chore: import upstream snapshot with attribution

examples/flows/chat/chat-basic-maf/chat_flow.py

@@ -0,0 +1,119 @@
+"""
+Basic Chat — Microsoft Agent Framework version.
+
+Migrated from the Prompt Flow chat-basic example.
+Original flow: Input (question + chat_history) → LLM node → answer
+
+This workflow uses a single Agent with FoundryChatClient to replicate
+the same chat behaviour: a helpful assistant that remembers conversation
+history and responds to user questions.
+"""
+
+import asyncio
+import os
+from dataclasses import dataclass
+
+from dotenv import load_dotenv
+from typing_extensions import Never
+
+from agent_framework import Agent, Executor, WorkflowBuilder, WorkflowContext, handler
+from agent_framework.openai import OpenAIChatClient
+
+load_dotenv()
+
+
+@dataclass
+class ChatInput:
+    """Mirrors the Prompt Flow inputs: question + chat_history."""
+
+    question: str
+    chat_history: list | None = None
+
+
+class InputExecutor(Executor):
+    """Replaces the Prompt Flow Input node.
+
+    Accepts a ChatInput, formats the conversation history into the prompt,
+    and forwards the assembled prompt string to the LLM executor.
+    """
+
+    @handler
+    async def receive(self, chat_input: ChatInput, ctx: WorkflowContext[str]) -> None:
+        parts = []
+
+        # Replay chat history as a formatted conversation
+        if chat_input.chat_history:
+            for turn in chat_input.chat_history:
+                parts.append(f"User: {turn['inputs']['question']}")
+                parts.append(f"Assistant: {turn['outputs']['answer']}")
+
+        # Append the current user question
+        parts.append(chat_input.question)
+
+        await ctx.send_message("\n".join(parts))
+
+
+class ChatExecutor(Executor):
+    """Replaces the Prompt Flow LLM (chat) node.
+
+    Uses OpenAIChatClient (Azure routing) + Agent with the same system prompt
+    as the original chat.jinja2 template: "You are a helpful assistant."
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        client = OpenAIChatClient(
+            azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+            model=os.environ["AZURE_OPENAI_DEPLOYMENT"],
+            api_key=os.environ["AZURE_OPENAI_API_KEY"],
+        )
+        self._agent = Agent(
+            client=client,
+            name="ChatAgent",
+            instructions="You are a helpful assistant.",
+        )
+
+    @handler
+    async def call_llm(self, question: str, ctx: WorkflowContext[Never, str]) -> None:
+        response = await self._agent.run(question)
+        await ctx.yield_output(response.text)
+
+
+# ── Build the workflow ────────────────────────────────────────────────────────
+def create_workflow():
+    """Return a fresh workflow instance (safe for concurrent / repeated runs)."""
+    _input = InputExecutor(id="input")
+    _chat = ChatExecutor(id="chat")
+    return (
+        WorkflowBuilder(name="BasicChatWorkflow", start_executor=_input)
+        .add_edge(_input, _chat)
+        .build()
+    )
+
+
+async def main():
+    # Simple single-turn test (no history)
+    workflow = create_workflow()
+    result = await workflow.run(ChatInput(question="What is ChatGPT?"))
+    print("Answer:", result.get_outputs()[0])
+    print()
+
+    # Multi-turn test (with chat history)
+    history = [
+        {
+            "inputs": {"question": "What is ChatGPT?"},
+            "outputs": {"answer": "ChatGPT is a large language model chatbot developed by OpenAI."},
+        }
+    ]
+    workflow2 = create_workflow()
+    result = await workflow2.run(
+        ChatInput(
+            question="What is the difference between ChatGPT and GPT-4?",
+            chat_history=history,
+        )
+    )
+    print("Answer:", result.get_outputs()[0])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())