fix: minor adjustments to intro labs

intro_acp_beeai/src/helpers.py

@@ -1,8 +1,9 @@
-from typing import List, Dict
+from typing import List
 from datetime import datetime, timezone
 from acp_sdk.models import Message, MessagePart
 import json
 
+
 def flatten_messages(messages: List[Message]) -> str:
     """
     Collapse the *text/plain* content of an entire Message list into one string.
@@ -21,12 +22,13 @@ def flatten_messages(messages: List[Message]) -> str:
         if part.content_type == "text/plain" and part.content is not None
     ).strip()
 
-def package_response(data: str | dict) -> Dict[str, List[Message]]:
-    if isinstance(data, dict):          # auto-convert
+
+def package_response(data: str | dict) -> Message:
+    if isinstance(data, dict):  # auto-convert
         data = json.dumps(data, separators=(",", ":"))
     assistant_message = Message(
         parts=[MessagePart(content=data)],
         created_at=datetime.now(timezone.utc),
         completed_at=datetime.now(timezone.utc),
     )
-    return {"messages": [assistant_message]}
+    return assistant_message

intro_acp_beeai/src/ticket_response_agent.py

@@ -1,38 +1,43 @@
-import sys
 import textwrap
 import os
-#Pydantic Framework
+
+# Pydantic Framework
 from pydantic_ai import Agent
 from pydantic_ai.models.openai import OpenAIModel
-from pydantic_ai.providers.openai import OpenAIProvider
-#ACP SDK
+
+# ACP SDK
 from acp_sdk import Metadata
-from acp_sdk.models import Message, MessagePart
+from acp_sdk.models import Message
 from acp_sdk.server import RunYield, RunYieldResume, Server
 from collections.abc import AsyncGenerator
-#Helpers
+
+# Helpers
 from helpers import package_response, flatten_messages
 
-#load environment variables
+# load environment variables
 from dotenv import load_dotenv
+
 load_dotenv()
 
 # Set up the ACP server
 server = Server()
 
-@server.agent(
-    metadata=Metadata(ui={"type": "hands-off"})
-)
-async def ticket_response_agent(input: list[Message]) -> AsyncGenerator[RunYield, RunYieldResume]:
+
+@server.agent(metadata=Metadata(ui={"type": "hands-off"}))
+async def ticket_response_agent(
+    input: list[Message],
+) -> AsyncGenerator[RunYield, RunYieldResume]:
     """
     An agent that responds to customer support tickets .
     """
     user_prompt = flatten_messages(input)
 
     model_name = os.getenv("MODEL_NAME", "gpt-4.1-mini-2025-04-14")
     model = OpenAIModel(model_name)
-    TicketResponseAgent = Agent( model=model,
-                            system_prompt=(textwrap.dedent("""
+    TicketResponseAgent = Agent(
+        model=model,
+        system_prompt=(
+            textwrap.dedent("""
                                            You are a helpful customer support agent that creates clear, helpful, human-sounding replies to a customer.
                                            Tone & Style Matrix:
                                             Category   | Primary Tone        | Secondary Goals
@@ -42,16 +47,18 @@ async def ticket_response_agent(input: list[Message]) -> AsyncGenerator[RunYield
                                             Account    | Professional, supportive | Clarify account status or changes, confirm security measures
                                             Feedback   | Appreciative, receptive | Thank the customer, highlight how feedback is used
                                             Other      | Warm, helpful        | Clarify intent, offer assistance
-                                           """)))
+                                           """)
+        ),
+    )
     response = await TicketResponseAgent.run(user_prompt)
-    
+
     yield package_response(response.output)
 
-#Run these agents on different ports
+
+# Run these agents on different ports
 def run():
     server.run(port=int(os.getenv("PORT", 8001)), self_registration=False)
 
 
 if __name__ == "__main__":
     run()
-

intro_acp_beeai/src/ticket_triage_agent.py

@@ -1,81 +1,78 @@
 import textwrap
 
-#Framework imports
+# Framework imports
 from beeai_framework.adapters.openai import OpenAIChatModel
 from beeai_framework.backend import UserMessage, SystemMessage
 
-#ACP SDK imports
+# ACP SDK imports
 from acp_sdk.models import Message
 from acp_sdk.server import RunYield, RunYieldResume, Server
 from collections.abc import AsyncGenerator
-#Helper imports     
+
+# Helper imports
 from helpers import package_response, flatten_messages
 from typing import List, Optional
 import os
 from pydantic import BaseModel, Field
 from dotenv import load_dotenv
 
-#load environment variables
+# load environment variables
 load_dotenv()
 
 # Set up the ACP server
 server = Server()
 
+
 class TicketClassifierOutput(BaseModel):
     """Structured payload returned by the LLM for a single ticket."""
+
     category: List[str] = Field(
         description="Options: Billing, Technical, Complaint, Account, Feedback, Other"
     )
     customer_name: Optional[str] = Field(
-        default=None,
-        description="Full customer name; null if not mentioned."
+        default=None, description="Full customer name; null if not mentioned."
     )
     account_id: Optional[str] = Field(
         default=None,
-        description="Exact account identifier as it appears in the ticket."
+        description="Exact account identifier as it appears in the ticket.",
     )
     product: Optional[str] = Field(
-        default=None,
-        description="Product/SKU referenced in the ticket."
+        default=None, description="Product/SKU referenced in the ticket."
     )
     issue_summary: str = Field(
         description="concise plain-language summary of the problem, extracting key insights."
     )
-    severity: str = Field(
-        description='One of: "critical", "high", "medium", "low".'
-    )
-    sentiment: str = Field(
-        description='One of: "negative", "neutral", "positive".'
-    )
+    severity: str = Field(description='One of: "critical", "high", "medium", "low".')
+    sentiment: str = Field(description='One of: "negative", "neutral", "positive".')
     incident_date: Optional[str] = Field(
-        default=None,
-        description="ISO-8601 date (YYYY-MM-DD) if provided."
+        default=None, description="ISO-8601 date (YYYY-MM-DD) if provided."
     )
 
 
 @server.agent()
-async def ticket_triage_agent(input: list[Message]) -> AsyncGenerator[RunYield, RunYieldResume]:
-        """ An agent that classifies customer support tickets.
-        """
-        user_prompt = flatten_messages(input[-1:])
-        model_name = os.getenv("MODEL_NAME", "gpt-4.1-mini-2025-04-14")
-        llm = OpenAIChatModel(model_name)
-        system_msg = SystemMessage(textwrap.dedent("""
+async def ticket_triage_agent(
+    input: list[Message],
+) -> AsyncGenerator[RunYield, RunYieldResume]:
+    """An agent that classifies customer support tickets."""
+    user_prompt = flatten_messages(input[-1:])
+    model_name = os.getenv("MODEL_NAME", "gpt-4.1-mini-2025-04-14")
+    llm = OpenAIChatModel(model_name)
+    system_msg = SystemMessage(
+        textwrap.dedent("""
             You are “Support-Sensei,” an AI assistant that must:
             1. Choose the single best ticket category.
             2. Extract the required fields.
-            """
-        ))
-        response = await llm.create_structure(
-            schema=TicketClassifierOutput,
-            messages=[system_msg, UserMessage(user_prompt)],
-        )
-        yield package_response(response.object)
+            """)
+    )
+    response = await llm.create(
+        messages=[system_msg, UserMessage(user_prompt)],
+    )
+    yield package_response(response.get_text_content())
 
 
 def run():
     server.run(port=int(os.getenv("PORT", 8000)), self_registration=False)
 
 
 if __name__ == "__main__":
-    run()
+    run()

intro_acp_beeai/src/ticket_workflow_agent.py

@@ -1,93 +1,101 @@
 import textwrap
 
-#Framework imports
+# Framework imports
 from beeai_framework.adapters.openai import OpenAIChatModel
 from beeai_framework.backend import UserMessage, SystemMessage
 from pydantic_ai import Agent
 from pydantic_ai.models.openai import OpenAIModel
 from pydantic_ai.providers.openai import OpenAIProvider
-#ACP SDK
+
+# ACP SDK
 from acp_sdk import Metadata
 from acp_sdk.models import Message, MessagePart
 from acp_sdk.server import RunYield, RunYieldResume, Server, Context
 from collections.abc import AsyncGenerator
 from acp_sdk.client import Client
-#Helpers
-from helpers import package_response, flatten_messages
+
+# Helpers
+from helpers import flatten_messages
 from pydantic import BaseModel, Field
 from typing import List, Optional
 import os
-#load environment variables
+
+# load environment variables
 from dotenv import load_dotenv
+
 load_dotenv()
 
-#set up the ACP server
+# set up the ACP server
 server = Server()
 
+
 class TicketClassifierOutput(BaseModel):
     """Structured payload returned by the LLM for a single ticket."""
+
     category: List[str] = Field(
         description="Options: Billing, Technical, Complaint, Account, Feedback, Other"
     )
     customer_name: Optional[str] = Field(
-        default=None,
-        description="Full customer name; null if not mentioned."
+        default=None, description="Full customer name; null if not mentioned."
     )
     account_id: Optional[str] = Field(
         default=None,
-        description="Exact account identifier as it appears in the ticket."
+        description="Exact account identifier as it appears in the ticket.",
     )
     product: Optional[str] = Field(
-        default=None,
-        description="Product/SKU referenced in the ticket."
+        default=None, description="Product/SKU referenced in the ticket."
     )
     issue_summary: str = Field(
         description="concise plain-language summary of the problem, extracting key insights."
     )
-    severity: str = Field(
-        description='One of: "critical", "high", "medium", "low".'
-    )
-    sentiment: str = Field(
-        description='One of: "negative", "neutral", "positive".'
-    )
+    severity: str = Field(description='One of: "critical", "high", "medium", "low".')
+    sentiment: str = Field(description='One of: "negative", "neutral", "positive".')
     incident_date: Optional[str] = Field(
-        default=None,
-        description="ISO-8601 date (YYYY-MM-DD) if provided."
+        default=None, description="ISO-8601 date (YYYY-MM-DD) if provided."
     )
 
 
-#make Agents ACP Compatible
+# make Agents ACP Compatible
+
 
 @server.agent(name="ticket_triage_agent", metadata=Metadata(ui={"type": "hands-off"}))
-async def ticket_triage_agent(input: list[Message]) -> AsyncGenerator[RunYield, RunYieldResume]:
+async def ticket_triage_agent(
+    input: list[Message],
+) -> AsyncGenerator[RunYield, RunYieldResume]:
     """An agent that classifies customer support tickets."""
     user_prompt = flatten_messages(input[-1:])
     model_name = os.getenv("MODEL_NAME", "gpt-4.1-mini-2025-04-14")
     llm = OpenAIChatModel(model_name)
-    system_msg = SystemMessage(textwrap.dedent("""
+    system_msg = SystemMessage(
+        textwrap.dedent("""
         You are “Support-Sensei,” an AI assistant that must:
         1. Choose the single best ticket category.
         2. Extract the required fields.
         """)
     )
-    response = await llm.create_structure(
-        schema=TicketClassifierOutput,
+    response = await llm.create(
         messages=[system_msg, UserMessage(user_prompt)],
     )
-    yield str(response.object)
+    yield str(response.get_text_content())
 
 
 @server.agent(name="ticket_response_agent", metadata=Metadata(ui={"type": "hands-off"}))
-async def ticket_response_agent(input: list[Message]) -> AsyncGenerator[RunYield, RunYieldResume]:
+async def ticket_response_agent(
+    input: list[Message],
+) -> AsyncGenerator[RunYield, RunYieldResume]:
     """
     An agent that responds to customer support tickets .
     """
     user_prompt = flatten_messages(input)
 
     model = os.getenv("MODEL_NAME", "gpt-4.1-mini-2025-04-14")
-    model = OpenAIModel(model, provider=OpenAIProvider(api_key=os.getenv('OPENAI_API_KEY')))
-    TicketResponseAgent = Agent( model=model,
-                            system_prompt=(textwrap.dedent("""
+    model = OpenAIModel(
+        model, provider=OpenAIProvider(api_key=os.getenv("OPENAI_API_KEY"))
+    )
+    TicketResponseAgent = Agent(
+        model=model,
+        system_prompt=(
+            textwrap.dedent("""
                                            You are a helpful customer support agent that creates clear, helpful, human-sounding replies to a customer.
                                            Tone & Style Matrix:
                                             Category   | Primary Tone        | Secondary Goals
@@ -97,32 +105,40 @@ async def ticket_response_agent(input: list[Message]) -> AsyncGenerator[RunYield
                                             Account    | Professional, supportive | Clarify account status or changes, confirm security measures
                                             Feedback   | Appreciative, receptive | Thank the customer, highlight how feedback is used
                                             Other      | Warm, helpful        | Clarify intent, offer assistance
-                                           """)))
+                                           """)
+        ),
+    )
     response = await TicketResponseAgent.run(user_prompt)
-    
+
     yield str(response.output)
 
 
-#Main ACP Agent that orchestrates the workflow
+# Main ACP Agent that orchestrates the workflow
 async def run_agent(agent: str, input: str) -> list[Message]:
     async with Client(base_url="http://localhost:8000") as client:
         run = await client.run_sync(
-            agent=agent, input=[Message(parts=[MessagePart(content=input, content_type="text/plain")])]
+            agent=agent,
+            input=[
+                Message(parts=[MessagePart(content=input, content_type="text/plain")])
+            ],
         )
     return run.output
 
+
 @server.agent(name="TicketWorkflow", metadata=Metadata(ui={"type": "hands-off"}))
 async def main_agent(input: list[Message], context: Context) -> AsyncGenerator:
     """
     Main agent that orchestrates the ticket triage and response workflow.
     """
     ticket_triage_response = await run_agent("ticket_triage_agent", str(input))
-    ticket_response_to_user = await run_agent("ticket_response_agent", str(ticket_triage_response))
+    ticket_response_to_user = await run_agent(
+        "ticket_response_agent", str(ticket_triage_response)
+    )
 
-    yield str(ticket_triage_response[0])
     yield str(ticket_response_to_user[0])
 
-#Run these agents
+
+# Run these agents
 def run():
     server.run()