Changes related to the next Meilisearch release (v1.11.0)

meilisearch/index.py

@@ -955,7 +955,7 @@ def get_settings(self) -> Dict[str, Any]:
 
         return settings
 
-    def update_settings(self, body: Mapping[str, Any]) -> TaskInfo:
+    def update_settings(self, body: MutableMapping[str, Any]) -> TaskInfo:
         """Update settings of the index.
 
         https://www.meilisearch.com/docs/reference/api/settings#update-settings
@@ -978,6 +978,11 @@ def update_settings(self, body: Mapping[str, Any]) -> TaskInfo:
         MeilisearchApiError
             An error containing details about why Meilisearch can't process your request. Meilisearch error codes are described here: https://www.meilisearch.com/docs/reference/errors/error_codes#meilisearch-errors
         """
+        if body.get("embedders"):
+            for _, v in body["embedders"].items():
+                if "documentTemplateMaxBytes" in v and v["documentTemplateMaxBytes"] is None:
+                    del v["documentTemplateMaxBytes"]
+
         task = self.http.patch(
             f"{self.config.paths.index}/{self.uid}/{self.config.paths.setting}", body
         )
@@ -1867,7 +1872,6 @@ def get_embedders(self) -> Embedders | None:
 
         embedders: dict[str, OpenAiEmbedder | HuggingFaceEmbedder | UserProvidedEmbedder] = {}
         for k, v in response.items():
-            print(v.get("source"))
             if v.get("source") == "openAi":
                 embedders[k] = OpenAiEmbedder(**v)
             elif v.get("source") == "huggingFace":
@@ -1877,7 +1881,7 @@ def get_embedders(self) -> Embedders | None:
 
         return Embedders(embedders=embedders)
 
-    def update_embedders(self, body: Union[Mapping[str, Any], None]) -> TaskInfo:
+    def update_embedders(self, body: Union[MutableMapping[str, Any], None]) -> TaskInfo:
         """Update embedders of the index.
 
         Parameters
@@ -1896,6 +1900,12 @@ def update_embedders(self, body: Union[Mapping[str, Any], None]) -> TaskInfo:
         MeilisearchApiError
             An error containing details about why Meilisearch can't process your request. Meilisearch error codes are described here: https://www.meilisearch.com/docs/reference/errors/error_codes#meilisearch-errors
         """
+
+        if body:
+            for _, v in body.items():
+                if "documentTemplateMaxBytes" in v and v["documentTemplateMaxBytes"] is None:
+                    del v["documentTemplateMaxBytes"]
+
         task = self.http.patch(self.__settings_url_for(self.config.paths.embedders), body)
 
         return TaskInfo(**task)

meilisearch/models/index.py

@@ -56,17 +56,19 @@ class ProximityPrecision(str, Enum):
 
 class OpenAiEmbedder(CamelBase):
     source: str = "openAi"
-    model: Optional[str] = None  # Defaults to text-embedding-ada-002
+    model: Optional[str] = None  # Defaults to text-embedding-3-small
     dimensions: Optional[int] = None  # Uses the model default
     api_key: Optional[str] = None  # Can be provided through a CLI option or environment variable
     document_template: Optional[str] = None
+    document_template_max_bytes: Optional[int] = None  # Default to 400
 
 
 class HuggingFaceEmbedder(CamelBase):
     source: str = "huggingFace"
     model: Optional[str] = None  # Defaults to BAAI/bge-base-en-v1.5
     revision: Optional[str] = None
     document_template: Optional[str] = None
+    document_template_max_bytes: Optional[int] = None  # Default to 400
 
 
 class UserProvidedEmbedder(CamelBase):

tests/index/test_index_search_meilisearch.py

@@ -505,7 +505,8 @@ def test_show_ranking_score(index_with_documents):
 @pytest.mark.usefixtures("enable_vector_search")
 def test_vector_search(index_with_documents_and_vectors):
     response = index_with_documents_and_vectors().search(
-        "", opt_params={"vector": [0.1, 0.2], "hybrid": {"semanticRatio": 1.0}}
+        "",
+        opt_params={"vector": [0.1, 0.2], "hybrid": {"semanticRatio": 1.0, "embedder": "default"}},
     )
     assert len(response["hits"]) > 0