chore: import upstream snapshot with attribution

examples/flows/evaluation/eval-perceived-intelligence-maf/workflow.py

@@ -0,0 +1,59 @@
+import os
+import re
+from dataclasses import dataclass
+from pathlib import Path
+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()
+
+_PROMPT_TEMPLATE = (
+    Path(__file__).parent / "gpt_perceived_intelligence.md"
+).read_text(encoding="utf-8")
+
+
+@dataclass
+class EvalInput:
+    question: str
+    answer: str
+    context: str
+
+
+def _render_prompt(question: str, answer: str, context: str) -> str:
+    prompt = _PROMPT_TEMPLATE
+    prompt = prompt.replace("{{question}}", question)
+    prompt = prompt.replace("{{answer}}", answer)
+    prompt = prompt.replace("{{context}}", context)
+    return prompt
+
+
+def _parse_score(gpt_score: str) -> float:
+    match = re.search(r"[-+]?\d*\.\d+|\d+", gpt_score)
+    if match:
+        return float(match.group())
+    return 0.0
+
+
+class PerceivedIntelligenceExecutor(Executor):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        client = OpenAIChatClient(
+            azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+            model=os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt-4"),
+            api_key=os.environ["AZURE_OPENAI_API_KEY"],
+        )
+        self._agent = Agent(client=client, name="PerceivedIntelligenceAgent", instructions="You are an evaluator.")
+
+    @handler
+    async def evaluate(self, input: EvalInput, ctx: WorkflowContext[Never, float]) -> None:
+        prompt = _render_prompt(input.question, input.answer, input.context)
+        response = await self._agent.run(prompt)
+        score = _parse_score(response.text)
+        await ctx.yield_output(score)
+
+
+def create_workflow():
+    _pi = PerceivedIntelligenceExecutor(id="perceived_intelligence")
+    return WorkflowBuilder(name="EvalPerceivedIntelligenceRow", start_executor=_pi).build()