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nodes.py

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+from typing import TypedDict, Dict, Any
+from langchain_community.llms import OpenAI
+import re
+
+# Global LLM instance
+llm = OpenAI(temperature=0.7)
+
+class ReflectState(TypedDict):
+    question: str
+    draft: str
+    critique: str
+    verdict: str
+    round: int
+    max_rounds: int
+
+def draft_answer(state: ReflectState) -> ReflectState:
+    prompt = (
+        f"Answer the following question in 5–10 sentences:\n\n"
+        f"Question: {state['question']}\n\n"
+        f"Answer:"
+    )
+    answer = llm.invoke(prompt).strip()
+    state["draft"] = answer
+    return state
+
+def reflect(state: ReflectState) -> ReflectState:
+    prompt = (
+        f"You are a critical reviewer. Evaluate the following draft answer.\n\n"
+        f"Draft:\n{state['draft']}\n\n"
+        f"Provide a verdict ('ok' or 'needs_revision') and 2–3 remarks.\n"
+        f"Format:\n"
+        f"Verdict: <ok|needs_revision>\n"
+        f"Remarks:\n<remarks>"
+    )
+    response = llm.invoke(prompt).strip()
+    # Parse verdict
+    verdict_match = re.search(r"Verdict:\s*(\w+)", response, re.IGNORECASE)
+    remarks_match = re.search(r"Remarks:\s*(.*)", response, re.DOTALL | re.IGNORECASE)
+    verdict = verdict_match.group(1).lower() if verdict_match else "needs_revision"
+    remarks = remarks_match.group(1).strip() if remarks_match else "No remarks provided."
+    state["verdict"] = verdict
+    state["critique"] = remarks
+    return state
+
+def rewrite(state: ReflectState) -> ReflectState:
+    prompt = (
+        f"Rewrite the following draft answer to address the remarks below.\n\n"
+        f"Draft:\n{state['draft']}\n\n"
+        f"Remarks:\n{state['critique']}\n\n"
+        f"Provide the revised answer in 5–10 sentences."
+    )
+    revised = llm.invoke(prompt).strip()
+    state["draft"] = revised
+    state["round"] += 1
+    return state