.

libs/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py

@@ -21,26 +21,35 @@ class MilvusCollectionHybridSearchRetriever(BaseRetriever):
 
     collection: Collection
     """Milvus Collection object."""
+
     rerank: BaseRanker
     """Milvus ranker object. Such as WeightedRanker or RRFRanker."""
+
     anns_fields: List[str]
     """The names of vector fields that are used for ANNS search."""
+
     field_embeddings: List[Union[Embeddings, BaseSparseEmbedding]]
     """The embedding functions of each vector fields, 
     which can be either Embeddings or BaseSparseEmbedding."""
+
     field_search_params: Optional[List[Dict]] = None
     """The search parameters of each vector fields. 
     If not specified, the default search parameters will be used."""
+
     field_limits: Optional[List[int]] = None
     """Limit number of results for each ANNS field. 
     If not specified, the default top_k will be used."""
+
     field_exprs: Optional[List[Optional[str]]] = None
     """The boolean expression for filtering the search results."""
+
     top_k: int = 4
     """Final top-K number of documents to retrieve."""
+
     text_field: str = "text"
     """The text field name, 
     which will be used as the `page_content` of a `Document` object."""
+
     output_fields: Optional[List[str]] = None
     """Final output fields of the documents. 
     If not specified, all fields except the vector fields will be used as output fields,
@@ -82,9 +91,9 @@ class MilvusCollectionHybridSearchRetriever(BaseRetriever):
         self.collection.load()
 
     def _validate_fields_num(self) -> None:
-        assert (
-            len(self.anns_fields) >= 2
-        ), "At least two fields are required for hybrid search."
+        assert len(self.anns_fields) >= 2, (
+            "At least two fields are required for hybrid search."
+        )
         lengths = [len(self.anns_fields)]
         if self.field_limits is not None:
             lengths.append(len(self.field_limits))
@@ -102,17 +111,17 @@ class MilvusCollectionHybridSearchRetriever(BaseRetriever):
     def _validate_fields_name(self) -> None:
         collection_fields = [x.name for x in self.collection.schema.fields]
         for field in self.anns_fields:
-            assert (
-                field in collection_fields
-            ), f"{field} is not a valid field in the collection."
-        assert (
-            self.text_field in collection_fields
-        ), f"{self.text_field} is not a valid field in the collection."
+            assert field in collection_fields, (
+                f"{field} is not a valid field in the collection."
+            )
+        assert self.text_field in collection_fields, (
+            f"{self.text_field} is not a valid field in the collection."
+        )
         for field in self.output_fields:  # type: ignore[union-attr]
             if not self.collection.schema.enable_dynamic_field:
-                assert (
-                    field in collection_fields
-                ), f"{field} is not a valid field in the collection."
+                assert field in collection_fields, (
+                    f"{field} is not a valid field in the collection."
+                )
 
     def _get_output_fields(self) -> List[str]:
         if self.output_fields:

libs/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py

@@ -9,7 +9,7 @@ from langchain_core.retrievers import BaseRetriever
 class ZillizCloudPipelineRetriever(BaseRetriever):
     """`Zilliz Cloud Pipeline` retriever.
 
-    Parameters:
+    Attributes:
         pipeline_ids: A dictionary of pipeline ids.
             Valid keys: "ingestion", "search", "deletion".
         token: Zilliz Cloud's token. Defaults to "".
@@ -18,7 +18,9 @@ class ZillizCloudPipelineRetriever(BaseRetriever):
     """
 
     pipeline_ids: Dict
+
     token: str = ""
+
     cloud_region: str = "gcp-us-west1"
 
     def _get_relevant_documents(

libs/milvus/langchain_milvus/vectorstores/milvus.py

@@ -326,9 +326,9 @@ class Milvus(VectorStore):
                 "Either `embedding_function` or `builtin_function` should be provided."
             )
 
-        self.embedding_func: Optional[
-            Union[EmbeddingType, List[EmbeddingType]]
-        ] = self._from_list(embedding_function)
+        self.embedding_func: Optional[Union[EmbeddingType, List[EmbeddingType]]] = (
+            self._from_list(embedding_function)
+        )
         self.builtin_func: Optional[
             Union[BaseMilvusBuiltInFunction, List[BaseMilvusBuiltInFunction]]
         ] = self._from_list(builtin_function)
@@ -1233,8 +1233,8 @@ class Milvus(VectorStore):
             metadatas: Optional metadata for each text
             ids: Optional IDs for each text
             force_ids: If force_ids, when auto_id is True and ids is not None,
-             it will return a list containing the customized ids, otherwise,
-             it will not contain the customized ids.
+                it will return a list containing the customized ids, otherwise,
+                it will not contain the customized ids.
 
         Returns:
             List of dictionaries ready for insertion
@@ -1242,14 +1242,14 @@ class Milvus(VectorStore):
         insert_list: list[dict] = []
 
         for vector_field_embeddings in embeddings:
-            assert len(texts) == len(
-                vector_field_embeddings
-            ), "Mismatched lengths of texts and embeddings."
+            assert len(texts) == len(vector_field_embeddings), (
+                "Mismatched lengths of texts and embeddings."
+            )
 
         if metadatas is not None:
-            assert len(texts) == len(
-                metadatas
-            ), "Mismatched lengths of texts and metadatas."
+            assert len(texts) == len(metadatas), (
+                "Mismatched lengths of texts and metadatas."
+            )
 
         for i, text in zip(range(len(texts)), texts):
             entity_dict = {}
@@ -1302,12 +1302,14 @@ class Milvus(VectorStore):
                 that they all fit in memory.
             metadatas (Optional[List[dict]]): Metadata dicts attached to each of
                 the texts. Defaults to None.
-            should be less than 65535 bytes. Required and work when auto_id is False.
             timeout (Optional[float]): Timeout for each batch insert. Defaults
                 to None.
             batch_size (int, optional): Batch size to use for insertion.
                 Defaults to 1000.
             ids (Optional[List[str]]): List of text ids. The length of each item
+                should be less than 65535 bytes.
+
+                Required and work when `auto_id` is `False`.
 
         Raises:
             MilvusException: Failure to add texts
@@ -1324,13 +1326,13 @@ class Milvus(VectorStore):
             )
             self.auto_id = True
         elif not self.auto_id and ids:  # Check ids
-            assert len(set(ids)) == len(
-                texts
-            ), "Different lengths of texts and unique ids are provided."
+            assert len(set(ids)) == len(texts), (
+                "Different lengths of texts and unique ids are provided."
+            )
             assert all(isinstance(x, str) for x in ids), "All ids should be strings."
-            assert all(
-                len(x.encode()) <= 65_535 for x in ids
-            ), "Each id should be a string less than 65535 bytes."
+            assert all(len(x.encode()) <= 65_535 for x in ids), (
+                "Each id should be a string less than 65535 bytes."
+            )
 
         elif self.auto_id and ids:
             logger.warning(
@@ -1417,12 +1419,12 @@ class Milvus(VectorStore):
                 or list of vectors for each text (in case of multi-vector)
             metadatas (Optional[List[dict]]): Metadata dicts attached to each of
                 the texts. Defaults to None.
-            should be less than 65535 bytes. Required and work when auto_id is False.
             timeout (Optional[float]): Timeout for each batch insert. Defaults
                 to None.
             batch_size (int, optional): Batch size to use for insertion.
                 Defaults to 1000.
             ids (Optional[List[str]]): List of text ids. The length of each item
+                should be less than 65535 bytes. Required and work when auto_id is False.
 
         Raises:
             MilvusException: Failure to add texts and embeddings
@@ -1523,7 +1525,7 @@ class Milvus(VectorStore):
             else:
                 log_entity[k] = v
         logger.error(
-            "Failed to %s batch starting at entity: %s/%s. " "First entity data: %s",
+            "Failed to %s batch starting at entity: %s/%s. First entity data: %s",
             operation_name,
             batch_index + 1,
             total_count,
@@ -1770,7 +1772,7 @@ class Milvus(VectorStore):
             query (str): The text being searched.
             k (int, optional): The amount of results to return. Defaults to 4.
             param (dict | list[dict], optional): The search params for the specified
-            index. Defaults to None.
+                index. Defaults to None.
             expr (str, optional): Filtering expression. Defaults to None.
             timeout (float, optional): How long to wait before timeout error.
                 Defaults to None.
@@ -2068,8 +2070,7 @@ class Milvus(VectorStore):
             return _map_ip_to_similarity
         else:
             raise ValueError(
-                "No supported normalization function"
-                f" for metric type: {metric_type}."
+                f"No supported normalization function for metric type: {metric_type}."
             )
 
     def delete(
@@ -2091,13 +2092,13 @@ class Milvus(VectorStore):
         if isinstance(ids, list) and len(ids) > 0:
             if expr is not None:
                 logger.warning(
-                    "Both ids and expr are provided. " "Ignore expr and delete by ids."
+                    "Both ids and expr are provided. Ignore expr and delete by ids."
                 )
             expr = f"{self._primary_field} in {ids}"
         else:
-            assert isinstance(
-                expr, str
-            ), "Either ids list or expr string must be provided."
+            assert isinstance(expr, str), (
+                "Either ids list or expr string must be provided."
+            )
         try:
             self.client.delete(self.collection_name, filter=expr, **kwargs)
             return True
@@ -2128,8 +2129,7 @@ class Milvus(VectorStore):
                     conn.schema_cache.pop(self.collection_name, None)
             except Exception as e:
                 logger.warning(
-                    f"Failed to clear sync schema cache for "
-                    f"{self.collection_name}: {e}"
+                    f"Failed to clear sync schema cache for {self.collection_name}: {e}"
                 )
 
             # Clear schema cache from async client
@@ -2201,7 +2201,7 @@ class Milvus(VectorStore):
         if isinstance(ids, list) and len(ids) > 0:
             if auto_id:
                 logger.warning(
-                    "Both ids and auto_id are provided. " "Ignore auto_id and use ids."
+                    "Both ids and auto_id are provided. Ignore auto_id and use ids."
                 )
             auto_id = False
         else:
@@ -2307,9 +2307,9 @@ class Milvus(VectorStore):
         if not ids:
             self.add_documents(documents=documents, **kwargs)
         else:
-            assert len(set(ids)) == len(
-                documents
-            ), "Different lengths of documents and unique ids are provided."
+            assert len(set(ids)) == len(documents), (
+                "Different lengths of documents and unique ids are provided."
+            )
 
         embeddings_functions: List[EmbeddingType] = self._as_list(self.embedding_func)
         embeddings: List = []
@@ -2474,12 +2474,12 @@ class Milvus(VectorStore):
                 that they all fit in memory.
             metadatas (Optional[List[dict]]): Metadata dicts attached to each of
                 the texts. Defaults to None.
-            should be less than 65535 bytes. Required and work when auto_id is False.
             timeout (Optional[float]): Timeout for each batch insert. Defaults
                 to None.
             batch_size (int, optional): Batch size to use for insertion.
                 Defaults to 1000.
             ids (Optional[List[str]]): List of text ids. The length of each item
+                should be less than 65535 bytes. Required and work when auto_id is False.
 
         Raises:
             MilvusException: Failure to add texts
@@ -2496,9 +2496,9 @@ class Milvus(VectorStore):
             )
             self.auto_id = True
         elif not self.auto_id and ids:  # Check ids
-            assert len(set(ids)) == len(
-                texts
-            ), "Different lengths of texts and unique ids are provided."
+            assert len(set(ids)) == len(texts), (
+                "Different lengths of texts and unique ids are provided."
+            )
 
         elif self.auto_id and ids:
             logger.warning(
@@ -2565,12 +2565,12 @@ class Milvus(VectorStore):
                 or list of vectors for each text (in case of multi-vector)
             metadatas (Optional[List[dict]]): Metadata dicts attached to each of
                 the texts. Defaults to None.
-            should be less than 65535 bytes. Required and work when auto_id is False.
             timeout (Optional[float]): Timeout for each batch insert. Defaults
                 to None.
             batch_size (int, optional): Batch size to use for insertion.
                 Defaults to 1000.
             ids (Optional[List[str]]): List of text ids. The length of each item
+                should be less than 65535 bytes. Required and work when auto_id is False.
 
         Raises:
             MilvusException: Failure to add texts and embeddings
@@ -2872,7 +2872,7 @@ class Milvus(VectorStore):
             query (str): The text being searched.
             k (int, optional): The amount of results to return. Defaults to 4.
             param (dict | list[dict], optional): The search params for the specified
-            index. Defaults to None.
+                index. Defaults to None.
             expr (str, optional): Filtering expression. Defaults to None.
             timeout (float, optional): How long to wait before timeout error.
                 Defaults to None.
@@ -3126,13 +3126,13 @@ class Milvus(VectorStore):
         if isinstance(ids, list) and len(ids) > 0:
             if expr is not None:
                 logger.warning(
-                    "Both ids and expr are provided. " "Ignore expr and delete by ids."
+                    "Both ids and expr are provided. Ignore expr and delete by ids."
                 )
             expr = f"{self._primary_field} in {ids}"
         else:
-            assert isinstance(
-                expr, str
-            ), "Either ids list or expr string must be provided."
+            assert isinstance(expr, str), (
+                "Either ids list or expr string must be provided."
+            )
         try:
             await self.aclient.delete(self.collection_name, filter=expr, **kwargs)
             return True
@@ -3197,7 +3197,7 @@ class Milvus(VectorStore):
         if isinstance(ids, list) and len(ids) > 0:
             if auto_id:
                 logger.warning(
-                    "Both ids and auto_id are provided. " "Ignore auto_id and use ids."
+                    "Both ids and auto_id are provided. Ignore auto_id and use ids."
                 )
             auto_id = False
         else:
@@ -3288,9 +3288,9 @@ class Milvus(VectorStore):
             await self.aadd_documents(documents=documents, **kwargs)
             return
 
-        assert len(set(ids)) == len(
-            documents
-        ), "Different lengths of documents and unique ids are provided."
+        assert len(set(ids)) == len(documents), (
+            "Different lengths of documents and unique ids are provided."
+        )
 
         embeddings_functions: List[EmbeddingType] = self._as_list(self.embedding_func)
         embeddings: List = []