List 5 roles/positions that benefit strongly from a high EQ, and list your reasons.

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>>>>>>>> USING AUTO REPLY...
1. generate_oai_reply for drafter to chat_manager. config: None
2. _generate_oai_reply_from_client for drafter. llm_client: [{'cache_seed': False, 'temperature': 0.5, 'model': 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q6_K.gguf'}], cache: None
2.b. _generate_oai_reply_from_client messages: [{'content': '[SYSTEM MESSAGE]"', 'role': 'system'}, {'content': '[PROMPT]', 'name': 'user_proxy_auto', 'role': 'user'}]
3. _generate_oai_reply_from_client response: ChatCompletion(id='chatcmpl-9jjt8tzao65chsce8vo6n', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content="[REPLY 1]", role='assistant', function_call=None, tool_calls=None))], created=1713971689, model='TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q6_K.gguf', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=340, prompt_tokens=340, total_tokens=680), cost=0, message_retrieval_function=<bound method OpenAIClient.message_retrieval of <autogen.oai.client.OpenAIClient object at 0x00000253849CA350>>, config_id=0, pass_filter=True)
drafter (to chat_manager):
[REPLY 1]

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1. Calling reflection_with_llm_as_summary from user_proxy_auto to chat_manager
1.b. llm_config from sender: {'cache_seed': False, 'temperature': 0.5, 'config_list': [{'model': 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q6_K.gguf', 'base_url': 'http://localhost:1234/v1', 'api_key': 'lm-studio'}], 'timeout': 120} and from recipient: {'cache_seed': False, 'temperature': 0.5, 'config_list': [{'model': 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q6_K.gguf', 'base_url': 'http://localhost:1234/v1', 'api_key': 'lm-studio'}], 'timeout': 120}
2. _generate_oai_reply_from_client for user_proxy_auto. llm_client: [{'cache_seed': False, 'temperature': 0.5, 'model': 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q6_K.gguf'}], cache: None
2.b. _generate_oai_reply_from_client messages: [{'content': '[PROMPT]', 'role': 'user', 'name': 'user_proxy_auto'}, {'content': "[REPLY 1]", 'role': 'user', 'name': 'drafter'}, {'role': 'system', 'content': 'List the final answer to the task.'}]
3. _generate_oai_reply_from_client response: ChatCompletion(id='chatcmpl-iv8jxgvqcydrcavkk0yrm', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='', role='assistant', function_call=None, tool_calls=None))], created=1713971727, model='TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q6_K.gguf', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=0, prompt_tokens=1, total_tokens=1), cost=0, message_retrieval_function=<bound method OpenAIClient.message_retrieval of <autogen.oai.client.OpenAIClient object at 0x00000253849F1780>>, config_id=0, pass_filter=True)
response:
chat_result history: [{'content': '[PROMPT]', 'role': 'assistant'}, {'content': "[REPLY 1]", 'name': 'drafter', 'role': 'user'}]
chat_result summary:

    def _reflection_with_llm(
        self, prompt, messages, llm_agent: Optional[Agent] = None, cache: Optional[AbstractCache] = None
    ) -> str:
        """Get a chat summary using reflection with an llm client based on the conversation history.

        Args:
            prompt (str): The prompt (in this method it is used as system prompt) used to get the summary.
            messages (list): The messages generated as part of a chat conversation.
            llm_agent: the agent with an llm client.
            cache (AbstractCache or None): the cache client to be used for this conversation.
        """
        system_msg = [
            {
                "role": "system", -> hardcoded
                "content": prompt, -> Programatically defined
            }
        ]

        messages = messages + system_msg
        if llm_agent and llm_agent.client is not None:
            llm_client = llm_agent.client
        elif self.client is not None:
            llm_client = self.client
        else:
            raise ValueError("No OpenAIWrapper client is found.")
        response = self._generate_oai_reply_from_client(llm_client=llm_client, messages=messages, cache=cache)
        print("response: ", response)
        return response