chore: import upstream snapshot with attribution

runtime/node/agent/thinking/self_reflection.py

@@ -0,0 +1,54 @@
+from entity.configs import ReflectionThinkingConfig
+from entity.messages import Message, MessageRole
+from runtime.node.agent.thinking.thinking_manager import (
+    ThinkingManagerBase,
+    AgentInvoker,
+    ThinkingPayload,
+)
+
+
+class SelfReflectionThinkingManager(ThinkingManagerBase):
+    """
+    A simple implementation of thinking manager, named self-reflection.
+    This part of the code is borrowed from ChatDev (https://github.com/OpenBMB/ChatDev) and adapted.
+    """
+
+    def __init__(self, config: ReflectionThinkingConfig):
+        super().__init__(config)
+        self.before_gen_think_enabled = False
+        self.after_gen_think_enabled = True
+        self.base_prompt = """Here is a conversation between two roles: {conversations} {reflection_prompt}"""
+
+        self.reflection_prompt = config.reflection_prompt or "Reflect on the given information and summarize key points in a few words."
+
+    def _before_gen_think(
+        self,
+        agent_invoker: AgentInvoker,
+        input_payload: ThinkingPayload,
+        agent_role: str,
+        memory: ThinkingPayload | None,
+    ) -> tuple[str, bool]:
+        ...
+
+    def _after_gen_think(
+        self,
+        agent_invoker: AgentInvoker,
+        input_payload: ThinkingPayload,
+        agent_role: str,
+        memory: ThinkingPayload | None,
+        gen_payload: ThinkingPayload,
+    ) -> tuple[str, bool]:
+        conversations = [
+            f"SYSTEM: {agent_role}",
+            f"USER: {input_payload.text}",
+            f"ASSISTANT: {gen_payload.text}",
+        ]
+        if memory and memory.text:
+            conversations = [memory.text] + conversations
+        prompt = self.base_prompt.format(conversations="\n\n".join(conversations),
+                                         reflection_prompt=self.reflection_prompt)
+
+        reflection_message = agent_invoker(
+            [Message(role=MessageRole.USER, content=prompt)]
+        )
+        return reflection_message.text_content(), True