mirror of
https://github.com/meilisearch/meilisearch.git
synced 2024-11-22 10:07:40 +08:00
Merge branch 'main' into change-matches-position-phrase-search
This commit is contained in:
F. Levi
commit
00ccf53ffa
16
Cargo.lock
generated
16
Cargo.lock
generated
@ -387,14 +387,14 @@ checksum = "96d30a06541fbafbc7f82ed10c06164cfbd2c401138f6addd8404629c4b16711"
|
||||
[[package]]
|
||||
name = "arroy"
|
||||
version = "0.4.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2ece9e5347e7fdaaea3181dec7f916677ad5f3fcbac183648ce1924eb4aeef9a"
|
||||
source = "git+https://github.com/meilisearch/arroy/?rev=2386594dfb009ce08821a925ccc89fb8e30bf73d#2386594dfb009ce08821a925ccc89fb8e30bf73d"
|
||||
dependencies = [
|
||||
"bytemuck",
|
||||
"byteorder",
|
||||
"heed",
|
||||
"log",
|
||||
"memmap2",
|
||||
"nohash",
|
||||
"ordered-float",
|
||||
"rand",
|
||||
"rayon",
|
||||
@ -933,9 +933,9 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "charabia"
|
||||
version = "0.9.0"
|
||||
version = "0.9.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "03cd8f290cae94934cdd0103c14c2de9faf2d7d85be0d24d511af2bf1b14119d"
|
||||
checksum = "55ff52497324e7d168505a16949ae836c14595606fab94687238d2f6c8d4c798"
|
||||
dependencies = [
|
||||
"aho-corasick",
|
||||
"csv",
|
||||
@ -2838,7 +2838,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "e310b3a6b5907f99202fcdb4960ff45b93735d7c7d96b760fcff8db2dc0e103d"
|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"windows-targets 0.48.1",
|
||||
"windows-targets 0.52.4",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -3686,6 +3686,12 @@ version = "0.0.3"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "6d02c0b00610773bb7fc61d85e13d86c7858cbdf00e1a120bfc41bc055dbaa0e"
|
||||
|
||||
[[package]]
|
||||
name = "nohash"
|
||||
version = "0.2.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "a0f889fb66f7acdf83442c35775764b51fed3c606ab9cee51500dbde2cf528ca"
|
||||
|
||||
[[package]]
|
||||
name = "nom"
|
||||
version = "7.1.3"
|
||||
|
||||
@ -45,14 +45,14 @@ See the list of all our example apps in our [demos repository](https://github.co
|
||||
## ✨ Features
|
||||
- **Hybrid search:** Combine the best of both [semantic](https://www.meilisearch.com/docs/learn/experimental/vector_search?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features) & full-text search to get the most relevant results
|
||||
- **Search-as-you-type:** Find & display results in less than 50 milliseconds to provide an intuitive experience
|
||||
- **[Typo tolerance](https://www.meilisearch.com/docs/learn/configuration/typo_tolerance?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** get relevant matches even when queries contain typos and misspellings
|
||||
- **[Typo tolerance](https://www.meilisearch.com/docs/learn/relevancy/typo_tolerance_settings?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** get relevant matches even when queries contain typos and misspellings
|
||||
- **[Filtering](https://www.meilisearch.com/docs/learn/fine_tuning_results/filtering?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features) and [faceted search](https://www.meilisearch.com/docs/learn/fine_tuning_results/faceted_search?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** enhance your users' search experience with custom filters and build a faceted search interface in a few lines of code
|
||||
- **[Sorting](https://www.meilisearch.com/docs/learn/fine_tuning_results/sorting?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** sort results based on price, date, or pretty much anything else your users need
|
||||
- **[Synonym support](https://www.meilisearch.com/docs/learn/configuration/synonyms?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** configure synonyms to include more relevant content in your search results
|
||||
- **[Synonym support](https://www.meilisearch.com/docs/learn/relevancy/synonyms?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** configure synonyms to include more relevant content in your search results
|
||||
- **[Geosearch](https://www.meilisearch.com/docs/learn/fine_tuning_results/geosearch?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** filter and sort documents based on geographic data
|
||||
- **[Extensive language support](https://www.meilisearch.com/docs/learn/what_is_meilisearch/language?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** search datasets in any language, with optimized support for Chinese, Japanese, Hebrew, and languages using the Latin alphabet
|
||||
- **[Security management](https://www.meilisearch.com/docs/learn/security/master_api_keys?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** control which users can access what data with API keys that allow fine-grained permissions handling
|
||||
- **[Multi-Tenancy](https://www.meilisearch.com/docs/learn/security/tenant_tokens?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** personalize search results for any number of application tenants
|
||||
- **[Multi-Tenancy](https://www.meilisearch.com/docs/learn/security/multitenancy_tenant_tokens?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** personalize search results for any number of application tenants
|
||||
- **Highly Customizable:** customize Meilisearch to your specific needs or use our out-of-the-box and hassle-free presets
|
||||
- **[RESTful API](https://www.meilisearch.com/docs/reference/api/overview?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=features):** integrate Meilisearch in your technical stack with our plugins and SDKs
|
||||
- **Easy to install, deploy, and maintain**
|
||||
|
||||
@ -255,6 +255,8 @@ pub(crate) mod test {
|
||||
}
|
||||
"###);
|
||||
|
||||
insta::assert_json_snapshot!(vector_index.settings().unwrap());
|
||||
|
||||
{
|
||||
let documents: Result<Vec<_>> = vector_index.documents().unwrap().collect();
|
||||
let mut documents = documents.unwrap();
|
||||
|
||||
@ -1,783 +1,56 @@
|
||||
---
|
||||
source: dump/src/reader/mod.rs
|
||||
expression: document
|
||||
expression: vector_index.settings().unwrap()
|
||||
---
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||||
{
|
||||
"id": "e3",
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"desc": "overriden vector + map",
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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,
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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,
|
||||
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,
|
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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,
|
||||
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,
|
||||
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
|
||||
]
|
||||
}
|
||||
}
|
||||
@ -27,6 +27,7 @@ pub enum Condition<'a> {
|
||||
LowerThanOrEqual(Token<'a>),
|
||||
Between { from: Token<'a>, to: Token<'a> },
|
||||
Contains { keyword: Token<'a>, word: Token<'a> },
|
||||
StartsWith { keyword: Token<'a>, word: Token<'a> },
|
||||
}
|
||||
|
||||
/// condition = value ("==" | ">" ...) value
|
||||
@ -121,6 +122,34 @@ pub fn parse_not_contains(input: Span) -> IResult<FilterCondition> {
|
||||
))
|
||||
}
|
||||
|
||||
/// starts with = value "CONTAINS" value
|
||||
pub fn parse_starts_with(input: Span) -> IResult<FilterCondition> {
|
||||
let (input, (fid, starts_with, value)) =
|
||||
tuple((parse_value, tag("STARTS WITH"), cut(parse_value)))(input)?;
|
||||
Ok((
|
||||
input,
|
||||
FilterCondition::Condition {
|
||||
fid,
|
||||
op: StartsWith { keyword: Token { span: starts_with, value: None }, word: value },
|
||||
},
|
||||
))
|
||||
}
|
||||
|
||||
/// starts with = value "NOT" WS+ "CONTAINS" value
|
||||
pub fn parse_not_starts_with(input: Span) -> IResult<FilterCondition> {
|
||||
let keyword = tuple((tag("NOT"), multispace1, tag("STARTS WITH")));
|
||||
let (input, (fid, (_not, _spaces, starts_with), value)) =
|
||||
tuple((parse_value, keyword, cut(parse_value)))(input)?;
|
||||
|
||||
Ok((
|
||||
input,
|
||||
FilterCondition::Not(Box::new(FilterCondition::Condition {
|
||||
fid,
|
||||
op: StartsWith { keyword: Token { span: starts_with, value: None }, word: value },
|
||||
})),
|
||||
))
|
||||
}
|
||||
|
||||
/// to = value value "TO" WS+ value
|
||||
pub fn parse_to(input: Span) -> IResult<FilterCondition> {
|
||||
let (input, (key, from, _, _, to)) =
|
||||
|
||||
@ -146,7 +146,7 @@ impl<'a> Display for Error<'a> {
|
||||
}
|
||||
ErrorKind::InvalidPrimary => {
|
||||
let text = if input.trim().is_empty() { "but instead got nothing.".to_string() } else { format!("at `{}`.", escaped_input) };
|
||||
writeln!(f, "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` {}", text)?
|
||||
writeln!(f, "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` {}", text)?
|
||||
}
|
||||
ErrorKind::InvalidEscapedNumber => {
|
||||
writeln!(f, "Found an invalid escaped sequence number: `{}`.", escaped_input)?
|
||||
|
||||
@ -49,7 +49,7 @@ use std::fmt::Debug;
|
||||
pub use condition::{parse_condition, parse_to, Condition};
|
||||
use condition::{
|
||||
parse_contains, parse_exists, parse_is_empty, parse_is_not_empty, parse_is_not_null,
|
||||
parse_is_null, parse_not_contains, parse_not_exists,
|
||||
parse_is_null, parse_not_contains, parse_not_exists, parse_not_starts_with, parse_starts_with,
|
||||
};
|
||||
use error::{cut_with_err, ExpectedValueKind, NomErrorExt};
|
||||
pub use error::{Error, ErrorKind};
|
||||
@ -166,7 +166,8 @@ impl<'a> FilterCondition<'a> {
|
||||
| Condition::LowerThan(_)
|
||||
| Condition::LowerThanOrEqual(_)
|
||||
| Condition::Between { .. } => None,
|
||||
Condition::Contains { keyword, word: _ } => Some(keyword),
|
||||
Condition::Contains { keyword, word: _ }
|
||||
| Condition::StartsWith { keyword, word: _ } => Some(keyword),
|
||||
},
|
||||
FilterCondition::Not(this) => this.use_contains_operator(),
|
||||
FilterCondition::Or(seq) | FilterCondition::And(seq) => {
|
||||
@ -484,6 +485,8 @@ fn parse_primary(input: Span, depth: usize) -> IResult<FilterCondition> {
|
||||
parse_to,
|
||||
parse_contains,
|
||||
parse_not_contains,
|
||||
parse_starts_with,
|
||||
parse_not_starts_with,
|
||||
// the next lines are only for error handling and are written at the end to have the less possible performance impact
|
||||
parse_geo,
|
||||
parse_geo_distance,
|
||||
@ -567,6 +570,7 @@ impl<'a> std::fmt::Display for Condition<'a> {
|
||||
Condition::LowerThanOrEqual(token) => write!(f, "<= {token}"),
|
||||
Condition::Between { from, to } => write!(f, "{from} TO {to}"),
|
||||
Condition::Contains { word, keyword: _ } => write!(f, "CONTAINS {word}"),
|
||||
Condition::StartsWith { word, keyword: _ } => write!(f, "STARTS WITH {word}"),
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -680,6 +684,13 @@ pub mod tests {
|
||||
insta::assert_snapshot!(p("NOT subscribers NOT CONTAINS 'hello'"), @"{subscribers} CONTAINS {hello}");
|
||||
insta::assert_snapshot!(p("subscribers NOT CONTAINS 'hello'"), @"NOT ({subscribers} CONTAINS {hello})");
|
||||
|
||||
// Test STARTS WITH + NOT STARTS WITH
|
||||
insta::assert_snapshot!(p("subscribers STARTS WITH 'hel'"), @"{subscribers} STARTS WITH {hel}");
|
||||
insta::assert_snapshot!(p("NOT subscribers STARTS WITH 'hel'"), @"NOT ({subscribers} STARTS WITH {hel})");
|
||||
insta::assert_snapshot!(p("subscribers NOT STARTS WITH hel"), @"NOT ({subscribers} STARTS WITH {hel})");
|
||||
insta::assert_snapshot!(p("NOT subscribers NOT STARTS WITH 'hel'"), @"{subscribers} STARTS WITH {hel}");
|
||||
insta::assert_snapshot!(p("subscribers NOT STARTS WITH 'hel'"), @"NOT ({subscribers} STARTS WITH {hel})");
|
||||
|
||||
// Test nested NOT
|
||||
insta::assert_snapshot!(p("NOT NOT NOT NOT x = 5"), @"{x} = {5}");
|
||||
insta::assert_snapshot!(p("NOT NOT (NOT NOT x = 5)"), @"{x} = {5}");
|
||||
@ -751,7 +762,7 @@ pub mod tests {
|
||||
"###);
|
||||
|
||||
insta::assert_snapshot!(p("'OR'"), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `\'OR\'`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `\'OR\'`.
|
||||
1:5 'OR'
|
||||
"###);
|
||||
|
||||
@ -761,12 +772,12 @@ pub mod tests {
|
||||
"###);
|
||||
|
||||
insta::assert_snapshot!(p("channel Ponce"), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `channel Ponce`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `channel Ponce`.
|
||||
1:14 channel Ponce
|
||||
"###);
|
||||
|
||||
insta::assert_snapshot!(p("channel = Ponce OR"), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` but instead got nothing.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` but instead got nothing.
|
||||
19:19 channel = Ponce OR
|
||||
"###);
|
||||
|
||||
@ -851,12 +862,12 @@ pub mod tests {
|
||||
"###);
|
||||
|
||||
insta::assert_snapshot!(p("colour NOT EXIST"), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `colour NOT EXIST`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `colour NOT EXIST`.
|
||||
1:17 colour NOT EXIST
|
||||
"###);
|
||||
|
||||
insta::assert_snapshot!(p("subscribers 100 TO1000"), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `subscribers 100 TO1000`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `subscribers 100 TO1000`.
|
||||
1:23 subscribers 100 TO1000
|
||||
"###);
|
||||
|
||||
@ -919,35 +930,35 @@ pub mod tests {
|
||||
"###);
|
||||
|
||||
insta::assert_snapshot!(p(r#"value NULL"#), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `value NULL`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `value NULL`.
|
||||
1:11 value NULL
|
||||
"###);
|
||||
insta::assert_snapshot!(p(r#"value NOT NULL"#), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `value NOT NULL`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `value NOT NULL`.
|
||||
1:15 value NOT NULL
|
||||
"###);
|
||||
insta::assert_snapshot!(p(r#"value EMPTY"#), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `value EMPTY`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `value EMPTY`.
|
||||
1:12 value EMPTY
|
||||
"###);
|
||||
insta::assert_snapshot!(p(r#"value NOT EMPTY"#), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `value NOT EMPTY`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `value NOT EMPTY`.
|
||||
1:16 value NOT EMPTY
|
||||
"###);
|
||||
insta::assert_snapshot!(p(r#"value IS"#), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `value IS`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `value IS`.
|
||||
1:9 value IS
|
||||
"###);
|
||||
insta::assert_snapshot!(p(r#"value IS NOT"#), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `value IS NOT`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `value IS NOT`.
|
||||
1:13 value IS NOT
|
||||
"###);
|
||||
insta::assert_snapshot!(p(r#"value IS EXISTS"#), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `value IS EXISTS`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `value IS EXISTS`.
|
||||
1:16 value IS EXISTS
|
||||
"###);
|
||||
insta::assert_snapshot!(p(r#"value IS NOT EXISTS"#), @r###"
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `value IS NOT EXISTS`.
|
||||
Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `value IS NOT EXISTS`.
|
||||
1:20 value IS NOT EXISTS
|
||||
"###);
|
||||
}
|
||||
|
||||
@ -212,6 +212,8 @@ fn is_keyword(s: &str) -> bool {
|
||||
| "NULL"
|
||||
| "EMPTY"
|
||||
| "CONTAINS"
|
||||
| "STARTS"
|
||||
| "WITH"
|
||||
| "_geoRadius"
|
||||
| "_geoBoundingBox"
|
||||
)
|
||||
|
||||
@ -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"] }
|
||||
|
||||
@ -87,7 +87,7 @@ impl RoFeatures {
|
||||
Ok(())
|
||||
} else {
|
||||
Err(FeatureNotEnabledError {
|
||||
disabled_action: "Using `CONTAINS` in a filter",
|
||||
disabled_action: "Using `CONTAINS` or `STARTS WITH` in a filter",
|
||||
feature: "contains filter",
|
||||
issue_link: "https://github.com/orgs/meilisearch/discussions/763",
|
||||
}
|
||||
|
||||
@ -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 []
|
||||
|
||||
@ -66,3 +66,5 @@ khmer = ["milli/khmer"]
|
||||
vietnamese = ["milli/vietnamese"]
|
||||
# force swedish character recomposition
|
||||
swedish-recomposition = ["milli/swedish-recomposition"]
|
||||
# force german character recomposition
|
||||
german = ["milli/german"]
|
||||
|
||||
@ -238,8 +238,14 @@ InvalidIndexLimit , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidIndexOffset , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidIndexPrimaryKey , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidIndexUid , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchFacets , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchFacetsByIndex , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchFacetOrder , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchFederated , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchFederationOptions , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchMaxValuesPerFacet , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchMergeFacets , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchQueryFacets , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchQueryPagination , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchQueryRankingRules , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidMultiSearchWeight , InvalidRequest , BAD_REQUEST ;
|
||||
@ -389,7 +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,
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
use std::borrow::Borrow;
|
||||
use std::error::Error;
|
||||
use std::fmt;
|
||||
use std::str::FromStr;
|
||||
@ -8,7 +9,7 @@ use crate::error::{Code, ErrorCode};
|
||||
|
||||
/// An index uid is composed of only ascii alphanumeric characters, - and _, between 1 and 400
|
||||
/// bytes long
|
||||
#[derive(Debug, Clone, PartialEq, Eq, Deserr)]
|
||||
#[derive(Debug, Clone, PartialEq, Eq, Deserr, PartialOrd, Ord)]
|
||||
#[deserr(try_from(String) = IndexUid::try_from -> IndexUidFormatError)]
|
||||
pub struct IndexUid(String);
|
||||
|
||||
@ -70,6 +71,12 @@ impl From<IndexUid> for String {
|
||||
}
|
||||
}
|
||||
|
||||
impl Borrow<String> for IndexUid {
|
||||
fn borrow(&self) -> &String {
|
||||
&self.0
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
pub struct IndexUidFormatError {
|
||||
pub invalid_uid: String,
|
||||
|
||||
@ -39,12 +39,14 @@ macro_rules! make_locale {
|
||||
pub enum Locale {
|
||||
$($iso_639_1,)+
|
||||
$($iso_639_3,)+
|
||||
Cmn,
|
||||
}
|
||||
|
||||
impl From<milli::tokenizer::Language> for Locale {
|
||||
fn from(other: milli::tokenizer::Language) -> Locale {
|
||||
match other {
|
||||
$(milli::tokenizer::Language::$iso_639_3 => Locale::$iso_639_3,)+
|
||||
milli::tokenizer::Language::Cmn => Locale::Cmn,
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -54,6 +56,7 @@ macro_rules! make_locale {
|
||||
match other {
|
||||
$(Locale::$iso_639_1 => milli::tokenizer::Language::$iso_639_3,)+
|
||||
$(Locale::$iso_639_3 => milli::tokenizer::Language::$iso_639_3,)+
|
||||
Locale::Cmn => milli::tokenizer::Language::Cmn,
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -65,6 +68,7 @@ macro_rules! make_locale {
|
||||
let locale = match s {
|
||||
$($iso_639_1_str => Locale::$iso_639_1,)+
|
||||
$($iso_639_3_str => Locale::$iso_639_3,)+
|
||||
"cmn" => Locale::Cmn,
|
||||
_ => return Err(LocaleFormatError { invalid_locale: s.to_string() }),
|
||||
};
|
||||
|
||||
@ -79,8 +83,9 @@ macro_rules! make_locale {
|
||||
|
||||
impl std::fmt::Display for LocaleFormatError {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
let valid_locales = [$($iso_639_1_str),+,$($iso_639_3_str),+].join(", ");
|
||||
write!(f, "Unsupported locale `{}`, expected one of {}", self.invalid_locale, valid_locales)
|
||||
let mut valid_locales = [$($iso_639_1_str),+,$($iso_639_3_str),+,"cmn"];
|
||||
valid_locales.sort_by(|left, right| left.len().cmp(&right.len()).then(left.cmp(right)));
|
||||
write!(f, "Unsupported locale `{}`, expected one of {}", self.invalid_locale, valid_locales.join(", "))
|
||||
}
|
||||
}
|
||||
|
||||
@ -99,7 +104,6 @@ make_locale!(
|
||||
(Bg, "bg") => (Bul, "bul"),
|
||||
(Ca, "ca") => (Cat, "cat"),
|
||||
(Cs, "cs") => (Ces, "ces"),
|
||||
(Zh, "zh") => (Cmn, "cmn"),
|
||||
(Da, "da") => (Dan, "dan"),
|
||||
(De, "de") => (Deu, "deu"),
|
||||
(El, "el") => (Ell, "ell"),
|
||||
@ -157,5 +161,6 @@ make_locale!(
|
||||
(Uz, "uz") => (Uzb, "uzb"),
|
||||
(Vi, "vi") => (Vie, "vie"),
|
||||
(Yi, "yi") => (Yid, "yid"),
|
||||
(Zh, "zh") => (Zho, "zho"),
|
||||
(Zu, "zu") => (Zul, "zul"),
|
||||
);
|
||||
|
||||
@ -153,6 +153,7 @@ greek = ["meilisearch-types/greek"]
|
||||
khmer = ["meilisearch-types/khmer"]
|
||||
vietnamese = ["meilisearch-types/vietnamese"]
|
||||
swedish-recomposition = ["meilisearch-types/swedish-recomposition"]
|
||||
german = ["meilisearch-types/german"]
|
||||
|
||||
[package.metadata.mini-dashboard]
|
||||
assets-url = "https://github.com/meilisearch/mini-dashboard/releases/download/v0.2.14/build.zip"
|
||||
|
||||
@ -646,8 +646,6 @@ pub struct SearchAggregator {
|
||||
max_vector_size: usize,
|
||||
// Whether the semantic ratio passed to a hybrid search equals the default ratio.
|
||||
semantic_ratio: bool,
|
||||
// Whether a non-default embedder was specified
|
||||
embedder: bool,
|
||||
hybrid: bool,
|
||||
retrieve_vectors: bool,
|
||||
|
||||
@ -795,7 +793,6 @@ impl SearchAggregator {
|
||||
|
||||
if let Some(hybrid) = hybrid {
|
||||
ret.semantic_ratio = hybrid.semantic_ratio != DEFAULT_SEMANTIC_RATIO();
|
||||
ret.embedder = hybrid.embedder.is_some();
|
||||
ret.hybrid = true;
|
||||
}
|
||||
|
||||
@ -863,7 +860,6 @@ impl SearchAggregator {
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
semantic_ratio,
|
||||
embedder,
|
||||
hybrid,
|
||||
total_degraded,
|
||||
total_used_negative_operator,
|
||||
@ -923,7 +919,6 @@ impl SearchAggregator {
|
||||
self.retrieve_vectors |= retrieve_vectors;
|
||||
self.semantic_ratio |= semantic_ratio;
|
||||
self.hybrid |= hybrid;
|
||||
self.embedder |= embedder;
|
||||
|
||||
// pagination
|
||||
self.max_limit = self.max_limit.max(max_limit);
|
||||
@ -999,7 +994,6 @@ impl SearchAggregator {
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
semantic_ratio,
|
||||
embedder,
|
||||
hybrid,
|
||||
total_degraded,
|
||||
total_used_negative_operator,
|
||||
@ -1051,7 +1045,6 @@ impl SearchAggregator {
|
||||
"hybrid": {
|
||||
"enabled": hybrid,
|
||||
"semantic_ratio": semantic_ratio,
|
||||
"embedder": embedder,
|
||||
},
|
||||
"pagination": {
|
||||
"max_limit": max_limit,
|
||||
@ -1782,7 +1775,6 @@ pub struct SimilarAggregator {
|
||||
used_syntax: HashMap<String, usize>,
|
||||
|
||||
// Whether a non-default embedder was specified
|
||||
embedder: bool,
|
||||
retrieve_vectors: bool,
|
||||
|
||||
// pagination
|
||||
@ -1803,7 +1795,7 @@ impl SimilarAggregator {
|
||||
pub fn from_query(query: &SimilarQuery, request: &HttpRequest) -> Self {
|
||||
let SimilarQuery {
|
||||
id: _,
|
||||
embedder,
|
||||
embedder: _,
|
||||
offset,
|
||||
limit,
|
||||
attributes_to_retrieve: _,
|
||||
@ -1851,7 +1843,6 @@ impl SimilarAggregator {
|
||||
ret.show_ranking_score_details = *show_ranking_score_details;
|
||||
ret.ranking_score_threshold = ranking_score_threshold.is_some();
|
||||
|
||||
ret.embedder = embedder.is_some();
|
||||
ret.retrieve_vectors = *retrieve_vectors;
|
||||
|
||||
ret
|
||||
@ -1883,7 +1874,6 @@ impl SimilarAggregator {
|
||||
max_attributes_to_retrieve,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
embedder,
|
||||
ranking_score_threshold,
|
||||
retrieve_vectors,
|
||||
} = other;
|
||||
@ -1914,7 +1904,6 @@ impl SimilarAggregator {
|
||||
*used_syntax = used_syntax.saturating_add(value);
|
||||
}
|
||||
|
||||
self.embedder |= embedder;
|
||||
self.retrieve_vectors |= retrieve_vectors;
|
||||
|
||||
// pagination
|
||||
@ -1948,7 +1937,6 @@ impl SimilarAggregator {
|
||||
max_attributes_to_retrieve,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
embedder,
|
||||
ranking_score_threshold,
|
||||
retrieve_vectors,
|
||||
} = self;
|
||||
@ -1980,9 +1968,6 @@ impl SimilarAggregator {
|
||||
"vector": {
|
||||
"retrieve_vectors": retrieve_vectors,
|
||||
},
|
||||
"hybrid": {
|
||||
"embedder": embedder,
|
||||
},
|
||||
"pagination": {
|
||||
"max_limit": max_limit,
|
||||
"max_offset": max_offset,
|
||||
|
||||
@ -4,6 +4,7 @@ use byte_unit::{Byte, UnitType};
|
||||
use meilisearch_types::document_formats::{DocumentFormatError, PayloadType};
|
||||
use meilisearch_types::error::{Code, ErrorCode, ResponseError};
|
||||
use meilisearch_types::index_uid::{IndexUid, IndexUidFormatError};
|
||||
use meilisearch_types::milli::OrderBy;
|
||||
use serde_json::Value;
|
||||
use tokio::task::JoinError;
|
||||
|
||||
@ -27,10 +28,20 @@ pub enum MeilisearchHttpError {
|
||||
EmptyFilter,
|
||||
#[error("Invalid syntax for the filter parameter: `expected {}, found: {1}`.", .0.join(", "))]
|
||||
InvalidExpression(&'static [&'static str], Value),
|
||||
#[error("Using `federationOptions` is not allowed in a non-federated search.\n Hint: remove `federationOptions` from query #{0} or add `federation: {{}}` to the request.")]
|
||||
#[error("Using `federationOptions` is not allowed in a non-federated search.\n - Hint: remove `federationOptions` from query #{0} or add `federation` to the request.")]
|
||||
FederationOptionsInNonFederatedRequest(usize),
|
||||
#[error("Inside `.queries[{0}]`: Using pagination options is not allowed in federated queries.\n Hint: remove `{1}` from query #{0} or remove `federation: {{}}` from the request")]
|
||||
#[error("Inside `.queries[{0}]`: Using pagination options is not allowed in federated queries.\n - Hint: remove `{1}` from query #{0} or remove `federation` from the request\n - Hint: pass `federation.limit` and `federation.offset` for pagination in federated search")]
|
||||
PaginationInFederatedQuery(usize, &'static str),
|
||||
#[error("Inside `.queries[{0}]`: Using facet options is not allowed in federated queries.\n - Hint: remove `facets` from query #{0} or remove `federation` from the request\n - Hint: pass `federation.facetsByIndex.{1}: {2:?}` for facets in federated search")]
|
||||
FacetsInFederatedQuery(usize, String, Vec<String>),
|
||||
#[error("Inconsistent order for values in facet `{facet}`: index `{previous_uid}` orders {previous_facet_order}, but index `{current_uid}` orders {index_facet_order}.\n - Hint: Remove `federation.mergeFacets` or change `faceting.sortFacetValuesBy` to be consistent in settings.")]
|
||||
InconsistentFacetOrder {
|
||||
facet: String,
|
||||
previous_facet_order: OrderBy,
|
||||
previous_uid: String,
|
||||
index_facet_order: OrderBy,
|
||||
current_uid: String,
|
||||
},
|
||||
#[error("A {0} payload is missing.")]
|
||||
MissingPayload(PayloadType),
|
||||
#[error("Too many search requests running at the same time: {0}. Retry after 10s.")]
|
||||
@ -61,7 +72,7 @@ pub enum MeilisearchHttpError {
|
||||
DocumentFormat(#[from] DocumentFormatError),
|
||||
#[error(transparent)]
|
||||
Join(#[from] JoinError),
|
||||
#[error("Invalid request: missing `hybrid` parameter when both `q` and `vector` are present.")]
|
||||
#[error("Invalid request: missing `hybrid` parameter when `vector` is present.")]
|
||||
MissingSearchHybrid,
|
||||
}
|
||||
|
||||
@ -96,6 +107,10 @@ impl ErrorCode for MeilisearchHttpError {
|
||||
MeilisearchHttpError::PaginationInFederatedQuery(_, _) => {
|
||||
Code::InvalidMultiSearchQueryPagination
|
||||
}
|
||||
MeilisearchHttpError::FacetsInFederatedQuery(..) => Code::InvalidMultiSearchQueryFacets,
|
||||
MeilisearchHttpError::InconsistentFacetOrder { .. } => {
|
||||
Code::InvalidMultiSearchFacetOrder
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -128,8 +128,10 @@ impl std::ops::Deref for SemanticRatioGet {
|
||||
}
|
||||
}
|
||||
|
||||
impl From<SearchQueryGet> for SearchQuery {
|
||||
fn from(other: SearchQueryGet) -> Self {
|
||||
impl TryFrom<SearchQueryGet> for SearchQuery {
|
||||
type Error = ResponseError;
|
||||
|
||||
fn try_from(other: SearchQueryGet) -> Result<Self, Self::Error> {
|
||||
let filter = match other.filter {
|
||||
Some(f) => match serde_json::from_str(&f) {
|
||||
Ok(v) => Some(v),
|
||||
@ -140,19 +142,28 @@ impl From<SearchQueryGet> for SearchQuery {
|
||||
|
||||
let hybrid = match (other.hybrid_embedder, other.hybrid_semantic_ratio) {
|
||||
(None, None) => None,
|
||||
(None, Some(semantic_ratio)) => {
|
||||
Some(HybridQuery { semantic_ratio: *semantic_ratio, embedder: None })
|
||||
(None, Some(_)) => {
|
||||
return Err(ResponseError::from_msg(
|
||||
"`hybridEmbedder` is mandatory when `hybridSemanticRatio` is present".into(),
|
||||
meilisearch_types::error::Code::InvalidHybridQuery,
|
||||
));
|
||||
}
|
||||
(Some(embedder), None) => {
|
||||
Some(HybridQuery { semantic_ratio: DEFAULT_SEMANTIC_RATIO(), embedder })
|
||||
}
|
||||
(Some(embedder), None) => Some(HybridQuery {
|
||||
semantic_ratio: DEFAULT_SEMANTIC_RATIO(),
|
||||
embedder: Some(embedder),
|
||||
}),
|
||||
(Some(embedder), Some(semantic_ratio)) => {
|
||||
Some(HybridQuery { semantic_ratio: *semantic_ratio, embedder: Some(embedder) })
|
||||
Some(HybridQuery { semantic_ratio: *semantic_ratio, embedder })
|
||||
}
|
||||
};
|
||||
|
||||
Self {
|
||||
if other.vector.is_some() && hybrid.is_none() {
|
||||
return Err(ResponseError::from_msg(
|
||||
"`hybridEmbedder` is mandatory when `vector` is present".into(),
|
||||
meilisearch_types::error::Code::MissingSearchHybrid,
|
||||
));
|
||||
}
|
||||
|
||||
Ok(Self {
|
||||
q: other.q,
|
||||
vector: other.vector.map(CS::into_inner),
|
||||
offset: other.offset.0,
|
||||
@ -179,7 +190,7 @@ impl From<SearchQueryGet> for SearchQuery {
|
||||
hybrid,
|
||||
ranking_score_threshold: other.ranking_score_threshold.map(|o| o.0),
|
||||
locales: other.locales.map(|o| o.into_iter().collect()),
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
@ -219,7 +230,7 @@ pub async fn search_with_url_query(
|
||||
debug!(parameters = ?params, "Search get");
|
||||
let index_uid = IndexUid::try_from(index_uid.into_inner())?;
|
||||
|
||||
let mut query: SearchQuery = params.into_inner().into();
|
||||
let mut query: SearchQuery = params.into_inner().try_into()?;
|
||||
|
||||
// Tenant token search_rules.
|
||||
if let Some(search_rules) = index_scheduler.filters().get_index_search_rules(&index_uid) {
|
||||
@ -312,44 +323,36 @@ pub fn search_kind(
|
||||
features.check_vector("Passing `hybrid` as a parameter")?;
|
||||
}
|
||||
|
||||
// regardless of anything, always do a keyword search when we don't have a vector and the query is whitespace or missing
|
||||
if query.vector.is_none() {
|
||||
match &query.q {
|
||||
Some(q) if q.trim().is_empty() => return Ok(SearchKind::KeywordOnly),
|
||||
None => return Ok(SearchKind::KeywordOnly),
|
||||
_ => {}
|
||||
// handle with care, the order of cases matters, the semantics is subtle
|
||||
match (query.q.as_deref(), &query.hybrid, query.vector.as_deref()) {
|
||||
// empty query, no vector => placeholder search
|
||||
(Some(q), _, None) if q.trim().is_empty() => Ok(SearchKind::KeywordOnly),
|
||||
// no query, no vector => placeholder search
|
||||
(None, _, None) => Ok(SearchKind::KeywordOnly),
|
||||
// hybrid.semantic_ratio == 1.0 => vector
|
||||
(_, Some(HybridQuery { semantic_ratio, embedder }), v) if **semantic_ratio == 1.0 => {
|
||||
SearchKind::semantic(index_scheduler, index, embedder, v.map(|v| v.len()))
|
||||
}
|
||||
}
|
||||
|
||||
match &query.hybrid {
|
||||
Some(HybridQuery { semantic_ratio, embedder }) if **semantic_ratio == 1.0 => {
|
||||
Ok(SearchKind::semantic(
|
||||
index_scheduler,
|
||||
index,
|
||||
embedder.as_deref(),
|
||||
query.vector.as_ref().map(Vec::len),
|
||||
)?)
|
||||
}
|
||||
Some(HybridQuery { semantic_ratio, embedder: _ }) if **semantic_ratio == 0.0 => {
|
||||
// hybrid.semantic_ratio == 0.0 => keyword
|
||||
(_, Some(HybridQuery { semantic_ratio, embedder: _ }), _) if **semantic_ratio == 0.0 => {
|
||||
Ok(SearchKind::KeywordOnly)
|
||||
}
|
||||
Some(HybridQuery { semantic_ratio, embedder }) => Ok(SearchKind::hybrid(
|
||||
// no query, hybrid, vector => semantic
|
||||
(None, Some(HybridQuery { semantic_ratio: _, embedder }), Some(v)) => {
|
||||
SearchKind::semantic(index_scheduler, index, embedder, Some(v.len()))
|
||||
}
|
||||
// query, no hybrid, no vector => keyword
|
||||
(Some(_), None, None) => Ok(SearchKind::KeywordOnly),
|
||||
// query, hybrid, maybe vector => hybrid
|
||||
(Some(_), Some(HybridQuery { semantic_ratio, embedder }), v) => SearchKind::hybrid(
|
||||
index_scheduler,
|
||||
index,
|
||||
embedder.as_deref(),
|
||||
embedder,
|
||||
**semantic_ratio,
|
||||
query.vector.as_ref().map(Vec::len),
|
||||
)?),
|
||||
None => match (query.q.as_deref(), query.vector.as_deref()) {
|
||||
(_query, None) => Ok(SearchKind::KeywordOnly),
|
||||
(None, Some(_vector)) => Ok(SearchKind::semantic(
|
||||
index_scheduler,
|
||||
index,
|
||||
None,
|
||||
query.vector.as_ref().map(Vec::len),
|
||||
)?),
|
||||
(Some(_), Some(_)) => Err(MeilisearchHttpError::MissingSearchHybrid.into()),
|
||||
},
|
||||
v.map(|v| v.len()),
|
||||
),
|
||||
|
||||
(_, None, Some(_)) => Err(MeilisearchHttpError::MissingSearchHybrid.into()),
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -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,
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
@ -102,8 +102,8 @@ async fn similar(
|
||||
|
||||
let index = index_scheduler.index(&index_uid)?;
|
||||
|
||||
let (embedder_name, embedder) =
|
||||
SearchKind::embedder(&index_scheduler, &index, query.embedder.as_deref(), None)?;
|
||||
let (embedder_name, embedder, quantized) =
|
||||
SearchKind::embedder(&index_scheduler, &index, &query.embedder, None)?;
|
||||
|
||||
tokio::task::spawn_blocking(move || {
|
||||
perform_similar(
|
||||
@ -111,6 +111,7 @@ async fn similar(
|
||||
query,
|
||||
embedder_name,
|
||||
embedder,
|
||||
quantized,
|
||||
retrieve_vectors,
|
||||
index_scheduler.features(),
|
||||
)
|
||||
@ -139,8 +140,8 @@ pub struct SimilarQueryGet {
|
||||
show_ranking_score_details: Param<bool>,
|
||||
#[deserr(default, error = DeserrQueryParamError<InvalidSimilarRankingScoreThreshold>, default)]
|
||||
pub ranking_score_threshold: Option<RankingScoreThresholdGet>,
|
||||
#[deserr(default, error = DeserrQueryParamError<InvalidEmbedder>)]
|
||||
pub embedder: Option<String>,
|
||||
#[deserr(error = DeserrQueryParamError<InvalidEmbedder>)]
|
||||
pub embedder: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, deserr::Deserr)]
|
||||
|
||||
@ -9,20 +9,24 @@ use std::vec::{IntoIter, Vec};
|
||||
|
||||
use actix_http::StatusCode;
|
||||
use index_scheduler::{IndexScheduler, RoFeatures};
|
||||
use indexmap::IndexMap;
|
||||
use meilisearch_types::deserr::DeserrJsonError;
|
||||
use meilisearch_types::error::deserr_codes::{
|
||||
InvalidMultiSearchWeight, InvalidSearchLimit, InvalidSearchOffset,
|
||||
InvalidMultiSearchFacetsByIndex, InvalidMultiSearchMaxValuesPerFacet,
|
||||
InvalidMultiSearchMergeFacets, InvalidMultiSearchWeight, InvalidSearchLimit,
|
||||
InvalidSearchOffset,
|
||||
};
|
||||
use meilisearch_types::error::ResponseError;
|
||||
use meilisearch_types::index_uid::IndexUid;
|
||||
use meilisearch_types::milli::score_details::{ScoreDetails, ScoreValue};
|
||||
use meilisearch_types::milli::{self, DocumentId, TimeBudget};
|
||||
use meilisearch_types::milli::{self, DocumentId, OrderBy, TimeBudget};
|
||||
use roaring::RoaringBitmap;
|
||||
use serde::Serialize;
|
||||
|
||||
use super::ranking_rules::{self, RankingRules};
|
||||
use super::{
|
||||
prepare_search, AttributesFormat, HitMaker, HitsInfo, RetrieveVectors, SearchHit, SearchKind,
|
||||
SearchQuery, SearchQueryWithIndex,
|
||||
compute_facet_distribution_stats, prepare_search, AttributesFormat, ComputedFacets, FacetStats,
|
||||
HitMaker, HitsInfo, RetrieveVectors, SearchHit, SearchKind, SearchQuery, SearchQueryWithIndex,
|
||||
};
|
||||
use crate::error::MeilisearchHttpError;
|
||||
use crate::routes::indexes::search::search_kind;
|
||||
@ -73,6 +77,17 @@ pub struct Federation {
|
||||
pub limit: usize,
|
||||
#[deserr(default = super::DEFAULT_SEARCH_OFFSET(), error = DeserrJsonError<InvalidSearchOffset>)]
|
||||
pub offset: usize,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidMultiSearchFacetsByIndex>)]
|
||||
pub facets_by_index: BTreeMap<IndexUid, Option<Vec<String>>>,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidMultiSearchMergeFacets>)]
|
||||
pub merge_facets: Option<MergeFacets>,
|
||||
}
|
||||
|
||||
#[derive(Copy, Clone, Debug, deserr::Deserr, Default)]
|
||||
#[deserr(error = DeserrJsonError<InvalidMultiSearchMergeFacets>, rename_all = camelCase, deny_unknown_fields)]
|
||||
pub struct MergeFacets {
|
||||
#[deserr(default, error = DeserrJsonError<InvalidMultiSearchMaxValuesPerFacet>)]
|
||||
pub max_values_per_facet: Option<usize>,
|
||||
}
|
||||
|
||||
#[derive(Debug, deserr::Deserr)]
|
||||
@ -82,7 +97,7 @@ pub struct FederatedSearch {
|
||||
#[deserr(default)]
|
||||
pub federation: Option<Federation>,
|
||||
}
|
||||
#[derive(Serialize, Clone, PartialEq)]
|
||||
#[derive(Serialize, Clone)]
|
||||
#[serde(rename_all = "camelCase")]
|
||||
pub struct FederatedSearchResult {
|
||||
pub hits: Vec<SearchHit>,
|
||||
@ -93,6 +108,13 @@ pub struct FederatedSearchResult {
|
||||
#[serde(skip_serializing_if = "Option::is_none")]
|
||||
pub semantic_hit_count: Option<u32>,
|
||||
|
||||
#[serde(skip_serializing_if = "Option::is_none")]
|
||||
pub facet_distribution: Option<BTreeMap<String, IndexMap<String, u64>>>,
|
||||
#[serde(skip_serializing_if = "Option::is_none")]
|
||||
pub facet_stats: Option<BTreeMap<String, FacetStats>>,
|
||||
#[serde(skip_serializing_if = "FederatedFacets::is_empty")]
|
||||
pub facets_by_index: FederatedFacets,
|
||||
|
||||
// These fields are only used for analytics purposes
|
||||
#[serde(skip)]
|
||||
pub degraded: bool,
|
||||
@ -109,6 +131,9 @@ impl fmt::Debug for FederatedSearchResult {
|
||||
semantic_hit_count,
|
||||
degraded,
|
||||
used_negative_operator,
|
||||
facet_distribution,
|
||||
facet_stats,
|
||||
facets_by_index,
|
||||
} = self;
|
||||
|
||||
let mut debug = f.debug_struct("SearchResult");
|
||||
@ -122,9 +147,18 @@ impl fmt::Debug for FederatedSearchResult {
|
||||
if *degraded {
|
||||
debug.field("degraded", degraded);
|
||||
}
|
||||
if let Some(facet_distribution) = facet_distribution {
|
||||
debug.field("facet_distribution", &facet_distribution);
|
||||
}
|
||||
if let Some(facet_stats) = facet_stats {
|
||||
debug.field("facet_stats", &facet_stats);
|
||||
}
|
||||
if let Some(semantic_hit_count) = semantic_hit_count {
|
||||
debug.field("semantic_hit_count", &semantic_hit_count);
|
||||
}
|
||||
if !facets_by_index.is_empty() {
|
||||
debug.field("facets_by_index", &facets_by_index);
|
||||
}
|
||||
|
||||
debug.finish()
|
||||
}
|
||||
@ -313,16 +347,104 @@ struct SearchHitByIndex {
|
||||
}
|
||||
|
||||
struct SearchResultByIndex {
|
||||
index: String,
|
||||
hits: Vec<SearchHitByIndex>,
|
||||
candidates: RoaringBitmap,
|
||||
estimated_total_hits: usize,
|
||||
degraded: bool,
|
||||
used_negative_operator: bool,
|
||||
facets: Option<ComputedFacets>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Default, Serialize)]
|
||||
pub struct FederatedFacets(pub BTreeMap<String, ComputedFacets>);
|
||||
|
||||
impl FederatedFacets {
|
||||
pub fn insert(&mut self, index: String, facets: Option<ComputedFacets>) {
|
||||
if let Some(facets) = facets {
|
||||
self.0.insert(index, facets);
|
||||
}
|
||||
}
|
||||
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.0.is_empty()
|
||||
}
|
||||
|
||||
pub fn merge(
|
||||
self,
|
||||
MergeFacets { max_values_per_facet }: MergeFacets,
|
||||
facet_order: BTreeMap<String, (String, OrderBy)>,
|
||||
) -> Option<ComputedFacets> {
|
||||
if self.is_empty() {
|
||||
return None;
|
||||
}
|
||||
|
||||
let mut distribution: BTreeMap<String, _> = Default::default();
|
||||
let mut stats: BTreeMap<String, FacetStats> = Default::default();
|
||||
|
||||
for facets_by_index in self.0.into_values() {
|
||||
for (facet, index_distribution) in facets_by_index.distribution {
|
||||
match distribution.entry(facet) {
|
||||
std::collections::btree_map::Entry::Vacant(entry) => {
|
||||
entry.insert(index_distribution);
|
||||
}
|
||||
std::collections::btree_map::Entry::Occupied(mut entry) => {
|
||||
let distribution = entry.get_mut();
|
||||
|
||||
for (value, index_count) in index_distribution {
|
||||
distribution
|
||||
.entry(value)
|
||||
.and_modify(|count| *count += index_count)
|
||||
.or_insert(index_count);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (facet, index_stats) in facets_by_index.stats {
|
||||
match stats.entry(facet) {
|
||||
std::collections::btree_map::Entry::Vacant(entry) => {
|
||||
entry.insert(index_stats);
|
||||
}
|
||||
std::collections::btree_map::Entry::Occupied(mut entry) => {
|
||||
let stats = entry.get_mut();
|
||||
|
||||
stats.min = f64::min(stats.min, index_stats.min);
|
||||
stats.max = f64::max(stats.max, index_stats.max);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// fixup order
|
||||
for (facet, values) in &mut distribution {
|
||||
let order_by = facet_order.get(facet).map(|(_, order)| *order).unwrap_or_default();
|
||||
|
||||
match order_by {
|
||||
OrderBy::Lexicographic => {
|
||||
values.sort_unstable_by(|left, _, right, _| left.cmp(right))
|
||||
}
|
||||
OrderBy::Count => {
|
||||
values.sort_unstable_by(|_, left, _, right| {
|
||||
left.cmp(right)
|
||||
// biggest first
|
||||
.reverse()
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(max_values_per_facet) = max_values_per_facet {
|
||||
values.truncate(max_values_per_facet)
|
||||
};
|
||||
}
|
||||
|
||||
Some(ComputedFacets { distribution, stats })
|
||||
}
|
||||
}
|
||||
|
||||
pub fn perform_federated_search(
|
||||
index_scheduler: &IndexScheduler,
|
||||
queries: Vec<SearchQueryWithIndex>,
|
||||
federation: Federation,
|
||||
mut federation: Federation,
|
||||
features: RoFeatures,
|
||||
) -> Result<FederatedSearchResult, ResponseError> {
|
||||
let before_search = std::time::Instant::now();
|
||||
@ -342,6 +464,16 @@ pub fn perform_federated_search(
|
||||
.into());
|
||||
}
|
||||
|
||||
if let Some(facets) = federated_query.has_facets() {
|
||||
let facets = facets.to_owned();
|
||||
return Err(MeilisearchHttpError::FacetsInFederatedQuery(
|
||||
query_index,
|
||||
federated_query.index_uid.into_inner(),
|
||||
facets,
|
||||
)
|
||||
.into());
|
||||
}
|
||||
|
||||
let (index_uid, query, federation_options) = federated_query.into_index_query_federation();
|
||||
|
||||
queries_by_index.entry(index_uid.into_inner()).or_default().push(QueryByIndex {
|
||||
@ -353,13 +485,24 @@ pub fn perform_federated_search(
|
||||
|
||||
// 2. perform queries, merge and make hits index by index
|
||||
let required_hit_count = federation.limit + federation.offset;
|
||||
|
||||
// In step (2), semantic_hit_count will be set to Some(0) if any search kind uses semantic
|
||||
// Then in step (3), we'll update its value if there is any semantic search
|
||||
let mut semantic_hit_count = None;
|
||||
let mut results_by_index = Vec::with_capacity(queries_by_index.len());
|
||||
let mut previous_query_data: Option<(RankingRules, usize, String)> = None;
|
||||
|
||||
// remember the order and name of first index for each facet when merging with index settings
|
||||
// to detect if the order is inconsistent for a facet.
|
||||
let mut facet_order: Option<BTreeMap<String, (String, OrderBy)>> = match federation.merge_facets
|
||||
{
|
||||
Some(MergeFacets { .. }) => Some(Default::default()),
|
||||
_ => None,
|
||||
};
|
||||
|
||||
for (index_uid, queries) in queries_by_index {
|
||||
let first_query_index = queries.first().map(|query| query.query_index);
|
||||
|
||||
let index = match index_scheduler.index(&index_uid) {
|
||||
Ok(index) => index,
|
||||
Err(err) => {
|
||||
@ -367,9 +510,8 @@ pub fn perform_federated_search(
|
||||
// Patch the HTTP status code to 400 as it defaults to 404 for `index_not_found`, but
|
||||
// here the resource not found is not part of the URL.
|
||||
err.code = StatusCode::BAD_REQUEST;
|
||||
if let Some(query) = queries.first() {
|
||||
err.message =
|
||||
format!("Inside `.queries[{}]`: {}", query.query_index, err.message);
|
||||
if let Some(query_index) = first_query_index {
|
||||
err.message = format!("Inside `.queries[{}]`: {}", query_index, err.message);
|
||||
}
|
||||
return Err(err);
|
||||
}
|
||||
@ -394,6 +536,23 @@ pub fn perform_federated_search(
|
||||
let mut used_negative_operator = false;
|
||||
let mut candidates = RoaringBitmap::new();
|
||||
|
||||
let facets_by_index = federation.facets_by_index.remove(&index_uid).flatten();
|
||||
|
||||
// TODO: recover the max size + facets_by_index as return value of this function so as not to ask it for all queries
|
||||
if let Err(mut error) =
|
||||
check_facet_order(&mut facet_order, &index_uid, &facets_by_index, &index, &rtxn)
|
||||
{
|
||||
error.message = format!(
|
||||
"Inside `.federation.facetsByIndex.{index_uid}`: {error}{}",
|
||||
if let Some(query_index) = first_query_index {
|
||||
format!("\n - Note: index `{index_uid}` used in `.queries[{query_index}]`")
|
||||
} else {
|
||||
Default::default()
|
||||
}
|
||||
);
|
||||
return Err(error);
|
||||
}
|
||||
|
||||
// 2.1. Compute all candidates for each query in the index
|
||||
let mut results_by_query = Vec::with_capacity(queries.len());
|
||||
|
||||
@ -562,34 +721,116 @@ pub fn perform_federated_search(
|
||||
.collect();
|
||||
|
||||
let merged_result = merged_result?;
|
||||
results_by_index.push(SearchResultByIndex {
|
||||
hits: merged_result,
|
||||
|
||||
let estimated_total_hits = candidates.len() as usize;
|
||||
|
||||
let facets = facets_by_index
|
||||
.map(|facets_by_index| {
|
||||
compute_facet_distribution_stats(
|
||||
&facets_by_index,
|
||||
&index,
|
||||
&rtxn,
|
||||
candidates,
|
||||
super::Route::MultiSearch,
|
||||
)
|
||||
})
|
||||
.transpose()
|
||||
.map_err(|mut error| {
|
||||
error.message = format!(
|
||||
"Inside `.federation.facetsByIndex.{index_uid}`: {}{}",
|
||||
error.message,
|
||||
if let Some(query_index) = first_query_index {
|
||||
format!("\n - Note: index `{index_uid}` used in `.queries[{query_index}]`")
|
||||
} else {
|
||||
Default::default()
|
||||
}
|
||||
);
|
||||
error
|
||||
})?;
|
||||
|
||||
results_by_index.push(SearchResultByIndex {
|
||||
index: index_uid,
|
||||
hits: merged_result,
|
||||
estimated_total_hits,
|
||||
degraded,
|
||||
used_negative_operator,
|
||||
facets,
|
||||
});
|
||||
}
|
||||
|
||||
// bonus step, make sure to return an error if an index wants a non-faceted field, even if no query actually uses that index.
|
||||
for (index_uid, facets) in federation.facets_by_index {
|
||||
let index = match index_scheduler.index(&index_uid) {
|
||||
Ok(index) => index,
|
||||
Err(err) => {
|
||||
let mut err = ResponseError::from(err);
|
||||
// Patch the HTTP status code to 400 as it defaults to 404 for `index_not_found`, but
|
||||
// here the resource not found is not part of the URL.
|
||||
err.code = StatusCode::BAD_REQUEST;
|
||||
err.message = format!(
|
||||
"Inside `.federation.facetsByIndex.{index_uid}`: {}\n - Note: index `{index_uid}` is not used in queries",
|
||||
err.message
|
||||
);
|
||||
return Err(err);
|
||||
}
|
||||
};
|
||||
|
||||
// Important: this is the only transaction we'll use for this index during this federated search
|
||||
let rtxn = index.read_txn()?;
|
||||
|
||||
if let Err(mut error) =
|
||||
check_facet_order(&mut facet_order, &index_uid, &facets, &index, &rtxn)
|
||||
{
|
||||
error.message = format!(
|
||||
"Inside `.federation.facetsByIndex.{index_uid}`: {error}\n - Note: index `{index_uid}` is not used in queries",
|
||||
);
|
||||
return Err(error);
|
||||
}
|
||||
|
||||
if let Some(facets) = facets {
|
||||
if let Err(mut error) = compute_facet_distribution_stats(
|
||||
&facets,
|
||||
&index,
|
||||
&rtxn,
|
||||
Default::default(),
|
||||
super::Route::MultiSearch,
|
||||
) {
|
||||
error.message =
|
||||
format!("Inside `.federation.facetsByIndex.{index_uid}`: {}\n - Note: index `{index_uid}` is not used in queries", error.message);
|
||||
return Err(error);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 3. merge hits and metadata across indexes
|
||||
// 3.1 merge metadata
|
||||
let (estimated_total_hits, degraded, used_negative_operator) = {
|
||||
let (estimated_total_hits, degraded, used_negative_operator, facets) = {
|
||||
let mut estimated_total_hits = 0;
|
||||
let mut degraded = false;
|
||||
let mut used_negative_operator = false;
|
||||
|
||||
let mut facets: FederatedFacets = FederatedFacets::default();
|
||||
|
||||
for SearchResultByIndex {
|
||||
index,
|
||||
hits: _,
|
||||
candidates,
|
||||
estimated_total_hits: estimated_total_hits_by_index,
|
||||
facets: facets_by_index,
|
||||
degraded: degraded_by_index,
|
||||
used_negative_operator: used_negative_operator_by_index,
|
||||
} in &results_by_index
|
||||
} in &mut results_by_index
|
||||
{
|
||||
estimated_total_hits += candidates.len() as usize;
|
||||
estimated_total_hits += *estimated_total_hits_by_index;
|
||||
degraded |= *degraded_by_index;
|
||||
used_negative_operator |= *used_negative_operator_by_index;
|
||||
|
||||
let facets_by_index = std::mem::take(facets_by_index);
|
||||
let index = std::mem::take(index);
|
||||
|
||||
facets.insert(index, facets_by_index);
|
||||
}
|
||||
|
||||
(estimated_total_hits, degraded, used_negative_operator)
|
||||
(estimated_total_hits, degraded, used_negative_operator, facets)
|
||||
};
|
||||
|
||||
// 3.2 merge hits
|
||||
@ -606,6 +847,20 @@ pub fn perform_federated_search(
|
||||
.map(|hit| hit.hit)
|
||||
.collect();
|
||||
|
||||
let (facet_distribution, facet_stats, facets_by_index) =
|
||||
match federation.merge_facets.zip(facet_order) {
|
||||
Some((merge_facets, facet_order)) => {
|
||||
let facets = facets.merge(merge_facets, facet_order);
|
||||
|
||||
let (facet_distribution, facet_stats) = facets
|
||||
.map(|ComputedFacets { distribution, stats }| (distribution, stats))
|
||||
.unzip();
|
||||
|
||||
(facet_distribution, facet_stats, FederatedFacets::default())
|
||||
}
|
||||
None => (None, None, facets),
|
||||
};
|
||||
|
||||
let search_result = FederatedSearchResult {
|
||||
hits: merged_hits,
|
||||
processing_time_ms: before_search.elapsed().as_millis(),
|
||||
@ -617,7 +872,39 @@ pub fn perform_federated_search(
|
||||
semantic_hit_count,
|
||||
degraded,
|
||||
used_negative_operator,
|
||||
facet_distribution,
|
||||
facet_stats,
|
||||
facets_by_index,
|
||||
};
|
||||
|
||||
Ok(search_result)
|
||||
}
|
||||
|
||||
fn check_facet_order(
|
||||
facet_order: &mut Option<BTreeMap<String, (String, OrderBy)>>,
|
||||
current_index: &str,
|
||||
facets_by_index: &Option<Vec<String>>,
|
||||
index: &milli::Index,
|
||||
rtxn: &milli::heed::RoTxn<'_>,
|
||||
) -> Result<(), ResponseError> {
|
||||
if let (Some(facet_order), Some(facets_by_index)) = (facet_order, facets_by_index) {
|
||||
let index_facet_order = index.sort_facet_values_by(rtxn)?;
|
||||
for facet in facets_by_index {
|
||||
let index_facet_order = index_facet_order.get(facet);
|
||||
let (previous_index, previous_facet_order) = facet_order
|
||||
.entry(facet.to_owned())
|
||||
.or_insert_with(|| (current_index.to_owned(), index_facet_order));
|
||||
if previous_facet_order != &index_facet_order {
|
||||
return Err(MeilisearchHttpError::InconsistentFacetOrder {
|
||||
facet: facet.clone(),
|
||||
previous_facet_order: *previous_facet_order,
|
||||
previous_uid: previous_index.clone(),
|
||||
current_uid: current_index.to_owned(),
|
||||
index_facet_order,
|
||||
}
|
||||
.into());
|
||||
}
|
||||
}
|
||||
};
|
||||
Ok(())
|
||||
}
|
||||
|
||||
@ -267,58 +267,54 @@ impl fmt::Debug for SearchQuery {
|
||||
pub struct HybridQuery {
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSearchSemanticRatio>, default)]
|
||||
pub semantic_ratio: SemanticRatio,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidEmbedder>, default)]
|
||||
pub embedder: Option<String>,
|
||||
#[deserr(error = DeserrJsonError<InvalidEmbedder>)]
|
||||
pub embedder: String,
|
||||
}
|
||||
|
||||
#[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 {
|
||||
pub(crate) fn semantic(
|
||||
index_scheduler: &index_scheduler::IndexScheduler,
|
||||
index: &Index,
|
||||
embedder_name: Option<&str>,
|
||||
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(
|
||||
index_scheduler: &index_scheduler::IndexScheduler,
|
||||
index: &Index,
|
||||
embedder_name: Option<&str>,
|
||||
embedder_name: &str,
|
||||
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(
|
||||
index_scheduler: &index_scheduler::IndexScheduler,
|
||||
index: &Index,
|
||||
embedder_name: Option<&str>,
|
||||
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_name = embedder_name.unwrap_or_else(|| embedders.get_default_embedder_name());
|
||||
|
||||
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() {
|
||||
@ -332,7 +328,7 @@ impl SearchKind {
|
||||
}
|
||||
}
|
||||
|
||||
Ok((embedder_name.to_owned(), embedder))
|
||||
Ok((embedder_name.to_owned(), embedder, quantized))
|
||||
}
|
||||
}
|
||||
|
||||
@ -441,9 +437,6 @@ pub struct SearchQueryWithIndex {
|
||||
}
|
||||
|
||||
impl SearchQueryWithIndex {
|
||||
pub fn has_federation_options(&self) -> bool {
|
||||
self.federation_options.is_some()
|
||||
}
|
||||
pub fn has_pagination(&self) -> Option<&'static str> {
|
||||
if self.offset.is_some() {
|
||||
Some("offset")
|
||||
@ -458,6 +451,10 @@ impl SearchQueryWithIndex {
|
||||
}
|
||||
}
|
||||
|
||||
pub fn has_facets(&self) -> Option<&[String]> {
|
||||
self.facets.as_deref().filter(|v| !v.is_empty())
|
||||
}
|
||||
|
||||
pub fn into_index_query_federation(self) -> (IndexUid, SearchQuery, Option<FederationOptions>) {
|
||||
let SearchQueryWithIndex {
|
||||
index_uid,
|
||||
@ -537,8 +534,8 @@ pub struct SimilarQuery {
|
||||
pub limit: usize,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSimilarFilter>)]
|
||||
pub filter: Option<Value>,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidEmbedder>, default)]
|
||||
pub embedder: Option<String>,
|
||||
#[deserr(error = DeserrJsonError<InvalidEmbedder>)]
|
||||
pub embedder: String,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSimilarAttributesToRetrieve>)]
|
||||
pub attributes_to_retrieve: Option<BTreeSet<String>>,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSimilarRetrieveVectors>)]
|
||||
@ -792,7 +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 => {
|
||||
@ -806,14 +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(),
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@ -987,39 +989,13 @@ pub fn perform_search(
|
||||
HitsInfo::OffsetLimit { limit, offset, estimated_total_hits: number_of_hits }
|
||||
};
|
||||
|
||||
let (facet_distribution, facet_stats) = match facets {
|
||||
Some(ref fields) => {
|
||||
let mut facet_distribution = index.facets_distribution(&rtxn);
|
||||
|
||||
let max_values_by_facet = index
|
||||
.max_values_per_facet(&rtxn)
|
||||
.map_err(milli::Error::from)?
|
||||
.map(|x| x as usize)
|
||||
.unwrap_or(DEFAULT_VALUES_PER_FACET);
|
||||
facet_distribution.max_values_per_facet(max_values_by_facet);
|
||||
|
||||
let sort_facet_values_by =
|
||||
index.sort_facet_values_by(&rtxn).map_err(milli::Error::from)?;
|
||||
|
||||
if fields.iter().all(|f| f != "*") {
|
||||
let fields: Vec<_> =
|
||||
fields.iter().map(|n| (n, sort_facet_values_by.get(n))).collect();
|
||||
facet_distribution.facets(fields);
|
||||
}
|
||||
|
||||
let distribution = facet_distribution
|
||||
.candidates(candidates)
|
||||
.default_order_by(sort_facet_values_by.get("*"))
|
||||
.execute()?;
|
||||
let stats = facet_distribution.compute_stats()?;
|
||||
(Some(distribution), Some(stats))
|
||||
}
|
||||
None => (None, None),
|
||||
};
|
||||
|
||||
let facet_stats = facet_stats.map(|stats| {
|
||||
stats.into_iter().map(|(k, (min, max))| (k, FacetStats { min, max })).collect()
|
||||
});
|
||||
let (facet_distribution, facet_stats) = facets
|
||||
.map(move |facets| {
|
||||
compute_facet_distribution_stats(&facets, index, &rtxn, candidates, Route::Search)
|
||||
})
|
||||
.transpose()?
|
||||
.map(|ComputedFacets { distribution, stats }| (distribution, stats))
|
||||
.unzip();
|
||||
|
||||
let result = SearchResult {
|
||||
hits: documents,
|
||||
@ -1035,6 +1011,61 @@ pub fn perform_search(
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Default, Serialize)]
|
||||
pub struct ComputedFacets {
|
||||
pub distribution: BTreeMap<String, IndexMap<String, u64>>,
|
||||
pub stats: BTreeMap<String, FacetStats>,
|
||||
}
|
||||
|
||||
enum Route {
|
||||
Search,
|
||||
MultiSearch,
|
||||
}
|
||||
|
||||
fn compute_facet_distribution_stats<S: AsRef<str>>(
|
||||
facets: &[S],
|
||||
index: &Index,
|
||||
rtxn: &RoTxn,
|
||||
candidates: roaring::RoaringBitmap,
|
||||
route: Route,
|
||||
) -> Result<ComputedFacets, ResponseError> {
|
||||
let mut facet_distribution = index.facets_distribution(rtxn);
|
||||
|
||||
let max_values_by_facet = index
|
||||
.max_values_per_facet(rtxn)
|
||||
.map_err(milli::Error::from)?
|
||||
.map(|x| x as usize)
|
||||
.unwrap_or(DEFAULT_VALUES_PER_FACET);
|
||||
|
||||
facet_distribution.max_values_per_facet(max_values_by_facet);
|
||||
|
||||
let sort_facet_values_by = index.sort_facet_values_by(rtxn).map_err(milli::Error::from)?;
|
||||
|
||||
// add specific facet if there is no placeholder
|
||||
if facets.iter().all(|f| f.as_ref() != "*") {
|
||||
let fields: Vec<_> =
|
||||
facets.iter().map(|n| (n, sort_facet_values_by.get(n.as_ref()))).collect();
|
||||
facet_distribution.facets(fields);
|
||||
}
|
||||
|
||||
let distribution = facet_distribution
|
||||
.candidates(candidates)
|
||||
.default_order_by(sort_facet_values_by.get("*"))
|
||||
.execute()
|
||||
.map_err(|error| match (error, route) {
|
||||
(
|
||||
error @ milli::Error::UserError(milli::UserError::InvalidFacetsDistribution {
|
||||
..
|
||||
}),
|
||||
Route::MultiSearch,
|
||||
) => ResponseError::from_msg(error.to_string(), Code::InvalidMultiSearchFacets),
|
||||
(error, _) => error.into(),
|
||||
})?;
|
||||
let stats = facet_distribution.compute_stats()?;
|
||||
let stats = stats.into_iter().map(|(k, (min, max))| (k, FacetStats { min, max })).collect();
|
||||
Ok(ComputedFacets { distribution, stats })
|
||||
}
|
||||
|
||||
pub fn search_from_kind(
|
||||
search_kind: SearchKind,
|
||||
search: milli::Search<'_>,
|
||||
@ -1413,6 +1444,7 @@ pub fn perform_similar(
|
||||
query: SimilarQuery,
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
quantized: bool,
|
||||
retrieve_vectors: RetrieveVectors,
|
||||
features: RoFeatures,
|
||||
) -> Result<SimilarResult, ResponseError> {
|
||||
@ -1441,8 +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)? {
|
||||
|
||||
@ -136,7 +136,7 @@ async fn get_all_documents_bad_filter() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `doggo`.\n1:6 doggo",
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `doggo`.\n1:6 doggo",
|
||||
"code": "invalid_document_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_document_filter"
|
||||
@ -525,7 +525,7 @@ async fn delete_document_by_filter() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `hello`.\n1:6 hello",
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `hello`.\n1:6 hello",
|
||||
"code": "invalid_document_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_document_filter"
|
||||
@ -723,7 +723,7 @@ async fn fetch_document_by_filter() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `cool doggo`.\n1:11 cool doggo",
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `cool doggo`.\n1:11 cool doggo",
|
||||
"code": "invalid_document_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_document_filter"
|
||||
|
||||
@ -646,7 +646,7 @@ async fn filter_invalid_syntax_object() {
|
||||
.search(json!({"filter": "title & Glass"}), |response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `title & Glass`.\n1:14 title & Glass",
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `title & Glass`.\n1:14 title & Glass",
|
||||
"code": "invalid_search_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_search_filter"
|
||||
@ -669,7 +669,7 @@ async fn filter_invalid_syntax_array() {
|
||||
.search(json!({"filter": ["title & Glass"]}), |response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `title & Glass`.\n1:14 title & Glass",
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `title & Glass`.\n1:14 title & Glass",
|
||||
"code": "invalid_search_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_search_filter"
|
||||
@ -1163,7 +1163,7 @@ async fn search_with_contains_without_enabling_the_feature() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Using `CONTAINS` in a filter requires enabling the `contains filter` experimental feature. See https://github.com/orgs/meilisearch/discussions/763\n7:15 doggo CONTAINS kefir",
|
||||
"message": "Using `CONTAINS` or `STARTS WITH` in a filter requires enabling the `contains filter` experimental feature. See https://github.com/orgs/meilisearch/discussions/763\n7:15 doggo CONTAINS kefir",
|
||||
"code": "feature_not_enabled",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#feature_not_enabled"
|
||||
@ -1176,7 +1176,7 @@ async fn search_with_contains_without_enabling_the_feature() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Using `CONTAINS` in a filter requires enabling the `contains filter` experimental feature. See https://github.com/orgs/meilisearch/discussions/763\n25:33 doggo != echo AND doggo CONTAINS kefir",
|
||||
"message": "Using `CONTAINS` or `STARTS WITH` in a filter requires enabling the `contains filter` experimental feature. See https://github.com/orgs/meilisearch/discussions/763\n25:33 doggo != echo AND doggo CONTAINS kefir",
|
||||
"code": "feature_not_enabled",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#feature_not_enabled"
|
||||
@ -1192,7 +1192,7 @@ async fn search_with_contains_without_enabling_the_feature() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Using `CONTAINS` in a filter requires enabling the `contains filter` experimental feature. See https://github.com/orgs/meilisearch/discussions/763\n7:15 doggo CONTAINS kefir",
|
||||
"message": "Using `CONTAINS` or `STARTS WITH` in a filter requires enabling the `contains filter` experimental feature. See https://github.com/orgs/meilisearch/discussions/763\n7:15 doggo CONTAINS kefir",
|
||||
"code": "feature_not_enabled",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#feature_not_enabled"
|
||||
@ -1204,7 +1204,7 @@ async fn search_with_contains_without_enabling_the_feature() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Using `CONTAINS` in a filter requires enabling the `contains filter` experimental feature. See https://github.com/orgs/meilisearch/discussions/763\n7:15 doggo CONTAINS kefir",
|
||||
"message": "Using `CONTAINS` or `STARTS WITH` in a filter requires enabling the `contains filter` experimental feature. See https://github.com/orgs/meilisearch/discussions/763\n7:15 doggo CONTAINS kefir",
|
||||
"code": "feature_not_enabled",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#feature_not_enabled"
|
||||
|
||||
@ -128,7 +128,7 @@ async fn simple_search() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.2}, "retrieveVectors": true}),
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.2, "embedder": "default"}, "retrieveVectors": true}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -137,7 +137,7 @@ async fn simple_search() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.5}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.5, "embedder": "default"}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -146,7 +146,7 @@ async fn simple_search() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.8}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.8, "embedder": "default"}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -161,7 +161,7 @@ async fn limit_offset() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.2}, "retrieveVectors": true, "offset": 1, "limit": 1}),
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.2, "embedder": "default"}, "retrieveVectors": true, "offset": 1, "limit": 1}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -174,7 +174,7 @@ async fn limit_offset() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.9}, "retrieveVectors": true, "offset": 1, "limit": 1}),
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 0.9, "embedder": "default"}, "retrieveVectors": true, "offset": 1, "limit": 1}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -188,8 +188,11 @@ async fn simple_search_hf() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents_hf(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
|
||||
|
||||
let (response, code) =
|
||||
index.search_post(json!({"q": "Captain", "hybrid": {"semanticRatio": 0.2}})).await;
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "hybrid": {"semanticRatio": 0.2, "embedder": "default"}}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2"},{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3"},{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1"}]"###);
|
||||
snapshot!(response["semanticHitCount"], @"0");
|
||||
@ -197,7 +200,7 @@ async fn simple_search_hf() {
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
// disable ranking score as the vectors between architectures are not equal
|
||||
json!({"q": "Captain", "hybrid": {"semanticRatio": 0.55}, "showRankingScore": false}),
|
||||
json!({"q": "Captain", "hybrid": {"embedder": "default", "semanticRatio": 0.55}, "showRankingScore": false}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -206,7 +209,7 @@ async fn simple_search_hf() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "hybrid": {"semanticRatio": 0.8}, "showRankingScore": false}),
|
||||
json!({"q": "Captain", "hybrid": {"embedder": "default", "semanticRatio": 0.8}, "showRankingScore": false}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -215,7 +218,7 @@ async fn simple_search_hf() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Movie World", "hybrid": {"semanticRatio": 0.2}, "showRankingScore": false}),
|
||||
json!({"q": "Movie World", "hybrid": {"embedder": "default", "semanticRatio": 0.2}, "showRankingScore": false}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -224,7 +227,7 @@ async fn simple_search_hf() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Wonder replacement", "hybrid": {"semanticRatio": 0.2}, "showRankingScore": false}),
|
||||
json!({"q": "Wonder replacement", "hybrid": {"embedder": "default", "semanticRatio": 0.2}, "showRankingScore": false}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -237,7 +240,7 @@ async fn distribution_shift() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
|
||||
|
||||
let search = json!({"q": "Captain", "vector": [1.0, 1.0], "showRankingScore": true, "hybrid": {"semanticRatio": 1.0}, "retrieveVectors": true});
|
||||
let search = json!({"q": "Captain", "vector": [1.0, 1.0], "showRankingScore": true, "hybrid": {"embedder": "default", "semanticRatio": 1.0}, "retrieveVectors": true});
|
||||
let (response, code) = index.search_post(search.clone()).await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3","_vectors":{"default":{"embeddings":[[2.0,3.0]],"regenerate":false}},"_rankingScore":0.990290343761444},{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2","_vectors":{"default":{"embeddings":[[1.0,2.0]],"regenerate":false}},"_rankingScore":0.974341630935669},{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1","_vectors":{"default":{"embeddings":[[1.0,3.0]],"regenerate":false}},"_rankingScore":0.9472135901451112}]"###);
|
||||
@ -271,7 +274,7 @@ async fn highlighter() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(json!({"q": "Captain Marvel", "vector": [1.0, 1.0],
|
||||
"hybrid": {"semanticRatio": 0.2},
|
||||
"hybrid": {"embedder": "default", "semanticRatio": 0.2},
|
||||
"retrieveVectors": true,
|
||||
"attributesToHighlight": [
|
||||
"desc",
|
||||
@ -287,7 +290,7 @@ async fn highlighter() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(json!({"q": "Captain Marvel", "vector": [1.0, 1.0],
|
||||
"hybrid": {"semanticRatio": 0.8},
|
||||
"hybrid": {"embedder": "default", "semanticRatio": 0.8},
|
||||
"retrieveVectors": true,
|
||||
"showRankingScore": true,
|
||||
"attributesToHighlight": [
|
||||
@ -304,7 +307,7 @@ async fn highlighter() {
|
||||
// no highlighting on full semantic
|
||||
let (response, code) = index
|
||||
.search_post(json!({"q": "Captain Marvel", "vector": [1.0, 1.0],
|
||||
"hybrid": {"semanticRatio": 1.0},
|
||||
"hybrid": {"embedder": "default", "semanticRatio": 1.0},
|
||||
"retrieveVectors": true,
|
||||
"showRankingScore": true,
|
||||
"attributesToHighlight": [
|
||||
@ -326,7 +329,7 @@ async fn invalid_semantic_ratio() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": 1.2}}),
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"embedder": "default", "semanticRatio": 1.2}}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
@ -341,7 +344,7 @@ async fn invalid_semantic_ratio() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"semanticRatio": -0.8}}),
|
||||
json!({"q": "Captain", "vector": [1.0, 1.0], "hybrid": {"embedder": "default", "semanticRatio": -0.8}}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
@ -357,7 +360,7 @@ async fn invalid_semantic_ratio() {
|
||||
let (response, code) = index
|
||||
.search_get(
|
||||
&yaup::to_string(
|
||||
&json!({"q": "Captain", "vector": [1.0, 1.0], "hybridSemanticRatio": 1.2}),
|
||||
&json!({"q": "Captain", "vector": [1.0, 1.0], "hybridEmbedder": "default", "hybridSemanticRatio": 1.2}),
|
||||
)
|
||||
.unwrap(),
|
||||
)
|
||||
@ -375,7 +378,7 @@ async fn invalid_semantic_ratio() {
|
||||
let (response, code) = index
|
||||
.search_get(
|
||||
&yaup::to_string(
|
||||
&json!({"q": "Captain", "vector": [1.0, 1.0], "hybridSemanticRatio": -0.2}),
|
||||
&json!({"q": "Captain", "vector": [1.0, 1.0], "hybridEmbedder": "default", "hybridSemanticRatio": -0.2}),
|
||||
)
|
||||
.unwrap(),
|
||||
)
|
||||
@ -398,7 +401,7 @@ async fn single_document() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"vector": [1.0, 3.0], "hybrid": {"semanticRatio": 1.0}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
json!({"vector": [1.0, 3.0], "hybrid": {"semanticRatio": 1.0, "embedder": "default"}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
)
|
||||
.await;
|
||||
|
||||
@ -414,7 +417,7 @@ async fn query_combination() {
|
||||
|
||||
// search without query and vector, but with hybrid => still placeholder
|
||||
let (response, code) = index
|
||||
.search_post(json!({"hybrid": {"semanticRatio": 1.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.search_post(json!({"hybrid": {"embedder": "default", "semanticRatio": 1.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.await;
|
||||
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -423,7 +426,7 @@ async fn query_combination() {
|
||||
|
||||
// same with a different semantic ratio
|
||||
let (response, code) = index
|
||||
.search_post(json!({"hybrid": {"semanticRatio": 0.76}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.search_post(json!({"hybrid": {"embedder": "default", "semanticRatio": 0.76}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.await;
|
||||
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -432,7 +435,7 @@ async fn query_combination() {
|
||||
|
||||
// wrong vector dimensions
|
||||
let (response, code) = index
|
||||
.search_post(json!({"vector": [1.0, 0.0, 1.0], "hybrid": {"semanticRatio": 1.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.search_post(json!({"vector": [1.0, 0.0, 1.0], "hybrid": {"embedder": "default", "semanticRatio": 1.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.await;
|
||||
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
@ -447,7 +450,7 @@ async fn query_combination() {
|
||||
|
||||
// full vector
|
||||
let (response, code) = index
|
||||
.search_post(json!({"vector": [1.0, 0.0], "hybrid": {"semanticRatio": 1.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.search_post(json!({"vector": [1.0, 0.0], "hybrid": {"embedder": "default", "semanticRatio": 1.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.await;
|
||||
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -456,7 +459,7 @@ async fn query_combination() {
|
||||
|
||||
// full keyword, without a query
|
||||
let (response, code) = index
|
||||
.search_post(json!({"vector": [1.0, 0.0], "hybrid": {"semanticRatio": 0.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.search_post(json!({"vector": [1.0, 0.0], "hybrid": {"embedder": "default", "semanticRatio": 0.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.await;
|
||||
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -465,7 +468,7 @@ async fn query_combination() {
|
||||
|
||||
// query + vector, full keyword => keyword
|
||||
let (response, code) = index
|
||||
.search_post(json!({"q": "Captain", "vector": [1.0, 0.0], "hybrid": {"semanticRatio": 0.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.search_post(json!({"q": "Captain", "vector": [1.0, 0.0], "hybrid": {"embedder": "default", "semanticRatio": 0.0}, "showRankingScore": true, "retrieveVectors": true}))
|
||||
.await;
|
||||
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -480,7 +483,7 @@ async fn query_combination() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"message": "Invalid request: missing `hybrid` parameter when both `q` and `vector` are present.",
|
||||
"message": "Invalid request: missing `hybrid` parameter when `vector` is present.",
|
||||
"code": "missing_search_hybrid",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#missing_search_hybrid"
|
||||
@ -490,7 +493,7 @@ async fn query_combination() {
|
||||
// full vector, without a vector => error
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "hybrid": {"semanticRatio": 1.0}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
json!({"q": "Captain", "hybrid": {"semanticRatio": 1.0, "embedder": "default"}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
)
|
||||
.await;
|
||||
|
||||
@ -507,7 +510,7 @@ async fn query_combination() {
|
||||
// hybrid without a vector => full keyword
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Planet", "hybrid": {"semanticRatio": 0.99}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
json!({"q": "Planet", "hybrid": {"semanticRatio": 0.99, "embedder": "default"}, "showRankingScore": true, "retrieveVectors": true}),
|
||||
)
|
||||
.await;
|
||||
|
||||
@ -523,7 +526,7 @@ async fn retrieve_vectors() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "hybrid": {"semanticRatio": 0.2}, "retrieveVectors": true}),
|
||||
json!({"q": "Captain", "hybrid": {"embedder": "default", "semanticRatio": 0.2}, "retrieveVectors": true}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -573,7 +576,7 @@ async fn retrieve_vectors() {
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "hybrid": {"semanticRatio": 0.2}, "retrieveVectors": true}),
|
||||
json!({"q": "Captain", "hybrid": {"embedder": "default", "semanticRatio": 0.2}, "retrieveVectors": true}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
|
||||
@ -922,7 +922,7 @@ async fn invalid_locales() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Unknown value `invalid` at `.locales[0]`: expected one of `af`, `ak`, `am`, `ar`, `az`, `be`, `bn`, `bg`, `ca`, `cs`, `zh`, `da`, `de`, `el`, `en`, `eo`, `et`, `fi`, `fr`, `gu`, `he`, `hi`, `hr`, `hu`, `hy`, `id`, `it`, `jv`, `ja`, `kn`, `ka`, `km`, `ko`, `la`, `lv`, `lt`, `ml`, `mr`, `mk`, `my`, `ne`, `nl`, `nb`, `or`, `pa`, `fa`, `pl`, `pt`, `ro`, `ru`, `si`, `sk`, `sl`, `sn`, `es`, `sr`, `sv`, `ta`, `te`, `tl`, `th`, `tk`, `tr`, `uk`, `ur`, `uz`, `vi`, `yi`, `zu`, `afr`, `aka`, `amh`, `ara`, `aze`, `bel`, `ben`, `bul`, `cat`, `ces`, `cmn`, `dan`, `deu`, `ell`, `eng`, `epo`, `est`, `fin`, `fra`, `guj`, `heb`, `hin`, `hrv`, `hun`, `hye`, `ind`, `ita`, `jav`, `jpn`, `kan`, `kat`, `khm`, `kor`, `lat`, `lav`, `lit`, `mal`, `mar`, `mkd`, `mya`, `nep`, `nld`, `nob`, `ori`, `pan`, `pes`, `pol`, `por`, `ron`, `rus`, `sin`, `slk`, `slv`, `sna`, `spa`, `srp`, `swe`, `tam`, `tel`, `tgl`, `tha`, `tuk`, `tur`, `ukr`, `urd`, `uzb`, `vie`, `yid`, `zul`",
|
||||
"message": "Unknown value `invalid` at `.locales[0]`: expected one of `af`, `ak`, `am`, `ar`, `az`, `be`, `bn`, `bg`, `ca`, `cs`, `da`, `de`, `el`, `en`, `eo`, `et`, `fi`, `fr`, `gu`, `he`, `hi`, `hr`, `hu`, `hy`, `id`, `it`, `jv`, `ja`, `kn`, `ka`, `km`, `ko`, `la`, `lv`, `lt`, `ml`, `mr`, `mk`, `my`, `ne`, `nl`, `nb`, `or`, `pa`, `fa`, `pl`, `pt`, `ro`, `ru`, `si`, `sk`, `sl`, `sn`, `es`, `sr`, `sv`, `ta`, `te`, `tl`, `th`, `tk`, `tr`, `uk`, `ur`, `uz`, `vi`, `yi`, `zh`, `zu`, `afr`, `aka`, `amh`, `ara`, `aze`, `bel`, `ben`, `bul`, `cat`, `ces`, `dan`, `deu`, `ell`, `eng`, `epo`, `est`, `fin`, `fra`, `guj`, `heb`, `hin`, `hrv`, `hun`, `hye`, `ind`, `ita`, `jav`, `jpn`, `kan`, `kat`, `khm`, `kor`, `lat`, `lav`, `lit`, `mal`, `mar`, `mkd`, `mya`, `nep`, `nld`, `nob`, `ori`, `pan`, `pes`, `pol`, `por`, `ron`, `rus`, `sin`, `slk`, `slv`, `sna`, `spa`, `srp`, `swe`, `tam`, `tel`, `tgl`, `tha`, `tuk`, `tur`, `ukr`, `urd`, `uzb`, `vie`, `yid`, `zho`, `zul`, `cmn`",
|
||||
"code": "invalid_search_locales",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_search_locales"
|
||||
@ -935,7 +935,7 @@ async fn invalid_locales() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value in parameter `locales`: Unsupported locale `invalid`, expected one of af, ak, am, ar, az, be, bn, bg, ca, cs, zh, da, de, el, en, eo, et, fi, fr, gu, he, hi, hr, hu, hy, id, it, jv, ja, kn, ka, km, ko, la, lv, lt, ml, mr, mk, my, ne, nl, nb, or, pa, fa, pl, pt, ro, ru, si, sk, sl, sn, es, sr, sv, ta, te, tl, th, tk, tr, uk, ur, uz, vi, yi, zu, afr, aka, amh, ara, aze, bel, ben, bul, cat, ces, cmn, dan, deu, ell, eng, epo, est, fin, fra, guj, heb, hin, hrv, hun, hye, ind, ita, jav, jpn, kan, kat, khm, kor, lat, lav, lit, mal, mar, mkd, mya, nep, nld, nob, ori, pan, pes, pol, por, ron, rus, sin, slk, slv, sna, spa, srp, swe, tam, tel, tgl, tha, tuk, tur, ukr, urd, uzb, vie, yid, zul",
|
||||
"message": "Invalid value in parameter `locales`: Unsupported locale `invalid`, expected one of af, ak, am, ar, az, be, bg, bn, ca, cs, da, de, el, en, eo, es, et, fa, fi, fr, gu, he, hi, hr, hu, hy, id, it, ja, jv, ka, km, kn, ko, la, lt, lv, mk, ml, mr, my, nb, ne, nl, or, pa, pl, pt, ro, ru, si, sk, sl, sn, sr, sv, ta, te, th, tk, tl, tr, uk, ur, uz, vi, yi, zh, zu, afr, aka, amh, ara, aze, bel, ben, bul, cat, ces, cmn, dan, deu, ell, eng, epo, est, fin, fra, guj, heb, hin, hrv, hun, hye, ind, ita, jav, jpn, kan, kat, khm, kor, lat, lav, lit, mal, mar, mkd, mya, nep, nld, nob, ori, pan, pes, pol, por, ron, rus, sin, slk, slv, sna, spa, srp, swe, tam, tel, tgl, tha, tuk, tur, ukr, urd, uzb, vie, yid, zho, zul",
|
||||
"code": "invalid_search_locales",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_search_locales"
|
||||
@ -957,7 +957,7 @@ async fn invalid_localized_attributes_rules() {
|
||||
.await;
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"message": "Unknown value `japan` at `.localizedAttributes[0].locales[0]`: expected one of `af`, `ak`, `am`, `ar`, `az`, `be`, `bn`, `bg`, `ca`, `cs`, `zh`, `da`, `de`, `el`, `en`, `eo`, `et`, `fi`, `fr`, `gu`, `he`, `hi`, `hr`, `hu`, `hy`, `id`, `it`, `jv`, `ja`, `kn`, `ka`, `km`, `ko`, `la`, `lv`, `lt`, `ml`, `mr`, `mk`, `my`, `ne`, `nl`, `nb`, `or`, `pa`, `fa`, `pl`, `pt`, `ro`, `ru`, `si`, `sk`, `sl`, `sn`, `es`, `sr`, `sv`, `ta`, `te`, `tl`, `th`, `tk`, `tr`, `uk`, `ur`, `uz`, `vi`, `yi`, `zu`, `afr`, `aka`, `amh`, `ara`, `aze`, `bel`, `ben`, `bul`, `cat`, `ces`, `cmn`, `dan`, `deu`, `ell`, `eng`, `epo`, `est`, `fin`, `fra`, `guj`, `heb`, `hin`, `hrv`, `hun`, `hye`, `ind`, `ita`, `jav`, `jpn`, `kan`, `kat`, `khm`, `kor`, `lat`, `lav`, `lit`, `mal`, `mar`, `mkd`, `mya`, `nep`, `nld`, `nob`, `ori`, `pan`, `pes`, `pol`, `por`, `ron`, `rus`, `sin`, `slk`, `slv`, `sna`, `spa`, `srp`, `swe`, `tam`, `tel`, `tgl`, `tha`, `tuk`, `tur`, `ukr`, `urd`, `uzb`, `vie`, `yid`, `zul`",
|
||||
"message": "Unknown value `japan` at `.localizedAttributes[0].locales[0]`: expected one of `af`, `ak`, `am`, `ar`, `az`, `be`, `bn`, `bg`, `ca`, `cs`, `da`, `de`, `el`, `en`, `eo`, `et`, `fi`, `fr`, `gu`, `he`, `hi`, `hr`, `hu`, `hy`, `id`, `it`, `jv`, `ja`, `kn`, `ka`, `km`, `ko`, `la`, `lv`, `lt`, `ml`, `mr`, `mk`, `my`, `ne`, `nl`, `nb`, `or`, `pa`, `fa`, `pl`, `pt`, `ro`, `ru`, `si`, `sk`, `sl`, `sn`, `es`, `sr`, `sv`, `ta`, `te`, `tl`, `th`, `tk`, `tr`, `uk`, `ur`, `uz`, `vi`, `yi`, `zh`, `zu`, `afr`, `aka`, `amh`, `ara`, `aze`, `bel`, `ben`, `bul`, `cat`, `ces`, `dan`, `deu`, `ell`, `eng`, `epo`, `est`, `fin`, `fra`, `guj`, `heb`, `hin`, `hrv`, `hun`, `hye`, `ind`, `ita`, `jav`, `jpn`, `kan`, `kat`, `khm`, `kor`, `lat`, `lav`, `lit`, `mal`, `mar`, `mkd`, `mya`, `nep`, `nld`, `nob`, `ori`, `pan`, `pes`, `pol`, `por`, `ron`, `rus`, `sin`, `slk`, `slv`, `sna`, `spa`, `srp`, `swe`, `tam`, `tel`, `tgl`, `tha`, `tuk`, `tur`, `ukr`, `urd`, `uzb`, `vie`, `yid`, `zho`, `zul`, `cmn`",
|
||||
"code": "invalid_settings_localized_attributes",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_settings_localized_attributes"
|
||||
@ -1143,3 +1143,195 @@ async fn facet_search_with_localized_attributes() {
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn swedish_search() {
|
||||
let server = Server::new().await;
|
||||
|
||||
let index = server.index("test");
|
||||
let documents = json!([
|
||||
{"id": "tra1-1", "product": "trä"},
|
||||
{"id": "tra2-1", "product": "traktor"},
|
||||
{"id": "tra1-2", "product": "träbjälke"},
|
||||
{"id": "tra2-2", "product": "trafiksignal"},
|
||||
]);
|
||||
index.add_documents(documents, None).await;
|
||||
let (_response, _) = index
|
||||
.update_settings(json!({
|
||||
"searchableAttributes": ["product"],
|
||||
"localizedAttributes": [
|
||||
// force swedish
|
||||
{"attributePatterns": ["product"], "locales": ["swe"]}
|
||||
]
|
||||
}))
|
||||
.await;
|
||||
index.wait_task(1).await;
|
||||
|
||||
// infer swedish
|
||||
index
|
||||
.search(json!({"q": "trä", "attributesToRetrieve": ["product"]}), |response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"hits": [
|
||||
{
|
||||
"product": "trä"
|
||||
},
|
||||
{
|
||||
"product": "träbjälke"
|
||||
}
|
||||
],
|
||||
"query": "trä",
|
||||
"processingTimeMs": "[duration]",
|
||||
"limit": 20,
|
||||
"offset": 0,
|
||||
"estimatedTotalHits": 2
|
||||
}
|
||||
"###);
|
||||
snapshot!(code, @"200 OK");
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "tra", "attributesToRetrieve": ["product"]}), |response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"hits": [
|
||||
{
|
||||
"product": "traktor"
|
||||
},
|
||||
{
|
||||
"product": "trafiksignal"
|
||||
}
|
||||
],
|
||||
"query": "tra",
|
||||
"processingTimeMs": "[duration]",
|
||||
"limit": 20,
|
||||
"offset": 0,
|
||||
"estimatedTotalHits": 2
|
||||
}
|
||||
"###);
|
||||
snapshot!(code, @"200 OK");
|
||||
})
|
||||
.await;
|
||||
|
||||
// force swedish
|
||||
index
|
||||
.search(
|
||||
json!({"q": "trä", "locales": ["swe"], "attributesToRetrieve": ["product"]}),
|
||||
|response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"hits": [
|
||||
{
|
||||
"product": "trä"
|
||||
},
|
||||
{
|
||||
"product": "träbjälke"
|
||||
}
|
||||
],
|
||||
"query": "trä",
|
||||
"processingTimeMs": "[duration]",
|
||||
"limit": 20,
|
||||
"offset": 0,
|
||||
"estimatedTotalHits": 2
|
||||
}
|
||||
"###);
|
||||
snapshot!(code, @"200 OK");
|
||||
},
|
||||
)
|
||||
.await;
|
||||
index
|
||||
.search(
|
||||
json!({"q": "tra", "locales": ["swe"], "attributesToRetrieve": ["product"]}),
|
||||
|response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"hits": [
|
||||
{
|
||||
"product": "traktor"
|
||||
},
|
||||
{
|
||||
"product": "trafiksignal"
|
||||
}
|
||||
],
|
||||
"query": "tra",
|
||||
"processingTimeMs": "[duration]",
|
||||
"limit": 20,
|
||||
"offset": 0,
|
||||
"estimatedTotalHits": 2
|
||||
}
|
||||
"###);
|
||||
snapshot!(code, @"200 OK");
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn german_search() {
|
||||
let server = Server::new().await;
|
||||
|
||||
let index = server.index("test");
|
||||
let documents = json!([
|
||||
{"id": 1, "product": "Interkulturalität"},
|
||||
{"id": 2, "product": "Wissensorganisation"},
|
||||
]);
|
||||
index.add_documents(documents, None).await;
|
||||
let (_response, _) = index
|
||||
.update_settings(json!({
|
||||
"searchableAttributes": ["product"],
|
||||
"localizedAttributes": [
|
||||
// force swedish
|
||||
{"attributePatterns": ["product"], "locales": ["deu"]}
|
||||
]
|
||||
}))
|
||||
.await;
|
||||
index.wait_task(1).await;
|
||||
|
||||
// infer swedish
|
||||
index
|
||||
.search(
|
||||
json!({"q": "kulturalität", "attributesToRetrieve": ["product"]}),
|
||||
|response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"hits": [
|
||||
{
|
||||
"product": "Interkulturalität"
|
||||
}
|
||||
],
|
||||
"query": "kulturalität",
|
||||
"processingTimeMs": "[duration]",
|
||||
"limit": 20,
|
||||
"offset": 0,
|
||||
"estimatedTotalHits": 1
|
||||
}
|
||||
"###);
|
||||
snapshot!(code, @"200 OK");
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(
|
||||
json!({"q": "organisation", "attributesToRetrieve": ["product"]}),
|
||||
|response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"hits": [
|
||||
{
|
||||
"product": "Wissensorganisation"
|
||||
}
|
||||
],
|
||||
"query": "organisation",
|
||||
"processingTimeMs": "[duration]",
|
||||
"limit": 20,
|
||||
"offset": 0,
|
||||
"estimatedTotalHits": 1
|
||||
}
|
||||
"###);
|
||||
snapshot!(code, @"200 OK");
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -1099,11 +1099,17 @@ async fn experimental_feature_vector_store() {
|
||||
index.add_documents(json!(documents), None).await;
|
||||
index.wait_task(0).await;
|
||||
|
||||
index
|
||||
.search(json!({
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"vector": [1.0, 2.0, 3.0],
|
||||
"hybrid": {
|
||||
"embedder": "manual",
|
||||
},
|
||||
"showRankingScore": true
|
||||
}), |response, code|{
|
||||
}))
|
||||
.await;
|
||||
|
||||
{
|
||||
meili_snap::snapshot!(code, @"400 Bad Request");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response), @r###"
|
||||
{
|
||||
@ -1113,8 +1119,8 @@ async fn experimental_feature_vector_store() {
|
||||
"link": "https://docs.meilisearch.com/errors#feature_not_enabled"
|
||||
}
|
||||
"###);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
index
|
||||
.search(json!({
|
||||
"retrieveVectors": true,
|
||||
@ -1162,6 +1168,9 @@ async fn experimental_feature_vector_store() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"vector": [1.0, 2.0, 3.0],
|
||||
"hybrid": {
|
||||
"embedder": "manual",
|
||||
},
|
||||
"showRankingScore": true,
|
||||
"retrieveVectors": true,
|
||||
}))
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -18,7 +18,7 @@ async fn similar_unexisting_index() {
|
||||
});
|
||||
|
||||
index
|
||||
.similar(json!({"id": 287947}), |response, code| {
|
||||
.similar(json!({"id": 287947, "embedder": "manual"}), |response, code| {
|
||||
assert_eq!(code, 404);
|
||||
assert_eq!(response, expected_response);
|
||||
})
|
||||
@ -44,7 +44,7 @@ async fn similar_feature_not_enabled() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
|
||||
let (response, code) = index.similar_post(json!({"id": 287947})).await;
|
||||
let (response, code) = index.similar_post(json!({"id": 287947, "embedder": "manual"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
@ -199,7 +199,8 @@ async fn similar_not_found_id() {
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"id": "definitely-doesnt-exist"})).await;
|
||||
let (response, code) =
|
||||
index.similar_post(json!({"id": "definitely-doesnt-exist", "embedder": "manual"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
@ -230,7 +231,8 @@ async fn similar_bad_offset() {
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"id": 287947, "offset": "doggo"})).await;
|
||||
let (response, code) =
|
||||
index.similar_post(json!({"id": 287947, "offset": "doggo", "embedder": "manual"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
@ -241,7 +243,7 @@ async fn similar_bad_offset() {
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index.similar_get("?id=287947&offset=doggo").await;
|
||||
let (response, code) = index.similar_get("?id=287947&offset=doggo&embedder=manual").await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
@ -272,7 +274,8 @@ async fn similar_bad_limit() {
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"id": 287947, "limit": "doggo"})).await;
|
||||
let (response, code) =
|
||||
index.similar_post(json!({"id": 287947, "limit": "doggo", "embedder": "manual"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
@ -283,7 +286,7 @@ async fn similar_bad_limit() {
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index.similar_get("?id=287946&limit=doggo").await;
|
||||
let (response, code) = index.similar_get("?id=287946&limit=doggo&embedder=manual").await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
@ -323,7 +326,8 @@ async fn similar_bad_filter() {
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({ "id": 287947, "filter": true })).await;
|
||||
let (response, code) =
|
||||
index.similar_post(json!({ "id": 287947, "filter": true, "embedder": "manual" })).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
@ -361,10 +365,10 @@ async fn filter_invalid_syntax_object() {
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "title & Glass"}), |response, code| {
|
||||
.similar(json!({"id": 287947, "filter": "title & Glass", "embedder": "manual"}), |response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `title & Glass`.\n1:14 title & Glass",
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `title & Glass`.\n1:14 title & Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
@ -400,10 +404,10 @@ async fn filter_invalid_syntax_array() {
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["title & Glass"]}), |response, code| {
|
||||
.similar(json!({"id": 287947, "filter": ["title & Glass"], "embedder": "manual"}), |response, code| {
|
||||
snapshot!(response, @r###"
|
||||
{
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `_geoRadius`, or `_geoBoundingBox` at `title & Glass`.\n1:14 title & Glass",
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `CONTAINS`, `NOT CONTAINS`, `STARTS WITH`, `NOT STARTS WITH`, `_geoRadius`, or `_geoBoundingBox` at `title & Glass`.\n1:14 title & Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
@ -446,7 +450,7 @@ async fn filter_invalid_syntax_string() {
|
||||
});
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": "title = Glass XOR title = Glass"}),
|
||||
json!({"id": 287947, "filter": "title = Glass XOR title = Glass", "embedder": "manual"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
@ -486,10 +490,13 @@ async fn filter_invalid_attribute_array() {
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["many = Glass"]}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": ["many = Glass"], "embedder": "manual"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -524,10 +531,13 @@ async fn filter_invalid_attribute_string() {
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "many = Glass"}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": "many = Glass", "embedder": "manual"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -562,10 +572,13 @@ async fn filter_reserved_geo_attribute_array() {
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["_geo = Glass"]}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": ["_geo = Glass"], "embedder": "manual"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -600,10 +613,13 @@ async fn filter_reserved_geo_attribute_string() {
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "_geo = Glass"}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": "_geo = Glass", "embedder": "manual"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -638,10 +654,13 @@ async fn filter_reserved_attribute_array() {
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["_geoDistance = Glass"]}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": ["_geoDistance = Glass"], "embedder": "manual"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -676,10 +695,13 @@ async fn filter_reserved_attribute_string() {
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "_geoDistance = Glass"}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": "_geoDistance = Glass", "embedder": "manual"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -714,10 +736,13 @@ async fn filter_reserved_geo_point_array() {
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["_geoPoint = Glass"]}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": ["_geoPoint = Glass"], "embedder": "manual"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -752,10 +777,13 @@ async fn filter_reserved_geo_point_string() {
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "_geoPoint = Glass"}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": "_geoPoint = Glass", "embedder": "manual"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -765,7 +793,8 @@ async fn similar_bad_retrieve_vectors() {
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
let index = server.index("test");
|
||||
|
||||
let (response, code) = index.similar_post(json!({"retrieveVectors": "doggo"})).await;
|
||||
let (response, code) =
|
||||
index.similar_post(json!({"retrieveVectors": "doggo", "embedder": "manual"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
@ -776,7 +805,8 @@ async fn similar_bad_retrieve_vectors() {
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index.similar_post(json!({"retrieveVectors": [true]})).await;
|
||||
let (response, code) =
|
||||
index.similar_post(json!({"retrieveVectors": [true], "embedder": "manual"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
|
||||
@ -80,7 +80,9 @@ async fn basic() {
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 143, "retrieveVectors": true}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 143, "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
@ -154,11 +156,14 @@ async fn basic() {
|
||||
}
|
||||
]
|
||||
"###);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": "299537", "retrieveVectors": true}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": "299537", "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
@ -232,7 +237,8 @@ async fn basic() {
|
||||
}
|
||||
]
|
||||
"###);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@ -272,7 +278,7 @@ async fn ranking_score_threshold() {
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0, "retrieveVectors": true}),
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0, "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @"4");
|
||||
@ -358,7 +364,7 @@ async fn ranking_score_threshold() {
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.2, "retrieveVectors": true}),
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.2, "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @"3");
|
||||
@ -426,7 +432,7 @@ async fn ranking_score_threshold() {
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.3, "retrieveVectors": true}),
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.3, "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @"2");
|
||||
@ -476,7 +482,7 @@ async fn ranking_score_threshold() {
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.6, "retrieveVectors": true}),
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.6, "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @"1");
|
||||
@ -508,7 +514,7 @@ async fn ranking_score_threshold() {
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.9, "retrieveVectors": true}),
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.9, "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @"[]");
|
||||
@ -553,7 +559,7 @@ async fn filter() {
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 522681, "filter": "release_year = 2019", "retrieveVectors": true}),
|
||||
json!({"id": 522681, "filter": "release_year = 2019", "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
@ -617,7 +623,7 @@ async fn filter() {
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 522681, "filter": "release_year < 2000", "retrieveVectors": true}),
|
||||
json!({"id": 522681, "filter": "release_year < 2000", "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
@ -681,7 +687,9 @@ async fn limit_and_offset() {
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 143, "limit": 1, "retrieveVectors": true}), |response, code| {
|
||||
.similar(
|
||||
json!({"id": 143, "limit": 1, "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
@ -704,12 +712,13 @@ async fn limit_and_offset() {
|
||||
}
|
||||
]
|
||||
"###);
|
||||
})
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "limit": 1, "offset": 1, "retrieveVectors": true}),
|
||||
json!({"id": 143, "limit": 1, "offset": 1, "retrieveVectors": true, "embedder": "manual"}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
|
||||
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;
|
||||
@ -624,7 +625,8 @@ async fn clear_documents() {
|
||||
"###);
|
||||
|
||||
// Make sure the arroy DB has been cleared
|
||||
let (documents, _code) = index.search_post(json!({ "vector": [1, 1, 1] })).await;
|
||||
let (documents, _code) =
|
||||
index.search_post(json!({ "vector": [1, 1, 1], "hybrid": {"embedder": "manual"} })).await;
|
||||
snapshot!(documents, @r###"
|
||||
{
|
||||
"hits": [],
|
||||
@ -685,7 +687,11 @@ async fn add_remove_one_vector_4588() {
|
||||
let task = index.wait_task(value.uid()).await;
|
||||
snapshot!(task, name: "document-deleted");
|
||||
|
||||
let (documents, _code) = index.search_post(json!({"vector": [1, 1, 1] })).await;
|
||||
let (documents, _code) = index
|
||||
.search_post(
|
||||
json!({"vector": [1, 1, 1], "hybrid": {"semanticRatio": 1.0, "embedder": "manual"} }),
|
||||
)
|
||||
.await;
|
||||
snapshot!(documents, @r###"
|
||||
{
|
||||
"hits": [
|
||||
|
||||
@ -449,7 +449,7 @@ async fn it_works() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "chien de chasse",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"},
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -489,7 +489,7 @@ async fn it_works() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "petit chien",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -529,7 +529,7 @@ async fn it_works() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "grand chien de berger des montagnes",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -616,7 +616,7 @@ async fn tokenize_long_text() {
|
||||
"q": "grand chien de berger des montagnes",
|
||||
"showRankingScore": true,
|
||||
"attributesToRetrieve": ["id"],
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1064,7 +1064,7 @@ async fn smaller_dimensions() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "chien de chasse",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1104,7 +1104,7 @@ async fn smaller_dimensions() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "petit chien",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1144,7 +1144,7 @@ async fn smaller_dimensions() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "grand chien de berger des montagnes",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1295,7 +1295,7 @@ async fn small_embedding_model() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "chien de chasse",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1335,7 +1335,7 @@ async fn small_embedding_model() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "petit chien",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1375,7 +1375,7 @@ async fn small_embedding_model() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "grand chien de berger des montagnes",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1525,7 +1525,7 @@ async fn legacy_embedding_model() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "chien de chasse",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1565,7 +1565,7 @@ async fn legacy_embedding_model() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "petit chien",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1605,7 +1605,7 @@ async fn legacy_embedding_model() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "grand chien de berger des montagnes",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1756,7 +1756,7 @@ async fn it_still_works() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "chien de chasse",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1796,7 +1796,7 @@ async fn it_still_works() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "petit chien",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
@ -1836,7 +1836,7 @@ async fn it_still_works() {
|
||||
let (response, code) = index
|
||||
.search_post(json!({
|
||||
"q": "grand chien de berger des montagnes",
|
||||
"hybrid": {"semanticRatio": 1.0}
|
||||
"hybrid": {"semanticRatio": 1.0, "embedder": "default"}
|
||||
}))
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
|
||||
@ -218,7 +218,8 @@ async fn reset_embedder_documents() {
|
||||
"###);
|
||||
|
||||
// Make sure the arroy DB has been cleared
|
||||
let (documents, _code) = index.search_post(json!({ "vector": [1, 1, 1] })).await;
|
||||
let (documents, _code) =
|
||||
index.search_post(json!({ "vector": [1, 1, 1], "hybrid": {"embedder": "default"} })).await;
|
||||
snapshot!(json_string!(documents), @r###"
|
||||
{
|
||||
"message": "Cannot find embedder with name `default`.",
|
||||
|
||||
@ -17,7 +17,7 @@ bincode = "1.3.3"
|
||||
bstr = "1.9.1"
|
||||
bytemuck = { version = "1.16.1", features = ["extern_crate_alloc"] }
|
||||
byteorder = "1.5.0"
|
||||
charabia = { version = "0.9.0", default-features = false }
|
||||
charabia = { version = "0.9.1", default-features = false }
|
||||
concat-arrays = "0.1.2"
|
||||
crossbeam-channel = "0.5.13"
|
||||
deserr = "0.6.2"
|
||||
@ -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"] }
|
||||
@ -106,6 +106,8 @@ all-tokenizations = [
|
||||
"charabia/greek",
|
||||
"charabia/khmer",
|
||||
"charabia/vietnamese",
|
||||
"charabia/swedish-recomposition",
|
||||
"charabia/german-segmentation",
|
||||
]
|
||||
|
||||
# Use POSIX semaphores instead of SysV semaphores in LMDB
|
||||
@ -138,6 +140,9 @@ khmer = ["charabia/khmer"]
|
||||
# allow vietnamese specialized tokenization
|
||||
vietnamese = ["charabia/vietnamese"]
|
||||
|
||||
# allow german specialized tokenization
|
||||
german = ["charabia/german-segmentation"]
|
||||
|
||||
# force swedish character recomposition
|
||||
swedish-recomposition = ["charabia/swedish-recomposition"]
|
||||
|
||||
|
||||
@ -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),
|
||||
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()
|
||||
})
|
||||
.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);
|
||||
.collect::<Result<Vec<_>>>()?;
|
||||
res.insert(config.name.to_owned(), embeddings);
|
||||
}
|
||||
Ok(res)
|
||||
}
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
use std::collections::{BTreeMap, HashMap, HashSet};
|
||||
use std::fmt::Display;
|
||||
use std::ops::ControlFlow;
|
||||
use std::{fmt, mem};
|
||||
|
||||
@ -37,6 +38,15 @@ pub enum OrderBy {
|
||||
Count,
|
||||
}
|
||||
|
||||
impl Display for OrderBy {
|
||||
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
|
||||
match self {
|
||||
OrderBy::Lexicographic => f.write_str("alphabetically"),
|
||||
OrderBy::Count => f.write_str("by count"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub struct FacetDistribution<'a> {
|
||||
facets: Option<HashMap<String, OrderBy>>,
|
||||
candidates: Option<RoaringBitmap>,
|
||||
@ -100,7 +110,6 @@ impl<'a> FacetDistribution<'a> {
|
||||
let mut lexicographic_distribution = BTreeMap::new();
|
||||
let mut key_buffer: Vec<_> = field_id.to_be_bytes().to_vec();
|
||||
|
||||
let distribution_prelength = distribution.len();
|
||||
let db = self.index.field_id_docid_facet_f64s;
|
||||
for docid in candidates {
|
||||
key_buffer.truncate(mem::size_of::<FieldId>());
|
||||
@ -113,23 +122,21 @@ impl<'a> FacetDistribution<'a> {
|
||||
for result in iter {
|
||||
let ((_, _, value), ()) = result?;
|
||||
*lexicographic_distribution.entry(value.to_string()).or_insert(0) += 1;
|
||||
|
||||
if lexicographic_distribution.len() - distribution_prelength
|
||||
== self.max_values_per_facet
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
distribution.extend(lexicographic_distribution);
|
||||
distribution.extend(
|
||||
lexicographic_distribution
|
||||
.into_iter()
|
||||
.take(self.max_values_per_facet.saturating_sub(distribution.len())),
|
||||
);
|
||||
}
|
||||
FacetType::String => {
|
||||
let mut normalized_distribution = BTreeMap::new();
|
||||
let mut key_buffer: Vec<_> = field_id.to_be_bytes().to_vec();
|
||||
|
||||
let db = self.index.field_id_docid_facet_strings;
|
||||
'outer: for docid in candidates {
|
||||
for docid in candidates {
|
||||
key_buffer.truncate(mem::size_of::<FieldId>());
|
||||
key_buffer.extend_from_slice(&docid.to_be_bytes());
|
||||
let iter = db
|
||||
@ -144,14 +151,14 @@ impl<'a> FacetDistribution<'a> {
|
||||
.or_insert_with(|| (original_value, 0));
|
||||
*count += 1;
|
||||
|
||||
if normalized_distribution.len() == self.max_values_per_facet {
|
||||
break 'outer;
|
||||
}
|
||||
// we'd like to break here if we have enough facet values, but we are collecting them by increasing docid,
|
||||
// so higher ranked facets could be in later docids
|
||||
}
|
||||
}
|
||||
|
||||
let iter = normalized_distribution
|
||||
.into_iter()
|
||||
.take(self.max_values_per_facet.saturating_sub(distribution.len()))
|
||||
.map(|(_normalized, (original, count))| (original.to_string(), count));
|
||||
distribution.extend(iter);
|
||||
}
|
||||
@ -467,7 +474,7 @@ mod tests {
|
||||
.execute()
|
||||
.unwrap();
|
||||
|
||||
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 1}}"###);
|
||||
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2}}"###);
|
||||
|
||||
let map = FacetDistribution::new(&txn, &index)
|
||||
.facets(iter::once(("colour", OrderBy::Count)))
|
||||
|
||||
@ -12,7 +12,7 @@ use serde_json::Value;
|
||||
use super::facet_range_search;
|
||||
use crate::error::{Error, UserError};
|
||||
use crate::heed_codec::facet::{
|
||||
FacetGroupKey, FacetGroupKeyCodec, FacetGroupValueCodec, OrderedF64Codec,
|
||||
FacetGroupKey, FacetGroupKeyCodec, FacetGroupValue, FacetGroupValueCodec, OrderedF64Codec,
|
||||
};
|
||||
use crate::index::db_name::FACET_ID_STRING_DOCIDS;
|
||||
use crate::{
|
||||
@ -336,6 +336,24 @@ impl<'a> Filter<'a> {
|
||||
|
||||
return Ok(docids);
|
||||
}
|
||||
Condition::StartsWith { keyword: _, word } => {
|
||||
let value = crate::normalize_facet(word.value());
|
||||
let base = FacetGroupKey { field_id, level: 0, left_bound: value.as_str() };
|
||||
let docids = strings_db
|
||||
.prefix_iter(rtxn, &base)?
|
||||
.map(|result| -> Result<RoaringBitmap> {
|
||||
match result {
|
||||
Ok((_facet_group_key, FacetGroupValue { bitmap, .. })) => Ok(bitmap),
|
||||
Err(_e) => Err(InternalError::from(SerializationError::Decoding {
|
||||
db_name: Some(FACET_ID_STRING_DOCIDS),
|
||||
})
|
||||
.into()),
|
||||
}
|
||||
})
|
||||
.union()?;
|
||||
|
||||
return Ok(docids);
|
||||
}
|
||||
};
|
||||
|
||||
let mut output = RoaringBitmap::new();
|
||||
|
||||
@ -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};
|
||||
|
||||
@ -208,10 +208,9 @@ 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)
|
||||
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;
|
||||
@ -241,7 +240,7 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
|
||||
let action = match action {
|
||||
ReindexAction::FullReindex => ExtractionAction::SettingsFullReindex,
|
||||
ReindexAction::RegeneratePrompts => {
|
||||
let Some((_, old_prompt)) = old_configs.get(name) else {
|
||||
let Some((_, old_prompt, _quantized)) = old_configs.get(name) else {
|
||||
tracing::error!(embedder = name, "Old embedder config not found");
|
||||
continue;
|
||||
};
|
||||
@ -260,13 +259,14 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
|
||||
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()));
|
||||
}
|
||||
}
|
||||
@ -1082,21 +1095,16 @@ 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()))?;
|
||||
// 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)?;
|
||||
}
|
||||
}
|
||||
EmbedderAction::WriteBackToDocuments(_) => { /* already removed */ }
|
||||
}
|
||||
}
|
||||
let updated_configs: Vec<IndexEmbeddingConfig> = updated_configs
|
||||
.into_iter()
|
||||
.filter_map(|(name, (config, user_provided))| match config {
|
||||
@ -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) => match entry.get() {
|
||||
EmbedderAction::WriteBackToDocuments(_) => { /* we are deleting this embedder, so no point in regeneration */
|
||||
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();
|
||||
}
|
||||
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>,
|
||||
@ -124,62 +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()
|
||||
}
|
||||
|
||||
/// Get the default embedder configuration, if any.
|
||||
pub fn get_default(&self) -> Option<(Arc<Embedder>, Arc<Prompt>)> {
|
||||
self.get(self.get_default_embedder_name())
|
||||
}
|
||||
|
||||
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
|
||||
}
|
||||
|
||||
/// Get the name of the default embedder configuration.
|
||||
///
|
||||
/// The default embedder is determined as follows:
|
||||
///
|
||||
/// - If there is only one embedder, it is always the default.
|
||||
/// - If there are multiple embedders and one of them is called `default`, then that one is the default embedder.
|
||||
/// - In all other cases, there is no default embedder.
|
||||
pub fn get_default_embedder_name(&self) -> &str {
|
||||
let mut it = self.0.keys();
|
||||
let first_name = it.next();
|
||||
let second_name = it.next();
|
||||
match (first_name, second_name) {
|
||||
(None, _) => "default",
|
||||
(Some(first), None) => first,
|
||||
(Some(_), Some(_)) => "default",
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
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