
|
||||
mut distribution,
|
||||
mut headers,
|
||||
mut document_template_max_bytes,
|
||||
binary_quantized: mut binary_quantize,
|
||||
} = old;
|
||||
|
||||
let EmbeddingSettings {
|
||||
@ -131,8 +175,17 @@ impl SettingsDiff {
|
||||
distribution: new_distribution,
|
||||
headers: new_headers,
|
||||
document_template_max_bytes: new_document_template_max_bytes,
|
||||
binary_quantized: new_binary_quantize,
|
||||
} = new;
|
||||
|
||||
if matches!(binary_quantize, Setting::Set(true))
|
||||
&& matches!(new_binary_quantize, Setting::Set(false))
|
||||
{
|
||||
return Err(UserError::InvalidDisableBinaryQuantization {
|
||||
embedder_name: embedder_name.to_string(),
|
||||
});
|
||||
}
|
||||
|
||||
let mut reindex_action = None;
|
||||
|
||||
// **Warning**: do not use short-circuiting || here, we want all these operations applied
|
||||
@ -172,6 +225,7 @@ impl SettingsDiff {
|
||||
_ => {}
|
||||
}
|
||||
}
|
||||
let binary_quantize_changed = binary_quantize.apply(new_binary_quantize);
|
||||
if url.apply(new_url) {
|
||||
match source {
|
||||
// do not regenerate on an url change in OpenAI
|
||||
@ -231,16 +285,27 @@ impl SettingsDiff {
|
||||
distribution,
|
||||
headers,
|
||||
document_template_max_bytes,
|
||||
binary_quantized: binary_quantize,
|
||||
};
|
||||
|
||||
match reindex_action {
|
||||
Some(action) => Self::Reindex { action, updated_settings },
|
||||
None => Self::UpdateWithoutReindex { updated_settings },
|
||||
Some(action) => Self::Reindex {
|
||||
action,
|
||||
updated_settings,
|
||||
quantize: binary_quantize_changed,
|
||||
},
|
||||
None => Self::UpdateWithoutReindex {
|
||||
updated_settings,
|
||||
quantize: binary_quantize_changed,
|
||||
},
|
||||
}
|
||||
}
|
||||
Setting::Reset => Self::Remove,
|
||||
Setting::NotSet => Self::UpdateWithoutReindex { updated_settings: old },
|
||||
}
|
||||
Setting::NotSet => {
|
||||
Self::UpdateWithoutReindex { updated_settings: old, quantize: false }
|
||||
}
|
||||
};
|
||||
Ok(ret)
|
||||
}
|
||||
}
|
||||
|
||||
@ -486,7 +551,7 @@ impl std::fmt::Display for EmbedderSource {
|
||||
|
||||
impl From<EmbeddingConfig> for EmbeddingSettings {
|
||||
fn from(value: EmbeddingConfig) -> Self {
|
||||
let EmbeddingConfig { embedder_options, prompt } = value;
|
||||
let EmbeddingConfig { embedder_options, prompt, quantized } = value;
|
||||
let document_template_max_bytes =
|
||||
Setting::Set(prompt.max_bytes.unwrap_or(default_max_bytes()).get());
|
||||
match embedder_options {
|
||||
@ -507,6 +572,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
|
||||
response: Setting::NotSet,
|
||||
headers: Setting::NotSet,
|
||||
distribution: Setting::some_or_not_set(distribution),
|
||||
binary_quantized: Setting::some_or_not_set(quantized),
|
||||
},
|
||||
super::EmbedderOptions::OpenAi(super::openai::EmbedderOptions {
|
||||
url,
|
||||
@ -527,6 +593,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
|
||||
response: Setting::NotSet,
|
||||
headers: Setting::NotSet,
|
||||
distribution: Setting::some_or_not_set(distribution),
|
||||
binary_quantized: Setting::some_or_not_set(quantized),
|
||||
},
|
||||
super::EmbedderOptions::Ollama(super::ollama::EmbedderOptions {
|
||||
embedding_model,
|
||||
@ -547,6 +614,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
|
||||
response: Setting::NotSet,
|
||||
headers: Setting::NotSet,
|
||||
distribution: Setting::some_or_not_set(distribution),
|
||||
binary_quantized: Setting::some_or_not_set(quantized),
|
||||
},
|
||||
super::EmbedderOptions::UserProvided(super::manual::EmbedderOptions {
|
||||
dimensions,
|
||||
@ -564,6 +632,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
|
||||
response: Setting::NotSet,
|
||||
headers: Setting::NotSet,
|
||||
distribution: Setting::some_or_not_set(distribution),
|
||||
binary_quantized: Setting::some_or_not_set(quantized),
|
||||
},
|
||||
super::EmbedderOptions::Rest(super::rest::EmbedderOptions {
|
||||
api_key,
|
||||
@ -586,6 +655,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
|
||||
response: Setting::Set(response),
|
||||
distribution: Setting::some_or_not_set(distribution),
|
||||
headers: Setting::Set(headers),
|
||||
binary_quantized: Setting::some_or_not_set(quantized),
|
||||
},
|
||||
}
|
||||
}
|
||||
@ -607,8 +677,11 @@ impl From<EmbeddingSettings> for EmbeddingConfig {
|
||||
response,
|
||||
distribution,
|
||||
headers,
|
||||
binary_quantized,
|
||||
} = value;
|
||||
|
||||
this.quantized = binary_quantized.set();
|
||||
|
||||
if let Some(source) = source.set() {
|
||||
match source {
|
||||
EmbedderSource::OpenAi => {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user