Add ranking_score_threshold parameter to search

docker-compose.yml

@@ -1,7 +1,7 @@
 services:
   meilisearch:
     # image: getmeili/meilisearch:latest
-    image: getmeili/meilisearch:v1.9.0-rc.1
+    image: getmeili/meilisearch:v1.9.0-rc.2
     ports:
       - "7700:7700"
     environment:

meilisearch_python_sdk/index.py

@@ -737,6 +737,7 @@ async def search(
         attributes_to_search_on: list[str] | None = None,
         show_ranking_score: bool = False,
         show_ranking_score_details: bool = False,
+        ranking_score_threshold: float | None = None,
         vector: list[float] | None = None,
         hybrid: Hybrid | None = None,
     ) -> SearchResults:
@@ -777,6 +778,9 @@ async def search(
                 Because this feature is experimental it may be removed or updated causing breaking
                 changes in this library without a major version bump so use with caution. This
                 feature became stable in Meiliseach v1.7.0.
+            ranking_score_threshold: If set, no document whose _rankingScore is under the
+                rankingScoreThreshold is returned. The value must be between 0.0 and 1.0. Defaults
+                to None.
             vector: List of vectors for vector search. Defaults to None. Note: This parameter can
                 only be used with Meilisearch >= v1.3.0, and is experimental in Meilisearch v1.3.0.
                 In order to use this feature in Meilisearch v1.3.0 you first need to enable the
@@ -808,6 +812,9 @@ async def search(
             >>>     index = client.index("movies")
             >>>     search_results = await index.search("Tron")
         """
+        if ranking_score_threshold:
+            _validate_ranking_score_threshold(ranking_score_threshold)
+
         body = _process_search_parameters(
             q=query,
             offset=offset,
@@ -831,6 +838,7 @@ async def search(
             show_ranking_score_details=show_ranking_score_details,
             vector=vector,
             hybrid=hybrid,
+            ranking_score_threshold=ranking_score_threshold,
         )
         search_url = f"{self._base_url_with_uid}/search"
 
@@ -4909,6 +4917,7 @@ def search(
         attributes_to_search_on: list[str] | None = None,
         show_ranking_score: bool = False,
         show_ranking_score_details: bool = False,
+        ranking_score_threshold: float | None = None,
         vector: list[float] | None = None,
         hybrid: Hybrid | None = None,
     ) -> SearchResults:
@@ -4949,6 +4958,9 @@ def search(
                 Because this feature is experimental it may be removed or updated causing breaking
                 changes in this library without a major version bump so use with caution. This
                 feature became stable in Meiliseach v1.7.0.
+            ranking_score_threshold: If set, no document whose _rankingScore is under the
+                rankingScoreThreshold is returned. The value must be between 0.0 and 1.0. Defaults
+                to None.
             vector: List of vectors for vector search. Defaults to None. Note: This parameter can
                 only be used with Meilisearch >= v1.3.0, and is experimental in Meilisearch v1.3.0.
                 In order to use this feature in Meilisearch v1.3.0 you first need to enable the
@@ -4980,6 +4992,9 @@ def search(
             >>> index = client.index("movies")
             >>> search_results = index.search("Tron")
         """
+        if ranking_score_threshold:
+            _validate_ranking_score_threshold(ranking_score_threshold)
+
         body = _process_search_parameters(
             q=query,
             offset=offset,
@@ -5003,6 +5018,7 @@ def search(
             show_ranking_score_details=show_ranking_score_details,
             vector=vector,
             hybrid=hybrid,
+            ranking_score_threshold=ranking_score_threshold,
         )
 
         if self._pre_search_plugins:
@@ -7999,6 +8015,7 @@ def _process_search_parameters(
     attributes_to_search_on: list[str] | None = None,
     show_ranking_score: bool = False,
     show_ranking_score_details: bool = False,
+    ranking_score_threshold: float | None = None,
     vector: list[float] | None = None,
     hybrid: Hybrid | None = None,
 ) -> JsonDict:
@@ -8025,6 +8042,7 @@ def _process_search_parameters(
         "page": page,
         "attributesToSearchOn": attributes_to_search_on,
         "showRankingScore": show_ranking_score,
+        "rankingScoreThreshold": ranking_score_threshold,
     }
 
     if facet_name:
@@ -8118,3 +8136,8 @@ def _embedder_json_to_settings_model(  # pragma: no cover
 def _validate_file_type(file_path: Path) -> None:
     if file_path.suffix not in (".json", ".csv", ".ndjson"):
         raise MeilisearchError("File must be a json, ndjson, or csv file")
+
+
+def _validate_ranking_score_threshold(ranking_score_threshold: float) -> None:
+    if not 0.0 <= ranking_score_threshold <= 1.0:
+        raise MeilisearchError("ranking_score_threshold must be between 0.0 and 1.0")

meilisearch_python_sdk/models/search.py

@@ -1,8 +1,11 @@
 from typing import List, Optional
+from warnings import warn
 
+import pydantic
 from camel_converter.pydantic_base import CamelBase
-from pydantic import Field
 
+from meilisearch_python_sdk._utils import is_pydantic_2
+from meilisearch_python_sdk.errors import MeilisearchError
 from meilisearch_python_sdk.types import Filter, JsonDict
 
 
@@ -24,7 +27,7 @@ class Hybrid(CamelBase):
 
 class SearchParams(CamelBase):
     index_uid: str
-    query: Optional[str] = Field(None, alias="q")
+    query: Optional[str] = pydantic.Field(None, alias="q")
     offset: int = 0
     limit: int = 20
     filter: Optional[Filter] = None
@@ -44,9 +47,35 @@ class SearchParams(CamelBase):
     attributes_to_search_on: Optional[List[str]] = None
     show_ranking_score: bool = False
     show_ranking_score_details: bool = False
+    ranking_score_threshold: Optional[float] = None
     vector: Optional[List[float]] = None
     hybrid: Optional[Hybrid] = None
 
+    if is_pydantic_2():
+
+        @pydantic.field_validator("ranking_score_threshold", mode="before")  # type: ignore[attr-defined]
+        @classmethod
+        def validate_ranking_score_threshold(cls, v: Optional[float]) -> Optional[float]:
+            if v and not 0.0 <= v <= 1.0:
+                raise MeilisearchError("ranking_score_threshold must be between 0.0 and 1.0")
+
+            return v
+
+    else:  # pragma: no cover
+        warn(
+            "The use of Pydantic less than version 2 is depreciated and will be removed in a future release",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
+        @pydantic.validator("ranking_score_threshold", pre=True)
+        @classmethod
+        def validate_expires_at(cls, v: Optional[float]) -> Optional[float]:
+            if v and not 0.0 <= v <= 1.0:
+                raise MeilisearchError("ranking_score_threshold must be between 0.0 and 1.0")
+
+            return v
+
 
 class SearchResults(CamelBase):
     hits: List[JsonDict]

tests/test_async_search.py

@@ -4,7 +4,7 @@
 
 from meilisearch_python_sdk import AsyncClient
 from meilisearch_python_sdk._task import async_wait_for_task
-from meilisearch_python_sdk.errors import MeilisearchApiError
+from meilisearch_python_sdk.errors import MeilisearchApiError, MeilisearchError
 from meilisearch_python_sdk.models.search import Hybrid, SearchParams
 
 
@@ -405,3 +405,49 @@ async def test_custom_facet_search(async_index_with_documents):
     )
     assert response.facet_hits[0].value == "cartoon"
     assert response.facet_hits[0].count == 1
+
+
+@pytest.mark.parametrize("ranking_score_threshold", (-0.1, 1.1))
+@pytest.mark.usefixtures("enable_vector_search")
+async def test_search_invalid_ranking_score_threshold(
+    ranking_score_threshold, async_index_with_documents
+):
+    index = await async_index_with_documents()
+    with pytest.raises(MeilisearchError) as e:
+        await index.search("", ranking_score_threshold=ranking_score_threshold)
+        assert "ranking_score_threshold must be between 0.0 and 1.0" in str(e.value)
+
+
+@pytest.mark.usefixtures("enable_vector_search")
+async def test_search_ranking_score_threshold(async_index_with_documents_and_vectors):
+    index = await async_index_with_documents_and_vectors()
+    result = await index.search("", ranking_score_threshold=0.5)
+    assert len(result.hits) > 0
+
+
+@pytest.mark.parametrize("ranking_score_threshold", (-0.1, 1.1))
+@pytest.mark.usefixtures("enable_vector_search")
+async def test_multi_search_invalid_ranking_score_threshold(
+    ranking_score_threshold, async_client, async_index_with_documents
+):
+    index1 = await async_index_with_documents()
+    with pytest.raises(MeilisearchError) as e:
+        await async_client.multi_search(
+            [
+                SearchParams(
+                    index_uid=index1.uid, query="", ranking_score_threshold=ranking_score_threshold
+                ),
+            ]
+        )
+        assert "ranking_score_threshold must be between 0.0 and 1.0" in str(e.value)
+
+
+@pytest.mark.usefixtures("enable_vector_search")
+async def test_multi_search_ranking_score_threshold(async_client, async_index_with_documents):
+    index1 = await async_index_with_documents()
+    result = await async_client.multi_search(
+        [
+            SearchParams(index_uid=index1.uid, query="", ranking_score_threshold=0.5),
+        ]
+    )
+    assert len(result[0].hits) > 0

tests/test_search.py

@@ -4,7 +4,7 @@
 
 from meilisearch_python_sdk import Client
 from meilisearch_python_sdk._task import wait_for_task
-from meilisearch_python_sdk.errors import MeilisearchApiError
+from meilisearch_python_sdk.errors import MeilisearchApiError, MeilisearchError
 from meilisearch_python_sdk.models.search import Hybrid, SearchParams
 
 
@@ -406,3 +406,49 @@ def test_custom_facet_search(index_with_documents):
     )
     assert response.facet_hits[0].value == "cartoon"
     assert response.facet_hits[0].count == 1
+
+
+@pytest.mark.parametrize("ranking_score_threshold", (-0.1, 1.1))
+@pytest.mark.usefixtures("enable_vector_search")
+def test_search_invalid_ranking_score_threshold(
+    ranking_score_threshold, index_with_documents_and_vectors
+):
+    index = index_with_documents_and_vectors()
+    with pytest.raises(MeilisearchError) as e:
+        index.search("", ranking_score_threshold=ranking_score_threshold)
+        assert "ranking_score_threshold must be between 0.0 and 1.0" in str(e.value)
+
+
+@pytest.mark.usefixtures("enable_vector_search")
+def test_search_ranking_score_threshold(index_with_documents_and_vectors):
+    index = index_with_documents_and_vectors()
+    result = index.search("", ranking_score_threshold=0.5)
+    assert len(result.hits) > 0
+
+
+@pytest.mark.parametrize("ranking_score_threshold", (-0.1, 1.1))
+@pytest.mark.usefixtures("enable_vector_search")
+def test_multi_search_invalid_ranking_score_threshold(
+    ranking_score_threshold, client, index_with_documents
+):
+    index1 = index_with_documents()
+    with pytest.raises(MeilisearchError) as e:
+        client.multi_search(
+            [
+                SearchParams(
+                    index_uid=index1.uid, query="", ranking_score_threshold=ranking_score_threshold
+                ),
+            ]
+        )
+        assert "ranking_score_threshold must be between 0.0 and 1.0" in str(e.value)
+
+
+@pytest.mark.usefixtures("enable_vector_search")
+def test_multi_search_ranking_score_threshold(client, index_with_documents):
+    index1 = index_with_documents()
+    result = client.multi_search(
+        [
+            SearchParams(index_uid=index1.uid, query="", ranking_score_threshold=0.5),
+        ]
+    )
+    assert len(result[0].hits) > 0