Releasing version 65.78.1

Releasing version 65.78.1

CHANGELOG.md

@@ -4,6 +4,16 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/)
 
+## 65.78.1 - 2024-11-12
+### Added 
+- Support for calling Oracle Cloud Infrastructure services in the `me-alain-1` region 
+- Support for connection refresh in the GoldenGate service 
+- Support for secret compartment id in import and export operations of deployment wallet in the GoldenGate service 
+- Support for creating metadata only backups in the GoldenGate service 
+- Support for Llama 3.2 unit shape in Generative AI service 
+- Support for Llama 3.2 vision in Generative AI Inference service 
+- Support for Cohere CommandR response format in Generative AI Inference service
+
 ## 65.78.0 - 2024-11-05
 ### Added 
 - Support for calling Oracle Cloud Infrastructure services in the `ap-seoul-2` region 

common/regions.go

@@ -138,6 +138,8 @@ const (
 	RegionEUCrissier1 Region = "eu-crissier-1"
 	//RegionMEAbudhabi3 region Abudhabi
 	RegionMEAbudhabi3 Region = "me-abudhabi-3"
+	//RegionMEAlain1 region Alain
+	RegionMEAlain1 Region = "me-alain-1"
 	//RegionMEAbudhabi2 region Abudhabi
 	RegionMEAbudhabi2 Region = "me-abudhabi-2"
 	//RegionMEAbudhabi4 region Abudhabi
@@ -218,6 +220,7 @@ var shortNameRegion = map[string]Region{
 	"avz": RegionEUDccZurich1,
 	"avf": RegionEUCrissier1,
 	"ahu": RegionMEAbudhabi3,
+	"rba": RegionMEAlain1,
 	"rkt": RegionMEAbudhabi2,
 	"shj": RegionMEAbudhabi4,
 	"dtz": RegionAPSeoul2,
@@ -327,6 +330,7 @@ var regionRealm = map[Region]string{
 	RegionEUCrissier1:  "oc24",
 
 	RegionMEAbudhabi3: "oc26",
+	RegionMEAlain1:    "oc26",
 
 	RegionMEAbudhabi2: "oc29",
 	RegionMEAbudhabi4: "oc29",

common/regions.json

@@ -430,5 +430,11 @@
         "realmKey": "oc35",
         "regionIdentifier": "ap-chuncheon-2",
         "realmDomainComponent": "oraclecloud35.com"
+    },
+    {
+        "regionKey": "rba",
+        "realmKey": "oc26",
+        "regionIdentifier": "me-alain-1",
+        "realmDomainComponent": "oraclecloud26.com"
     }
 ]

generativeai/create_dedicated_ai_cluster_details.go

@@ -39,10 +39,14 @@ type CreateDedicatedAiClusterDetails struct {
 	// - LARGE_COHERE_V2
 	// - SMALL_COHERE
 	// - SMALL_COHERE_V2
+	// - SMALL_COHERE_4
 	// - EMBED_COHERE
 	// - LLAMA2_70
 	// - LARGE_GENERIC
 	// - LARGE_COHERE_V2_2
+	// - LARGE_GENERIC_4
+	// - SMALL_GENERIC_V2
+	// - LARGE_GENERIC_2
 	UnitShape DedicatedAiClusterUnitShapeEnum `mandatory:"true" json:"unitShape"`
 
 	// A user-friendly name. Does not have to be unique, and it's changeable.

generativeai/dedicated_ai_cluster.go

@@ -21,7 +21,7 @@ import (
 
 // DedicatedAiCluster Dedicated AI clusters are compute resources that you can use for fine-tuning custom models or for hosting endpoints for custom models. The clusters are dedicated to your models and not shared with users in other tenancies.
 // To use any of the API operations, you must be authorized in an IAM policy. If you're not authorized, talk to an administrator who gives OCI resource access to users. See
-// Getting Started with Policies (https://docs.cloud.oracle.com/iaas/Content/Identity/policiesgs/get-started-with-policies.htm) and Getting Access to Generative AI Resouces (https://docs.cloud.oracle.com/iaas/Content/generative-ai/iam-policies.htm).
+// Getting Started with Policies (https://docs.cloud.oracle.com/iaas/Content/Identity/policiesgs/get-started-with-policies.htm) and Getting Access to Generative AI Resources (https://docs.cloud.oracle.com/iaas/Content/generative-ai/iam-policies.htm).
 type DedicatedAiCluster struct {
 
 	// The OCID (https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the dedicated AI cluster.
@@ -283,35 +283,47 @@ const (
 	DedicatedAiClusterUnitShapeLargeCohereV2  DedicatedAiClusterUnitShapeEnum = "LARGE_COHERE_V2"
 	DedicatedAiClusterUnitShapeSmallCohere    DedicatedAiClusterUnitShapeEnum = "SMALL_COHERE"
 	DedicatedAiClusterUnitShapeSmallCohereV2  DedicatedAiClusterUnitShapeEnum = "SMALL_COHERE_V2"
+	DedicatedAiClusterUnitShapeSmallCohere4   DedicatedAiClusterUnitShapeEnum = "SMALL_COHERE_4"
 	DedicatedAiClusterUnitShapeEmbedCohere    DedicatedAiClusterUnitShapeEnum = "EMBED_COHERE"
 	DedicatedAiClusterUnitShapeLlama270       DedicatedAiClusterUnitShapeEnum = "LLAMA2_70"
 	DedicatedAiClusterUnitShapeLargeGeneric   DedicatedAiClusterUnitShapeEnum = "LARGE_GENERIC"
 	DedicatedAiClusterUnitShapeLargeCohereV22 DedicatedAiClusterUnitShapeEnum = "LARGE_COHERE_V2_2"
 	DedicatedAiClusterUnitShapeLargeGeneric4  DedicatedAiClusterUnitShapeEnum = "LARGE_GENERIC_4"
+	DedicatedAiClusterUnitShapeSmallGenericV2 DedicatedAiClusterUnitShapeEnum = "SMALL_GENERIC_V2"
+	DedicatedAiClusterUnitShapeLargeGeneric2  DedicatedAiClusterUnitShapeEnum = "LARGE_GENERIC_2"
+	DedicatedAiClusterUnitShapeLargeGenericV2 DedicatedAiClusterUnitShapeEnum = "LARGE_GENERIC_V2"
 )
 
 var mappingDedicatedAiClusterUnitShapeEnum = map[string]DedicatedAiClusterUnitShapeEnum{
 	"LARGE_COHERE":      DedicatedAiClusterUnitShapeLargeCohere,
 	"LARGE_COHERE_V2":   DedicatedAiClusterUnitShapeLargeCohereV2,
 	"SMALL_COHERE":      DedicatedAiClusterUnitShapeSmallCohere,
 	"SMALL_COHERE_V2":   DedicatedAiClusterUnitShapeSmallCohereV2,
+	"SMALL_COHERE_4":    DedicatedAiClusterUnitShapeSmallCohere4,
 	"EMBED_COHERE":      DedicatedAiClusterUnitShapeEmbedCohere,
 	"LLAMA2_70":         DedicatedAiClusterUnitShapeLlama270,
 	"LARGE_GENERIC":     DedicatedAiClusterUnitShapeLargeGeneric,
 	"LARGE_COHERE_V2_2": DedicatedAiClusterUnitShapeLargeCohereV22,
 	"LARGE_GENERIC_4":   DedicatedAiClusterUnitShapeLargeGeneric4,
+	"SMALL_GENERIC_V2":  DedicatedAiClusterUnitShapeSmallGenericV2,
+	"LARGE_GENERIC_2":   DedicatedAiClusterUnitShapeLargeGeneric2,
+	"LARGE_GENERIC_V2":  DedicatedAiClusterUnitShapeLargeGenericV2,
 }
 
 var mappingDedicatedAiClusterUnitShapeEnumLowerCase = map[string]DedicatedAiClusterUnitShapeEnum{
 	"large_cohere":      DedicatedAiClusterUnitShapeLargeCohere,
 	"large_cohere_v2":   DedicatedAiClusterUnitShapeLargeCohereV2,
 	"small_cohere":      DedicatedAiClusterUnitShapeSmallCohere,
 	"small_cohere_v2":   DedicatedAiClusterUnitShapeSmallCohereV2,
+	"small_cohere_4":    DedicatedAiClusterUnitShapeSmallCohere4,
 	"embed_cohere":      DedicatedAiClusterUnitShapeEmbedCohere,
 	"llama2_70":         DedicatedAiClusterUnitShapeLlama270,
 	"large_generic":     DedicatedAiClusterUnitShapeLargeGeneric,
 	"large_cohere_v2_2": DedicatedAiClusterUnitShapeLargeCohereV22,
 	"large_generic_4":   DedicatedAiClusterUnitShapeLargeGeneric4,
+	"small_generic_v2":  DedicatedAiClusterUnitShapeSmallGenericV2,
+	"large_generic_2":   DedicatedAiClusterUnitShapeLargeGeneric2,
+	"large_generic_v2":  DedicatedAiClusterUnitShapeLargeGenericV2,
 }
 
 // GetDedicatedAiClusterUnitShapeEnumValues Enumerates the set of values for DedicatedAiClusterUnitShapeEnum
@@ -330,11 +342,15 @@ func GetDedicatedAiClusterUnitShapeEnumStringValues() []string {
 		"LARGE_COHERE_V2",
 		"SMALL_COHERE",
 		"SMALL_COHERE_V2",
+		"SMALL_COHERE_4",
 		"EMBED_COHERE",
 		"LLAMA2_70",
 		"LARGE_GENERIC",
 		"LARGE_COHERE_V2_2",
 		"LARGE_GENERIC_4",
+		"SMALL_GENERIC_V2",
+		"LARGE_GENERIC_2",
+		"LARGE_GENERIC_V2",
 	}
 }
 

generativeai/endpoint.go

@@ -20,7 +20,7 @@ import (
 
 // Endpoint To host a custom model for inference, create an endpoint for that model on a dedicated AI cluster of type HOSTING.
 // To use any of the API operations, you must be authorized in an IAM policy. If you're not authorized, talk to an administrator who gives OCI resource access to users. See
-// Getting Started with Policies (https://docs.cloud.oracle.com/iaas/Content/Identity/policiesgs/get-started-with-policies.htm) and Getting Access to Generative AI Resouces (https://docs.cloud.oracle.com/iaas/Content/generative-ai/iam-policies.htm).
+// Getting Started with Policies (https://docs.cloud.oracle.com/iaas/Content/Identity/policiesgs/get-started-with-policies.htm) and Getting Access to Generative AI Resources (https://docs.cloud.oracle.com/iaas/Content/generative-ai/iam-policies.htm).
 type Endpoint struct {
 
 	// An OCID that uniquely identifies this endpoint resource.

generativeai/model.go

@@ -21,7 +21,7 @@ import (
 
 // Model You can create a custom model by using your dataset to fine-tune an out-of-the-box text generation base model. Have your dataset ready before you create a custom model. See Training Data Requirements (https://docs.cloud.oracle.com/iaas/Content/generative-ai/training-data-requirements.htm).
 // To use any of the API operations, you must be authorized in an IAM policy. If you're not authorized, talk to an administrator who gives OCI resource access to users. See
-// Getting Started with Policies (https://docs.cloud.oracle.com/iaas/Content/Identity/policiesgs/get-started-with-policies.htm) and Getting Access to Generative AI Resouces (https://docs.cloud.oracle.com/iaas/Content/generative-ai/iam-policies.htm).
+// Getting Started with Policies (https://docs.cloud.oracle.com/iaas/Content/Identity/policiesgs/get-started-with-policies.htm) and Getting Access to Generative AI Resources (https://docs.cloud.oracle.com/iaas/Content/generative-ai/iam-policies.htm).
 type Model struct {
 
 	// An ID that uniquely identifies a pretrained or fine-tuned model.

generativeaiinference/assistant_message.go

@@ -5,8 +5,8 @@
 // Generative AI Service Inference API
 //
 // OCI Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases for text generation, summarization, and text embeddings.
-// Use the Generative AI service inference API to access your custom model endpoints, or to try the out-of-the-box models to Chat, GenerateText, SummarizeText, and EmbedText.
-// To use a Generative AI custom model for inference, you must first create an endpoint for that model. Use the Generative AI service management API (https://docs.cloud.oracle.com/#/en/generative-ai/latest/) to Model by fine-tuning an out-of-the-box model, or a previous version of a custom model, using your own data. Fine-tune the custom model on a  DedicatedAiCluster. Then, create a DedicatedAiCluster with an Endpoint to host your custom model. For resource management in the Generative AI service, use the Generative AI service management API (https://docs.cloud.oracle.com/#/en/generative-ai/latest/).
+// Use the Generative AI service inference API to access your custom model endpoints, or to try the out-of-the-box models to /EN/generative-ai-inference/latest/ChatResult/Chat, /EN/generative-ai-inference/latest/GenerateTextResult/GenerateText, /EN/generative-ai-inference/latest/SummarizeTextResult/SummarizeText, and /EN/generative-ai-inference/latest/EmbedTextResult/EmbedText.
+// To use a Generative AI custom model for inference, you must first create an endpoint for that model. Use the /EN/generative-ai/latest/ to /EN/generative-ai/latest/Model/ by fine-tuning an out-of-the-box model, or a previous version of a custom model, using your own data. Fine-tune the custom model on a /EN/generative-ai/latest/DedicatedAiCluster/. Then, create a /EN/generative-ai/latest/DedicatedAiCluster/ with an Endpoint to host your custom model. For resource management in the Generative AI service, use the /EN/generative-ai/latest/.
 // To learn more about the service, see the Generative AI documentation (https://docs.cloud.oracle.com/iaas/Content/generative-ai/home.htm).
 //
 

generativeaiinference/base_chat_request.go

@@ -5,8 +5,8 @@
 // Generative AI Service Inference API
 //
 // OCI Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases for text generation, summarization, and text embeddings.
-// Use the Generative AI service inference API to access your custom model endpoints, or to try the out-of-the-box models to Chat, GenerateText, SummarizeText, and EmbedText.
-// To use a Generative AI custom model for inference, you must first create an endpoint for that model. Use the Generative AI service management API (https://docs.cloud.oracle.com/#/en/generative-ai/latest/) to Model by fine-tuning an out-of-the-box model, or a previous version of a custom model, using your own data. Fine-tune the custom model on a  DedicatedAiCluster. Then, create a DedicatedAiCluster with an Endpoint to host your custom model. For resource management in the Generative AI service, use the Generative AI service management API (https://docs.cloud.oracle.com/#/en/generative-ai/latest/).
+// Use the Generative AI service inference API to access your custom model endpoints, or to try the out-of-the-box models to /EN/generative-ai-inference/latest/ChatResult/Chat, /EN/generative-ai-inference/latest/GenerateTextResult/GenerateText, /EN/generative-ai-inference/latest/SummarizeTextResult/SummarizeText, and /EN/generative-ai-inference/latest/EmbedTextResult/EmbedText.
+// To use a Generative AI custom model for inference, you must first create an endpoint for that model. Use the /EN/generative-ai/latest/ to /EN/generative-ai/latest/Model/ by fine-tuning an out-of-the-box model, or a previous version of a custom model, using your own data. Fine-tune the custom model on a /EN/generative-ai/latest/DedicatedAiCluster/. Then, create a /EN/generative-ai/latest/DedicatedAiCluster/ with an Endpoint to host your custom model. For resource management in the Generative AI service, use the /EN/generative-ai/latest/.
 // To learn more about the service, see the Generative AI documentation (https://docs.cloud.oracle.com/iaas/Content/generative-ai/home.htm).
 //
 

generativeaiinference/base_chat_response.go

@@ -5,8 +5,8 @@
 // Generative AI Service Inference API
 //
 // OCI Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases for text generation, summarization, and text embeddings.
-// Use the Generative AI service inference API to access your custom model endpoints, or to try the out-of-the-box models to Chat, GenerateText, SummarizeText, and EmbedText.
-// To use a Generative AI custom model for inference, you must first create an endpoint for that model. Use the Generative AI service management API (https://docs.cloud.oracle.com/#/en/generative-ai/latest/) to Model by fine-tuning an out-of-the-box model, or a previous version of a custom model, using your own data. Fine-tune the custom model on a  DedicatedAiCluster. Then, create a DedicatedAiCluster with an Endpoint to host your custom model. For resource management in the Generative AI service, use the Generative AI service management API (https://docs.cloud.oracle.com/#/en/generative-ai/latest/).
+// Use the Generative AI service inference API to access your custom model endpoints, or to try the out-of-the-box models to /EN/generative-ai-inference/latest/ChatResult/Chat, /EN/generative-ai-inference/latest/GenerateTextResult/GenerateText, /EN/generative-ai-inference/latest/SummarizeTextResult/SummarizeText, and /EN/generative-ai-inference/latest/EmbedTextResult/EmbedText.
+// To use a Generative AI custom model for inference, you must first create an endpoint for that model. Use the /EN/generative-ai/latest/ to /EN/generative-ai/latest/Model/ by fine-tuning an out-of-the-box model, or a previous version of a custom model, using your own data. Fine-tune the custom model on a /EN/generative-ai/latest/DedicatedAiCluster/. Then, create a /EN/generative-ai/latest/DedicatedAiCluster/ with an Endpoint to host your custom model. For resource management in the Generative AI service, use the /EN/generative-ai/latest/.
 // To learn more about the service, see the Generative AI documentation (https://docs.cloud.oracle.com/iaas/Content/generative-ai/home.htm).
 //
 

generativeaiinference/chat_choice.go

@@ -5,8 +5,8 @@
 // Generative AI Service Inference API
 //
 // OCI Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases for text generation, summarization, and text embeddings.
-// Use the Generative AI service inference API to access your custom model endpoints, or to try the out-of-the-box models to Chat, GenerateText, SummarizeText, and EmbedText.
-// To use a Generative AI custom model for inference, you must first create an endpoint for that model. Use the Generative AI service management API (https://docs.cloud.oracle.com/#/en/generative-ai/latest/) to Model by fine-tuning an out-of-the-box model, or a previous version of a custom model, using your own data. Fine-tune the custom model on a  DedicatedAiCluster. Then, create a DedicatedAiCluster with an Endpoint to host your custom model. For resource management in the Generative AI service, use the Generative AI service management API (https://docs.cloud.oracle.com/#/en/generative-ai/latest/).
+// Use the Generative AI service inference API to access your custom model endpoints, or to try the out-of-the-box models to /EN/generative-ai-inference/latest/ChatResult/Chat, /EN/generative-ai-inference/latest/GenerateTextResult/GenerateText, /EN/generative-ai-inference/latest/SummarizeTextResult/SummarizeText, and /EN/generative-ai-inference/latest/EmbedTextResult/EmbedText.
+// To use a Generative AI custom model for inference, you must first create an endpoint for that model. Use the /EN/generative-ai/latest/ to /EN/generative-ai/latest/Model/ by fine-tuning an out-of-the-box model, or a previous version of a custom model, using your own data. Fine-tune the custom model on a /EN/generative-ai/latest/DedicatedAiCluster/. Then, create a /EN/generative-ai/latest/DedicatedAiCluster/ with an Endpoint to host your custom model. For resource management in the Generative AI service, use the /EN/generative-ai/latest/.
 // To learn more about the service, see the Generative AI documentation (https://docs.cloud.oracle.com/iaas/Content/generative-ai/home.htm).
 //