Add support for Google VertexAI language model provider

src/dotnet/Common/Constants/Configuration/APIEndpointProviders.cs

@@ -16,13 +16,18 @@ public static class APIEndpointProviders
         public const string OPENAI = "openai";
 
         /// <summary>
-        /// Bedrock
+        /// Amazon Bedrock
         /// </summary>
         public const string BEDROCK = "bedrock";
 
+        /// <summary>
+        /// Google VertexAI
+        /// </summary>
+        public const string VERTEXAI = "vertexai";
+
         /// <summary>
         /// All providers.
         /// </summary>
-        public readonly static string[] All = [MICROSOFT, OPENAI, BEDROCK];
+        public readonly static string[] All = [MICROSOFT, OPENAI, BEDROCK, VERTEXAI];
     }
 }

src/python/LangChainAPI/requirements.txt

@@ -8,17 +8,18 @@ azure-search-documents==11.6.0b9
 azure-storage-blob==12.21.0
 email-validator==2.2.0
 fastapi==0.115.6
-langchain==0.3.7
-langchain-aws==0.2.7
-langchain-experimental==0.3.3
+langchain==0.3.17
+langchain-aws==0.2.12
+langchain-experimental==0.3.4
+langchain-google-vertexai==2.0.13
 langchain-openai==0.3.3
 langchain-azure-dynamic-sessions==0.2.0
 langchain-deepseek-official==0.1.0
 langgraph==0.2.53
-openai==1.60.2
+openai==1.61.0
 pandas==2.2.2
 pyarrow==18.1.0
-pydantic==2.8.2
+pydantic==2.10.6
 pylint==3.2.6
 pyodbc==5.2.0
 sqlalchemy==2.0.36

src/python/PythonSDK/foundationallm/langchain/agents/langchain_agent_base.py

@@ -1,11 +1,13 @@
 import boto3
+import json
 from abc import abstractmethod
 from typing import List
 from azure.identity import DefaultAzureCredential, get_bearer_token_provider
-
+from google.oauth2 import service_account
 from langchain_core.language_models import BaseLanguageModel
 from langchain_aws import ChatBedrockConverse
 from langchain_core.messages import BaseMessage, AIMessage, HumanMessage
+from langchain_google_vertexai import ChatVertexAI
 from langchain_openai import AzureChatOpenAI, ChatOpenAI, OpenAI
 from openai import AsyncAzureOpenAI as async_aoi
 from foundationallm.config import Configuration, UserIdentity
@@ -397,6 +399,26 @@ def _get_language_model(self, override_operation_type: OperationTypes = None) ->
                         aws_access_key_id = access_key,
                         aws_secret_access_key = secret_key
                     )
+            case LanguageModelProvider.VERTEXAI:
+                # Only supports service account authentication via JSON credentials stored in key vault. 
+                # Uses the authentication parameter: service_account_credentials to get the application configuration key for this value.
+                try:
+                    service_account_credentials_definition = json.loads(self.config.get_value(self.api_endpoint.authentication_parameters.get('service_account_credentials')))
+                except Exception as e:
+                    raise LangChainException(f"Failed to retrieve service account credentials: {str(e)}", 500)
+
+                if not service_account_credentials_definition:
+                    raise LangChainException("Service account credentials are missing from the configuration settings.", 400)
+
+                service_account_credentials = service_account.Credentials.from_service_account_info(service_account_credentials_definition)                
+                language_model = ChatVertexAI(                   
+                    model=self.ai_model.deployment_name,
+                    temperature=0,
+                    max_tokens=None,
+                    max_retries=6,
+                    stop=None,
+                    credentials=service_account_credentials
+                )
 
         # Set model parameters.
         for key, value in self.ai_model.model_parameters.items():

src/python/PythonSDK/foundationallm/langchain/agents/langchain_knowledge_management_agent.py

@@ -491,7 +491,7 @@ async def invoke_async(self, request: KnowledgeManagementCompletionRequest) -> C
                             if isinstance(item, ContentArtifact):
                                 content_artifacts.append(item)
 
-            final_message = response["messages"][-1]
+            final_message = response["messages"][-1]            
             response_content = OpenAITextMessageContentItem(
                 value = final_message.content,
                 agent_capability_category = AgentCapabilityCategories.FOUNDATIONALLM_KNOWLEDGE_MANAGEMENT
@@ -599,27 +599,7 @@ async def invoke_async(self, request: KnowledgeManagementCompletionRequest) -> C
 
         retvalue = None
 
-        if self.api_endpoint.provider == LanguageModelProvider.BEDROCK:
-            if self.has_retriever:
-                completion = chain.invoke(request.user_prompt)
-            else:
-                completion = await chain.ainvoke(request.user_prompt)
-            response_content = OpenAITextMessageContentItem(
-                value = completion.content,
-                agent_capability_category = AgentCapabilityCategories.FOUNDATIONALLM_KNOWLEDGE_MANAGEMENT
-            )
-            retvalue = CompletionResponse(
-                operation_id = request.operation_id,
-                content = [response_content],
-                user_prompt = request.user_prompt,
-                user_prompt_rewrite = request.user_prompt_rewrite,
-                full_prompt = self.full_prompt.text,
-                completion_tokens = completion.usage_metadata["output_tokens"] + image_analysis_token_usage.completion_tokens,
-                prompt_tokens = completion.usage_metadata["input_tokens"] + image_analysis_token_usage.prompt_tokens,
-                total_tokens = completion.usage_metadata["total_tokens"] + image_analysis_token_usage.total_tokens,
-                total_cost = 0
-            )
-        else:
+        if self.api_endpoint.provider == LanguageModelProvider.MICROSOFT or self.api_endpoint.provider == LanguageModelProvider.OPENAI:     
             # OpenAI compatible models
             with get_openai_callback() as cb:
                 # add output parser to openai callback
@@ -647,6 +627,26 @@ async def invoke_async(self, request: KnowledgeManagementCompletionRequest) -> C
                     )
                 except Exception as e:
                     raise LangChainException(f"An unexpected exception occurred when executing the completion request: {str(e)}", 500)
+        else:
+            if self.has_retriever:
+                completion = chain.invoke(request.user_prompt)
+            else:
+                completion = await chain.ainvoke(request.user_prompt)
+            response_content = OpenAITextMessageContentItem(
+                value = completion.content,
+                agent_capability_category = AgentCapabilityCategories.FOUNDATIONALLM_KNOWLEDGE_MANAGEMENT
+            )
+            retvalue = CompletionResponse(
+                operation_id = request.operation_id,
+                content = [response_content],
+                user_prompt = request.user_prompt,
+                user_prompt_rewrite = request.user_prompt_rewrite,
+                full_prompt = self.full_prompt.text,
+                completion_tokens = completion.usage_metadata["output_tokens"] + image_analysis_token_usage.completion_tokens,
+                prompt_tokens = completion.usage_metadata["input_tokens"] + image_analysis_token_usage.prompt_tokens,
+                total_tokens = completion.usage_metadata["total_tokens"] + image_analysis_token_usage.total_tokens,
+                total_cost = 0
+            )
 
         if isinstance(retriever, ContentArtifactRetrievalBase):
             retvalue.content_artifacts = retriever.get_document_content_artifacts() or []

src/python/PythonSDK/foundationallm/models/language_models/language_model_provider.py

@@ -6,3 +6,4 @@ class LanguageModelProvider(str, Enum):
     MICROSOFT = "microsoft"
     OPENAI = "openai"
     BEDROCK = "bedrock"
+    VERTEXAI = "vertexai"

src/python/PythonSDK/requirements.txt

@@ -6,17 +6,18 @@ azure-monitor-opentelemetry==1.6.2
 azure-monitor-opentelemetry-exporter==1.0.0b28
 azure-search-documents==11.6.0b9
 azure-storage-blob==12.21.0
-boto3==1.35.67
-langchain==0.3.7
-langchain-aws==0.2.7
-langchain-experimental==0.3.3
-langchain-openai==0.2.9
+boto3==1.36.13
+langchain==0.3.17
+langchain-aws==0.2.12
+langchain-experimental==0.3.4
+langchain-google-vertexai==2.0.13
+langchain-openai==0.3.3
 langchain-azure-dynamic-sessions==0.2.0
 langgraph==0.2.53
-openai==1.55.3
+openai==1.61.0
 opentelemetry-api==1.27.0
 opentelemetry-sdk==1.27.0
 pandas==2.2.2
-pydantic==2.8.2
+pydantic==2.10.6
 unidecode==1.3.8
 wikipedia==1.4.0