Compare commits

...

63 Commits

Author SHA1 Message Date
Clément Renault
af5b1a88a4 Expose a route to show advanced index stats 2024-06-04 14:08:01 -04:00
meili-bors[bot]
d6bd88ce4f Merge #4667
4667: Frequency matching strategy r=Kerollmops a=ManyTheFish

# Pull Request

## Related issue
Fixes #3773

## What does this PR do?
- add test for matching strategy
- implement frequency matching strategy

See the [PRD for more details](https://www.notion.so/meilisearch/Frequency-Matching-Strategy-0f3ba08833a442a39590a53a1505ab00).

[Public API](https://www.notion.so/meilisearch/frequency-matching-strategy-89868fb7fc584026bc56e378eb854a7f).


Co-authored-by: ManyTheFish <many@meilisearch.com>
2024-05-30 14:53:31 +00:00
ManyTheFish
3f1a510069 Add tests and fix matching strategy 2024-05-30 12:02:42 +02:00
meili-bors[bot]
06a9803544 Merge #4664
4664: Update README.md r=curquiza a=tpayet

Add hybrid & semantic as a feature

# Pull Request

## Related issue
Fixes #<issue_number>

## What does this PR do?
- ...

## PR checklist
Please check if your PR fulfills the following requirements:
- [ ] Does this PR fix an existing issue, or have you listed the changes applied in the PR description (and why they are needed)?
- [ ] Have you read the contributing guidelines?
- [ ] Have you made sure that the title is accurate and descriptive of the changes?

Thank you so much for contributing to Meilisearch!


Co-authored-by: Thomas Payet <thomas@meilisearch.com>
2024-05-29 16:55:20 +00:00
Thomas Payet
b2588d8101 Update README.md
Add hybrid & semantic as a feature
2024-05-29 17:48:48 +02:00
meili-bors[bot]
62d27172f4 Merge #4663
4663: Bring back release v1.8.1 into main r=ManyTheFish a=ManyTheFish



Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: ManyTheFish <many@meilisearch.com>
Co-authored-by: meili-bors[bot] <89034592+meili-bors[bot]@users.noreply.github.com>
Co-authored-by: ManyTheFish <ManyTheFish@users.noreply.github.com>
Co-authored-by: Many the fish <many@meilisearch.com>
2024-05-29 14:47:38 +00:00
ManyTheFish
1ab88e10b9 Merge branch 'main' into merge-release-v1.8.1-in-main 2024-05-29 16:24:00 +02:00
ManyTheFish
6a4b2516aa WIP 2024-05-29 16:21:24 +02:00
ManyTheFish
abdc4afcca Implement Frequency matching strategy 2024-05-29 13:59:08 +02:00
meili-bors[bot]
75d5c0ae1f Merge #4647
4647: Feature: get similar documents r=dureuill a=dureuill

# Pull Request

## Related issue
Fixes #4610 

## What does this PR do?
[Usage](https://meilisearch.notion.site/Get-similar-documents-usage-540919ca755c4da0b7cdee273db3f290)

Co-authored-by: Louis Dureuil <louis@meilisearch.com>
2024-05-29 11:42:23 +00:00
meili-bors[bot]
a88554216a Merge #4657
4657: Update version for the next release (v1.9.0) in Cargo.toml r=curquiza a=meili-bot

⚠️ This PR is automatically generated. Check the new version is the expected one and Cargo.lock has been updated before merging.

Co-authored-by: curquiza <curquiza@users.noreply.github.com>
2024-05-29 11:14:19 +00:00
Louis Dureuil
2cf3e1c80a Temporarily ignore perform snapshot test under Windows 2024-05-29 12:42:47 +02:00
Many the fish
e1fbfde6c4 Merge branch 'main' into merge-release-v1.8.1-in-main 2024-05-29 11:31:03 +02:00
ManyTheFish
27b75ec648 merge main into v1.8.1 2024-05-29 11:26:07 +02:00
curquiza
07fdb081a4 Update version for the next release (v1.9.0) in Cargo.toml 2024-05-28 14:19:40 +00:00
Louis Dureuil
ca006e38ec Basic tests 2024-05-28 15:28:19 +02:00
Louis Dureuil
e26bd87780 Error tests for similar routes 2024-05-28 15:28:19 +02:00
Louis Dureuil
c01e498a63 Test server can call similar 2024-05-28 15:28:19 +02:00
Louis Dureuil
ca6cc4654b Add similar route 2024-05-28 15:28:19 +02:00
Louis Dureuil
3bd9d2478c Add error codes 2024-05-28 15:27:43 +02:00
Louis Dureuil
54b15059a0 Analytics changes 2024-05-28 15:27:43 +02:00
Louis Dureuil
d35278320e Add support functions for accessing arroy writers and readers 2024-05-28 15:27:43 +02:00
Louis Dureuil
e172e938e7 add search rules directly takes the filter rather than the searchquery 2024-05-28 15:22:25 +02:00
Louis Dureuil
02b3d82c60 filtered_universe accepts index and txn instead of SearchContext 2024-05-28 15:22:12 +02:00
Louis Dureuil
fd2c95999d Change validate_document_id to public and remove extra layer of result 2024-05-28 15:21:19 +02:00
meili-bors[bot]
e248d2a1e6 Merge #4655
4655: Remove `exportPuffinReport` experimental feature r=Kerollmops a=Kerollmops

This PR fixes #4605 by removing every trace of Puffin. Puffin is a great tool, but we use a better approach to measuring performance.

Co-authored-by: Clément Renault <clement@meilisearch.com>
2024-05-28 07:01:16 +00:00
Clément Renault
487431a035 Fix tests 2024-05-27 16:12:20 +02:00
Clément Renault
b6d450d484 Remove puffin experimental feature 2024-05-27 15:59:28 +02:00
Clément Renault
dc949ab46a Remove puffin usage 2024-05-27 15:59:14 +02:00
Clément Renault
7f3e51349e Remove puffin for the dependencies 2024-05-27 15:53:06 +02:00
meili-bors[bot]
19acc65ad2 Merge #4646
4646: Reduce `Transform`'s disk usage r=Kerollmops a=Kerollmops

This PR implements what is described in #4485. It reduces the number of disk writes and disk usage.

Co-authored-by: Clément Renault <clement@meilisearch.com>
2024-05-23 16:06:50 +00:00
meili-bors[bot]
3a3ab17714 Merge #4651
4651: Allow to comment with the results of benchmark invocation r=Kerollmops a=dureuill



Co-authored-by: Louis Dureuil <louis@meilisearch.com>
2024-05-23 15:32:09 +00:00
Louis Dureuil
eaf57056ca comment with the results of benchmarks 2024-05-23 15:34:39 +02:00
Louis Dureuil
e340705634 Change benchmark outputs
- logs to stderr instead of stdout
- prints links to the dashboard when there is a dashboard
2024-05-23 15:29:06 +02:00
Clément Renault
fe17c0f52e Construct the minimal OBKVs according to the settings diff 2024-05-23 11:23:57 +02:00
meili-bors[bot]
14bc80e3df Merge #4633
4633: Allow to mark vectors as "userProvided" r=Kerollmops a=dureuill

# Pull Request

## Related issue
Fixes #4606 

## What does this PR do?

[See usage in PRD](https://meilisearch.notion.site/v1-9-AI-search-changes-e90d6803eca8417aa70a1ac5d0225697#deb96fb0595947bda7d4a371100326eb)

- Extends the shape of the special `_vectors` field in documents.
    - previously, the `_vectors` field had to be an object, with each field the name of a configured embedder, and each value either `null`, an embedding (array of numbers), or an array of embeddings.
    - In this PR, the value of an embedder in the `_vectors` field can additionally be an object. The object has two fields:
      1. `embeddings`: `null`, an embedding (array of numbers), or an array of embeddings.
      2. `userProvided`: a boolean indicating if the vector was provided by the user.
    - The previous form `embedder_or_array_of_embedders` is semantically equivalent to:
    ```json
    {
        "embeddings": embedder_or_array_of_embedders,
        "userProvided": true
    }
    ```
- During the indexing step, the subfields and values of the `_vectors` field that have `userProvided` set to **false** are added in the vector DB, but not in the documents DB: that means that future modifications of the documents will trigger a regeneration of that particular vector using the document template.
- This allows **importing** embeddings as a one-shot process, while still retaining the ability to regenerate embeddings on document change.
- The dump process now uses this ability: it enriches the `_vectors` fields of documents with the embeddings that were autogenerated, marking them as not `userProvided`. This allows importing the vectors from a dump without regenerating them.

### Tests

This PR adds the following tests

- Long-needed hybrid search tests of a simple hf embedder
- Dump test that imports vectors. Due to the difficulty of actually importing a dump in tests, we just read the dump and check it contains the expected content.
- Tests in the index-scheduler: this tests that documents containing the same kind of instructions as in the dump indexes as expected


Co-authored-by: Louis Dureuil <louis@meilisearch.com>
2024-05-23 08:17:54 +00:00
Clément Renault
bc5663e673 FieldIdsMap no longer useful thanks to #4631 2024-05-22 16:06:15 +02:00
Louis Dureuil
8a941c0241 Smaller review changes 2024-05-22 14:44:42 +02:00
Louis Dureuil
3412e7fbcf "[]" is deserialized as 0 embedding rather than 1 embedding of dim 0 2024-05-22 12:25:21 +02:00
Louis Dureuil
16037e2169 Don't remove embedders that are not in the config from the document DB 2024-05-22 12:24:51 +02:00
Louis Dureuil
8f7c8ca7f0 Remove now unused error variant 2024-05-22 12:23:43 +02:00
meili-bors[bot]
ba75d23bfe Merge #4648
4648: Update version for the next release (v1.8.1) in Cargo.toml r=ManyTheFish a=meili-bot

⚠️ This PR is automatically generated. Check the new version is the expected one and Cargo.lock has been updated before merging.

Co-authored-by: ManyTheFish <ManyTheFish@users.noreply.github.com>
2024-05-21 16:38:36 +00:00
ManyTheFish
7fbb3bf8e8 Update version for the next release (v1.8.1) in Cargo.toml 2024-05-21 15:13:03 +00:00
Clément Renault
500ddc76b5 Make the flattened sorter optional 2024-05-21 16:16:36 +02:00
meili-bors[bot]
9066a446a3 Merge #4642
4642: Index the _geo fields when changing the setting while there is already documents in the DB r=ManyTheFish a=irevoire

# Pull Request

## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/4640
Fixes https://github.com/meilisearch/meilisearch/issues/4628

## What does this PR do?
- Add an integration test that first indexes the document and then changes the settings
- Fix `extract_geo_point` by detecting if the `_geo` field has been faceted in this setting change and index all documents

Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: ManyTheFish <many@meilisearch.com>
2024-05-21 13:16:11 +00:00
Louis Dureuil
eccbcf5130 Increase index-scheduler test timeouts 2024-05-21 14:59:08 +02:00
Clément Renault
943f8dba0c Make clippy happy 2024-05-21 14:58:41 +02:00
Clément Renault
1aa8ed9ef7 Make the original sorter optional 2024-05-21 14:53:26 +02:00
ManyTheFish
f762307838 Fix clippy 2024-05-21 13:44:20 +02:00
ManyTheFish
3e94a90722 Fixes 2024-05-21 13:39:46 +02:00
Louis Dureuil
9969f7a638 Add test on index-scheduler 2024-05-20 14:44:10 +02:00
Louis Dureuil
b17cb56dee Test array of vectors 2024-05-20 14:44:10 +02:00
Louis Dureuil
afcd7b9f0c Test hybrid search with hf embedder 2024-05-20 14:44:10 +02:00
ManyTheFish
fc7e817221 Index geo points based on the settings differences 2024-05-20 12:27:26 +02:00
Tamo
0f78703b85 add a test reproducing the bug 2024-05-20 10:58:08 +02:00
Louis Dureuil
30cf972987 Add test with a dump 2024-05-20 10:36:18 +02:00
Louis Dureuil
d05d49ffd8 Fix tests 2024-05-20 10:36:18 +02:00
Louis Dureuil
0462ebbe58 Don't write an empty _vectors field 2024-05-20 10:36:18 +02:00
Louis Dureuil
2f7a8a4efb Don't write vectors that weren't autogenerated in document DB 2024-05-20 10:36:18 +02:00
Louis Dureuil
02714ef5ed Add vectors from vector DB in dump 2024-05-20 10:36:18 +02:00
Louis Dureuil
52d9cb6e5a Refactor vector indexing
- use the parsed_vectors module
- only parse `_vectors` once per document, instead of once per embedder per document
2024-05-20 10:36:17 +02:00
Louis Dureuil
261de888b7 Add function to get the embeddings of a document in an index 2024-05-20 10:36:17 +02:00
Louis Dureuil
98c811247e Add parsed vectors module 2024-05-20 10:25:59 +02:00
97 changed files with 7482 additions and 965 deletions

View File

@@ -43,4 +43,11 @@ 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 }})" -- ${{ steps.command.outputs.command-arguments }}
cargo xtask bench --api-key "${{ 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

60
Cargo.lock generated
View File

@@ -500,7 +500,7 @@ checksum = "8c3c1a368f70d6cf7302d78f8f7093da241fb8e8807c05cc9e51a125895a6d5b"
[[package]]
name = "benchmarks"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"anyhow",
"bytes",
@@ -645,7 +645,7 @@ dependencies = [
[[package]]
name = "build-info"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"anyhow",
"time",
@@ -1545,7 +1545,7 @@ dependencies = [
[[package]]
name = "dump"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"anyhow",
"big_s",
@@ -1793,7 +1793,7 @@ dependencies = [
[[package]]
name = "file-store"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"faux",
"tempfile",
@@ -1816,7 +1816,7 @@ dependencies = [
[[package]]
name = "filter-parser"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"insta",
"nom",
@@ -1836,7 +1836,7 @@ dependencies = [
[[package]]
name = "flatten-serde-json"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"criterion",
"serde_json",
@@ -1954,7 +1954,7 @@ dependencies = [
[[package]]
name = "fuzzers"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"arbitrary",
"clap",
@@ -2447,7 +2447,7 @@ checksum = "206ca75c9c03ba3d4ace2460e57b189f39f43de612c2f85836e65c929701bb2d"
[[package]]
name = "index-scheduler"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"anyhow",
"big_s",
@@ -2464,7 +2464,6 @@ dependencies = [
"meilisearch-auth",
"meilisearch-types",
"page_size 0.5.0",
"puffin",
"rayon",
"roaring",
"serde",
@@ -2643,7 +2642,7 @@ dependencies = [
[[package]]
name = "json-depth-checker"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"criterion",
"serde_json",
@@ -3231,12 +3230,6 @@ version = "0.4.21"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "90ed8c1e510134f979dbc4f070f87d4313098b704861a105fe34231c70a3901c"
[[package]]
name = "lz4_flex"
version = "0.10.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "8b8c72594ac26bfd34f2d99dfced2edfaddfe8a476e3ff2ca0eb293d925c4f83"
[[package]]
name = "macro_rules_attribute"
version = "0.2.0"
@@ -3279,7 +3272,7 @@ checksum = "490cc448043f947bae3cbee9c203358d62dbee0db12107a74be5c30ccfd09771"
[[package]]
name = "meili-snap"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"insta",
"md5",
@@ -3288,7 +3281,7 @@ dependencies = [
[[package]]
name = "meilisearch"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"actix-cors",
"actix-http",
@@ -3341,7 +3334,6 @@ dependencies = [
"pin-project-lite",
"platform-dirs",
"prometheus",
"puffin",
"rand",
"rayon",
"regex",
@@ -3381,7 +3373,7 @@ dependencies = [
[[package]]
name = "meilisearch-auth"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"base64 0.21.7",
"enum-iterator",
@@ -3400,7 +3392,7 @@ dependencies = [
[[package]]
name = "meilisearch-types"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"actix-web",
"anyhow",
@@ -3430,7 +3422,7 @@ dependencies = [
[[package]]
name = "meilitool"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"anyhow",
"clap",
@@ -3469,7 +3461,7 @@ dependencies = [
[[package]]
name = "milli"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"arroy",
"big_s",
@@ -3509,7 +3501,6 @@ dependencies = [
"obkv",
"once_cell",
"ordered-float",
"puffin",
"rand",
"rand_pcg",
"rayon",
@@ -3910,7 +3901,7 @@ checksum = "e3148f5046208a5d56bcfc03053e3ca6334e51da8dfb19b6cdc8b306fae3283e"
[[package]]
name = "permissive-json-pointer"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"big_s",
"serde_json",
@@ -4180,23 +4171,6 @@ version = "2.28.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "106dd99e98437432fed6519dedecfade6a06a73bb7b2a1e019fdd2bee5778d94"
[[package]]
name = "puffin"
version = "0.16.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "76425abd4e1a0ad4bd6995dd974b52f414fca9974171df8e3708b3e660d05a21"
dependencies = [
"anyhow",
"bincode",
"byteorder",
"cfg-if",
"instant",
"lz4_flex",
"once_cell",
"parking_lot",
"serde",
]
[[package]]
name = "pulp"
version = "0.18.9"
@@ -6078,7 +6052,7 @@ dependencies = [
[[package]]
name = "xtask"
version = "1.8.0"
version = "1.9.0"
dependencies = [
"anyhow",
"build-info",

View File

@@ -22,7 +22,7 @@ members = [
]
[workspace.package]
version = "1.8.0"
version = "1.9.0"
authors = [
"Quentin de Quelen <quentin@dequelen.me>",
"Clément Renault <clement@meilisearch.com>",

View File

@@ -25,7 +25,7 @@
<p align="center">⚡ A lightning-fast search engine that fits effortlessly into your apps, websites, and workflow 🔍</p>
Meilisearch helps you shape a delightful search experience in a snap, offering features that work out-of-the-box to speed up your workflow.
[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.
<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 @@ Meilisearch helps you shape a delightful search experience in a snap, offering f
🔥 [**Try it!**](https://where2watch.meilisearch.com/?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=demo-link) 🔥
## ✨ Features
- **Search-as-you-type:** find search results in less than 50 milliseconds
- **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
- **[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 @@ Meilisearch helps you shape a delightful search experience in a snap, offering f
## 📖 Documentation
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).
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).
## 🚀 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). No credit card 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). Additional features include analytics & monitoring in many regions around the world. No credit card is 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). Don't forget 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). Remember 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), 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)
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)
🗞 [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.

View File

@@ -197,6 +197,140 @@ 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();

View File

@@ -0,0 +1,783 @@
---
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,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1
],
"toto": [
0.1
]
}
}

View File

@@ -0,0 +1,786 @@
---
source: dump/src/reader/mod.rs
expression: document
---
{
"id": "e2",
"desc": "natural vector + map",
"_vectors": {
"toto": [],
"default": {
"embeddings": [
[
-0.05189208313822746,
-0.9273212552070618,
0.1443813145160675,
0.0932632014155388,
0.2665371894836426,
0.36266782879829407,
0.6402910947799683,
0.32014018297195435,
0.030915971845388412,
-0.9312191605567932,
-0.3718109726905823,
-0.2700554132461548,
-1.1014580726623535,
0.9154956936836244,
-0.3406888246536255,
1.0077725648880005,
0.6577560901641846,
-0.3955195546150207,
-0.4148270785808563,
0.1855088472366333,
0.5062315464019775,
-0.3632686734199524,
-0.2277890294790268,
0.2560805082321167,
-0.3853609561920166,
-0.1604762226343155,
-0.13947471976280212,
-0.20147813856601715,
-0.4466346800327301,
-0.3761846721172333,
0.1443382054567337,
0.18205296993255615,
0.49359792470932007,
-0.22538000345230105,
-0.4996317625045776,
-0.22734887897968292,
-0.6034309267997742,
-0.7857939600944519,
-0.34923747181892395,
-0.3466345965862274,
0.21176661550998688,
-0.5101462006568909,
-0.3403083384037018,
0.000315118464641273,
0.236465722322464,
-0.10246097296476364,
-1.3013339042663574,
0.3419138789176941,
-0.32963496446609497,
-0.0901619717478752,
-0.5426247119903564,
0.22656650841236117,
-0.44758284091949463,
0.14151698350906372,
-0.1089438870549202,
0.5500766634941101,
-0.670711100101471,
-0.6227269768714905,
0.3894464075565338,
-0.27609574794769287,
0.7028202414512634,
-0.19697771966457367,
0.328511506319046,
0.5063360929489136,
0.4065195322036743,
0.2614171802997589,
-0.30274391174316406,
1.0393824577331543,
-0.7742937207221985,
-0.7874112129211426,
-0.6749666929244995,
0.5190866589546204,
0.004123548045754433,
-0.28312963247299194,
-0.038731709122657776,
-1.0142987966537476,
-0.09519586712121964,
0.8755272626876831,
0.4876938760280609,
0.7811151742935181,
0.85174959897995,
0.11826585978269576,
0.5373436808586121,
0.3649002015590668,
0.19064077734947205,
-0.00287026260048151,
-0.7305403351783752,
-0.015206154435873032,
-0.7899249196052551,
0.19407285749912265,
0.08596625179052353,
-0.28976231813430786,
-0.1525907665491104,
0.3798313438892365,
0.050306469202041626,
-0.5697937607765198,
0.4219021201133728,
0.276252806186676,
0.1559903472661972,
0.10030482709407806,
-0.4043720066547394,
-0.1969818025827408,
0.5739826560020447,
0.2116064727306366,
-1.4620544910430908,
-0.7802462577819824,
-0.24739810824394223,
-0.09791352599859238,
-0.4413802027702331,
0.21549351513385773,
-0.9520436525344848,
-0.08762510865926743,
0.08154498040676117,
-0.6154940724372864,
-1.01079523563385,
0.885427713394165,
0.6967288851737976,
0.27186504006385803,
-0.43194177746772766,
-0.11248451471328735,
0.7576630711555481,
0.4998855590820313,
0.0264343973249197,
0.9872855544090272,
0.5634694695472717,
0.053698331117630005,
0.19410227239131927,
0.3570743501186371,
-0.23670297861099243,
-0.9114483594894408,
0.07884842902421951,
0.7318344116210938,
0.44630110263824463,
0.08745364099740982,
-0.347101628780365,
-0.4314247667789459,
-0.5060274004936218,
0.003706763498485088,
0.44320008158683777,
-0.00788921769708395,
-0.1368623524904251,
-0.17391923069953918,
0.14473655819892883,
0.10927865654230118,
0.6974599361419678,
0.005052129738032818,
-0.016953065991401672,
-0.1256176233291626,
-0.036742497235536575,
0.5591985583305359,
-0.37619709968566895,
0.22429119050502777,
0.5403043031692505,
-0.8603790998458862,
-0.3456307053565979,
0.9292937517166138,
0.5074859261512756,
0.6310645937919617,
-0.3091641068458557,
0.46902573108673096,
0.7891915440559387,
0.4499550759792328,
0.2744995653629303,
0.2712305784225464,
-0.04349074140191078,
-0.3638863265514374,
0.7839881777763367,
0.7352104783058167,
-0.19457511603832245,
-0.5957832932472229,
-0.43704694509506226,
-1.084769368171692,
0.4904985725879669,
0.5385226011276245,
0.1891629993915558,
0.12338479608297348,
0.8315675258636475,
-0.07830192148685455,
1.0916285514831543,
-0.28066861629486084,
-1.3585069179534912,
0.5203898549079895,
0.08678033947944641,
-0.2566044330596924,
0.09484415501356123,
-0.0180208683013916,
1.0264745950698853,
-0.023572135716676712,
0.5864979028701782,
0.7625196576118469,
-0.2543414533138275,
-0.8877770900726318,
0.7611982822418213,
-0.06220436468720436,
0.937336564064026,
0.2704363465309143,
-0.37733694911003113,
0.5076137781143188,
-0.30641937255859375,
0.6252772808074951,
-0.0823579877614975,
-0.03736555948853493,
0.4131673276424408,
-0.6514252424240112,
0.12918265163898468,
-0.4483584463596344,
0.6750786304473877,
-0.37008383870124817,
-0.02324833907186985,
0.38027650117874146,
-0.26374951004981995,
0.4346931278705597,
0.42882832884788513,
-0.48798441886901855,
1.1882442235946655,
0.5132288336753845,
0.5284568667411804,
-0.03538886830210686,
0.29620853066444397,
-1.0683696269989014,
0.25936177372932434,
0.10404160618782043,
-0.25796034932136536,
0.027896970510482788,
-0.09225251525640488,
1.4811025857925415,
0.641173779964447,
-0.13838383555412292,
-0.3437179923057556,
0.5667019486427307,
-0.5400741696357727,
0.31090837717056274,
0.6470608115196228,
-0.3747067153453827,
-0.7364534735679626,
-0.07431528717279434,
0.5173454880714417,
-0.6578747034072876,
0.7107478976249695,
-0.7918999791145325,
-0.0648345872759819,
0.609937846660614,
-0.7329513430595398,
0.9741371870040894,
0.17912346124649048,
-0.02658769302070141,
0.5162150859832764,
-0.3978803157806397,
-0.7833885550498962,
-0.6497276425361633,
-0.3898126780986786,
-0.0952848568558693,
0.2663288116455078,
-0.1604052186012268,
0.373076468706131,
-0.8357769250869751,
-0.05217683315277099,
-0.2680160701274872,
0.8389158248901367,
0.6833611130714417,
-0.6712407469749451,
0.7406917214393616,
-0.44522786140441895,
-0.34645363688468933,
-0.27384576201438904,
-0.9878405928611756,
-0.8166060447692871,
0.06268279999494553,
0.38567957282066345,
-0.3274703919887543,
0.5296315550804138,
-0.11810623109340668,
0.23029841482639313,
0.08616159111261368,
-0.2195747196674347,
0.09430307894945145,
0.4057176411151886,
0.4892159104347229,
-0.1636916548013687,
-0.6071445345878601,
0.41256585717201233,
0.622254490852356,
-0.41223976016044617,
-0.6686707139015198,
-0.7474371790885925,
-0.8509522080421448,
-0.16754287481307983,
-0.9078601002693176,
-0.29653599858283997,
-0.5020652413368225,
0.4692700505256653,
0.01281109917908907,
-0.16071580350399017,
0.03388889133930206,
-0.020511148497462273,
0.5027827024459839,
-0.20729811489582065,
0.48107290267944336,
0.33669769763946533,
-0.5275911688804626,
0.48271527886390686,
0.2738940715789795,
-0.033152539283037186,
-0.13629786670207977,
-0.05965912342071533,
-0.26200807094573975,
0.04002794995903969,
-0.34095603227615356,
-3.986898899078369,
-0.46819332242012024,
-0.422744482755661,
-0.169097900390625,
0.6008929014205933,
0.058016058057546616,
-0.11401277780532836,
-0.3077819049358368,
-0.09595538675785063,
0.6723822355270386,
0.19367831945419312,
0.28304359316825867,
0.1609862744808197,
0.7567598819732666,
0.6889985799789429,
0.06907720118761063,
-0.04188092052936554,
-0.7434936165809631,
0.13321782648563385,
0.8456063270568848,
-0.10364038497209548,
-0.45084846019744873,
-0.4758241474628449,
0.43882066011428833,
-0.6432598829269409,
0.7217311859130859,
-0.24189773201942444,
0.12737572193145752,
-1.1008601188659668,
-0.3305315673351288,
0.14614742994308472,
-0.7819333076477051,
0.5287120342254639,
-0.055538054555654526,
0.1877404749393463,
-0.6907662153244019,
0.5616975426673889,
-0.4611121714115143,
-0.26109233498573303,
-0.12898315489292145,
-0.3724522292613983,
-0.7191406488418579,
-0.4425233602523804,
-0.644108235836029,
0.8424481153488159,
0.17532426118850708,
-0.5121750235557556,
-0.6467239260673523,
-0.0008507720194756985,
0.7866212129592896,
-0.02644744887948036,
-0.005045140627771616,
0.015782782807946205,
0.16334445774555206,
-0.1913367658853531,
-0.13697923719882965,
-0.6684983372688293,
0.18346354365348816,
-0.341105580329895,
0.5427411198616028,
0.3779832422733307,
-0.6778115034103394,
-0.2931850254535675,
-0.8805161714553833,
-0.4212774932384491,
-0.5368952751159668,
-1.3937891721725464,
-1.225494146347046,
0.4276703894138336,
1.1205668449401855,
-0.6005299687385559,
0.15732505917549133,
-0.3914784789085388,
-1.357046604156494,
-0.4707142114639282,
-0.1497287154197693,
-0.25035548210144043,
-0.34328439831733704,
0.39083412289619446,
0.1623048633337021,
-0.9275814294815063,
-0.6430015563964844,
0.2973862886428833,
0.5580436587333679,
-0.6232585310935974,
-0.6611042022705078,
0.4015969038009643,
-1.0232892036437988,
-0.2585645020008087,
-0.5431421399116516,
0.5021264553070068,
-0.48601630330085754,
-0.010242084041237833,
0.5862035155296326,
0.7316920161247253,
0.4036808013916016,
0.4269520044326782,
-0.705938458442688,
0.7747307419776917,
0.10164368897676468,
0.7887958884239197,
-0.9612497091293336,
0.12755516171455383,
0.06812842190265656,
-0.022603651508688927,
0.14722754061222076,
-0.5588505268096924,
-0.20689940452575684,
0.3557641804218292,
-0.6812759637832642,
0.2860803008079529,
-0.38954633474349976,
0.1759403496980667,
-0.5678874850273132,
-0.1692986786365509,
-0.14578519761562347,
0.5711379051208496,
1.0208125114440918,
0.7759483456611633,
-0.372348427772522,
-0.5460885763168335,
0.7190321683883667,
-0.6914990544319153,
0.13365162909030914,
-0.4854792356491089,
0.4054908752441406,
0.4502798914909363,
-0.3041122555732727,
-0.06726965308189392,
-0.05570871382951737,
-0.0455719493329525,
0.4785125255584717,
0.8867972493171692,
0.4107886850833893,
0.6121342182159424,
-0.20477132499217987,
-0.5598517656326294,
-0.6443566679954529,
-0.5905212759971619,
-0.5571200251579285,
0.17573799192905426,
-0.28621870279312134,
0.1685224026441574,
0.09719007462263109,
-0.04223639518022537,
-0.28623101115226746,
-0.1449810117483139,
-0.3789580464363098,
-0.5227636098861694,
-0.049728814512491226,
0.7849089503288269,
0.16792525351047516,
0.9849340915679932,
-0.6559549570083618,
0.35723909735679626,
-0.6822739243507385,
1.2873116731643677,
0.19993330538272855,
0.03512010723352432,
-0.6972134113311768,
0.18453484773635864,
-0.2437680810689926,
0.2156416028738022,
0.5230382680892944,
0.22020135819911957,
0.8314080238342285,
0.15627102553844452,
-0.7330264449119568,
0.3888184726238251,
-0.22034703195095065,
0.5457669496536255,
-0.48084837198257446,
-0.45576658844947815,
-0.09287727624177931,
-0.06968110054731369,
0.35125672817230225,
-0.4278119504451752,
0.2038476765155792,
0.11392722278833388,
0.9433983564376832,
-0.4097744226455689,
0.035297419875860214,
-0.4274404048919678,
-0.25100165605545044,
1.0943366289138794,
-0.07634022831916809,
-0.2925529479980469,
-0.7512530088424683,
0.2649727463722229,
-0.4078235328197479,
-0.3372223973274231,
0.05190162733197212,
0.005654910113662481,
-0.0001571219472680241,
-0.35445958375930786,
-0.7837416529655457,
0.1500556766986847,
0.4383024573326111,
0.6099548935890198,
0.05951934307813645,
-0.21325334906578064,
0.0199207104742527,
-0.22704418003559113,
-0.6481077671051025,
0.37442275881767273,
-1.015955924987793,
0.38637226819992065,
-0.06489371508359909,
-0.494120329618454,
0.3469836115837097,
0.15402406454086304,
-0.7660972476005554,
-0.7053225040435791,
-0.25964751839637756,
0.014004424214363098,
-0.2860170006752014,
-0.17565494775772095,
-0.45117494463920593,
-0.0031954257283359766,
0.09676837921142578,
-0.514464259147644,
0.41698193550109863,
-0.21642713248729703,
-0.5398141145706177,
-0.3647628426551819,
0.37005379796028137,
0.239425927400589,
-0.08833975344896317,
0.934946596622467,
-0.48340797424316406,
0.6241437792778015,
-0.7253676652908325,
-0.04303571209311485,
1.1125205755233765,
-0.15692919492721558,
-0.2914651036262512,
-0.5117168426513672,
0.21365483105182648,
0.4924402534961701,
0.5269662141799927,
0.0352792888879776,
-0.149167999625206,
-0.6019760370254517,
0.08245442807674408,
0.4900692105293274,
0.518824577331543,
-0.00005570516441366635,
-0.553304135799408,
0.22217543423175812,
0.5047767758369446,
0.135724738240242,
1.1511540412902832,
-0.3541218340396881,
-0.9712511897087096,
0.8353699445724487,
-0.39227569103240967,
-0.9117669463157654,
-0.26349931955337524,
0.05597023293375969,
0.20695461332798004,
0.3178807199001312,
1.0663238763809204,
0.5062212347984314,
0.7288597822189331,
0.09899299591779707,
0.553720235824585,
0.675009548664093,
-0.20067055523395536,
0.3138423264026642,
-0.6886593103408813,
-0.2910398542881012,
-1.3186300992965698,
-0.4684459865093231,
-0.095743365585804,
-0.1257995069026947,
-0.4858281314373016,
-0.4935407340526581,
-0.3266896903514862,
-0.3928797245025635,
-0.40803104639053345,
-0.9975396394729614,
0.4229583740234375,
0.37309643626213074,
0.4431034922599793,
0.30364808440208435,
-0.3765178918838501,
0.5616499185562134,
0.16904796659946442,
-0.7343707084655762,
0.2560209631919861,
0.6166825294494629,
0.3200829327106476,
-0.4483652710914612,
0.16224201023578644,
-0.31495288014411926,
-0.42713335156440735,
0.7270734906196594,
0.7049484848976135,
-0.0571461021900177,
0.04477125033736229,
-0.6647796034812927,
1.183672308921814,
0.36199676990509033,
0.046881116926670074,
0.4515796303749085,
0.9278061985969543,
0.31471705436706543,
-0.7073333859443665,
-0.3443860113620758,
0.5440067052841187,
-0.15020819008350372,
-0.541202962398529,
0.5203295946121216,
1.2192286252975464,
-0.9983593225479126,
-0.18758884072303772,
0.2758221924304962,
-0.6511523723602295,
-0.1584404855966568,
-0.236241415143013,
0.2692437767982483,
-0.4941152036190033,
0.4987454116344452,
-0.3331359028816223,
0.3163745701313019,
0.745529294013977,
-0.2905873656272888,
0.13602906465530396,
0.4679684340953827,
1.0555986166000366,
1.075700044631958,
0.5368486046791077,
-0.5118206739425659,
0.8668332099914551,
-0.5726966857910156,
-0.7811751961708069,
0.1938626915216446,
-0.1929349899291992,
0.1757766306400299,
0.6384295225143433,
0.26462844014167786,
0.9542630314826964,
0.19313029944896695,
1.264248013496399,
-0.6304428577423096,
0.0487106591463089,
-0.16211535036563873,
-0.7894763350486755,
0.3582514822483063,
-0.04153040423989296,
0.635784387588501,
0.6554391980171204,
-0.47010496258735657,
-0.8302040696144104,
-0.1350124627351761,
0.2568812072277069,
0.13614831864833832,
-0.2563649117946625,
-1.0434694290161133,
0.3232482671737671,
0.47882452607154846,
0.4298652410507202,
1.0563770532608032,
-0.28917592763900757,
-0.8533256649971008,
0.10648339986801147,
0.6376127004623413,
-0.20832888782024384,
0.2370245456695557,
0.0018312990432605147,
-0.2034837007522583,
0.01051164511591196,
-1.105310082435608,
0.29724350571632385,
0.15604574978351593,
0.1973688006401062,
0.44394731521606445,
0.3974513411521912,
-0.13625948131084442,
0.9571986198425292,
0.2257384955883026,
0.2323588728904724,
-0.5583669543266296,
-0.7854922413825989,
0.1647188365459442,
-1.6098142862319946,
0.318587988615036,
-0.13399995863437653,
-0.2172701060771942,
-0.767514705657959,
-0.5813586711883545,
-0.3195130527019501,
-0.04894036799669266,
0.2929930090904236,
-0.8213384747505188,
0.07181350141763687,
0.7469993829727173,
0.6407455801963806,
0.16365697979927063,
0.7870153188705444,
0.6524736881256104,
0.6399973630905151,
-0.04992736503481865,
-0.03959266096353531,
-0.2512352466583252,
0.8448855876922607,
-0.1422702670097351,
0.1216789186000824,
-1.2647287845611572,
0.5931149125099182,
0.7186052203178406,
-0.06118432432413101,
-1.1942816972732544,
-0.17677085101604462,
0.31543800234794617,
-0.32252824306488037,
0.8255583047866821,
-0.14529970288276672,
-0.2695446312427521,
-0.33378756046295166,
-0.1653425395488739,
0.1454019844532013,
-0.3920115828514099,
0.912214994430542,
-0.7279734015464783,
0.7374742031097412,
0.933980405330658,
0.13429680466651917,
-0.514870285987854,
0.3989711999893189,
-0.11613689363002776,
0.4022413492202759,
-0.9990655779838562,
-0.33749932050704956,
-0.4334589838981629,
-1.376373291015625,
-0.2993924915790558,
-0.09454808384180068,
-0.01314175222069025,
-0.001090060803107917,
0.2137461006641388,
0.2938512861728668,
0.17508235573768616,
0.8260607123374939,
-0.7218498587608337,
0.2414487451314926,
-0.47296759486198425,
-0.3002610504627228,
-1.238540768623352,
0.08663805574178696,
0.6805586218833923,
0.5909030437469482,
-0.42807504534721375,
-0.22887496650218964,
0.47537800669670105,
-1.0474627017974854,
0.6338009238243103,
0.06548397243022919,
0.4971011281013489,
1.3484878540039063
]
],
"userProvided": false
}
}
}

View File

@@ -0,0 +1,785 @@
---
source: dump/src/reader/mod.rs
expression: document
---
{
"id": "e1",
"desc": "natural vector",
"_vectors": {
"default": {
"embeddings": [
[
-0.2979458272457123,
-0.5288640856742859,
-0.019957859069108963,
-0.18495318293571472,
0.7429973483085632,
0.5238497257232666,
0.432366281747818,
0.32744166254997253,
0.0020762972999364138,
-0.9507834911346436,
-0.35097137093544006,
0.08469701558351517,
-1.4176613092422483,
0.4647577106952667,
-0.69340580701828,
1.0372896194458008,
0.3716741800308227,
0.06031008064746857,
-0.6152024269104004,
0.007914665155112743,
0.7954924702644348,
-0.20773003995418549,
0.09376765787601472,
0.04508133605122566,
-0.2084471583366394,
-0.1518009901046753,
0.018195509910583496,
-0.07044368237257004,
-0.18119366466999057,
-0.4480230510234833,
0.3822529911994934,
0.1911812424659729,
0.4674372375011444,
0.06963984668254852,
-0.09341949224472046,
0.005675444379448891,
-0.6774799227714539,
-0.7066726684570313,
-0.39256376028060913,
0.04005039855837822,
0.2084812968969345,
-0.7872875928878784,
-0.8205880522727966,
0.2919981777667999,
-0.06004738807678223,
-0.4907574355602264,
-1.5937862396240234,
0.24249385297298431,
-0.14709846675395966,
-0.11860740929841997,
-0.8299489617347717,
0.472964346408844,
-0.497518390417099,
-0.22205302119255063,
-0.4196169078350067,
0.32697558403015137,
-0.360930860042572,
-0.9789686799049376,
0.1887447088956833,
-0.403737336397171,
0.18524253368377688,
0.3768732249736786,
0.3666233420372009,
0.3511938452720642,
0.6985810995101929,
0.41721710562705994,
0.09754953533411026,
0.6204307079315186,
-1.0762996673583984,
-0.06263761967420578,
-0.7376511693000793,
0.6849768161773682,
-0.1745152473449707,
-0.40449759364128113,
0.20757411420345304,
-0.8424443006515503,
0.330015629529953,
0.3489064872264862,
1.0954371690750122,
0.8487558960914612,
1.1076823472976685,
0.61430823802948,
0.4155903458595276,
0.4111340939998626,
0.05753209814429283,
-0.06429877132177353,
-0.765606164932251,
-0.41703930497169495,
-0.508820652961731,
0.19859947264194489,
-0.16607828438282013,
-0.28112146258354187,
0.11032675206661224,
0.38809511065483093,
-0.36498191952705383,
-0.48671194911003113,
0.6755134463310242,
0.03958442434668541,
0.4478721618652344,
-0.10335399955511092,
-0.9546685814857484,
-0.6087718605995178,
0.17498846352100372,
0.08320838958024979,
-1.4478336572647097,
-0.605027437210083,
-0.5867993235588074,
-0.14711688458919525,
-0.5447602272033691,
-0.026259321719408035,
-0.6997418403625488,
-0.07349082082509995,
0.10638900846242905,
-0.7133527398109436,
-0.9396815299987792,
1.087092399597168,
1.1885089874267578,
0.4011896848678589,
-0.4089202582836151,
-0.10938972979784012,
0.6726722121238708,
0.24576938152313232,
-0.24247920513153076,
1.1499971151351929,
0.47813335061073303,
-0.05331678315997124,
0.32338133454322815,
0.4870913326740265,
-0.23144258558750153,
-1.2023426294326782,
0.2349330335855484,
1.080536961555481,
0.29334118962287903,
0.391574501991272,
-0.15818795561790466,
-0.2948290705680847,
-0.024689948186278343,
0.06602869182825089,
0.5937030911445618,
-0.047901444137096405,
-0.512734591960907,
-0.35780075192451477,
0.28751692175865173,
0.4298716187477112,
0.9242428541183472,
-0.17208744585514069,
0.11515070497989656,
-0.0335976779460907,
-0.3422986567020416,
0.5344581604003906,
0.19895796477794647,
0.33001241087913513,
0.6390730142593384,
-0.6074934005737305,
-0.2553696632385254,
0.9644920229911804,
0.2699219584465027,
0.6403993368148804,
-0.6380003690719604,
-0.027310986071825027,
0.638815701007843,
0.27719101309776306,
-0.13553589582443237,
0.750195324420929,
0.1224869191646576,
-0.20613941550254825,
0.8444448709487915,
0.16200250387191772,
-0.24750925600528717,
-0.739950954914093,
-0.28443849086761475,
-1.176282525062561,
0.516107976436615,
0.3774825632572174,
0.10906043648719788,
0.07962015271186829,
0.7384604215621948,
-0.051241904497146606,
1.1730090379714966,
-0.4828610122203827,
-1.404372215270996,
0.8811132311820984,
-0.3839482367038727,
0.022516896948218346,
-0.0491158664226532,
-0.43027013540267944,
1.2049334049224854,
-0.27309560775756836,
0.6883630752563477,
0.8264574408531189,
-0.5020735263824463,
-0.4874092042446137,
0.6007202863693237,
-0.4965405762195587,
1.1302915811538696,
0.032572727650403976,
-0.3731859028339386,
0.658271849155426,
-0.9023059010505676,
0.7400162220001221,
0.014550759457051754,
-0.19699542224407196,
0.2319706380367279,
-0.789058268070221,
-0.14905710518360138,
-0.5826214551925659,
0.207652747631073,
-0.4507439732551574,
-0.3163885474205017,
0.3604124188423157,
-0.45119962096214294,
0.3428427278995514,
0.3005594313144684,
-0.36026081442832947,
1.1014249324798584,
0.40884315967559814,
0.34991952776908875,
-0.1806638240814209,
0.27440476417541504,
-0.7118373513221741,
0.4645499587059021,
0.214790478348732,
-0.2343102991580963,
0.10500429570674896,
-0.28034430742263794,
1.2267805337905884,
1.0561333894729614,
-0.497364342212677,
-0.6143305897712708,
0.24963727593421936,
-0.33136463165283203,
-0.01473914459347725,
0.495918869972229,
-0.6985538005828857,
-1.0033197402954102,
0.35937801003456116,
0.6325868368148804,
-0.6808838844299316,
1.0354058742523191,
-0.7214401960372925,
-0.33318862318992615,
0.874398410320282,
-0.6594992280006409,
0.6830640435218811,
-0.18534131348133087,
0.024834271520376205,
0.19901277124881744,
-0.5992477536201477,
-1.2126628160476685,
-0.9245557188987732,
-0.3898217976093292,
-0.1286519467830658,
0.4217943847179413,
-0.1143646091222763,
0.5630772709846497,
-0.5240639448165894,
0.21152715384960177,
-0.3792001008987427,
0.8266305327415466,
1.170984387397766,
-0.8072142004966736,
0.11382893472909927,
-0.17953898012638092,
-0.1789460331201553,
-0.15078622102737427,
-1.2082908153533936,
-0.7812382578849792,
-0.10903695970773696,
0.7303897142410278,
-0.39054441452026367,
0.19511254131793976,
-0.09121843427419662,
0.22400228679180145,
0.30143046379089355,
0.1141919493675232,
0.48112115263938904,
0.7307931780815125,
0.09701362252235413,
-0.2795647978782654,
-0.3997688889503479,
0.5540812611579895,
0.564578115940094,
-0.40065160393714905,
-0.3629159033298493,
-0.3789091110229492,
-0.7298538088798523,
-0.6996853351593018,
-0.4477842152118683,
-0.289089560508728,
-0.6430277824401855,
0.2344944179058075,
0.3742927014827728,
-0.5079357028007507,
0.28841453790664673,
0.06515737622976303,
0.707315981388092,
0.09498685598373412,
0.8365515470504761,
0.10002726316452026,
-0.7695478200912476,
0.6264724135398865,
0.7562043070793152,
-0.23112858831882477,
-0.2871039807796478,
-0.25010058283805847,
0.2783474028110504,
-0.03224996477365494,
-0.9119359850883484,
-3.6940200328826904,
-0.5099936127662659,
-0.1604711413383484,
0.17453284561634064,
0.41759559512138367,
0.1419190913438797,
-0.11362407356500626,
-0.33312007784843445,
0.11511333286762238,
0.4667884409427643,
-0.0031647447030991316,
0.15879854559898376,
0.3042248487472534,
0.5404849052429199,
0.8515422344207764,
0.06286454200744629,
0.43790125846862793,
-0.8682025074958801,
-0.06363756954669952,
0.5547921657562256,
-0.01483887154608965,
-0.07361344993114471,
-0.929947018623352,
0.3502565622329712,
-0.5080993175506592,
1.0380364656448364,
-0.2017953395843506,
0.21319580078125,
-1.0763001441955566,
-0.556368887424469,
0.1949922740459442,
-0.6445739269256592,
0.6791343688964844,
0.21188358962535855,
0.3736183941364288,
-0.21800459921360016,
0.7597446441650391,
-0.3732394874095917,
-0.4710160195827484,
0.025146087631583217,
0.05341297015547752,
-0.9522109627723694,
-0.6000866889953613,
-0.08469046652317047,
0.5966026186943054,
0.3444081246852875,
-0.461188405752182,
-0.5279349088668823,
0.10296865552663804,
0.5175143480300903,
-0.20671147108078003,
0.13392412662506104,
0.4812754988670349,
0.2993808686733246,
-0.3005635440349579,
0.5141698122024536,
-0.6239235401153564,
0.2877119481563568,
-0.4452739953994751,
0.5621107816696167,
0.5047508478164673,
-0.4226335883140564,
-0.18578553199768064,
-1.1967322826385498,
0.28178197145462036,
-0.8692031502723694,
-1.1812998056411743,
-1.4526212215423584,
0.4645712077617645,
0.9327932000160216,
-0.6560136675834656,
0.461549699306488,
-0.5621527433395386,
-1.328449010848999,
-0.08676894754171371,
0.00021918353741057217,
-0.18864136934280396,
0.1259666532278061,
0.18240638077259064,
-0.14919660985469818,
-0.8965857625007629,
-0.7539900541305542,
0.013973715715110302,
0.504276692867279,
-0.704748272895813,
-0.6428424119949341,
0.6303996443748474,
-0.5404738187789917,
-0.31176653504371643,
-0.21262824535369873,
0.18736739456653595,
-0.7998970746994019,
0.039946746081113815,
0.7390344738960266,
0.4283199906349182,
0.3795057237148285,
0.07204607129096985,
-0.9230587482452391,
0.9440426230430604,
0.26272690296173096,
0.5598306655883789,
-1.0520871877670288,
-0.2677186131477356,
-0.1888762265443802,
0.30426350235939026,
0.4746131896972656,
-0.5746733546257019,
-0.4197768568992615,
0.8565112948417664,
-0.6767723560333252,
0.23448683321475983,
-0.2010004222393036,
0.4112907350063324,
-0.6497949957847595,
-0.418667733669281,
-0.4950824975967407,
0.44438859820365906,
1.026281714439392,
0.482397586107254,
-0.26220494508743286,
-0.3640787005424499,
0.5907743573188782,
-0.8771642446517944,
0.09708411991596222,
-0.3671700060367584,
0.4331349730491638,
0.619417667388916,
-0.2684665620326996,
-0.5123821496963501,
-0.1502324342727661,
-0.012190685607492924,
0.3580845892429352,
0.8617186546325684,
0.3493645489215851,
1.0270192623138428,
0.18297909200191495,
-0.5881339311599731,
-0.1733516901731491,
-0.5040576457977295,
-0.340370237827301,
-0.26767754554748535,
-0.28570041060447693,
-0.032928116619586945,
0.6029254794120789,
0.17397655546665192,
0.09346921741962431,
0.27815181016921997,
-0.46699589490890503,
-0.8148876428604126,
-0.3964351713657379,
0.3812595009803772,
0.13547226786613464,
0.7126688361167908,
-0.3473474085330963,
-0.06573959439992905,
-0.6483767032623291,
1.4808889627456665,
0.30924928188323975,
-0.5085946917533875,
-0.8613000512123108,
0.3048902451992035,
-0.4241599142551422,
0.15909206867218018,
0.5764641761779785,
-0.07879110425710678,
1.015336513519287,
0.07599356025457382,
-0.7025855779647827,
0.30047643184661865,
-0.35094937682151794,
0.2522146999835968,
-0.2338722199201584,
-0.8326804637908936,
-0.13695412874221802,
-0.03452421352267265,
0.47974953055381775,
-0.18385636806488037,
0.32438594102859497,
0.1797013282775879,
0.787494957447052,
-0.12579888105392456,
-0.07507286965847015,
-0.4389670491218567,
0.2720070779323578,
0.8138866424560547,
0.01974171027541161,
-0.3057698905467987,
-0.6709924936294556,
0.0885881632566452,
-0.2862754464149475,
0.03475658595561981,
-0.1285519152879715,
0.3838353455066681,
-0.2944154739379883,
-0.4204859137535095,
-0.4416137933731079,
0.13426260650157928,
0.36733248829841614,
0.573428750038147,
-0.14928072690963745,
-0.026076916605234143,
0.33286052942276,
-0.5340145826339722,
-0.17279052734375,
-0.01154550164937973,
-0.6620771884918213,
0.18390542268753052,
-0.08265615254640579,
-0.2489682286977768,
0.2429984211921692,
-0.044153645634651184,
-0.986578404903412,
-0.33574509620666504,
-0.5387663841247559,
0.19767941534519196,
0.12540718913078308,
-0.3403128981590271,
-0.4154576361179352,
0.17275673151016235,
0.09407442808151244,
-0.5414086580276489,
0.4393929839134216,
0.1725579798221588,
-0.4998118281364441,
-0.6926208138465881,
0.16552448272705078,
0.6659538149833679,
-0.10949844866991044,
0.986426830291748,
0.01748848147690296,
0.4003709554672241,
-0.5430638194084167,
0.35347291827201843,
0.6887399554252625,
0.08274628221988678,
0.13407137989997864,
-0.591465950012207,
0.3446292281150818,
0.6069018244743347,
0.1935492902994156,
-0.0989871397614479,
0.07008486241102219,
-0.8503749370574951,
-0.09507356584072112,
0.6259510517120361,
0.13934025168418884,
0.06392545253038406,
-0.4112265408039093,
-0.08475656062364578,
0.4974113404750824,
-0.30606114864349365,
1.111435890197754,
-0.018766529858112335,
-0.8422622680664063,
0.4325508773326874,
-0.2832120656967163,
-0.4859798848628998,
-0.41498348116874695,
0.015977520495653152,
0.5292825698852539,
0.4538311660289765,
1.1328668594360352,
0.22632671892642975,
0.7918671369552612,
0.33401933312416077,
0.7306135296821594,
0.3548600673675537,
0.12506209313869476,
0.8573207855224609,
-0.5818327069282532,
-0.6953738927841187,
-1.6171947717666626,
-0.1699674427509308,
0.6318262815475464,
-0.05671752244234085,
-0.28145185112953186,
-0.3976689279079437,
-0.2041076272726059,
-0.5495951175689697,
-0.5152917504310608,
-0.9309796094894408,
0.101932130753994,
0.1367802917957306,
0.1490798443555832,
0.5304336547851563,
-0.5082434415817261,
0.06688683480024338,
0.14657628536224365,
-0.782435953617096,
0.2962816655635834,
0.6965363621711731,
0.8496337532997131,
-0.3042965829372406,
0.04343798756599426,
0.0330701619386673,
-0.5662598013877869,
1.1086925268173218,
0.756072998046875,
-0.204134538769722,
0.2404300570487976,
-0.47848284244537354,
1.3659011125564575,
0.5645433068275452,
-0.15836156904697418,
0.43395575881004333,
0.5944653749465942,
1.0043466091156006,
-0.49446743726730347,
-0.5954391360282898,
0.5341240763664246,
0.020598189905285835,
-0.4036853015422821,
0.4473709762096405,
1.1998231410980225,
-0.9317775368690492,
-0.23321466147899628,
0.2052552700042725,
-0.7423108816146851,
-0.19917210936546328,
-0.1722569614648819,
-0.034072667360305786,
-0.00671181408688426,
0.46396249532699585,
-0.1372445821762085,
0.053376372903585434,
0.7392690777778625,
-0.38447609543800354,
0.07497968524694443,
0.5197252631187439,
1.3746477365493774,
0.9060075879096984,
0.20000585913658145,
-0.4053704142570496,
0.7497360110282898,
-0.34087055921554565,
-1.101803183555603,
0.273650586605072,
-0.5125769376754761,
0.22472351789474487,
0.480757474899292,
-0.19845178723335263,
0.8857700824737549,
0.30752456188201904,
1.1109285354614258,
-0.6768012642860413,
0.524367094039917,
-0.22495046257972717,
-0.4224412739276886,
0.40753406286239624,
-0.23133376240730288,
0.3297771215438843,
0.4905449151992798,
-0.6813114285469055,
-0.7543983459472656,
-0.5599071383476257,
0.14351597428321838,
-0.029278717935085297,
-0.3970443606376648,
-0.303079217672348,
0.24161772429943085,
0.008353390730917454,
-0.0062365154735744,
1.0824860334396362,
-0.3704061508178711,
-1.0337258577346802,
0.04638749733567238,
1.163011074066162,
-0.31737643480300903,
0.013986887410283089,
0.19223114848136905,
-0.2260770797729492,
-0.210910826921463,
-1.0191949605941772,
0.22356095910072327,
0.09353553503751756,
0.18096882104873657,
0.14867214858531952,
0.43408671021461487,
-0.33312076330184937,
0.8173948526382446,
0.6428242921829224,
0.20215003192424777,
-0.6634518504142761,
-0.4132290482521057,
0.29815030097961426,
-1.579406976699829,
-0.0981958732008934,
-0.03941014781594277,
0.1709178239107132,
-0.5481140613555908,
-0.5338194966316223,
-0.3528362512588501,
-0.11561278253793716,
-0.21793591976165771,
-1.1570470333099363,
0.2157980799674988,
0.42083489894866943,
0.9639263153076172,
0.09747201204299928,
0.15671424567699432,
0.4034591615200043,
0.6728067994117737,
-0.5216875672340393,
0.09657668322324751,
-0.2416689097881317,
0.747975766658783,
0.1021689772605896,
0.11652665585279463,
-1.0484966039657593,
0.8489304780960083,
0.7169828414916992,
-0.09012343734502792,
-1.3173753023147583,
0.057890523225069046,
-0.006231260951608419,
-0.1018214002251625,
0.936040461063385,
-0.0502331368625164,
-0.4284322261810303,
-0.38209280371665955,
-0.22668412327766416,
0.0782942995429039,
-0.4881664514541626,
0.9268959760665894,
0.001867273123934865,
0.42261114716529846,
0.8283362984657288,
0.4256294071674347,
-0.7965338826179504,
0.4840078353881836,
-0.19861412048339844,
0.33977967500686646,
-0.4604192078113556,
-0.3107339143753052,
-0.2839638590812683,
-1.5734281539916992,
0.005220232997089624,
0.09239906817674635,
-0.7828494906425476,
-0.1397123783826828,
0.2576255202293396,
0.21372435986995697,
-0.23169949650764465,
0.4016408920288086,
-0.462497353553772,
-0.2186472862958908,
-0.5617868900299072,
-0.3649831712245941,
-1.1585862636566162,
-0.08222806453704834,
0.931126832962036,
0.4327389597892761,
-0.46451422572135925,
-0.5430706143379211,
-0.27434298396110535,
-0.9479129314422609,
0.1845661848783493,
0.3972720205783844,
0.4883299469947815,
1.04031240940094
]
],
"userProvided": false
}
}
}

View File

@@ -0,0 +1,780 @@
---
source: dump/src/reader/mod.rs
expression: document
---
{
"id": "e0",
"desc": "overriden vector",
"_vectors": {
"default": [
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1,
0.1
]
}
}

Binary file not shown.

View File

@@ -22,7 +22,6 @@ 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"] }

View File

@@ -31,6 +31,9 @@ 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};
@@ -526,8 +529,6 @@ 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;
@@ -636,8 +637,6 @@ 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.
@@ -916,8 +915,55 @@ impl IndexScheduler {
if self.must_stop_processing.get() {
return Err(Error::AbortedTask);
}
let (_id, doc) = ret?;
let document = milli::obkv_to_json(&all_fields, &fields_ids_map, doc)?;
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()
});
}
}
index_dumper.push_document(&document)?;
}
@@ -1176,8 +1222,6 @@ 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()?;

View File

@@ -68,19 +68,6 @@ 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 {

View File

@@ -32,7 +32,6 @@ 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: _,

View File

@@ -33,7 +33,6 @@ 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};
@@ -59,7 +58,6 @@ 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;
@@ -344,9 +342,6 @@ 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,
@@ -401,7 +396,6 @@ 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(),
@@ -500,7 +494,6 @@ 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,
@@ -621,10 +614,6 @@ 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(),
@@ -636,31 +625,6 @@ 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();
@@ -1774,6 +1738,7 @@ 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;
@@ -1851,7 +1816,7 @@ 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(1)) {
let breakpoint = match receiver.recv_timeout(std::time::Duration::from_secs(10)) {
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.")
@@ -1962,7 +1927,7 @@ mod tests {
fn advance(&mut self) -> Breakpoint {
let (breakpoint_1, b) = match self
.test_breakpoint_rcv
.recv_timeout(std::time::Duration::from_secs(5))
.recv_timeout(std::time::Duration::from_secs(50))
{
Ok(b) => b,
Err(RecvTimeoutError::Timeout) => {
@@ -1983,7 +1948,7 @@ mod tests {
let (breakpoint_2, b) = match self
.test_breakpoint_rcv
.recv_timeout(std::time::Duration::from_secs(5))
.recv_timeout(std::time::Duration::from_secs(50))
{
Ok(b) => b,
Err(RecvTimeoutError::Timeout) => {
@@ -4982,4 +4947,233 @@ 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]"});
}
}
}
}

View File

@@ -0,0 +1,19 @@
---
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
]
}
}

View File

@@ -0,0 +1,20 @@
---
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"
}
}
}

View File

@@ -0,0 +1,23 @@
---
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"
}
}

View File

@@ -0,0 +1,11 @@
---
source: index-scheduler/src/lib.rs
expression: simple_hf_config.embedder_options
---
{
"HuggingFace": {
"model": "sentence-transformers/all-MiniLM-L6-v2",
"revision": "e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
"distribution": null
}
}

View File

@@ -0,0 +1,19 @@
---
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
]
}
}

View File

@@ -0,0 +1,20 @@
---
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"
}
}
}

View File

@@ -0,0 +1,49 @@
---
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:
----------------------------------------------------------------------

View File

@@ -0,0 +1,48 @@
---
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
----------------------------------------------------------------------

View File

@@ -0,0 +1,45 @@
---
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:
----------------------------------------------------------------------

View File

@@ -0,0 +1,44 @@
---
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
----------------------------------------------------------------------

View File

@@ -0,0 +1,36 @@
---
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:
----------------------------------------------------------------------

View File

@@ -0,0 +1,40 @@
---
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:
----------------------------------------------------------------------

View File

@@ -189,3 +189,4 @@ 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!(InvalidSimilarId);

View File

@@ -239,18 +239,23 @@ InvalidIndexUid , InvalidRequest , BAD_REQUEST ;
InvalidSearchAttributesToSearchOn , InvalidRequest , BAD_REQUEST ;
InvalidSearchAttributesToCrop , InvalidRequest , BAD_REQUEST ;
InvalidSearchAttributesToHighlight , InvalidRequest , BAD_REQUEST ;
InvalidSimilarAttributesToRetrieve , InvalidRequest , BAD_REQUEST ;
InvalidSearchAttributesToRetrieve , 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 ;
@@ -259,7 +264,9 @@ 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 ;
@@ -322,7 +329,8 @@ UnretrievableErrorCode , InvalidRequest , BAD_REQUEST ;
UnsupportedMediaType , InvalidRequest , UNSUPPORTED_MEDIA_TYPE ;
// Experimental features
VectorEmbeddingError , InvalidRequest , BAD_REQUEST
VectorEmbeddingError , InvalidRequest , BAD_REQUEST ;
NotFoundSimilarId , InvalidRequest , BAD_REQUEST
}
impl ErrorCode for JoinError {
@@ -384,7 +392,6 @@ 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(_, _) => {
@@ -487,6 +494,17 @@ 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 (_)."
)
}
}
#[macro_export]
macro_rules! internal_error {
($target:ty : $($other:path), *) => {

View File

@@ -6,7 +6,6 @@ pub struct RuntimeTogglableFeatures {
pub vector_store: bool,
pub metrics: bool,
pub logs_route: bool,
pub export_puffin_reports: bool,
}
#[derive(Default, Debug, Clone, Copy)]

View File

@@ -67,7 +67,6 @@ 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"

View File

@@ -25,6 +25,18 @@ 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;
@@ -66,6 +78,8 @@ 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(

View File

@@ -22,6 +22,8 @@ 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;
@@ -32,6 +34,8 @@ 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;
@@ -86,6 +90,12 @@ 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);

View File

@@ -36,8 +36,9 @@ use crate::routes::indexes::facet_search::FacetSearchQuery;
use crate::routes::{create_all_stats, Stats};
use crate::search::{
FacetSearchResult, MatchingStrategy, SearchQuery, SearchQueryWithIndex, SearchResult,
DEFAULT_CROP_LENGTH, DEFAULT_CROP_MARKER, DEFAULT_HIGHLIGHT_POST_TAG,
DEFAULT_HIGHLIGHT_PRE_TAG, DEFAULT_SEARCH_LIMIT, DEFAULT_SEMANTIC_RATIO,
SimilarQuery, SimilarResult, DEFAULT_CROP_LENGTH, DEFAULT_CROP_MARKER,
DEFAULT_HIGHLIGHT_POST_TAG, DEFAULT_HIGHLIGHT_PRE_TAG, DEFAULT_SEARCH_LIMIT,
DEFAULT_SEMANTIC_RATIO,
};
use crate::Opt;
@@ -73,6 +74,8 @@ pub enum AnalyticsMsg {
BatchMessage(Track),
AggregateGetSearch(SearchAggregator),
AggregatePostSearch(SearchAggregator),
AggregateGetSimilar(SimilarAggregator),
AggregatePostSimilar(SimilarAggregator),
AggregatePostMultiSearch(MultiSearchAggregator),
AggregatePostFacetSearch(FacetSearchAggregator),
AggregateAddDocuments(DocumentsAggregator),
@@ -149,6 +152,8 @@ 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()));
@@ -184,6 +189,14 @@ 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));
}
@@ -379,6 +392,8 @@ 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 {
@@ -441,6 +456,8 @@ 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 => (),
}
}
@@ -494,6 +511,8 @@ 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) =
@@ -541,6 +560,18 @@ 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;
}
}
@@ -1558,3 +1589,235 @@ 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,
}
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,
} = 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.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,
} = 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;
}
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,
} = 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,
},
});
Some(Track {
timestamp,
user: user.clone(),
event: event_name.to_string(),
properties,
..Default::default()
})
}
}
}

View File

@@ -47,8 +47,6 @@ 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(
@@ -68,21 +66,13 @@ 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,
export_puffin_reports,
} = new_features;
let meilisearch_types::features::RuntimeTogglableFeatures { vector_store, metrics, logs_route } =
new_features;
analytics.publish(
"Experimental features Updated".to_string(),
@@ -90,7 +80,6 @@ async fn patch_features(
"vector_store": vector_store,
"metrics": metrics,
"logs_route": logs_route,
"export_puffin_reports": export_puffin_reports,
}),
Some(&req),
);

View File

@@ -69,7 +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, search_rules);
add_search_rules(&mut search_query.filter, search_rules);
}
let index = index_scheduler.index(&index_uid)?;

View File

@@ -29,6 +29,7 @@ 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(
@@ -45,9 +46,14 @@ pub fn configure(cfg: &mut web::ServiceConfig) {
.route(web::delete().to(SeqHandler(delete_index))),
)
.service(web::resource("/stats").route(web::get().to(SeqHandler(get_index_stats))))
.service(
web::resource("/advanced-stats")
.route(web::get().to(SeqHandler(get_advanced_index_stats))),
)
.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)),
);
}
@@ -276,3 +282,16 @@ pub async fn get_index_stats(
debug!(returns = ?stats, "Get index stats");
Ok(HttpResponse::Ok().json(stats))
}
pub async fn get_advanced_index_stats(
index_scheduler: GuardedData<ActionPolicy<{ actions::STATS_GET }>, Data<IndexScheduler>>,
index_uid: web::Path<String>,
) -> Result<HttpResponse, ResponseError> {
let index_uid = IndexUid::try_from(index_uid.into_inner())?;
let index = index_scheduler.index(&index_uid)?;
let rtxn = index.read_txn()?;
let advanced_stats = index.advanced_stats(&rtxn)?;
debug!(returns = ?advanced_stats, "Get advanced index stats");
Ok(HttpResponse::Ok().json(advanced_stats))
}

View File

@@ -196,7 +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, search_rules);
add_search_rules(&mut query.filter, search_rules);
}
let mut aggregate = SearchAggregator::from_query(&query, &req);
@@ -235,7 +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, search_rules);
add_search_rules(&mut query.filter, search_rules);
}
let mut aggregate = SearchAggregator::from_query(&query, &req);

View File

@@ -0,0 +1,171 @@
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, 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, 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().map_err(|code: InvalidSimilarId| {
ResponseError::from_msg(code.to_string(), code.error_code())
})?;
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<InvalidEmbedder>)]
pub embedder: Option<String>,
}
impl TryFrom<SimilarQueryGet> for SimilarQuery {
type Error = InvalidSimilarId;
fn try_from(
SimilarQueryGet {
id,
offset,
limit,
attributes_to_retrieve,
filter,
show_ranking_score,
show_ranking_score_details,
embedder,
}: 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()?,
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,
})
}
}

View File

@@ -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, search_rules);
add_search_rules(&mut query.filter, search_rules);
}
let index = index_scheduler

View File

@@ -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::ResponseError;
use meilisearch_types::error::{Code, ResponseError};
use meilisearch_types::heed::RoTxn;
use meilisearch_types::index_uid::IndexUid;
use meilisearch_types::milli::score_details::{ScoreDetails, ScoringStrategy};
@@ -231,7 +231,7 @@ impl SearchKind {
Ok(Self::Hybrid { embedder_name, embedder, semantic_ratio })
}
fn embedder(
pub(crate) fn embedder(
index_scheduler: &index_scheduler::IndexScheduler,
index: &Index,
embedder_name: Option<&str>,
@@ -417,6 +417,59 @@ 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,
}
#[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 {
@@ -424,6 +477,8 @@ 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 {
@@ -437,6 +492,7 @@ impl From<MatchingStrategy> for TermsMatchingStrategy {
match other {
MatchingStrategy::Last => Self::Last,
MatchingStrategy::All => Self::All,
MatchingStrategy::Frequency => Self::Frequency,
}
}
}
@@ -538,6 +594,16 @@ 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 {
@@ -570,8 +636,8 @@ pub struct FacetSearchResult {
}
/// Incorporate search rules in search query
pub fn add_search_rules(query: &mut SearchQuery, rules: IndexSearchRules) {
query.filter = match (query.filter.take(), rules.filter) {
pub fn add_search_rules(filter: &mut Option<Value>, rules: IndexSearchRules) {
*filter = match (filter.take(), rules.filter) {
(None, rules_filter) => rules_filter,
(filter, None) => filter,
(Some(filter), Some(rules_filter)) => {
@@ -719,131 +785,52 @@ pub fn perform_search(
SearchKind::Hybrid { semantic_ratio, .. } => search.execute_hybrid(*semantic_ratio)?,
};
let fields_ids_map = index.fields_ids_map(&rtxn).unwrap();
let SearchQuery {
q,
vector: _,
hybrid: _,
// already computed from prepare_search
offset: _,
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,
filter: _,
sort,
facets,
highlight_pre_tag,
highlight_post_tag,
crop_marker,
matching_strategy: _,
attributes_to_search_on: _,
} = query;
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 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,
};
// 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 documents =
make_hits(index, &rtxn, format, matching_words, documents_ids, document_scores)?;
let number_of_hits = min(candidates.len() as usize, max_total_hits);
let hits_info = if is_finite_pagination {
let hits_per_page = query.hits_per_page.unwrap_or_else(DEFAULT_SEARCH_LIMIT);
let hits_per_page = 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)
@@ -851,15 +838,15 @@ pub fn perform_search(
HitsInfo::Pagination {
hits_per_page,
page: query.page.unwrap_or(1),
page: page.unwrap_or(1),
total_pages,
total_hits: number_of_hits,
}
} else {
HitsInfo::OffsetLimit { limit: query.limit, offset, estimated_total_hits: number_of_hits }
HitsInfo::OffsetLimit { limit, offset, estimated_total_hits: number_of_hits }
};
let (facet_distribution, facet_stats) = match query.facets {
let (facet_distribution, facet_stats) = match facets {
Some(ref fields) => {
let mut facet_distribution = index.facets_distribution(&rtxn);
@@ -896,7 +883,7 @@ pub fn perform_search(
let result = SearchResult {
hits: documents,
hits_info,
query: query.q.unwrap_or_default(),
query: q.unwrap_or_default(),
processing_time_ms: before_search.elapsed().as_millis(),
facet_distribution,
facet_stats,
@@ -907,6 +894,130 @@ 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,
@@ -941,6 +1052,95 @@ 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,
} = 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);
}
}
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 =

View File

@@ -380,6 +380,43 @@ 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

View File

@@ -1859,8 +1859,7 @@ async fn import_dump_v6_containing_experimental_features() {
{
"vectorStore": false,
"metrics": false,
"logsRoute": false,
"exportPuffinReports": false
"logsRoute": false
}
"###);

View File

@@ -20,8 +20,7 @@ async fn experimental_features() {
{
"vectorStore": false,
"metrics": false,
"logsRoute": false,
"exportPuffinReports": false
"logsRoute": false
}
"###);
@@ -32,8 +31,7 @@ async fn experimental_features() {
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"exportPuffinReports": false
"logsRoute": false
}
"###);
@@ -44,8 +42,7 @@ async fn experimental_features() {
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"exportPuffinReports": false
"logsRoute": false
}
"###);
@@ -57,8 +54,7 @@ async fn experimental_features() {
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"exportPuffinReports": false
"logsRoute": false
}
"###);
@@ -70,8 +66,7 @@ async fn experimental_features() {
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"exportPuffinReports": false
"logsRoute": false
}
"###);
}
@@ -90,8 +85,7 @@ async fn experimental_feature_metrics() {
{
"vectorStore": false,
"metrics": true,
"logsRoute": false,
"exportPuffinReports": false
"logsRoute": false
}
"###);
@@ -146,7 +140,7 @@ 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`, `exportPuffinReports`",
"message": "Unknown field `NotAFeature`: expected one of `vectorStore`, `metrics`, `logsRoute`",
"code": "bad_request",
"type": "invalid_request",
"link": "https://docs.meilisearch.com/errors#bad_request"

View File

@@ -8,6 +8,7 @@ mod index;
mod logs;
mod search;
mod settings;
mod similar;
mod snapshot;
mod stats;
mod swap_indexes;

View File

@@ -505,7 +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`",
"message": "Unknown value `doggo` at `.matchingStrategy`: expected one of `last`, `all`, `frequency`",
"code": "invalid_search_matching_strategy",
"type": "invalid_request",
"link": "https://docs.meilisearch.com/errors#invalid_search_matching_strategy"
@@ -527,7 +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`",
"message": "Unknown value `doggo` for parameter `matchingStrategy`: expected one of `last`, `all`, `frequency`",
"code": "invalid_search_matching_strategy",
"type": "invalid_request",
"link": "https://docs.meilisearch.com/errors#invalid_search_matching_strategy"

View File

@@ -117,3 +117,69 @@ 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;
}

View File

@@ -5,7 +5,10 @@ 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> {
async fn index_with_documents_user_provided<'a>(
server: &'a Server,
documents: &Value,
) -> Index<'a> {
let index = server.index("test");
let (response, code) = server.set_features(json!({"vectorStore": true})).await;
@@ -15,8 +18,7 @@ async fn index_with_documents<'a>(server: &'a Server, documents: &Value) -> Inde
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"exportPuffinReports": false
"logsRoute": false
}
"###);
@@ -34,7 +36,38 @@ async fn index_with_documents<'a>(server: &'a Server, documents: &Value) -> Inde
index
}
static SIMPLE_SEARCH_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
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(|| {
json!([
{
"title": "Shazam!",
@@ -56,7 +89,7 @@ static SIMPLE_SEARCH_DOCUMENTS: Lazy<Value> = Lazy::new(|| {
}])
});
static SINGLE_DOCUMENT: Lazy<Value> = Lazy::new(|| {
static SINGLE_DOCUMENT_VEC: Lazy<Value> = Lazy::new(|| {
json!([{
"title": "Shazam!",
"desc": "a Captain Marvel ersatz",
@@ -65,10 +98,29 @@ static SINGLE_DOCUMENT: 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(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
let (response, code) = index
.search_post(
@@ -98,10 +150,59 @@ 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(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
let search = json!({"q": "Captain", "vector": [1.0, 1.0], "showRankingScore": true, "hybrid": {"semanticRatio": 1.0}});
let (response, code) = index.search_post(search.clone()).await;
@@ -133,7 +234,7 @@ async fn distribution_shift() {
#[actix_rt::test]
async fn highlighter() {
let server = Server::new().await;
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
let (response, code) = index
.search_post(json!({"q": "Captain Marvel", "vector": [1.0, 1.0],
@@ -184,7 +285,7 @@ async fn highlighter() {
#[actix_rt::test]
async fn invalid_semantic_ratio() {
let server = Server::new().await;
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
let (response, code) = index
.search_post(
@@ -256,7 +357,7 @@ async fn invalid_semantic_ratio() {
#[actix_rt::test]
async fn single_document() {
let server = Server::new().await;
let index = index_with_documents(&server, &SINGLE_DOCUMENT).await;
let index = index_with_documents_user_provided(&server, &SINGLE_DOCUMENT_VEC).await;
let (response, code) = index
.search_post(
@@ -272,7 +373,7 @@ async fn single_document() {
#[actix_rt::test]
async fn query_combination() {
let server = Server::new().await;
let index = index_with_documents(&server, &SIMPLE_SEARCH_DOCUMENTS).await;
let index = index_with_documents_user_provided(&server, &SIMPLE_SEARCH_DOCUMENTS_VEC).await;
// search without query and vector, but with hybrid => still placeholder
let (response, code) = index

View File

@@ -0,0 +1,128 @@
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;
}

View File

@@ -7,6 +7,7 @@ mod facet_search;
mod formatted;
mod geo;
mod hybrid;
mod matching_strategy;
mod multi;
mod pagination;
mod restrict_searchable;
@@ -680,6 +681,26 @@ 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]
@@ -895,9 +916,9 @@ async fn test_score_details() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
},
"_rankingScoreDetails": {
@@ -1096,9 +1117,9 @@ async fn experimental_feature_vector_store() {
"id": "287947",
"_vectors": {
"manual": [
1,
2,
3
1.0,
2.0,
3.0
]
},
"_rankingScore": 1.0
@@ -1108,9 +1129,9 @@ async fn experimental_feature_vector_store() {
"id": "299537",
"_vectors": {
"manual": [
1,
2,
54
1.0,
2.0,
54.0
]
},
"_rankingScore": 0.9129111766815186
@@ -1120,9 +1141,9 @@ async fn experimental_feature_vector_store() {
"id": "450465",
"_vectors": {
"manual": [
-100,
340,
90
-100.0,
340.0,
90.0
]
},
"_rankingScore": 0.8106412887573242
@@ -1132,9 +1153,9 @@ async fn experimental_feature_vector_store() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
},
"_rankingScore": 0.7412010431289673
@@ -1144,9 +1165,9 @@ async fn experimental_feature_vector_store() {
"id": "522681",
"_vectors": {
"manual": [
10,
-23,
32
10.0,
-23.0,
32.0
]
},
"_rankingScore": 0.6972063183784485
@@ -1405,9 +1426,9 @@ async fn simple_search_with_strange_synonyms() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
}
}
@@ -1426,9 +1447,9 @@ async fn simple_search_with_strange_synonyms() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
}
}
@@ -1447,9 +1468,9 @@ async fn simple_search_with_strange_synonyms() {
"id": "166428",
"_vectors": {
"manual": [
-100,
231,
32
-100.0,
231.0,
32.0
]
}
}

View File

@@ -75,9 +75,9 @@ async fn simple_search_single_index() {
"id": "450465",
"_vectors": {
"manual": [
-100,
340,
90
-100.0,
340.0,
90.0
]
}
}
@@ -96,9 +96,9 @@ async fn simple_search_single_index() {
"id": "299537",
"_vectors": {
"manual": [
1,
2,
54
1.0,
2.0,
54.0
]
}
}
@@ -194,9 +194,9 @@ async fn simple_search_two_indexes() {
"id": "450465",
"_vectors": {
"manual": [
-100,
340,
90
-100.0,
340.0,
90.0
]
}
}
@@ -227,9 +227,9 @@ async fn simple_search_two_indexes() {
"cattos": "pésti",
"_vectors": {
"manual": [
1,
2,
3
1.0,
2.0,
3.0
]
}
},
@@ -249,9 +249,9 @@ async fn simple_search_two_indexes() {
],
"_vectors": {
"manual": [
1,
2,
54
1.0,
2.0,
54.0
]
}
}

View File

@@ -98,8 +98,7 @@ async fn secrets_are_hidden_in_settings() {
{
"vectorStore": true,
"metrics": false,
"logsRoute": false,
"exportPuffinReports": false
"logsRoute": false
}
"###);

View File

@@ -0,0 +1,696 @@
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_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;
}

View File

@@ -0,0 +1,373 @@
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 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;
}

View File

@@ -31,6 +31,7 @@ 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();

View File

@@ -67,9 +67,6 @@ 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" }

View File

@@ -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, &None)?;
let universe = filtered_universe(ctx.index, ctx.txn, &None)?;
let docs = execute_search(
&mut ctx,

View File

@@ -12,7 +12,10 @@ use bimap::BiHashMap;
pub use builder::DocumentsBatchBuilder;
pub use enriched::{EnrichedDocument, EnrichedDocumentsBatchCursor, EnrichedDocumentsBatchReader};
use obkv::KvReader;
pub use primary_key::{DocumentIdExtractionError, FieldIdMapper, PrimaryKey, DEFAULT_PRIMARY_KEY};
pub use primary_key::{
validate_document_id_value, DocumentIdExtractionError, FieldIdMapper, PrimaryKey,
DEFAULT_PRIMARY_KEY,
};
pub use reader::{DocumentsBatchCursor, DocumentsBatchCursorError, DocumentsBatchReader};
use serde::{Deserialize, Serialize};

View File

@@ -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) -> Result<StdResult<String, UserError>> {
pub fn validate_document_id_value(document_id: Value) -> StdResult<String, UserError> {
match document_id {
Value::String(string) => match validate_document_id(&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) })),
Some(s) if s.len() == string.len() => Ok(string),
Some(s) => Ok(s.to_string()),
None => Err(UserError::InvalidDocumentId { document_id: Value::String(string) }),
},
Value::Number(number) if number.is_i64() => Ok(Ok(number.to_string())),
content => Ok(Err(UserError::InvalidDocumentId { document_id: content })),
Value::Number(number) if number.is_i64() => Ok(number.to_string()),
content => Err(UserError::InvalidDocumentId { document_id: content }),
}
}

View File

@@ -117,10 +117,8 @@ 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: Value, value: Value },
InvalidVectorsMapType { document_id: String, value: Value },
#[error("{0}")]
InvalidFilter(String),
#[error("Invalid type for filter subexpression: expected: {}, found: {1}.", .0.join(", "))]

View File

@@ -195,7 +195,7 @@ mod tests {
fn merge_cbo_roaring_bitmaps() {
let mut buffer = Vec::new();
let small_data = vec![
let small_data = [
RoaringBitmap::from_sorted_iter(1..4).unwrap(),
RoaringBitmap::from_sorted_iter(2..5).unwrap(),
RoaringBitmap::from_sorted_iter(4..6).unwrap(),
@@ -209,7 +209,7 @@ mod tests {
let expected = RoaringBitmap::from_sorted_iter(1..6).unwrap();
assert_eq!(bitmap, expected);
let medium_data = vec![
let medium_data = [
RoaringBitmap::from_sorted_iter(1..4).unwrap(),
RoaringBitmap::from_sorted_iter(2..5).unwrap(),
RoaringBitmap::from_sorted_iter(4..8).unwrap(),

View File

@@ -9,6 +9,7 @@ use heed::types::*;
use heed::{CompactionOption, Database, RoTxn, RwTxn, Unspecified};
use roaring::RoaringBitmap;
use rstar::RTree;
use serde::Serialize;
use time::OffsetDateTime;
use crate::documents::PrimaryKey;
@@ -23,7 +24,7 @@ use crate::heed_codec::{
};
use crate::order_by_map::OrderByMap;
use crate::proximity::ProximityPrecision;
use crate::vector::EmbeddingConfig;
use crate::vector::{Embedding, EmbeddingConfig};
use crate::{
default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds,
FacetDistribution, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldIdWordCountCodec,
@@ -324,6 +325,87 @@ impl Index {
self.env.info().map_size
}
pub fn advanced_stats(&self, rtxn: &heed::RoTxn) -> Result<AdvancedStats> {
use db_name::*;
let mut dbs = BTreeMap::new();
dbs.insert(WORD_DOCIDS, advanced_database_stats(rtxn, self.word_docids)?);
dbs.insert(
WORD_PAIR_PROXIMITY_DOCIDS,
advanced_database_stats(rtxn, self.word_pair_proximity_docids)?,
);
dbs.insert(WORD_PREFIX_DOCIDS, advanced_database_stats(rtxn, self.word_prefix_docids)?);
dbs.insert(WORD_FIELD_ID_DOCIDS, advanced_database_stats(rtxn, self.word_fid_docids)?);
dbs.insert(WORD_POSITION_DOCIDS, advanced_database_stats(rtxn, self.word_position_docids)?);
dbs.insert(DOCUMENTS, advanced_database_stats_no_bitmap(rtxn, self.documents)?);
fn advanced_database_stats<KC>(
rtxn: &heed::RoTxn,
db: Database<KC, CboRoaringBitmapCodec>,
) -> Result<AdvancedDatabaseStats> {
let db = db.remap_key_type::<Bytes>().lazily_decode_data();
let mut entries_count = 0;
let mut total_bitmap_size = 0;
let mut total_bitmap_len = 0;
let mut total_key_size = 0;
for result in db.iter(rtxn)? {
let (bytes_key, lazy_value) = result?;
entries_count += 1;
total_bitmap_size += lazy_value.remap::<Bytes>().decode().unwrap().len();
let bitmap = lazy_value.decode().map_err(heed::Error::Decoding)?;
total_bitmap_len += bitmap.len();
total_key_size += bytes_key.len();
}
Ok(AdvancedDatabaseStats {
entries_count,
average_bitmap_len: Some(total_bitmap_len as f64 / entries_count as f64),
median_bitmap_len: None,
average_value_size: Some(total_bitmap_size as f64 / entries_count as f64),
median_value_size: None,
average_key_size: Some(total_key_size as f64 / entries_count as f64),
median_key_size: None,
})
}
fn advanced_database_stats_no_bitmap<KC, DC>(
rtxn: &heed::RoTxn,
db: Database<KC, DC>,
) -> Result<AdvancedDatabaseStats> {
let db = db.remap_types::<Bytes, Bytes>();
let mut entries_count = 0;
let mut total_value_size = 0;
let mut total_key_size = 0;
for result in db.iter(rtxn)? {
let (bytes_key, bytes_value) = result?;
entries_count += 1;
total_value_size += bytes_value.len();
total_key_size += bytes_key.len();
}
Ok(AdvancedDatabaseStats {
entries_count,
average_bitmap_len: None,
median_bitmap_len: None,
average_value_size: Some(total_value_size as f64 / entries_count as f64),
median_value_size: None,
average_key_size: Some(total_key_size as f64 / entries_count as f64),
median_key_size: None,
})
}
Ok(AdvancedStats {
map_size: self.map_size(),
non_free_pages_size: self.on_disk_size()?,
on_disk_size: self.on_disk_size()?,
databases: dbs,
})
}
pub fn copy_to_file<P: AsRef<Path>>(&self, path: P, option: CompactionOption) -> Result<File> {
self.env.copy_to_file(path, option).map_err(Into::into)
}
@@ -1595,6 +1677,22 @@ 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)
}
@@ -1606,6 +1704,74 @@ 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)
}
}
#[derive(Clone, Debug, Serialize)]
pub struct AdvancedStats {
/// Size of the data memory map.
map_size: usize,
/// Returns the size used by all the databases in the environment without the free pages.
non_free_pages_size: u64,
/// The size of the data file on disk.
on_disk_size: u64,
/// Databases advanced stats.
databases: BTreeMap<&'static str, AdvancedDatabaseStats>,
}
#[derive(Clone, Debug, Serialize)]
pub struct AdvancedDatabaseStats {
/// The number of entries in this database.
entries_count: usize,
/// The average number of entries in the bitmaps of this database.
average_bitmap_len: Option<f64>,
/// The median number of entries in the bitmaps of this database.
median_bitmap_len: Option<f64>,
/// The average size of values of this database.
average_value_size: Option<f64>,
/// The median size of values of this database.
median_value_size: Option<f64>,
/// The average size of keys of this database.
average_key_size: Option<f64>,
/// The mediane size of keys of this database.
median_key_size: Option<f64>,
}
#[cfg(test)]

View File

@@ -63,6 +63,7 @@ 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,
@@ -354,43 +355,13 @@ 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(|c| c == '.').unwrap_or(true)
field.starts_with(facet) && field[facet.len()..].chars().next().map_or(true, |c| c == '.')
}
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;

View File

@@ -24,6 +24,7 @@ pub mod facet;
mod fst_utils;
pub mod hybrid;
pub mod new;
pub mod similar;
#[derive(Debug, Clone)]
pub struct SemanticSearch {
@@ -148,7 +149,7 @@ impl<'a> Search<'a> {
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, &self.filter)
filtered_universe(ctx.index, ctx.txn, &self.filter)
} else {
Ok(self.execute()?.candidates)
}
@@ -161,7 +162,7 @@ impl<'a> Search<'a> {
ctx.attributes_to_search_on(searchable_attributes)?;
}
let universe = filtered_universe(&ctx, &self.filter)?;
let universe = filtered_universe(ctx.index, ctx.txn, &self.filter)?;
let PartialSearchResult {
located_query_terms,
candidates,
@@ -276,6 +277,8 @@ pub enum TermsMatchingStrategy {
Last,
// all words are mandatory
All,
// remove more frequent word first
Frequency,
}
impl Default for TermsMatchingStrategy {

View File

@@ -164,6 +164,21 @@ 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 {

View File

@@ -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, &None).unwrap();
let universe = filtered_universe(ctx.index, ctx.txn, &None).unwrap();
let crate::search::PartialSearchResult { located_query_terms, .. } = execute_search(
&mut ctx,
Some(query),

View File

@@ -197,6 +197,11 @@ 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);
@@ -543,11 +548,15 @@ fn resolve_sort_criteria<'ctx, Query: RankingRuleQueryTrait>(
Ok(())
}
pub fn filtered_universe(ctx: &SearchContext, filters: &Option<Filter>) -> Result<RoaringBitmap> {
pub fn filtered_universe(
index: &Index,
txn: &RoTxn<'_>,
filters: &Option<Filter>,
) -> Result<RoaringBitmap> {
Ok(if let Some(filters) = filters {
filters.evaluate(ctx.txn, ctx.index)?
filters.evaluate(txn, index)?
} else {
ctx.index.documents_ids(ctx.txn)?
index.documents_ids(txn)?
})
}

View File

@@ -1,8 +1,9 @@
use std::cmp::Ordering;
use std::cmp::{Ordering, Reverse};
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::{
@@ -11,6 +12,7 @@ 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`].
@@ -290,6 +292,49 @@ 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,
@@ -315,10 +360,19 @@ 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() {
@@ -329,7 +383,7 @@ impl QueryGraph {
}
let mut cost = 0;
for id in t.term_ids.clone() {
cost = std::cmp::max(cost, cost_of_term_idx(id));
cost = std::cmp::max(cost, order(id));
}
nodes_to_remove
.entry(cost)

View File

@@ -49,19 +49,8 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
ctx: &mut SearchContext<'_>,
vector_candidates: &RoaringBitmap,
) -> Result<()> {
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: std::result::Result<Vec<_>, _> =
ctx.index.arroy_readers(ctx.txn, self.embedder_index).collect();
let readers = readers?;
let target = &self.target;

111
milli/src/search/similar.rs Normal file
View File

@@ -0,0 +1,111 @@
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>,
}
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 }
}
pub fn filter(&mut self, filter: Filter<'a>) -> &mut Self {
self.filter = Some(filter);
self
}
pub fn execute(&self) -> Result<SearchResult> {
let universe = filtered_universe(self.index, self.rtxn, &self.filter)?;
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);
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)
{
documents_ids.push(docid);
let score = 1.0 - distance;
let score = self
.embedder
.distribution()
.map(|distribution| distribution.shift(score))
.unwrap_or(score);
let score = ScoreDetails::Vector(score_details::Vector { similarity: Some(score) });
document_scores.push(vec![score]);
}
Ok(SearchResult {
matching_words: Default::default(),
candidates: universe,
documents_ids,
document_scores,
degraded: false,
used_negative_operator: false,
})
}
}

View File

@@ -21,8 +21,6 @@ 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,

View File

@@ -29,8 +29,6 @@ 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)?;

View File

@@ -29,8 +29,6 @@ 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();

View File

@@ -23,8 +23,6 @@ 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(

View File

@@ -28,8 +28,6 @@ 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() };

View File

@@ -45,10 +45,7 @@ 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(
@@ -127,12 +124,18 @@ pub fn extract_fid_docid_facet_values<R: io::Read + io::Seek>(
add_exists.insert(document);
}
let geo_support =
geo_fields_ids.map_or(false, |(lat, lng)| field_id == lat || field_id == lng);
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, geo_support));
del_value.map(|value| extract_facet_values(&value, del_geo_support));
let add_filterable_values =
add_value.map(|value| extract_facet_values(&value, geo_support));
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| {

View File

@@ -26,8 +26,6 @@ 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(

View File

@@ -8,6 +8,7 @@ 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.
@@ -18,10 +19,8 @@ pub fn extract_geo_points<R: io::Read + io::Seek>(
obkv_documents: grenad::Reader<R>,
indexer: GrenadParameters,
primary_key_id: FieldId,
(lat_fid, lng_fid): (FieldId, FieldId),
settings_diff: &InnerIndexSettingsDiff,
) -> Result<grenad::Reader<BufReader<File>>> {
puffin::profile_function!();
let mut writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
@@ -40,47 +39,27 @@ pub fn extract_geo_points<R: io::Read + io::Seek>(
serde_json::from_slice(document_id).unwrap()
};
// 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);
// 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)?;
// 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 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)?;
}
(None, Some(_)) => {
return Err(GeoError::MissingLatitude { document_id: document_id() }.into())
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)?;
}
(Some(_), None) => {
return Err(GeoError::MissingLongitude { document_id: document_id() }.into())
}
(None, None) => (),
let bytes = obkv.into_inner()?;
writer.insert(docid_bytes, bytes)?;
}
}
@@ -88,16 +67,37 @@ 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(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 })?;
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 })?;
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])
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),
}
}

View File

@@ -10,16 +10,16 @@ use bytemuck::cast_slice;
use grenad::Writer;
use itertools::EitherOrBoth;
use ordered_float::OrderedFloat;
use serde_json::{from_slice, Value};
use serde_json::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, InternalError, Result, ThreadPoolNoAbort, VectorOrArrayOfVectors};
use crate::{DocumentId, Result, ThreadPoolNoAbort};
/// The length of the elements that are always in the buffer when inserting new values.
const TRUNCATE_SIZE: usize = size_of::<DocumentId>();
@@ -31,6 +31,10 @@ 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 {
@@ -65,6 +69,19 @@ 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>
@@ -73,34 +90,52 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
obkv_documents: grenad::Reader<R>,
indexer: GrenadParameters,
settings_diff: &InnerIndexSettingsDiff,
prompt: &Prompt,
embedder_name: &str,
) -> Result<ExtractedVectorPoints> {
puffin::profile_function!();
) -> Result<Vec<ExtractedVectorPoints>> {
let reindex_vectors = settings_diff.reindex_vectors();
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);
// (docid, _index) -> KvWriterDelAdd -> Vector
let mut manual_vectors_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
let new_vectors_fid = new_fields_ids_map.id(RESERVED_VECTORS_FIELD_NAME);
// (docid) -> (prompt)
let mut prompts_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) -> ()
let mut remove_vectors_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
// (docid) -> (prompt)
let prompts_writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
tempfile::tempfile()?,
);
// (docid) -> ()
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,
});
}
let mut key_buffer = Vec::new();
let mut cursor = obkv_documents.into_cursor()?;
@@ -114,152 +149,138 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
key_buffer.clear();
key_buffer.extend_from_slice(docid_bytes);
// since we only needs the primary key when we throw an error we create this getter to
// since we only need 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() };
// 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());
let mut parsed_vectors = ParsedVectorsDiff::new(obkv, old_vectors_fid, new_vectors_fid)
.map_err(|error| error.to_crate_error(document_id().to_string()))?;
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)
};
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 (del_map, add_map) = vectors_field;
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(),
)));
}
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 {
tracing::trace!("⏭️ Prompt unmodified, skipping");
VectorStateDelta::NoChange
if add_vectors.len() > usize::from(u8::MAX) {
return Err(crate::Error::UserError(crate::UserError::TooManyVectors(
document_id().to_string(),
add_vectors.len(),
)));
}
} else {
VectorStateDelta::NowRemoved
}
}
};
// 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,
)?;
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 = 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,
)?;
}
}
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)?,
})
}
let mut results = Vec::new();
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
})
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)
}
/// Computes the diff between both Del and Add numbers and
@@ -270,14 +291,13 @@ fn push_vectors_diff(
manual_vectors_writer: &mut Writer<BufWriter<File>>,
key_buffer: &mut Vec<u8>,
delta: VectorStateDelta,
settings_diff: &InnerIndexSettingsDiff,
reindex_vectors: bool,
) -> 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.
&& !settings_diff.reindex_vectors()
&& !reindex_vectors
{
key_buffer.truncate(TRUNCATE_SIZE);
remove_vectors_writer.insert(&key_buffer, [])?;
@@ -308,7 +328,7 @@ 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 !settings_diff.reindex_vectors() {
if !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();
@@ -336,26 +356,6 @@ 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
@@ -364,7 +364,6 @@ 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

View File

@@ -36,8 +36,6 @@ 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(
@@ -167,8 +165,6 @@ 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};

View File

@@ -26,7 +26,6 @@ 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;
@@ -71,8 +70,6 @@ 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();
@@ -163,7 +160,6 @@ 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();
@@ -176,7 +172,6 @@ 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();

View File

@@ -25,8 +25,6 @@ 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(
@@ -104,8 +102,6 @@ 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};

View File

@@ -43,12 +43,9 @@ 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
@@ -72,7 +69,6 @@ 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,
)
@@ -90,7 +86,6 @@ 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::<
_,
@@ -117,7 +112,6 @@ pub(crate) fn data_from_obkv_documents(
word_fid_docids_reader,
}
},
"word-docids",
);
run_extraction_task::<_, _, grenad::Reader<BufReader<File>>>(
@@ -127,7 +121,6 @@ pub(crate) fn data_from_obkv_documents(
lmdb_writer_sx.clone(),
extract_word_position_docids,
TypedChunk::WordPositionDocids,
"word-position-docids",
);
run_extraction_task::<
@@ -141,7 +134,6 @@ 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>>>(
@@ -151,7 +143,6 @@ 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>>>(
@@ -161,7 +152,6 @@ pub(crate) fn data_from_obkv_documents(
lmdb_writer_sx.clone(),
extract_word_pair_proximity_docids,
TypedChunk::WordPairProximityDocids,
"word-pair-proximity-docids",
);
}
@@ -185,7 +175,6 @@ 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>,
@@ -203,7 +192,7 @@ fn run_extraction_task<FE, FS, M>(
rayon::spawn(move || {
let child_span = tracing::trace_span!(target: "indexing::extract::details", parent: &current_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)));
@@ -226,27 +215,31 @@ 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()?;
if settings_diff.reindex_vectors() || !settings_diff.settings_update_only() {
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 {
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 || {
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 }) => {
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
{
let embeddings = match extract_embeddings(
prompts,
indexer,
@@ -255,28 +248,26 @@ fn send_original_documents_data(
) {
Ok(results) => Some(results),
Err(error) => {
let _ = lmdb_writer_sx_cloned.send(Err(error));
let _ = lmdb_writer_sx.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_cloned.send(Ok(TypedChunk::VectorPoints {
let _ = lmdb_writer_sx.send(Ok(TypedChunk::VectorPoints {
remove_vectors,
embeddings,
expected_dimension: embedder.dimensions(),
manual_vectors,
embedder_name: name,
embedder_name,
}));
}
}
Err(error) => {
let _ = lmdb_writer_sx_cloned.send(Err(error));
}
}
Err(error) => {
let _ = lmdb_writer_sx.send(Err(error));
}
}
});
@@ -300,7 +291,6 @@ 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<(
@@ -310,12 +300,13 @@ fn send_and_extract_flattened_documents_data(
let flattened_documents_chunk =
flattened_documents_chunk.and_then(|c| unsafe { as_cloneable_grenad(&c) })?;
if let Some(geo_fields_ids) = geo_fields_ids {
if settings_diff.run_geo_indexing() {
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, geo_fields_ids);
extract_geo_points(documents_chunk_cloned, indexer, primary_key_id, &settings_diff);
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)),
@@ -354,7 +345,6 @@ 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

View File

@@ -61,7 +61,6 @@ pub fn sorter_into_reader(
sorter: grenad::Sorter<MergeFn>,
indexer: GrenadParameters,
) -> Result<grenad::Reader<BufReader<File>>> {
puffin::profile_function!();
let mut writer = create_writer(
indexer.chunk_compression_type,
indexer.chunk_compression_level,
@@ -182,8 +181,6 @@ where
FS: for<'a> Fn(&'a [u8], &'a mut Vec<u8>) -> Result<&'a [u8]>,
FM: for<'a> Fn(&[u8], &[u8], &'a mut Vec<u8>) -> Result<Option<&'a [u8]>>,
{
puffin::profile_function!();
let mut buffer = Vec::new();
let database = database.remap_types::<Bytes, Bytes>();

View File

@@ -6,6 +6,7 @@ mod typed_chunk;
use std::collections::{HashMap, HashSet};
use std::io::{Read, Seek};
use std::iter;
use std::num::NonZeroU32;
use std::result::Result as StdResult;
use std::sync::Arc;
@@ -140,8 +141,6 @@ where
mut self,
reader: DocumentsBatchReader<R>,
) -> Result<(Self, StdResult<u64, UserError>)> {
puffin::profile_function!();
// Early return when there is no document to add
if reader.is_empty() {
return Ok((self, Ok(0)));
@@ -186,8 +185,6 @@ where
mut self,
to_delete: Vec<String>,
) -> Result<(Self, StdResult<u64, UserError>)> {
puffin::profile_function!();
// Early return when there is no document to add
if to_delete.is_empty() {
// Maintains Invariant: remove documents actually always returns Ok for the inner result
@@ -222,8 +219,6 @@ where
mut self,
to_delete: &RoaringBitmap,
) -> Result<(Self, u64)> {
puffin::profile_function!();
// Early return when there is no document to add
if to_delete.is_empty() {
return Ok((self, 0));
@@ -248,8 +243,6 @@ where
name = "index_documents"
)]
pub fn execute(mut self) -> Result<DocumentAdditionResult> {
puffin::profile_function!();
if self.added_documents == 0 && self.deleted_documents == 0 {
let number_of_documents = self.index.number_of_documents(self.wtxn)?;
return Ok(DocumentAdditionResult { indexed_documents: 0, number_of_documents });
@@ -278,8 +271,6 @@ where
FP: Fn(UpdateIndexingStep) + Sync,
FA: Fn() -> bool + Sync,
{
puffin::profile_function!();
let TransformOutput {
primary_key,
mut settings_diff,
@@ -324,28 +315,6 @@ where
// get the primary key field id
let primary_key_id = settings_diff.new.fields_ids_map.id(&primary_key).unwrap();
// get the fid of the `_geo.lat` and `_geo.lng` fields.
let mut field_id_map = self.index.fields_ids_map(self.wtxn)?;
// self.index.fields_ids_map($a)? ==>> field_id_map
let geo_fields_ids = match field_id_map.id("_geo") {
Some(gfid) => {
let is_sortable = self.index.sortable_fields_ids(self.wtxn)?.contains(&gfid);
let is_filterable = self.index.filterable_fields_ids(self.wtxn)?.contains(&gfid);
// if `_geo` is faceted then we get the `lat` and `lng`
if is_sortable || is_filterable {
let field_ids = field_id_map
.insert("_geo.lat")
.zip(field_id_map.insert("_geo.lng"))
.ok_or(UserError::AttributeLimitReached)?;
Some(field_ids)
} else {
None
}
}
None => None,
};
let pool_params = GrenadParameters {
chunk_compression_type: self.indexer_config.chunk_compression_type,
chunk_compression_level: self.indexer_config.chunk_compression_level,
@@ -359,7 +328,10 @@ where
let min_chunk_size = 1024 * 512; // 512KiB
// compute the chunk size from the number of available threads and the inputed data size.
let total_size = flattened_documents.metadata().map(|m| m.len());
let total_size = match flattened_documents.as_ref() {
Some(flattened_documents) => flattened_documents.metadata().map(|m| m.len()),
None => Ok(default_chunk_size as u64),
};
let current_num_threads = pool.current_num_threads();
// if we have more than 2 thread, create a number of chunk equal to 3/4 threads count
let chunk_count = if current_num_threads > 2 {
@@ -373,8 +345,14 @@ where
}
};
let original_documents = grenad::Reader::new(original_documents)?;
let flattened_documents = grenad::Reader::new(flattened_documents)?;
let original_documents = match original_documents {
Some(original_documents) => Some(grenad::Reader::new(original_documents)?),
None => None,
};
let flattened_documents = match flattened_documents {
Some(flattened_documents) => Some(grenad::Reader::new(flattened_documents)?),
None => None,
};
let max_positions_per_attributes = self.indexer_config.max_positions_per_attributes;
@@ -393,15 +371,23 @@ where
pool.install(|| {
rayon::spawn(move || {
let child_span = tracing::trace_span!(target: "indexing::details", parent: &current_span, "extract_and_send_grenad_chunks");
let _enter = child_span.enter();
puffin::profile_scope!("extract_and_send_grenad_chunks");
// split obkv file into several chunks
let original_chunk_iter =
grenad_obkv_into_chunks(original_documents, pool_params, documents_chunk_size);
let _enter = child_span.enter();
// split obkv file into several chunks
let flattened_chunk_iter =
grenad_obkv_into_chunks(flattened_documents, pool_params, documents_chunk_size);
let original_chunk_iter = match original_documents {
Some(original_documents) => {
grenad_obkv_into_chunks(original_documents,pool_params,documents_chunk_size).map(either::Left)
},
None => Ok(either::Right(iter::empty())),
};
// split obkv file into several chunks
let flattened_chunk_iter = match flattened_documents {
Some(flattened_documents) => {
grenad_obkv_into_chunks(flattened_documents, pool_params, documents_chunk_size).map(either::Left)
},
None => Ok(either::Right(iter::empty())),
};
let result = original_chunk_iter.and_then(|original_chunk| {
let flattened_chunk = flattened_chunk_iter?;
@@ -412,7 +398,6 @@ where
pool_params,
lmdb_writer_sx.clone(),
primary_key_id,
geo_fields_ids,
settings_diff.clone(),
max_positions_per_attributes,
)
@@ -553,10 +538,8 @@ where
)?;
pool.install(|| {
let writer_index = (embedder_index as u16) << 8;
for k in 0..=u8::MAX {
let writer =
arroy::Writer::new(vector_arroy, writer_index | (k as u16), dimension);
for k in crate::vector::arroy_db_range_for_embedder(embedder_index) {
let writer = arroy::Writer::new(vector_arroy, k, dimension);
if writer.is_empty(wtxn)? {
break;
}
@@ -594,8 +577,6 @@ where
FP: Fn(UpdateIndexingStep) + Sync,
FA: Fn() -> bool + Sync,
{
puffin::profile_function!();
// Merged databases are already been indexed, we start from this count;
let mut databases_seen = MERGED_DATABASE_COUNT;
@@ -639,7 +620,6 @@ where
{
let span = tracing::trace_span!(target: "indexing::details", "compute_prefix_diffs");
let _entered = span.enter();
puffin::profile_scope!("compute_prefix_diffs");
current_prefix_fst = self.index.words_prefixes_fst(self.wtxn)?;
@@ -779,8 +759,6 @@ fn execute_word_prefix_docids(
common_prefix_fst_words: &[&[String]],
del_prefix_fst_words: &HashSet<Vec<u8>>,
) -> Result<()> {
puffin::profile_function!();
let mut builder = WordPrefixDocids::new(txn, word_docids_db, word_prefix_docids_db);
builder.chunk_compression_type = indexer_config.chunk_compression_type;
builder.chunk_compression_level = indexer_config.chunk_compression_level;

View File

@@ -1,7 +1,7 @@
use std::borrow::Cow;
use std::collections::btree_map::Entry as BEntry;
use std::collections::hash_map::Entry as HEntry;
use std::collections::HashMap;
use std::collections::{HashMap, HashSet};
use std::fs::File;
use std::io::{Read, Seek};
@@ -20,21 +20,21 @@ use super::{IndexDocumentsMethod, IndexerConfig};
use crate::documents::{DocumentsBatchIndex, EnrichedDocument, EnrichedDocumentsBatchReader};
use crate::error::{Error, InternalError, UserError};
use crate::index::{db_name, main_key};
use crate::update::del_add::{
del_add_from_two_obkvs, into_del_add_obkv, DelAdd, DelAddOperation, KvReaderDelAdd,
};
use crate::update::del_add::{into_del_add_obkv, DelAdd, DelAddOperation, KvReaderDelAdd};
use crate::update::index_documents::GrenadParameters;
use crate::update::settings::{InnerIndexSettings, InnerIndexSettingsDiff};
use crate::update::{AvailableDocumentsIds, UpdateIndexingStep};
use crate::{FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldsIdsMap, Index, Result};
use crate::{
is_faceted_by, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldsIdsMap, Index, Result,
};
pub struct TransformOutput {
pub primary_key: String,
pub settings_diff: InnerIndexSettingsDiff,
pub field_distribution: FieldDistribution,
pub documents_count: usize,
pub original_documents: File,
pub flattened_documents: File,
pub original_documents: Option<File>,
pub flattened_documents: Option<File>,
}
/// Extract the external ids, deduplicate and compute the new internal documents ids
@@ -161,8 +161,6 @@ impl<'a, 'i> Transform<'a, 'i> {
FP: Fn(UpdateIndexingStep) + Sync,
FA: Fn() -> bool + Sync,
{
puffin::profile_function!();
let (mut cursor, fields_index) = reader.into_cursor_and_fields_index();
let external_documents_ids = self.index.external_documents_ids();
let mapping = create_fields_mapping(&mut self.fields_ids_map, &fields_index)?;
@@ -375,8 +373,6 @@ impl<'a, 'i> Transform<'a, 'i> {
where
FA: Fn() -> bool + Sync,
{
puffin::profile_function!();
// there may be duplicates in the documents to remove.
to_remove.sort_unstable();
to_remove.dedup();
@@ -466,8 +462,6 @@ impl<'a, 'i> Transform<'a, 'i> {
where
FA: Fn() -> bool + Sync,
{
puffin::profile_function!();
let mut documents_deleted = 0;
let mut document_sorter_value_buffer = Vec::new();
let mut document_sorter_key_buffer = Vec::new();
@@ -686,8 +680,6 @@ impl<'a, 'i> Transform<'a, 'i> {
where
F: Fn(UpdateIndexingStep) + Sync,
{
puffin::profile_function!();
let primary_key = self
.index
.primary_key(wtxn)?
@@ -808,11 +800,15 @@ impl<'a, 'i> Transform<'a, 'i> {
})?;
let old_inner_settings = InnerIndexSettings::from_index(self.index, wtxn)?;
let fields_ids_map = self.fields_ids_map;
let primary_key_id = self.index.primary_key(wtxn)?.and_then(|name| fields_ids_map.id(name));
let mut new_inner_settings = old_inner_settings.clone();
new_inner_settings.fields_ids_map = self.fields_ids_map;
new_inner_settings.fields_ids_map = fields_ids_map;
let settings_diff = InnerIndexSettingsDiff {
old: old_inner_settings,
new: new_inner_settings,
primary_key_id,
embedding_configs_updated: false,
settings_update_only: false,
};
@@ -822,10 +818,12 @@ impl<'a, 'i> Transform<'a, 'i> {
settings_diff,
field_distribution,
documents_count: self.documents_count,
original_documents: original_documents.into_inner().map_err(|err| err.into_error())?,
flattened_documents: flattened_documents
.into_inner()
.map_err(|err| err.into_error())?,
original_documents: Some(
original_documents.into_inner().map_err(|err| err.into_error())?,
),
flattened_documents: Some(
flattened_documents.into_inner().map_err(|err| err.into_error())?,
),
})
}
@@ -835,34 +833,66 @@ impl<'a, 'i> Transform<'a, 'i> {
fn rebind_existing_document(
old_obkv: KvReader<FieldId>,
settings_diff: &InnerIndexSettingsDiff,
original_obkv_buffer: &mut Vec<u8>,
flattened_obkv_buffer: &mut Vec<u8>,
modified_faceted_fields: &HashSet<String>,
original_obkv_buffer: Option<&mut Vec<u8>>,
flattened_obkv_buffer: Option<&mut Vec<u8>>,
) -> Result<()> {
let mut old_fields_ids_map = settings_diff.old.fields_ids_map.clone();
let mut new_fields_ids_map = settings_diff.new.fields_ids_map.clone();
// Always keep the primary key.
let is_primary_key = |id: FieldId| -> bool { settings_diff.primary_key_id == Some(id) };
// If only the `searchableAttributes` has been changed, keep only the searchable fields.
let must_reindex_searchables = settings_diff.reindex_searchable();
let necessary_searchable_field = |id: FieldId| -> bool {
must_reindex_searchables
&& (settings_diff.old.searchable_fields_ids.contains(&id)
|| settings_diff.new.searchable_fields_ids.contains(&id))
};
// If only a faceted field has been added, keep only this field.
let must_reindex_facets = settings_diff.reindex_facets();
let necessary_faceted_field = |id: FieldId| -> bool {
let field_name = settings_diff.new.fields_ids_map.name(id).unwrap();
must_reindex_facets
&& modified_faceted_fields
.iter()
.any(|long| is_faceted_by(long, field_name) || is_faceted_by(field_name, long))
};
// Alway provide all fields when vectors are involved because
// we need the fields for the prompt/templating.
let reindex_vectors = settings_diff.reindex_vectors();
let mut obkv_writer = KvWriter::<_, FieldId>::memory();
// We iterate over the new `FieldsIdsMap` ids in order and construct the new obkv.
for (id, name) in new_fields_ids_map.iter() {
if let Some(val) = old_fields_ids_map.id(name).and_then(|id| old_obkv.get(id)) {
for (id, val) in old_obkv.iter() {
if is_primary_key(id)
|| necessary_searchable_field(id)
|| necessary_faceted_field(id)
|| reindex_vectors
{
obkv_writer.insert(id, val)?;
}
}
let data = obkv_writer.into_inner()?;
let new_obkv = KvReader::<FieldId>::new(&data);
let obkv = KvReader::<FieldId>::new(&data);
// take the non-flattened version if flatten_from_fields_ids_map returns None.
let old_flattened = Self::flatten_from_fields_ids_map(&old_obkv, &mut old_fields_ids_map)?;
let old_flattened =
old_flattened.as_deref().map_or_else(|| old_obkv, KvReader::<FieldId>::new);
let new_flattened = Self::flatten_from_fields_ids_map(&new_obkv, &mut new_fields_ids_map)?;
let new_flattened =
new_flattened.as_deref().map_or_else(|| new_obkv, KvReader::<FieldId>::new);
if let Some(original_obkv_buffer) = original_obkv_buffer {
original_obkv_buffer.clear();
into_del_add_obkv(obkv, DelAddOperation::DeletionAndAddition, original_obkv_buffer)?;
}
original_obkv_buffer.clear();
flattened_obkv_buffer.clear();
if let Some(flattened_obkv_buffer) = flattened_obkv_buffer {
// take the non-flattened version if flatten_from_fields_ids_map returns None.
let mut fields_ids_map = settings_diff.new.fields_ids_map.clone();
let flattened = Self::flatten_from_fields_ids_map(&obkv, &mut fields_ids_map)?;
let flattened = flattened.as_deref().map_or(obkv, KvReader::new);
del_add_from_two_obkvs(&old_obkv, &new_obkv, original_obkv_buffer)?;
del_add_from_two_obkvs(&old_flattened, &new_flattened, flattened_obkv_buffer)?;
flattened_obkv_buffer.clear();
into_del_add_obkv(
flattened,
DelAddOperation::DeletionAndAddition,
flattened_obkv_buffer,
)?;
}
Ok(())
}
@@ -891,46 +921,63 @@ impl<'a, 'i> Transform<'a, 'i> {
let documents_count = documents_ids.len() as usize;
// We initialize the sorter with the user indexing settings.
let mut original_sorter = create_sorter(
grenad::SortAlgorithm::Stable,
keep_first,
self.indexer_settings.chunk_compression_type,
self.indexer_settings.chunk_compression_level,
self.indexer_settings.max_nb_chunks,
self.indexer_settings.max_memory.map(|mem| mem / 2),
);
let mut original_sorter = if settings_diff.reindex_vectors() {
Some(create_sorter(
grenad::SortAlgorithm::Stable,
keep_first,
self.indexer_settings.chunk_compression_type,
self.indexer_settings.chunk_compression_level,
self.indexer_settings.max_nb_chunks,
self.indexer_settings.max_memory.map(|mem| mem / 2),
))
} else {
None
};
// We initialize the sorter with the user indexing settings.
let mut flattened_sorter = create_sorter(
grenad::SortAlgorithm::Stable,
keep_first,
self.indexer_settings.chunk_compression_type,
self.indexer_settings.chunk_compression_level,
self.indexer_settings.max_nb_chunks,
self.indexer_settings.max_memory.map(|mem| mem / 2),
);
let mut flattened_sorter =
if settings_diff.reindex_searchable() || settings_diff.reindex_facets() {
Some(create_sorter(
grenad::SortAlgorithm::Stable,
keep_first,
self.indexer_settings.chunk_compression_type,
self.indexer_settings.chunk_compression_level,
self.indexer_settings.max_nb_chunks,
self.indexer_settings.max_memory.map(|mem| mem / 2),
))
} else {
None
};
let mut original_obkv_buffer = Vec::new();
let mut flattened_obkv_buffer = Vec::new();
let mut document_sorter_key_buffer = Vec::new();
for result in self.index.external_documents_ids().iter(wtxn)? {
let (external_id, docid) = result?;
let old_obkv = self.index.documents.get(wtxn, &docid)?.ok_or(
InternalError::DatabaseMissingEntry { db_name: db_name::DOCUMENTS, key: None },
)?;
if original_sorter.is_some() || flattened_sorter.is_some() {
let modified_faceted_fields = settings_diff.modified_faceted_fields();
let mut original_obkv_buffer = Vec::new();
let mut flattened_obkv_buffer = Vec::new();
let mut document_sorter_key_buffer = Vec::new();
for result in self.index.external_documents_ids().iter(wtxn)? {
let (external_id, docid) = result?;
let old_obkv = self.index.documents.get(wtxn, &docid)?.ok_or(
InternalError::DatabaseMissingEntry { db_name: db_name::DOCUMENTS, key: None },
)?;
Self::rebind_existing_document(
old_obkv,
&settings_diff,
&mut original_obkv_buffer,
&mut flattened_obkv_buffer,
)?;
Self::rebind_existing_document(
old_obkv,
&settings_diff,
&modified_faceted_fields,
Some(&mut original_obkv_buffer).filter(|_| original_sorter.is_some()),
Some(&mut flattened_obkv_buffer).filter(|_| flattened_sorter.is_some()),
)?;
document_sorter_key_buffer.clear();
document_sorter_key_buffer.extend_from_slice(&docid.to_be_bytes());
document_sorter_key_buffer.extend_from_slice(external_id.as_bytes());
original_sorter.insert(&document_sorter_key_buffer, &original_obkv_buffer)?;
flattened_sorter.insert(docid.to_be_bytes(), &flattened_obkv_buffer)?;
if let Some(original_sorter) = original_sorter.as_mut() {
document_sorter_key_buffer.clear();
document_sorter_key_buffer.extend_from_slice(&docid.to_be_bytes());
document_sorter_key_buffer.extend_from_slice(external_id.as_bytes());
original_sorter.insert(&document_sorter_key_buffer, &original_obkv_buffer)?;
}
if let Some(flattened_sorter) = flattened_sorter.as_mut() {
flattened_sorter.insert(docid.to_be_bytes(), &flattened_obkv_buffer)?;
}
}
}
let grenad_params = GrenadParameters {
@@ -941,17 +988,22 @@ impl<'a, 'i> Transform<'a, 'i> {
};
// Once we have written all the documents, we merge everything into a Reader.
let original_documents = sorter_into_reader(original_sorter, grenad_params)?;
let flattened_documents = sorter_into_reader(flattened_sorter, grenad_params)?;
let flattened_documents = match flattened_sorter {
Some(flattened_sorter) => Some(sorter_into_reader(flattened_sorter, grenad_params)?),
None => None,
};
let original_documents = match original_sorter {
Some(original_sorter) => Some(sorter_into_reader(original_sorter, grenad_params)?),
None => None,
};
Ok(TransformOutput {
primary_key,
field_distribution,
settings_diff,
documents_count,
original_documents: original_documents.into_inner().into_inner(),
flattened_documents: flattened_documents.into_inner().into_inner(),
original_documents: original_documents.map(|od| od.into_inner().into_inner()),
flattened_documents: flattened_documents.map(|fd| fd.into_inner().into_inner()),
})
}
}

View File

@@ -1,4 +1,4 @@
use std::collections::HashMap;
use std::collections::{BTreeSet, HashMap};
use std::convert::TryInto;
use std::fs::File;
use std::io::{self, BufReader};
@@ -118,65 +118,6 @@ impl TypedChunk {
}
}
impl TypedChunk {
pub fn to_debug_string(&self) -> String {
match self {
TypedChunk::FieldIdDocidFacetStrings(grenad) => {
format!("FieldIdDocidFacetStrings {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::FieldIdDocidFacetNumbers(grenad) => {
format!("FieldIdDocidFacetNumbers {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::Documents(grenad) => {
format!("Documents {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::FieldIdWordCountDocids(grenad) => {
format!("FieldIdWordcountDocids {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::WordDocids {
word_docids_reader,
exact_word_docids_reader,
word_fid_docids_reader,
} => format!(
"WordDocids {{ word_docids_reader: {}, exact_word_docids_reader: {}, word_fid_docids_reader: {} }}",
word_docids_reader.len(),
exact_word_docids_reader.len(),
word_fid_docids_reader.len()
),
TypedChunk::WordPositionDocids(grenad) => {
format!("WordPositionDocids {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::WordPairProximityDocids(grenad) => {
format!("WordPairProximityDocids {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::FieldIdFacetStringDocids((grenad, _)) => {
format!("FieldIdFacetStringDocids {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::FieldIdFacetNumberDocids(grenad) => {
format!("FieldIdFacetNumberDocids {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::FieldIdFacetExistsDocids(grenad) => {
format!("FieldIdFacetExistsDocids {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::FieldIdFacetIsNullDocids(grenad) => {
format!("FieldIdFacetIsNullDocids {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::FieldIdFacetIsEmptyDocids(grenad) => {
format!("FieldIdFacetIsEmptyDocids {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::GeoPoints(grenad) => {
format!("GeoPoints {{ number_of_entries: {} }}", grenad.len())
}
TypedChunk::VectorPoints{ remove_vectors, manual_vectors, embeddings, expected_dimension, embedder_name } => {
format!("VectorPoints {{ remove_vectors: {}, manual_vectors: {}, embeddings: {}, dimension: {}, embedder_name: {} }}", remove_vectors.len(), manual_vectors.len(), embeddings.as_ref().map(|e| e.len()).unwrap_or_default(), expected_dimension, embedder_name)
}
TypedChunk::ScriptLanguageDocids(sl_map) => {
format!("ScriptLanguageDocids {{ number_of_entries: {} }}", sl_map.len())
}
}
}
}
/// Write typed chunk in the corresponding LMDB database of the provided index.
/// Return new documents seen.
#[tracing::instrument(level = "trace", skip_all, target = "indexing::write_db")]
@@ -185,14 +126,16 @@ pub(crate) fn write_typed_chunk_into_index(
index: &Index,
wtxn: &mut RwTxn,
) -> Result<(RoaringBitmap, bool)> {
puffin::profile_function!(typed_chunks[0].to_debug_string());
let mut is_merged_database = false;
match typed_chunks[0] {
TypedChunk::Documents(_) => {
let span = tracing::trace_span!(target: "indexing::write_db", "documents");
let _entered = span.enter();
let fields_ids_map = index.fields_ids_map(wtxn)?;
let vectors_fid =
fields_ids_map.id(crate::vector::parsed_vectors::RESERVED_VECTORS_FIELD_NAME);
let mut builder = MergerBuilder::new(keep_latest_obkv as MergeFn);
for typed_chunk in typed_chunks {
let TypedChunk::Documents(chunk) = typed_chunk else {
@@ -206,6 +149,10 @@ pub(crate) fn write_typed_chunk_into_index(
let mut docids = index.documents_ids(wtxn)?;
let mut iter = merger.into_stream_merger_iter()?;
let embedders: BTreeSet<_> =
index.embedding_configs(wtxn)?.into_iter().map(|(k, _v)| k).collect();
let mut vectors_buffer = Vec::new();
while let Some((key, reader)) = iter.next()? {
let mut writer: KvWriter<_, FieldId> = KvWriter::memory();
let reader: KvReader<FieldId> = KvReader::new(reader);
@@ -219,7 +166,35 @@ pub(crate) fn write_typed_chunk_into_index(
let del_add_reader = KvReaderDelAdd::new(value);
if let Some(addition) = del_add_reader.get(DelAdd::Addition) {
writer.insert(field_id, addition)?;
let addition = if vectors_fid == Some(field_id) {
'vectors: {
vectors_buffer.clear();
let Ok(mut vectors) =
crate::vector::parsed_vectors::ParsedVectors::from_bytes(
addition,
)
else {
// if the `_vectors` field cannot be parsed as map of vectors, just write it as-is
break 'vectors Some(addition);
};
vectors.retain_user_provided_vectors(&embedders);
let crate::vector::parsed_vectors::ParsedVectors(vectors) = vectors;
if vectors.is_empty() {
// skip writing empty `_vectors` map
break 'vectors None;
}
serde_json::to_writer(&mut vectors_buffer, &vectors)
.map_err(InternalError::SerdeJson)?;
Some(vectors_buffer.as_slice())
}
} else {
Some(addition)
};
if let Some(addition) = addition {
writer.insert(field_id, addition)?;
}
}
}
@@ -659,16 +634,9 @@ pub(crate) fn write_typed_chunk_into_index(
let embedder_index = index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or(
InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None },
)?;
let writer_index = (embedder_index as u16) << 8;
// FIXME: allow customizing distance
let writers: Vec<_> = (0..=u8::MAX)
.map(|k| {
arroy::Writer::new(
index.vector_arroy,
writer_index | (k as u16),
expected_dimension,
)
})
let writers: Vec<_> = crate::vector::arroy_db_range_for_embedder(embedder_index)
.map(|k| arroy::Writer::new(index.vector_arroy, k, expected_dimension))
.collect();
// remove vectors for docids we want them removed
@@ -841,7 +809,6 @@ where
FS: for<'a> Fn(&'a [u8], &'a mut Vec<u8>) -> Result<&'a [u8]>,
FM: for<'a> Fn(&[u8], &[u8], &'a mut Vec<u8>) -> Result<Option<&'a [u8]>>,
{
puffin::profile_function!();
let mut buffer = Vec::new();
let database = database.remap_types::<Bytes, Bytes>();

View File

@@ -398,8 +398,6 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
FP: Fn(UpdateIndexingStep) + Sync,
FA: Fn() -> bool + Sync,
{
puffin::profile_function!();
// if the settings are set before any document update, we don't need to do anything, and
// will set the primary key during the first document addition.
if self.index.number_of_documents(self.wtxn)? == 0 {
@@ -1067,10 +1065,17 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
// 3. Keep the old vectors but reattempt indexing on a prompt change: only actually changed prompt will need embedding + storage
let embedding_configs_updated = self.update_embedding_configs()?;
let new_inner_settings = InnerIndexSettings::from_index(self.index, self.wtxn)?;
let mut new_inner_settings = InnerIndexSettings::from_index(self.index, self.wtxn)?;
new_inner_settings.recompute_facets(self.wtxn, self.index)?;
let primary_key_id = self
.index
.primary_key(self.wtxn)?
.and_then(|name| new_inner_settings.fields_ids_map.id(name));
let inner_settings_diff = InnerIndexSettingsDiff {
old: old_inner_settings,
new: new_inner_settings,
primary_key_id,
embedding_configs_updated,
settings_update_only: true,
};
@@ -1086,10 +1091,9 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
pub struct InnerIndexSettingsDiff {
pub(crate) old: InnerIndexSettings,
pub(crate) new: InnerIndexSettings,
pub(crate) primary_key_id: Option<FieldId>,
// TODO: compare directly the embedders.
pub(crate) embedding_configs_updated: bool,
pub(crate) settings_update_only: bool,
}
@@ -1099,13 +1103,8 @@ impl InnerIndexSettingsDiff {
}
pub fn reindex_searchable(&self) -> bool {
self.old
.fields_ids_map
.iter()
.zip(self.new.fields_ids_map.iter())
.any(|(old, new)| old != new)
|| self.old.stop_words.as_ref().map(|set| set.as_fst().as_bytes())
!= self.new.stop_words.as_ref().map(|set| set.as_fst().as_bytes())
self.old.stop_words.as_ref().map(|set| set.as_fst().as_bytes())
!= self.new.stop_words.as_ref().map(|set| set.as_fst().as_bytes())
|| self.old.allowed_separators != self.new.allowed_separators
|| self.old.dictionary != self.new.dictionary
|| self.old.user_defined_searchable_fields != self.new.user_defined_searchable_fields
@@ -1132,15 +1131,7 @@ impl InnerIndexSettingsDiff {
return true;
}
let faceted_updated =
(existing_fields - old_faceted_fields) != (existing_fields - new_faceted_fields);
self.old
.fields_ids_map
.iter()
.zip(self.new.fields_ids_map.iter())
.any(|(old, new)| old != new)
|| faceted_updated
(existing_fields - old_faceted_fields) != (existing_fields - new_faceted_fields)
}
pub fn reindex_vectors(&self) -> bool {
@@ -1150,6 +1141,15 @@ impl InnerIndexSettingsDiff {
pub fn settings_update_only(&self) -> bool {
self.settings_update_only
}
pub fn run_geo_indexing(&self) -> bool {
self.old.geo_fields_ids != self.new.geo_fields_ids
|| (!self.settings_update_only && self.new.geo_fields_ids.is_some())
}
pub fn modified_faceted_fields(&self) -> HashSet<String> {
&self.old.user_defined_faceted_fields ^ &self.new.user_defined_faceted_fields
}
}
#[derive(Clone)]
@@ -1166,6 +1166,7 @@ pub(crate) struct InnerIndexSettings {
pub proximity_precision: ProximityPrecision,
pub embedding_configs: EmbeddingConfigs,
pub existing_fields: HashSet<String>,
pub geo_fields_ids: Option<(FieldId, FieldId)>,
}
impl InnerIndexSettings {
@@ -1174,7 +1175,7 @@ impl InnerIndexSettings {
let stop_words = stop_words.map(|sw| sw.map_data(Vec::from).unwrap());
let allowed_separators = index.allowed_separators(rtxn)?;
let dictionary = index.dictionary(rtxn)?;
let fields_ids_map = index.fields_ids_map(rtxn)?;
let mut fields_ids_map = index.fields_ids_map(rtxn)?;
let user_defined_searchable_fields = index.user_defined_searchable_fields(rtxn)?;
let user_defined_searchable_fields =
user_defined_searchable_fields.map(|sf| sf.into_iter().map(String::from).collect());
@@ -1189,6 +1190,24 @@ impl InnerIndexSettings {
.into_iter()
.filter_map(|(field, count)| (count != 0).then_some(field))
.collect();
// index.fields_ids_map($a)? ==>> fields_ids_map
let geo_fields_ids = match fields_ids_map.id("_geo") {
Some(gfid) => {
let is_sortable = index.sortable_fields_ids(rtxn)?.contains(&gfid);
let is_filterable = index.filterable_fields_ids(rtxn)?.contains(&gfid);
// if `_geo` is faceted then we get the `lat` and `lng`
if is_sortable || is_filterable {
let field_ids = fields_ids_map
.insert("_geo.lat")
.zip(fields_ids_map.insert("_geo.lng"))
.ok_or(UserError::AttributeLimitReached)?;
Some(field_ids)
} else {
None
}
}
None => None,
};
Ok(Self {
stop_words,
@@ -1203,6 +1222,7 @@ impl InnerIndexSettings {
proximity_precision,
embedding_configs,
existing_fields,
geo_fields_ids,
})
}

View File

@@ -52,8 +52,6 @@ impl<'t, 'i> WordPrefixDocids<'t, 'i> {
common_prefix_fst_words: &[&[String]],
del_prefix_fst_words: &HashSet<Vec<u8>>,
) -> Result<()> {
puffin::profile_function!();
// It is forbidden to keep a mutable reference into the database
// and write into it at the same time, therefore we write into another file.
let mut prefix_docids_sorter = create_sorter(

View File

@@ -57,7 +57,6 @@ impl<'t, 'i> WordPrefixIntegerDocids<'t, 'i> {
common_prefix_fst_words: &[&[String]],
del_prefix_fst_words: &HashSet<Vec<u8>>,
) -> Result<()> {
puffin::profile_function!();
debug!("Computing and writing the word levels integers docids into LMDB on disk...");
let mut prefix_integer_docids_sorter = create_sorter(

View File

@@ -45,8 +45,6 @@ impl<'t, 'i> WordsPrefixesFst<'t, 'i> {
name = "words_prefix_fst"
)]
pub fn execute(self) -> Result<()> {
puffin::profile_function!();
let words_fst = self.index.words_fst(self.wtxn)?;
let mut current_prefix = vec![SmallString32::new(); self.max_prefix_length];

View File

@@ -13,6 +13,7 @@ pub mod error;
pub mod hf;
pub mod manual;
pub mod openai;
pub mod parsed_vectors;
pub mod settings;
pub mod ollama;
@@ -147,6 +148,10 @@ impl EmbeddingConfigs {
self.get(self.get_default_embedder_name())
}
pub fn inner_as_ref(&self) -> &HashMap<String, (Arc<Embedder>, Arc<Prompt>)> {
&self.0
}
/// Get the name of the default embedder configuration.
///
/// The default embedder is determined as follows:
@@ -437,3 +442,9 @@ impl DistributionShift {
pub const fn is_cuda_enabled() -> bool {
cfg!(feature = "cuda")
}
pub fn arroy_db_range_for_embedder(embedder_id: u8) -> impl Iterator<Item = u16> {
let embedder_id = (embedder_id as u16) << 8;
(0..=u8::MAX).map(move |k| embedder_id | (k as u16))
}

View File

@@ -0,0 +1,207 @@
use std::collections::{BTreeMap, BTreeSet};
use obkv::KvReader;
use serde_json::{from_slice, Value};
use super::Embedding;
use crate::update::del_add::{DelAdd, KvReaderDelAdd};
use crate::{FieldId, InternalError, UserError};
pub const RESERVED_VECTORS_FIELD_NAME: &str = "_vectors";
#[derive(serde::Serialize, serde::Deserialize, Debug)]
#[serde(untagged)]
pub enum Vectors {
ImplicitlyUserProvided(VectorOrArrayOfVectors),
Explicit(ExplicitVectors),
}
impl Vectors {
pub fn into_array_of_vectors(self) -> Vec<Embedding> {
match self {
Vectors::ImplicitlyUserProvided(embeddings)
| Vectors::Explicit(ExplicitVectors { embeddings, user_provided: _ }) => {
embeddings.into_array_of_vectors().unwrap_or_default()
}
}
}
}
#[derive(serde::Serialize, serde::Deserialize, Debug)]
#[serde(rename_all = "camelCase")]
pub struct ExplicitVectors {
pub embeddings: VectorOrArrayOfVectors,
pub user_provided: bool,
}
pub struct ParsedVectorsDiff {
pub old: Option<BTreeMap<String, Vectors>>,
pub new: Option<BTreeMap<String, Vectors>>,
}
impl ParsedVectorsDiff {
pub fn new(
documents_diff: KvReader<'_, FieldId>,
old_vectors_fid: Option<FieldId>,
new_vectors_fid: Option<FieldId>,
) -> Result<Self, Error> {
let old = match old_vectors_fid
.and_then(|vectors_fid| documents_diff.get(vectors_fid))
.map(KvReaderDelAdd::new)
.map(|obkv| to_vector_map(obkv, DelAdd::Deletion))
.transpose()
{
Ok(del) => del,
// ignore wrong shape for old version of documents, use an empty map in this case
Err(Error::InvalidMap(value)) => {
tracing::warn!(%value, "Previous version of the `_vectors` field had a wrong shape");
Default::default()
}
Err(error) => {
return Err(error);
}
}
.flatten();
let new = new_vectors_fid
.and_then(|vectors_fid| documents_diff.get(vectors_fid))
.map(KvReaderDelAdd::new)
.map(|obkv| to_vector_map(obkv, DelAdd::Addition))
.transpose()?
.flatten();
Ok(Self { old, new })
}
pub fn remove(&mut self, embedder_name: &str) -> (Option<Vectors>, Option<Vectors>) {
let old = self.old.as_mut().and_then(|old| old.remove(embedder_name));
let new = self.new.as_mut().and_then(|new| new.remove(embedder_name));
(old, new)
}
}
pub struct ParsedVectors(pub BTreeMap<String, Vectors>);
impl ParsedVectors {
pub fn from_bytes(value: &[u8]) -> Result<Self, Error> {
let Ok(value) = from_slice(value) else {
let value = from_slice(value).map_err(Error::InternalSerdeJson)?;
return Err(Error::InvalidMap(value));
};
Ok(ParsedVectors(value))
}
pub fn retain_user_provided_vectors(&mut self, embedders: &BTreeSet<String>) {
self.0.retain(|k, v| match v {
Vectors::ImplicitlyUserProvided(_) => true,
Vectors::Explicit(ExplicitVectors { embeddings: _, user_provided }) => {
*user_provided
// if the embedder is not in the config, then never touch it
|| !embedders.contains(k)
}
});
}
}
pub enum Error {
InvalidMap(Value),
InternalSerdeJson(serde_json::Error),
}
impl Error {
pub fn to_crate_error(self, document_id: String) -> crate::Error {
match self {
Error::InvalidMap(value) => {
crate::Error::UserError(UserError::InvalidVectorsMapType { document_id, value })
}
Error::InternalSerdeJson(error) => {
crate::Error::InternalError(InternalError::SerdeJson(error))
}
}
}
}
fn to_vector_map(
obkv: KvReaderDelAdd,
side: DelAdd,
) -> Result<Option<BTreeMap<String, Vectors>>, Error> {
Ok(if let Some(value) = obkv.get(side) {
let ParsedVectors(parsed_vectors) = ParsedVectors::from_bytes(value)?;
Some(parsed_vectors)
} else {
None
})
}
/// 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<Embedding>, Embedding>>,
}
impl VectorOrArrayOfVectors {
pub fn into_array_of_vectors(self) -> Option<Vec<Embedding>> {
match self.inner? {
either::Either::Left(vectors) => Some(vectors),
either::Either::Right(vector) => Some(vec![vector]),
}
}
pub fn from_array_of_vectors(array_of_vec: Vec<Embedding>) -> Self {
Self { inner: Some(either::Either::Left(array_of_vec)) }
}
}
#[cfg(test)]
mod test {
use super::VectorOrArrayOfVectors;
#[test]
fn array_of_vectors() {
let null: VectorOrArrayOfVectors = serde_json::from_str("null").unwrap();
let empty: VectorOrArrayOfVectors = serde_json::from_str("[]").unwrap();
let one: VectorOrArrayOfVectors = serde_json::from_str("[0.1]").unwrap();
let two: VectorOrArrayOfVectors = serde_json::from_str("[0.1, 0.2]").unwrap();
let one_vec: VectorOrArrayOfVectors = serde_json::from_str("[[0.1, 0.2]]").unwrap();
let two_vecs: VectorOrArrayOfVectors =
serde_json::from_str("[[0.1, 0.2], [0.3, 0.4]]").unwrap();
insta::assert_json_snapshot!(null.into_array_of_vectors(), @"null");
insta::assert_json_snapshot!(empty.into_array_of_vectors(), @"[]");
insta::assert_json_snapshot!(one.into_array_of_vectors(), @r###"
[
[
0.1
]
]
"###);
insta::assert_json_snapshot!(two.into_array_of_vectors(), @r###"
[
[
0.1,
0.2
]
]
"###);
insta::assert_json_snapshot!(one_vec.into_array_of_vectors(), @r###"
[
[
0.1,
0.2
]
]
"###);
insta::assert_json_snapshot!(two_vecs.into_array_of_vectors(), @r###"
[
[
0.1,
0.2
],
[
0.3,
0.4
]
]
"###);
}
}

View File

@@ -159,6 +159,7 @@ pub fn expected_order(
match optional_words {
TermsMatchingStrategy::Last => groups.into_iter().flatten().collect(),
TermsMatchingStrategy::Frequency => groups.into_iter().flatten().collect(),
TermsMatchingStrategy::All => {
groups.into_iter().flatten().filter(|d| d.word_rank == 0).collect()
}

View File

@@ -55,6 +55,10 @@ impl Client {
pub fn delete(&self, route: &str) -> reqwest::RequestBuilder {
self.request(reqwest::Method::DELETE, route)
}
pub fn base_url(&self) -> Option<&str> {
self.base_url.as_deref()
}
}
#[derive(Debug, Clone, Copy, Deserialize)]

View File

@@ -18,12 +18,9 @@ pub enum DashboardClient {
}
impl DashboardClient {
pub fn new(dashboard_url: &str, api_key: Option<&str>) -> anyhow::Result<Self> {
let dashboard_client = Client::new(
Some(format!("{}/api/v1", dashboard_url)),
api_key,
Some(std::time::Duration::from_secs(60)),
)?;
pub fn new(dashboard_url: String, api_key: Option<&str>) -> anyhow::Result<Self> {
let dashboard_client =
Client::new(Some(dashboard_url), api_key, Some(std::time::Duration::from_secs(60)))?;
Ok(Self::Client(dashboard_client))
}
@@ -36,7 +33,7 @@ impl DashboardClient {
let Self::Client(dashboard_client) = self else { return Ok(()) };
let response = dashboard_client
.put("machine")
.put("/api/v1/machine")
.json(&json!({"hostname": env.hostname}))
.send()
.await
@@ -62,7 +59,7 @@ impl DashboardClient {
let Self::Client(dashboard_client) = self else { return Ok(Uuid::now_v7()) };
let response = dashboard_client
.put("invocation")
.put("/api/v1/invocation")
.json(&json!({
"commit": {
"sha1": build_info.commit_sha1,
@@ -97,7 +94,7 @@ impl DashboardClient {
let Self::Client(dashboard_client) = self else { return Ok(Uuid::now_v7()) };
let response = dashboard_client
.put("workload")
.put("/api/v1/workload")
.json(&json!({
"invocation_uuid": invocation_uuid,
"name": &workload.name,
@@ -124,7 +121,7 @@ impl DashboardClient {
let Self::Client(dashboard_client) = self else { return Ok(()) };
let response = dashboard_client
.put("run")
.put("/api/v1/run")
.json(&json!({
"workload_uuid": workload_uuid,
"data": report
@@ -159,7 +156,7 @@ impl DashboardClient {
pub async fn mark_as_failed(&self, invocation_uuid: Uuid, failure_reason: Option<String>) {
if let DashboardClient::Client(client) = self {
let response = client
.post("cancel-invocation")
.post("/api/v1/cancel-invocation")
.json(&json!({
"invocation_uuid": invocation_uuid,
"failure_reason": failure_reason,
@@ -186,4 +183,28 @@ impl DashboardClient {
tracing::warn!(%invocation_uuid, "marked invocation as failed or canceled");
}
/// Result URL in markdown
pub(crate) fn result_url(
&self,
workload_name: &str,
build_info: &build_info::BuildInfo,
baseline_branch: &str,
) -> String {
let Self::Client(client) = self else { return Default::default() };
let Some(base_url) = client.base_url() else { return Default::default() };
let Some(commit_sha1) = build_info.commit_sha1 else { return Default::default() };
// https://bench.meilisearch.dev/view_spans?commit_sha1=500ddc76b549fb9f1af54b2dd6abfa15960381bb&workload_name=settings-add-remove-filters.json&target_branch=reduce-transform-disk-usage&baseline_branch=main
let mut url = format!(
"{base_url}/view_spans?commit_sha1={commit_sha1}&workload_name={workload_name}"
);
if let Some(target_branch) = build_info.branch {
url += &format!("&target_branch={target_branch}&baseline_branch={baseline_branch}");
}
format!("[{workload_name} compared with {baseline_branch}]({url})")
}
}

View File

@@ -6,6 +6,7 @@ mod env_info;
mod meili_process;
mod workload;
use std::io::LineWriter;
use std::path::PathBuf;
use anyhow::Context;
@@ -90,6 +91,7 @@ pub fn run(args: BenchDeriveArgs) -> anyhow::Result<()> {
let subscriber = tracing_subscriber::registry().with(
tracing_subscriber::fmt::layer()
.with_writer(|| LineWriter::new(std::io::stderr()))
.with_span_events(FmtSpan::NEW | FmtSpan::CLOSE)
.with_filter(filter),
);
@@ -110,7 +112,7 @@ pub fn run(args: BenchDeriveArgs) -> anyhow::Result<()> {
let dashboard_client = if args.no_dashboard {
dashboard::DashboardClient::new_dry()
} else {
dashboard::DashboardClient::new(&args.dashboard_url, args.api_key.as_deref())?
dashboard::DashboardClient::new(args.dashboard_url.clone(), args.api_key.as_deref())?
};
// reporting uses its own client because keeping the stream open to wait for entries
@@ -136,7 +138,7 @@ pub fn run(args: BenchDeriveArgs) -> anyhow::Result<()> {
let commit_message = build_info.commit_msg.context("missing commit message")?.split('\n').next().unwrap();
let max_workloads = args.workload_file.len();
let reason: Option<&str> = args.reason.as_deref();
let invocation_uuid = dashboard_client.create_invocation( build_info, commit_message, env, max_workloads, reason).await?;
let invocation_uuid = dashboard_client.create_invocation(build_info.clone(), commit_message, env, max_workloads, reason).await?;
tracing::info!(workload_count = args.workload_file.len(), "handling workload files");
@@ -144,6 +146,7 @@ pub fn run(args: BenchDeriveArgs) -> anyhow::Result<()> {
let workload_runs = tokio::spawn(
{
let dashboard_client = dashboard_client.clone();
let mut dashboard_urls = Vec::new();
async move {
for workload_file in args.workload_file.iter() {
let workload: Workload = serde_json::from_reader(
@@ -152,6 +155,8 @@ pub fn run(args: BenchDeriveArgs) -> anyhow::Result<()> {
)
.with_context(|| format!("error parsing {} as JSON", workload_file.display()))?;
let workload_name = workload.name.clone();
workload::execute(
&assets_client,
&dashboard_client,
@@ -163,8 +168,23 @@ pub fn run(args: BenchDeriveArgs) -> anyhow::Result<()> {
&args,
)
.await?;
let result_url = dashboard_client.result_url(&workload_name, &build_info, "main");
if !result_url.is_empty() {
dashboard_urls.push(result_url);
}
if let Some(branch) = build_info.branch {
let result_url = dashboard_client.result_url(&workload_name, &build_info, branch);
if !result_url.is_empty() {
dashboard_urls.push(result_url);
}
}
}
Ok::<(), anyhow::Error>(())
Ok::<_, anyhow::Error>(dashboard_urls)
}});
// handle ctrl-c
@@ -176,13 +196,19 @@ pub fn run(args: BenchDeriveArgs) -> anyhow::Result<()> {
// wait for the end of the main task, handle result
match workload_runs.await {
Ok(Ok(_)) => {
Ok(Ok(urls)) => {
tracing::info!("Success");
println!("☀️ Benchmark invocation completed, please find the results for your workloads below:");
for url in urls {
println!("- {url}");
}
Ok::<(), anyhow::Error>(())
}
Ok(Err(error)) => {
tracing::error!(%invocation_uuid, error = %error, "invocation failed, attempting to report the failure to dashboard");
dashboard_client.mark_as_failed(invocation_uuid, Some(error.to_string())).await;
println!("☔️ Benchmark invocation failed...");
println!("{error}");
tracing::warn!(%invocation_uuid, "invocation marked as failed following error");
Err(error)
},
@@ -191,10 +217,20 @@ pub fn run(args: BenchDeriveArgs) -> anyhow::Result<()> {
Ok(panic) => {
tracing::error!("invocation panicked, attempting to report the failure to dashboard");
dashboard_client.mark_as_failed( invocation_uuid, Some("Panicked".into())).await;
println!("‼️ Benchmark invocation panicked 😱");
let msg = match panic.downcast_ref::<&'static str>() {
Some(s) => *s,
None => match panic.downcast_ref::<String>() {
Some(s) => &s[..],
None => "Box<dyn Any>",
},
};
println!("panicked at {msg}");
std::panic::resume_unwind(panic)
}
Err(_) => {
tracing::warn!("task was canceled");
println!("🚫 Benchmark invocation was canceled");
Ok(())
}
}