Support API CreateDBInstanceIPArray.

Author: sdk-team

gpdb-20160503/ChangeLog.md

@@ -1,3 +1,21 @@
+2025-11-28 Version: 4.15.0
+- Support API CreateDBInstanceIPArray.
+- Support API CreateDatabase.
+- Support API DeleteDBInstanceIPArray.
+- Support API DeleteDatabase.
+- Support API DeletePrivateRAGService.
+- Support API DeployPrivateRAGService.
+- Support API DescribeDatabase.
+- Support API DescribeExtension.
+- Support API DescribePrivateRAGService.
+- Support API DescribeRebalanceStatus.
+- Support API DescribeZonesPrivateRAGService.
+- Support API DownloadSlowSQLRecords.
+- Support API ListDatabaseExtensions.
+- Support API ListInstanceDatabases.
+- Support API ListSlowSQLRecords.
+
+
 2025-10-30 Version: 4.14.0
 - Support API QueryKnowledgeBasesContent.
 - Update API ChatWithKnowledgeBase: add request parameters RegionId.

gpdb-20160503/alibabacloud_gpdb20160503/__init__.py

@@ -1 +1 @@
-__version__ = '4.14.0'
+__version__ = '4.15.0'

gpdb-20160503/alibabacloud_gpdb20160503/client.py

@@ -14,9 +14,14 @@ def __init__(
         ainode_spec_infos: List[main_models.AddAINodeRequestAINodeSpecInfos] = None,
         dbinstance_id: str = None,
     ):
+        # The ID of the resource pool to which the AI node belongs.
         self.ainode_pool_id = ainode_pool_id
+        # The AI node specifications.
+        # 
         # This parameter is required.
         self.ainode_spec_infos = ainode_spec_infos
+        # The cluster ID.
+        # 
         # This parameter is required.
         self.dbinstance_id = dbinstance_id
 
@@ -68,8 +73,41 @@ def __init__(
         node_num: str = None,
         node_spec: str = None,
     ):
+        # The number of AI nodes.
+        # 
         # This parameter is required.
         self.node_num = node_num
+        # The AI node specifications.
+        # 
+        #     ADB.AIStandard.1
+        #     ADB.AIMedium.1
+        #     ADB.AILarge.1
+        #     ADB.AIStandard.2
+        #     ADB.AIMedium.2
+        #     ADB.AILarge.2
+        #     ADB.AIXLarge.2
+        #     ADB.AIStandard.6
+        #     ADB.AIMedium.6
+        #     ADB.AILarge.6
+        #     ADB.AIXLarge.6
+        #     ADB.AIStandard.3
+        #     ADB.AIMedium.3
+        #     ADB.AILarge.3
+        #     ADB.AIXLarge.3
+        #     ADB.AIStandard.4
+        #     ADB.AIMedium.4
+        #     ADB.AILarge.4
+        #     ADB.AIXLarge.4
+        #     ADB.AIStandard.5
+        #     ADB.AIMedium.5
+        #     ADB.AILarge.5
+        #     ADB.AIXLarge.5
+        #     ADB.AIStandard.8
+        #     ADB.AIMedium.8
+        #     ADB.AILarge.8
+        #     ADB.AIXLarge.8
+        #     ADB.AI2XLarge.8
+        # 
         # This parameter is required.
         self.node_spec = node_spec
 

gpdb-20160503/alibabacloud_gpdb20160503/models/__init__.py

@@ -16,18 +16,46 @@ def __init__(
         region_id: str = None,
         workspace_id: str = None,
     ):
+        # The name of the collection.
+        # 
+        # > You can call the [ListCollections](https://help.aliyun.com/document_detail/2401503.html) operation to query a list of collections.
+        # 
         # This parameter is required.
         self.collection = collection
+        # The cluster ID.
+        # 
+        # > You can call the [DescribeDBInstances](https://help.aliyun.com/document_detail/196830.html) operation to query the information about all AnalyticDB for PostgreSQL instances within a region, including instance IDs.
         self.dbinstance_id = dbinstance_id
+        # The ID of the index creation job, which is returned from the `CreateIndex` operation.
+        # 
         # This parameter is required.
         self.job_id = job_id
+        # The namespace name.
+        # 
+        # >  You can call the [CreateNamespace](https://help.aliyun.com/document_detail/2401495.html) operation to create a namespace and call the [ListNamespaces](https://help.aliyun.com/document_detail/2401502.html) operation to query a list of namespaces.
+        # 
         # This parameter is required.
         self.namespace = namespace
+        # The password of the namespace.
+        # 
+        # > The value of this parameter is specified when you call the CreateNamespace operation.
+        # 
         # This parameter is required.
         self.namespace_password = namespace_password
         self.owner_id = owner_id
+        # The region ID of the cluster.
+        # 
+        # > You can call the [DescribeRegions](https://help.aliyun.com/document_detail/86912.html) operation to query the most recent region list.
+        # 
         # This parameter is required.
         self.region_id = region_id
+        # The ID of the workspace that consists of multiple AnalyticDB for PostgreSQL instances. You must specify one of the WorkspaceId and DBInstanceId parameters. If you specify both parameters, the WorkspaceId parameter takes precedence.
+        # 
+        # Valid values:
+        # 
+        # *   ip
+        # *   l2
+        # *   cosine
         self.workspace_id = workspace_id
 
     def validate(self):

gpdb-20160503/alibabacloud_gpdb20160503/models/_add_ainode_request.py

@@ -11,8 +11,14 @@ def __init__(
         request_id: str = None,
         status: str = None,
     ):
+        # The returned message.
         self.message = message
+        # The unique ID of the request.
         self.request_id = request_id
+        # The status of the operation. Valid values:
+        # 
+        # *   **success**
+        # *   **fail**
         self.status = status
 
     def validate(self):

gpdb-20160503/alibabacloud_gpdb20160503/models/_cancel_create_index_job_request.py

@@ -18,14 +18,24 @@ def __init__(
         prompt_params: str = None,
         region_id: str = None,
     ):
+        # The cluster ID.
+        # 
+        # >  You can call the [DescribeDBInstances](https://help.aliyun.com/document_detail/196830.html) operation to query the information about all AnalyticDB for PostgreSQL instances within a region, including instance IDs.
+        # 
         # This parameter is required.
         self.dbinstance_id = dbinstance_id
+        # Whether to return the retrieved result. Default value: false.
         self.include_knowledge_base_results = include_knowledge_base_results
+        # The knowledge retrieval parameter object. If you do not specify this parameter, only chat mode is enabled.
         self.knowledge_params = knowledge_params
+        # The Large Language Model (LLM) invocation parameter object.
+        # 
         # This parameter is required.
         self.model_params = model_params
         self.owner_id = owner_id
+        # The system prompt template, which should include {{ text_chunks }},{{ user_system_prompt }},{{ graph_entities },{{ graph_relations }}. If any of these placeholders are not specified, the corresponding section should have no effect.
         self.prompt_params = prompt_params
+        # 实例所在的地域ID
         self.region_id = region_id
 
     def validate(self):
@@ -103,17 +113,29 @@ def __init__(
         tools: List[main_models.ChatWithKnowledgeBaseRequestModelParamsTools] = None,
         top_p: float = None,
     ):
+        # Maximum number of tokens to generate.
         self.max_tokens = max_tokens
+        # Message list.
+        # 
         # This parameter is required.
         self.messages = messages
+        # The model name. See [Model Studio Document](https://help.aliyun.com/zh/model-studio/compatibility-of-openai-with-dashscope?spm=a2c4g.11186623.help-menu-2400256.d_2_10_0.45b5516eZIciC8\\&scm=20140722.H_2833609._.OR_help-T_cn~zh-V_1#eadfc13038jd5) for the available models.
+        # 
         # This parameter is required.
         self.model = model
+        # The number of candidate responses to generate.
         self.n = n
+        # Presence penalty coefficient (-2.0 to 2.0).
         self.presence_penalty = presence_penalty
+        # The random seed.
         self.seed = seed
+        # Stop words.
         self.stop = stop
+        # Sampling temperature (0~2).
         self.temperature = temperature
+        # Tools
         self.tools = tools
+        # Top-p (nucleus) sampling threshold (0–1).
         self.top_p = top_p
 
     def validate(self):
@@ -212,6 +234,7 @@ def __init__(
         self,
         function: main_models.ChatWithKnowledgeBaseRequestModelParamsToolsFunction = None,
     ):
+        # The information about a function.
         self.function = function
 
     def validate(self):
@@ -243,8 +266,11 @@ def __init__(
         name: str = None,
         parameters: Any = None,
     ):
+        # The description of the function.
         self.description = description
+        # The name of the function.
         self.name = name
+        # JSON Schema for function parameters.
         self.parameters = parameters
 
     def validate(self):
@@ -285,7 +311,13 @@ def __init__(
         content: str = None,
         role: str = None,
     ):
+        # The message content.
         self.content = content
+        # The message role. Valid values:
+        # 
+        # *   system
+        # *   user
+        # *   assistant
         self.role = role
 
     def validate(self):
@@ -323,11 +355,26 @@ def __init__(
         source_collection: List[main_models.ChatWithKnowledgeBaseRequestKnowledgeParamsSourceCollection] = None,
         top_k: int = None,
     ):
+        # The method used to merge multiple knowledge bases. Default value: RRF. Optional:
+        # 
+        # *   RRF
+        # *   Weight
         self.merge_method = merge_method
+        # Parameters for multi-knowledge-base fusion.
         self.merge_method_args = merge_method_args
+        # The rerank factor. If you specify this parameter, the search result is reranked once again. Valid values: 1\\<RerankFactor<=5.
+        # 
+        # > 
+        # 
+        # *   If the document is segmented into sparse parts, reranking is inefficient.
+        # 
+        # *   We recommend that the number of reranked results (the ceiling of TopK × RerankFactor) not exceed 50.
         self.rerank_factor = rerank_factor
+        # Knowledge base.
+        # 
         # This parameter is required.
         self.source_collection = source_collection
+        # Specifies the number of top results to return after merging retrieved results from multiple vector collections.
         self.top_k = top_k
 
     def validate(self):
@@ -393,11 +440,21 @@ def __init__(
         namespace_password: str = None,
         query_params: main_models.ChatWithKnowledgeBaseRequestKnowledgeParamsSourceCollectionQueryParams = None,
     ):
+        # The name of the collection to be recalled.
+        # 
         # This parameter is required.
         self.collection = collection
+        # The name of the namespace. Default value: public.
+        # 
+        # >  You can call the [CreateNamespace](https://help.aliyun.com/document_detail/2401495.html) operation to create a namespace and call the [ListNamespaces](https://help.aliyun.com/document_detail/2401502.html) operation to query a list of namespaces.
         self.namespace = namespace
+        # The password of the namespace.
+        # 
+        # >  The value of this parameter is specified when you call the CreateNamespace operation.
+        # 
         # This parameter is required.
         self.namespace_password = namespace_password
+        # Parameters related to the knowledge base retrieval.
         self.query_params = query_params
 
     def validate(self):
@@ -454,15 +511,67 @@ def __init__(
         top_k: int = None,
         use_full_text_retrieval: bool = None,
     ):
+        # The condition that is used to filter the data to be updated. Specify this parameter in a format that is the same as the WHERE clause.
         self.filter = filter
+        # Whether to enable knowledge graph enhancement. Default value: false.
         self.graph_enhance = graph_enhance
+        # Returns the top number of entities and relationship edges. Default value: 60.
         self.graph_search_args = graph_search_args
+        # The dual-path retrieval algorithm. This parameter is empty by default, which specifies that scores of vector retrieval and full-text retrieval are directly compared and sorted together.
+        # 
+        # Valid values:
+        # 
+        # *   RRF: The reciprocal rank fusion (RRF) algorithm uses a constant k to control the fusion effect. For more information, see the description of the HybridSearchArgs parameter.
+        # *   Weight: This algorithm uses the alpha parameter to specify the proportion of the vector search score and the full-text search score and then sorts by weight. For more information, see the description of the HybridSearchArgs parameter.
+        # *   Cascaded: This algorithm performs first full-text retrieval and then vector retrieval.
         self.hybrid_search = hybrid_search
+        # The parameters of the dual-path retrieval algorithm. RRF and Weight are supported at this time:
+        # 
+        # *   RRF: Specifies the smoothing constant k in the formula to calculate the score: `1/(k + rank_i)`. The k constant must be a positive integer greater than 1. The format:
+        # 
+        # <!---->
+        # 
+        #     { 
+        #        "RRF": {
+        #         "k": 60
+        #        }
+        #     }
+        # 
+        # *   Weight: The score is computed as `alpha * vector_score + (1 - alpha) * text_score`. The parameter alpha controls the weighting between vector search and full-text search scores, with a valid range of [0, 1]. 0 specifies only full-text search score. 1 specifies only vector search score.
+        # 
+        # <!---->
+        # 
+        #     { 
+        #        "Weight": {
+        #         "alpha": 0.5
+        #        }
+        #     }
         self.hybrid_search_args = hybrid_search_args
+        # The method that is used to create vector indexes. Valid values:
+        # 
+        # *   l2: Euclidean distance.
+        # *   ip: Inner product distance.
+        # *   cosine: Cosine similarity.
         self.metrics = metrics
+        # The retrieval window. If you specify this parameter, the context of the retrieved result is added in the output. Format: List\\<A, B>. Valid values: -10<=A<=0 and 0<=B<=10.
+        # 
+        # > 
+        # 
+        # *   We recommend that you specify this parameter if the source document is segmented into large numbers of pieces, which may result in loss of contextual information during retrieval.
+        # 
+        # *   Perform re-ranking before windowing.
         self.recall_window = recall_window
+        # The rerank factor. If you specify this parameter, the search result is reranked once again. Valid values: 1\\<RerankFactor<=5.
+        # 
+        # > 
+        # 
+        # *   If the document is segmented into sparse parts, reranking is inefficient.
+        # 
+        # *   We recommend that the number of reranked results (the ceiling of TopK × RerankFactor) not exceed 50.
         self.rerank_factor = rerank_factor
+        # The number of top results.
         self.top_k = top_k
+        # Specifies whether to use full-text retrieval (dual-path retrieval). The default value is false, which means only vector retrieval is used.
         self.use_full_text_retrieval = use_full_text_retrieval
 
     def validate(self):
@@ -546,6 +655,7 @@ def __init__(
         self,
         graph_top_k: int = None,
     ):
+        # Returns the top number of entities and relationship edges. Default value: 60.
         self.graph_top_k = graph_top_k
 
     def validate(self):
@@ -574,7 +684,9 @@ def __init__(
         rrf: main_models.ChatWithKnowledgeBaseRequestKnowledgeParamsMergeMethodArgsRrf = None,
         weight: main_models.ChatWithKnowledgeBaseRequestKnowledgeParamsMergeMethodArgsWeight = None,
     ):
+        # The parameter that can be configured when the MergeMethod parameter is set to RRF.
         self.rrf = rrf
+        # The parameter that you can configure when you set the MergeMethod parameter to Weight.
         self.weight = weight
 
     def validate(self):
@@ -613,6 +725,7 @@ def __init__(
         self,
         weights: List[float] = None,
     ):
+        # An array of weights for each SourceCollection.
         self.weights = weights
 
     def validate(self):
@@ -640,6 +753,7 @@ def __init__(
         self,
         k: int = None,
     ):
+        # The smoothing constant k in the formula to calculate the score: 1/(k + rank_i). It must be a positive integer greater than 1.
         self.k = k
 
     def validate(self):