I am running my prompt flow code as a pipeline. However, it seems like that the connection that I created in prompt flow which is referred in flow.dag.yaml. Same connection can be used in UI interface of promptflow without any issue. Serverless connection is recognized in Azure documentation as one of the connection type. Also I am using the latest version of prompt-flow. Error: packages/azureml_sys/parallel_run/job_starter.py\", line 222, in start_tasks\n connections_dict[name] = connection_provider.get_connection_dict(name)\n File \"/tmp/ad766d1f-9561-49f4/prs_prod/lib/python3.8/site-packages/azureml_sys/parallel_run/promptflow/connections/connection_providers.py\", line 43, in get_connection_dict\n raise ResolveConnectionsObjectError from exec", "cause": ["module": "azureml_sys.parallel_run.promptflow.exceptions", "name": "UnknownConnectionType", "message": "Unknown connection Llama-4-Scout-17B-16E-Instruct-j category Serverless, please upgrade your promptflow sdk version and retry.", flow.dag.yaml $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json id: template_standard_flow name: evaluation_response_flow inputs: source: type: string default: "" is_chat_input: false Description: type: string is_chat_input: false Groundtruth: type: string default: "" is_chat_input: false line: type: string default: "" is_chat_input: false expense_account: type: string default: "" is_chat_input: false supplier: type: string default: "" is_chat_input: false outputs: output_LLM_prompt_v1: type: string reference: ${process_llm_response_v1.output} output_LLM_prompt_v2: type: string reference: ${process_llm_response_v2.output} output_LLM_prompt_v3: type: string reference: ${process_llm_response_v3.output} evaluation_prompt_v1: type: string reference: ${process_llm_response_v1.output} evaluation_prompt_v2: type: string reference: ${process_llm_response_v3.output} output_prompt_v1: type: string reference: ${v1_llm_response.output} output_prompt_v2: type: string reference: ${v2_llm_response.output} output_prompt_v3: type: string reference: ${v3_llm_response.output} Evaluation: type: string reference: ${evaluation.output} nodes: - name: evaluation type: python source: type: code path: evaluation.py inputs: groundtruth: ${inputs.Groundtruth} llm_response_v1: ${v1_llm_response.output} llm_response_v2: ${v2_llm_response.output} llm_response_v3: ${v3_llm_response.output} use_variants: false - name: prompt_v1 type: prompt source: type: code path: prompt_v1.jinja2 inputs: description: ${inputs.Description} use_variants: false - name: prompt_v2 type: prompt source: type: code path: prompt_v2.jinja2 inputs: description: ${inputs.Description} use_variants: false - name: prompt_v3 type: prompt source: type: code path: prompt_v3.jinja2 inputs: description: ${inputs.Description} use_variants: false - name: v1_llm_response type: llm source: type: code path: v1_llm_response.jinja2 inputs: temperature: 1 top_p: 1 response_format: type: text prompt_v1: ${prompt_v1.output} provider: Serverless connection: Llama-4-Scout-17B-16E-Instruct-j api: chat module: promptflow.tools.openai use_variants: false - name: v2_llm_response type: llm source: type: code path: v2_llm_response.jinja2 inputs: temperature: 1 top_p: 1 response_format: type: text prompt_v2: ${prompt_v2.output} provider: Serverless connection: Llama-4-Scout-17B-16E-Instruct-j api: chat module: promptflow.tools.openai use_variants: false - name: v3_llm_response type: llm source: type: code path: v3_llm_response.jinja2 inputs: temperature: 1 top_p: 1 response_format: type: text prompt_v3: ${prompt_v3.output} provider: Serverless connection: Llama-4-Scout-17B-16E-Instruct-j api: chat module: promptflow.tools.openai use_variants: false - name: process_llm_response_v1 type: python source: type: code path: process_llm_response_v1.py inputs: source: ${inputs.source} expense_account: ${inputs.expense_account} line: ${inputs.line} llm_response: ${v1_llm_response.output} supplier: ${inputs.supplier} use_variants: false - name: process_llm_response_v2 type: python source: type: code path: process_llm_response_v2.py inputs: source: ${inputs.source} expense_account: ${inputs.expense_account} line: ${inputs.line} llm_response: ${v2_llm_response.output} supplier: ${inputs.supplier} use_variants: false - name: process_llm_response_v3 type: python source: type: code path: process_llm_response_v3.py inputs: source: ${inputs.source} expense_account: ${inputs.expense_account} line: ${inputs.line} llm_response: ${v3_llm_response.output} supplier: ${inputs.supplier} use_variants: false node_variants: {} environment: python_requirements_txt: requirements.txt orchestrator.py import os import uuid import json import tempfile import mlflow import azureml.mlflow from azure.identity import DefaultAzureCredential from azure.ai.ml import MLClient, load_component from azure.ai.ml.constants import AssetTypes from azure.ai.ml.dsl import pipeline from azure.ai.ml import MLClient, load_component, dsl, Input # ------------------------------------------------------------------------------ # 1. Workspace & MLflow Tracking Configuration # ------------------------------------------------------------------------------ subscription_id = "foo" resource_group = "bar" workspace_name = "baz" os.environ['subscription_id'] = subscription_id os.environ['resource_group'] = resource_group os.environ['workspace_name'] = workspace_name cred = DefaultAzureCredential() ml_client = MLClient( credential = cred, subscription_id = subscription_id, resource_group_name = resource_group, workspace_name = workspace_name, ) # get the MLflow tracking URI from your workspace tracking_uri = ml_client.workspaces.get(workspace_name).mlflow_tracking_uri mlflow.set_tracking_uri(tracking_uri) mlflow.set_experiment("promptflow_pipeline") cluster_name = "LLM-Prompt-Flow" print(ml_client.compute.get(cluster_name)) # ------------------------------------------------------------------------------ # 2. Turn your Prompt Flow into a reusable Component # ------------------------------------------------------------------------------ flow_component = load_component(source="flow.dag.yaml") ml_client.components.create_or_update(flow_component, version="1") # ------------------------------------------------------------------------------ # 3. Build the DSL Pipeline that invokes your flow component # ------------------------------------------------------------------------------ # create a tiny JSONL file for inputs sample = {"source": "test", "Description": "test", "Groundtruth":"test", "line":"test", "expense_account": "test", "supplier":"test"} tmp = tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False, encoding="utf-8") json.dump(sample, tmp); tmp.write("\n"); tmp.flush(); tmp.close() eval_data = Input( type=AssetTypes.URI_FILE, path=tmp.name, mode="ro_mount", ) @pipeline() def eval_pipeline(): eval_node = flow_component( data=eval_data, source="${data.source}", Description="${data.Description}", Groundtruth="${data.Groundtruth}", line="${data.line}", expense="${data.expense}", supplier="${data.supplier}", ) eval_node.compute = cluster_name eval_node.max_concurrency_per_instance = 1 eval_node.mini_batch_error_threshold = 5 pipeline_job = eval_pipeline() pipeline_job.settings.default_compute = cluster_name pipeline_job.name = f"eval-{uuid.uuid4().hex[:8]}" pipeline_job_exec = ml_client.jobs.create_or_update(pipeline_job) ml_client.jobs.stream(pipeline_job_exec.name) I am looking for a solution to fix the connection issue. 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