1791 Commits

Author SHA1 Message Date
Kerollmops
e9a3029c30
Use the right field id to write the string facet values FST 2023-06-28 15:01:51 +02:00
Kerollmops
ed0ff47551
Return an empty list of results if attribute is set as filterable 2023-06-28 15:01:51 +02:00
Clément Renault
e1b8fb48ee
Use the minWordSizeForTypos index settings 2023-06-28 15:01:51 +02:00
Clément Renault
87e22e436a
Fix compilation issues 2023-06-28 15:01:51 +02:00
Clément Renault
0252cfe8b6
Simplify the placeholder search of the facet-search route 2023-06-28 15:01:50 +02:00
Clément Renault
f35ad96afa
Use the disableOnAttributes parameter on the facet-search route 2023-06-28 15:01:50 +02:00
Clément Renault
2ceb781c73
Use the disableOnWords parameter on the facet-search route 2023-06-28 15:01:50 +02:00
Clément Renault
7bd67543dd
Support the typoTolerant.enabled parameter 2023-06-28 15:01:50 +02:00
Clément Renault
8e86eb91bb
Log an error when a facet value is missing from the database 2023-06-28 15:01:50 +02:00
Clément Renault
55c17aa38b
Rename the SearchForFacetValues struct 2023-06-28 15:01:50 +02:00
Clément Renault
aadbe88048
Return an internal error when a field id is missing 2023-06-28 15:01:50 +02:00
Clément Renault
f36de2115f
Make clippy happy 2023-06-28 15:01:50 +02:00
Clément Renault
702041b7e1
Improve the returned errors from the facet-search route 2023-06-28 15:01:48 +02:00
Clément Renault
a05074e675
Fix the max number of facets to be returned to 100 2023-06-28 14:58:42 +02:00
Clément Renault
93f30e65a9
Return the correct response JSON object from the facet-search route 2023-06-28 14:58:42 +02:00
Clément Renault
e81809aae7
Make the search for facet work 2023-06-28 14:58:41 +02:00
Kerollmops
ce7e7f12c8
Introduce the facet search route 2023-06-28 14:58:41 +02:00
Kerollmops
addb21f110
Restrict the number of facet search results to 1000 2023-06-28 14:58:41 +02:00
Kerollmops
c34de05106
Introduce the SearchForFacetValue struct 2023-06-28 14:58:41 +02:00
Clément Renault
15a4c05379
Store the facet string values in multiple FSTs 2023-06-28 14:58:41 +02:00
meili-bors[bot]
d4f10800f2
Merge #3834
3834: Define searchable fields at runtime r=Kerollmops a=ManyTheFish

## Summary
This feature allows the end-user to search in one or multiple attributes using the search parameter `attributesToSearchOn`:

```json
{
  "q": "Captain Marvel",
  "attributesToSearchOn": ["title"]
}
```

This feature act like a filter, forcing Meilisearch to only return the documents containing the requested words in the attributes-to-search-on. Note that, with the matching strategy `last`, Meilisearch will only ensure that the first word is in the attributes-to-search-on, but, the retrieved documents will be ordered taking into account the word contained in the attributes-to-search-on. 

## Trying the prototype

A dedicated docker image has been released for this feature:

#### last prototype version:

```bash
docker pull getmeili/meilisearch:prototype-define-searchable-fields-at-search-time-1
```

#### others prototype versions:

```bash
docker pull getmeili/meilisearch:prototype-define-searchable-fields-at-search-time-0
```

## Technical Detail

The attributes-to-search-on list is given to the search context, then, the search context uses the `fid_word_docids`database using only the allowed field ids instead of the global `word_docids` database. This is the same for the prefix databases.
The database cache is updated with the merged values, meaning that the union of the field-id-database values is only made if the requested key is missing from the cache.

### Relevancy limits

Almost all ranking rules behave as expected when ordering the documents.
Only `proximity` could miss-order documents if all the searched words are in the restricted attribute but a better proximity is found in an ignored attribute in a document that should be ranked lower. I put below a failing test showing it:
```rust
#[actix_rt::test]
async fn proximity_ranking_rule_order() {
    let server = Server::new().await;
    let index = index_with_documents(
        &server,
        &json!([
        {
            "title": "Captain super mega cool. A Marvel story",
            // Perfect distance between words in an ignored attribute
            "desc": "Captain Marvel",
            "id": "1",
        },
        {
            "title": "Captain America from Marvel",
            "desc": "a Shazam ersatz",
            "id": "2",
        }]),
    )
    .await;

    // Document 2 should appear before document 1.
    index
        .search(json!({"q": "Captain Marvel", "attributesToSearchOn": ["title"], "attributesToRetrieve": ["id"]}), |response, code| {
            assert_eq!(code, 200, "{}", response);
            assert_eq!(
                response["hits"],
                json!([
                    {"id": "2"},
                    {"id": "1"},
                ])
            );
        })
        .await;
}
```

Fixing this would force us to create a `fid_word_pair_proximity_docids` and a `fid_word_prefix_pair_proximity_docids` databases which may multiply the keys of `word_pair_proximity_docids` and `word_prefix_pair_proximity_docids` by the number of attributes in the searchable_attributes list. If we think we should fix this test, I'll suggest doing it in another PR.

## Related

Fixes #3772

Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: ManyTheFish <many@meilisearch.com>
2023-06-28 08:19:23 +00:00
Clément Renault
30741d17fa
Change the TODO message 2023-06-27 12:32:43 +02:00
Clément Renault
ebad1f396f
Remove the useless euclidean distance implementation 2023-06-27 12:32:43 +02:00
Clément Renault
29d8268c94
Fix the vector query part by using the correct universe 2023-06-27 12:32:43 +02:00
Clément Renault
63bfe1cee2
Ignore when there are too many vectors 2023-06-27 12:32:43 +02:00
Kerollmops
7c2f5f77b8
Make clippy and fmt happy 2023-06-27 12:32:42 +02:00
Kerollmops
66b8cfd8c8
Introduce a way to store the HNSW on multiple LMDB entries 2023-06-27 12:32:42 +02:00
Kerollmops
ff3664431f
Make rustfmt happy 2023-06-27 12:32:42 +02:00
Kerollmops
531748c536
Return a user error when the _vectors type is invalid 2023-06-27 12:32:41 +02:00
Kerollmops
7aa1275337
Display the _semanticSimilarity even if the _vectors field is not displayed 2023-06-27 12:32:41 +02:00
Kerollmops
737aec1705
Expose an _semanticSimilarity as a dot product in the documents 2023-06-27 12:32:41 +02:00
Kerollmops
3e3c743392
Make Rustfmt happy 2023-06-27 12:32:41 +02:00
Kerollmops
5c5a4e075d
Make clippy happy 2023-06-27 12:32:41 +02:00
Kerollmops
ab9f2269aa
Normalize the vectors during indexation and search 2023-06-27 12:32:41 +02:00
Kerollmops
321ec5f3fa
Accept multiple vectors by documents using the _vectors field 2023-06-27 12:32:40 +02:00
Kerollmops
717d4fddd4
Remove the unused distance 2023-06-27 12:32:40 +02:00
Kerollmops
a7e0f0de89
Introduce a new error message for invalid vector dimensions 2023-06-27 12:32:40 +02:00
Kerollmops
3b560ef7d0
Make clippy happy 2023-06-27 12:32:40 +02:00
Kerollmops
2cf747cb89
Fix the tests 2023-06-27 12:32:40 +02:00
Kerollmops
3c31e1cdd1
Support more pages but in an ugly way 2023-06-27 12:32:39 +02:00
Kerollmops
23eaaf1001
Change the name of the distance module 2023-06-27 12:32:39 +02:00
Kerollmops
c2a402f3ae
Implement an ugly deletion of values in the HNSW 2023-06-27 12:32:39 +02:00
Kerollmops
436a10bef4
Replace the euclidean with a dot product 2023-06-27 12:32:39 +02:00
Kerollmops
8debf6fe81
Use a basic euclidean distance function 2023-06-27 12:32:39 +02:00
Kerollmops
c79e82c62a
Move back to the hnsw crate
This reverts commit 7a4b6c065482f988b01298642f4c18775503f92f.
2023-06-27 12:32:39 +02:00
Kerollmops
aca305bb77
Log more to make sure we insert vectors in the hgg data-structure 2023-06-27 12:32:38 +02:00
Kerollmops
5816008139
Introduce an optimized version of the euclidean distance function 2023-06-27 12:32:38 +02:00
Kerollmops
268a9ef416
Move to the hgg crate 2023-06-27 12:32:38 +02:00
Clément Renault
642b0f3a1b
Expose a new vector field on the search route 2023-06-27 12:32:38 +02:00
Clément Renault
4571e512d2
Store the vectors in an HNSW in LMDB 2023-06-27 12:32:38 +02:00