added sample morningstar agent

scenarios/docs-samples/python/agents/project-sdk/sample_morningstar_agent.py

@@ -0,0 +1,169 @@
+# pylint: disable=line-too-long,useless-suppression
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to use agent operations with the
+    Morningstar OpenAPI tool, from the Azure Agents service using a synchronous client.
+    To learn more about OpenAPI specs, visit https://learn.microsoft.com/openapi
+    Link to the Morningstar Agent OpenAPI spec: https://developer.morningstar.com/direct-web-services/documentation/intelligence-engine/apps/morningstar-agent-api
+    Download the OpenAPI spec and store it as iep_openapi.json
+USAGE:
+    python sample_agents_openapi_morningstar.py
+    Before running the sample:
+    pip install azure-ai-projects azure-identity jsonref
+    Set these environment variables with your own values:
+    1) PROJECT_CONNECTION_STRING - The project connection string, as found in the overview page of your
+       Azure AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
+       the "Models + endpoints" tab in your Azure AI Foundry project.
+    3) PROJECT_CONNECTION_ID - The project connection id of your AI Foundry project.
+    You'll also need a JWT token to authorize with the Morningstar service.
+    You can fetch this JWT token by using the below code:
+    import requests
+    from requests.auth import HTTPBasicAuth
+    url = "https://www.us-api.morningstar.com/token/oauth"
+    auth = HTTPBasicAuth("john.doe@example.com", "SecureP@ssw0rd")
+    response = requests.post(url, auth=auth)
+    print(response.text)
+    Note: This token expires, please remember to refresh your credentials.
+"""
+
+import os
+import jsonref
+from azure.ai.projects import AIProjectClient
+from azure.ai.projects.models import (
+    OpenApiConnectionAuthDetails,
+    OpenApiConnectionSecurityScheme,
+    OpenApiTool,
+)
+from azure.identity import DefaultAzureCredential
+
+project_client = AIProjectClient.from_connection_string(
+    credential=DefaultAzureCredential(),
+    conn_str=os.environ["PROJECT_CONNECTION_STRING"],
+)
+
+with open("./iep_openapi.json", "r") as f:
+    openapi_spec = jsonref.loads(f.read())
+
+# Create Auth object for the OpenApiTool (note that connection or managed identity auth setup requires additional setup in Azure)
+connection_id = os.environ["PROJECT_CONNECTION_ID"]
+security_scheme = OpenApiConnectionSecurityScheme(connection_id=connection_id)
+auth = OpenApiConnectionAuthDetails(security_scheme=security_scheme)
+
+# Create the OpenAPI tool
+openapi = OpenApiTool(
+    name="get_answer",
+    spec=openapi_spec,
+    description="Retrieves information from Morningstar.",
+    auth=auth,
+)
+
+INSTRUCTIONS = """
+You are a helpful assistant that answers questions using Morningstar's data. Only refer to the information found in the **"answer"** and **"sources"** fields of provided responses to generate your answers. If the user asks a complex question, break it down into simpler, more manageable sub-queries to guide your process effectively.
+# Guidelines
+- **Data Usage**: Strictly rely on the "answer" and "sources" fields for information. Do not infer or assume beyond what is explicitly provided. 
+- **Complex Questions**: If the question is challenging or multi-faceted, first decompose it into smaller, simpler queries, ensuring each step aligns with the provided data.
+- **Clarity in Explanation**: When delivering the final answer, clearly articulate the reasoning used to arrive at the conclusion based on the Morningstar data provided.
+- **Consistency**: Ensure the terminology and phrasing used aligns with the financial and investment language standard for Morningstar.
+# Steps
+1. Determine if the question is straightforward or complex:
+   - If straightforward, proceed directly to formulating an answer based on the "answer" and "sources" fields.
+   - If complex, break it into simpler sub-queries, each directly addressable via data from the "answer" and "sources" fields.
+2. Reference only the "answer" and "sources" fields to gather information for responding.
+3. Construct a clear, concise answer or explanation using the gathered data.
+4. If necessary, list the sources referenced to provide users with transparency.
+# Output Format
+The response should be in plain text, clearly structured, and concise. 
+For simple queries:
+- A direct, succinct answer based on the given data.
+- Include a reference to the sources field if needed.
+For complex queries:
+- Briefly outline the parallel or sequential reasoning steps taken to address the question.
+- Conclude with the final, comprehensive answer.
+- Optionally: Cite sources derived from the provided data.
+# Example
+### Input:
+"What are the key financial metrics of XYZ Company? What is my best course of action for diversification?"
+### Process:
+1. **Simplify the queries**:
+   - What are the key financial metrics of XYZ Company?
+   - What strategies can be considered for diversification?
+2. Consult the "answer" and "sources" fields for data relevant to each sub-query.
+### Output:
+- XYZ Company's key financial metrics are as follows:
+  - Revenue: [Insert data from answer field]
+  - Net Income: [Insert data from answer field]
+  - P/E Ratio: [Insert data from answer field]
+  (Source: [Insert relevant sources here])
+- Regarding diversification:
+  - Based on provided data, one approach might be to [Insert strategy].
+  (Source: [Insert relevant sources here])
+# Notes
+- Do not speculate or provide opinions—use only the supplied "answer" and "sources" fields.
+- When data from Morningstar is insufficient to form a complete answer, explicitly communicate the limitation in the response."""
+
+# Create agent with OpenApi tool and process assistant run
+with project_client:
+    agent = project_client.agents.create_agent(
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        name="morningstar-agent",
+        instructions=INSTRUCTIONS,
+        tools=openapi.definitions,
+    )
+
+    print(f"Created agent, ID: {agent.id}")
+
+    # Create thread for communication
+    thread = project_client.agents.create_thread()
+    print(f"Created thread, ID: {thread.id}")
+
+    # Create message to thread
+    message = project_client.agents.create_message(
+        thread_id=thread.id,
+        role="user",
+        content="What is value investing according to Morningstar?",
+    )
+    print(f"Created message, ID: {message.id}")
+
+    # Create and process agent run in thread with tools
+    run = project_client.agents.create_and_process_run(
+        thread_id=thread.id,
+        assistant_id=agent.id,
+    )
+    print(f"Run finished with status: {run.status}")
+    if run.status == "failed":
+        print(f"Run failed: {run.last_error}")
+
+    run_steps = project_client.agents.list_run_steps(thread_id=thread.id, run_id=run.id)
+
+    # Loop through each step
+    for step in run_steps.data:
+        print(f"Step {step['id']} status: {step['status']}")
+
+        # Check if there are tool calls in the step details
+        step_details = step.get("step_details", {})
+        tool_calls = step_details.get("tool_calls", [])
+
+        if tool_calls:
+            print("  Tool calls:")
+            for call in tool_calls:
+                print(f"    Tool Call ID: {call.get('id')}")
+                print(f"    Type: {call.get('type')}")
+
+                function_details = call.get("function", {})
+                if function_details:
+                    print(f"    Function name: {function_details.get('name')}")
+        print()  # add an extra newline between steps
+
+    # Fetch and log all messages
+    messages = project_client.agents.list_messages(thread_id=thread.id)
+    print(f"Messages: {messages}")
+
+    # Delete the assistant when done
+    project_client.agents.delete_agent(agent.id)
+    print("Deleted agent")