Update for Meilisearch v1.13.0

meilisearch_python_sdk/_client.py

@@ -623,15 +623,18 @@ async def multi_search(
             >>>     search_results = await client.search(queries)
         """
         url = "multi-search"
-        if federation:
-            processed_queries = []
-            for query in queries:
-                q = query.model_dump(by_alias=True)
+        processed_queries = []
+        for query in queries:
+            q = query.model_dump(by_alias=True)
+
+            if query.retrieve_vectors is None:
+                del q["retrieveVectors"]
+
+            if federation:
                 del q["limit"]
                 del q["offset"]
-                processed_queries.append(q)
-        else:
-            processed_queries = [x.model_dump(by_alias=True) for x in queries]
+
+            processed_queries.append(q)
 
         if federation:
             federation_payload = federation.model_dump(by_alias=True)
@@ -1479,15 +1482,18 @@ def multi_search(
             >>> search_results = client.search(queries)
         """
         url = "multi-search"
-        if federation:
-            processed_queries = []
-            for query in queries:
-                q = query.model_dump(by_alias=True)
+        processed_queries = []
+        for query in queries:
+            q = query.model_dump(by_alias=True)
+
+            if query.retrieve_vectors is None:
+                del q["retrieveVectors"]
+
+            if federation:
                 del q["limit"]
                 del q["offset"]
-                processed_queries.append(q)
-        else:
-            processed_queries = [x.model_dump(by_alias=True) for x in queries]
+
+            processed_queries.append(q)
 
         if federation:
             federation_payload = federation.model_dump(by_alias=True)

meilisearch_python_sdk/index.py

@@ -721,6 +721,7 @@ async def search(
         vector: list[float] | None = None,
         hybrid: Hybrid | None = None,
         locales: list[str] | None = None,
+        retrieve_vectors: bool | None = None,
     ) -> SearchResults:
         """Search the index.
 
@@ -781,6 +782,7 @@ async def search(
                 with caution.
             locales: Specifies the languages for the search. This parameter can only be used with
                 Milisearch >= v1.10.0. Defaults to None letting the Meilisearch pick.
+            retrieve_vectors: Return document vector data with search result.
 
         Returns:
             Results of the search
@@ -824,6 +826,7 @@ async def search(
             hybrid=hybrid,
             ranking_score_threshold=ranking_score_threshold,
             locales=locales,
+            retrieve_vectors=retrieve_vectors,
         )
         search_url = f"{self._base_url_with_uid}/search"
 
@@ -986,6 +989,7 @@ async def facet_search(
         ranking_score_threshold: float | None = None,
         vector: list[float] | None = None,
         locales: list[str] | None = None,
+        retrieve_vectors: bool | None = None,
     ) -> FacetSearchResults:
         """Search the index.
 
@@ -1038,6 +1042,7 @@ async def facet_search(
                 with caution.
             locales: Specifies the languages for the search. This parameter can only be used with
                 Milisearch >= v1.10.0. Defaults to None letting the Meilisearch pick.
+            retrieve_vectors: Return document vector data with search result.
 
         Returns:
             Results of the search
@@ -1085,6 +1090,7 @@ async def facet_search(
             ranking_score_threshold=ranking_score_threshold,
             vector=vector,
             locales=locales,
+            retrieve_vectors=retrieve_vectors,
         )
         search_url = f"{self._base_url_with_uid}/facet-search"
 
@@ -5085,6 +5091,7 @@ def search(
         vector: list[float] | None = None,
         hybrid: Hybrid | None = None,
         locales: list[str] | None = None,
+        retrieve_vectors: bool | None = None,
     ) -> SearchResults:
         """Search the index.
 
@@ -5145,6 +5152,7 @@ def search(
                 with caution.
             locales: Specifies the languages for the search. This parameter can only be used with
                 Milisearch >= v1.10.0. Defaults to None letting the Meilisearch pick.
+            retrieve_vectors: Return document vector data with search result.
 
         Returns:
             Results of the search
@@ -5188,6 +5196,7 @@ def search(
             hybrid=hybrid,
             ranking_score_threshold=ranking_score_threshold,
             locales=locales,
+            retrieve_vectors=retrieve_vectors,
         )
 
         if self._pre_search_plugins:
@@ -5256,6 +5265,7 @@ def facet_search(
         ranking_score_threshold: float | None = None,
         vector: list[float] | None = None,
         locales: list[str] | None = None,
+        retrieve_vectors: bool | None = None,
     ) -> FacetSearchResults:
         """Search the index.
 
@@ -5308,6 +5318,7 @@ def facet_search(
                 with caution.
             locales: Specifies the languages for the search. This parameter can only be used with
                 Milisearch >= v1.10.0. Defaults to None letting the Meilisearch pick.
+            retrieve_vectors: Return document vector data with search result.
 
         Returns:
             Results of the search
@@ -5355,6 +5366,7 @@ def facet_search(
             ranking_score_threshold=ranking_score_threshold,
             vector=vector,
             locales=locales,
+            retrieve_vectors=retrieve_vectors,
         )
 
         if self._pre_facet_search_plugins:
@@ -8262,6 +8274,7 @@ def _process_search_parameters(
     vector: list[float] | None = None,
     hybrid: Hybrid | None = None,
     locales: list[str] | None = None,
+    retrieve_vectors: bool | None = None,
 ) -> JsonDict:
     if attributes_to_retrieve is None:
         attributes_to_retrieve = ["*"]
@@ -8310,6 +8323,9 @@ def _process_search_parameters(
     if locales:
         body["locales"] = locales
 
+    if retrieve_vectors is not None:
+        body["retrieveVectors"] = retrieve_vectors
+
     return body
 
 

meilisearch_python_sdk/models/search.py

@@ -70,6 +70,7 @@ class SearchParams(CamelBase):
     vector: list[float] | None = None
     hybrid: Hybrid | None = None
     locales: list[str] | None = None
+    retrieve_vectors: bool | None = None
 
     @field_validator("ranking_score_threshold", mode="before")  # type: ignore[attr-defined]
     @classmethod

meilisearch_python_sdk/models/settings.py

@@ -56,6 +56,7 @@ class OpenAiEmbedder(CamelBase):
     document_template: str | None = None
     document_template_max_bytes: int | None = None
     distribution: Distribution | None = None
+    binary_quantized: bool | None = None
 
 
 class HuggingFaceEmbedder(CamelBase):
@@ -65,6 +66,8 @@ class HuggingFaceEmbedder(CamelBase):
     document_template: str | None = None
     document_template_max_bytes: int | None = None
     distribution: Distribution | None = None
+    dimensions: int | None = None
+    binary_quantized: bool | None = None
 
 
 class OllamaEmbedder(CamelBase):
@@ -76,6 +79,7 @@ class OllamaEmbedder(CamelBase):
     document_template: str | None = None
     document_template_max_bytes: int | None = None
     distribution: Distribution | None = None
+    binary_quantized: bool | None = None
 
 
 class RestEmbedder(CamelBase):
@@ -89,12 +93,16 @@ class RestEmbedder(CamelBase):
     headers: JsonDict | None = None
     request: JsonDict
     response: JsonDict
+    binary_quantized: bool | None = None
 
 
 class UserProvidedEmbedder(CamelBase):
     source: str = "userProvided"
     dimensions: int
     distribution: Distribution | None = None
+    document_template: str | None = None
+    document_template_max_bytes: int | None = None
+    binary_quantized: bool | None = None
 
 
 class Embedders(CamelBase):

tests/conftest.py

@@ -295,15 +295,6 @@ async def default_search_key(async_client):
             return key
 
 
-@pytest.fixture(scope="session", autouse=True)
-async def enable_vector_search(base_url, ssl_verify):
-    async with HttpxAsyncClient(
-        base_url=base_url, headers={"Authorization": f"Bearer {MASTER_KEY}"}, verify=ssl_verify
-    ) as client:
-        await client.patch("/experimental-features", json={"vectorStore": True})
-        yield
-
-
 @pytest.fixture(scope="session", autouse=True)
 async def enable_edit_by_function(base_url, ssl_verify):
     async with HttpxAsyncClient(

tests/test_async_index.py

@@ -139,7 +139,7 @@ async def test_get_settings_default(
     assert response.non_separator_tokens == []
     assert response.search_cutoff_ms is None
     assert response.dictionary == []
-    assert response.embedders is None
+    assert response.embedders == {}
     assert response.facet_search is True
     assert response.prefix_search == "indexingTime"
 
@@ -233,7 +233,7 @@ async def test_reset_settings(async_empty_index, new_settings, default_ranking_r
     assert response.faceting.max_values_per_facet == 100
     assert response.pagination.max_total_hits == 1000
     assert response.proximity_precision is ProximityPrecision.BY_WORD
-    assert response.embedders is None
+    assert response.embedders == {}
 
 
 async def test_get_ranking_rules_default(async_empty_index, default_ranking_rules):

tests/test_async_search.py

@@ -506,6 +506,30 @@ async def test_vector_search(async_index_with_documents_and_vectors):
     assert len(response.hits) >= 1
 
 
+async def test_vector_search_retrieve_vectors(async_index_with_documents_and_vectors):
+    index = await async_index_with_documents_and_vectors()
+    response = await index.search(
+        "",
+        vector=[0.1, 0.2],
+        hybrid=Hybrid(semantic_ratio=1.0, embedder="default"),
+        retrieve_vectors=True,
+    )
+    assert len(response.hits) >= 1
+    assert response.hits[0].get("_vectors") is not None
+
+
+async def test_vector_search_retrieve_vectors_false(async_index_with_documents_and_vectors):
+    index = await async_index_with_documents_and_vectors()
+    response = await index.search(
+        "",
+        vector=[0.1, 0.2],
+        hybrid=Hybrid(semantic_ratio=1.0, embedder="default"),
+        retrieve_vectors=False,
+    )
+    assert len(response.hits) >= 1
+    assert response.hits[0].get("_vectors") is None
+
+
 async def test_basic_facet_search(async_index_with_documents):
     index = await async_index_with_documents()
     update = await index.update_filterable_attributes(["genre"])

tests/test_index.py

@@ -131,7 +131,7 @@ def test_get_settings_default(
     assert response.non_separator_tokens == []
     assert response.search_cutoff_ms is None
     assert response.dictionary == []
-    assert response.embedders is None
+    assert response.embedders == {}
     assert response.facet_search is True
     assert response.prefix_search == "indexingTime"
 
@@ -225,7 +225,7 @@ def test_reset_settings(empty_index, new_settings, default_ranking_rules):
     assert response.faceting.max_values_per_facet == 100
     assert response.pagination.max_total_hits == 1000
     assert response.proximity_precision is ProximityPrecision.BY_WORD
-    assert response.embedders is None
+    assert response.embedders == {}
 
 
 def test_get_ranking_rules_default(empty_index, default_ranking_rules):

tests/test_search.py

@@ -483,6 +483,30 @@ def test_vector_search(index_with_documents_and_vectors):
     assert len(response.hits) >= 1
 
 
+def test_vector_search_retrieve_vectors(index_with_documents_and_vectors):
+    index = index_with_documents_and_vectors()
+    response = index.search(
+        "",
+        vector=[0.1, 0.2],
+        hybrid=Hybrid(semantic_ratio=1.0, embedder="default"),
+        retrieve_vectors=True,
+    )
+    assert len(response.hits) >= 1
+    assert response.hits[0].get("_vectors") is not None
+
+
+def test_vector_search_retrieve_vectors_false(index_with_documents_and_vectors):
+    index = index_with_documents_and_vectors()
+    response = index.search(
+        "",
+        vector=[0.1, 0.2],
+        hybrid=Hybrid(semantic_ratio=1.0, embedder="default"),
+        retrieve_vectors=False,
+    )
+    assert len(response.hits) >= 1
+    assert response.hits[0].get("_vectors") is None
+
+
 def test_basic_facet_search(index_with_documents):
     index = index_with_documents()
     update = index.update_filterable_attributes(["genre"])