mirror of
https://github.com/meilisearch/meilisearch.git
synced 2025-12-27 06:56:53 +00:00
Compare commits
2 Commits
refactor-s
...
cleanup-de
| Author | SHA1 | Date | |
|---|---|---|---|
|
5f4b0a0b25 | ||
|
|
b1f5a72d0d |
9
.github/workflows/bench-pr.yml
vendored
9
.github/workflows/bench-pr.yml
vendored
@@ -43,11 +43,4 @@ jobs:
|
||||
|
||||
- name: Run benchmarks on PR ${{ github.event.issue.id }}
|
||||
run: |
|
||||
cargo xtask bench --api-key "${{ secrets.BENCHMARK_API_KEY }}" \
|
||||
--dashboard-url "${{ vars.BENCHMARK_DASHBOARD_URL }}" \
|
||||
--reason "[Comment](${{ github.event.comment.html_url }}) on [#${{ github.event.issue.number }}](${{ github.event.issue.html_url }})" \
|
||||
-- ${{ steps.command.outputs.command-arguments }} > benchlinks.txt
|
||||
|
||||
- name: Send comment in PR
|
||||
run: |
|
||||
gh pr comment ${{github.event.issue.number}} --body-file benchlinks.txt
|
||||
cargo xtask bench --api-key "${{ secrets.BENCHMARK_API_KEY }}" --dashboard-url "${{ vars.BENCHMARK_DASHBOARD_URL }}" --reason "[Comment](${{ github.event.comment.html_url }}) on [#${{ github.event.issue.number }}](${{ github.event.issue.html_url }})" -- ${{ steps.command.outputs.command-arguments }}
|
||||
598
Cargo.lock
generated
598
Cargo.lock
generated
File diff suppressed because it is too large
Load Diff
@@ -22,7 +22,7 @@ members = [
|
||||
]
|
||||
|
||||
[workspace.package]
|
||||
version = "1.9.0"
|
||||
version = "1.8.0"
|
||||
authors = [
|
||||
"Quentin de Quelen <quentin@dequelen.me>",
|
||||
"Clément Renault <clement@meilisearch.com>",
|
||||
|
||||
16
README.md
16
README.md
@@ -25,7 +25,7 @@
|
||||
|
||||
<p align="center">⚡ A lightning-fast search engine that fits effortlessly into your apps, websites, and workflow 🔍</p>
|
||||
|
||||
[Meilisearch](https://www.meilisearch.com) helps you shape a delightful search experience in a snap, offering features that work out of the box to speed up your workflow.
|
||||
Meilisearch helps you shape a delightful search experience in a snap, offering features that work out-of-the-box to speed up your workflow.
|
||||
|
||||
<p align="center" name="demo">
|
||||
<a href="https://where2watch.meilisearch.com/?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=demo-gif#gh-light-mode-only" target="_blank">
|
||||
@@ -39,8 +39,8 @@
|
||||
🔥 [**Try it!**](https://where2watch.meilisearch.com/?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=demo-link) 🔥
|
||||
|
||||
## ✨ Features
|
||||
- **Hybrid search:** Combine the best of both [semantic](https://www.meilisearch.com/docs/learn/experimental/vector_search) & 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
|
||||
|
||||
- **Search-as-you-type:** find search results in less than 50 milliseconds
|
||||
- **[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
|
||||
- **[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
|
||||
@@ -55,15 +55,15 @@
|
||||
|
||||
## 📖 Documentation
|
||||
|
||||
You can consult Meilisearch's documentation at [meilisearch.com/docs](https://www.meilisearch.com/docs/?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=docs).
|
||||
You can consult Meilisearch's documentation at [https://www.meilisearch.com/docs](https://www.meilisearch.com/docs/?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=docs).
|
||||
|
||||
## 🚀 Getting started
|
||||
|
||||
For basic instructions on how to set up Meilisearch, add documents to an index, and search for documents, take a look at our [Quick Start](https://www.meilisearch.com/docs/learn/getting_started/quick_start?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=get-started) guide.
|
||||
|
||||
## 🌍 Supercharge your Meilisearch experience
|
||||
## ⚡ Supercharge your Meilisearch experience
|
||||
|
||||
Say goodbye to server deployment and manual updates with [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=oss&utm_source=github&utm_medium=meilisearch). Additional features include analytics & monitoring in many regions around the world. No credit card is required.
|
||||
Say goodbye to server deployment and manual updates with [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=oss&utm_source=github&utm_medium=meilisearch). No credit card required.
|
||||
|
||||
## 🧰 SDKs & integration tools
|
||||
|
||||
@@ -85,13 +85,13 @@ Finally, for more in-depth information, refer to our articles explaining fundame
|
||||
|
||||
Meilisearch collects **anonymized** data from users to help us improve our product. You can [deactivate this](https://www.meilisearch.com/docs/learn/what_is_meilisearch/telemetry?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=telemetry#how-to-disable-data-collection) whenever you want.
|
||||
|
||||
To request deletion of collected data, please write to us at [privacy@meilisearch.com](mailto:privacy@meilisearch.com). Remember to include your `Instance UID` in the message, as this helps us quickly find and delete your data.
|
||||
To request deletion of collected data, please write to us at [privacy@meilisearch.com](mailto:privacy@meilisearch.com). Don't forget to include your `Instance UID` in the message, as this helps us quickly find and delete your data.
|
||||
|
||||
If you want to know more about the kind of data we collect and what we use it for, check the [telemetry section](https://www.meilisearch.com/docs/learn/what_is_meilisearch/telemetry?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=telemetry#how-to-disable-data-collection) of our documentation.
|
||||
|
||||
## 📫 Get in touch!
|
||||
|
||||
Meilisearch is a search engine created by [Meili]([https://www.welcometothejungle.com/en/companies/meilisearch](https://www.meilisearch.com/careers)), a software development company headquartered in France and with team members all over the world. Want to know more about us? [Check out our blog!](https://blog.meilisearch.com/?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=contact)
|
||||
Meilisearch is a search engine created by [Meili](https://www.welcometothejungle.com/en/companies/meilisearch), a software development company based in France and with team members all over the world. Want to know more about us? [Check out our blog!](https://blog.meilisearch.com/?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=contact)
|
||||
|
||||
🗞 [Subscribe to our newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e) if you don't want to miss any updates! We promise we won't clutter your mailbox: we only send one edition every two months.
|
||||
|
||||
|
||||
@@ -15,4 +15,4 @@ time = { version = "0.3.34", features = ["parsing"] }
|
||||
|
||||
[build-dependencies]
|
||||
anyhow = "1.0.80"
|
||||
vergen-git2 = "1.0.0-beta.2"
|
||||
vergen-gitcl = "1.0.0-beta.2"
|
||||
|
||||
@@ -8,7 +8,7 @@ fn emit_git_variables() -> anyhow::Result<()> {
|
||||
// Note: any code that needs VERGEN_ environment variables should take care to define them manually in the Dockerfile and pass them
|
||||
// in the corresponding GitHub workflow (publish_docker.yml).
|
||||
// This is due to the Dockerfile building the binary outside of the git directory.
|
||||
let mut builder = vergen_git2::Git2Builder::default();
|
||||
let mut builder = vergen_gitcl::GitclBuilder::default();
|
||||
|
||||
builder.branch(true);
|
||||
builder.commit_timestamp(true);
|
||||
@@ -16,7 +16,6 @@ fn emit_git_variables() -> anyhow::Result<()> {
|
||||
builder.describe(true, true, None);
|
||||
builder.sha(false);
|
||||
|
||||
let git2 = builder.build()?;
|
||||
|
||||
vergen_git2::Emitter::default().fail_on_error().add_instructions(&git2)?.emit()
|
||||
let gitcl = builder.build()?;
|
||||
vergen_gitcl::Emitter::default().fail_on_error().add_instructions(&gitcl)?.emit()
|
||||
}
|
||||
|
||||
@@ -197,140 +197,6 @@ pub(crate) mod test {
|
||||
use super::*;
|
||||
use crate::reader::v6::RuntimeTogglableFeatures;
|
||||
|
||||
#[test]
|
||||
fn import_dump_v6_with_vectors() {
|
||||
// dump containing two indexes
|
||||
//
|
||||
// "vector", configured with an embedder
|
||||
// contains:
|
||||
// - one document with an overriden vector,
|
||||
// - one document with a natural vector
|
||||
// - one document with a _vectors map containing one additional embedder name and a natural vector
|
||||
// - one document with a _vectors map containing one additional embedder name and an overriden vector
|
||||
//
|
||||
// "novector", no embedder
|
||||
// contains:
|
||||
// - a document without vector
|
||||
// - a document with a random _vectors field
|
||||
let dump = File::open("tests/assets/v6-with-vectors.dump").unwrap();
|
||||
let mut dump = DumpReader::open(dump).unwrap();
|
||||
|
||||
// top level infos
|
||||
insta::assert_display_snapshot!(dump.date().unwrap(), @"2024-05-16 15:51:34.151044 +00:00:00");
|
||||
insta::assert_debug_snapshot!(dump.instance_uid().unwrap(), @"None");
|
||||
|
||||
// tasks
|
||||
let tasks = dump.tasks().unwrap().collect::<Result<Vec<_>>>().unwrap();
|
||||
let (tasks, update_files): (Vec<_>, Vec<_>) = tasks.into_iter().unzip();
|
||||
meili_snap::snapshot_hash!(meili_snap::json_string!(tasks), @"278f63325ef06ca04d01df98d8207b94");
|
||||
assert_eq!(update_files.len(), 10);
|
||||
assert!(update_files[0].is_none()); // the dump creation
|
||||
assert!(update_files[1].is_none());
|
||||
assert!(update_files[2].is_none());
|
||||
assert!(update_files[3].is_none());
|
||||
assert!(update_files[4].is_none());
|
||||
assert!(update_files[5].is_none());
|
||||
assert!(update_files[6].is_none());
|
||||
assert!(update_files[7].is_none());
|
||||
assert!(update_files[8].is_none());
|
||||
assert!(update_files[9].is_none());
|
||||
|
||||
// indexes
|
||||
let mut indexes = dump.indexes().unwrap().collect::<Result<Vec<_>>>().unwrap();
|
||||
// the index are not ordered in any way by default
|
||||
indexes.sort_by_key(|index| index.metadata().uid.to_string());
|
||||
|
||||
let mut vector_index = indexes.pop().unwrap();
|
||||
let mut novector_index = indexes.pop().unwrap();
|
||||
assert!(indexes.is_empty());
|
||||
|
||||
// vector
|
||||
|
||||
insta::assert_json_snapshot!(vector_index.metadata(), @r###"
|
||||
{
|
||||
"uid": "vector",
|
||||
"primaryKey": "id",
|
||||
"createdAt": "2024-05-16T15:33:17.240962Z",
|
||||
"updatedAt": "2024-05-16T15:40:55.723052Z"
|
||||
}
|
||||
"###);
|
||||
|
||||
{
|
||||
let documents: Result<Vec<_>> = vector_index.documents().unwrap().collect();
|
||||
let mut documents = documents.unwrap();
|
||||
assert_eq!(documents.len(), 4);
|
||||
|
||||
documents.sort_by_key(|doc| doc.get("id").unwrap().to_string());
|
||||
|
||||
{
|
||||
let document = documents.pop().unwrap();
|
||||
insta::assert_json_snapshot!(document);
|
||||
}
|
||||
|
||||
{
|
||||
let document = documents.pop().unwrap();
|
||||
insta::assert_json_snapshot!(document);
|
||||
}
|
||||
|
||||
{
|
||||
let document = documents.pop().unwrap();
|
||||
insta::assert_json_snapshot!(document);
|
||||
}
|
||||
|
||||
{
|
||||
let document = documents.pop().unwrap();
|
||||
insta::assert_json_snapshot!(document);
|
||||
}
|
||||
}
|
||||
|
||||
// novector
|
||||
|
||||
insta::assert_json_snapshot!(novector_index.metadata(), @r###"
|
||||
{
|
||||
"uid": "novector",
|
||||
"primaryKey": "id",
|
||||
"createdAt": "2024-05-16T15:33:03.568055Z",
|
||||
"updatedAt": "2024-05-16T15:33:07.530217Z"
|
||||
}
|
||||
"###);
|
||||
|
||||
insta::assert_json_snapshot!(novector_index.settings().unwrap().embedders, @"null");
|
||||
|
||||
{
|
||||
let documents: Result<Vec<_>> = novector_index.documents().unwrap().collect();
|
||||
let mut documents = documents.unwrap();
|
||||
assert_eq!(documents.len(), 2);
|
||||
|
||||
documents.sort_by_key(|doc| doc.get("id").unwrap().to_string());
|
||||
|
||||
{
|
||||
let document = documents.pop().unwrap();
|
||||
insta::assert_json_snapshot!(document, @r###"
|
||||
{
|
||||
"id": "e1",
|
||||
"other": "random1",
|
||||
"_vectors": "toto"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
{
|
||||
let document = documents.pop().unwrap();
|
||||
insta::assert_json_snapshot!(document, @r###"
|
||||
{
|
||||
"id": "e0",
|
||||
"other": "random0"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
}
|
||||
|
||||
assert_eq!(
|
||||
dump.features().unwrap().unwrap(),
|
||||
RuntimeTogglableFeatures { vector_store: true, ..Default::default() }
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn import_dump_v6_experimental() {
|
||||
let dump = File::open("tests/assets/v6-with-experimental.dump").unwrap();
|
||||
|
||||
@@ -1,783 +0,0 @@
|
||||
---
|
||||
source: dump/src/reader/mod.rs
|
||||
expression: document
|
||||
---
|
||||
{
|
||||
"id": "e3",
|
||||
"desc": "overriden vector + map",
|
||||
"_vectors": {
|
||||
"default": [
|
||||
0.2,
|
||||
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,
|
||||
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,
|
||||
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}
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@@ -1,786 +0,0 @@
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---
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source: dump/src/reader/mod.rs
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expression: document
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---
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{
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"id": "e2",
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"desc": "natural vector + map",
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"default": {
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0.1,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0.1,
|
||||
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|
||||
0.1,
|
||||
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|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
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0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1
|
||||
]
|
||||
}
|
||||
}
|
||||
Binary file not shown.
@@ -22,6 +22,7 @@ flate2 = "1.0.28"
|
||||
meilisearch-auth = { path = "../meilisearch-auth" }
|
||||
meilisearch-types = { path = "../meilisearch-types" }
|
||||
page_size = "0.5.0"
|
||||
puffin = { version = "0.16.0", features = ["serialization"] }
|
||||
rayon = "1.8.1"
|
||||
roaring = { version = "0.10.2", features = ["serde"] }
|
||||
serde = { version = "1.0.195", features = ["derive"] }
|
||||
|
||||
@@ -31,9 +31,6 @@ use meilisearch_types::milli::heed::CompactionOption;
|
||||
use meilisearch_types::milli::update::{
|
||||
IndexDocumentsConfig, IndexDocumentsMethod, IndexerConfig, Settings as MilliSettings,
|
||||
};
|
||||
use meilisearch_types::milli::vector::parsed_vectors::{
|
||||
ExplicitVectors, VectorOrArrayOfVectors, RESERVED_VECTORS_FIELD_NAME,
|
||||
};
|
||||
use meilisearch_types::milli::{self, Filter};
|
||||
use meilisearch_types::settings::{apply_settings_to_builder, Settings, Unchecked};
|
||||
use meilisearch_types::tasks::{Details, IndexSwap, Kind, KindWithContent, Status, Task};
|
||||
@@ -529,6 +526,8 @@ impl IndexScheduler {
|
||||
#[cfg(test)]
|
||||
self.maybe_fail(crate::tests::FailureLocation::InsideCreateBatch)?;
|
||||
|
||||
puffin::profile_function!();
|
||||
|
||||
let enqueued = &self.get_status(rtxn, Status::Enqueued)?;
|
||||
let to_cancel = self.get_kind(rtxn, Kind::TaskCancelation)? & enqueued;
|
||||
|
||||
@@ -637,6 +636,8 @@ impl IndexScheduler {
|
||||
self.breakpoint(crate::Breakpoint::InsideProcessBatch);
|
||||
}
|
||||
|
||||
puffin::profile_function!(batch.to_string());
|
||||
|
||||
match batch {
|
||||
Batch::TaskCancelation { mut task, previous_started_at, previous_processing_tasks } => {
|
||||
// 1. Retrieve the tasks that matched the query at enqueue-time.
|
||||
@@ -915,55 +916,8 @@ impl IndexScheduler {
|
||||
if self.must_stop_processing.get() {
|
||||
return Err(Error::AbortedTask);
|
||||
}
|
||||
|
||||
let (id, doc) = ret?;
|
||||
|
||||
let mut document = milli::obkv_to_json(&all_fields, &fields_ids_map, doc)?;
|
||||
|
||||
'inject_vectors: {
|
||||
let embeddings = index.embeddings(&rtxn, id)?;
|
||||
|
||||
if embeddings.is_empty() {
|
||||
break 'inject_vectors;
|
||||
}
|
||||
|
||||
let vectors = document
|
||||
.entry(RESERVED_VECTORS_FIELD_NAME.to_owned())
|
||||
.or_insert(serde_json::Value::Object(Default::default()));
|
||||
|
||||
let serde_json::Value::Object(vectors) = vectors else {
|
||||
return Err(milli::Error::UserError(
|
||||
milli::UserError::InvalidVectorsMapType {
|
||||
document_id: {
|
||||
if let Ok(Some(Ok(index))) = index
|
||||
.external_id_of(&rtxn, std::iter::once(id))
|
||||
.map(|it| it.into_iter().next())
|
||||
{
|
||||
index
|
||||
} else {
|
||||
format!("internal docid={id}")
|
||||
}
|
||||
},
|
||||
value: vectors.clone(),
|
||||
},
|
||||
)
|
||||
.into());
|
||||
};
|
||||
|
||||
for (embedder_name, embeddings) in embeddings {
|
||||
// don't change the entry if it already exists, because it was user-provided
|
||||
vectors.entry(embedder_name).or_insert_with(|| {
|
||||
let embeddings = ExplicitVectors {
|
||||
embeddings: VectorOrArrayOfVectors::from_array_of_vectors(
|
||||
embeddings,
|
||||
),
|
||||
user_provided: false,
|
||||
};
|
||||
serde_json::to_value(embeddings).unwrap()
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
let (_id, doc) = ret?;
|
||||
let document = milli::obkv_to_json(&all_fields, &fields_ids_map, doc)?;
|
||||
index_dumper.push_document(&document)?;
|
||||
}
|
||||
|
||||
@@ -1222,6 +1176,8 @@ impl IndexScheduler {
|
||||
index: &'i Index,
|
||||
operation: IndexOperation,
|
||||
) -> Result<Vec<Task>> {
|
||||
puffin::profile_function!();
|
||||
|
||||
match operation {
|
||||
IndexOperation::DocumentClear { mut tasks, .. } => {
|
||||
let count = milli::update::ClearDocuments::new(index_wtxn, index).execute()?;
|
||||
|
||||
@@ -68,6 +68,19 @@ impl RoFeatures {
|
||||
.into())
|
||||
}
|
||||
}
|
||||
|
||||
pub fn check_puffin(&self) -> Result<()> {
|
||||
if self.runtime.export_puffin_reports {
|
||||
Ok(())
|
||||
} else {
|
||||
Err(FeatureNotEnabledError {
|
||||
disabled_action: "Outputting Puffin reports to disk",
|
||||
feature: "export puffin reports",
|
||||
issue_link: "https://github.com/meilisearch/product/discussions/693",
|
||||
}
|
||||
.into())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl FeatureData {
|
||||
|
||||
@@ -32,6 +32,7 @@ pub fn snapshot_index_scheduler(scheduler: &IndexScheduler) -> String {
|
||||
features: _,
|
||||
max_number_of_tasks: _,
|
||||
max_number_of_batched_tasks: _,
|
||||
puffin_frame: _,
|
||||
wake_up: _,
|
||||
dumps_path: _,
|
||||
snapshots_path: _,
|
||||
|
||||
@@ -33,6 +33,7 @@ pub type Result<T> = std::result::Result<T, Error>;
|
||||
pub type TaskId = u32;
|
||||
|
||||
use std::collections::{BTreeMap, HashMap};
|
||||
use std::fs::File;
|
||||
use std::io::{self, BufReader, Read};
|
||||
use std::ops::{Bound, RangeBounds};
|
||||
use std::path::{Path, PathBuf};
|
||||
@@ -58,6 +59,7 @@ use meilisearch_types::milli::vector::{Embedder, EmbedderOptions, EmbeddingConfi
|
||||
use meilisearch_types::milli::{self, CboRoaringBitmapCodec, Index, RoaringBitmapCodec, BEU32};
|
||||
use meilisearch_types::task_view::TaskView;
|
||||
use meilisearch_types::tasks::{Kind, KindWithContent, Status, Task};
|
||||
use puffin::FrameView;
|
||||
use rayon::current_num_threads;
|
||||
use rayon::prelude::{IntoParallelIterator, ParallelIterator};
|
||||
use roaring::RoaringBitmap;
|
||||
@@ -342,6 +344,9 @@ pub struct IndexScheduler {
|
||||
/// The Authorization header to send to the webhook URL.
|
||||
pub(crate) webhook_authorization_header: Option<String>,
|
||||
|
||||
/// A frame to output the indexation profiling files to disk.
|
||||
pub(crate) puffin_frame: Arc<puffin::GlobalFrameView>,
|
||||
|
||||
/// The path used to create the dumps.
|
||||
pub(crate) dumps_path: PathBuf,
|
||||
|
||||
@@ -396,6 +401,7 @@ impl IndexScheduler {
|
||||
cleanup_enabled: self.cleanup_enabled,
|
||||
max_number_of_tasks: self.max_number_of_tasks,
|
||||
max_number_of_batched_tasks: self.max_number_of_batched_tasks,
|
||||
puffin_frame: self.puffin_frame.clone(),
|
||||
snapshots_path: self.snapshots_path.clone(),
|
||||
dumps_path: self.dumps_path.clone(),
|
||||
auth_path: self.auth_path.clone(),
|
||||
@@ -494,6 +500,7 @@ impl IndexScheduler {
|
||||
env,
|
||||
// we want to start the loop right away in case meilisearch was ctrl+Ced while processing things
|
||||
wake_up: Arc::new(SignalEvent::auto(true)),
|
||||
puffin_frame: Arc::new(puffin::GlobalFrameView::default()),
|
||||
autobatching_enabled: options.autobatching_enabled,
|
||||
cleanup_enabled: options.cleanup_enabled,
|
||||
max_number_of_tasks: options.max_number_of_tasks,
|
||||
@@ -614,6 +621,10 @@ impl IndexScheduler {
|
||||
run.wake_up.wait();
|
||||
|
||||
loop {
|
||||
let puffin_enabled = run.features().check_puffin().is_ok();
|
||||
puffin::set_scopes_on(puffin_enabled);
|
||||
puffin::GlobalProfiler::lock().new_frame();
|
||||
|
||||
match run.tick() {
|
||||
Ok(TickOutcome::TickAgain(_)) => (),
|
||||
Ok(TickOutcome::WaitForSignal) => run.wake_up.wait(),
|
||||
@@ -625,6 +636,31 @@ impl IndexScheduler {
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Let's write the previous frame to disk but only if
|
||||
// the user wanted to profile with puffin.
|
||||
if puffin_enabled {
|
||||
let mut frame_view = run.puffin_frame.lock();
|
||||
if !frame_view.is_empty() {
|
||||
let now = OffsetDateTime::now_utc();
|
||||
let mut file = match File::create(format!("{}.puffin", now)) {
|
||||
Ok(file) => file,
|
||||
Err(e) => {
|
||||
tracing::error!("{e}");
|
||||
continue;
|
||||
}
|
||||
};
|
||||
if let Err(e) = frame_view.save_to_writer(&mut file) {
|
||||
tracing::error!("{e}");
|
||||
}
|
||||
if let Err(e) = file.sync_all() {
|
||||
tracing::error!("{e}");
|
||||
}
|
||||
// We erase this frame view as it is no more useful. We want to
|
||||
// measure the new frames now that we exported the previous ones.
|
||||
*frame_view = FrameView::default();
|
||||
}
|
||||
}
|
||||
}
|
||||
})
|
||||
.unwrap();
|
||||
@@ -1738,7 +1774,6 @@ mod tests {
|
||||
use big_s::S;
|
||||
use crossbeam::channel::RecvTimeoutError;
|
||||
use file_store::File;
|
||||
use insta::assert_json_snapshot;
|
||||
use meili_snap::{json_string, snapshot};
|
||||
use meilisearch_auth::AuthFilter;
|
||||
use meilisearch_types::document_formats::DocumentFormatError;
|
||||
@@ -1816,7 +1851,7 @@ mod tests {
|
||||
|
||||
// To be 100% consistent between all test we're going to start the scheduler right now
|
||||
// and ensure it's in the expected starting state.
|
||||
let breakpoint = match receiver.recv_timeout(std::time::Duration::from_secs(10)) {
|
||||
let breakpoint = match receiver.recv_timeout(std::time::Duration::from_secs(1)) {
|
||||
Ok(b) => b,
|
||||
Err(RecvTimeoutError::Timeout) => {
|
||||
panic!("The scheduler seems to be waiting for a new task while your test is waiting for a breakpoint.")
|
||||
@@ -1927,7 +1962,7 @@ mod tests {
|
||||
fn advance(&mut self) -> Breakpoint {
|
||||
let (breakpoint_1, b) = match self
|
||||
.test_breakpoint_rcv
|
||||
.recv_timeout(std::time::Duration::from_secs(50))
|
||||
.recv_timeout(std::time::Duration::from_secs(5))
|
||||
{
|
||||
Ok(b) => b,
|
||||
Err(RecvTimeoutError::Timeout) => {
|
||||
@@ -1948,7 +1983,7 @@ mod tests {
|
||||
|
||||
let (breakpoint_2, b) = match self
|
||||
.test_breakpoint_rcv
|
||||
.recv_timeout(std::time::Duration::from_secs(50))
|
||||
.recv_timeout(std::time::Duration::from_secs(5))
|
||||
{
|
||||
Ok(b) => b,
|
||||
Err(RecvTimeoutError::Timeout) => {
|
||||
@@ -4947,233 +4982,4 @@ mod tests {
|
||||
----------------------------------------------------------------------
|
||||
"###);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn import_vectors() {
|
||||
use meilisearch_types::settings::{Settings, Unchecked};
|
||||
use milli::update::Setting;
|
||||
|
||||
let (index_scheduler, mut handle) = IndexScheduler::test(true, vec![]);
|
||||
|
||||
let mut new_settings: Box<Settings<Unchecked>> = Box::default();
|
||||
let mut embedders = BTreeMap::default();
|
||||
let embedding_settings = milli::vector::settings::EmbeddingSettings {
|
||||
source: Setting::Set(milli::vector::settings::EmbedderSource::Rest),
|
||||
api_key: Setting::Set(S("My super secret")),
|
||||
url: Setting::Set(S("http://localhost:7777")),
|
||||
dimensions: Setting::Set(384),
|
||||
..Default::default()
|
||||
};
|
||||
embedders.insert(S("A_fakerest"), Setting::Set(embedding_settings));
|
||||
|
||||
let embedding_settings = milli::vector::settings::EmbeddingSettings {
|
||||
source: Setting::Set(milli::vector::settings::EmbedderSource::HuggingFace),
|
||||
model: Setting::Set(S("sentence-transformers/all-MiniLM-L6-v2")),
|
||||
revision: Setting::Set(S("e4ce9877abf3edfe10b0d82785e83bdcb973e22e")),
|
||||
document_template: Setting::Set(S("{{doc.doggo}} the {{doc.breed}} best doggo")),
|
||||
..Default::default()
|
||||
};
|
||||
embedders.insert(S("B_small_hf"), Setting::Set(embedding_settings));
|
||||
|
||||
new_settings.embedders = Setting::Set(embedders);
|
||||
|
||||
index_scheduler
|
||||
.register(
|
||||
KindWithContent::SettingsUpdate {
|
||||
index_uid: S("doggos"),
|
||||
new_settings,
|
||||
is_deletion: false,
|
||||
allow_index_creation: true,
|
||||
},
|
||||
None,
|
||||
false,
|
||||
)
|
||||
.unwrap();
|
||||
index_scheduler.assert_internally_consistent();
|
||||
|
||||
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "after_registering_settings_task_vectors");
|
||||
|
||||
{
|
||||
let rtxn = index_scheduler.read_txn().unwrap();
|
||||
let task = index_scheduler.get_task(&rtxn, 0).unwrap().unwrap();
|
||||
let task = meilisearch_types::task_view::TaskView::from_task(&task);
|
||||
insta::assert_json_snapshot!(task.details);
|
||||
}
|
||||
|
||||
handle.advance_n_successful_batches(1);
|
||||
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "settings_update_processed_vectors");
|
||||
|
||||
{
|
||||
let rtxn = index_scheduler.read_txn().unwrap();
|
||||
let task = index_scheduler.get_task(&rtxn, 0).unwrap().unwrap();
|
||||
let task = meilisearch_types::task_view::TaskView::from_task(&task);
|
||||
insta::assert_json_snapshot!(task.details);
|
||||
}
|
||||
|
||||
let (fakerest_name, simple_hf_name, beagle_embed, lab_embed, patou_embed) = {
|
||||
let index = index_scheduler.index("doggos").unwrap();
|
||||
let rtxn = index.read_txn().unwrap();
|
||||
|
||||
let configs = index.embedding_configs(&rtxn).unwrap();
|
||||
// for consistency with the below
|
||||
#[allow(clippy::get_first)]
|
||||
let (name, fakerest_config) = configs.get(0).unwrap();
|
||||
insta::assert_json_snapshot!(name, @r###""A_fakerest""###);
|
||||
insta::assert_json_snapshot!(fakerest_config.embedder_options);
|
||||
let fakerest_name = name.clone();
|
||||
|
||||
let (name, simple_hf_config) = configs.get(1).unwrap();
|
||||
insta::assert_json_snapshot!(name, @r###""B_small_hf""###);
|
||||
insta::assert_json_snapshot!(simple_hf_config.embedder_options);
|
||||
let simple_hf_name = name.clone();
|
||||
|
||||
let configs = index_scheduler.embedders(configs).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();
|
||||
(fakerest_name, simple_hf_name, beagle_embed, lab_embed, patou_embed)
|
||||
};
|
||||
|
||||
// add one doc, specifying vectors
|
||||
|
||||
let doc = serde_json::json!(
|
||||
{
|
||||
"id": 0,
|
||||
"doggo": "Intel",
|
||||
"breed": "beagle",
|
||||
"_vectors": {
|
||||
&fakerest_name: {
|
||||
// this will never trigger regeneration, which is good because we can't actually generate with
|
||||
// this embedder
|
||||
"userProvided": true,
|
||||
"embeddings": beagle_embed,
|
||||
},
|
||||
&simple_hf_name: {
|
||||
// this will be regenerated on updates
|
||||
"userProvided": false,
|
||||
"embeddings": lab_embed,
|
||||
},
|
||||
"noise": [0.1, 0.2, 0.3]
|
||||
}
|
||||
}
|
||||
);
|
||||
|
||||
let (uuid, mut file) = index_scheduler.create_update_file_with_uuid(0u128).unwrap();
|
||||
let documents_count = read_json(doc.to_string().as_bytes(), &mut file).unwrap();
|
||||
assert_eq!(documents_count, 1);
|
||||
file.persist().unwrap();
|
||||
|
||||
index_scheduler
|
||||
.register(
|
||||
KindWithContent::DocumentAdditionOrUpdate {
|
||||
index_uid: S("doggos"),
|
||||
primary_key: Some(S("id")),
|
||||
method: UpdateDocuments,
|
||||
content_file: uuid,
|
||||
documents_count,
|
||||
allow_index_creation: true,
|
||||
},
|
||||
None,
|
||||
false,
|
||||
)
|
||||
.unwrap();
|
||||
index_scheduler.assert_internally_consistent();
|
||||
|
||||
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "after adding Intel");
|
||||
|
||||
handle.advance_one_successful_batch();
|
||||
|
||||
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "adding Intel succeeds");
|
||||
|
||||
// check embeddings
|
||||
{
|
||||
let index = index_scheduler.index("doggos").unwrap();
|
||||
let rtxn = index.read_txn().unwrap();
|
||||
|
||||
let embeddings = index.embeddings(&rtxn, 0).unwrap();
|
||||
|
||||
assert_json_snapshot!(embeddings[&simple_hf_name][0] == lab_embed, @"true");
|
||||
assert_json_snapshot!(embeddings[&fakerest_name][0] == beagle_embed, @"true");
|
||||
|
||||
let doc = index.documents(&rtxn, std::iter::once(0)).unwrap()[0].1;
|
||||
let fields_ids_map = index.fields_ids_map(&rtxn).unwrap();
|
||||
let doc = obkv_to_json(
|
||||
&[
|
||||
fields_ids_map.id("doggo").unwrap(),
|
||||
fields_ids_map.id("breed").unwrap(),
|
||||
fields_ids_map.id("_vectors").unwrap(),
|
||||
],
|
||||
&fields_ids_map,
|
||||
doc,
|
||||
)
|
||||
.unwrap();
|
||||
assert_json_snapshot!(doc, {"._vectors.A_fakerest.embeddings" => "[vector]"});
|
||||
}
|
||||
|
||||
// update the doc, specifying vectors
|
||||
|
||||
let doc = serde_json::json!(
|
||||
{
|
||||
"id": 0,
|
||||
"doggo": "kefir",
|
||||
"breed": "patou",
|
||||
}
|
||||
);
|
||||
|
||||
let (uuid, mut file) = index_scheduler.create_update_file_with_uuid(1u128).unwrap();
|
||||
let documents_count = read_json(doc.to_string().as_bytes(), &mut file).unwrap();
|
||||
assert_eq!(documents_count, 1);
|
||||
file.persist().unwrap();
|
||||
|
||||
index_scheduler
|
||||
.register(
|
||||
KindWithContent::DocumentAdditionOrUpdate {
|
||||
index_uid: S("doggos"),
|
||||
primary_key: None,
|
||||
method: UpdateDocuments,
|
||||
content_file: uuid,
|
||||
documents_count,
|
||||
allow_index_creation: true,
|
||||
},
|
||||
None,
|
||||
false,
|
||||
)
|
||||
.unwrap();
|
||||
index_scheduler.assert_internally_consistent();
|
||||
|
||||
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "Intel to kefir");
|
||||
|
||||
handle.advance_one_successful_batch();
|
||||
snapshot!(snapshot_index_scheduler(&index_scheduler), name: "Intel to kefir succeeds");
|
||||
|
||||
{
|
||||
// check embeddings
|
||||
{
|
||||
let index = index_scheduler.index("doggos").unwrap();
|
||||
let rtxn = index.read_txn().unwrap();
|
||||
|
||||
let embeddings = index.embeddings(&rtxn, 0).unwrap();
|
||||
|
||||
// automatically changed to patou
|
||||
assert_json_snapshot!(embeddings[&simple_hf_name][0] == patou_embed, @"true");
|
||||
// remained beagle because set to userProvided
|
||||
assert_json_snapshot!(embeddings[&fakerest_name][0] == beagle_embed, @"true");
|
||||
|
||||
let doc = index.documents(&rtxn, std::iter::once(0)).unwrap()[0].1;
|
||||
let fields_ids_map = index.fields_ids_map(&rtxn).unwrap();
|
||||
let doc = obkv_to_json(
|
||||
&[
|
||||
fields_ids_map.id("doggo").unwrap(),
|
||||
fields_ids_map.id("breed").unwrap(),
|
||||
fields_ids_map.id("_vectors").unwrap(),
|
||||
],
|
||||
&fields_ids_map,
|
||||
doc,
|
||||
)
|
||||
.unwrap();
|
||||
assert_json_snapshot!(doc, {"._vectors.A_fakerest.embeddings" => "[vector]"});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,19 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
expression: doc
|
||||
---
|
||||
{
|
||||
"doggo": "kefir",
|
||||
"breed": "patou",
|
||||
"_vectors": {
|
||||
"A_fakerest": {
|
||||
"embeddings": "[vector]",
|
||||
"userProvided": true
|
||||
},
|
||||
"noise": [
|
||||
0.1,
|
||||
0.2,
|
||||
0.3
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1,20 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
expression: task.details
|
||||
---
|
||||
{
|
||||
"embedders": {
|
||||
"A_fakerest": {
|
||||
"source": "rest",
|
||||
"apiKey": "MyXXXX...",
|
||||
"dimensions": 384,
|
||||
"url": "http://localhost:7777"
|
||||
},
|
||||
"B_small_hf": {
|
||||
"source": "huggingFace",
|
||||
"model": "sentence-transformers/all-MiniLM-L6-v2",
|
||||
"revision": "e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
|
||||
"documentTemplate": "{{doc.doggo}} the {{doc.breed}} best doggo"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,23 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
expression: fakerest_config.embedder_options
|
||||
---
|
||||
{
|
||||
"Rest": {
|
||||
"api_key": "My super secret",
|
||||
"distribution": null,
|
||||
"dimensions": 384,
|
||||
"url": "http://localhost:7777",
|
||||
"query": null,
|
||||
"input_field": [
|
||||
"input"
|
||||
],
|
||||
"path_to_embeddings": [
|
||||
"data"
|
||||
],
|
||||
"embedding_object": [
|
||||
"embedding"
|
||||
],
|
||||
"input_type": "text"
|
||||
}
|
||||
}
|
||||
@@ -1,11 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
expression: simple_hf_config.embedder_options
|
||||
---
|
||||
{
|
||||
"HuggingFace": {
|
||||
"model": "sentence-transformers/all-MiniLM-L6-v2",
|
||||
"revision": "e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
|
||||
"distribution": null
|
||||
}
|
||||
}
|
||||
@@ -1,19 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
expression: doc
|
||||
---
|
||||
{
|
||||
"doggo": "Intel",
|
||||
"breed": "beagle",
|
||||
"_vectors": {
|
||||
"A_fakerest": {
|
||||
"embeddings": "[vector]",
|
||||
"userProvided": true
|
||||
},
|
||||
"noise": [
|
||||
0.1,
|
||||
0.2,
|
||||
0.3
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1,20 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
expression: task.details
|
||||
---
|
||||
{
|
||||
"embedders": {
|
||||
"A_fakerest": {
|
||||
"source": "rest",
|
||||
"apiKey": "MyXXXX...",
|
||||
"dimensions": 384,
|
||||
"url": "http://localhost:7777"
|
||||
},
|
||||
"B_small_hf": {
|
||||
"source": "huggingFace",
|
||||
"model": "sentence-transformers/all-MiniLM-L6-v2",
|
||||
"revision": "e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
|
||||
"documentTemplate": "{{doc.doggo}} the {{doc.breed}} best doggo"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,49 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
---
|
||||
### Autobatching Enabled = true
|
||||
### Processing Tasks:
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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 }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
enqueued []
|
||||
succeeded [0,1,2,]
|
||||
----------------------------------------------------------------------
|
||||
### Kind:
|
||||
"documentAdditionOrUpdate" [1,2,]
|
||||
"settingsUpdate" [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Tasks:
|
||||
doggos [0,1,2,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Mapper:
|
||||
doggos: { number_of_documents: 1, field_distribution: {"_vectors": 1, "breed": 1, "doggo": 1, "id": 1} }
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Canceled By:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Enqueued At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
[timestamp] [2,]
|
||||
----------------------------------------------------------------------
|
||||
### Started At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
[timestamp] [2,]
|
||||
----------------------------------------------------------------------
|
||||
### Finished At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
[timestamp] [2,]
|
||||
----------------------------------------------------------------------
|
||||
### File Store:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
|
||||
@@ -1,48 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
---
|
||||
### Autobatching Enabled = true
|
||||
### Processing Tasks:
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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 }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
enqueued [2,]
|
||||
succeeded [0,1,]
|
||||
----------------------------------------------------------------------
|
||||
### Kind:
|
||||
"documentAdditionOrUpdate" [1,2,]
|
||||
"settingsUpdate" [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Tasks:
|
||||
doggos [0,1,2,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Mapper:
|
||||
doggos: { number_of_documents: 1, field_distribution: {"_vectors": 1, "breed": 1, "doggo": 1, "id": 1} }
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Canceled By:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Enqueued At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
[timestamp] [2,]
|
||||
----------------------------------------------------------------------
|
||||
### Started At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
----------------------------------------------------------------------
|
||||
### Finished At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
----------------------------------------------------------------------
|
||||
### File Store:
|
||||
00000000-0000-0000-0000-000000000001
|
||||
|
||||
----------------------------------------------------------------------
|
||||
|
||||
@@ -1,45 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
---
|
||||
### Autobatching Enabled = true
|
||||
### Processing Tasks:
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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:
|
||||
enqueued []
|
||||
succeeded [0,1,]
|
||||
----------------------------------------------------------------------
|
||||
### Kind:
|
||||
"documentAdditionOrUpdate" [1,]
|
||||
"settingsUpdate" [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Tasks:
|
||||
doggos [0,1,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Mapper:
|
||||
doggos: { number_of_documents: 1, field_distribution: {"_vectors": 1, "breed": 1, "doggo": 1, "id": 1} }
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Canceled By:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Enqueued At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
----------------------------------------------------------------------
|
||||
### Started At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
----------------------------------------------------------------------
|
||||
### Finished At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
----------------------------------------------------------------------
|
||||
### File Store:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
|
||||
@@ -1,44 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
---
|
||||
### Autobatching Enabled = true
|
||||
### Processing Tasks:
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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:
|
||||
enqueued [1,]
|
||||
succeeded [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Kind:
|
||||
"documentAdditionOrUpdate" [1,]
|
||||
"settingsUpdate" [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Tasks:
|
||||
doggos [0,1,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Mapper:
|
||||
doggos: { number_of_documents: 0, field_distribution: {} }
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Canceled By:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Enqueued At:
|
||||
[timestamp] [0,]
|
||||
[timestamp] [1,]
|
||||
----------------------------------------------------------------------
|
||||
### Started At:
|
||||
[timestamp] [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Finished At:
|
||||
[timestamp] [0,]
|
||||
----------------------------------------------------------------------
|
||||
### File Store:
|
||||
00000000-0000-0000-0000-000000000000
|
||||
|
||||
----------------------------------------------------------------------
|
||||
|
||||
@@ -1,36 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
---
|
||||
### Autobatching Enabled = true
|
||||
### Processing Tasks:
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
enqueued [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Kind:
|
||||
"settingsUpdate" [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Tasks:
|
||||
doggos [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Mapper:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Canceled By:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Enqueued At:
|
||||
[timestamp] [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Started At:
|
||||
----------------------------------------------------------------------
|
||||
### Finished At:
|
||||
----------------------------------------------------------------------
|
||||
### File Store:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
|
||||
@@ -1,40 +0,0 @@
|
||||
---
|
||||
source: index-scheduler/src/lib.rs
|
||||
---
|
||||
### Autobatching Enabled = true
|
||||
### Processing Tasks:
|
||||
[]
|
||||
----------------------------------------------------------------------
|
||||
### 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: 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, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: 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"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
|
||||
----------------------------------------------------------------------
|
||||
### Status:
|
||||
enqueued []
|
||||
succeeded [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Kind:
|
||||
"settingsUpdate" [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Tasks:
|
||||
doggos [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Index Mapper:
|
||||
doggos: { number_of_documents: 0, field_distribution: {} }
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Canceled By:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
### Enqueued At:
|
||||
[timestamp] [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Started At:
|
||||
[timestamp] [0,]
|
||||
----------------------------------------------------------------------
|
||||
### Finished At:
|
||||
[timestamp] [0,]
|
||||
----------------------------------------------------------------------
|
||||
### File Store:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
|
||||
@@ -11,7 +11,7 @@ edition.workspace = true
|
||||
license.workspace = true
|
||||
|
||||
[dependencies]
|
||||
actix-web = { version = "4.6.0", default-features = false }
|
||||
actix-web = { version = "4.5.1", default-features = false }
|
||||
anyhow = "1.0.79"
|
||||
convert_case = "0.6.0"
|
||||
csv = "1.3.0"
|
||||
@@ -30,12 +30,7 @@ serde_json = "1.0.111"
|
||||
tar = "0.4.40"
|
||||
tempfile = "3.9.0"
|
||||
thiserror = "1.0.56"
|
||||
time = { version = "0.3.31", features = [
|
||||
"serde-well-known",
|
||||
"formatting",
|
||||
"parsing",
|
||||
"macros",
|
||||
] }
|
||||
time = { version = "0.3.31", features = ["serde-well-known", "formatting", "parsing", "macros"] }
|
||||
tokio = "1.35"
|
||||
uuid = { version = "1.6.1", features = ["serde", "v4"] }
|
||||
|
||||
|
||||
@@ -189,6 +189,3 @@ merge_with_error_impl_take_error_message!(ParseTaskKindError);
|
||||
merge_with_error_impl_take_error_message!(ParseTaskStatusError);
|
||||
merge_with_error_impl_take_error_message!(IndexUidFormatError);
|
||||
merge_with_error_impl_take_error_message!(InvalidSearchSemanticRatio);
|
||||
merge_with_error_impl_take_error_message!(InvalidSearchRankingScoreThreshold);
|
||||
merge_with_error_impl_take_error_message!(InvalidSimilarRankingScoreThreshold);
|
||||
merge_with_error_impl_take_error_message!(InvalidSimilarId);
|
||||
|
||||
@@ -239,25 +239,18 @@ InvalidIndexUid , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchAttributesToSearchOn , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchAttributesToCrop , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchAttributesToHighlight , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSimilarAttributesToRetrieve , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchAttributesToRetrieve , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchRankingScoreThreshold , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSimilarRankingScoreThreshold , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchCropLength , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchCropMarker , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchFacets , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchSemanticRatio , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidFacetSearchFacetName , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSimilarId , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchFilter , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSimilarFilter , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchHighlightPostTag , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchHighlightPreTag , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchHitsPerPage , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSimilarLimit , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchLimit , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchMatchingStrategy , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSimilarOffset , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchOffset , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchPage , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchQ , InvalidRequest , BAD_REQUEST ;
|
||||
@@ -266,9 +259,7 @@ InvalidFacetSearchName , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchVector , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchShowMatchesPosition , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchShowRankingScore , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSimilarShowRankingScore , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchShowRankingScoreDetails , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSimilarShowRankingScoreDetails , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSearchSort , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSettingsDisplayedAttributes , InvalidRequest , BAD_REQUEST ;
|
||||
InvalidSettingsDistinctAttribute , InvalidRequest , BAD_REQUEST ;
|
||||
@@ -331,8 +322,7 @@ UnretrievableErrorCode , InvalidRequest , BAD_REQUEST ;
|
||||
UnsupportedMediaType , InvalidRequest , UNSUPPORTED_MEDIA_TYPE ;
|
||||
|
||||
// Experimental features
|
||||
VectorEmbeddingError , InvalidRequest , BAD_REQUEST ;
|
||||
NotFoundSimilarId , InvalidRequest , BAD_REQUEST
|
||||
VectorEmbeddingError , InvalidRequest , BAD_REQUEST
|
||||
}
|
||||
|
||||
impl ErrorCode for JoinError {
|
||||
@@ -394,6 +384,7 @@ impl ErrorCode for milli::Error {
|
||||
UserError::InvalidGeoField { .. } => Code::InvalidDocumentGeoField,
|
||||
UserError::InvalidVectorDimensions { .. } => Code::InvalidVectorDimensions,
|
||||
UserError::InvalidVectorsMapType { .. } => Code::InvalidVectorsType,
|
||||
UserError::InvalidVectorsType { .. } => Code::InvalidVectorsType,
|
||||
UserError::TooManyVectors(_, _) => Code::TooManyVectors,
|
||||
UserError::SortError(_) => Code::InvalidSearchSort,
|
||||
UserError::InvalidMinTypoWordLenSetting(_, _) => {
|
||||
@@ -496,32 +487,6 @@ impl fmt::Display for deserr_codes::InvalidSearchSemanticRatio {
|
||||
}
|
||||
}
|
||||
|
||||
impl fmt::Display for deserr_codes::InvalidSimilarId {
|
||||
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
|
||||
write!(
|
||||
f,
|
||||
"the value of `id` is invalid. \
|
||||
A document identifier can be of type integer or string, \
|
||||
only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and underscores (_)."
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl fmt::Display for deserr_codes::InvalidSearchRankingScoreThreshold {
|
||||
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
|
||||
write!(
|
||||
f,
|
||||
"the value of `rankingScoreThreshold` is invalid, expected a float between `0.0` and `1.0`."
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl fmt::Display for deserr_codes::InvalidSimilarRankingScoreThreshold {
|
||||
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
|
||||
deserr_codes::InvalidSearchRankingScoreThreshold.fmt(f)
|
||||
}
|
||||
}
|
||||
|
||||
#[macro_export]
|
||||
macro_rules! internal_error {
|
||||
($target:ty : $($other:path), *) => {
|
||||
|
||||
@@ -6,6 +6,7 @@ pub struct RuntimeTogglableFeatures {
|
||||
pub vector_store: bool,
|
||||
pub metrics: bool,
|
||||
pub logs_route: bool,
|
||||
pub export_puffin_reports: bool,
|
||||
}
|
||||
|
||||
#[derive(Default, Debug, Clone, Copy)]
|
||||
|
||||
@@ -14,20 +14,20 @@ default-run = "meilisearch"
|
||||
|
||||
[dependencies]
|
||||
actix-cors = "0.7.0"
|
||||
actix-http = { version = "3.7.0", default-features = false, features = [
|
||||
actix-http = { version = "3.6.0", default-features = false, features = [
|
||||
"compress-brotli",
|
||||
"compress-gzip",
|
||||
"rustls-0_21",
|
||||
] }
|
||||
actix-utils = "3.0.1"
|
||||
actix-web = { version = "4.6.0", default-features = false, features = [
|
||||
actix-web = { version = "4.5.1", default-features = false, features = [
|
||||
"macros",
|
||||
"compress-brotli",
|
||||
"compress-gzip",
|
||||
"cookies",
|
||||
"rustls-0_21",
|
||||
] }
|
||||
actix-web-static-files = { version = "4.0.1", optional = true }
|
||||
actix-web-static-files = { git = "https://github.com/kilork/actix-web-static-files.git", rev = "2d3b6160", optional = true }
|
||||
anyhow = { version = "1.0.79", features = ["backtrace"] }
|
||||
async-stream = "0.3.5"
|
||||
async-trait = "0.1.77"
|
||||
@@ -67,6 +67,7 @@ permissive-json-pointer = { path = "../permissive-json-pointer" }
|
||||
pin-project-lite = "0.2.13"
|
||||
platform-dirs = "0.3.0"
|
||||
prometheus = { version = "0.13.3", features = ["process"] }
|
||||
puffin = { version = "0.16.0", features = ["serialization"] }
|
||||
rand = "0.8.5"
|
||||
rayon = "1.8.0"
|
||||
regex = "1.10.2"
|
||||
@@ -105,13 +106,13 @@ url = { version = "2.5.0", features = ["serde"] }
|
||||
tracing = "0.1.40"
|
||||
tracing-subscriber = { version = "0.3.18", features = ["json"] }
|
||||
tracing-trace = { version = "0.1.0", path = "../tracing-trace" }
|
||||
tracing-actix-web = "0.7.10"
|
||||
tracing-actix-web = "0.7.9"
|
||||
build-info = { version = "1.7.0", path = "../build-info" }
|
||||
|
||||
[dev-dependencies]
|
||||
actix-rt = "2.9.0"
|
||||
assert-json-diff = "2.0.2"
|
||||
brotli = "6.0.0"
|
||||
brotli = "3.4.0"
|
||||
insta = "1.34.0"
|
||||
manifest-dir-macros = "0.1.18"
|
||||
maplit = "1.0.2"
|
||||
@@ -131,7 +132,7 @@ reqwest = { version = "0.11.23", features = [
|
||||
sha-1 = { version = "0.10.1", optional = true }
|
||||
static-files = { version = "0.2.3", optional = true }
|
||||
tempfile = { version = "3.9.0", optional = true }
|
||||
zip = { version = "0.6.6", optional = true }
|
||||
zip = { version = "1.3.1", features = ["deflate"], default-features = false, optional = true }
|
||||
|
||||
[features]
|
||||
default = ["analytics", "meilisearch-types/all-tokenizations", "mini-dashboard"]
|
||||
|
||||
@@ -25,18 +25,6 @@ impl SearchAggregator {
|
||||
pub fn succeed(&mut self, _: &dyn Any) {}
|
||||
}
|
||||
|
||||
#[derive(Default)]
|
||||
pub struct SimilarAggregator;
|
||||
|
||||
#[allow(dead_code)]
|
||||
impl SimilarAggregator {
|
||||
pub fn from_query(_: &dyn Any, _: &dyn Any) -> Self {
|
||||
Self
|
||||
}
|
||||
|
||||
pub fn succeed(&mut self, _: &dyn Any) {}
|
||||
}
|
||||
|
||||
#[derive(Default)]
|
||||
pub struct MultiSearchAggregator;
|
||||
|
||||
@@ -78,8 +66,6 @@ impl Analytics for MockAnalytics {
|
||||
fn publish(&self, _event_name: String, _send: Value, _request: Option<&HttpRequest>) {}
|
||||
fn get_search(&self, _aggregate: super::SearchAggregator) {}
|
||||
fn post_search(&self, _aggregate: super::SearchAggregator) {}
|
||||
fn get_similar(&self, _aggregate: super::SimilarAggregator) {}
|
||||
fn post_similar(&self, _aggregate: super::SimilarAggregator) {}
|
||||
fn post_multi_search(&self, _aggregate: super::MultiSearchAggregator) {}
|
||||
fn post_facet_search(&self, _aggregate: super::FacetSearchAggregator) {}
|
||||
fn add_documents(
|
||||
|
||||
@@ -22,8 +22,6 @@ pub type SegmentAnalytics = mock_analytics::MockAnalytics;
|
||||
#[cfg(not(feature = "analytics"))]
|
||||
pub type SearchAggregator = mock_analytics::SearchAggregator;
|
||||
#[cfg(not(feature = "analytics"))]
|
||||
pub type SimilarAggregator = mock_analytics::SimilarAggregator;
|
||||
#[cfg(not(feature = "analytics"))]
|
||||
pub type MultiSearchAggregator = mock_analytics::MultiSearchAggregator;
|
||||
#[cfg(not(feature = "analytics"))]
|
||||
pub type FacetSearchAggregator = mock_analytics::FacetSearchAggregator;
|
||||
@@ -34,8 +32,6 @@ pub type SegmentAnalytics = segment_analytics::SegmentAnalytics;
|
||||
#[cfg(feature = "analytics")]
|
||||
pub type SearchAggregator = segment_analytics::SearchAggregator;
|
||||
#[cfg(feature = "analytics")]
|
||||
pub type SimilarAggregator = segment_analytics::SimilarAggregator;
|
||||
#[cfg(feature = "analytics")]
|
||||
pub type MultiSearchAggregator = segment_analytics::MultiSearchAggregator;
|
||||
#[cfg(feature = "analytics")]
|
||||
pub type FacetSearchAggregator = segment_analytics::FacetSearchAggregator;
|
||||
@@ -90,12 +86,6 @@ pub trait Analytics: Sync + Send {
|
||||
/// This method should be called to aggregate a post search
|
||||
fn post_search(&self, aggregate: SearchAggregator);
|
||||
|
||||
/// This method should be called to aggregate a get similar request
|
||||
fn get_similar(&self, aggregate: SimilarAggregator);
|
||||
|
||||
/// This method should be called to aggregate a post similar request
|
||||
fn post_similar(&self, aggregate: SimilarAggregator);
|
||||
|
||||
/// This method should be called to aggregate a post array of searches
|
||||
fn post_multi_search(&self, aggregate: MultiSearchAggregator);
|
||||
|
||||
|
||||
@@ -36,9 +36,8 @@ use crate::routes::indexes::facet_search::FacetSearchQuery;
|
||||
use crate::routes::{create_all_stats, Stats};
|
||||
use crate::search::{
|
||||
FacetSearchResult, MatchingStrategy, SearchQuery, SearchQueryWithIndex, SearchResult,
|
||||
SimilarQuery, SimilarResult, DEFAULT_CROP_LENGTH, DEFAULT_CROP_MARKER,
|
||||
DEFAULT_HIGHLIGHT_POST_TAG, DEFAULT_HIGHLIGHT_PRE_TAG, DEFAULT_SEARCH_LIMIT,
|
||||
DEFAULT_SEMANTIC_RATIO,
|
||||
DEFAULT_CROP_LENGTH, DEFAULT_CROP_MARKER, DEFAULT_HIGHLIGHT_POST_TAG,
|
||||
DEFAULT_HIGHLIGHT_PRE_TAG, DEFAULT_SEARCH_LIMIT, DEFAULT_SEMANTIC_RATIO,
|
||||
};
|
||||
use crate::Opt;
|
||||
|
||||
@@ -74,8 +73,6 @@ pub enum AnalyticsMsg {
|
||||
BatchMessage(Track),
|
||||
AggregateGetSearch(SearchAggregator),
|
||||
AggregatePostSearch(SearchAggregator),
|
||||
AggregateGetSimilar(SimilarAggregator),
|
||||
AggregatePostSimilar(SimilarAggregator),
|
||||
AggregatePostMultiSearch(MultiSearchAggregator),
|
||||
AggregatePostFacetSearch(FacetSearchAggregator),
|
||||
AggregateAddDocuments(DocumentsAggregator),
|
||||
@@ -152,8 +149,6 @@ impl SegmentAnalytics {
|
||||
update_documents_aggregator: DocumentsAggregator::default(),
|
||||
get_fetch_documents_aggregator: DocumentsFetchAggregator::default(),
|
||||
post_fetch_documents_aggregator: DocumentsFetchAggregator::default(),
|
||||
get_similar_aggregator: SimilarAggregator::default(),
|
||||
post_similar_aggregator: SimilarAggregator::default(),
|
||||
});
|
||||
tokio::spawn(segment.run(index_scheduler.clone(), auth_controller.clone()));
|
||||
|
||||
@@ -189,14 +184,6 @@ impl super::Analytics for SegmentAnalytics {
|
||||
let _ = self.sender.try_send(AnalyticsMsg::AggregatePostSearch(aggregate));
|
||||
}
|
||||
|
||||
fn get_similar(&self, aggregate: SimilarAggregator) {
|
||||
let _ = self.sender.try_send(AnalyticsMsg::AggregateGetSimilar(aggregate));
|
||||
}
|
||||
|
||||
fn post_similar(&self, aggregate: SimilarAggregator) {
|
||||
let _ = self.sender.try_send(AnalyticsMsg::AggregatePostSimilar(aggregate));
|
||||
}
|
||||
|
||||
fn post_facet_search(&self, aggregate: FacetSearchAggregator) {
|
||||
let _ = self.sender.try_send(AnalyticsMsg::AggregatePostFacetSearch(aggregate));
|
||||
}
|
||||
@@ -392,8 +379,6 @@ pub struct Segment {
|
||||
update_documents_aggregator: DocumentsAggregator,
|
||||
get_fetch_documents_aggregator: DocumentsFetchAggregator,
|
||||
post_fetch_documents_aggregator: DocumentsFetchAggregator,
|
||||
get_similar_aggregator: SimilarAggregator,
|
||||
post_similar_aggregator: SimilarAggregator,
|
||||
}
|
||||
|
||||
impl Segment {
|
||||
@@ -456,8 +441,6 @@ impl Segment {
|
||||
Some(AnalyticsMsg::AggregateUpdateDocuments(agreg)) => self.update_documents_aggregator.aggregate(agreg),
|
||||
Some(AnalyticsMsg::AggregateGetFetchDocuments(agreg)) => self.get_fetch_documents_aggregator.aggregate(agreg),
|
||||
Some(AnalyticsMsg::AggregatePostFetchDocuments(agreg)) => self.post_fetch_documents_aggregator.aggregate(agreg),
|
||||
Some(AnalyticsMsg::AggregateGetSimilar(agreg)) => self.get_similar_aggregator.aggregate(agreg),
|
||||
Some(AnalyticsMsg::AggregatePostSimilar(agreg)) => self.post_similar_aggregator.aggregate(agreg),
|
||||
None => (),
|
||||
}
|
||||
}
|
||||
@@ -511,8 +494,6 @@ impl Segment {
|
||||
update_documents_aggregator,
|
||||
get_fetch_documents_aggregator,
|
||||
post_fetch_documents_aggregator,
|
||||
get_similar_aggregator,
|
||||
post_similar_aggregator,
|
||||
} = self;
|
||||
|
||||
if let Some(get_search) =
|
||||
@@ -560,18 +541,6 @@ impl Segment {
|
||||
{
|
||||
let _ = self.batcher.push(post_fetch_documents).await;
|
||||
}
|
||||
|
||||
if let Some(get_similar_documents) =
|
||||
take(get_similar_aggregator).into_event(user, "Similar GET")
|
||||
{
|
||||
let _ = self.batcher.push(get_similar_documents).await;
|
||||
}
|
||||
|
||||
if let Some(post_similar_documents) =
|
||||
take(post_similar_aggregator).into_event(user, "Similar POST")
|
||||
{
|
||||
let _ = self.batcher.push(post_similar_documents).await;
|
||||
}
|
||||
let _ = self.batcher.flush().await;
|
||||
}
|
||||
}
|
||||
@@ -648,7 +617,6 @@ pub struct SearchAggregator {
|
||||
// scoring
|
||||
show_ranking_score: bool,
|
||||
show_ranking_score_details: bool,
|
||||
ranking_score_threshold: bool,
|
||||
}
|
||||
|
||||
impl SearchAggregator {
|
||||
@@ -677,7 +645,6 @@ impl SearchAggregator {
|
||||
matching_strategy,
|
||||
attributes_to_search_on,
|
||||
hybrid,
|
||||
ranking_score_threshold,
|
||||
} = query;
|
||||
|
||||
let mut ret = Self::default();
|
||||
@@ -750,7 +717,6 @@ impl SearchAggregator {
|
||||
|
||||
ret.show_ranking_score = *show_ranking_score;
|
||||
ret.show_ranking_score_details = *show_ranking_score_details;
|
||||
ret.ranking_score_threshold = ranking_score_threshold.is_some();
|
||||
|
||||
if let Some(hybrid) = hybrid {
|
||||
ret.semantic_ratio = hybrid.semantic_ratio != DEFAULT_SEMANTIC_RATIO();
|
||||
@@ -824,7 +790,6 @@ impl SearchAggregator {
|
||||
hybrid,
|
||||
total_degraded,
|
||||
total_used_negative_operator,
|
||||
ranking_score_threshold,
|
||||
} = other;
|
||||
|
||||
if self.timestamp.is_none() {
|
||||
@@ -908,7 +873,6 @@ impl SearchAggregator {
|
||||
// scoring
|
||||
self.show_ranking_score |= show_ranking_score;
|
||||
self.show_ranking_score_details |= show_ranking_score_details;
|
||||
self.ranking_score_threshold |= ranking_score_threshold;
|
||||
}
|
||||
|
||||
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
|
||||
@@ -950,7 +914,6 @@ impl SearchAggregator {
|
||||
hybrid,
|
||||
total_degraded,
|
||||
total_used_negative_operator,
|
||||
ranking_score_threshold,
|
||||
} = self;
|
||||
|
||||
if total_received == 0 {
|
||||
@@ -1021,7 +984,6 @@ impl SearchAggregator {
|
||||
"scoring": {
|
||||
"show_ranking_score": show_ranking_score,
|
||||
"show_ranking_score_details": show_ranking_score_details,
|
||||
"ranking_score_threshold": ranking_score_threshold,
|
||||
},
|
||||
});
|
||||
|
||||
@@ -1094,7 +1056,6 @@ impl MultiSearchAggregator {
|
||||
matching_strategy: _,
|
||||
attributes_to_search_on: _,
|
||||
hybrid: _,
|
||||
ranking_score_threshold: _,
|
||||
} = query;
|
||||
|
||||
index_uid.as_str()
|
||||
@@ -1242,7 +1203,6 @@ impl FacetSearchAggregator {
|
||||
matching_strategy,
|
||||
attributes_to_search_on,
|
||||
hybrid,
|
||||
ranking_score_threshold,
|
||||
} = query;
|
||||
|
||||
let mut ret = Self::default();
|
||||
@@ -1257,8 +1217,7 @@ impl FacetSearchAggregator {
|
||||
|| filter.is_some()
|
||||
|| *matching_strategy != MatchingStrategy::default()
|
||||
|| attributes_to_search_on.is_some()
|
||||
|| hybrid.is_some()
|
||||
|| ranking_score_threshold.is_some();
|
||||
|| hybrid.is_some();
|
||||
|
||||
ret
|
||||
}
|
||||
@@ -1599,242 +1558,3 @@ impl DocumentsFetchAggregator {
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Default)]
|
||||
pub struct SimilarAggregator {
|
||||
timestamp: Option<OffsetDateTime>,
|
||||
|
||||
// context
|
||||
user_agents: HashSet<String>,
|
||||
|
||||
// requests
|
||||
total_received: usize,
|
||||
total_succeeded: usize,
|
||||
time_spent: BinaryHeap<usize>,
|
||||
|
||||
// filter
|
||||
filter_with_geo_radius: bool,
|
||||
filter_with_geo_bounding_box: bool,
|
||||
// every time a request has a filter, this field must be incremented by the number of terms it contains
|
||||
filter_sum_of_criteria_terms: usize,
|
||||
// every time a request has a filter, this field must be incremented by one
|
||||
filter_total_number_of_criteria: usize,
|
||||
used_syntax: HashMap<String, usize>,
|
||||
|
||||
// Whether a non-default embedder was specified
|
||||
embedder: bool,
|
||||
|
||||
// pagination
|
||||
max_limit: usize,
|
||||
max_offset: usize,
|
||||
|
||||
// formatting
|
||||
max_attributes_to_retrieve: usize,
|
||||
|
||||
// scoring
|
||||
show_ranking_score: bool,
|
||||
show_ranking_score_details: bool,
|
||||
ranking_score_threshold: bool,
|
||||
}
|
||||
|
||||
impl SimilarAggregator {
|
||||
#[allow(clippy::field_reassign_with_default)]
|
||||
pub fn from_query(query: &SimilarQuery, request: &HttpRequest) -> Self {
|
||||
let SimilarQuery {
|
||||
id: _,
|
||||
embedder,
|
||||
offset,
|
||||
limit,
|
||||
attributes_to_retrieve: _,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
filter,
|
||||
ranking_score_threshold,
|
||||
} = query;
|
||||
|
||||
let mut ret = Self::default();
|
||||
ret.timestamp = Some(OffsetDateTime::now_utc());
|
||||
|
||||
ret.total_received = 1;
|
||||
ret.user_agents = extract_user_agents(request).into_iter().collect();
|
||||
|
||||
if let Some(ref filter) = filter {
|
||||
static RE: Lazy<Regex> = Lazy::new(|| Regex::new("AND | OR").unwrap());
|
||||
ret.filter_total_number_of_criteria = 1;
|
||||
|
||||
let syntax = match filter {
|
||||
Value::String(_) => "string".to_string(),
|
||||
Value::Array(values) => {
|
||||
if values.iter().map(|v| v.to_string()).any(|s| RE.is_match(&s)) {
|
||||
"mixed".to_string()
|
||||
} else {
|
||||
"array".to_string()
|
||||
}
|
||||
}
|
||||
_ => "none".to_string(),
|
||||
};
|
||||
// convert the string to a HashMap
|
||||
ret.used_syntax.insert(syntax, 1);
|
||||
|
||||
let stringified_filters = filter.to_string();
|
||||
ret.filter_with_geo_radius = stringified_filters.contains("_geoRadius(");
|
||||
ret.filter_with_geo_bounding_box = stringified_filters.contains("_geoBoundingBox(");
|
||||
ret.filter_sum_of_criteria_terms = RE.split(&stringified_filters).count();
|
||||
}
|
||||
|
||||
ret.max_limit = *limit;
|
||||
ret.max_offset = *offset;
|
||||
|
||||
ret.show_ranking_score = *show_ranking_score;
|
||||
ret.show_ranking_score_details = *show_ranking_score_details;
|
||||
ret.ranking_score_threshold = ranking_score_threshold.is_some();
|
||||
|
||||
ret.embedder = embedder.is_some();
|
||||
|
||||
ret
|
||||
}
|
||||
|
||||
pub fn succeed(&mut self, result: &SimilarResult) {
|
||||
let SimilarResult { id: _, hits: _, processing_time_ms, hits_info: _ } = result;
|
||||
|
||||
self.total_succeeded = self.total_succeeded.saturating_add(1);
|
||||
|
||||
self.time_spent.push(*processing_time_ms as usize);
|
||||
}
|
||||
|
||||
/// Aggregate one [SimilarAggregator] into another.
|
||||
pub fn aggregate(&mut self, mut other: Self) {
|
||||
let Self {
|
||||
timestamp,
|
||||
user_agents,
|
||||
total_received,
|
||||
total_succeeded,
|
||||
ref mut time_spent,
|
||||
filter_with_geo_radius,
|
||||
filter_with_geo_bounding_box,
|
||||
filter_sum_of_criteria_terms,
|
||||
filter_total_number_of_criteria,
|
||||
used_syntax,
|
||||
max_limit,
|
||||
max_offset,
|
||||
max_attributes_to_retrieve,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
embedder,
|
||||
ranking_score_threshold,
|
||||
} = other;
|
||||
|
||||
if self.timestamp.is_none() {
|
||||
self.timestamp = timestamp;
|
||||
}
|
||||
|
||||
// context
|
||||
for user_agent in user_agents.into_iter() {
|
||||
self.user_agents.insert(user_agent);
|
||||
}
|
||||
|
||||
// request
|
||||
self.total_received = self.total_received.saturating_add(total_received);
|
||||
self.total_succeeded = self.total_succeeded.saturating_add(total_succeeded);
|
||||
self.time_spent.append(time_spent);
|
||||
|
||||
// filter
|
||||
self.filter_with_geo_radius |= filter_with_geo_radius;
|
||||
self.filter_with_geo_bounding_box |= filter_with_geo_bounding_box;
|
||||
self.filter_sum_of_criteria_terms =
|
||||
self.filter_sum_of_criteria_terms.saturating_add(filter_sum_of_criteria_terms);
|
||||
self.filter_total_number_of_criteria =
|
||||
self.filter_total_number_of_criteria.saturating_add(filter_total_number_of_criteria);
|
||||
for (key, value) in used_syntax.into_iter() {
|
||||
let used_syntax = self.used_syntax.entry(key).or_insert(0);
|
||||
*used_syntax = used_syntax.saturating_add(value);
|
||||
}
|
||||
|
||||
self.embedder |= embedder;
|
||||
|
||||
// pagination
|
||||
self.max_limit = self.max_limit.max(max_limit);
|
||||
self.max_offset = self.max_offset.max(max_offset);
|
||||
|
||||
// formatting
|
||||
self.max_attributes_to_retrieve =
|
||||
self.max_attributes_to_retrieve.max(max_attributes_to_retrieve);
|
||||
|
||||
// scoring
|
||||
self.show_ranking_score |= show_ranking_score;
|
||||
self.show_ranking_score_details |= show_ranking_score_details;
|
||||
self.ranking_score_threshold |= ranking_score_threshold;
|
||||
}
|
||||
|
||||
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
|
||||
let Self {
|
||||
timestamp,
|
||||
user_agents,
|
||||
total_received,
|
||||
total_succeeded,
|
||||
time_spent,
|
||||
filter_with_geo_radius,
|
||||
filter_with_geo_bounding_box,
|
||||
filter_sum_of_criteria_terms,
|
||||
filter_total_number_of_criteria,
|
||||
used_syntax,
|
||||
max_limit,
|
||||
max_offset,
|
||||
max_attributes_to_retrieve,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
embedder,
|
||||
ranking_score_threshold,
|
||||
} = self;
|
||||
|
||||
if total_received == 0 {
|
||||
None
|
||||
} else {
|
||||
// we get all the values in a sorted manner
|
||||
let time_spent = time_spent.into_sorted_vec();
|
||||
// the index of the 99th percentage of value
|
||||
let percentile_99th = time_spent.len() * 99 / 100;
|
||||
// We are only interested by the slowest value of the 99th fastest results
|
||||
let time_spent = time_spent.get(percentile_99th);
|
||||
|
||||
let properties = json!({
|
||||
"user-agent": user_agents,
|
||||
"requests": {
|
||||
"99th_response_time": time_spent.map(|t| format!("{:.2}", t)),
|
||||
"total_succeeded": total_succeeded,
|
||||
"total_failed": total_received.saturating_sub(total_succeeded), // just to be sure we never panics
|
||||
"total_received": total_received,
|
||||
},
|
||||
"filter": {
|
||||
"with_geoRadius": filter_with_geo_radius,
|
||||
"with_geoBoundingBox": filter_with_geo_bounding_box,
|
||||
"avg_criteria_number": format!("{:.2}", filter_sum_of_criteria_terms as f64 / filter_total_number_of_criteria as f64),
|
||||
"most_used_syntax": used_syntax.iter().max_by_key(|(_, v)| *v).map(|(k, _)| json!(k)).unwrap_or_else(|| json!(null)),
|
||||
},
|
||||
"hybrid": {
|
||||
"embedder": embedder,
|
||||
},
|
||||
"pagination": {
|
||||
"max_limit": max_limit,
|
||||
"max_offset": max_offset,
|
||||
},
|
||||
"formatting": {
|
||||
"max_attributes_to_retrieve": max_attributes_to_retrieve,
|
||||
},
|
||||
"scoring": {
|
||||
"show_ranking_score": show_ranking_score,
|
||||
"show_ranking_score_details": show_ranking_score_details,
|
||||
"ranking_score_threshold": ranking_score_threshold,
|
||||
},
|
||||
});
|
||||
|
||||
Some(Track {
|
||||
timestamp,
|
||||
user: user.clone(),
|
||||
event: event_name.to_string(),
|
||||
properties,
|
||||
..Default::default()
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -47,6 +47,8 @@ pub struct RuntimeTogglableFeatures {
|
||||
pub metrics: Option<bool>,
|
||||
#[deserr(default)]
|
||||
pub logs_route: Option<bool>,
|
||||
#[deserr(default)]
|
||||
pub export_puffin_reports: Option<bool>,
|
||||
}
|
||||
|
||||
async fn patch_features(
|
||||
@@ -66,13 +68,21 @@ async fn patch_features(
|
||||
vector_store: new_features.0.vector_store.unwrap_or(old_features.vector_store),
|
||||
metrics: new_features.0.metrics.unwrap_or(old_features.metrics),
|
||||
logs_route: new_features.0.logs_route.unwrap_or(old_features.logs_route),
|
||||
export_puffin_reports: new_features
|
||||
.0
|
||||
.export_puffin_reports
|
||||
.unwrap_or(old_features.export_puffin_reports),
|
||||
};
|
||||
|
||||
// explicitly destructure for analytics rather than using the `Serialize` implementation, because
|
||||
// the it renames to camelCase, which we don't want for analytics.
|
||||
// **Do not** ignore fields with `..` or `_` here, because we want to add them in the future.
|
||||
let meilisearch_types::features::RuntimeTogglableFeatures { vector_store, metrics, logs_route } =
|
||||
new_features;
|
||||
let meilisearch_types::features::RuntimeTogglableFeatures {
|
||||
vector_store,
|
||||
metrics,
|
||||
logs_route,
|
||||
export_puffin_reports,
|
||||
} = new_features;
|
||||
|
||||
analytics.publish(
|
||||
"Experimental features Updated".to_string(),
|
||||
@@ -80,6 +90,7 @@ async fn patch_features(
|
||||
"vector_store": vector_store,
|
||||
"metrics": metrics,
|
||||
"logs_route": logs_route,
|
||||
"export_puffin_reports": export_puffin_reports,
|
||||
}),
|
||||
Some(&req),
|
||||
);
|
||||
|
||||
@@ -14,8 +14,8 @@ use crate::extractors::authentication::policies::*;
|
||||
use crate::extractors::authentication::GuardedData;
|
||||
use crate::routes::indexes::search::search_kind;
|
||||
use crate::search::{
|
||||
add_search_rules, perform_facet_search, HybridQuery, MatchingStrategy, RankingScoreThreshold,
|
||||
SearchQuery, DEFAULT_CROP_LENGTH, DEFAULT_CROP_MARKER, DEFAULT_HIGHLIGHT_POST_TAG,
|
||||
add_search_rules, perform_facet_search, HybridQuery, MatchingStrategy, SearchQuery,
|
||||
DEFAULT_CROP_LENGTH, DEFAULT_CROP_MARKER, DEFAULT_HIGHLIGHT_POST_TAG,
|
||||
DEFAULT_HIGHLIGHT_PRE_TAG, DEFAULT_SEARCH_LIMIT, DEFAULT_SEARCH_OFFSET,
|
||||
};
|
||||
use crate::search_queue::SearchQueue;
|
||||
@@ -46,8 +46,6 @@ pub struct FacetSearchQuery {
|
||||
pub matching_strategy: MatchingStrategy,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSearchAttributesToSearchOn>, default)]
|
||||
pub attributes_to_search_on: Option<Vec<String>>,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSearchRankingScoreThreshold>, default)]
|
||||
pub ranking_score_threshold: Option<RankingScoreThreshold>,
|
||||
}
|
||||
|
||||
pub async fn search(
|
||||
@@ -71,7 +69,7 @@ pub async fn search(
|
||||
|
||||
// Tenant token search_rules.
|
||||
if let Some(search_rules) = index_scheduler.filters().get_index_search_rules(&index_uid) {
|
||||
add_search_rules(&mut search_query.filter, search_rules);
|
||||
add_search_rules(&mut search_query, search_rules);
|
||||
}
|
||||
|
||||
let index = index_scheduler.index(&index_uid)?;
|
||||
@@ -105,7 +103,6 @@ impl From<FacetSearchQuery> for SearchQuery {
|
||||
matching_strategy,
|
||||
attributes_to_search_on,
|
||||
hybrid,
|
||||
ranking_score_threshold,
|
||||
} = value;
|
||||
|
||||
SearchQuery {
|
||||
@@ -131,7 +128,6 @@ impl From<FacetSearchQuery> for SearchQuery {
|
||||
vector,
|
||||
attributes_to_search_on,
|
||||
hybrid,
|
||||
ranking_score_threshold,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -29,7 +29,6 @@ pub mod documents;
|
||||
pub mod facet_search;
|
||||
pub mod search;
|
||||
pub mod settings;
|
||||
pub mod similar;
|
||||
|
||||
pub fn configure(cfg: &mut web::ServiceConfig) {
|
||||
cfg.service(
|
||||
@@ -49,7 +48,6 @@ pub fn configure(cfg: &mut web::ServiceConfig) {
|
||||
.service(web::scope("/documents").configure(documents::configure))
|
||||
.service(web::scope("/search").configure(search::configure))
|
||||
.service(web::scope("/facet-search").configure(facet_search::configure))
|
||||
.service(web::scope("/similar").configure(similar::configure))
|
||||
.service(web::scope("/settings").configure(settings::configure)),
|
||||
);
|
||||
}
|
||||
|
||||
@@ -19,10 +19,9 @@ use crate::extractors::authentication::GuardedData;
|
||||
use crate::extractors::sequential_extractor::SeqHandler;
|
||||
use crate::metrics::MEILISEARCH_DEGRADED_SEARCH_REQUESTS;
|
||||
use crate::search::{
|
||||
add_search_rules, perform_search, HybridQuery, MatchingStrategy, RankingScoreThreshold,
|
||||
SearchKind, SearchQuery, SemanticRatio, DEFAULT_CROP_LENGTH, DEFAULT_CROP_MARKER,
|
||||
DEFAULT_HIGHLIGHT_POST_TAG, DEFAULT_HIGHLIGHT_PRE_TAG, DEFAULT_SEARCH_LIMIT,
|
||||
DEFAULT_SEARCH_OFFSET, DEFAULT_SEMANTIC_RATIO,
|
||||
add_search_rules, perform_search, HybridQuery, MatchingStrategy, SearchKind, SearchQuery,
|
||||
SemanticRatio, DEFAULT_CROP_LENGTH, DEFAULT_CROP_MARKER, DEFAULT_HIGHLIGHT_POST_TAG,
|
||||
DEFAULT_HIGHLIGHT_PRE_TAG, DEFAULT_SEARCH_LIMIT, DEFAULT_SEARCH_OFFSET, DEFAULT_SEMANTIC_RATIO,
|
||||
};
|
||||
use crate::search_queue::SearchQueue;
|
||||
|
||||
@@ -83,21 +82,6 @@ pub struct SearchQueryGet {
|
||||
pub hybrid_embedder: Option<String>,
|
||||
#[deserr(default, error = DeserrQueryParamError<InvalidSearchSemanticRatio>)]
|
||||
pub hybrid_semantic_ratio: Option<SemanticRatioGet>,
|
||||
#[deserr(default, error = DeserrQueryParamError<InvalidSearchRankingScoreThreshold>)]
|
||||
pub ranking_score_threshold: Option<RankingScoreThresholdGet>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, deserr::Deserr)]
|
||||
#[deserr(try_from(String) = TryFrom::try_from -> InvalidSearchRankingScoreThreshold)]
|
||||
pub struct RankingScoreThresholdGet(RankingScoreThreshold);
|
||||
|
||||
impl std::convert::TryFrom<String> for RankingScoreThresholdGet {
|
||||
type Error = InvalidSearchRankingScoreThreshold;
|
||||
|
||||
fn try_from(s: String) -> Result<Self, Self::Error> {
|
||||
let f: f64 = s.parse().map_err(|_| InvalidSearchRankingScoreThreshold)?;
|
||||
Ok(RankingScoreThresholdGet(RankingScoreThreshold::try_from(f)?))
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, deserr::Deserr)]
|
||||
@@ -168,7 +152,6 @@ impl From<SearchQueryGet> for SearchQuery {
|
||||
matching_strategy: other.matching_strategy,
|
||||
attributes_to_search_on: other.attributes_to_search_on.map(|o| o.into_iter().collect()),
|
||||
hybrid,
|
||||
ranking_score_threshold: other.ranking_score_threshold.map(|o| o.0),
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -213,7 +196,7 @@ pub async fn search_with_url_query(
|
||||
|
||||
// Tenant token search_rules.
|
||||
if let Some(search_rules) = index_scheduler.filters().get_index_search_rules(&index_uid) {
|
||||
add_search_rules(&mut query.filter, search_rules);
|
||||
add_search_rules(&mut query, search_rules);
|
||||
}
|
||||
|
||||
let mut aggregate = SearchAggregator::from_query(&query, &req);
|
||||
@@ -252,7 +235,7 @@ pub async fn search_with_post(
|
||||
|
||||
// Tenant token search_rules.
|
||||
if let Some(search_rules) = index_scheduler.filters().get_index_search_rules(&index_uid) {
|
||||
add_search_rules(&mut query.filter, search_rules);
|
||||
add_search_rules(&mut query, search_rules);
|
||||
}
|
||||
|
||||
let mut aggregate = SearchAggregator::from_query(&query, &req);
|
||||
|
||||
@@ -1,188 +0,0 @@
|
||||
use actix_web::web::{self, Data};
|
||||
use actix_web::{HttpRequest, HttpResponse};
|
||||
use deserr::actix_web::{AwebJson, AwebQueryParameter};
|
||||
use index_scheduler::IndexScheduler;
|
||||
use meilisearch_types::deserr::query_params::Param;
|
||||
use meilisearch_types::deserr::{DeserrJsonError, DeserrQueryParamError};
|
||||
use meilisearch_types::error::deserr_codes::{
|
||||
InvalidEmbedder, InvalidSimilarAttributesToRetrieve, InvalidSimilarFilter, InvalidSimilarId,
|
||||
InvalidSimilarLimit, InvalidSimilarOffset, InvalidSimilarRankingScoreThreshold,
|
||||
InvalidSimilarShowRankingScore, InvalidSimilarShowRankingScoreDetails,
|
||||
};
|
||||
use meilisearch_types::error::{ErrorCode as _, ResponseError};
|
||||
use meilisearch_types::index_uid::IndexUid;
|
||||
use meilisearch_types::keys::actions;
|
||||
use meilisearch_types::serde_cs::vec::CS;
|
||||
use serde_json::Value;
|
||||
use tracing::debug;
|
||||
|
||||
use super::ActionPolicy;
|
||||
use crate::analytics::{Analytics, SimilarAggregator};
|
||||
use crate::extractors::authentication::GuardedData;
|
||||
use crate::extractors::sequential_extractor::SeqHandler;
|
||||
use crate::search::{
|
||||
add_search_rules, perform_similar, RankingScoreThresholdSimilar, SearchKind, SimilarQuery,
|
||||
SimilarResult, DEFAULT_SEARCH_LIMIT, DEFAULT_SEARCH_OFFSET,
|
||||
};
|
||||
|
||||
pub fn configure(cfg: &mut web::ServiceConfig) {
|
||||
cfg.service(
|
||||
web::resource("")
|
||||
.route(web::get().to(SeqHandler(similar_get)))
|
||||
.route(web::post().to(SeqHandler(similar_post))),
|
||||
);
|
||||
}
|
||||
|
||||
pub async fn similar_get(
|
||||
index_scheduler: GuardedData<ActionPolicy<{ actions::SEARCH }>, Data<IndexScheduler>>,
|
||||
index_uid: web::Path<String>,
|
||||
params: AwebQueryParameter<SimilarQueryGet, DeserrQueryParamError>,
|
||||
req: HttpRequest,
|
||||
analytics: web::Data<dyn Analytics>,
|
||||
) -> Result<HttpResponse, ResponseError> {
|
||||
let index_uid = IndexUid::try_from(index_uid.into_inner())?;
|
||||
|
||||
let query = params.0.try_into()?;
|
||||
|
||||
let mut aggregate = SimilarAggregator::from_query(&query, &req);
|
||||
|
||||
debug!(parameters = ?query, "Similar get");
|
||||
|
||||
let similar = similar(index_scheduler, index_uid, query).await;
|
||||
|
||||
if let Ok(similar) = &similar {
|
||||
aggregate.succeed(similar);
|
||||
}
|
||||
analytics.get_similar(aggregate);
|
||||
|
||||
let similar = similar?;
|
||||
|
||||
debug!(returns = ?similar, "Similar get");
|
||||
Ok(HttpResponse::Ok().json(similar))
|
||||
}
|
||||
|
||||
pub async fn similar_post(
|
||||
index_scheduler: GuardedData<ActionPolicy<{ actions::SEARCH }>, Data<IndexScheduler>>,
|
||||
index_uid: web::Path<String>,
|
||||
params: AwebJson<SimilarQuery, DeserrJsonError>,
|
||||
req: HttpRequest,
|
||||
analytics: web::Data<dyn Analytics>,
|
||||
) -> Result<HttpResponse, ResponseError> {
|
||||
let index_uid = IndexUid::try_from(index_uid.into_inner())?;
|
||||
|
||||
let query = params.into_inner();
|
||||
debug!(parameters = ?query, "Similar post");
|
||||
|
||||
let mut aggregate = SimilarAggregator::from_query(&query, &req);
|
||||
|
||||
let similar = similar(index_scheduler, index_uid, query).await;
|
||||
|
||||
if let Ok(similar) = &similar {
|
||||
aggregate.succeed(similar);
|
||||
}
|
||||
analytics.post_similar(aggregate);
|
||||
|
||||
let similar = similar?;
|
||||
|
||||
debug!(returns = ?similar, "Similar post");
|
||||
Ok(HttpResponse::Ok().json(similar))
|
||||
}
|
||||
|
||||
async fn similar(
|
||||
index_scheduler: GuardedData<ActionPolicy<{ actions::SEARCH }>, Data<IndexScheduler>>,
|
||||
index_uid: IndexUid,
|
||||
mut query: SimilarQuery,
|
||||
) -> Result<SimilarResult, ResponseError> {
|
||||
let features = index_scheduler.features();
|
||||
|
||||
features.check_vector("Using the similar API")?;
|
||||
|
||||
// Tenant token search_rules.
|
||||
if let Some(search_rules) = index_scheduler.filters().get_index_search_rules(&index_uid) {
|
||||
add_search_rules(&mut query.filter, search_rules);
|
||||
}
|
||||
|
||||
let index = index_scheduler.index(&index_uid)?;
|
||||
|
||||
let (embedder_name, embedder) =
|
||||
SearchKind::embedder(&index_scheduler, &index, query.embedder.as_deref(), None)?;
|
||||
|
||||
tokio::task::spawn_blocking(move || perform_similar(&index, query, embedder_name, embedder))
|
||||
.await?
|
||||
}
|
||||
|
||||
#[derive(Debug, deserr::Deserr)]
|
||||
#[deserr(error = DeserrQueryParamError, rename_all = camelCase, deny_unknown_fields)]
|
||||
pub struct SimilarQueryGet {
|
||||
#[deserr(error = DeserrQueryParamError<InvalidSimilarId>)]
|
||||
id: Param<String>,
|
||||
#[deserr(default = Param(DEFAULT_SEARCH_OFFSET()), error = DeserrQueryParamError<InvalidSimilarOffset>)]
|
||||
offset: Param<usize>,
|
||||
#[deserr(default = Param(DEFAULT_SEARCH_LIMIT()), error = DeserrQueryParamError<InvalidSimilarLimit>)]
|
||||
limit: Param<usize>,
|
||||
#[deserr(default, error = DeserrQueryParamError<InvalidSimilarAttributesToRetrieve>)]
|
||||
attributes_to_retrieve: Option<CS<String>>,
|
||||
#[deserr(default, error = DeserrQueryParamError<InvalidSimilarFilter>)]
|
||||
filter: Option<String>,
|
||||
#[deserr(default, error = DeserrQueryParamError<InvalidSimilarShowRankingScore>)]
|
||||
show_ranking_score: Param<bool>,
|
||||
#[deserr(default, error = DeserrQueryParamError<InvalidSimilarShowRankingScoreDetails>)]
|
||||
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>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, deserr::Deserr)]
|
||||
#[deserr(try_from(String) = TryFrom::try_from -> InvalidSimilarRankingScoreThreshold)]
|
||||
pub struct RankingScoreThresholdGet(RankingScoreThresholdSimilar);
|
||||
|
||||
impl std::convert::TryFrom<String> for RankingScoreThresholdGet {
|
||||
type Error = InvalidSimilarRankingScoreThreshold;
|
||||
|
||||
fn try_from(s: String) -> Result<Self, Self::Error> {
|
||||
let f: f64 = s.parse().map_err(|_| InvalidSimilarRankingScoreThreshold)?;
|
||||
Ok(RankingScoreThresholdGet(RankingScoreThresholdSimilar::try_from(f)?))
|
||||
}
|
||||
}
|
||||
|
||||
impl TryFrom<SimilarQueryGet> for SimilarQuery {
|
||||
type Error = ResponseError;
|
||||
|
||||
fn try_from(
|
||||
SimilarQueryGet {
|
||||
id,
|
||||
offset,
|
||||
limit,
|
||||
attributes_to_retrieve,
|
||||
filter,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
embedder,
|
||||
ranking_score_threshold,
|
||||
}: SimilarQueryGet,
|
||||
) -> Result<Self, Self::Error> {
|
||||
let filter = match filter {
|
||||
Some(f) => match serde_json::from_str(&f) {
|
||||
Ok(v) => Some(v),
|
||||
_ => Some(Value::String(f)),
|
||||
},
|
||||
None => None,
|
||||
};
|
||||
|
||||
Ok(SimilarQuery {
|
||||
id: id.0.try_into().map_err(|code: InvalidSimilarId| {
|
||||
ResponseError::from_msg(code.to_string(), code.error_code())
|
||||
})?,
|
||||
offset: offset.0,
|
||||
limit: limit.0,
|
||||
filter,
|
||||
embedder,
|
||||
attributes_to_retrieve: attributes_to_retrieve.map(|o| o.into_iter().collect()),
|
||||
show_ranking_score: show_ranking_score.0,
|
||||
show_ranking_score_details: show_ranking_score_details.0,
|
||||
ranking_score_threshold: ranking_score_threshold.map(|x| x.0),
|
||||
})
|
||||
}
|
||||
}
|
||||
@@ -67,7 +67,7 @@ pub async fn multi_search_with_post(
|
||||
// Apply search rules from tenant token
|
||||
if let Some(search_rules) = index_scheduler.filters().get_index_search_rules(&index_uid)
|
||||
{
|
||||
add_search_rules(&mut query.filter, search_rules);
|
||||
add_search_rules(&mut query, search_rules);
|
||||
}
|
||||
|
||||
let index = index_scheduler
|
||||
|
||||
@@ -11,7 +11,7 @@ use indexmap::IndexMap;
|
||||
use meilisearch_auth::IndexSearchRules;
|
||||
use meilisearch_types::deserr::DeserrJsonError;
|
||||
use meilisearch_types::error::deserr_codes::*;
|
||||
use meilisearch_types::error::{Code, ResponseError};
|
||||
use meilisearch_types::error::ResponseError;
|
||||
use meilisearch_types::heed::RoTxn;
|
||||
use meilisearch_types::index_uid::IndexUid;
|
||||
use meilisearch_types::milli::score_details::{ScoreDetails, ScoringStrategy};
|
||||
@@ -87,44 +87,6 @@ pub struct SearchQuery {
|
||||
pub matching_strategy: MatchingStrategy,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSearchAttributesToSearchOn>, default)]
|
||||
pub attributes_to_search_on: Option<Vec<String>>,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSearchRankingScoreThreshold>, default)]
|
||||
pub ranking_score_threshold: Option<RankingScoreThreshold>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Deserr)]
|
||||
#[deserr(try_from(f64) = TryFrom::try_from -> InvalidSearchRankingScoreThreshold)]
|
||||
pub struct RankingScoreThreshold(f64);
|
||||
|
||||
impl std::convert::TryFrom<f64> for RankingScoreThreshold {
|
||||
type Error = InvalidSearchRankingScoreThreshold;
|
||||
|
||||
fn try_from(f: f64) -> Result<Self, Self::Error> {
|
||||
// the suggested "fix" is: `!(0.0..=1.0).contains(&f)`` which is allegedly less readable
|
||||
#[allow(clippy::manual_range_contains)]
|
||||
if f > 1.0 || f < 0.0 {
|
||||
Err(InvalidSearchRankingScoreThreshold)
|
||||
} else {
|
||||
Ok(RankingScoreThreshold(f))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Deserr)]
|
||||
#[deserr(try_from(f64) = TryFrom::try_from -> InvalidSimilarRankingScoreThreshold)]
|
||||
pub struct RankingScoreThresholdSimilar(f64);
|
||||
|
||||
impl std::convert::TryFrom<f64> for RankingScoreThresholdSimilar {
|
||||
type Error = InvalidSimilarRankingScoreThreshold;
|
||||
|
||||
fn try_from(f: f64) -> Result<Self, Self::Error> {
|
||||
// the suggested "fix" is: `!(0.0..=1.0).contains(&f)`` which is allegedly less readable
|
||||
#[allow(clippy::manual_range_contains)]
|
||||
if f > 1.0 || f < 0.0 {
|
||||
Err(InvalidSimilarRankingScoreThreshold)
|
||||
} else {
|
||||
Ok(Self(f))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Since this structure is logged A LOT we're going to reduce the number of things it logs to the bare minimum.
|
||||
@@ -155,7 +117,6 @@ impl fmt::Debug for SearchQuery {
|
||||
crop_marker,
|
||||
matching_strategy,
|
||||
attributes_to_search_on,
|
||||
ranking_score_threshold,
|
||||
} = self;
|
||||
|
||||
let mut debug = f.debug_struct("SearchQuery");
|
||||
@@ -227,9 +188,6 @@ impl fmt::Debug for SearchQuery {
|
||||
debug.field("highlight_pre_tag", &highlight_pre_tag);
|
||||
debug.field("highlight_post_tag", &highlight_post_tag);
|
||||
debug.field("crop_marker", &crop_marker);
|
||||
if let Some(ranking_score_threshold) = ranking_score_threshold {
|
||||
debug.field("ranking_score_threshold", &ranking_score_threshold);
|
||||
}
|
||||
|
||||
debug.finish()
|
||||
}
|
||||
@@ -273,7 +231,7 @@ impl SearchKind {
|
||||
Ok(Self::Hybrid { embedder_name, embedder, semantic_ratio })
|
||||
}
|
||||
|
||||
pub(crate) fn embedder(
|
||||
fn embedder(
|
||||
index_scheduler: &index_scheduler::IndexScheduler,
|
||||
index: &Index,
|
||||
embedder_name: Option<&str>,
|
||||
@@ -398,8 +356,6 @@ pub struct SearchQueryWithIndex {
|
||||
pub matching_strategy: MatchingStrategy,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSearchAttributesToSearchOn>, default)]
|
||||
pub attributes_to_search_on: Option<Vec<String>>,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSearchRankingScoreThreshold>, default)]
|
||||
pub ranking_score_threshold: Option<RankingScoreThreshold>,
|
||||
}
|
||||
|
||||
impl SearchQueryWithIndex {
|
||||
@@ -428,7 +384,6 @@ impl SearchQueryWithIndex {
|
||||
matching_strategy,
|
||||
attributes_to_search_on,
|
||||
hybrid,
|
||||
ranking_score_threshold,
|
||||
} = self;
|
||||
(
|
||||
index_uid,
|
||||
@@ -455,7 +410,6 @@ impl SearchQueryWithIndex {
|
||||
matching_strategy,
|
||||
attributes_to_search_on,
|
||||
hybrid,
|
||||
ranking_score_threshold,
|
||||
// do not use ..Default::default() here,
|
||||
// rather add any missing field from `SearchQuery` to `SearchQueryWithIndex`
|
||||
},
|
||||
@@ -463,61 +417,6 @@ impl SearchQueryWithIndex {
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, PartialEq, Deserr)]
|
||||
#[deserr(error = DeserrJsonError, rename_all = camelCase, deny_unknown_fields)]
|
||||
pub struct SimilarQuery {
|
||||
#[deserr(error = DeserrJsonError<InvalidSimilarId>)]
|
||||
pub id: ExternalDocumentId,
|
||||
#[deserr(default = DEFAULT_SEARCH_OFFSET(), error = DeserrJsonError<InvalidSimilarOffset>)]
|
||||
pub offset: usize,
|
||||
#[deserr(default = DEFAULT_SEARCH_LIMIT(), error = DeserrJsonError<InvalidSimilarLimit>)]
|
||||
pub limit: usize,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSimilarFilter>)]
|
||||
pub filter: Option<Value>,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidEmbedder>, default)]
|
||||
pub embedder: Option<String>,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSimilarAttributesToRetrieve>)]
|
||||
pub attributes_to_retrieve: Option<BTreeSet<String>>,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSimilarShowRankingScore>, default)]
|
||||
pub show_ranking_score: bool,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSimilarShowRankingScoreDetails>, default)]
|
||||
pub show_ranking_score_details: bool,
|
||||
#[deserr(default, error = DeserrJsonError<InvalidSimilarRankingScoreThreshold>, default)]
|
||||
pub ranking_score_threshold: Option<RankingScoreThresholdSimilar>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, PartialEq, Deserr)]
|
||||
#[deserr(try_from(Value) = TryFrom::try_from -> InvalidSimilarId)]
|
||||
pub struct ExternalDocumentId(String);
|
||||
|
||||
impl AsRef<str> for ExternalDocumentId {
|
||||
fn as_ref(&self) -> &str {
|
||||
&self.0
|
||||
}
|
||||
}
|
||||
|
||||
impl ExternalDocumentId {
|
||||
pub fn into_inner(self) -> String {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
impl TryFrom<String> for ExternalDocumentId {
|
||||
type Error = InvalidSimilarId;
|
||||
|
||||
fn try_from(value: String) -> Result<Self, Self::Error> {
|
||||
serde_json::Value::String(value).try_into()
|
||||
}
|
||||
}
|
||||
|
||||
impl TryFrom<Value> for ExternalDocumentId {
|
||||
type Error = InvalidSimilarId;
|
||||
|
||||
fn try_from(value: Value) -> Result<Self, Self::Error> {
|
||||
Ok(Self(milli::documents::validate_document_id_value(value).map_err(|_| InvalidSimilarId)?))
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Copy, Clone, PartialEq, Eq, Deserr)]
|
||||
#[deserr(rename_all = camelCase)]
|
||||
pub enum MatchingStrategy {
|
||||
@@ -525,8 +424,6 @@ pub enum MatchingStrategy {
|
||||
Last,
|
||||
/// All query words are mandatory
|
||||
All,
|
||||
/// Remove query words from the most frequent to the least
|
||||
Frequency,
|
||||
}
|
||||
|
||||
impl Default for MatchingStrategy {
|
||||
@@ -540,7 +437,6 @@ impl From<MatchingStrategy> for TermsMatchingStrategy {
|
||||
match other {
|
||||
MatchingStrategy::Last => Self::Last,
|
||||
MatchingStrategy::All => Self::All,
|
||||
MatchingStrategy::Frequency => Self::Frequency,
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -642,16 +538,6 @@ impl fmt::Debug for SearchResult {
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Serialize, Debug, Clone, PartialEq)]
|
||||
#[serde(rename_all = "camelCase")]
|
||||
pub struct SimilarResult {
|
||||
pub hits: Vec<SearchHit>,
|
||||
pub id: String,
|
||||
pub processing_time_ms: u128,
|
||||
#[serde(flatten)]
|
||||
pub hits_info: HitsInfo,
|
||||
}
|
||||
|
||||
#[derive(Serialize, Debug, Clone, PartialEq)]
|
||||
#[serde(rename_all = "camelCase")]
|
||||
pub struct SearchResultWithIndex {
|
||||
@@ -684,8 +570,8 @@ pub struct FacetSearchResult {
|
||||
}
|
||||
|
||||
/// Incorporate search rules in search query
|
||||
pub fn add_search_rules(filter: &mut Option<Value>, rules: IndexSearchRules) {
|
||||
*filter = match (filter.take(), rules.filter) {
|
||||
pub fn add_search_rules(query: &mut SearchQuery, rules: IndexSearchRules) {
|
||||
query.filter = match (query.filter.take(), rules.filter) {
|
||||
(None, rules_filter) => rules_filter,
|
||||
(filter, None) => filter,
|
||||
(Some(filter), Some(rules_filter)) => {
|
||||
@@ -712,9 +598,6 @@ fn prepare_search<'t>(
|
||||
) -> Result<(milli::Search<'t>, bool, usize, usize), MeilisearchHttpError> {
|
||||
let mut search = index.search(rtxn);
|
||||
search.time_budget(time_budget);
|
||||
if let Some(ranking_score_threshold) = query.ranking_score_threshold {
|
||||
search.ranking_score_threshold(ranking_score_threshold.0);
|
||||
}
|
||||
|
||||
match search_kind {
|
||||
SearchKind::KeywordOnly => {
|
||||
@@ -756,16 +639,11 @@ fn prepare_search<'t>(
|
||||
.unwrap_or(DEFAULT_PAGINATION_MAX_TOTAL_HITS);
|
||||
|
||||
search.exhaustive_number_hits(is_finite_pagination);
|
||||
search.scoring_strategy(
|
||||
if query.show_ranking_score
|
||||
|| query.show_ranking_score_details
|
||||
|| query.ranking_score_threshold.is_some()
|
||||
{
|
||||
ScoringStrategy::Detailed
|
||||
} else {
|
||||
ScoringStrategy::Skip
|
||||
},
|
||||
);
|
||||
search.scoring_strategy(if query.show_ranking_score || query.show_ranking_score_details {
|
||||
ScoringStrategy::Detailed
|
||||
} else {
|
||||
ScoringStrategy::Skip
|
||||
});
|
||||
|
||||
// compute the offset on the limit depending on the pagination mode.
|
||||
let (offset, limit) = if is_finite_pagination {
|
||||
@@ -841,53 +719,131 @@ pub fn perform_search(
|
||||
SearchKind::Hybrid { semantic_ratio, .. } => search.execute_hybrid(*semantic_ratio)?,
|
||||
};
|
||||
|
||||
let SearchQuery {
|
||||
q,
|
||||
limit,
|
||||
page,
|
||||
hits_per_page,
|
||||
attributes_to_retrieve,
|
||||
attributes_to_crop,
|
||||
crop_length,
|
||||
attributes_to_highlight,
|
||||
show_matches_position,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
sort,
|
||||
facets,
|
||||
highlight_pre_tag,
|
||||
highlight_post_tag,
|
||||
crop_marker,
|
||||
// already used in prepare_search
|
||||
vector: _,
|
||||
hybrid: _,
|
||||
offset: _,
|
||||
ranking_score_threshold: _,
|
||||
matching_strategy: _,
|
||||
attributes_to_search_on: _,
|
||||
filter: _,
|
||||
} = query;
|
||||
let fields_ids_map = index.fields_ids_map(&rtxn).unwrap();
|
||||
|
||||
let format = AttributesFormat {
|
||||
attributes_to_retrieve,
|
||||
attributes_to_highlight,
|
||||
attributes_to_crop,
|
||||
crop_length,
|
||||
crop_marker,
|
||||
highlight_pre_tag,
|
||||
highlight_post_tag,
|
||||
show_matches_position,
|
||||
sort,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
let displayed_ids = index
|
||||
.displayed_fields_ids(&rtxn)?
|
||||
.map(|fields| fields.into_iter().collect::<BTreeSet<_>>())
|
||||
.unwrap_or_else(|| fields_ids_map.iter().map(|(id, _)| id).collect());
|
||||
|
||||
let fids = |attrs: &BTreeSet<String>| {
|
||||
let mut ids = BTreeSet::new();
|
||||
for attr in attrs {
|
||||
if attr == "*" {
|
||||
ids.clone_from(&displayed_ids);
|
||||
break;
|
||||
}
|
||||
|
||||
if let Some(id) = fields_ids_map.id(attr) {
|
||||
ids.insert(id);
|
||||
}
|
||||
}
|
||||
ids
|
||||
};
|
||||
|
||||
let documents =
|
||||
make_hits(index, &rtxn, format, matching_words, documents_ids, document_scores)?;
|
||||
// The attributes to retrieve are the ones explicitly marked as to retrieve (all by default),
|
||||
// but these attributes must be also be present
|
||||
// - in the fields_ids_map
|
||||
// - in the displayed attributes
|
||||
let to_retrieve_ids: BTreeSet<_> = query
|
||||
.attributes_to_retrieve
|
||||
.as_ref()
|
||||
.map(fids)
|
||||
.unwrap_or_else(|| displayed_ids.clone())
|
||||
.intersection(&displayed_ids)
|
||||
.cloned()
|
||||
.collect();
|
||||
|
||||
let attr_to_highlight = query.attributes_to_highlight.unwrap_or_default();
|
||||
|
||||
let attr_to_crop = query.attributes_to_crop.unwrap_or_default();
|
||||
|
||||
// Attributes in `formatted_options` correspond to the attributes that will be in `_formatted`
|
||||
// These attributes are:
|
||||
// - the attributes asked to be highlighted or cropped (with `attributesToCrop` or `attributesToHighlight`)
|
||||
// - the attributes asked to be retrieved: these attributes will not be highlighted/cropped
|
||||
// But these attributes must be also present in displayed attributes
|
||||
let formatted_options = compute_formatted_options(
|
||||
&attr_to_highlight,
|
||||
&attr_to_crop,
|
||||
query.crop_length,
|
||||
&to_retrieve_ids,
|
||||
&fields_ids_map,
|
||||
&displayed_ids,
|
||||
);
|
||||
|
||||
let mut tokenizer_builder = TokenizerBuilder::default();
|
||||
tokenizer_builder.create_char_map(true);
|
||||
|
||||
let script_lang_map = index.script_language(&rtxn)?;
|
||||
if !script_lang_map.is_empty() {
|
||||
tokenizer_builder.allow_list(&script_lang_map);
|
||||
}
|
||||
|
||||
let separators = index.allowed_separators(&rtxn)?;
|
||||
let separators: Option<Vec<_>> =
|
||||
separators.as_ref().map(|x| x.iter().map(String::as_str).collect());
|
||||
if let Some(ref separators) = separators {
|
||||
tokenizer_builder.separators(separators);
|
||||
}
|
||||
|
||||
let dictionary = index.dictionary(&rtxn)?;
|
||||
let dictionary: Option<Vec<_>> =
|
||||
dictionary.as_ref().map(|x| x.iter().map(String::as_str).collect());
|
||||
if let Some(ref dictionary) = dictionary {
|
||||
tokenizer_builder.words_dict(dictionary);
|
||||
}
|
||||
|
||||
let mut formatter_builder = MatcherBuilder::new(matching_words, tokenizer_builder.build());
|
||||
formatter_builder.crop_marker(query.crop_marker);
|
||||
formatter_builder.highlight_prefix(query.highlight_pre_tag);
|
||||
formatter_builder.highlight_suffix(query.highlight_post_tag);
|
||||
|
||||
let mut documents = Vec::new();
|
||||
let documents_iter = index.documents(&rtxn, documents_ids)?;
|
||||
|
||||
for ((_id, obkv), score) in documents_iter.into_iter().zip(document_scores.into_iter()) {
|
||||
// First generate a document with all the displayed fields
|
||||
let displayed_document = make_document(&displayed_ids, &fields_ids_map, obkv)?;
|
||||
|
||||
// select the attributes to retrieve
|
||||
let attributes_to_retrieve = to_retrieve_ids
|
||||
.iter()
|
||||
.map(|&fid| fields_ids_map.name(fid).expect("Missing field name"));
|
||||
let mut document =
|
||||
permissive_json_pointer::select_values(&displayed_document, attributes_to_retrieve);
|
||||
|
||||
let (matches_position, formatted) = format_fields(
|
||||
&displayed_document,
|
||||
&fields_ids_map,
|
||||
&formatter_builder,
|
||||
&formatted_options,
|
||||
query.show_matches_position,
|
||||
&displayed_ids,
|
||||
)?;
|
||||
|
||||
if let Some(sort) = query.sort.as_ref() {
|
||||
insert_geo_distance(sort, &mut document);
|
||||
}
|
||||
|
||||
let ranking_score =
|
||||
query.show_ranking_score.then(|| ScoreDetails::global_score(score.iter()));
|
||||
let ranking_score_details =
|
||||
query.show_ranking_score_details.then(|| ScoreDetails::to_json_map(score.iter()));
|
||||
|
||||
let hit = SearchHit {
|
||||
document,
|
||||
formatted,
|
||||
matches_position,
|
||||
ranking_score_details,
|
||||
ranking_score,
|
||||
};
|
||||
documents.push(hit);
|
||||
}
|
||||
|
||||
let number_of_hits = min(candidates.len() as usize, max_total_hits);
|
||||
let hits_info = if is_finite_pagination {
|
||||
let hits_per_page = hits_per_page.unwrap_or_else(DEFAULT_SEARCH_LIMIT);
|
||||
let hits_per_page = query.hits_per_page.unwrap_or_else(DEFAULT_SEARCH_LIMIT);
|
||||
// If hit_per_page is 0, then pages can't be computed and so we respond 0.
|
||||
let total_pages = (number_of_hits + hits_per_page.saturating_sub(1))
|
||||
.checked_div(hits_per_page)
|
||||
@@ -895,15 +851,15 @@ pub fn perform_search(
|
||||
|
||||
HitsInfo::Pagination {
|
||||
hits_per_page,
|
||||
page: page.unwrap_or(1),
|
||||
page: query.page.unwrap_or(1),
|
||||
total_pages,
|
||||
total_hits: number_of_hits,
|
||||
}
|
||||
} else {
|
||||
HitsInfo::OffsetLimit { limit, offset, estimated_total_hits: number_of_hits }
|
||||
HitsInfo::OffsetLimit { limit: query.limit, offset, estimated_total_hits: number_of_hits }
|
||||
};
|
||||
|
||||
let (facet_distribution, facet_stats) = match facets {
|
||||
let (facet_distribution, facet_stats) = match query.facets {
|
||||
Some(ref fields) => {
|
||||
let mut facet_distribution = index.facets_distribution(&rtxn);
|
||||
|
||||
@@ -940,7 +896,7 @@ pub fn perform_search(
|
||||
let result = SearchResult {
|
||||
hits: documents,
|
||||
hits_info,
|
||||
query: q.unwrap_or_default(),
|
||||
query: query.q.unwrap_or_default(),
|
||||
processing_time_ms: before_search.elapsed().as_millis(),
|
||||
facet_distribution,
|
||||
facet_stats,
|
||||
@@ -951,130 +907,6 @@ pub fn perform_search(
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
struct AttributesFormat {
|
||||
attributes_to_retrieve: Option<BTreeSet<String>>,
|
||||
attributes_to_highlight: Option<HashSet<String>>,
|
||||
attributes_to_crop: Option<Vec<String>>,
|
||||
crop_length: usize,
|
||||
crop_marker: String,
|
||||
highlight_pre_tag: String,
|
||||
highlight_post_tag: String,
|
||||
show_matches_position: bool,
|
||||
sort: Option<Vec<String>>,
|
||||
show_ranking_score: bool,
|
||||
show_ranking_score_details: bool,
|
||||
}
|
||||
|
||||
fn make_hits(
|
||||
index: &Index,
|
||||
rtxn: &RoTxn<'_>,
|
||||
format: AttributesFormat,
|
||||
matching_words: milli::MatchingWords,
|
||||
documents_ids: Vec<u32>,
|
||||
document_scores: Vec<Vec<ScoreDetails>>,
|
||||
) -> Result<Vec<SearchHit>, MeilisearchHttpError> {
|
||||
let fields_ids_map = index.fields_ids_map(rtxn).unwrap();
|
||||
let displayed_ids = index
|
||||
.displayed_fields_ids(rtxn)?
|
||||
.map(|fields| fields.into_iter().collect::<BTreeSet<_>>())
|
||||
.unwrap_or_else(|| fields_ids_map.iter().map(|(id, _)| id).collect());
|
||||
let fids = |attrs: &BTreeSet<String>| {
|
||||
let mut ids = BTreeSet::new();
|
||||
for attr in attrs {
|
||||
if attr == "*" {
|
||||
ids.clone_from(&displayed_ids);
|
||||
break;
|
||||
}
|
||||
|
||||
if let Some(id) = fields_ids_map.id(attr) {
|
||||
ids.insert(id);
|
||||
}
|
||||
}
|
||||
ids
|
||||
};
|
||||
let to_retrieve_ids: BTreeSet<_> = format
|
||||
.attributes_to_retrieve
|
||||
.as_ref()
|
||||
.map(fids)
|
||||
.unwrap_or_else(|| displayed_ids.clone())
|
||||
.intersection(&displayed_ids)
|
||||
.cloned()
|
||||
.collect();
|
||||
let attr_to_highlight = format.attributes_to_highlight.unwrap_or_default();
|
||||
let attr_to_crop = format.attributes_to_crop.unwrap_or_default();
|
||||
let formatted_options = compute_formatted_options(
|
||||
&attr_to_highlight,
|
||||
&attr_to_crop,
|
||||
format.crop_length,
|
||||
&to_retrieve_ids,
|
||||
&fields_ids_map,
|
||||
&displayed_ids,
|
||||
);
|
||||
let mut tokenizer_builder = TokenizerBuilder::default();
|
||||
tokenizer_builder.create_char_map(true);
|
||||
let script_lang_map = index.script_language(rtxn)?;
|
||||
if !script_lang_map.is_empty() {
|
||||
tokenizer_builder.allow_list(&script_lang_map);
|
||||
}
|
||||
let separators = index.allowed_separators(rtxn)?;
|
||||
let separators: Option<Vec<_>> =
|
||||
separators.as_ref().map(|x| x.iter().map(String::as_str).collect());
|
||||
if let Some(ref separators) = separators {
|
||||
tokenizer_builder.separators(separators);
|
||||
}
|
||||
let dictionary = index.dictionary(rtxn)?;
|
||||
let dictionary: Option<Vec<_>> =
|
||||
dictionary.as_ref().map(|x| x.iter().map(String::as_str).collect());
|
||||
if let Some(ref dictionary) = dictionary {
|
||||
tokenizer_builder.words_dict(dictionary);
|
||||
}
|
||||
let mut formatter_builder = MatcherBuilder::new(matching_words, tokenizer_builder.build());
|
||||
formatter_builder.crop_marker(format.crop_marker);
|
||||
formatter_builder.highlight_prefix(format.highlight_pre_tag);
|
||||
formatter_builder.highlight_suffix(format.highlight_post_tag);
|
||||
let mut documents = Vec::new();
|
||||
let documents_iter = index.documents(rtxn, documents_ids)?;
|
||||
for ((_id, obkv), score) in documents_iter.into_iter().zip(document_scores.into_iter()) {
|
||||
// First generate a document with all the displayed fields
|
||||
let displayed_document = make_document(&displayed_ids, &fields_ids_map, obkv)?;
|
||||
|
||||
// select the attributes to retrieve
|
||||
let attributes_to_retrieve = to_retrieve_ids
|
||||
.iter()
|
||||
.map(|&fid| fields_ids_map.name(fid).expect("Missing field name"));
|
||||
let mut document =
|
||||
permissive_json_pointer::select_values(&displayed_document, attributes_to_retrieve);
|
||||
|
||||
let (matches_position, formatted) = format_fields(
|
||||
&displayed_document,
|
||||
&fields_ids_map,
|
||||
&formatter_builder,
|
||||
&formatted_options,
|
||||
format.show_matches_position,
|
||||
&displayed_ids,
|
||||
)?;
|
||||
|
||||
if let Some(sort) = format.sort.as_ref() {
|
||||
insert_geo_distance(sort, &mut document);
|
||||
}
|
||||
|
||||
let ranking_score =
|
||||
format.show_ranking_score.then(|| ScoreDetails::global_score(score.iter()));
|
||||
let ranking_score_details =
|
||||
format.show_ranking_score_details.then(|| ScoreDetails::to_json_map(score.iter()));
|
||||
|
||||
let hit = SearchHit {
|
||||
document,
|
||||
formatted,
|
||||
matches_position,
|
||||
ranking_score_details,
|
||||
ranking_score,
|
||||
};
|
||||
documents.push(hit);
|
||||
}
|
||||
Ok(documents)
|
||||
}
|
||||
|
||||
pub fn perform_facet_search(
|
||||
index: &Index,
|
||||
search_query: SearchQuery,
|
||||
@@ -1109,100 +941,6 @@ pub fn perform_facet_search(
|
||||
})
|
||||
}
|
||||
|
||||
pub fn perform_similar(
|
||||
index: &Index,
|
||||
query: SimilarQuery,
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
) -> Result<SimilarResult, ResponseError> {
|
||||
let before_search = Instant::now();
|
||||
let rtxn = index.read_txn()?;
|
||||
|
||||
let SimilarQuery {
|
||||
id,
|
||||
offset,
|
||||
limit,
|
||||
filter: _,
|
||||
embedder: _,
|
||||
attributes_to_retrieve,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
ranking_score_threshold,
|
||||
} = query;
|
||||
|
||||
// using let-else rather than `?` so that the borrow checker identifies we're always returning here,
|
||||
// preventing a use-after-move
|
||||
let Some(internal_id) = index.external_documents_ids().get(&rtxn, &id)? else {
|
||||
return Err(ResponseError::from_msg(
|
||||
MeilisearchHttpError::DocumentNotFound(id.into_inner()).to_string(),
|
||||
Code::NotFoundSimilarId,
|
||||
));
|
||||
};
|
||||
|
||||
let mut similar =
|
||||
milli::Similar::new(internal_id, offset, limit, index, &rtxn, embedder_name, embedder);
|
||||
|
||||
if let Some(ref filter) = query.filter {
|
||||
if let Some(facets) = parse_filter(filter)
|
||||
// inject InvalidSimilarFilter code
|
||||
.map_err(|e| ResponseError::from_msg(e.to_string(), Code::InvalidSimilarFilter))?
|
||||
{
|
||||
similar.filter(facets);
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(ranking_score_threshold) = ranking_score_threshold {
|
||||
similar.ranking_score_threshold(ranking_score_threshold.0);
|
||||
}
|
||||
|
||||
let milli::SearchResult {
|
||||
documents_ids,
|
||||
matching_words: _,
|
||||
candidates,
|
||||
document_scores,
|
||||
degraded: _,
|
||||
used_negative_operator: _,
|
||||
} = similar.execute().map_err(|err| match err {
|
||||
milli::Error::UserError(milli::UserError::InvalidFilter(_)) => {
|
||||
ResponseError::from_msg(err.to_string(), Code::InvalidSimilarFilter)
|
||||
}
|
||||
err => err.into(),
|
||||
})?;
|
||||
|
||||
let format = AttributesFormat {
|
||||
attributes_to_retrieve,
|
||||
attributes_to_highlight: None,
|
||||
attributes_to_crop: None,
|
||||
crop_length: DEFAULT_CROP_LENGTH(),
|
||||
crop_marker: DEFAULT_CROP_MARKER(),
|
||||
highlight_pre_tag: DEFAULT_HIGHLIGHT_PRE_TAG(),
|
||||
highlight_post_tag: DEFAULT_HIGHLIGHT_POST_TAG(),
|
||||
show_matches_position: false,
|
||||
sort: None,
|
||||
show_ranking_score,
|
||||
show_ranking_score_details,
|
||||
};
|
||||
|
||||
let hits = make_hits(index, &rtxn, format, Default::default(), documents_ids, document_scores)?;
|
||||
|
||||
let max_total_hits = index
|
||||
.pagination_max_total_hits(&rtxn)
|
||||
.map_err(milli::Error::from)?
|
||||
.map(|x| x as usize)
|
||||
.unwrap_or(DEFAULT_PAGINATION_MAX_TOTAL_HITS);
|
||||
|
||||
let number_of_hits = min(candidates.len() as usize, max_total_hits);
|
||||
let hits_info = HitsInfo::OffsetLimit { limit, offset, estimated_total_hits: number_of_hits };
|
||||
|
||||
let result = SimilarResult {
|
||||
hits,
|
||||
hits_info,
|
||||
id: id.into_inner(),
|
||||
processing_time_ms: before_search.elapsed().as_millis(),
|
||||
};
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
fn insert_geo_distance(sorts: &[String], document: &mut Document) {
|
||||
lazy_static::lazy_static! {
|
||||
static ref GEO_REGEX: Regex =
|
||||
|
||||
@@ -40,9 +40,8 @@ pub struct Permit {
|
||||
|
||||
impl Drop for Permit {
|
||||
fn drop(&mut self) {
|
||||
let sender = self.sender.clone();
|
||||
// if the channel is closed then the whole instance is down
|
||||
std::mem::drop(tokio::spawn(async move { sender.send(()).await }));
|
||||
let _ = futures::executor::block_on(self.sender.send(()));
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -380,43 +380,6 @@ impl Index<'_> {
|
||||
self.service.get(url).await
|
||||
}
|
||||
|
||||
/// Performs both GET and POST similar queries
|
||||
pub async fn similar(
|
||||
&self,
|
||||
query: Value,
|
||||
test: impl Fn(Value, StatusCode) + UnwindSafe + Clone,
|
||||
) {
|
||||
let post = self.similar_post(query.clone()).await;
|
||||
|
||||
let query = yaup::to_string(&query).unwrap();
|
||||
let get = self.similar_get(&query).await;
|
||||
|
||||
insta::allow_duplicates! {
|
||||
let (response, code) = post;
|
||||
let t = test.clone();
|
||||
if let Err(e) = catch_unwind(move || t(response, code)) {
|
||||
eprintln!("Error with post search");
|
||||
resume_unwind(e);
|
||||
}
|
||||
|
||||
let (response, code) = get;
|
||||
if let Err(e) = catch_unwind(move || test(response, code)) {
|
||||
eprintln!("Error with get search");
|
||||
resume_unwind(e);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn similar_post(&self, query: Value) -> (Value, StatusCode) {
|
||||
let url = format!("/indexes/{}/similar", urlencode(self.uid.as_ref()));
|
||||
self.service.post_encoded(url, query, self.encoder).await
|
||||
}
|
||||
|
||||
pub async fn similar_get(&self, query: &str) -> (Value, StatusCode) {
|
||||
let url = format!("/indexes/{}/similar?{}", urlencode(self.uid.as_ref()), query);
|
||||
self.service.get(url).await
|
||||
}
|
||||
|
||||
pub async fn facet_search(&self, query: Value) -> (Value, StatusCode) {
|
||||
let url = format!("/indexes/{}/facet-search", urlencode(self.uid.as_ref()));
|
||||
self.service.post_encoded(url, query, self.encoder).await
|
||||
|
||||
@@ -1859,7 +1859,8 @@ async fn import_dump_v6_containing_experimental_features() {
|
||||
{
|
||||
"vectorStore": false,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
"logsRoute": false,
|
||||
"exportPuffinReports": false
|
||||
}
|
||||
"###);
|
||||
|
||||
|
||||
@@ -20,7 +20,8 @@ async fn experimental_features() {
|
||||
{
|
||||
"vectorStore": false,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
"logsRoute": false,
|
||||
"exportPuffinReports": false
|
||||
}
|
||||
"###);
|
||||
|
||||
@@ -31,7 +32,8 @@ async fn experimental_features() {
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
"logsRoute": false,
|
||||
"exportPuffinReports": false
|
||||
}
|
||||
"###);
|
||||
|
||||
@@ -42,7 +44,8 @@ async fn experimental_features() {
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
"logsRoute": false,
|
||||
"exportPuffinReports": false
|
||||
}
|
||||
"###);
|
||||
|
||||
@@ -54,7 +57,8 @@ async fn experimental_features() {
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
"logsRoute": false,
|
||||
"exportPuffinReports": false
|
||||
}
|
||||
"###);
|
||||
|
||||
@@ -66,7 +70,8 @@ async fn experimental_features() {
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
"logsRoute": false,
|
||||
"exportPuffinReports": false
|
||||
}
|
||||
"###);
|
||||
}
|
||||
@@ -85,7 +90,8 @@ async fn experimental_feature_metrics() {
|
||||
{
|
||||
"vectorStore": false,
|
||||
"metrics": true,
|
||||
"logsRoute": false
|
||||
"logsRoute": false,
|
||||
"exportPuffinReports": false
|
||||
}
|
||||
"###);
|
||||
|
||||
@@ -140,7 +146,7 @@ async fn errors() {
|
||||
meili_snap::snapshot!(code, @"400 Bad Request");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response), @r###"
|
||||
{
|
||||
"message": "Unknown field `NotAFeature`: expected one of `vectorStore`, `metrics`, `logsRoute`",
|
||||
"message": "Unknown field `NotAFeature`: expected one of `vectorStore`, `metrics`, `logsRoute`, `exportPuffinReports`",
|
||||
"code": "bad_request",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#bad_request"
|
||||
|
||||
@@ -8,7 +8,6 @@ mod index;
|
||||
mod logs;
|
||||
mod search;
|
||||
mod settings;
|
||||
mod similar;
|
||||
mod snapshot;
|
||||
mod stats;
|
||||
mod swap_indexes;
|
||||
|
||||
@@ -321,40 +321,6 @@ async fn search_bad_facets() {
|
||||
// Can't make the `attributes_to_highlight` fail with a get search since it'll accept anything as an array of strings.
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn search_bad_threshold() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
|
||||
let (response, code) = index.search_post(json!({"rankingScoreThreshold": "doggo"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value type at `.rankingScoreThreshold`: expected a number, but found a string: `\"doggo\"`",
|
||||
"code": "invalid_search_ranking_score_threshold",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_search_ranking_score_threshold"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn search_invalid_threshold() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
|
||||
let (response, code) = index.search_post(json!({"rankingScoreThreshold": 42})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value at `.rankingScoreThreshold`: the value of `rankingScoreThreshold` is invalid, expected a float between `0.0` and `1.0`.",
|
||||
"code": "invalid_search_ranking_score_threshold",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_search_ranking_score_threshold"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn search_non_filterable_facets() {
|
||||
let server = Server::new().await;
|
||||
@@ -539,7 +505,7 @@ async fn search_bad_matching_strategy() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Unknown value `doggo` at `.matchingStrategy`: expected one of `last`, `all`, `frequency`",
|
||||
"message": "Unknown value `doggo` at `.matchingStrategy`: expected one of `last`, `all`",
|
||||
"code": "invalid_search_matching_strategy",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_search_matching_strategy"
|
||||
@@ -561,7 +527,7 @@ async fn search_bad_matching_strategy() {
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Unknown value `doggo` for parameter `matchingStrategy`: expected one of `last`, `all`, `frequency`",
|
||||
"message": "Unknown value `doggo` for parameter `matchingStrategy`: expected one of `last`, `all`",
|
||||
"code": "invalid_search_matching_strategy",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_search_matching_strategy"
|
||||
|
||||
@@ -117,69 +117,3 @@ async fn geo_bounding_box_with_string_and_number() {
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn bug_4640() {
|
||||
// https://github.com/meilisearch/meilisearch/issues/4640
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
index.add_documents(documents, None).await;
|
||||
index.update_settings_filterable_attributes(json!(["_geo"])).await;
|
||||
let (ret, _code) = index.update_settings_sortable_attributes(json!(["_geo"])).await;
|
||||
index.wait_task(ret.uid()).await;
|
||||
|
||||
// Sort the document with the second one first
|
||||
index
|
||||
.search(
|
||||
json!({
|
||||
"sort": ["_geoPoint(45.4777599, 9.1967508):asc"],
|
||||
}),
|
||||
|response, code| {
|
||||
assert_eq!(code, 200, "{}", response);
|
||||
snapshot!(json_string!(response, { ".processingTimeMs" => "[time]" }), @r###"
|
||||
{
|
||||
"hits": [
|
||||
{
|
||||
"id": 2,
|
||||
"name": "La Bella Italia",
|
||||
"address": "456 Elm Street, Townsville",
|
||||
"type": "Italian",
|
||||
"rating": 9,
|
||||
"_geo": {
|
||||
"lat": "45.4777599",
|
||||
"lng": "9.1967508"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"name": "Taco Truck",
|
||||
"address": "444 Salsa Street, Burritoville",
|
||||
"type": "Mexican",
|
||||
"rating": 9,
|
||||
"_geo": {
|
||||
"lat": 34.0522,
|
||||
"lng": -118.2437
|
||||
},
|
||||
"_geoDistance": 9714063
|
||||
},
|
||||
{
|
||||
"id": 3,
|
||||
"name": "Crêpe Truck",
|
||||
"address": "2 Billig Avenue, Rouenville",
|
||||
"type": "French",
|
||||
"rating": 10
|
||||
}
|
||||
],
|
||||
"query": "",
|
||||
"processingTimeMs": "[time]",
|
||||
"limit": 20,
|
||||
"offset": 0,
|
||||
"estimatedTotalHits": 3
|
||||
}
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
@@ -5,10 +5,7 @@ use crate::common::index::Index;
|
||||
use crate::common::{Server, Value};
|
||||
use crate::json;
|
||||
|
||||
async fn index_with_documents_user_provided<'a>(
|
||||
server: &'a Server,
|
||||
documents: &Value,
|
||||
) -> Index<'a> {
|
||||
async fn index_with_documents<'a>(server: &'a Server, documents: &Value) -> Index<'a> {
|
||||
let index = server.index("test");
|
||||
|
||||
let (response, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
@@ -18,7 +15,8 @@ async fn index_with_documents_user_provided<'a>(
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
"logsRoute": false,
|
||||
"exportPuffinReports": false
|
||||
}
|
||||
"###);
|
||||
|
||||
@@ -36,38 +34,7 @@ async fn index_with_documents_user_provided<'a>(
|
||||
index
|
||||
}
|
||||
|
||||
async fn index_with_documents_hf<'a>(server: &'a Server, documents: &Value) -> Index<'a> {
|
||||
let index = server.index("test");
|
||||
|
||||
let (response, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
meili_snap::snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response), @r###"
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({ "embedders": {"default": {
|
||||
"source": "huggingFace",
|
||||
"model": "sentence-transformers/all-MiniLM-L6-v2",
|
||||
"revision": "e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
|
||||
"documentTemplate": "{{doc.title}}, {{doc.desc}}"
|
||||
}}} ))
|
||||
.await;
|
||||
assert_eq!(202, code, "{:?}", response);
|
||||
index.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.add_documents(documents.clone(), None).await;
|
||||
assert_eq!(202, code, "{:?}", response);
|
||||
index.wait_task(response.uid()).await;
|
||||
index
|
||||
}
|
||||
|
||||
static SIMPLE_SEARCH_DOCUMENTS_VEC: Lazy<Value> = Lazy::new(|| {
|
||||
static SIMPLE_SEARCH_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
|
||||
json!([
|
||||
{
|
||||
"title": "Shazam!",
|
||||
@@ -89,7 +56,7 @@ static SIMPLE_SEARCH_DOCUMENTS_VEC: Lazy<Value> = Lazy::new(|| {
|
||||
}])
|
||||
});
|
||||
|
||||
static SINGLE_DOCUMENT_VEC: Lazy<Value> = Lazy::new(|| {
|
||||
static SINGLE_DOCUMENT: Lazy<Value> = Lazy::new(|| {
|
||||
json!([{
|
||||
"title": "Shazam!",
|
||||
"desc": "a Captain Marvel ersatz",
|
||||
@@ -98,29 +65,10 @@ static SINGLE_DOCUMENT_VEC: Lazy<Value> = Lazy::new(|| {
|
||||
}])
|
||||
});
|
||||
|
||||
static SIMPLE_SEARCH_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
|
||||
json!([
|
||||
{
|
||||
"title": "Shazam!",
|
||||
"desc": "a Captain Marvel ersatz",
|
||||
"id": "1",
|
||||
},
|
||||
{
|
||||
"title": "Captain Planet",
|
||||
"desc": "He's not part of the Marvel Cinematic Universe",
|
||||
"id": "2",
|
||||
},
|
||||
{
|
||||
"title": "Captain Marvel",
|
||||
"desc": "a Shazam ersatz",
|
||||
"id": "3",
|
||||
}])
|
||||
});
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn simple_search() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
|
||||
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
@@ -150,59 +98,10 @@ async fn simple_search() {
|
||||
snapshot!(response["semanticHitCount"], @"3");
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
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;
|
||||
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");
|
||||
|
||||
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}),
|
||||
)
|
||||
.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"], @"1");
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Captain", "hybrid": {"semanticRatio": 0.8}, "showRankingScore": false}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1"},{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3"},{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2"}]"###);
|
||||
snapshot!(response["semanticHitCount"], @"3");
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Movie World", "hybrid": {"semanticRatio": 0.2}, "showRankingScore": false}),
|
||||
)
|
||||
.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":"Shazam!","desc":"a Captain Marvel ersatz","id":"1"},{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3"}]"###);
|
||||
snapshot!(response["semanticHitCount"], @"3");
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
json!({"q": "Wonder replacement", "hybrid": {"semanticRatio": 0.2}, "showRankingScore": false}),
|
||||
)
|
||||
.await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3"},{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1"},{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2"}]"###);
|
||||
snapshot!(response["semanticHitCount"], @"3");
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn distribution_shift() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
|
||||
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
|
||||
|
||||
let search = json!({"q": "Captain", "vector": [1.0, 1.0], "showRankingScore": true, "hybrid": {"semanticRatio": 1.0}});
|
||||
let (response, code) = index.search_post(search.clone()).await;
|
||||
@@ -234,7 +133,7 @@ async fn distribution_shift() {
|
||||
#[actix_rt::test]
|
||||
async fn highlighter() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
|
||||
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(json!({"q": "Captain Marvel", "vector": [1.0, 1.0],
|
||||
@@ -285,7 +184,7 @@ async fn highlighter() {
|
||||
#[actix_rt::test]
|
||||
async fn invalid_semantic_ratio() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
|
||||
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
@@ -357,7 +256,7 @@ async fn invalid_semantic_ratio() {
|
||||
#[actix_rt::test]
|
||||
async fn single_document() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents_user_provided(&server, &SINGLE_DOCUMENT_VEC).await;
|
||||
let index = index_with_documents(&server, &SINGLE_DOCUMENT).await;
|
||||
|
||||
let (response, code) = index
|
||||
.search_post(
|
||||
@@ -373,7 +272,7 @@ async fn single_document() {
|
||||
#[actix_rt::test]
|
||||
async fn query_combination() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
|
||||
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
|
||||
|
||||
// search without query and vector, but with hybrid => still placeholder
|
||||
let (response, code) = index
|
||||
|
||||
@@ -1,128 +0,0 @@
|
||||
use meili_snap::snapshot;
|
||||
use once_cell::sync::Lazy;
|
||||
|
||||
use crate::common::index::Index;
|
||||
use crate::common::{Server, Value};
|
||||
use crate::json;
|
||||
|
||||
async fn index_with_documents<'a>(server: &'a Server, documents: &Value) -> Index<'a> {
|
||||
let index = server.index("test");
|
||||
|
||||
index.add_documents(documents.clone(), None).await;
|
||||
index.wait_task(0).await;
|
||||
index
|
||||
}
|
||||
|
||||
static SIMPLE_SEARCH_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
|
||||
json!([
|
||||
{
|
||||
"title": "Shazam!",
|
||||
"id": "1",
|
||||
},
|
||||
{
|
||||
"title": "Captain Planet",
|
||||
"id": "2",
|
||||
},
|
||||
{
|
||||
"title": "Captain Marvel",
|
||||
"id": "3",
|
||||
},
|
||||
{
|
||||
"title": "a Captain Marvel ersatz",
|
||||
"id": "4"
|
||||
},
|
||||
{
|
||||
"title": "He's not part of the Marvel Cinematic Universe",
|
||||
"id": "5"
|
||||
},
|
||||
{
|
||||
"title": "a Shazam ersatz, but better than Captain Planet",
|
||||
"id": "6"
|
||||
},
|
||||
{
|
||||
"title": "Capitain CAAAAAVEEERNE!!!!",
|
||||
"id": "7"
|
||||
}
|
||||
])
|
||||
});
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn simple_search() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "Captain Marvel", "matchingStrategy": "last", "attributesToRetrieve": ["id"]}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"id":"3"},{"id":"4"},{"id":"2"},{"id":"6"},{"id":"7"}]"###);
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "Captain Marvel", "matchingStrategy": "all", "attributesToRetrieve": ["id"]}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"id":"3"},{"id":"4"}]"###);
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "Captain Marvel", "matchingStrategy": "frequency", "attributesToRetrieve": ["id"]}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"id":"3"},{"id":"4"},{"id":"5"}]"###);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn search_with_typo() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "Capitain Marvel", "matchingStrategy": "last", "attributesToRetrieve": ["id"]}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"id":"3"},{"id":"4"},{"id":"7"},{"id":"2"},{"id":"6"}]"###);
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "Capitain Marvel", "matchingStrategy": "all", "attributesToRetrieve": ["id"]}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"id":"3"},{"id":"4"}]"###);
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "Capitain Marvel", "matchingStrategy": "frequency", "attributesToRetrieve": ["id"]}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"id":"3"},{"id":"4"},{"id":"5"}]"###);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn search_with_unknown_word() {
|
||||
let server = Server::new().await;
|
||||
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "Captain Supercopter Marvel", "matchingStrategy": "last", "attributesToRetrieve": ["id"]}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"id":"2"},{"id":"3"},{"id":"4"},{"id":"6"},{"id":"7"}]"###);
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "Captain Supercopter Marvel", "matchingStrategy": "all", "attributesToRetrieve": ["id"]}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @"[]");
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(json!({"q": "Captain Supercopter Marvel", "matchingStrategy": "frequency", "attributesToRetrieve": ["id"]}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(response["hits"], @r###"[{"id":"3"},{"id":"4"},{"id":"5"}]"###);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
@@ -7,7 +7,6 @@ mod facet_search;
|
||||
mod formatted;
|
||||
mod geo;
|
||||
mod hybrid;
|
||||
mod matching_strategy;
|
||||
mod multi;
|
||||
mod pagination;
|
||||
mod restrict_searchable;
|
||||
@@ -48,31 +47,6 @@ static DOCUMENTS: Lazy<Value> = Lazy::new(|| {
|
||||
])
|
||||
});
|
||||
|
||||
static SCORE_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
|
||||
json!([
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 1",
|
||||
"id": "A",
|
||||
},
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 2",
|
||||
"id": "B",
|
||||
},
|
||||
{
|
||||
"title": "Batman Returns",
|
||||
"id": "C",
|
||||
},
|
||||
{
|
||||
"title": "Batman",
|
||||
"id": "D",
|
||||
},
|
||||
{
|
||||
"title": "Badman",
|
||||
"id": "E",
|
||||
}
|
||||
])
|
||||
});
|
||||
|
||||
static NESTED_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
|
||||
json!([
|
||||
{
|
||||
@@ -706,26 +680,6 @@ async fn search_facet_distribution() {
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index.update_settings(json!({"filterableAttributes": ["doggos.name"]})).await;
|
||||
index.wait_task(5).await;
|
||||
|
||||
index
|
||||
.search(
|
||||
json!({
|
||||
"facets": ["doggos.name"]
|
||||
}),
|
||||
|response, code| {
|
||||
assert_eq!(code, 200, "{}", response);
|
||||
let dist = response["facetDistribution"].as_object().unwrap();
|
||||
assert_eq!(dist.len(), 1);
|
||||
assert_eq!(
|
||||
dist["doggos.name"],
|
||||
json!({ "bobby": 1, "buddy": 1, "gros bill": 1, "turbo": 1, "fast": 1})
|
||||
);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
@@ -941,9 +895,9 @@ async fn test_score_details() {
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-100.0,
|
||||
231.0,
|
||||
32.0
|
||||
-100,
|
||||
231,
|
||||
32
|
||||
]
|
||||
},
|
||||
"_rankingScoreDetails": {
|
||||
@@ -985,213 +939,6 @@ async fn test_score_details() {
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn test_score() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
|
||||
let documents = SCORE_DOCUMENTS.clone();
|
||||
|
||||
let res = index.add_documents(json!(documents), None).await;
|
||||
index.wait_task(res.0.uid()).await;
|
||||
|
||||
index
|
||||
.search(
|
||||
json!({
|
||||
"q": "Badman the dark knight returns 1",
|
||||
"showRankingScore": true,
|
||||
}),
|
||||
|response, code| {
|
||||
meili_snap::snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 1",
|
||||
"id": "A",
|
||||
"_rankingScore": 0.9746605609456898
|
||||
},
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 2",
|
||||
"id": "B",
|
||||
"_rankingScore": 0.8055252965383685
|
||||
},
|
||||
{
|
||||
"title": "Badman",
|
||||
"id": "E",
|
||||
"_rankingScore": 0.16666666666666666
|
||||
},
|
||||
{
|
||||
"title": "Batman Returns",
|
||||
"id": "C",
|
||||
"_rankingScore": 0.07702020202020202
|
||||
},
|
||||
{
|
||||
"title": "Batman",
|
||||
"id": "D",
|
||||
"_rankingScore": 0.07702020202020202
|
||||
}
|
||||
]
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn test_score_threshold() {
|
||||
let query = "Badman dark returns 1";
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
|
||||
let documents = SCORE_DOCUMENTS.clone();
|
||||
|
||||
let res = index.add_documents(json!(documents), None).await;
|
||||
index.wait_task(res.0.uid()).await;
|
||||
|
||||
index
|
||||
.search(
|
||||
json!({
|
||||
"q": query,
|
||||
"showRankingScore": true,
|
||||
"rankingScoreThreshold": 0.0
|
||||
}),
|
||||
|response, code| {
|
||||
meili_snap::snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @"5");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 1",
|
||||
"id": "A",
|
||||
"_rankingScore": 0.93430081300813
|
||||
},
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 2",
|
||||
"id": "B",
|
||||
"_rankingScore": 0.6685627880184332
|
||||
},
|
||||
{
|
||||
"title": "Badman",
|
||||
"id": "E",
|
||||
"_rankingScore": 0.25
|
||||
},
|
||||
{
|
||||
"title": "Batman Returns",
|
||||
"id": "C",
|
||||
"_rankingScore": 0.11553030303030302
|
||||
},
|
||||
{
|
||||
"title": "Batman",
|
||||
"id": "D",
|
||||
"_rankingScore": 0.11553030303030302
|
||||
}
|
||||
]
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(
|
||||
json!({
|
||||
"q": query,
|
||||
"showRankingScore": true,
|
||||
"rankingScoreThreshold": 0.2
|
||||
}),
|
||||
|response, code| {
|
||||
meili_snap::snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @r###"3"###);
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 1",
|
||||
"id": "A",
|
||||
"_rankingScore": 0.93430081300813
|
||||
},
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 2",
|
||||
"id": "B",
|
||||
"_rankingScore": 0.6685627880184332
|
||||
},
|
||||
{
|
||||
"title": "Badman",
|
||||
"id": "E",
|
||||
"_rankingScore": 0.25
|
||||
}
|
||||
]
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(
|
||||
json!({
|
||||
"q": query,
|
||||
"showRankingScore": true,
|
||||
"rankingScoreThreshold": 0.5
|
||||
}),
|
||||
|response, code| {
|
||||
meili_snap::snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @r###"2"###);
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 1",
|
||||
"id": "A",
|
||||
"_rankingScore": 0.93430081300813
|
||||
},
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 2",
|
||||
"id": "B",
|
||||
"_rankingScore": 0.6685627880184332
|
||||
}
|
||||
]
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(
|
||||
json!({
|
||||
"q": query,
|
||||
"showRankingScore": true,
|
||||
"rankingScoreThreshold": 0.8
|
||||
}),
|
||||
|response, code| {
|
||||
meili_snap::snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @r###"1"###);
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Batman the dark knight returns: Part 1",
|
||||
"id": "A",
|
||||
"_rankingScore": 0.93430081300813
|
||||
}
|
||||
]
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.search(
|
||||
json!({
|
||||
"q": query,
|
||||
"showRankingScore": true,
|
||||
"rankingScoreThreshold": 1.0
|
||||
}),
|
||||
|response, code| {
|
||||
meili_snap::snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @r###"0"###);
|
||||
// nobody is perfect
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["hits"]), @"[]");
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn test_degraded_score_details() {
|
||||
let server = Server::new().await;
|
||||
@@ -1349,9 +1096,9 @@ async fn experimental_feature_vector_store() {
|
||||
"id": "287947",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
1.0,
|
||||
2.0,
|
||||
3.0
|
||||
1,
|
||||
2,
|
||||
3
|
||||
]
|
||||
},
|
||||
"_rankingScore": 1.0
|
||||
@@ -1361,9 +1108,9 @@ async fn experimental_feature_vector_store() {
|
||||
"id": "299537",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
1.0,
|
||||
2.0,
|
||||
54.0
|
||||
1,
|
||||
2,
|
||||
54
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.9129111766815186
|
||||
@@ -1373,9 +1120,9 @@ async fn experimental_feature_vector_store() {
|
||||
"id": "450465",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-100.0,
|
||||
340.0,
|
||||
90.0
|
||||
-100,
|
||||
340,
|
||||
90
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.8106412887573242
|
||||
@@ -1385,9 +1132,9 @@ async fn experimental_feature_vector_store() {
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-100.0,
|
||||
231.0,
|
||||
32.0
|
||||
-100,
|
||||
231,
|
||||
32
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.7412010431289673
|
||||
@@ -1397,9 +1144,9 @@ async fn experimental_feature_vector_store() {
|
||||
"id": "522681",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
10.0,
|
||||
-23.0,
|
||||
32.0
|
||||
10,
|
||||
-23,
|
||||
32
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.6972063183784485
|
||||
@@ -1658,9 +1405,9 @@ async fn simple_search_with_strange_synonyms() {
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-100.0,
|
||||
231.0,
|
||||
32.0
|
||||
-100,
|
||||
231,
|
||||
32
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1679,9 +1426,9 @@ async fn simple_search_with_strange_synonyms() {
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-100.0,
|
||||
231.0,
|
||||
32.0
|
||||
-100,
|
||||
231,
|
||||
32
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1700,9 +1447,9 @@ async fn simple_search_with_strange_synonyms() {
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-100.0,
|
||||
231.0,
|
||||
32.0
|
||||
-100,
|
||||
231,
|
||||
32
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
@@ -75,9 +75,9 @@ async fn simple_search_single_index() {
|
||||
"id": "450465",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-100.0,
|
||||
340.0,
|
||||
90.0
|
||||
-100,
|
||||
340,
|
||||
90
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -96,9 +96,9 @@ async fn simple_search_single_index() {
|
||||
"id": "299537",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
1.0,
|
||||
2.0,
|
||||
54.0
|
||||
1,
|
||||
2,
|
||||
54
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -194,9 +194,9 @@ async fn simple_search_two_indexes() {
|
||||
"id": "450465",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-100.0,
|
||||
340.0,
|
||||
90.0
|
||||
-100,
|
||||
340,
|
||||
90
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -227,9 +227,9 @@ async fn simple_search_two_indexes() {
|
||||
"cattos": "pésti",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
1.0,
|
||||
2.0,
|
||||
3.0
|
||||
1,
|
||||
2,
|
||||
3
|
||||
]
|
||||
}
|
||||
},
|
||||
@@ -249,9 +249,9 @@ async fn simple_search_two_indexes() {
|
||||
],
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
1.0,
|
||||
2.0,
|
||||
54.0
|
||||
1,
|
||||
2,
|
||||
54
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
@@ -98,7 +98,8 @@ async fn secrets_are_hidden_in_settings() {
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
"logsRoute": false,
|
||||
"exportPuffinReports": false
|
||||
}
|
||||
"###);
|
||||
|
||||
|
||||
@@ -1,758 +0,0 @@
|
||||
use meili_snap::*;
|
||||
|
||||
use super::DOCUMENTS;
|
||||
use crate::common::Server;
|
||||
use crate::json;
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_unexisting_index() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "Index `test` not found.",
|
||||
"code": "index_not_found",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#index_not_found"
|
||||
});
|
||||
|
||||
index
|
||||
.similar(json!({"id": 287947}), |response, code| {
|
||||
assert_eq!(code, 404);
|
||||
assert_eq!(response, expected_response);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_unexisting_parameter() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 287947, "marin": "hello"}), |response, code| {
|
||||
assert_eq!(code, 400, "{}", response);
|
||||
assert_eq!(response["code"], "bad_request");
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
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;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Using the similar API requires enabling the `vector store` experimental feature. See https://github.com/meilisearch/product/discussions/677",
|
||||
"code": "feature_not_enabled",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#feature_not_enabled"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_bad_id() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"id": ["doggo"]})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value at `.id`: the value of `id` is invalid. A document identifier can be of type integer or string, only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and underscores (_).",
|
||||
"code": "invalid_similar_id",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_id"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_bad_ranking_score_threshold() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"rankingScoreThreshold": ["doggo"]})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value type at `.rankingScoreThreshold`: expected a number, but found an array: `[\"doggo\"]`",
|
||||
"code": "invalid_similar_ranking_score_threshold",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_ranking_score_threshold"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_invalid_ranking_score_threshold() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"rankingScoreThreshold": 42})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value at `.rankingScoreThreshold`: the value of `rankingScoreThreshold` is invalid, expected a float between `0.0` and `1.0`.",
|
||||
"code": "invalid_similar_ranking_score_threshold",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_ranking_score_threshold"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_invalid_id() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"id": "http://invalid-docid/"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value at `.id`: the value of `id` is invalid. A document identifier can be of type integer or string, only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and underscores (_).",
|
||||
"code": "invalid_similar_id",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_id"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_not_found_id() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"id": "definitely-doesnt-exist"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Document `definitely-doesnt-exist` not found.",
|
||||
"code": "not_found_similar_id",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#not_found_similar_id"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_bad_offset() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"id": 287947, "offset": "doggo"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value type at `.offset`: expected a positive integer, but found a string: `\"doggo\"`",
|
||||
"code": "invalid_similar_offset",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_offset"
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index.similar_get("id=287947&offset=doggo").await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value in parameter `offset`: could not parse `doggo` as a positive integer",
|
||||
"code": "invalid_similar_offset",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_offset"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_bad_limit() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({"id": 287947, "limit": "doggo"})).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value type at `.limit`: expected a positive integer, but found a string: `\"doggo\"`",
|
||||
"code": "invalid_similar_limit",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_limit"
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index.similar_get("id=287946&limit=doggo").await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid value in parameter `limit`: could not parse `doggo` as a positive integer",
|
||||
"code": "invalid_similar_limit",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_limit"
|
||||
}
|
||||
"###);
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn similar_bad_filter() {
|
||||
// Since a filter is deserialized as a json Value it will never fail to deserialize.
|
||||
// Thus the error message is not generated by deserr but written by us.
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
snapshot!(code, @"202 Accepted");
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let (response, code) = index.similar_post(json!({ "id": 287947, "filter": true })).await;
|
||||
snapshot!(code, @"400 Bad Request");
|
||||
snapshot!(json_string!(response), @r###"
|
||||
{
|
||||
"message": "Invalid syntax for the filter parameter: `expected String, Array, found: true`.",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
}
|
||||
"###);
|
||||
// Can't make the `filter` fail with a get search since it'll accept anything as a strings.
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_invalid_syntax_object() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `_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"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "title & Glass"}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_invalid_syntax_array() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "Was expecting an operation `=`, `!=`, `>=`, `>`, `<=`, `<`, `IN`, `NOT IN`, `TO`, `EXISTS`, `NOT EXISTS`, `IS NULL`, `IS NOT NULL`, `IS EMPTY`, `IS NOT EMPTY`, `_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"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["title & Glass"]}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_invalid_syntax_string() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "Found unexpected characters at the end of the filter: `XOR title = Glass`. You probably forgot an `OR` or an `AND` rule.\n15:32 title = Glass XOR title = Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 287947, "filter": "title = Glass XOR title = Glass"}),
|
||||
|response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_invalid_attribute_array() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "Attribute `many` is not filterable. Available filterable attributes are: `title`.\n1:5 many = Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["many = Glass"]}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_invalid_attribute_string() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "Attribute `many` is not filterable. Available filterable attributes are: `title`.\n1:5 many = Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "many = Glass"}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_reserved_geo_attribute_array() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "`_geo` is a reserved keyword and thus can't be used as a filter expression. Use the `_geoRadius(latitude, longitude, distance)` or `_geoBoundingBox([latitude, longitude], [latitude, longitude])` built-in rules to filter on `_geo` coordinates.\n1:13 _geo = Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["_geo = Glass"]}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_reserved_geo_attribute_string() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "`_geo` is a reserved keyword and thus can't be used as a filter expression. Use the `_geoRadius(latitude, longitude, distance)` or `_geoBoundingBox([latitude, longitude], [latitude, longitude])` built-in rules to filter on `_geo` coordinates.\n1:13 _geo = Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "_geo = Glass"}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_reserved_attribute_array() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "`_geoDistance` is a reserved keyword and thus can't be used as a filter expression. Use the `_geoRadius(latitude, longitude, distance)` or `_geoBoundingBox([latitude, longitude], [latitude, longitude])` built-in rules to filter on `_geo` coordinates.\n1:21 _geoDistance = Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["_geoDistance = Glass"]}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_reserved_attribute_string() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "`_geoDistance` is a reserved keyword and thus can't be used as a filter expression. Use the `_geoRadius(latitude, longitude, distance)` or `_geoBoundingBox([latitude, longitude], [latitude, longitude])` built-in rules to filter on `_geo` coordinates.\n1:21 _geoDistance = Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "_geoDistance = Glass"}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_reserved_geo_point_array() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "`_geoPoint` is a reserved keyword and thus can't be used as a filter expression. Use the `_geoRadius(latitude, longitude, distance)` or `_geoBoundingBox([latitude, longitude], [latitude, longitude])` built-in rules to filter on `_geo` coordinates.\n1:18 _geoPoint = Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": ["_geoPoint = Glass"]}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter_reserved_geo_point_string() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
server.set_features(json!({"vectorStore": true})).await;
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
let expected_response = json!({
|
||||
"message": "`_geoPoint` is a reserved keyword and thus can't be used as a filter expression. Use the `_geoRadius(latitude, longitude, distance)` or `_geoBoundingBox([latitude, longitude], [latitude, longitude])` built-in rules to filter on `_geo` coordinates.\n1:18 _geoPoint = Glass",
|
||||
"code": "invalid_similar_filter",
|
||||
"type": "invalid_request",
|
||||
"link": "https://docs.meilisearch.com/errors#invalid_similar_filter"
|
||||
});
|
||||
index
|
||||
.similar(json!({"id": 287947, "filter": "_geoPoint = Glass"}), |response, code| {
|
||||
assert_eq!(response, expected_response);
|
||||
assert_eq!(code, 400);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
@@ -1,602 +0,0 @@
|
||||
mod errors;
|
||||
|
||||
use meili_snap::{json_string, snapshot};
|
||||
use once_cell::sync::Lazy;
|
||||
|
||||
use crate::common::{Server, Value};
|
||||
use crate::json;
|
||||
|
||||
static DOCUMENTS: Lazy<Value> = Lazy::new(|| {
|
||||
json!([
|
||||
{
|
||||
"title": "Shazam!",
|
||||
"release_year": 2019,
|
||||
"id": "287947",
|
||||
// Three semantic properties:
|
||||
// 1. magic, anything that reminds you of magic
|
||||
// 2. authority, anything that inspires command
|
||||
// 3. horror, anything that inspires fear or dread
|
||||
"_vectors": { "manual": [0.8, 0.4, -0.5]},
|
||||
},
|
||||
{
|
||||
"title": "Captain Marvel",
|
||||
"release_year": 2019,
|
||||
"id": "299537",
|
||||
"_vectors": { "manual": [0.6, 0.8, -0.2] },
|
||||
},
|
||||
{
|
||||
"title": "Escape Room",
|
||||
"release_year": 2019,
|
||||
"id": "522681",
|
||||
"_vectors": { "manual": [0.1, 0.6, 0.8] },
|
||||
},
|
||||
{
|
||||
"title": "How to Train Your Dragon: The Hidden World",
|
||||
"release_year": 2019,
|
||||
"id": "166428",
|
||||
"_vectors": { "manual": [0.7, 0.7, -0.4] },
|
||||
},
|
||||
{
|
||||
"title": "All Quiet on the Western Front",
|
||||
"release_year": 1930,
|
||||
"id": "143",
|
||||
"_vectors": { "manual": [-0.5, 0.3, 0.85] },
|
||||
}
|
||||
])
|
||||
});
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn basic() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(value, @r###"
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 143}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Escape Room",
|
||||
"release_year": 2019,
|
||||
"id": "522681",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.1,
|
||||
0.6,
|
||||
0.8
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"title": "Captain Marvel",
|
||||
"release_year": 2019,
|
||||
"id": "299537",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.6,
|
||||
0.8,
|
||||
-0.2
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"title": "How to Train Your Dragon: The Hidden World",
|
||||
"release_year": 2019,
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.7,
|
||||
0.7,
|
||||
-0.4
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"title": "Shazam!",
|
||||
"release_year": 2019,
|
||||
"id": "287947",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.8,
|
||||
0.4,
|
||||
-0.5
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
"###);
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": "299537"}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "How to Train Your Dragon: The Hidden World",
|
||||
"release_year": 2019,
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.7,
|
||||
0.7,
|
||||
-0.4
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"title": "Shazam!",
|
||||
"release_year": 2019,
|
||||
"id": "287947",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.8,
|
||||
0.4,
|
||||
-0.5
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"title": "Escape Room",
|
||||
"release_year": 2019,
|
||||
"id": "522681",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.1,
|
||||
0.6,
|
||||
0.8
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"title": "All Quiet on the Western Front",
|
||||
"release_year": 1930,
|
||||
"id": "143",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-0.5,
|
||||
0.3,
|
||||
0.85
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
"###);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn ranking_score_threshold() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(value, @r###"
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @"4");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Escape Room",
|
||||
"release_year": 2019,
|
||||
"id": "522681",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.1,
|
||||
0.6,
|
||||
0.8
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.890957772731781
|
||||
},
|
||||
{
|
||||
"title": "Captain Marvel",
|
||||
"release_year": 2019,
|
||||
"id": "299537",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.6,
|
||||
0.8,
|
||||
-0.2
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.39060014486312866
|
||||
},
|
||||
{
|
||||
"title": "How to Train Your Dragon: The Hidden World",
|
||||
"release_year": 2019,
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.7,
|
||||
0.7,
|
||||
-0.4
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.2819308042526245
|
||||
},
|
||||
{
|
||||
"title": "Shazam!",
|
||||
"release_year": 2019,
|
||||
"id": "287947",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.8,
|
||||
0.4,
|
||||
-0.5
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.1662663221359253
|
||||
}
|
||||
]
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.2}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @"3");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Escape Room",
|
||||
"release_year": 2019,
|
||||
"id": "522681",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.1,
|
||||
0.6,
|
||||
0.8
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.890957772731781
|
||||
},
|
||||
{
|
||||
"title": "Captain Marvel",
|
||||
"release_year": 2019,
|
||||
"id": "299537",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.6,
|
||||
0.8,
|
||||
-0.2
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.39060014486312866
|
||||
},
|
||||
{
|
||||
"title": "How to Train Your Dragon: The Hidden World",
|
||||
"release_year": 2019,
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.7,
|
||||
0.7,
|
||||
-0.4
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.2819308042526245
|
||||
}
|
||||
]
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.3}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @"2");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Escape Room",
|
||||
"release_year": 2019,
|
||||
"id": "522681",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.1,
|
||||
0.6,
|
||||
0.8
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.890957772731781
|
||||
},
|
||||
{
|
||||
"title": "Captain Marvel",
|
||||
"release_year": 2019,
|
||||
"id": "299537",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.6,
|
||||
0.8,
|
||||
-0.2
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.39060014486312866
|
||||
}
|
||||
]
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.6}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
meili_snap::snapshot!(meili_snap::json_string!(response["estimatedTotalHits"]), @"1");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Escape Room",
|
||||
"release_year": 2019,
|
||||
"id": "522681",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.1,
|
||||
0.6,
|
||||
0.8
|
||||
]
|
||||
},
|
||||
"_rankingScore": 0.890957772731781
|
||||
}
|
||||
]
|
||||
"###);
|
||||
},
|
||||
)
|
||||
.await;
|
||||
|
||||
index
|
||||
.similar(
|
||||
json!({"id": 143, "showRankingScore": true, "rankingScoreThreshold": 0.9}),
|
||||
|response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @"[]");
|
||||
},
|
||||
)
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn filter() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(value, @r###"
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title", "release_year"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 522681, "filter": "release_year = 2019"}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Captain Marvel",
|
||||
"release_year": 2019,
|
||||
"id": "299537",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.6,
|
||||
0.8,
|
||||
-0.2
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"title": "How to Train Your Dragon: The Hidden World",
|
||||
"release_year": 2019,
|
||||
"id": "166428",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.7,
|
||||
0.7,
|
||||
-0.4
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"title": "Shazam!",
|
||||
"release_year": 2019,
|
||||
"id": "287947",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.8,
|
||||
0.4,
|
||||
-0.5
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
"###);
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 522681, "filter": "release_year < 2000"}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "All Quiet on the Western Front",
|
||||
"release_year": 1930,
|
||||
"id": "143",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
-0.5,
|
||||
0.3,
|
||||
0.85
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
"###);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
async fn limit_and_offset() {
|
||||
let server = Server::new().await;
|
||||
let index = server.index("test");
|
||||
let (value, code) = server.set_features(json!({"vectorStore": true})).await;
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(value, @r###"
|
||||
{
|
||||
"vectorStore": true,
|
||||
"metrics": false,
|
||||
"logsRoute": false
|
||||
}
|
||||
"###);
|
||||
|
||||
let (response, code) = index
|
||||
.update_settings(json!({
|
||||
"embedders": {
|
||||
"manual": {
|
||||
"source": "userProvided",
|
||||
"dimensions": 3,
|
||||
}
|
||||
},
|
||||
"filterableAttributes": ["title"]}))
|
||||
.await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
server.wait_task(response.uid()).await;
|
||||
|
||||
let documents = DOCUMENTS.clone();
|
||||
let (value, code) = index.add_documents(documents, None).await;
|
||||
snapshot!(code, @"202 Accepted");
|
||||
index.wait_task(value.uid()).await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 143, "limit": 1}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Escape Room",
|
||||
"release_year": 2019,
|
||||
"id": "522681",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.1,
|
||||
0.6,
|
||||
0.8
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
"###);
|
||||
})
|
||||
.await;
|
||||
|
||||
index
|
||||
.similar(json!({"id": 143, "limit": 1, "offset": 1}), |response, code| {
|
||||
snapshot!(code, @"200 OK");
|
||||
snapshot!(json_string!(response["hits"]), @r###"
|
||||
[
|
||||
{
|
||||
"title": "Captain Marvel",
|
||||
"release_year": 2019,
|
||||
"id": "299537",
|
||||
"_vectors": {
|
||||
"manual": [
|
||||
0.6,
|
||||
0.8,
|
||||
-0.2
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
"###);
|
||||
})
|
||||
.await;
|
||||
}
|
||||
@@ -31,7 +31,6 @@ macro_rules! verify_snapshot {
|
||||
}
|
||||
|
||||
#[actix_rt::test]
|
||||
#[cfg_attr(target_os = "windows", ignore)]
|
||||
async fn perform_snapshot() {
|
||||
let temp = tempfile::tempdir().unwrap();
|
||||
let snapshot_dir = tempfile::tempdir().unwrap();
|
||||
|
||||
@@ -17,7 +17,7 @@ bincode = "1.3.3"
|
||||
bstr = "1.9.0"
|
||||
bytemuck = { version = "1.14.0", features = ["extern_crate_alloc"] }
|
||||
byteorder = "1.5.0"
|
||||
charabia = { version = "0.8.11", default-features = false }
|
||||
charabia = { version = "0.8.10", default-features = false }
|
||||
concat-arrays = "0.1.2"
|
||||
crossbeam-channel = "0.5.11"
|
||||
deserr = "0.6.1"
|
||||
@@ -67,6 +67,9 @@ filter-parser = { path = "../filter-parser" }
|
||||
# documents words self-join
|
||||
itertools = "0.11.0"
|
||||
|
||||
# profiling
|
||||
puffin = "0.16.0"
|
||||
|
||||
csv = "1.3.0"
|
||||
candle-core = { version = "0.4.1" }
|
||||
candle-transformers = { version = "0.4.1" }
|
||||
|
||||
@@ -49,7 +49,7 @@ fn main() -> Result<(), Box<dyn Error>> {
|
||||
let start = Instant::now();
|
||||
|
||||
let mut ctx = SearchContext::new(&index, &txn)?;
|
||||
let universe = filtered_universe(ctx.index, ctx.txn, &None)?;
|
||||
let universe = filtered_universe(&ctx, &None)?;
|
||||
|
||||
let docs = execute_search(
|
||||
&mut ctx,
|
||||
@@ -66,7 +66,6 @@ fn main() -> Result<(), Box<dyn Error>> {
|
||||
&mut DefaultSearchLogger,
|
||||
logger,
|
||||
TimeBudget::max(),
|
||||
None,
|
||||
)?;
|
||||
if let Some((logger, dir)) = detailed_logger {
|
||||
logger.finish(&mut ctx, Path::new(dir))?;
|
||||
|
||||
@@ -12,10 +12,7 @@ use bimap::BiHashMap;
|
||||
pub use builder::DocumentsBatchBuilder;
|
||||
pub use enriched::{EnrichedDocument, EnrichedDocumentsBatchCursor, EnrichedDocumentsBatchReader};
|
||||
use obkv::KvReader;
|
||||
pub use primary_key::{
|
||||
validate_document_id_value, DocumentIdExtractionError, FieldIdMapper, PrimaryKey,
|
||||
DEFAULT_PRIMARY_KEY,
|
||||
};
|
||||
pub use primary_key::{DocumentIdExtractionError, FieldIdMapper, PrimaryKey, DEFAULT_PRIMARY_KEY};
|
||||
pub use reader::{DocumentsBatchCursor, DocumentsBatchCursorError, DocumentsBatchReader};
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
|
||||
@@ -60,7 +60,7 @@ impl<'a> PrimaryKey<'a> {
|
||||
Some(document_id_bytes) => {
|
||||
let document_id = serde_json::from_slice(document_id_bytes)
|
||||
.map_err(InternalError::SerdeJson)?;
|
||||
match validate_document_id_value(document_id) {
|
||||
match validate_document_id_value(document_id)? {
|
||||
Ok(document_id) => Ok(Ok(document_id)),
|
||||
Err(user_error) => {
|
||||
Ok(Err(DocumentIdExtractionError::InvalidDocumentId(user_error)))
|
||||
@@ -88,7 +88,7 @@ impl<'a> PrimaryKey<'a> {
|
||||
}
|
||||
|
||||
match matching_documents_ids.pop() {
|
||||
Some(document_id) => match validate_document_id_value(document_id) {
|
||||
Some(document_id) => match validate_document_id_value(document_id)? {
|
||||
Ok(document_id) => Ok(Ok(document_id)),
|
||||
Err(user_error) => {
|
||||
Ok(Err(DocumentIdExtractionError::InvalidDocumentId(user_error)))
|
||||
@@ -159,14 +159,14 @@ fn validate_document_id(document_id: &str) -> Option<&str> {
|
||||
}
|
||||
}
|
||||
|
||||
pub fn validate_document_id_value(document_id: Value) -> StdResult<String, UserError> {
|
||||
pub fn validate_document_id_value(document_id: Value) -> Result<StdResult<String, UserError>> {
|
||||
match document_id {
|
||||
Value::String(string) => match validate_document_id(&string) {
|
||||
Some(s) if s.len() == string.len() => Ok(string),
|
||||
Some(s) => Ok(s.to_string()),
|
||||
None => Err(UserError::InvalidDocumentId { document_id: Value::String(string) }),
|
||||
Some(s) if s.len() == string.len() => Ok(Ok(string)),
|
||||
Some(s) => Ok(Ok(s.to_string())),
|
||||
None => Ok(Err(UserError::InvalidDocumentId { document_id: Value::String(string) })),
|
||||
},
|
||||
Value::Number(number) if number.is_i64() => Ok(number.to_string()),
|
||||
content => Err(UserError::InvalidDocumentId { document_id: content }),
|
||||
Value::Number(number) if number.is_i64() => Ok(Ok(number.to_string())),
|
||||
content => Ok(Err(UserError::InvalidDocumentId { document_id: content })),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -117,8 +117,10 @@ only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and undersco
|
||||
InvalidGeoField(#[from] GeoError),
|
||||
#[error("Invalid vector dimensions: expected: `{}`, found: `{}`.", .expected, .found)]
|
||||
InvalidVectorDimensions { expected: usize, found: usize },
|
||||
#[error("The `_vectors.{subfield}` field in the document with id: `{document_id}` is not an array. Was expecting an array of floats or an array of arrays of floats but instead got `{value}`.")]
|
||||
InvalidVectorsType { document_id: Value, value: Value, subfield: String },
|
||||
#[error("The `_vectors` field in the document with id: `{document_id}` is not an object. Was expecting an object with a key for each embedder with manually provided vectors, but instead got `{value}`")]
|
||||
InvalidVectorsMapType { document_id: String, value: Value },
|
||||
InvalidVectorsMapType { document_id: Value, value: Value },
|
||||
#[error("{0}")]
|
||||
InvalidFilter(String),
|
||||
#[error("Invalid type for filter subexpression: expected: {}, found: {1}.", .0.join(", "))]
|
||||
|
||||
@@ -47,12 +47,6 @@ pub struct FacetGroupValue {
|
||||
pub bitmap: RoaringBitmap,
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
pub struct FacetGroupLazyValue<'b> {
|
||||
pub size: u8,
|
||||
pub bitmap_bytes: &'b [u8],
|
||||
}
|
||||
|
||||
pub struct FacetGroupKeyCodec<T> {
|
||||
_phantom: PhantomData<T>,
|
||||
}
|
||||
@@ -75,7 +69,6 @@ where
|
||||
Ok(Cow::Owned(v))
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a, T> heed::BytesDecode<'a> for FacetGroupKeyCodec<T>
|
||||
where
|
||||
T: BytesDecode<'a>,
|
||||
@@ -91,7 +84,6 @@ where
|
||||
}
|
||||
|
||||
pub struct FacetGroupValueCodec;
|
||||
|
||||
impl<'a> heed::BytesEncode<'a> for FacetGroupValueCodec {
|
||||
type EItem = FacetGroupValue;
|
||||
|
||||
@@ -101,23 +93,11 @@ impl<'a> heed::BytesEncode<'a> for FacetGroupValueCodec {
|
||||
Ok(Cow::Owned(v))
|
||||
}
|
||||
}
|
||||
|
||||
impl<'a> heed::BytesDecode<'a> for FacetGroupValueCodec {
|
||||
type DItem = FacetGroupValue;
|
||||
|
||||
fn bytes_decode(bytes: &'a [u8]) -> Result<Self::DItem, BoxedError> {
|
||||
let size = bytes[0];
|
||||
let bitmap = CboRoaringBitmapCodec::deserialize_from(&bytes[1..])?;
|
||||
Ok(FacetGroupValue { size, bitmap })
|
||||
}
|
||||
}
|
||||
|
||||
pub struct FacetGroupLazyValueCodec;
|
||||
|
||||
impl<'a> heed::BytesDecode<'a> for FacetGroupLazyValueCodec {
|
||||
type DItem = FacetGroupLazyValue<'a>;
|
||||
|
||||
fn bytes_decode(bytes: &'a [u8]) -> Result<Self::DItem, BoxedError> {
|
||||
Ok(FacetGroupLazyValue { size: bytes[0], bitmap_bytes: &bytes[1..] })
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
use std::borrow::Cow;
|
||||
use std::io::{self, Cursor};
|
||||
use std::io;
|
||||
use std::mem::size_of;
|
||||
|
||||
use byteorder::{NativeEndian, ReadBytesExt, WriteBytesExt};
|
||||
@@ -57,24 +57,6 @@ impl CboRoaringBitmapCodec {
|
||||
}
|
||||
}
|
||||
|
||||
pub fn intersection_with_serialized(
|
||||
mut bytes: &[u8],
|
||||
other: &RoaringBitmap,
|
||||
) -> io::Result<RoaringBitmap> {
|
||||
// See above `deserialize_from` method for implementation details.
|
||||
if bytes.len() <= THRESHOLD * size_of::<u32>() {
|
||||
let mut bitmap = RoaringBitmap::new();
|
||||
while let Ok(integer) = bytes.read_u32::<NativeEndian>() {
|
||||
if other.contains(integer) {
|
||||
bitmap.insert(integer);
|
||||
}
|
||||
}
|
||||
Ok(bitmap)
|
||||
} else {
|
||||
other.intersection_with_serialized_unchecked(Cursor::new(bytes))
|
||||
}
|
||||
}
|
||||
|
||||
/// Merge serialized CboRoaringBitmaps in a buffer.
|
||||
///
|
||||
/// if the merged values length is under the threshold, values are directly
|
||||
@@ -213,7 +195,7 @@ mod tests {
|
||||
fn merge_cbo_roaring_bitmaps() {
|
||||
let mut buffer = Vec::new();
|
||||
|
||||
let small_data = [
|
||||
let small_data = vec![
|
||||
RoaringBitmap::from_sorted_iter(1..4).unwrap(),
|
||||
RoaringBitmap::from_sorted_iter(2..5).unwrap(),
|
||||
RoaringBitmap::from_sorted_iter(4..6).unwrap(),
|
||||
@@ -227,7 +209,7 @@ mod tests {
|
||||
let expected = RoaringBitmap::from_sorted_iter(1..6).unwrap();
|
||||
assert_eq!(bitmap, expected);
|
||||
|
||||
let medium_data = [
|
||||
let medium_data = vec![
|
||||
RoaringBitmap::from_sorted_iter(1..4).unwrap(),
|
||||
RoaringBitmap::from_sorted_iter(2..5).unwrap(),
|
||||
RoaringBitmap::from_sorted_iter(4..8).unwrap(),
|
||||
|
||||
@@ -23,7 +23,7 @@ use crate::heed_codec::{
|
||||
};
|
||||
use crate::order_by_map::OrderByMap;
|
||||
use crate::proximity::ProximityPrecision;
|
||||
use crate::vector::{Embedding, EmbeddingConfig};
|
||||
use crate::vector::EmbeddingConfig;
|
||||
use crate::{
|
||||
default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds,
|
||||
FacetDistribution, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldIdWordCountCodec,
|
||||
@@ -1595,22 +1595,6 @@ impl Index {
|
||||
.unwrap_or_default())
|
||||
}
|
||||
|
||||
pub fn arroy_readers<'a>(
|
||||
&'a self,
|
||||
rtxn: &'a RoTxn<'a>,
|
||||
embedder_id: u8,
|
||||
) -> impl Iterator<Item = Result<arroy::Reader<arroy::distances::Angular>>> + '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()
|
||||
})
|
||||
}
|
||||
|
||||
pub(crate) fn put_search_cutoff(&self, wtxn: &mut RwTxn<'_>, cutoff: u64) -> heed::Result<()> {
|
||||
self.main.remap_types::<Str, BEU64>().put(wtxn, main_key::SEARCH_CUTOFF, &cutoff)
|
||||
}
|
||||
@@ -1622,44 +1606,6 @@ impl Index {
|
||||
pub(crate) fn delete_search_cutoff(&self, wtxn: &mut RwTxn<'_>) -> heed::Result<bool> {
|
||||
self.main.remap_key_type::<Str>().delete(wtxn, main_key::SEARCH_CUTOFF)
|
||||
}
|
||||
|
||||
pub fn embeddings(
|
||||
&self,
|
||||
rtxn: &RoTxn<'_>,
|
||||
docid: DocumentId,
|
||||
) -> Result<BTreeMap<String, Vec<Embedding>>> {
|
||||
let mut res = BTreeMap::new();
|
||||
for row in self.embedder_category_id.iter(rtxn)? {
|
||||
let (embedder_name, embedder_id) = row?;
|
||||
let embedder_id = (embedder_id as u16) << 8;
|
||||
let mut embeddings = Vec::new();
|
||||
'vectors: for i in 0..=u8::MAX {
|
||||
let reader = arroy::Reader::open(rtxn, embedder_id | (i as u16), self.vector_arroy)
|
||||
.map(Some)
|
||||
.or_else(|e| match e {
|
||||
arroy::Error::MissingMetadata => Ok(None),
|
||||
e => Err(e),
|
||||
})
|
||||
.transpose();
|
||||
|
||||
let Some(reader) = reader else {
|
||||
break 'vectors;
|
||||
};
|
||||
|
||||
let embedding = reader?.item_vector(rtxn, docid)?;
|
||||
if let Some(embedding) = embedding {
|
||||
embeddings.push(embedding)
|
||||
} else {
|
||||
break 'vectors;
|
||||
}
|
||||
}
|
||||
|
||||
if !embeddings.is_empty() {
|
||||
res.insert(embedder_name.to_owned(), embeddings);
|
||||
}
|
||||
}
|
||||
Ok(res)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
|
||||
@@ -63,7 +63,6 @@ pub use self::heed_codec::{
|
||||
};
|
||||
pub use self::index::Index;
|
||||
pub use self::search::facet::{FacetValueHit, SearchForFacetValues};
|
||||
pub use self::search::similar::Similar;
|
||||
pub use self::search::{
|
||||
FacetDistribution, Filter, FormatOptions, MatchBounds, MatcherBuilder, MatchingWords, OrderBy,
|
||||
Search, SearchResult, SemanticSearch, TermsMatchingStrategy, DEFAULT_VALUES_PER_FACET,
|
||||
@@ -355,13 +354,43 @@ pub fn is_faceted(field: &str, faceted_fields: impl IntoIterator<Item = impl AsR
|
||||
/// assert!(!is_faceted_by("animaux.chien", "animaux.chie"));
|
||||
/// ```
|
||||
pub fn is_faceted_by(field: &str, facet: &str) -> bool {
|
||||
field.starts_with(facet) && field[facet.len()..].chars().next().map_or(true, |c| c == '.')
|
||||
field.starts_with(facet)
|
||||
&& field[facet.len()..].chars().next().map(|c| c == '.').unwrap_or(true)
|
||||
}
|
||||
|
||||
pub fn normalize_facet(original: &str) -> String {
|
||||
CompatibilityDecompositionNormalizer.normalize_str(original.trim()).to_lowercase()
|
||||
}
|
||||
|
||||
/// Represents either a vector or an array of multiple vectors.
|
||||
#[derive(serde::Serialize, serde::Deserialize, Debug)]
|
||||
#[serde(transparent)]
|
||||
pub struct VectorOrArrayOfVectors {
|
||||
#[serde(with = "either::serde_untagged_optional")]
|
||||
inner: Option<either::Either<Vec<f32>, Vec<Vec<f32>>>>,
|
||||
}
|
||||
|
||||
impl VectorOrArrayOfVectors {
|
||||
pub fn into_array_of_vectors(self) -> Option<Vec<Vec<f32>>> {
|
||||
match self.inner? {
|
||||
either::Either::Left(vector) => Some(vec![vector]),
|
||||
either::Either::Right(vectors) => Some(vectors),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Normalize a vector by dividing the dimensions by the length of it.
|
||||
pub fn normalize_vector(mut vector: Vec<f32>) -> Vec<f32> {
|
||||
let squared: f32 = vector.iter().map(|x| x * x).sum();
|
||||
let length = squared.sqrt();
|
||||
if length <= f32::EPSILON {
|
||||
vector
|
||||
} else {
|
||||
vector.iter_mut().for_each(|x| *x /= length);
|
||||
vector
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use serde_json::json;
|
||||
|
||||
@@ -38,7 +38,7 @@ where
|
||||
field_id,
|
||||
)?;
|
||||
|
||||
if let Some(first_bound) = get_first_facet_value::<BytesRefCodec, _>(rtxn, db, field_id)? {
|
||||
if let Some(first_bound) = get_first_facet_value::<BytesRefCodec>(rtxn, db, field_id)? {
|
||||
fd.iterate(candidates, highest_level, first_bound, usize::MAX)?;
|
||||
Ok(())
|
||||
} else {
|
||||
@@ -81,7 +81,7 @@ where
|
||||
field_id,
|
||||
)?;
|
||||
|
||||
if let Some(first_bound) = get_first_facet_value::<BytesRefCodec, _>(rtxn, db, field_id)? {
|
||||
if let Some(first_bound) = get_first_facet_value::<BytesRefCodec>(rtxn, db, field_id)? {
|
||||
// We first fill the heap with values from the highest level
|
||||
let starting_key =
|
||||
FacetGroupKey { field_id, level: highest_level, left_bound: first_bound };
|
||||
|
||||
@@ -4,11 +4,9 @@ use heed::BytesEncode;
|
||||
use roaring::RoaringBitmap;
|
||||
|
||||
use super::{get_first_facet_value, get_highest_level, get_last_facet_value};
|
||||
use crate::heed_codec::facet::{
|
||||
FacetGroupKey, FacetGroupKeyCodec, FacetGroupLazyValueCodec, FacetGroupValueCodec,
|
||||
};
|
||||
use crate::heed_codec::facet::{FacetGroupKey, FacetGroupKeyCodec, FacetGroupValueCodec};
|
||||
use crate::heed_codec::BytesRefCodec;
|
||||
use crate::{CboRoaringBitmapCodec, Result};
|
||||
use crate::Result;
|
||||
|
||||
/// Find all the document ids for which the given field contains a value contained within
|
||||
/// the two bounds.
|
||||
@@ -18,7 +16,6 @@ pub fn find_docids_of_facet_within_bounds<'t, BoundCodec>(
|
||||
field_id: u16,
|
||||
left: &'t Bound<<BoundCodec as BytesEncode<'t>>::EItem>,
|
||||
right: &'t Bound<<BoundCodec as BytesEncode<'t>>::EItem>,
|
||||
universe: Option<&RoaringBitmap>,
|
||||
docids: &mut RoaringBitmap,
|
||||
) -> Result<()>
|
||||
where
|
||||
@@ -49,15 +46,13 @@ where
|
||||
}
|
||||
Bound::Unbounded => Bound::Unbounded,
|
||||
};
|
||||
let db = db.remap_types::<FacetGroupKeyCodec<BytesRefCodec>, FacetGroupLazyValueCodec>();
|
||||
let mut f = FacetRangeSearch { rtxn, db, field_id, left, right, universe, docids };
|
||||
let db = db.remap_key_type::<FacetGroupKeyCodec<BytesRefCodec>>();
|
||||
let mut f = FacetRangeSearch { rtxn, db, field_id, left, right, docids };
|
||||
let highest_level = get_highest_level(rtxn, db, field_id)?;
|
||||
|
||||
if let Some(starting_left_bound) =
|
||||
get_first_facet_value::<BytesRefCodec, _>(rtxn, db, field_id)?
|
||||
{
|
||||
if let Some(starting_left_bound) = get_first_facet_value::<BytesRefCodec>(rtxn, db, field_id)? {
|
||||
let rightmost_bound =
|
||||
Bound::Included(get_last_facet_value::<BytesRefCodec, _>(rtxn, db, field_id)?.unwrap()); // will not fail because get_first_facet_value succeeded
|
||||
Bound::Included(get_last_facet_value::<BytesRefCodec>(rtxn, db, field_id)?.unwrap()); // will not fail because get_first_facet_value succeeded
|
||||
let group_size = usize::MAX;
|
||||
f.run(highest_level, starting_left_bound, rightmost_bound, group_size)?;
|
||||
Ok(())
|
||||
@@ -69,16 +64,12 @@ where
|
||||
/// Fetch the document ids that have a facet with a value between the two given bounds
|
||||
struct FacetRangeSearch<'t, 'b, 'bitmap> {
|
||||
rtxn: &'t heed::RoTxn<'t>,
|
||||
db: heed::Database<FacetGroupKeyCodec<BytesRefCodec>, FacetGroupLazyValueCodec>,
|
||||
db: heed::Database<FacetGroupKeyCodec<BytesRefCodec>, FacetGroupValueCodec>,
|
||||
field_id: u16,
|
||||
left: Bound<&'b [u8]>,
|
||||
right: Bound<&'b [u8]>,
|
||||
/// The subset of documents ids that are useful for this search.
|
||||
/// Great performance optimizations can be achieved by only fetching values matching this subset.
|
||||
universe: Option<&'bitmap RoaringBitmap>,
|
||||
docids: &'bitmap mut RoaringBitmap,
|
||||
}
|
||||
|
||||
impl<'t, 'b, 'bitmap> FacetRangeSearch<'t, 'b, 'bitmap> {
|
||||
fn run_level_0(&mut self, starting_left_bound: &'t [u8], group_size: usize) -> Result<()> {
|
||||
let left_key =
|
||||
@@ -113,13 +104,7 @@ impl<'t, 'b, 'bitmap> FacetRangeSearch<'t, 'b, 'bitmap> {
|
||||
}
|
||||
|
||||
if RangeBounds::<&[u8]>::contains(&(self.left, self.right), &key.left_bound) {
|
||||
*self.docids |= match self.universe {
|
||||
Some(universe) => CboRoaringBitmapCodec::intersection_with_serialized(
|
||||
value.bitmap_bytes,
|
||||
universe,
|
||||
)?,
|
||||
None => CboRoaringBitmapCodec::deserialize_from(value.bitmap_bytes)?,
|
||||
};
|
||||
*self.docids |= value.bitmap;
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
@@ -210,13 +195,7 @@ impl<'t, 'b, 'bitmap> FacetRangeSearch<'t, 'b, 'bitmap> {
|
||||
left_condition && right_condition
|
||||
};
|
||||
if should_take_whole_group {
|
||||
*self.docids |= match self.universe {
|
||||
Some(universe) => CboRoaringBitmapCodec::intersection_with_serialized(
|
||||
previous_value.bitmap_bytes,
|
||||
universe,
|
||||
)?,
|
||||
None => CboRoaringBitmapCodec::deserialize_from(previous_value.bitmap_bytes)?,
|
||||
};
|
||||
*self.docids |= &previous_value.bitmap;
|
||||
previous_key = next_key;
|
||||
previous_value = next_value;
|
||||
continue;
|
||||
@@ -312,13 +291,7 @@ impl<'t, 'b, 'bitmap> FacetRangeSearch<'t, 'b, 'bitmap> {
|
||||
left_condition && right_condition
|
||||
};
|
||||
if should_take_whole_group {
|
||||
*self.docids |= match self.universe {
|
||||
Some(universe) => CboRoaringBitmapCodec::intersection_with_serialized(
|
||||
previous_value.bitmap_bytes,
|
||||
universe,
|
||||
)?,
|
||||
None => CboRoaringBitmapCodec::deserialize_from(previous_value.bitmap_bytes)?,
|
||||
};
|
||||
*self.docids |= &previous_value.bitmap;
|
||||
} else {
|
||||
let level = level - 1;
|
||||
let starting_left_bound = previous_key.left_bound;
|
||||
@@ -392,7 +365,6 @@ mod tests {
|
||||
0,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -412,7 +384,6 @@ mod tests {
|
||||
0,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -447,7 +418,6 @@ mod tests {
|
||||
0,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -469,7 +439,6 @@ mod tests {
|
||||
0,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -505,7 +474,6 @@ mod tests {
|
||||
0,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -531,7 +499,6 @@ mod tests {
|
||||
0,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -570,7 +537,6 @@ mod tests {
|
||||
0,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -590,7 +556,6 @@ mod tests {
|
||||
0,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -606,7 +571,6 @@ mod tests {
|
||||
0,
|
||||
&Bound::Unbounded,
|
||||
&Bound::Unbounded,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -622,7 +586,6 @@ mod tests {
|
||||
1,
|
||||
&Bound::Unbounded,
|
||||
&Bound::Unbounded,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -658,7 +621,6 @@ mod tests {
|
||||
0,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
@@ -672,7 +634,6 @@ mod tests {
|
||||
1,
|
||||
&start,
|
||||
&end,
|
||||
None,
|
||||
&mut docids,
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
@@ -36,7 +36,7 @@ pub fn ascending_facet_sort<'t>(
|
||||
candidates: RoaringBitmap,
|
||||
) -> Result<impl Iterator<Item = Result<(RoaringBitmap, &'t [u8])>> + 't> {
|
||||
let highest_level = get_highest_level(rtxn, db, field_id)?;
|
||||
if let Some(first_bound) = get_first_facet_value::<BytesRefCodec, _>(rtxn, db, field_id)? {
|
||||
if let Some(first_bound) = get_first_facet_value::<BytesRefCodec>(rtxn, db, field_id)? {
|
||||
let first_key = FacetGroupKey { field_id, level: highest_level, left_bound: first_bound };
|
||||
let iter = db.range(rtxn, &(first_key..)).unwrap().take(usize::MAX);
|
||||
|
||||
|
||||
@@ -19,9 +19,9 @@ pub fn descending_facet_sort<'t>(
|
||||
candidates: RoaringBitmap,
|
||||
) -> Result<impl Iterator<Item = Result<(RoaringBitmap, &'t [u8])>> + 't> {
|
||||
let highest_level = get_highest_level(rtxn, db, field_id)?;
|
||||
if let Some(first_bound) = get_first_facet_value::<BytesRefCodec, _>(rtxn, db, field_id)? {
|
||||
if let Some(first_bound) = get_first_facet_value::<BytesRefCodec>(rtxn, db, field_id)? {
|
||||
let first_key = FacetGroupKey { field_id, level: highest_level, left_bound: first_bound };
|
||||
let last_bound = get_last_facet_value::<BytesRefCodec, _>(rtxn, db, field_id)?.unwrap();
|
||||
let last_bound = get_last_facet_value::<BytesRefCodec>(rtxn, db, field_id)?.unwrap();
|
||||
let last_key = FacetGroupKey { field_id, level: highest_level, left_bound: last_bound };
|
||||
let iter = db.rev_range(rtxn, &(first_key..=last_key))?.take(usize::MAX);
|
||||
Ok(itertools::Either::Left(DescendingFacetSort {
|
||||
|
||||
@@ -4,7 +4,7 @@ use std::ops::Bound::{self, Excluded, Included};
|
||||
|
||||
use either::Either;
|
||||
pub use filter_parser::{Condition, Error as FPError, FilterCondition, Token};
|
||||
use roaring::{MultiOps, RoaringBitmap};
|
||||
use roaring::RoaringBitmap;
|
||||
use serde_json::Value;
|
||||
|
||||
use super::facet_range_search;
|
||||
@@ -224,14 +224,14 @@ impl<'a> Filter<'a> {
|
||||
pub fn evaluate(&self, rtxn: &heed::RoTxn, index: &Index) -> Result<RoaringBitmap> {
|
||||
// to avoid doing this for each recursive call we're going to do it ONCE ahead of time
|
||||
let filterable_fields = index.filterable_fields(rtxn)?;
|
||||
self.inner_evaluate(rtxn, index, &filterable_fields, None)
|
||||
|
||||
self.inner_evaluate(rtxn, index, &filterable_fields)
|
||||
}
|
||||
|
||||
fn evaluate_operator(
|
||||
rtxn: &heed::RoTxn,
|
||||
index: &Index,
|
||||
field_id: FieldId,
|
||||
universe: Option<&RoaringBitmap>,
|
||||
operator: &Condition<'a>,
|
||||
) -> Result<RoaringBitmap> {
|
||||
let numbers_db = index.facet_id_f64_docids;
|
||||
@@ -291,22 +291,14 @@ impl<'a> Filter<'a> {
|
||||
}
|
||||
Condition::NotEqual(val) => {
|
||||
let operator = Condition::Equal(val.clone());
|
||||
let docids = Self::evaluate_operator(rtxn, index, field_id, None, &operator)?;
|
||||
let docids = Self::evaluate_operator(rtxn, index, field_id, &operator)?;
|
||||
let all_ids = index.documents_ids(rtxn)?;
|
||||
return Ok(all_ids - docids);
|
||||
}
|
||||
};
|
||||
|
||||
let mut output = RoaringBitmap::new();
|
||||
Self::explore_facet_number_levels(
|
||||
rtxn,
|
||||
numbers_db,
|
||||
field_id,
|
||||
left,
|
||||
right,
|
||||
universe,
|
||||
&mut output,
|
||||
)?;
|
||||
Self::explore_facet_number_levels(rtxn, numbers_db, field_id, left, right, &mut output)?;
|
||||
Ok(output)
|
||||
}
|
||||
|
||||
@@ -318,7 +310,6 @@ impl<'a> Filter<'a> {
|
||||
field_id: FieldId,
|
||||
left: Bound<f64>,
|
||||
right: Bound<f64>,
|
||||
universe: Option<&RoaringBitmap>,
|
||||
output: &mut RoaringBitmap,
|
||||
) -> Result<()> {
|
||||
match (left, right) {
|
||||
@@ -330,7 +321,7 @@ impl<'a> Filter<'a> {
|
||||
(_, _) => (),
|
||||
}
|
||||
facet_range_search::find_docids_of_facet_within_bounds::<OrderedF64Codec>(
|
||||
rtxn, db, field_id, &left, &right, universe, output,
|
||||
rtxn, db, field_id, &left, &right, output,
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
@@ -341,37 +332,31 @@ impl<'a> Filter<'a> {
|
||||
rtxn: &heed::RoTxn,
|
||||
index: &Index,
|
||||
filterable_fields: &HashSet<String>,
|
||||
universe: Option<&RoaringBitmap>,
|
||||
) -> Result<RoaringBitmap> {
|
||||
if universe.map_or(false, |u| u.is_empty()) {
|
||||
return Ok(RoaringBitmap::new());
|
||||
}
|
||||
|
||||
match &self.condition {
|
||||
FilterCondition::Not(f) => {
|
||||
let all_ids = index.documents_ids(rtxn)?;
|
||||
let selected = Self::inner_evaluate(
|
||||
&(f.as_ref().clone()).into(),
|
||||
rtxn,
|
||||
index,
|
||||
filterable_fields,
|
||||
universe,
|
||||
)?;
|
||||
match universe {
|
||||
Some(universe) => Ok(universe - selected),
|
||||
None => {
|
||||
let all_ids = index.documents_ids(rtxn)?;
|
||||
Ok(all_ids - selected)
|
||||
}
|
||||
}
|
||||
Ok(all_ids - selected)
|
||||
}
|
||||
FilterCondition::In { fid, els } => {
|
||||
if crate::is_faceted(fid.value(), filterable_fields) {
|
||||
let field_ids_map = index.fields_ids_map(rtxn)?;
|
||||
|
||||
if let Some(fid) = field_ids_map.id(fid.value()) {
|
||||
els.iter()
|
||||
.map(|el| Condition::Equal(el.clone()))
|
||||
.map(|op| Self::evaluate_operator(rtxn, index, fid, universe, &op))
|
||||
.union()
|
||||
let mut bitmap = RoaringBitmap::new();
|
||||
|
||||
for el in els {
|
||||
let op = Condition::Equal(el.clone());
|
||||
let el_bitmap = Self::evaluate_operator(rtxn, index, fid, &op)?;
|
||||
bitmap |= el_bitmap;
|
||||
}
|
||||
Ok(bitmap)
|
||||
} else {
|
||||
Ok(RoaringBitmap::new())
|
||||
}
|
||||
@@ -386,7 +371,7 @@ impl<'a> Filter<'a> {
|
||||
if crate::is_faceted(fid.value(), filterable_fields) {
|
||||
let field_ids_map = index.fields_ids_map(rtxn)?;
|
||||
if let Some(fid) = field_ids_map.id(fid.value()) {
|
||||
Self::evaluate_operator(rtxn, index, fid, universe, op)
|
||||
Self::evaluate_operator(rtxn, index, fid, op)
|
||||
} else {
|
||||
Ok(RoaringBitmap::new())
|
||||
}
|
||||
@@ -397,11 +382,14 @@ impl<'a> Filter<'a> {
|
||||
}))?
|
||||
}
|
||||
}
|
||||
FilterCondition::Or(subfilters) => subfilters
|
||||
.iter()
|
||||
.cloned()
|
||||
.map(|f| Self::inner_evaluate(&f.into(), rtxn, index, filterable_fields, universe))
|
||||
.union(),
|
||||
FilterCondition::Or(subfilters) => {
|
||||
let mut bitmap = RoaringBitmap::new();
|
||||
for f in subfilters {
|
||||
bitmap |=
|
||||
Self::inner_evaluate(&(f.clone()).into(), rtxn, index, filterable_fields)?;
|
||||
}
|
||||
Ok(bitmap)
|
||||
}
|
||||
FilterCondition::And(subfilters) => {
|
||||
let mut subfilters_iter = subfilters.iter();
|
||||
if let Some(first_subfilter) = subfilters_iter.next() {
|
||||
@@ -410,21 +398,16 @@ impl<'a> Filter<'a> {
|
||||
rtxn,
|
||||
index,
|
||||
filterable_fields,
|
||||
universe,
|
||||
)?;
|
||||
for f in subfilters_iter {
|
||||
if bitmap.is_empty() {
|
||||
return Ok(bitmap);
|
||||
}
|
||||
// TODO We are doing the intersections two times,
|
||||
// it could be more efficient
|
||||
// Can't I just replace this `&=` by an `=`?
|
||||
bitmap &= Self::inner_evaluate(
|
||||
&(f.clone()).into(),
|
||||
rtxn,
|
||||
index,
|
||||
filterable_fields,
|
||||
Some(&bitmap),
|
||||
)?;
|
||||
}
|
||||
Ok(bitmap)
|
||||
@@ -524,7 +507,6 @@ impl<'a> Filter<'a> {
|
||||
rtxn,
|
||||
index,
|
||||
filterable_fields,
|
||||
universe,
|
||||
)?;
|
||||
|
||||
let geo_lng_token = Token::new(
|
||||
@@ -557,7 +539,6 @@ impl<'a> Filter<'a> {
|
||||
rtxn,
|
||||
index,
|
||||
filterable_fields,
|
||||
universe,
|
||||
)?;
|
||||
|
||||
let condition_right = FilterCondition::Condition {
|
||||
@@ -571,7 +552,6 @@ impl<'a> Filter<'a> {
|
||||
rtxn,
|
||||
index,
|
||||
filterable_fields,
|
||||
universe,
|
||||
)?;
|
||||
|
||||
left | right
|
||||
@@ -587,7 +567,6 @@ impl<'a> Filter<'a> {
|
||||
rtxn,
|
||||
index,
|
||||
filterable_fields,
|
||||
universe,
|
||||
)?
|
||||
};
|
||||
|
||||
|
||||
@@ -7,7 +7,7 @@ use roaring::RoaringBitmap;
|
||||
pub use self::facet_distribution::{FacetDistribution, OrderBy, DEFAULT_VALUES_PER_FACET};
|
||||
pub use self::filter::{BadGeoError, Filter};
|
||||
pub use self::search::{FacetValueHit, SearchForFacetValues};
|
||||
use crate::heed_codec::facet::{FacetGroupKeyCodec, OrderedF64Codec};
|
||||
use crate::heed_codec::facet::{FacetGroupKeyCodec, FacetGroupValueCodec, OrderedF64Codec};
|
||||
use crate::heed_codec::BytesRefCodec;
|
||||
use crate::{Index, Result};
|
||||
|
||||
@@ -54,9 +54,9 @@ pub fn facet_max_value<'t>(
|
||||
}
|
||||
|
||||
/// Get the first facet value in the facet database
|
||||
pub(crate) fn get_first_facet_value<'t, BoundCodec, DC>(
|
||||
pub(crate) fn get_first_facet_value<'t, BoundCodec>(
|
||||
txn: &'t RoTxn,
|
||||
db: heed::Database<FacetGroupKeyCodec<BytesRefCodec>, DC>,
|
||||
db: heed::Database<FacetGroupKeyCodec<BytesRefCodec>, FacetGroupValueCodec>,
|
||||
field_id: u16,
|
||||
) -> heed::Result<Option<BoundCodec::DItem>>
|
||||
where
|
||||
@@ -78,9 +78,9 @@ where
|
||||
}
|
||||
|
||||
/// Get the last facet value in the facet database
|
||||
pub(crate) fn get_last_facet_value<'t, BoundCodec, DC>(
|
||||
pub(crate) fn get_last_facet_value<'t, BoundCodec>(
|
||||
txn: &'t RoTxn,
|
||||
db: heed::Database<FacetGroupKeyCodec<BytesRefCodec>, DC>,
|
||||
db: heed::Database<FacetGroupKeyCodec<BytesRefCodec>, FacetGroupValueCodec>,
|
||||
field_id: u16,
|
||||
) -> heed::Result<Option<BoundCodec::DItem>>
|
||||
where
|
||||
@@ -102,9 +102,9 @@ where
|
||||
}
|
||||
|
||||
/// Get the height of the highest level in the facet database
|
||||
pub(crate) fn get_highest_level<'t, DC>(
|
||||
pub(crate) fn get_highest_level<'t>(
|
||||
txn: &'t RoTxn<'t>,
|
||||
db: heed::Database<FacetGroupKeyCodec<BytesRefCodec>, DC>,
|
||||
db: heed::Database<FacetGroupKeyCodec<BytesRefCodec>, FacetGroupValueCodec>,
|
||||
field_id: u16,
|
||||
) -> heed::Result<u8> {
|
||||
let field_id_prefix = &field_id.to_be_bytes();
|
||||
|
||||
@@ -169,7 +169,6 @@ impl<'a> Search<'a> {
|
||||
index: self.index,
|
||||
semantic: self.semantic.clone(),
|
||||
time_budget: self.time_budget.clone(),
|
||||
ranking_score_threshold: self.ranking_score_threshold,
|
||||
};
|
||||
|
||||
let semantic = search.semantic.take();
|
||||
|
||||
@@ -24,7 +24,6 @@ pub mod facet;
|
||||
mod fst_utils;
|
||||
pub mod hybrid;
|
||||
pub mod new;
|
||||
pub mod similar;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct SemanticSearch {
|
||||
@@ -50,7 +49,6 @@ pub struct Search<'a> {
|
||||
index: &'a Index,
|
||||
semantic: Option<SemanticSearch>,
|
||||
time_budget: TimeBudget,
|
||||
ranking_score_threshold: Option<f64>,
|
||||
}
|
||||
|
||||
impl<'a> Search<'a> {
|
||||
@@ -71,7 +69,6 @@ impl<'a> Search<'a> {
|
||||
index,
|
||||
semantic: None,
|
||||
time_budget: TimeBudget::max(),
|
||||
ranking_score_threshold: None,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -148,15 +145,10 @@ impl<'a> Search<'a> {
|
||||
self
|
||||
}
|
||||
|
||||
pub fn ranking_score_threshold(&mut self, ranking_score_threshold: f64) -> &mut Search<'a> {
|
||||
self.ranking_score_threshold = Some(ranking_score_threshold);
|
||||
self
|
||||
}
|
||||
|
||||
pub fn execute_for_candidates(&self, has_vector_search: bool) -> Result<RoaringBitmap> {
|
||||
if has_vector_search {
|
||||
let ctx = SearchContext::new(self.index, self.rtxn)?;
|
||||
filtered_universe(ctx.index, ctx.txn, &self.filter)
|
||||
filtered_universe(&ctx, &self.filter)
|
||||
} else {
|
||||
Ok(self.execute()?.candidates)
|
||||
}
|
||||
@@ -169,7 +161,7 @@ impl<'a> Search<'a> {
|
||||
ctx.attributes_to_search_on(searchable_attributes)?;
|
||||
}
|
||||
|
||||
let universe = filtered_universe(ctx.index, ctx.txn, &self.filter)?;
|
||||
let universe = filtered_universe(&ctx, &self.filter)?;
|
||||
let PartialSearchResult {
|
||||
located_query_terms,
|
||||
candidates,
|
||||
@@ -191,7 +183,6 @@ impl<'a> Search<'a> {
|
||||
embedder_name,
|
||||
embedder,
|
||||
self.time_budget.clone(),
|
||||
self.ranking_score_threshold,
|
||||
)?
|
||||
}
|
||||
_ => execute_search(
|
||||
@@ -209,7 +200,6 @@ impl<'a> Search<'a> {
|
||||
&mut DefaultSearchLogger,
|
||||
&mut DefaultSearchLogger,
|
||||
self.time_budget.clone(),
|
||||
self.ranking_score_threshold,
|
||||
)?,
|
||||
};
|
||||
|
||||
@@ -248,7 +238,6 @@ impl fmt::Debug for Search<'_> {
|
||||
index: _,
|
||||
semantic,
|
||||
time_budget,
|
||||
ranking_score_threshold,
|
||||
} = self;
|
||||
f.debug_struct("Search")
|
||||
.field("query", query)
|
||||
@@ -267,7 +256,6 @@ impl fmt::Debug for Search<'_> {
|
||||
&semantic.as_ref().map(|semantic| &semantic.embedder_name),
|
||||
)
|
||||
.field("time_budget", time_budget)
|
||||
.field("ranking_score_threshold", ranking_score_threshold)
|
||||
.finish()
|
||||
}
|
||||
}
|
||||
@@ -288,8 +276,6 @@ pub enum TermsMatchingStrategy {
|
||||
Last,
|
||||
// all words are mandatory
|
||||
All,
|
||||
// remove more frequent word first
|
||||
Frequency,
|
||||
}
|
||||
|
||||
impl Default for TermsMatchingStrategy {
|
||||
|
||||
@@ -28,7 +28,6 @@ pub fn bucket_sort<'ctx, Q: RankingRuleQueryTrait>(
|
||||
scoring_strategy: ScoringStrategy,
|
||||
logger: &mut dyn SearchLogger<Q>,
|
||||
time_budget: TimeBudget,
|
||||
ranking_score_threshold: Option<f64>,
|
||||
) -> Result<BucketSortOutput> {
|
||||
logger.initial_query(query);
|
||||
logger.ranking_rules(&ranking_rules);
|
||||
@@ -165,19 +164,7 @@ pub fn bucket_sort<'ctx, Q: RankingRuleQueryTrait>(
|
||||
loop {
|
||||
let bucket = std::mem::take(&mut ranking_rule_universes[cur_ranking_rule_index]);
|
||||
ranking_rule_scores.push(ScoreDetails::Skipped);
|
||||
|
||||
// remove candidates from the universe without adding them to result if their score is below the threshold
|
||||
if let Some(ranking_score_threshold) = ranking_score_threshold {
|
||||
let current_score = ScoreDetails::global_score(ranking_rule_scores.iter());
|
||||
if current_score < ranking_score_threshold {
|
||||
all_candidates -= bucket | &ranking_rule_universes[cur_ranking_rule_index];
|
||||
back!();
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
maybe_add_to_results!(bucket);
|
||||
|
||||
ranking_rule_scores.pop();
|
||||
|
||||
if cur_ranking_rule_index == 0 {
|
||||
@@ -233,18 +220,6 @@ pub fn bucket_sort<'ctx, Q: RankingRuleQueryTrait>(
|
||||
debug_assert!(
|
||||
ranking_rule_universes[cur_ranking_rule_index].is_superset(&next_bucket.candidates)
|
||||
);
|
||||
|
||||
// remove candidates from the universe without adding them to result if their score is below the threshold
|
||||
if let Some(ranking_score_threshold) = ranking_score_threshold {
|
||||
let current_score = ScoreDetails::global_score(ranking_rule_scores.iter());
|
||||
if current_score < ranking_score_threshold {
|
||||
all_candidates -=
|
||||
next_bucket.candidates | &ranking_rule_universes[cur_ranking_rule_index];
|
||||
back!();
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
ranking_rule_universes[cur_ranking_rule_index] -= &next_bucket.candidates;
|
||||
|
||||
if cur_ranking_rule_index == ranking_rules_len - 1
|
||||
|
||||
@@ -164,21 +164,6 @@ impl<'ctx, G: RankingRuleGraphTrait> RankingRule<'ctx, QueryGraph> for GraphBase
|
||||
}
|
||||
costs
|
||||
}
|
||||
TermsMatchingStrategy::Frequency => {
|
||||
let removal_order =
|
||||
query_graph.removal_order_for_terms_matching_strategy_frequency(ctx)?;
|
||||
let mut forbidden_nodes =
|
||||
SmallBitmap::for_interned_values_in(&query_graph.nodes);
|
||||
let mut costs = query_graph.nodes.map(|_| None);
|
||||
// FIXME: this works because only words uses termsmatchingstrategy at the moment.
|
||||
for ns in removal_order {
|
||||
for n in ns.iter() {
|
||||
*costs.get_mut(n) = Some((1, forbidden_nodes.clone()));
|
||||
}
|
||||
forbidden_nodes.union(&ns);
|
||||
}
|
||||
costs
|
||||
}
|
||||
TermsMatchingStrategy::All => query_graph.nodes.map(|_| None),
|
||||
}
|
||||
} else {
|
||||
|
||||
@@ -507,7 +507,7 @@ mod tests {
|
||||
impl<'a> MatcherBuilder<'a> {
|
||||
fn new_test(rtxn: &'a heed::RoTxn, index: &'a TempIndex, query: &str) -> Self {
|
||||
let mut ctx = SearchContext::new(index, rtxn).unwrap();
|
||||
let universe = filtered_universe(ctx.index, ctx.txn, &None).unwrap();
|
||||
let universe = filtered_universe(&ctx, &None).unwrap();
|
||||
let crate::search::PartialSearchResult { located_query_terms, .. } = execute_search(
|
||||
&mut ctx,
|
||||
Some(query),
|
||||
@@ -523,7 +523,6 @@ mod tests {
|
||||
&mut crate::DefaultSearchLogger,
|
||||
&mut crate::DefaultSearchLogger,
|
||||
TimeBudget::max(),
|
||||
None,
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
|
||||
@@ -197,11 +197,6 @@ fn resolve_maximally_reduced_query_graph(
|
||||
.iter()
|
||||
.flat_map(|x| x.iter())
|
||||
.collect(),
|
||||
TermsMatchingStrategy::Frequency => query_graph
|
||||
.removal_order_for_terms_matching_strategy_frequency(ctx)?
|
||||
.iter()
|
||||
.flat_map(|x| x.iter())
|
||||
.collect(),
|
||||
TermsMatchingStrategy::All => vec![],
|
||||
};
|
||||
graph.remove_nodes_keep_edges(&nodes_to_remove);
|
||||
@@ -548,16 +543,11 @@ fn resolve_sort_criteria<'ctx, Query: RankingRuleQueryTrait>(
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[tracing::instrument(level = "trace", skip_all, target = "search")]
|
||||
pub fn filtered_universe(
|
||||
index: &Index,
|
||||
txn: &RoTxn<'_>,
|
||||
filters: &Option<Filter>,
|
||||
) -> Result<RoaringBitmap> {
|
||||
pub fn filtered_universe(ctx: &SearchContext, filters: &Option<Filter>) -> Result<RoaringBitmap> {
|
||||
Ok(if let Some(filters) = filters {
|
||||
filters.evaluate(txn, index)?
|
||||
filters.evaluate(ctx.txn, ctx.index)?
|
||||
} else {
|
||||
index.documents_ids(txn)?
|
||||
ctx.index.documents_ids(ctx.txn)?
|
||||
})
|
||||
}
|
||||
|
||||
@@ -574,7 +564,6 @@ pub fn execute_vector_search(
|
||||
embedder_name: &str,
|
||||
embedder: &Embedder,
|
||||
time_budget: TimeBudget,
|
||||
ranking_score_threshold: Option<f64>,
|
||||
) -> Result<PartialSearchResult> {
|
||||
check_sort_criteria(ctx, sort_criteria.as_ref())?;
|
||||
|
||||
@@ -604,7 +593,6 @@ pub fn execute_vector_search(
|
||||
scoring_strategy,
|
||||
placeholder_search_logger,
|
||||
time_budget,
|
||||
ranking_score_threshold,
|
||||
)?;
|
||||
|
||||
Ok(PartialSearchResult {
|
||||
@@ -634,7 +622,6 @@ pub fn execute_search(
|
||||
placeholder_search_logger: &mut dyn SearchLogger<PlaceholderQuery>,
|
||||
query_graph_logger: &mut dyn SearchLogger<QueryGraph>,
|
||||
time_budget: TimeBudget,
|
||||
ranking_score_threshold: Option<f64>,
|
||||
) -> Result<PartialSearchResult> {
|
||||
check_sort_criteria(ctx, sort_criteria.as_ref())?;
|
||||
|
||||
@@ -723,7 +710,6 @@ pub fn execute_search(
|
||||
scoring_strategy,
|
||||
query_graph_logger,
|
||||
time_budget,
|
||||
ranking_score_threshold,
|
||||
)?
|
||||
} else {
|
||||
let ranking_rules =
|
||||
@@ -738,7 +724,6 @@ pub fn execute_search(
|
||||
scoring_strategy,
|
||||
placeholder_search_logger,
|
||||
time_budget,
|
||||
ranking_score_threshold,
|
||||
)?
|
||||
};
|
||||
|
||||
|
||||
@@ -1,9 +1,8 @@
|
||||
use std::cmp::{Ordering, Reverse};
|
||||
use std::cmp::Ordering;
|
||||
use std::collections::BTreeMap;
|
||||
use std::hash::{Hash, Hasher};
|
||||
|
||||
use fxhash::{FxHashMap, FxHasher};
|
||||
use roaring::RoaringBitmap;
|
||||
|
||||
use super::interner::{FixedSizeInterner, Interned};
|
||||
use super::query_term::{
|
||||
@@ -12,7 +11,6 @@ use super::query_term::{
|
||||
use super::small_bitmap::SmallBitmap;
|
||||
use super::SearchContext;
|
||||
use crate::search::new::interner::Interner;
|
||||
use crate::search::new::resolve_query_graph::compute_query_term_subset_docids;
|
||||
use crate::Result;
|
||||
|
||||
/// A node of the [`QueryGraph`].
|
||||
@@ -292,49 +290,6 @@ impl QueryGraph {
|
||||
}
|
||||
}
|
||||
|
||||
pub fn removal_order_for_terms_matching_strategy_frequency(
|
||||
&self,
|
||||
ctx: &mut SearchContext,
|
||||
) -> Result<Vec<SmallBitmap<QueryNode>>> {
|
||||
// lookup frequency for each term
|
||||
let mut term_with_frequency: Vec<(u8, u64)> = {
|
||||
let mut term_docids: BTreeMap<u8, RoaringBitmap> = Default::default();
|
||||
for (_, node) in self.nodes.iter() {
|
||||
match &node.data {
|
||||
QueryNodeData::Term(t) => {
|
||||
let docids = compute_query_term_subset_docids(ctx, &t.term_subset)?;
|
||||
for id in t.term_ids.clone() {
|
||||
term_docids
|
||||
.entry(id)
|
||||
.and_modify(|curr| *curr |= &docids)
|
||||
.or_insert_with(|| docids.clone());
|
||||
}
|
||||
}
|
||||
QueryNodeData::Deleted | QueryNodeData::Start | QueryNodeData::End => continue,
|
||||
}
|
||||
}
|
||||
term_docids
|
||||
.into_iter()
|
||||
.map(|(idx, docids)| match docids.len() {
|
||||
0 => (idx, u64::max_value()),
|
||||
frequency => (idx, frequency),
|
||||
})
|
||||
.collect()
|
||||
};
|
||||
term_with_frequency.sort_by_key(|(_, frequency)| Reverse(*frequency));
|
||||
let mut term_weight = BTreeMap::new();
|
||||
let mut weight: u16 = 1;
|
||||
let mut peekable = term_with_frequency.into_iter().peekable();
|
||||
while let Some((idx, frequency)) = peekable.next() {
|
||||
term_weight.insert(idx, weight);
|
||||
if peekable.peek().map_or(false, |(_, f)| frequency != *f) {
|
||||
weight += 1;
|
||||
}
|
||||
}
|
||||
let cost_of_term_idx = move |term_idx: u8| *term_weight.get(&term_idx).unwrap();
|
||||
Ok(self.removal_order_for_terms_matching_strategy(ctx, cost_of_term_idx))
|
||||
}
|
||||
|
||||
pub fn removal_order_for_terms_matching_strategy_last(
|
||||
&self,
|
||||
ctx: &SearchContext,
|
||||
@@ -360,19 +315,10 @@ impl QueryGraph {
|
||||
if first_term_idx >= last_term_idx {
|
||||
return vec![];
|
||||
}
|
||||
|
||||
let cost_of_term_idx = |term_idx: u8| {
|
||||
let rank = 1 + last_term_idx - term_idx;
|
||||
rank as u16
|
||||
};
|
||||
self.removal_order_for_terms_matching_strategy(ctx, cost_of_term_idx)
|
||||
}
|
||||
|
||||
pub fn removal_order_for_terms_matching_strategy(
|
||||
&self,
|
||||
ctx: &SearchContext,
|
||||
order: impl Fn(u8) -> u16,
|
||||
) -> Vec<SmallBitmap<QueryNode>> {
|
||||
let mut nodes_to_remove = BTreeMap::<u16, SmallBitmap<QueryNode>>::new();
|
||||
let mut at_least_one_mandatory_term = false;
|
||||
for (node_id, node) in self.nodes.iter() {
|
||||
@@ -383,7 +329,7 @@ impl QueryGraph {
|
||||
}
|
||||
let mut cost = 0;
|
||||
for id in t.term_ids.clone() {
|
||||
cost = std::cmp::max(cost, order(id));
|
||||
cost = std::cmp::max(cost, cost_of_term_idx(id));
|
||||
}
|
||||
nodes_to_remove
|
||||
.entry(cost)
|
||||
|
||||
@@ -49,8 +49,19 @@ 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 writer_index = (self.embedder_index as u16) << 8;
|
||||
let readers: std::result::Result<Vec<_>, _> = (0..=u8::MAX)
|
||||
.map_while(|k| {
|
||||
arroy::Reader::open(ctx.txn, writer_index | (k as u16), ctx.index.vector_arroy)
|
||||
.map(Some)
|
||||
.or_else(|e| match e {
|
||||
arroy::Error::MissingMetadata => Ok(None),
|
||||
e => Err(e),
|
||||
})
|
||||
.transpose()
|
||||
})
|
||||
.collect();
|
||||
|
||||
let readers = readers?;
|
||||
|
||||
let target = &self.target;
|
||||
|
||||
@@ -1,146 +0,0 @@
|
||||
use std::sync::Arc;
|
||||
|
||||
use ordered_float::OrderedFloat;
|
||||
use roaring::RoaringBitmap;
|
||||
|
||||
use crate::score_details::{self, ScoreDetails};
|
||||
use crate::vector::Embedder;
|
||||
use crate::{filtered_universe, DocumentId, Filter, Index, Result, SearchResult};
|
||||
|
||||
pub struct Similar<'a> {
|
||||
id: DocumentId,
|
||||
// this should be linked to the String in the query
|
||||
filter: Option<Filter<'a>>,
|
||||
offset: usize,
|
||||
limit: usize,
|
||||
rtxn: &'a heed::RoTxn<'a>,
|
||||
index: &'a Index,
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
ranking_score_threshold: Option<f64>,
|
||||
}
|
||||
|
||||
impl<'a> Similar<'a> {
|
||||
pub fn new(
|
||||
id: DocumentId,
|
||||
offset: usize,
|
||||
limit: usize,
|
||||
index: &'a Index,
|
||||
rtxn: &'a heed::RoTxn<'a>,
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
) -> Self {
|
||||
Self {
|
||||
id,
|
||||
filter: None,
|
||||
offset,
|
||||
limit,
|
||||
rtxn,
|
||||
index,
|
||||
embedder_name,
|
||||
embedder,
|
||||
ranking_score_threshold: None,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn filter(&mut self, filter: Filter<'a>) -> &mut Self {
|
||||
self.filter = Some(filter);
|
||||
self
|
||||
}
|
||||
|
||||
pub fn ranking_score_threshold(&mut self, ranking_score_threshold: f64) -> &mut Self {
|
||||
self.ranking_score_threshold = Some(ranking_score_threshold);
|
||||
self
|
||||
}
|
||||
|
||||
pub fn execute(&self) -> Result<SearchResult> {
|
||||
let mut universe = filtered_universe(self.index, self.rtxn, &self.filter)?;
|
||||
|
||||
// we never want to receive the docid
|
||||
universe.remove(self.id);
|
||||
|
||||
let universe = universe;
|
||||
|
||||
let embedder_index =
|
||||
self.index
|
||||
.embedder_category_id
|
||||
.get(self.rtxn, &self.embedder_name)?
|
||||
.ok_or_else(|| crate::UserError::InvalidEmbedder(self.embedder_name.to_owned()))?;
|
||||
|
||||
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(
|
||||
self.rtxn,
|
||||
self.id,
|
||||
self.limit + self.offset + 1,
|
||||
None,
|
||||
Some(&universe),
|
||||
)?;
|
||||
if let Some(mut nns_by_item) = nns_by_item {
|
||||
results.append(&mut nns_by_item);
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));
|
||||
|
||||
let mut documents_ids = Vec::with_capacity(self.limit);
|
||||
let mut document_scores = Vec::with_capacity(self.limit);
|
||||
// list of documents we've already seen, so that we don't return the same document multiple times.
|
||||
// initialized to the target document, that we never want to return.
|
||||
let mut documents_seen = RoaringBitmap::new();
|
||||
documents_seen.insert(self.id);
|
||||
|
||||
let mut candidates = universe;
|
||||
|
||||
for (docid, distance) in results
|
||||
.into_iter()
|
||||
// skip documents we've already seen & mark that we saw the current document
|
||||
.filter(|(docid, _)| documents_seen.insert(*docid))
|
||||
.skip(self.offset)
|
||||
// take **after** filter and skip so that we get exactly limit elements if available
|
||||
.take(self.limit)
|
||||
{
|
||||
let score = 1.0 - distance;
|
||||
let score = self
|
||||
.embedder
|
||||
.distribution()
|
||||
.map(|distribution| distribution.shift(score))
|
||||
.unwrap_or(score);
|
||||
|
||||
let score_details =
|
||||
vec![ScoreDetails::Vector(score_details::Vector { similarity: Some(score) })];
|
||||
|
||||
let score = ScoreDetails::global_score(score_details.iter());
|
||||
|
||||
if let Some(ranking_score_threshold) = &self.ranking_score_threshold {
|
||||
if score < *ranking_score_threshold {
|
||||
// this document is no longer a candidate
|
||||
candidates.remove(docid);
|
||||
// any document after this one is no longer a candidate either, so restrict the set to documents already seen.
|
||||
candidates &= documents_seen;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
documents_ids.push(docid);
|
||||
document_scores.push(score_details);
|
||||
}
|
||||
|
||||
Ok(SearchResult {
|
||||
matching_words: Default::default(),
|
||||
candidates,
|
||||
documents_ids,
|
||||
document_scores,
|
||||
degraded: false,
|
||||
used_negative_operator: false,
|
||||
})
|
||||
}
|
||||
}
|
||||
@@ -21,6 +21,8 @@ impl<'t, 'i> ClearDocuments<'t, 'i> {
|
||||
name = "clear_documents"
|
||||
)]
|
||||
pub fn execute(self) -> Result<u64> {
|
||||
puffin::profile_function!();
|
||||
|
||||
self.index.set_updated_at(self.wtxn, &OffsetDateTime::now_utc())?;
|
||||
let Index {
|
||||
env: _env,
|
||||
|
||||
@@ -40,26 +40,11 @@ pub fn into_del_add_obkv<K: obkv::Key + PartialOrd>(
|
||||
operation: DelAddOperation,
|
||||
buffer: &mut Vec<u8>,
|
||||
) -> Result<(), std::io::Error> {
|
||||
into_del_add_obkv_conditional_operation(reader, buffer, |_| operation)
|
||||
}
|
||||
|
||||
/// Akin to the [into_del_add_obkv] function but lets you
|
||||
/// conditionally define the `DelAdd` variant based on the obkv key.
|
||||
pub fn into_del_add_obkv_conditional_operation<K, F>(
|
||||
reader: obkv::KvReader<K>,
|
||||
buffer: &mut Vec<u8>,
|
||||
operation: F,
|
||||
) -> std::io::Result<()>
|
||||
where
|
||||
K: obkv::Key + PartialOrd,
|
||||
F: Fn(K) -> DelAddOperation,
|
||||
{
|
||||
let mut writer = obkv::KvWriter::new(buffer);
|
||||
let mut value_buffer = Vec::new();
|
||||
for (key, value) in reader.iter() {
|
||||
value_buffer.clear();
|
||||
let mut value_writer = KvWriterDelAdd::new(&mut value_buffer);
|
||||
let operation = operation(key);
|
||||
if matches!(operation, DelAddOperation::Deletion | DelAddOperation::DeletionAndAddition) {
|
||||
value_writer.insert(DelAdd::Deletion, value)?;
|
||||
}
|
||||
|
||||
@@ -29,6 +29,8 @@ pub fn enrich_documents_batch<R: Read + Seek>(
|
||||
autogenerate_docids: bool,
|
||||
reader: DocumentsBatchReader<R>,
|
||||
) -> Result<StdResult<EnrichedDocumentsBatchReader<R>, UserError>> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let (mut cursor, mut documents_batch_index) = reader.into_cursor_and_fields_index();
|
||||
|
||||
let mut external_ids = tempfile::tempfile().map(BufWriter::new).map(grenad::Writer::new)?;
|
||||
|
||||
@@ -29,6 +29,8 @@ pub fn extract_docid_word_positions<R: io::Read + io::Seek>(
|
||||
settings_diff: &InnerIndexSettingsDiff,
|
||||
max_positions_per_attributes: Option<u32>,
|
||||
) -> Result<(grenad::Reader<BufReader<File>>, ScriptLanguageDocidsMap)> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let max_positions_per_attributes = max_positions_per_attributes
|
||||
.map_or(MAX_POSITION_PER_ATTRIBUTE, |max| max.min(MAX_POSITION_PER_ATTRIBUTE));
|
||||
let max_memory = indexer.max_memory_by_thread();
|
||||
|
||||
@@ -23,6 +23,8 @@ pub fn extract_facet_number_docids<R: io::Read + io::Seek>(
|
||||
indexer: GrenadParameters,
|
||||
_settings_diff: &InnerIndexSettingsDiff,
|
||||
) -> Result<grenad::Reader<BufReader<File>>> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let max_memory = indexer.max_memory_by_thread();
|
||||
|
||||
let mut facet_number_docids_sorter = create_sorter(
|
||||
|
||||
@@ -28,6 +28,8 @@ pub fn extract_facet_string_docids<R: io::Read + io::Seek>(
|
||||
indexer: GrenadParameters,
|
||||
_settings_diff: &InnerIndexSettingsDiff,
|
||||
) -> Result<(grenad::Reader<BufReader<File>>, grenad::Reader<BufReader<File>>)> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let max_memory = indexer.max_memory_by_thread();
|
||||
let options = NormalizerOption { lossy: true, ..Default::default() };
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
use std::borrow::Cow;
|
||||
use std::collections::{BTreeMap, BTreeSet};
|
||||
use std::collections::BTreeMap;
|
||||
use std::convert::TryInto;
|
||||
use std::fs::File;
|
||||
use std::io::{self, BufReader};
|
||||
@@ -9,7 +9,7 @@ use std::result::Result as StdResult;
|
||||
use bytemuck::bytes_of;
|
||||
use grenad::Sorter;
|
||||
use heed::BytesEncode;
|
||||
use itertools::{merge_join_by, EitherOrBoth};
|
||||
use itertools::EitherOrBoth;
|
||||
use ordered_float::OrderedFloat;
|
||||
use roaring::RoaringBitmap;
|
||||
use serde_json::{from_slice, Value};
|
||||
@@ -18,7 +18,7 @@ use FilterableValues::{Empty, Null, Values};
|
||||
use super::helpers::{create_sorter, keep_first, sorter_into_reader, GrenadParameters};
|
||||
use crate::error::InternalError;
|
||||
use crate::facet::value_encoding::f64_into_bytes;
|
||||
use crate::update::del_add::{DelAdd, KvReaderDelAdd, KvWriterDelAdd};
|
||||
use crate::update::del_add::{DelAdd, KvWriterDelAdd};
|
||||
use crate::update::index_documents::{create_writer, writer_into_reader};
|
||||
use crate::update::settings::InnerIndexSettingsDiff;
|
||||
use crate::{CboRoaringBitmapCodec, DocumentId, Error, FieldId, Result, MAX_FACET_VALUE_LENGTH};
|
||||
@@ -45,7 +45,10 @@ pub fn extract_fid_docid_facet_values<R: io::Read + io::Seek>(
|
||||
obkv_documents: grenad::Reader<R>,
|
||||
indexer: GrenadParameters,
|
||||
settings_diff: &InnerIndexSettingsDiff,
|
||||
geo_fields_ids: Option<(FieldId, FieldId)>,
|
||||
) -> Result<ExtractedFacetValues> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let max_memory = indexer.max_memory_by_thread();
|
||||
|
||||
let mut fid_docid_facet_numbers_sorter = create_sorter(
|
||||
@@ -75,181 +78,143 @@ pub fn extract_fid_docid_facet_values<R: io::Read + io::Seek>(
|
||||
let mut numbers_key_buffer = Vec::new();
|
||||
let mut strings_key_buffer = Vec::new();
|
||||
|
||||
let old_faceted_fids: BTreeSet<_> =
|
||||
settings_diff.old.faceted_fields_ids.iter().copied().collect();
|
||||
let new_faceted_fids: BTreeSet<_> =
|
||||
settings_diff.new.faceted_fields_ids.iter().copied().collect();
|
||||
let mut cursor = obkv_documents.into_cursor()?;
|
||||
while let Some((docid_bytes, value)) = cursor.move_on_next()? {
|
||||
let obkv = obkv::KvReader::new(value);
|
||||
|
||||
if !settings_diff.settings_update_only || old_faceted_fids != new_faceted_fids {
|
||||
let mut cursor = obkv_documents.into_cursor()?;
|
||||
while let Some((docid_bytes, value)) = cursor.move_on_next()? {
|
||||
let obkv = obkv::KvReader::new(value);
|
||||
let get_document_json_value = move |field_id, side| {
|
||||
obkv.get(field_id)
|
||||
.map(KvReaderDelAdd::new)
|
||||
.and_then(|kv| kv.get(side))
|
||||
.map(from_slice)
|
||||
.transpose()
|
||||
.map_err(InternalError::SerdeJson)
|
||||
};
|
||||
// iterate over the faceted fields instead of over the whole document.
|
||||
for eob in
|
||||
merge_join_by(old_faceted_fids.iter(), new_faceted_fids.iter(), |old, new| {
|
||||
old.cmp(new)
|
||||
})
|
||||
{
|
||||
let (field_id, del_value, add_value) = match eob {
|
||||
EitherOrBoth::Left(&field_id) => {
|
||||
let del_value = get_document_json_value(field_id, DelAdd::Deletion)?;
|
||||
for (field_id, field_bytes) in obkv.iter() {
|
||||
let delete_faceted = settings_diff.old.faceted_fields_ids.contains(&field_id);
|
||||
let add_faceted = settings_diff.new.faceted_fields_ids.contains(&field_id);
|
||||
if delete_faceted || add_faceted {
|
||||
numbers_key_buffer.clear();
|
||||
strings_key_buffer.clear();
|
||||
|
||||
// deletion only
|
||||
(field_id, del_value, None)
|
||||
}
|
||||
EitherOrBoth::Right(&field_id) => {
|
||||
let add_value = get_document_json_value(field_id, DelAdd::Addition)?;
|
||||
// Set key to the field_id
|
||||
// Note: this encoding is consistent with FieldIdCodec
|
||||
numbers_key_buffer.extend_from_slice(&field_id.to_be_bytes());
|
||||
strings_key_buffer.extend_from_slice(&field_id.to_be_bytes());
|
||||
|
||||
// addition only
|
||||
(field_id, None, add_value)
|
||||
}
|
||||
EitherOrBoth::Both(&field_id, _) => {
|
||||
// during settings update, recompute the changing settings only.
|
||||
if settings_diff.settings_update_only {
|
||||
continue;
|
||||
}
|
||||
let document: [u8; 4] = docid_bytes[..4].try_into().ok().unwrap();
|
||||
let document = DocumentId::from_be_bytes(document);
|
||||
|
||||
let del_value = get_document_json_value(field_id, DelAdd::Deletion)?;
|
||||
let add_value = get_document_json_value(field_id, DelAdd::Addition)?;
|
||||
// For the other extraction tasks, prefix the key with the field_id and the document_id
|
||||
numbers_key_buffer.extend_from_slice(docid_bytes);
|
||||
strings_key_buffer.extend_from_slice(docid_bytes);
|
||||
|
||||
(field_id, del_value, add_value)
|
||||
}
|
||||
let del_add_obkv = obkv::KvReader::new(field_bytes);
|
||||
let del_value = match del_add_obkv.get(DelAdd::Deletion).filter(|_| delete_faceted)
|
||||
{
|
||||
Some(bytes) => Some(from_slice(bytes).map_err(InternalError::SerdeJson)?),
|
||||
None => None,
|
||||
};
|
||||
let add_value = match del_add_obkv.get(DelAdd::Addition).filter(|_| add_faceted) {
|
||||
Some(bytes) => Some(from_slice(bytes).map_err(InternalError::SerdeJson)?),
|
||||
None => None,
|
||||
};
|
||||
|
||||
if del_value.is_some() || add_value.is_some() {
|
||||
numbers_key_buffer.clear();
|
||||
strings_key_buffer.clear();
|
||||
// We insert the document id on the Del and the Add side if the field exists.
|
||||
let (ref mut del_exists, ref mut add_exists) =
|
||||
facet_exists_docids.entry(field_id).or_default();
|
||||
let (ref mut del_is_null, ref mut add_is_null) =
|
||||
facet_is_null_docids.entry(field_id).or_default();
|
||||
let (ref mut del_is_empty, ref mut add_is_empty) =
|
||||
facet_is_empty_docids.entry(field_id).or_default();
|
||||
|
||||
// Set key to the field_id
|
||||
// Note: this encoding is consistent with FieldIdCodec
|
||||
numbers_key_buffer.extend_from_slice(&field_id.to_be_bytes());
|
||||
strings_key_buffer.extend_from_slice(&field_id.to_be_bytes());
|
||||
if del_value.is_some() {
|
||||
del_exists.insert(document);
|
||||
}
|
||||
if add_value.is_some() {
|
||||
add_exists.insert(document);
|
||||
}
|
||||
|
||||
let document: [u8; 4] = docid_bytes[..4].try_into().ok().unwrap();
|
||||
let document = DocumentId::from_be_bytes(document);
|
||||
let geo_support =
|
||||
geo_fields_ids.map_or(false, |(lat, lng)| field_id == lat || field_id == lng);
|
||||
let del_filterable_values =
|
||||
del_value.map(|value| extract_facet_values(&value, geo_support));
|
||||
let add_filterable_values =
|
||||
add_value.map(|value| extract_facet_values(&value, geo_support));
|
||||
|
||||
// For the other extraction tasks, prefix the key with the field_id and the document_id
|
||||
numbers_key_buffer.extend_from_slice(docid_bytes);
|
||||
strings_key_buffer.extend_from_slice(docid_bytes);
|
||||
// Those closures are just here to simplify things a bit.
|
||||
let mut insert_numbers_diff = |del_numbers, add_numbers| {
|
||||
insert_numbers_diff(
|
||||
&mut fid_docid_facet_numbers_sorter,
|
||||
&mut numbers_key_buffer,
|
||||
del_numbers,
|
||||
add_numbers,
|
||||
)
|
||||
};
|
||||
let mut insert_strings_diff = |del_strings, add_strings| {
|
||||
insert_strings_diff(
|
||||
&mut fid_docid_facet_strings_sorter,
|
||||
&mut strings_key_buffer,
|
||||
del_strings,
|
||||
add_strings,
|
||||
)
|
||||
};
|
||||
|
||||
// We insert the document id on the Del and the Add side if the field exists.
|
||||
let (ref mut del_exists, ref mut add_exists) =
|
||||
facet_exists_docids.entry(field_id).or_default();
|
||||
let (ref mut del_is_null, ref mut add_is_null) =
|
||||
facet_is_null_docids.entry(field_id).or_default();
|
||||
let (ref mut del_is_empty, ref mut add_is_empty) =
|
||||
facet_is_empty_docids.entry(field_id).or_default();
|
||||
|
||||
if del_value.is_some() {
|
||||
del_exists.insert(document);
|
||||
}
|
||||
if add_value.is_some() {
|
||||
add_exists.insert(document);
|
||||
}
|
||||
|
||||
let del_geo_support = settings_diff
|
||||
.old
|
||||
.geo_fields_ids
|
||||
.map_or(false, |(lat, lng)| field_id == lat || field_id == lng);
|
||||
let add_geo_support = settings_diff
|
||||
.new
|
||||
.geo_fields_ids
|
||||
.map_or(false, |(lat, lng)| field_id == lat || field_id == lng);
|
||||
let del_filterable_values =
|
||||
del_value.map(|value| extract_facet_values(&value, del_geo_support));
|
||||
let add_filterable_values =
|
||||
add_value.map(|value| extract_facet_values(&value, add_geo_support));
|
||||
|
||||
// Those closures are just here to simplify things a bit.
|
||||
let mut insert_numbers_diff = |del_numbers, add_numbers| {
|
||||
insert_numbers_diff(
|
||||
&mut fid_docid_facet_numbers_sorter,
|
||||
&mut numbers_key_buffer,
|
||||
del_numbers,
|
||||
add_numbers,
|
||||
)
|
||||
};
|
||||
let mut insert_strings_diff = |del_strings, add_strings| {
|
||||
insert_strings_diff(
|
||||
&mut fid_docid_facet_strings_sorter,
|
||||
&mut strings_key_buffer,
|
||||
del_strings,
|
||||
add_strings,
|
||||
)
|
||||
};
|
||||
|
||||
match (del_filterable_values, add_filterable_values) {
|
||||
(None, None) => (),
|
||||
(Some(del_filterable_values), None) => match del_filterable_values {
|
||||
Null => {
|
||||
match (del_filterable_values, add_filterable_values) {
|
||||
(None, None) => (),
|
||||
(Some(del_filterable_values), None) => match del_filterable_values {
|
||||
Null => {
|
||||
del_is_null.insert(document);
|
||||
}
|
||||
Empty => {
|
||||
del_is_empty.insert(document);
|
||||
}
|
||||
Values { numbers, strings } => {
|
||||
insert_numbers_diff(numbers, vec![])?;
|
||||
insert_strings_diff(strings, vec![])?;
|
||||
}
|
||||
},
|
||||
(None, Some(add_filterable_values)) => match add_filterable_values {
|
||||
Null => {
|
||||
add_is_null.insert(document);
|
||||
}
|
||||
Empty => {
|
||||
add_is_empty.insert(document);
|
||||
}
|
||||
Values { numbers, strings } => {
|
||||
insert_numbers_diff(vec![], numbers)?;
|
||||
insert_strings_diff(vec![], strings)?;
|
||||
}
|
||||
},
|
||||
(Some(del_filterable_values), Some(add_filterable_values)) => {
|
||||
match (del_filterable_values, add_filterable_values) {
|
||||
(Null, Null) | (Empty, Empty) => (),
|
||||
(Null, Empty) => {
|
||||
del_is_null.insert(document);
|
||||
add_is_empty.insert(document);
|
||||
}
|
||||
(Empty, Null) => {
|
||||
del_is_empty.insert(document);
|
||||
add_is_null.insert(document);
|
||||
}
|
||||
(Null, Values { numbers, strings }) => {
|
||||
insert_numbers_diff(vec![], numbers)?;
|
||||
insert_strings_diff(vec![], strings)?;
|
||||
del_is_null.insert(document);
|
||||
}
|
||||
Empty => {
|
||||
(Empty, Values { numbers, strings }) => {
|
||||
insert_numbers_diff(vec![], numbers)?;
|
||||
insert_strings_diff(vec![], strings)?;
|
||||
del_is_empty.insert(document);
|
||||
}
|
||||
Values { numbers, strings } => {
|
||||
(Values { numbers, strings }, Null) => {
|
||||
add_is_null.insert(document);
|
||||
insert_numbers_diff(numbers, vec![])?;
|
||||
insert_strings_diff(strings, vec![])?;
|
||||
}
|
||||
},
|
||||
(None, Some(add_filterable_values)) => match add_filterable_values {
|
||||
Null => {
|
||||
add_is_null.insert(document);
|
||||
}
|
||||
Empty => {
|
||||
(Values { numbers, strings }, Empty) => {
|
||||
add_is_empty.insert(document);
|
||||
insert_numbers_diff(numbers, vec![])?;
|
||||
insert_strings_diff(strings, vec![])?;
|
||||
}
|
||||
Values { numbers, strings } => {
|
||||
insert_numbers_diff(vec![], numbers)?;
|
||||
insert_strings_diff(vec![], strings)?;
|
||||
}
|
||||
},
|
||||
(Some(del_filterable_values), Some(add_filterable_values)) => {
|
||||
match (del_filterable_values, add_filterable_values) {
|
||||
(Null, Null) | (Empty, Empty) => (),
|
||||
(Null, Empty) => {
|
||||
del_is_null.insert(document);
|
||||
add_is_empty.insert(document);
|
||||
}
|
||||
(Empty, Null) => {
|
||||
del_is_empty.insert(document);
|
||||
add_is_null.insert(document);
|
||||
}
|
||||
(Null, Values { numbers, strings }) => {
|
||||
insert_numbers_diff(vec![], numbers)?;
|
||||
insert_strings_diff(vec![], strings)?;
|
||||
del_is_null.insert(document);
|
||||
}
|
||||
(Empty, Values { numbers, strings }) => {
|
||||
insert_numbers_diff(vec![], numbers)?;
|
||||
insert_strings_diff(vec![], strings)?;
|
||||
del_is_empty.insert(document);
|
||||
}
|
||||
(Values { numbers, strings }, Null) => {
|
||||
add_is_null.insert(document);
|
||||
insert_numbers_diff(numbers, vec![])?;
|
||||
insert_strings_diff(strings, vec![])?;
|
||||
}
|
||||
(Values { numbers, strings }, Empty) => {
|
||||
add_is_empty.insert(document);
|
||||
insert_numbers_diff(numbers, vec![])?;
|
||||
insert_strings_diff(strings, vec![])?;
|
||||
}
|
||||
(
|
||||
Values { numbers: del_numbers, strings: del_strings },
|
||||
Values { numbers: add_numbers, strings: add_strings },
|
||||
) => {
|
||||
insert_numbers_diff(del_numbers, add_numbers)?;
|
||||
insert_strings_diff(del_strings, add_strings)?;
|
||||
}
|
||||
(
|
||||
Values { numbers: del_numbers, strings: del_strings },
|
||||
Values { numbers: add_numbers, strings: add_strings },
|
||||
) => {
|
||||
insert_numbers_diff(del_numbers, add_numbers)?;
|
||||
insert_strings_diff(del_strings, add_strings)?;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -26,6 +26,8 @@ pub fn extract_fid_word_count_docids<R: io::Read + io::Seek>(
|
||||
indexer: GrenadParameters,
|
||||
_settings_diff: &InnerIndexSettingsDiff,
|
||||
) -> Result<grenad::Reader<BufReader<File>>> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let max_memory = indexer.max_memory_by_thread();
|
||||
|
||||
let mut fid_word_count_docids_sorter = create_sorter(
|
||||
|
||||
@@ -8,7 +8,6 @@ use super::helpers::{create_writer, writer_into_reader, GrenadParameters};
|
||||
use crate::error::GeoError;
|
||||
use crate::update::del_add::{DelAdd, KvReaderDelAdd, KvWriterDelAdd};
|
||||
use crate::update::index_documents::extract_finite_float_from_value;
|
||||
use crate::update::settings::{InnerIndexSettings, InnerIndexSettingsDiff};
|
||||
use crate::{FieldId, InternalError, Result};
|
||||
|
||||
/// Extracts the geographical coordinates contained in each document under the `_geo` field.
|
||||
@@ -19,8 +18,10 @@ pub fn extract_geo_points<R: io::Read + io::Seek>(
|
||||
obkv_documents: grenad::Reader<R>,
|
||||
indexer: GrenadParameters,
|
||||
primary_key_id: FieldId,
|
||||
settings_diff: &InnerIndexSettingsDiff,
|
||||
(lat_fid, lng_fid): (FieldId, FieldId),
|
||||
) -> Result<grenad::Reader<BufReader<File>>> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let mut writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
@@ -39,27 +40,47 @@ pub fn extract_geo_points<R: io::Read + io::Seek>(
|
||||
serde_json::from_slice(document_id).unwrap()
|
||||
};
|
||||
|
||||
// extract old version
|
||||
let del_lat_lng =
|
||||
extract_lat_lng(&obkv, &settings_diff.old, DelAdd::Deletion, document_id)?;
|
||||
// extract new version
|
||||
let add_lat_lng =
|
||||
extract_lat_lng(&obkv, &settings_diff.new, DelAdd::Addition, document_id)?;
|
||||
// first we get the two fields
|
||||
match (obkv.get(lat_fid), obkv.get(lng_fid)) {
|
||||
(Some(lat), Some(lng)) => {
|
||||
let deladd_lat_obkv = KvReaderDelAdd::new(lat);
|
||||
let deladd_lng_obkv = KvReaderDelAdd::new(lng);
|
||||
|
||||
if del_lat_lng != add_lat_lng {
|
||||
let mut obkv = KvWriterDelAdd::memory();
|
||||
if let Some([lat, lng]) = del_lat_lng {
|
||||
#[allow(clippy::drop_non_drop)]
|
||||
let bytes: [u8; 16] = concat_arrays![lat.to_ne_bytes(), lng.to_ne_bytes()];
|
||||
obkv.insert(DelAdd::Deletion, bytes)?;
|
||||
// then we extract the values
|
||||
let del_lat_lng = deladd_lat_obkv
|
||||
.get(DelAdd::Deletion)
|
||||
.zip(deladd_lng_obkv.get(DelAdd::Deletion))
|
||||
.map(|(lat, lng)| extract_lat_lng(lat, lng, document_id))
|
||||
.transpose()?;
|
||||
let add_lat_lng = deladd_lat_obkv
|
||||
.get(DelAdd::Addition)
|
||||
.zip(deladd_lng_obkv.get(DelAdd::Addition))
|
||||
.map(|(lat, lng)| extract_lat_lng(lat, lng, document_id))
|
||||
.transpose()?;
|
||||
|
||||
if del_lat_lng != add_lat_lng {
|
||||
let mut obkv = KvWriterDelAdd::memory();
|
||||
if let Some([lat, lng]) = del_lat_lng {
|
||||
#[allow(clippy::drop_non_drop)]
|
||||
let bytes: [u8; 16] = concat_arrays![lat.to_ne_bytes(), lng.to_ne_bytes()];
|
||||
obkv.insert(DelAdd::Deletion, bytes)?;
|
||||
}
|
||||
if let Some([lat, lng]) = add_lat_lng {
|
||||
#[allow(clippy::drop_non_drop)]
|
||||
let bytes: [u8; 16] = concat_arrays![lat.to_ne_bytes(), lng.to_ne_bytes()];
|
||||
obkv.insert(DelAdd::Addition, bytes)?;
|
||||
}
|
||||
let bytes = obkv.into_inner()?;
|
||||
writer.insert(docid_bytes, bytes)?;
|
||||
}
|
||||
}
|
||||
if let Some([lat, lng]) = add_lat_lng {
|
||||
#[allow(clippy::drop_non_drop)]
|
||||
let bytes: [u8; 16] = concat_arrays![lat.to_ne_bytes(), lng.to_ne_bytes()];
|
||||
obkv.insert(DelAdd::Addition, bytes)?;
|
||||
(None, Some(_)) => {
|
||||
return Err(GeoError::MissingLatitude { document_id: document_id() }.into())
|
||||
}
|
||||
let bytes = obkv.into_inner()?;
|
||||
writer.insert(docid_bytes, bytes)?;
|
||||
(Some(_), None) => {
|
||||
return Err(GeoError::MissingLongitude { document_id: document_id() }.into())
|
||||
}
|
||||
(None, None) => (),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -67,37 +88,16 @@ pub fn extract_geo_points<R: io::Read + io::Seek>(
|
||||
}
|
||||
|
||||
/// Extract the finite floats lat and lng from two bytes slices.
|
||||
fn extract_lat_lng(
|
||||
document: &obkv::KvReader<FieldId>,
|
||||
settings: &InnerIndexSettings,
|
||||
deladd: DelAdd,
|
||||
document_id: impl Fn() -> Value,
|
||||
) -> Result<Option<[f64; 2]>> {
|
||||
match settings.geo_fields_ids {
|
||||
Some((lat_fid, lng_fid)) => {
|
||||
let lat = document.get(lat_fid).map(KvReaderDelAdd::new).and_then(|r| r.get(deladd));
|
||||
let lng = document.get(lng_fid).map(KvReaderDelAdd::new).and_then(|r| r.get(deladd));
|
||||
let (lat, lng) = match (lat, lng) {
|
||||
(Some(lat), Some(lng)) => (lat, lng),
|
||||
(Some(_), None) => {
|
||||
return Err(GeoError::MissingLatitude { document_id: document_id() }.into())
|
||||
}
|
||||
(None, Some(_)) => {
|
||||
return Err(GeoError::MissingLongitude { document_id: document_id() }.into())
|
||||
}
|
||||
(None, None) => return Ok(None),
|
||||
};
|
||||
let lat = extract_finite_float_from_value(
|
||||
serde_json::from_slice(lat).map_err(InternalError::SerdeJson)?,
|
||||
)
|
||||
.map_err(|lat| GeoError::BadLatitude { document_id: document_id(), value: lat })?;
|
||||
fn extract_lat_lng(lat: &[u8], lng: &[u8], document_id: impl Fn() -> Value) -> Result<[f64; 2]> {
|
||||
let lat = extract_finite_float_from_value(
|
||||
serde_json::from_slice(lat).map_err(InternalError::SerdeJson)?,
|
||||
)
|
||||
.map_err(|lat| GeoError::BadLatitude { document_id: document_id(), value: lat })?;
|
||||
|
||||
let lng = extract_finite_float_from_value(
|
||||
serde_json::from_slice(lng).map_err(InternalError::SerdeJson)?,
|
||||
)
|
||||
.map_err(|lng| GeoError::BadLongitude { document_id: document_id(), value: lng })?;
|
||||
Ok(Some([lat, lng]))
|
||||
}
|
||||
None => Ok(None),
|
||||
}
|
||||
let lng = extract_finite_float_from_value(
|
||||
serde_json::from_slice(lng).map_err(InternalError::SerdeJson)?,
|
||||
)
|
||||
.map_err(|lng| GeoError::BadLongitude { document_id: document_id(), value: lng })?;
|
||||
|
||||
Ok([lat, lng])
|
||||
}
|
||||
|
||||
@@ -10,16 +10,16 @@ use bytemuck::cast_slice;
|
||||
use grenad::Writer;
|
||||
use itertools::EitherOrBoth;
|
||||
use ordered_float::OrderedFloat;
|
||||
use serde_json::Value;
|
||||
use serde_json::{from_slice, Value};
|
||||
|
||||
use super::helpers::{create_writer, writer_into_reader, GrenadParameters};
|
||||
use crate::error::UserError;
|
||||
use crate::prompt::Prompt;
|
||||
use crate::update::del_add::{DelAdd, KvReaderDelAdd, KvWriterDelAdd};
|
||||
use crate::update::index_documents::helpers::try_split_at;
|
||||
use crate::update::settings::InnerIndexSettingsDiff;
|
||||
use crate::vector::parsed_vectors::{ParsedVectorsDiff, RESERVED_VECTORS_FIELD_NAME};
|
||||
use crate::vector::Embedder;
|
||||
use crate::{DocumentId, Result, ThreadPoolNoAbort};
|
||||
use crate::{DocumentId, InternalError, Result, ThreadPoolNoAbort, VectorOrArrayOfVectors};
|
||||
|
||||
/// The length of the elements that are always in the buffer when inserting new values.
|
||||
const TRUNCATE_SIZE: usize = size_of::<DocumentId>();
|
||||
@@ -31,10 +31,6 @@ pub struct ExtractedVectorPoints {
|
||||
pub remove_vectors: grenad::Reader<BufReader<File>>,
|
||||
// docid -> prompt
|
||||
pub prompts: grenad::Reader<BufReader<File>>,
|
||||
|
||||
// embedder
|
||||
pub embedder_name: String,
|
||||
pub embedder: Arc<Embedder>,
|
||||
}
|
||||
|
||||
enum VectorStateDelta {
|
||||
@@ -69,19 +65,6 @@ impl VectorStateDelta {
|
||||
}
|
||||
}
|
||||
|
||||
struct EmbedderVectorExtractor {
|
||||
embedder_name: String,
|
||||
embedder: Arc<Embedder>,
|
||||
prompt: Arc<Prompt>,
|
||||
|
||||
// (docid, _index) -> KvWriterDelAdd -> Vector
|
||||
manual_vectors_writer: Writer<BufWriter<File>>,
|
||||
// (docid) -> (prompt)
|
||||
prompts_writer: Writer<BufWriter<File>>,
|
||||
// (docid) -> ()
|
||||
remove_vectors_writer: Writer<BufWriter<File>>,
|
||||
}
|
||||
|
||||
/// Extracts the embedding vector contained in each document under the `_vectors` field.
|
||||
///
|
||||
/// Returns the generated grenad reader containing the docid as key associated to the Vec<f32>
|
||||
@@ -90,52 +73,34 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
|
||||
obkv_documents: grenad::Reader<R>,
|
||||
indexer: GrenadParameters,
|
||||
settings_diff: &InnerIndexSettingsDiff,
|
||||
) -> Result<Vec<ExtractedVectorPoints>> {
|
||||
let reindex_vectors = settings_diff.reindex_vectors();
|
||||
prompt: &Prompt,
|
||||
embedder_name: &str,
|
||||
) -> Result<ExtractedVectorPoints> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let old_fields_ids_map = &settings_diff.old.fields_ids_map;
|
||||
let new_fields_ids_map = &settings_diff.new.fields_ids_map;
|
||||
// the vector field id may have changed
|
||||
let old_vectors_fid = old_fields_ids_map.id(RESERVED_VECTORS_FIELD_NAME);
|
||||
// filter the old vector fid if the settings has been changed forcing reindexing.
|
||||
let old_vectors_fid = old_vectors_fid.filter(|_| !reindex_vectors);
|
||||
|
||||
let new_vectors_fid = new_fields_ids_map.id(RESERVED_VECTORS_FIELD_NAME);
|
||||
// (docid, _index) -> KvWriterDelAdd -> Vector
|
||||
let mut manual_vectors_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
let mut extractors = Vec::new();
|
||||
for (embedder_name, (embedder, prompt)) in
|
||||
settings_diff.new.embedding_configs.clone().into_iter()
|
||||
{
|
||||
// (docid, _index) -> KvWriterDelAdd -> Vector
|
||||
let manual_vectors_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
// (docid) -> (prompt)
|
||||
let mut prompts_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
// (docid) -> (prompt)
|
||||
let prompts_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
// (docid) -> ()
|
||||
let remove_vectors_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
extractors.push(EmbedderVectorExtractor {
|
||||
embedder_name,
|
||||
embedder,
|
||||
prompt,
|
||||
manual_vectors_writer,
|
||||
prompts_writer,
|
||||
remove_vectors_writer,
|
||||
});
|
||||
}
|
||||
// (docid) -> ()
|
||||
let mut remove_vectors_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
let mut key_buffer = Vec::new();
|
||||
let mut cursor = obkv_documents.into_cursor()?;
|
||||
@@ -149,138 +114,152 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
|
||||
key_buffer.clear();
|
||||
key_buffer.extend_from_slice(docid_bytes);
|
||||
|
||||
// since we only need the primary key when we throw an error we create this getter to
|
||||
// since we only needs the primary key when we throw an error we create this getter to
|
||||
// lazily get it when needed
|
||||
let document_id = || -> Value { from_utf8(external_id_bytes).unwrap().into() };
|
||||
|
||||
let mut parsed_vectors = ParsedVectorsDiff::new(obkv, old_vectors_fid, new_vectors_fid)
|
||||
.map_err(|error| error.to_crate_error(document_id().to_string()))?;
|
||||
// the vector field id may have changed
|
||||
let old_vectors_fid = old_fields_ids_map.id("_vectors");
|
||||
// filter the old vector fid if the settings has been changed forcing reindexing.
|
||||
let old_vectors_fid = old_vectors_fid.filter(|_| !settings_diff.reindex_vectors());
|
||||
|
||||
for EmbedderVectorExtractor {
|
||||
embedder_name,
|
||||
embedder: _,
|
||||
prompt,
|
||||
manual_vectors_writer,
|
||||
prompts_writer,
|
||||
remove_vectors_writer,
|
||||
} in extractors.iter_mut()
|
||||
{
|
||||
let delta = match parsed_vectors.remove(embedder_name) {
|
||||
(Some(old), Some(new)) => {
|
||||
// no autogeneration
|
||||
let del_vectors = old.into_array_of_vectors();
|
||||
let add_vectors = new.into_array_of_vectors();
|
||||
let new_vectors_fid = new_fields_ids_map.id("_vectors");
|
||||
let vectors_field = {
|
||||
let del = old_vectors_fid
|
||||
.and_then(|vectors_fid| obkv.get(vectors_fid))
|
||||
.map(KvReaderDelAdd::new)
|
||||
.map(|obkv| to_vector_map(obkv, DelAdd::Deletion, &document_id))
|
||||
.transpose()?
|
||||
.flatten();
|
||||
let add = new_vectors_fid
|
||||
.and_then(|vectors_fid| obkv.get(vectors_fid))
|
||||
.map(KvReaderDelAdd::new)
|
||||
.map(|obkv| to_vector_map(obkv, DelAdd::Addition, &document_id))
|
||||
.transpose()?
|
||||
.flatten();
|
||||
(del, add)
|
||||
};
|
||||
|
||||
if add_vectors.len() > usize::from(u8::MAX) {
|
||||
return Err(crate::Error::UserError(crate::UserError::TooManyVectors(
|
||||
document_id().to_string(),
|
||||
add_vectors.len(),
|
||||
)));
|
||||
}
|
||||
let (del_map, add_map) = vectors_field;
|
||||
|
||||
VectorStateDelta::ManualDelta(del_vectors, add_vectors)
|
||||
let del_value = del_map.and_then(|mut map| map.remove(embedder_name));
|
||||
let add_value = add_map.and_then(|mut map| map.remove(embedder_name));
|
||||
|
||||
let delta = match (del_value, add_value) {
|
||||
(Some(old), Some(new)) => {
|
||||
// no autogeneration
|
||||
let del_vectors = extract_vectors(old, document_id, embedder_name)?;
|
||||
let add_vectors = extract_vectors(new, document_id, embedder_name)?;
|
||||
|
||||
if add_vectors.len() > usize::from(u8::MAX) {
|
||||
return Err(crate::Error::UserError(crate::UserError::TooManyVectors(
|
||||
document_id().to_string(),
|
||||
add_vectors.len(),
|
||||
)));
|
||||
}
|
||||
(Some(_old), None) => {
|
||||
// Do we keep this document?
|
||||
let document_is_kept = obkv
|
||||
.iter()
|
||||
.map(|(_, deladd)| KvReaderDelAdd::new(deladd))
|
||||
.any(|deladd| deladd.get(DelAdd::Addition).is_some());
|
||||
if document_is_kept {
|
||||
// becomes autogenerated
|
||||
VectorStateDelta::NowGenerated(prompt.render(
|
||||
obkv,
|
||||
DelAdd::Addition,
|
||||
new_fields_ids_map,
|
||||
)?)
|
||||
|
||||
VectorStateDelta::ManualDelta(del_vectors, add_vectors)
|
||||
}
|
||||
(Some(_old), None) => {
|
||||
// Do we keep this document?
|
||||
let document_is_kept = obkv
|
||||
.iter()
|
||||
.map(|(_, deladd)| KvReaderDelAdd::new(deladd))
|
||||
.any(|deladd| deladd.get(DelAdd::Addition).is_some());
|
||||
if document_is_kept {
|
||||
// becomes autogenerated
|
||||
VectorStateDelta::NowGenerated(prompt.render(
|
||||
obkv,
|
||||
DelAdd::Addition,
|
||||
new_fields_ids_map,
|
||||
)?)
|
||||
} else {
|
||||
VectorStateDelta::NowRemoved
|
||||
}
|
||||
}
|
||||
(None, Some(new)) => {
|
||||
// was possibly autogenerated, remove all vectors for that document
|
||||
let add_vectors = extract_vectors(new, document_id, embedder_name)?;
|
||||
if add_vectors.len() > usize::from(u8::MAX) {
|
||||
return Err(crate::Error::UserError(crate::UserError::TooManyVectors(
|
||||
document_id().to_string(),
|
||||
add_vectors.len(),
|
||||
)));
|
||||
}
|
||||
|
||||
VectorStateDelta::WasGeneratedNowManual(add_vectors)
|
||||
}
|
||||
(None, None) => {
|
||||
// Do we keep this document?
|
||||
let document_is_kept = obkv
|
||||
.iter()
|
||||
.map(|(_, deladd)| KvReaderDelAdd::new(deladd))
|
||||
.any(|deladd| deladd.get(DelAdd::Addition).is_some());
|
||||
|
||||
if document_is_kept {
|
||||
// Don't give up if the old prompt was failing
|
||||
let old_prompt = Some(prompt)
|
||||
// TODO: this filter works because we erase the vec database when a embedding setting changes.
|
||||
// When vector pipeline will be optimized, this should be removed.
|
||||
.filter(|_| !settings_diff.reindex_vectors())
|
||||
.map(|p| {
|
||||
p.render(obkv, DelAdd::Deletion, old_fields_ids_map).unwrap_or_default()
|
||||
});
|
||||
let new_prompt = prompt.render(obkv, DelAdd::Addition, new_fields_ids_map)?;
|
||||
if old_prompt.as_ref() != Some(&new_prompt) {
|
||||
let old_prompt = old_prompt.unwrap_or_default();
|
||||
tracing::trace!(
|
||||
"🚀 Changing prompt from\n{old_prompt}\n===to===\n{new_prompt}"
|
||||
);
|
||||
VectorStateDelta::NowGenerated(new_prompt)
|
||||
} else {
|
||||
VectorStateDelta::NowRemoved
|
||||
tracing::trace!("⏭️ Prompt unmodified, skipping");
|
||||
VectorStateDelta::NoChange
|
||||
}
|
||||
} else {
|
||||
VectorStateDelta::NowRemoved
|
||||
}
|
||||
(None, Some(new)) => {
|
||||
// was possibly autogenerated, remove all vectors for that document
|
||||
let add_vectors = new.into_array_of_vectors();
|
||||
if add_vectors.len() > usize::from(u8::MAX) {
|
||||
return Err(crate::Error::UserError(crate::UserError::TooManyVectors(
|
||||
document_id().to_string(),
|
||||
add_vectors.len(),
|
||||
)));
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
VectorStateDelta::WasGeneratedNowManual(add_vectors)
|
||||
}
|
||||
(None, None) => {
|
||||
// Do we keep this document?
|
||||
let document_is_kept = obkv
|
||||
.iter()
|
||||
.map(|(_, deladd)| KvReaderDelAdd::new(deladd))
|
||||
.any(|deladd| deladd.get(DelAdd::Addition).is_some());
|
||||
|
||||
if document_is_kept {
|
||||
// Don't give up if the old prompt was failing
|
||||
let old_prompt = Some(&prompt)
|
||||
// TODO: this filter works because we erase the vec database when a embedding setting changes.
|
||||
// When vector pipeline will be optimized, this should be removed.
|
||||
.filter(|_| !settings_diff.reindex_vectors())
|
||||
.map(|p| {
|
||||
p.render(obkv, DelAdd::Deletion, old_fields_ids_map)
|
||||
.unwrap_or_default()
|
||||
});
|
||||
let new_prompt =
|
||||
prompt.render(obkv, DelAdd::Addition, new_fields_ids_map)?;
|
||||
if old_prompt.as_ref() != Some(&new_prompt) {
|
||||
let old_prompt = old_prompt.unwrap_or_default();
|
||||
tracing::trace!(
|
||||
"🚀 Changing prompt from\n{old_prompt}\n===to===\n{new_prompt}"
|
||||
);
|
||||
VectorStateDelta::NowGenerated(new_prompt)
|
||||
} else {
|
||||
tracing::trace!("⏭️ Prompt unmodified, skipping");
|
||||
VectorStateDelta::NoChange
|
||||
}
|
||||
} else {
|
||||
VectorStateDelta::NowRemoved
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// and we finally push the unique vectors into the writer
|
||||
push_vectors_diff(
|
||||
remove_vectors_writer,
|
||||
prompts_writer,
|
||||
manual_vectors_writer,
|
||||
&mut key_buffer,
|
||||
delta,
|
||||
reindex_vectors,
|
||||
)?;
|
||||
}
|
||||
// and we finally push the unique vectors into the writer
|
||||
push_vectors_diff(
|
||||
&mut remove_vectors_writer,
|
||||
&mut prompts_writer,
|
||||
&mut manual_vectors_writer,
|
||||
&mut key_buffer,
|
||||
delta,
|
||||
settings_diff,
|
||||
)?;
|
||||
}
|
||||
|
||||
let mut results = Vec::new();
|
||||
Ok(ExtractedVectorPoints {
|
||||
// docid, _index -> KvWriterDelAdd -> Vector
|
||||
manual_vectors: writer_into_reader(manual_vectors_writer)?,
|
||||
// docid -> ()
|
||||
remove_vectors: writer_into_reader(remove_vectors_writer)?,
|
||||
// docid -> prompt
|
||||
prompts: writer_into_reader(prompts_writer)?,
|
||||
})
|
||||
}
|
||||
|
||||
for EmbedderVectorExtractor {
|
||||
embedder_name,
|
||||
embedder,
|
||||
prompt: _,
|
||||
manual_vectors_writer,
|
||||
prompts_writer,
|
||||
remove_vectors_writer,
|
||||
} in extractors
|
||||
{
|
||||
results.push(ExtractedVectorPoints {
|
||||
// docid, _index -> KvWriterDelAdd -> Vector
|
||||
manual_vectors: writer_into_reader(manual_vectors_writer)?,
|
||||
// docid -> ()
|
||||
remove_vectors: writer_into_reader(remove_vectors_writer)?,
|
||||
// docid -> prompt
|
||||
prompts: writer_into_reader(prompts_writer)?,
|
||||
|
||||
embedder,
|
||||
embedder_name,
|
||||
})
|
||||
}
|
||||
|
||||
Ok(results)
|
||||
fn to_vector_map(
|
||||
obkv: KvReaderDelAdd,
|
||||
side: DelAdd,
|
||||
document_id: &impl Fn() -> Value,
|
||||
) -> Result<Option<serde_json::Map<String, Value>>> {
|
||||
Ok(if let Some(value) = obkv.get(side) {
|
||||
let Ok(value) = from_slice(value) else {
|
||||
let value = from_slice(value).map_err(InternalError::SerdeJson)?;
|
||||
return Err(crate::Error::UserError(UserError::InvalidVectorsMapType {
|
||||
document_id: document_id(),
|
||||
value,
|
||||
}));
|
||||
};
|
||||
Some(value)
|
||||
} else {
|
||||
None
|
||||
})
|
||||
}
|
||||
|
||||
/// Computes the diff between both Del and Add numbers and
|
||||
@@ -291,13 +270,14 @@ fn push_vectors_diff(
|
||||
manual_vectors_writer: &mut Writer<BufWriter<File>>,
|
||||
key_buffer: &mut Vec<u8>,
|
||||
delta: VectorStateDelta,
|
||||
reindex_vectors: bool,
|
||||
settings_diff: &InnerIndexSettingsDiff,
|
||||
) -> Result<()> {
|
||||
puffin::profile_function!();
|
||||
let (must_remove, prompt, (mut del_vectors, mut add_vectors)) = delta.into_values();
|
||||
if must_remove
|
||||
// TODO: the below condition works because we erase the vec database when a embedding setting changes.
|
||||
// When vector pipeline will be optimized, this should be removed.
|
||||
&& !reindex_vectors
|
||||
&& !settings_diff.reindex_vectors()
|
||||
{
|
||||
key_buffer.truncate(TRUNCATE_SIZE);
|
||||
remove_vectors_writer.insert(&key_buffer, [])?;
|
||||
@@ -328,7 +308,7 @@ fn push_vectors_diff(
|
||||
EitherOrBoth::Left(vector) => {
|
||||
// TODO: the below condition works because we erase the vec database when a embedding setting changes.
|
||||
// When vector pipeline will be optimized, this should be removed.
|
||||
if !reindex_vectors {
|
||||
if !settings_diff.reindex_vectors() {
|
||||
// We insert only the Del part of the Obkv to inform
|
||||
// that we only want to remove all those vectors.
|
||||
let mut obkv = KvWriterDelAdd::memory();
|
||||
@@ -356,6 +336,26 @@ fn compare_vectors(a: &[f32], b: &[f32]) -> Ordering {
|
||||
a.iter().copied().map(OrderedFloat).cmp(b.iter().copied().map(OrderedFloat))
|
||||
}
|
||||
|
||||
/// Extracts the vectors from a JSON value.
|
||||
fn extract_vectors(
|
||||
value: Value,
|
||||
document_id: impl Fn() -> Value,
|
||||
name: &str,
|
||||
) -> Result<Vec<Vec<f32>>> {
|
||||
// FIXME: ugly clone of the vectors here
|
||||
match serde_json::from_value(value.clone()) {
|
||||
Ok(vectors) => {
|
||||
Ok(VectorOrArrayOfVectors::into_array_of_vectors(vectors).unwrap_or_default())
|
||||
}
|
||||
Err(_) => Err(UserError::InvalidVectorsType {
|
||||
document_id: document_id(),
|
||||
value,
|
||||
subfield: name.to_owned(),
|
||||
}
|
||||
.into()),
|
||||
}
|
||||
}
|
||||
|
||||
#[tracing::instrument(level = "trace", skip_all, target = "indexing::extract")]
|
||||
pub fn extract_embeddings<R: io::Read + io::Seek>(
|
||||
// docid, prompt
|
||||
@@ -364,6 +364,7 @@ pub fn extract_embeddings<R: io::Read + io::Seek>(
|
||||
embedder: Arc<Embedder>,
|
||||
request_threads: &ThreadPoolNoAbort,
|
||||
) -> Result<grenad::Reader<BufReader<File>>> {
|
||||
puffin::profile_function!();
|
||||
let n_chunks = embedder.chunk_count_hint(); // chunk level parallelism
|
||||
let n_vectors_per_chunk = embedder.prompt_count_in_chunk_hint(); // number of vectors in a single chunk
|
||||
|
||||
|
||||
@@ -36,6 +36,8 @@ pub fn extract_word_docids<R: io::Read + io::Seek>(
|
||||
grenad::Reader<BufReader<File>>,
|
||||
grenad::Reader<BufReader<File>>,
|
||||
)> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let max_memory = indexer.max_memory_by_thread();
|
||||
|
||||
let mut word_fid_docids_sorter = create_sorter(
|
||||
@@ -165,6 +167,8 @@ fn words_into_sorter(
|
||||
add_words: &BTreeSet<Vec<u8>>,
|
||||
word_fid_docids_sorter: &mut grenad::Sorter<MergeFn>,
|
||||
) -> Result<()> {
|
||||
puffin::profile_function!();
|
||||
|
||||
use itertools::merge_join_by;
|
||||
use itertools::EitherOrBoth::{Both, Left, Right};
|
||||
|
||||
|
||||
@@ -26,8 +26,12 @@ pub fn extract_word_pair_proximity_docids<R: io::Read + io::Seek>(
|
||||
indexer: GrenadParameters,
|
||||
settings_diff: &InnerIndexSettingsDiff,
|
||||
) -> Result<grenad::Reader<BufReader<File>>> {
|
||||
puffin::profile_function!();
|
||||
let any_deletion = settings_diff.old.proximity_precision == ProximityPrecision::ByWord;
|
||||
let any_addition = settings_diff.new.proximity_precision == ProximityPrecision::ByWord;
|
||||
|
||||
// early return if the data shouldn't be deleted nor created.
|
||||
if settings_diff.settings_update_only && !settings_diff.reindex_proximities() {
|
||||
if !any_deletion && !any_addition {
|
||||
let writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
@@ -36,10 +40,8 @@ pub fn extract_word_pair_proximity_docids<R: io::Read + io::Seek>(
|
||||
return writer_into_reader(writer);
|
||||
}
|
||||
|
||||
let any_deletion = settings_diff.old.proximity_precision == ProximityPrecision::ByWord;
|
||||
let any_addition = settings_diff.new.proximity_precision == ProximityPrecision::ByWord;
|
||||
|
||||
let max_memory = indexer.max_memory_by_thread();
|
||||
|
||||
let mut word_pair_proximity_docids_sorters: Vec<_> = (1..MAX_DISTANCE)
|
||||
.map(|_| {
|
||||
create_sorter(
|
||||
@@ -69,6 +71,8 @@ pub fn extract_word_pair_proximity_docids<R: io::Read + io::Seek>(
|
||||
|
||||
// if we change document, we fill the sorter
|
||||
if current_document_id.map_or(false, |id| id != document_id) {
|
||||
puffin::profile_scope!("Document into sorter");
|
||||
|
||||
// FIXME: span inside of a hot loop might degrade performance and create big reports
|
||||
let span = tracing::trace_span!(target: "indexing::details", "document_into_sorter");
|
||||
let _entered = span.enter();
|
||||
@@ -159,6 +163,7 @@ pub fn extract_word_pair_proximity_docids<R: io::Read + io::Seek>(
|
||||
}
|
||||
|
||||
if let Some(document_id) = current_document_id {
|
||||
puffin::profile_scope!("Final document into sorter");
|
||||
// FIXME: span inside of a hot loop might degrade performance and create big reports
|
||||
let span = tracing::trace_span!(target: "indexing::details", "final_document_into_sorter");
|
||||
let _entered = span.enter();
|
||||
@@ -171,6 +176,7 @@ pub fn extract_word_pair_proximity_docids<R: io::Read + io::Seek>(
|
||||
)?;
|
||||
}
|
||||
{
|
||||
puffin::profile_scope!("sorter_into_reader");
|
||||
// FIXME: span inside of a hot loop might degrade performance and create big reports
|
||||
let span = tracing::trace_span!(target: "indexing::details", "sorter_into_reader");
|
||||
let _entered = span.enter();
|
||||
|
||||
@@ -25,6 +25,8 @@ pub fn extract_word_position_docids<R: io::Read + io::Seek>(
|
||||
indexer: GrenadParameters,
|
||||
_settings_diff: &InnerIndexSettingsDiff,
|
||||
) -> Result<grenad::Reader<BufReader<File>>> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let max_memory = indexer.max_memory_by_thread();
|
||||
|
||||
let mut word_position_docids_sorter = create_sorter(
|
||||
@@ -102,6 +104,8 @@ fn words_position_into_sorter(
|
||||
add_word_positions: &BTreeSet<(u16, Vec<u8>)>,
|
||||
word_position_docids_sorter: &mut grenad::Sorter<MergeFn>,
|
||||
) -> Result<()> {
|
||||
puffin::profile_function!();
|
||||
|
||||
use itertools::merge_join_by;
|
||||
use itertools::EitherOrBoth::{Both, Left, Right};
|
||||
|
||||
|
||||
@@ -8,7 +8,6 @@ mod extract_vector_points;
|
||||
mod extract_word_docids;
|
||||
mod extract_word_pair_proximity_docids;
|
||||
mod extract_word_position_docids;
|
||||
// mod searchable;
|
||||
|
||||
use std::fs::File;
|
||||
use std::io::BufReader;
|
||||
@@ -44,9 +43,12 @@ pub(crate) fn data_from_obkv_documents(
|
||||
indexer: GrenadParameters,
|
||||
lmdb_writer_sx: Sender<Result<TypedChunk>>,
|
||||
primary_key_id: FieldId,
|
||||
geo_fields_ids: Option<(FieldId, FieldId)>,
|
||||
settings_diff: Arc<InnerIndexSettingsDiff>,
|
||||
max_positions_per_attributes: Option<u32>,
|
||||
) -> Result<()> {
|
||||
puffin::profile_function!();
|
||||
|
||||
let (original_pipeline_result, flattened_pipeline_result): (Result<_>, Result<_>) = rayon::join(
|
||||
|| {
|
||||
original_obkv_chunks
|
||||
@@ -70,6 +72,7 @@ pub(crate) fn data_from_obkv_documents(
|
||||
indexer,
|
||||
lmdb_writer_sx.clone(),
|
||||
primary_key_id,
|
||||
geo_fields_ids,
|
||||
settings_diff.clone(),
|
||||
max_positions_per_attributes,
|
||||
)
|
||||
@@ -87,6 +90,7 @@ pub(crate) fn data_from_obkv_documents(
|
||||
lmdb_writer_sx.clone(),
|
||||
extract_fid_word_count_docids,
|
||||
TypedChunk::FieldIdWordCountDocids,
|
||||
"field-id-wordcount-docids",
|
||||
);
|
||||
run_extraction_task::<
|
||||
_,
|
||||
@@ -113,6 +117,7 @@ pub(crate) fn data_from_obkv_documents(
|
||||
word_fid_docids_reader,
|
||||
}
|
||||
},
|
||||
"word-docids",
|
||||
);
|
||||
|
||||
run_extraction_task::<_, _, grenad::Reader<BufReader<File>>>(
|
||||
@@ -122,6 +127,7 @@ pub(crate) fn data_from_obkv_documents(
|
||||
lmdb_writer_sx.clone(),
|
||||
extract_word_position_docids,
|
||||
TypedChunk::WordPositionDocids,
|
||||
"word-position-docids",
|
||||
);
|
||||
|
||||
run_extraction_task::<
|
||||
@@ -135,6 +141,7 @@ pub(crate) fn data_from_obkv_documents(
|
||||
lmdb_writer_sx.clone(),
|
||||
extract_facet_string_docids,
|
||||
TypedChunk::FieldIdFacetStringDocids,
|
||||
"field-id-facet-string-docids",
|
||||
);
|
||||
|
||||
run_extraction_task::<_, _, grenad::Reader<BufReader<File>>>(
|
||||
@@ -144,6 +151,7 @@ pub(crate) fn data_from_obkv_documents(
|
||||
lmdb_writer_sx.clone(),
|
||||
extract_facet_number_docids,
|
||||
TypedChunk::FieldIdFacetNumberDocids,
|
||||
"field-id-facet-number-docids",
|
||||
);
|
||||
|
||||
run_extraction_task::<_, _, grenad::Reader<BufReader<File>>>(
|
||||
@@ -153,6 +161,7 @@ pub(crate) fn data_from_obkv_documents(
|
||||
lmdb_writer_sx.clone(),
|
||||
extract_word_pair_proximity_docids,
|
||||
TypedChunk::WordPairProximityDocids,
|
||||
"word-pair-proximity-docids",
|
||||
);
|
||||
}
|
||||
|
||||
@@ -176,6 +185,7 @@ fn run_extraction_task<FE, FS, M>(
|
||||
lmdb_writer_sx: Sender<Result<TypedChunk>>,
|
||||
extract_fn: FE,
|
||||
serialize_fn: FS,
|
||||
name: &'static str,
|
||||
) where
|
||||
FE: Fn(
|
||||
grenad::Reader<CursorClonableMmap>,
|
||||
@@ -193,7 +203,7 @@ fn run_extraction_task<FE, FS, M>(
|
||||
rayon::spawn(move || {
|
||||
let child_span = tracing::trace_span!(target: "indexing::extract::details", parent: ¤t_span, "extract_multiple_chunks");
|
||||
let _entered = child_span.enter();
|
||||
|
||||
puffin::profile_scope!("extract_multiple_chunks", name);
|
||||
match extract_fn(chunk, indexer, &settings_diff) {
|
||||
Ok(chunk) => {
|
||||
let _ = lmdb_writer_sx.send(Ok(serialize_fn(chunk)));
|
||||
@@ -216,31 +226,27 @@ fn send_original_documents_data(
|
||||
let original_documents_chunk =
|
||||
original_documents_chunk.and_then(|c| unsafe { as_cloneable_grenad(&c) })?;
|
||||
|
||||
let documents_chunk_cloned = original_documents_chunk.clone();
|
||||
let lmdb_writer_sx_cloned = lmdb_writer_sx.clone();
|
||||
|
||||
let request_threads = ThreadPoolNoAbortBuilder::new()
|
||||
.num_threads(crate::vector::REQUEST_PARALLELISM)
|
||||
.thread_name(|index| format!("embedding-request-{index}"))
|
||||
.build()?;
|
||||
|
||||
let index_vectors = (settings_diff.reindex_vectors() || !settings_diff.settings_update_only())
|
||||
// no point in indexing vectors without embedders
|
||||
&& (!settings_diff.new.embedding_configs.inner_as_ref().is_empty());
|
||||
|
||||
if index_vectors {
|
||||
if settings_diff.reindex_vectors() || !settings_diff.settings_update_only() {
|
||||
let settings_diff = settings_diff.clone();
|
||||
|
||||
let original_documents_chunk = original_documents_chunk.clone();
|
||||
let lmdb_writer_sx = lmdb_writer_sx.clone();
|
||||
rayon::spawn(move || {
|
||||
match extract_vector_points(original_documents_chunk.clone(), indexer, &settings_diff) {
|
||||
Ok(extracted_vectors) => {
|
||||
for ExtractedVectorPoints {
|
||||
manual_vectors,
|
||||
remove_vectors,
|
||||
prompts,
|
||||
embedder_name,
|
||||
embedder,
|
||||
} in extracted_vectors
|
||||
{
|
||||
for (name, (embedder, prompt)) in settings_diff.new.embedding_configs.clone() {
|
||||
let result = extract_vector_points(
|
||||
documents_chunk_cloned.clone(),
|
||||
indexer,
|
||||
&settings_diff,
|
||||
&prompt,
|
||||
&name,
|
||||
);
|
||||
match result {
|
||||
Ok(ExtractedVectorPoints { manual_vectors, remove_vectors, prompts }) => {
|
||||
let embeddings = match extract_embeddings(
|
||||
prompts,
|
||||
indexer,
|
||||
@@ -249,26 +255,28 @@ fn send_original_documents_data(
|
||||
) {
|
||||
Ok(results) => Some(results),
|
||||
Err(error) => {
|
||||
let _ = lmdb_writer_sx.send(Err(error));
|
||||
let _ = lmdb_writer_sx_cloned.send(Err(error));
|
||||
None
|
||||
}
|
||||
};
|
||||
|
||||
if !(remove_vectors.is_empty()
|
||||
&& manual_vectors.is_empty()
|
||||
&& embeddings.as_ref().map_or(true, |e| e.is_empty()))
|
||||
{
|
||||
let _ = lmdb_writer_sx.send(Ok(TypedChunk::VectorPoints {
|
||||
let _ = lmdb_writer_sx_cloned.send(Ok(TypedChunk::VectorPoints {
|
||||
remove_vectors,
|
||||
embeddings,
|
||||
expected_dimension: embedder.dimensions(),
|
||||
manual_vectors,
|
||||
embedder_name,
|
||||
embedder_name: name,
|
||||
}));
|
||||
}
|
||||
}
|
||||
}
|
||||
Err(error) => {
|
||||
let _ = lmdb_writer_sx.send(Err(error));
|
||||
|
||||
Err(error) => {
|
||||
let _ = lmdb_writer_sx_cloned.send(Err(error));
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
@@ -292,6 +300,7 @@ fn send_and_extract_flattened_documents_data(
|
||||
indexer: GrenadParameters,
|
||||
lmdb_writer_sx: Sender<Result<TypedChunk>>,
|
||||
primary_key_id: FieldId,
|
||||
geo_fields_ids: Option<(FieldId, FieldId)>,
|
||||
settings_diff: Arc<InnerIndexSettingsDiff>,
|
||||
max_positions_per_attributes: Option<u32>,
|
||||
) -> Result<(
|
||||
@@ -301,13 +310,12 @@ fn send_and_extract_flattened_documents_data(
|
||||
let flattened_documents_chunk =
|
||||
flattened_documents_chunk.and_then(|c| unsafe { as_cloneable_grenad(&c) })?;
|
||||
|
||||
if settings_diff.run_geo_indexing() {
|
||||
if let Some(geo_fields_ids) = geo_fields_ids {
|
||||
let documents_chunk_cloned = flattened_documents_chunk.clone();
|
||||
let lmdb_writer_sx_cloned = lmdb_writer_sx.clone();
|
||||
let settings_diff = settings_diff.clone();
|
||||
rayon::spawn(move || {
|
||||
let result =
|
||||
extract_geo_points(documents_chunk_cloned, indexer, primary_key_id, &settings_diff);
|
||||
extract_geo_points(documents_chunk_cloned, indexer, primary_key_id, geo_fields_ids);
|
||||
let _ = match result {
|
||||
Ok(geo_points) => lmdb_writer_sx_cloned.send(Ok(TypedChunk::GeoPoints(geo_points))),
|
||||
Err(error) => lmdb_writer_sx_cloned.send(Err(error)),
|
||||
@@ -346,6 +354,7 @@ fn send_and_extract_flattened_documents_data(
|
||||
flattened_documents_chunk.clone(),
|
||||
indexer,
|
||||
&settings_diff,
|
||||
geo_fields_ids,
|
||||
)?;
|
||||
|
||||
// send fid_docid_facet_numbers_chunk to DB writer
|
||||
|
||||
@@ -1,211 +0,0 @@
|
||||
use std::collections::HashMap;
|
||||
|
||||
use charabia::normalizer::NormalizedTokenIter;
|
||||
use charabia::{Language, Script, SeparatorKind, Token, TokenKind, Tokenizer, TokenizerBuilder};
|
||||
use roaring::RoaringBitmap;
|
||||
use serde_json::Value;
|
||||
|
||||
use crate::update::settings::InnerIndexSettings;
|
||||
use crate::{InternalError, Result, MAX_POSITION_PER_ATTRIBUTE, MAX_WORD_LENGTH};
|
||||
|
||||
pub type ScriptLanguageDocidsMap = HashMap<(Script, Language), (RoaringBitmap, RoaringBitmap)>;
|
||||
|
||||
pub struct FieldWordPositionExtractorBuilder<'a> {
|
||||
max_positions_per_attributes: u16,
|
||||
stop_words: Option<&'a fst::Set<Vec<u8>>>,
|
||||
separators: Option<Vec<&'a str>>,
|
||||
dictionary: Option<Vec<&'a str>>,
|
||||
}
|
||||
|
||||
impl<'a> FieldWordPositionExtractorBuilder<'a> {
|
||||
pub fn new(
|
||||
max_positions_per_attributes: Option<u32>,
|
||||
settings: &'a InnerIndexSettings,
|
||||
) -> Result<Self> {
|
||||
let stop_words = settings.stop_words.as_ref();
|
||||
let separators: Option<Vec<_>> =
|
||||
settings.allowed_separators.as_ref().map(|s| s.iter().map(String::as_str).collect());
|
||||
let dictionary: Option<Vec<_>> =
|
||||
settings.dictionary.as_ref().map(|s| s.iter().map(String::as_str).collect());
|
||||
Ok(Self {
|
||||
max_positions_per_attributes: max_positions_per_attributes
|
||||
.map_or(MAX_POSITION_PER_ATTRIBUTE as u16, |max| {
|
||||
max.min(MAX_POSITION_PER_ATTRIBUTE) as u16
|
||||
}),
|
||||
stop_words,
|
||||
separators,
|
||||
dictionary,
|
||||
})
|
||||
}
|
||||
|
||||
pub fn build(&'a self) -> FieldWordPositionExtractor<'a> {
|
||||
let builder = tokenizer_builder(
|
||||
self.stop_words,
|
||||
self.separators.as_deref(),
|
||||
self.dictionary.as_deref(),
|
||||
None,
|
||||
);
|
||||
|
||||
FieldWordPositionExtractor {
|
||||
tokenizer: builder.into_tokenizer(),
|
||||
max_positions_per_attributes: self.max_positions_per_attributes,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub struct FieldWordPositionExtractor<'a> {
|
||||
tokenizer: Tokenizer<'a>,
|
||||
max_positions_per_attributes: u16,
|
||||
}
|
||||
|
||||
impl<'a> FieldWordPositionExtractor<'a> {
|
||||
pub fn extract<'b>(
|
||||
&'a self,
|
||||
field_bytes: &[u8],
|
||||
buffer: &'b mut String,
|
||||
) -> Result<ExtractedFieldWordPosition<'a, 'b>> {
|
||||
let field_value = serde_json::from_slice(field_bytes).map_err(InternalError::SerdeJson)?;
|
||||
Ok(ExtractedFieldWordPosition {
|
||||
tokenizer: &self.tokenizer,
|
||||
max_positions_per_attributes: self.max_positions_per_attributes,
|
||||
field_value,
|
||||
buffer: buffer,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
pub struct ExtractedFieldWordPosition<'a, 'b> {
|
||||
tokenizer: &'a Tokenizer<'a>,
|
||||
max_positions_per_attributes: u16,
|
||||
field_value: Value,
|
||||
buffer: &'b mut String,
|
||||
}
|
||||
|
||||
impl<'a> ExtractedFieldWordPosition<'a, '_> {
|
||||
pub fn iter<'o>(&'o mut self) -> FieldWordPositionIter<'o> {
|
||||
self.buffer.clear();
|
||||
let inner = match json_to_string(&self.field_value, &mut self.buffer) {
|
||||
Some(field) => Some(self.tokenizer.tokenize(field)),
|
||||
None => None,
|
||||
};
|
||||
|
||||
// create an iterator of token with their positions.
|
||||
FieldWordPositionIter {
|
||||
inner,
|
||||
max_positions_per_attributes: self.max_positions_per_attributes,
|
||||
position: 0,
|
||||
prev_kind: None,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub struct FieldWordPositionIter<'a> {
|
||||
inner: Option<NormalizedTokenIter<'a, 'a>>,
|
||||
max_positions_per_attributes: u16,
|
||||
position: u16,
|
||||
prev_kind: Option<TokenKind>,
|
||||
}
|
||||
|
||||
impl<'a> Iterator for FieldWordPositionIter<'a> {
|
||||
type Item = (u16, Token<'a>);
|
||||
|
||||
fn next(&mut self) -> Option<Self::Item> {
|
||||
if self.position >= self.max_positions_per_attributes {
|
||||
return None;
|
||||
}
|
||||
|
||||
let token = self.inner.as_mut().map(|i| i.next()).flatten()?;
|
||||
|
||||
match token.kind {
|
||||
TokenKind::Word | TokenKind::StopWord if !token.lemma().is_empty() => {
|
||||
self.position += match self.prev_kind {
|
||||
Some(TokenKind::Separator(SeparatorKind::Hard)) => 8,
|
||||
Some(_) => 1,
|
||||
None => 0,
|
||||
};
|
||||
self.prev_kind = Some(token.kind)
|
||||
}
|
||||
TokenKind::Separator(_) if self.position == 0 => {
|
||||
return self.next();
|
||||
}
|
||||
TokenKind::Separator(SeparatorKind::Hard) => {
|
||||
self.prev_kind = Some(token.kind);
|
||||
}
|
||||
TokenKind::Separator(SeparatorKind::Soft)
|
||||
if self.prev_kind != Some(TokenKind::Separator(SeparatorKind::Hard)) =>
|
||||
{
|
||||
self.prev_kind = Some(token.kind);
|
||||
}
|
||||
_ => return self.next(),
|
||||
}
|
||||
|
||||
if !token.is_word() {
|
||||
return self.next();
|
||||
}
|
||||
|
||||
// keep a word only if it is not empty and fit in a LMDB key.
|
||||
let lemma = token.lemma().trim();
|
||||
if !lemma.is_empty() && lemma.len() <= MAX_WORD_LENGTH {
|
||||
Some((self.position, token))
|
||||
} else {
|
||||
self.next()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Factorize tokenizer building.
|
||||
pub fn tokenizer_builder<'a>(
|
||||
stop_words: Option<&'a fst::Set<Vec<u8>>>,
|
||||
allowed_separators: Option<&'a [&str]>,
|
||||
dictionary: Option<&'a [&str]>,
|
||||
script_language: Option<&'a HashMap<Script, Vec<Language>>>,
|
||||
) -> TokenizerBuilder<'a, Vec<u8>> {
|
||||
let mut tokenizer_builder = TokenizerBuilder::new();
|
||||
if let Some(stop_words) = stop_words {
|
||||
tokenizer_builder.stop_words(stop_words);
|
||||
}
|
||||
if let Some(dictionary) = dictionary {
|
||||
tokenizer_builder.words_dict(dictionary);
|
||||
}
|
||||
if let Some(separators) = allowed_separators {
|
||||
tokenizer_builder.separators(separators);
|
||||
}
|
||||
|
||||
if let Some(script_language) = script_language {
|
||||
tokenizer_builder.allow_list(script_language);
|
||||
}
|
||||
|
||||
tokenizer_builder
|
||||
}
|
||||
|
||||
/// Transform a JSON value into a string that can be indexed.
|
||||
fn json_to_string<'a>(value: &'a Value, buffer: &'a mut String) -> Option<&'a str> {
|
||||
fn inner(value: &Value, output: &mut String) -> bool {
|
||||
use std::fmt::Write;
|
||||
match value {
|
||||
Value::Null | Value::Object(_) => false,
|
||||
Value::Bool(boolean) => write!(output, "{}", boolean).is_ok(),
|
||||
Value::Number(number) => write!(output, "{}", number).is_ok(),
|
||||
Value::String(string) => write!(output, "{}", string).is_ok(),
|
||||
Value::Array(array) => {
|
||||
let mut count = 0;
|
||||
for value in array {
|
||||
if inner(value, output) {
|
||||
output.push_str(". ");
|
||||
count += 1;
|
||||
}
|
||||
}
|
||||
// check that at least one value was written
|
||||
count != 0
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if let Value::String(string) = value {
|
||||
Some(string)
|
||||
} else if inner(value, buffer) {
|
||||
Some(buffer)
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
@@ -1,114 +0,0 @@
|
||||
use std::collections::{BTreeMap, BTreeSet};
|
||||
use std::convert::TryInto;
|
||||
use std::fs::File;
|
||||
use std::io;
|
||||
use std::io::BufReader;
|
||||
|
||||
use field_word_position::FieldWordPositionExtractorBuilder;
|
||||
use obkv::KvReader;
|
||||
use roaring::RoaringBitmap;
|
||||
use word_docids::{WordDocidsDump, WordDocidsExtractor};
|
||||
|
||||
use crate::update::del_add::{DelAdd, KvReaderDelAdd};
|
||||
use crate::update::index_documents::extract::extract_docid_word_positions::ScriptLanguageDocidsMap;
|
||||
use crate::update::index_documents::GrenadParameters;
|
||||
use crate::update::settings::InnerIndexSettingsDiff;
|
||||
use crate::{FieldId, Result, SerializationError};
|
||||
|
||||
mod field_word_position;
|
||||
mod word_docids;
|
||||
|
||||
#[tracing::instrument(level = "trace", skip_all, target = "indexing::extract")]
|
||||
pub fn extract_searchable_data<R: io::Read + io::Seek>(
|
||||
obkv_documents: grenad::Reader<R>,
|
||||
indexer: GrenadParameters,
|
||||
settings_diff: &InnerIndexSettingsDiff,
|
||||
max_positions_per_attributes: Option<u32>,
|
||||
) -> Result<(grenad::Reader<BufReader<File>>, ScriptLanguageDocidsMap)> {
|
||||
let searchable_fields_to_index = settings_diff.searchable_fields_to_index();
|
||||
|
||||
let mut documents_ids = RoaringBitmap::new();
|
||||
|
||||
let add_builder =
|
||||
FieldWordPositionExtractorBuilder::new(max_positions_per_attributes, &settings_diff.new)?;
|
||||
let add_token_positions_extractor = add_builder.build();
|
||||
let del_builder;
|
||||
let del_token_positions_extractor = if settings_diff.settings_update_only {
|
||||
del_builder = FieldWordPositionExtractorBuilder::new(
|
||||
max_positions_per_attributes,
|
||||
&settings_diff.old,
|
||||
)?;
|
||||
del_builder.build()
|
||||
} else {
|
||||
add_builder.build()
|
||||
};
|
||||
let token_positions_extractor = &[del_token_positions_extractor, add_token_positions_extractor];
|
||||
|
||||
let mut word_map = BTreeMap::new();
|
||||
let mut word_docids_extractor = WordDocidsExtractor::new(settings_diff);
|
||||
|
||||
let mut cursor = obkv_documents.into_cursor()?;
|
||||
// loop over documents
|
||||
while let Some((key, value)) = cursor.move_on_next()? {
|
||||
let document_id = key
|
||||
.try_into()
|
||||
.map(u32::from_be_bytes)
|
||||
.map_err(|_| SerializationError::InvalidNumberSerialization)?;
|
||||
let obkv = KvReader::<FieldId>::new(value);
|
||||
// if the searchable fields didn't change, skip the searchable indexing for this document.
|
||||
if !settings_diff.reindex_searchable()
|
||||
&& !searchable_fields_changed(&obkv, &searchable_fields_to_index)
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
documents_ids.push(document_id);
|
||||
|
||||
let mut buffer = String::new();
|
||||
for field_id in searchable_fields_to_index.iter() {
|
||||
let Some(field_obkv) = obkv.get(*field_id).map(KvReaderDelAdd::new) else { continue };
|
||||
|
||||
for (deladd, field_bytes) in field_obkv {
|
||||
let mut extracted_positions =
|
||||
token_positions_extractor[deladd as usize].extract(field_bytes, &mut buffer)?;
|
||||
for (position, token) in extracted_positions.iter() {
|
||||
let word = token.lemma().trim();
|
||||
if !word_map.contains_key(word) {
|
||||
word_map.insert(word.to_string(), word_map.len() as u32);
|
||||
}
|
||||
let word_id = word_map.get(word).unwrap();
|
||||
word_docids_extractor.insert(*word_id, *field_id, document_id, deladd);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if word_docids_extractor.rough_size_estimate()
|
||||
> indexer.max_memory.map_or(512 * 1024 * 1024, |s| s.min(512 * 1024 * 1024))
|
||||
{
|
||||
let WordDocidsDump { .. } =
|
||||
word_docids_extractor.dump(&word_map, &searchable_fields_to_index, indexer)?;
|
||||
}
|
||||
}
|
||||
|
||||
todo!()
|
||||
}
|
||||
|
||||
/// Check if any searchable fields of a document changed.
|
||||
fn searchable_fields_changed(
|
||||
obkv: &KvReader<FieldId>,
|
||||
searchable_fields: &BTreeSet<FieldId>,
|
||||
) -> bool {
|
||||
for field_id in searchable_fields {
|
||||
let Some(field_obkv) = obkv.get(*field_id).map(KvReaderDelAdd::new) else { continue };
|
||||
match (field_obkv.get(DelAdd::Deletion), field_obkv.get(DelAdd::Addition)) {
|
||||
// if both fields are None, check the next field.
|
||||
(None, None) => (),
|
||||
// if both contains a value and values are the same, check the next field.
|
||||
(Some(del), Some(add)) if del == add => (),
|
||||
// otherwise the fields are different, return true.
|
||||
_otherwise => return true,
|
||||
}
|
||||
}
|
||||
|
||||
false
|
||||
}
|
||||
@@ -1,203 +0,0 @@
|
||||
use std::collections::hash_map::Entry::{Occupied, Vacant};
|
||||
use std::collections::{BTreeMap, BTreeSet, HashMap, HashSet};
|
||||
use std::fs::File;
|
||||
use std::hash::Hash;
|
||||
use std::io::BufReader;
|
||||
use std::mem::size_of;
|
||||
|
||||
use roaring::RoaringBitmap;
|
||||
|
||||
use crate::update::del_add::KvWriterDelAdd;
|
||||
use crate::update::index_documents::extract::searchable::DelAdd;
|
||||
use crate::update::index_documents::{create_writer, writer_into_reader, GrenadParameters};
|
||||
use crate::update::settings::InnerIndexSettingsDiff;
|
||||
use crate::{CboRoaringBitmapCodec, DocumentId, FieldId, Result};
|
||||
|
||||
pub struct WordDocidsExtractor<'a> {
|
||||
word_fid_docids: RevertedIndex<(u32, FieldId)>,
|
||||
settings_diff: &'a InnerIndexSettingsDiff,
|
||||
}
|
||||
|
||||
impl<'a> WordDocidsExtractor<'a> {
|
||||
pub fn new(settings_diff: &'a InnerIndexSettingsDiff) -> Self {
|
||||
Self { word_fid_docids: RevertedIndex::new(), settings_diff }
|
||||
}
|
||||
pub fn insert(&mut self, wordid: u32, fieldid: FieldId, docid: DocumentId, del_add: DelAdd) {
|
||||
self.word_fid_docids.insert((wordid, fieldid), docid, del_add);
|
||||
}
|
||||
|
||||
pub fn rough_size_estimate(&self) -> usize {
|
||||
self.word_fid_docids.rough_size_estimate()
|
||||
}
|
||||
|
||||
pub fn dump(
|
||||
&mut self,
|
||||
word_map: &BTreeMap<String, u32>,
|
||||
fields: &BTreeSet<FieldId>,
|
||||
indexer: GrenadParameters,
|
||||
) -> Result<WordDocidsDump> {
|
||||
let mut word_fid_docids_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
let mut word_docids_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
let mut exact_word_docids_writer = create_writer(
|
||||
indexer.chunk_compression_type,
|
||||
indexer.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
let mut exact_word_deletion = RoaringBitmap::new();
|
||||
let mut exact_word_addition = RoaringBitmap::new();
|
||||
let mut word_deletion = RoaringBitmap::new();
|
||||
let mut word_addition = RoaringBitmap::new();
|
||||
let mut key_buffer = Vec::new();
|
||||
let mut bitmap_buffer = Vec::new();
|
||||
let mut obkv_buffer = Vec::new();
|
||||
for (word, wid) in word_map {
|
||||
exact_word_deletion.clear();
|
||||
exact_word_addition.clear();
|
||||
word_deletion.clear();
|
||||
word_addition.clear();
|
||||
for fid in fields {
|
||||
if let Some((deletion, addition)) = self.word_fid_docids.inner.get(&(*wid, *fid)) {
|
||||
if self.settings_diff.old.exact_attributes.contains(&fid) {
|
||||
exact_word_deletion |= deletion;
|
||||
} else {
|
||||
word_deletion |= deletion;
|
||||
}
|
||||
|
||||
if self.settings_diff.new.exact_attributes.contains(&fid) {
|
||||
exact_word_addition |= addition;
|
||||
} else {
|
||||
word_addition |= addition;
|
||||
}
|
||||
|
||||
if deletion != addition {
|
||||
key_buffer.clear();
|
||||
key_buffer.extend_from_slice(word.as_bytes());
|
||||
key_buffer.push(0);
|
||||
key_buffer.extend_from_slice(&fid.to_be_bytes());
|
||||
let value = bitmaps_into_deladd_obkv(
|
||||
deletion,
|
||||
addition,
|
||||
&mut obkv_buffer,
|
||||
&mut bitmap_buffer,
|
||||
)?;
|
||||
word_fid_docids_writer.insert(&key_buffer, value)?;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
key_buffer.clear();
|
||||
key_buffer.extend_from_slice(word.as_bytes());
|
||||
if exact_word_deletion != exact_word_addition {
|
||||
let value = bitmaps_into_deladd_obkv(
|
||||
&exact_word_deletion,
|
||||
&exact_word_addition,
|
||||
&mut obkv_buffer,
|
||||
&mut bitmap_buffer,
|
||||
)?;
|
||||
exact_word_docids_writer.insert(&key_buffer, value)?;
|
||||
}
|
||||
|
||||
if word_deletion != word_addition {
|
||||
let value = bitmaps_into_deladd_obkv(
|
||||
&word_deletion,
|
||||
&word_addition,
|
||||
&mut obkv_buffer,
|
||||
&mut bitmap_buffer,
|
||||
)?;
|
||||
word_docids_writer.insert(&key_buffer, value)?;
|
||||
}
|
||||
}
|
||||
|
||||
self.word_fid_docids.clear();
|
||||
|
||||
Ok(WordDocidsDump {
|
||||
word_fid_docids: writer_into_reader(word_fid_docids_writer)?,
|
||||
word_docids: writer_into_reader(word_docids_writer)?,
|
||||
exact_word_docids: writer_into_reader(exact_word_docids_writer)?,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
fn bitmaps_into_deladd_obkv<'a>(
|
||||
deletion: &RoaringBitmap,
|
||||
addition: &RoaringBitmap,
|
||||
obkv_buffer: &'a mut Vec<u8>,
|
||||
bitmap_buffer: &mut Vec<u8>,
|
||||
) -> Result<&'a mut Vec<u8>> {
|
||||
obkv_buffer.clear();
|
||||
let mut value_writer = KvWriterDelAdd::new(obkv_buffer);
|
||||
if !deletion.is_empty() {
|
||||
bitmap_buffer.clear();
|
||||
CboRoaringBitmapCodec::serialize_into(deletion, bitmap_buffer);
|
||||
value_writer.insert(DelAdd::Deletion, &*bitmap_buffer)?;
|
||||
}
|
||||
if !addition.is_empty() {
|
||||
bitmap_buffer.clear();
|
||||
CboRoaringBitmapCodec::serialize_into(addition, bitmap_buffer);
|
||||
value_writer.insert(DelAdd::Addition, &*bitmap_buffer)?;
|
||||
}
|
||||
Ok(value_writer.into_inner()?)
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
struct RevertedIndex<K> {
|
||||
inner: HashMap<K, (RoaringBitmap, RoaringBitmap)>,
|
||||
max_value_size: usize,
|
||||
}
|
||||
|
||||
impl<K: PartialEq + Eq + Hash> RevertedIndex<K> {
|
||||
pub fn insert(&mut self, key: K, docid: DocumentId, del_add: DelAdd) {
|
||||
let size = match self.inner.entry(key) {
|
||||
Occupied(mut entry) => {
|
||||
let (ref mut del, ref mut add) = entry.get_mut();
|
||||
match del_add {
|
||||
DelAdd::Deletion => del.insert(docid),
|
||||
DelAdd::Addition => add.insert(docid),
|
||||
};
|
||||
del.serialized_size() + add.serialized_size()
|
||||
}
|
||||
Vacant(entry) => {
|
||||
let mut bitmap = RoaringBitmap::new();
|
||||
bitmap.insert(docid);
|
||||
let size = bitmap.serialized_size();
|
||||
match del_add {
|
||||
DelAdd::Deletion => entry.insert((bitmap, RoaringBitmap::new())),
|
||||
DelAdd::Addition => entry.insert((RoaringBitmap::new(), bitmap)),
|
||||
};
|
||||
size * 2
|
||||
}
|
||||
};
|
||||
|
||||
self.max_value_size = self.max_value_size.max(size);
|
||||
}
|
||||
|
||||
pub fn new() -> Self {
|
||||
Self { inner: HashMap::new(), max_value_size: 0 }
|
||||
}
|
||||
|
||||
pub fn rough_size_estimate(&self) -> usize {
|
||||
self.inner.len() * size_of::<K>() + self.inner.len() * self.max_value_size
|
||||
}
|
||||
|
||||
fn clear(&mut self) {
|
||||
self.max_value_size = 0;
|
||||
self.inner.clear();
|
||||
}
|
||||
}
|
||||
|
||||
pub struct WordDocidsDump {
|
||||
pub word_fid_docids: grenad::Reader<BufReader<File>>,
|
||||
pub word_docids: grenad::Reader<BufReader<File>>,
|
||||
pub exact_word_docids: grenad::Reader<BufReader<File>>,
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user