mockup outline for foundry quickstart

Author: qubitron

doc-samples/getting-started/README.md

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+
+## Install dependencies
+
+ - install azure CLI
+ - pip install azure-ai-projects openai azure-identity
+ - 
+
+## Create a project
+see: create_project.py
+
+## Create a project client
+see: create_project.py
+
+## Run a chat completion
+
+see: quickstart.py
+
+## Create and run an agent
+
+see: quickstart.py
+
+## Add file search to agent
+
+see: quickstart.py
+
+## Evaluate agent run
+
+This will return scores for how good the agent performed on the task
+
+see: quickstart.py

doc-samples/getting-started/csharp/quickstart.cs

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+# source: https://github.com/Azure/agent-first-sdk/blob/main/tests/management_sdk/manage_ai_foundry.ipynb
+
+# <create_project>
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.cognitiveservices import CognitiveServicesManagementClient
+import os
+import json
+
+subscription_id = 'your-subscription-id'
+resource_group_name = 'your-resource-group-name'
+foundry_resource_name = 'your-foundry-resource-name'
+foundry_project_name = 'your-foundry-project-name'
+location = 'eastus'
+
+# TODO: add code to create create a new resource group
+
+client = CognitiveServicesManagementClient(
+    subscription_id=subscription_id,
+    credential=DefaultAzureCredential(), 
+    api_version="2025-04-01-preview"
+)
+
+account = client.accounts.begin_create(
+    resource_group_name=resource_group_name,
+    account_name=foundry_resource_name,
+    foundry_project_name=foundry_project_name,
+    account={
+        "location": location,
+        "kind": "AIServices",
+        "sku": {
+            "name": "S0",
+        },
+        "identity": {
+            "type": "SystemAssigned"
+        },
+        "properties": {
+            "allowProjectManagement": True
+        }
+    }
+)
+
+# TODO: code to do role assignment to give user project manager role on the account
+
+# </create_project>
+
+# <deploy_model>
+
+# </deploy_model>

doc-samples/getting-started/java/quickstart.java

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+from dotenv import load_dotenv
+load_dotenv()
+
+## - pre-reqs: install openai and azure-ai-projects packages
+##   pip install openai azure-ai-projects azure-identity
+## - deploy a gpt-4o model
+
+## <chat_completion>
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+from azure.ai.projects import FileSearchTool
+
+project = AIProjectClient(
+    endpoint="https://your-foundry-resource-name.ai.azure.com/api/projects",
+    credential=DefaultAzureCredential(),
+)
+
+openai = project.inference.get_azure_openai_client(api_version="2024-06-01")
+response = openai.chat.completions.create(
+    model="gpt-4o",
+    messages=[
+        {"role": "system", "content": "You are a helpful writing assistant"},
+        {"role": "user", "content": "Write me a poem about flowers"},
+    ],
+)
+
+print(response.choices[0].message.content)
+# </chat_completion>
+
+# <create_and_run_agent>
+agent = project.agents.create_agent(
+    model="gpt-4o"
+    name="my-agent",
+    instructions="You are a helpful writing assistant")
+
+thread = project.agents.create_thread()
+message = agents_client.create_message(
+    thread_id=thread.id, 
+    role="user", 
+    content="Write me a poem about flowers")
+
+run = project.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
+if run.status == "failed":
+    # Check if you got "Rate limit is exceeded.", then you want to get more quota
+    print(f"Run failed: {run.last_error}")
+
+# Get messages from the thread
+messages = project.agents.list_messages(thread_id=thread.id)
+
+# Get the last message from the sender
+last_msg = messages.get_last_text_message_by_role("assistant")
+if last_msg:
+    print(f"Last Message: {last_msg.text.value}")
+
+# Delete the agent once done
+project.agents.delete_agent(agent.id)
+print("Deleted agent")
+# </create_and_run_agent>
+
+
+# <create_filesearch_agent>
+# Upload file and create vector store
+file = project.agents.upload_file(file_path="product_info_1.md", purpose="agents")
+vector_store = project.agents.create_vector_store_and_poll(file_ids=[file.id], name="my_vectorstore")
+
+# Create file search tool and agent
+file_search = FileSearchTool(vector_store_ids=[vector_store.id])
+agent = project.agents.create_agent(
+    model="gpt-4o",
+    name="my-assistant",
+    instructions="You are a helpful assistant and can search information from uploaded files",
+    tools=file_search.definitions,
+    tool_resources=file_search.resources,
+)
+
+# Create thread and process user message
+thread = project.agents.create_thread()
+project.agents.create_message(thread_id=thread.id, role="user", content="Hello, what Contoso products do you know?")
+run = project.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
+
+# Handle run status
+if run.status == "failed":
+    print(f"Run failed: {run.last_error}")
+
+# Cleanup resources
+project.agents.delete_vector_store(vector_store.id)
+project.agents.delete_file(file_id=file.id)
+project.agents.delete_agent(agent.id)
+
+# Print thread messages
+for message in project.agents.list_messages(thread_id=thread.id).text_messages:
+    print(message)
+# </create_filesearch_agent>
+
+# <evaluate_agent_run>
+from azure.ai.projects import EvaluatorIds
+
+result = project.evaluation.create_agent_evaluation(
+    thread=thread.id,
+    run=run.id, 
+    evaluators=[EvaluatorIds.AGENT_QUALITY_EVALUATOR])
+
+# wait for evaluation to complete
+result.wait_for_completion()
+
+# result
+print(result.output())
+# </evaluate_agent_run>

doc-samples/getting-started/javascript/quickstart.js

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+openai
+azure-ai-projects
+azure-identity