1) Complete new Agent Behavior: triage_tasks
2) Fix bugs.
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@@ -15,22 +15,22 @@ Your name is Jarvis, the super personal assistant to the master, The focus of wo
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"""
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[behavior.on_message]
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type="LLMAgentMessageProcess"
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type="AgentMessageProcess"
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# TODO: 是否应该自动记录 inner function和action的执行细节
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process_description="""
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1. Based on your role, combined with existing information, make a brief and efficient reply.
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1. Based on your role and the existing information, please think and then make a brief and efficient reply.
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2. Be mindful of the identity of the person you are chatting with and provide services accordingly based on their status.
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3. Understand the intention of the dialogue, while using the necessary reply, use the appropriate, supported ACTION.
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4. If you feel that there is a potential Task in the dialogue, you can create these tasks through appropriate ACTION. Be careful to query whether there are the same task before creating. Using the query interface is a high-cost behavior.
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5. You are proficient in the languages of various countries and try to communicate with each other's mother tongue.
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"""
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# Not work: tags: ['tag1', 'tag2'], #Optional,If the conversation involves important things and people, you can mark by 1-3 tags.
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reply_format = """
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The Response must be directly parsed by `python json.loads`. Here is an example:
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{
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think:'$think step-by-step to be sure you have the right answer.'
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think:'$think step-by-step to be sure you have the right reply.'
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resp: '$What you want to reply',
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tags: ['tag1', 'tag2'], #Optional,If the conversation involves important things and people, you can mark by 1-3 tags.
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actions: [{
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name: '$action_name',
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$param_name: '$parm' #Optional, fill in only if the action has parameters.
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@@ -48,8 +48,35 @@ tools_tips = """
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llm_context.actions.enable = ["agent.workspace.create_task"]
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llm_context.functions.enable = ["agent.workspace.list_task"]
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[behavior.review_task]
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type="ReviewTaskProcess"
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[behavior.triage_tasks]
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## 处理任务列表,任务列表里会包含所有未执行过,且未过期的任务
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## 对于简单的任务会一次性完成处理
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type="AgentTriageTaskList"
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process_description="""
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You are expected to effectively TRIAGE the task list described in JSON format, in accordance with your role. Your GOAL is :
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1. Adjust the priority of the task and set up a reasonable processing time.(update_task)
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2. Immediately perform a simple (similar to reminding one category) task. Send a message using send_message, set the task to complete the use of update_task.
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3. Organize tasks to remove tasks beyond your ability. And merge the repeated tasks.(update_task + cancel_task)
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"""
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reply_format = """
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The Response must be directly parsed by `python json.loads`. Here is an example:
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{
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think:'$think step-by-step to be sure you can triage tasks well.'
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resp : '$determine, summary what you do',
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actions: [{
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name: '$action_name',
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$param_name: '$parm' #Optional, fill in only if the action has parameters.
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}]
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}
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"""
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context="Your master now in {location}, time: {now}, weather: {weather}."
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llm_context.actions.enable = ["agent.workspace.update_task","agent.workspace.cancel_task","post_message"]
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[behavior.plan_task]
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## 首次处理任务
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type="AgentPlanTask"
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process_description="""
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你得到的输入来自你自己之前记录在TaskList系统里的一个Task。现在你并不需要完成该Task,而是结合已知信息对Task进行一次Review.Review的过程是你独立完成的,你在形成结论的过程中可以使用工具,但不能和其它人交流。
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1. 理性的思考如何一步一步的高效的,在潜在的截止时间前完成该Task。明确拒绝超出自己能力范围的Task。
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@@ -75,20 +102,18 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
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}
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"""
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# action_list: ['cancle','confirm', 'execute']
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LLMContext.action_list = ['cancle','confirm', 'execute']
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llm_context.actions.enable = ["agent.workspace.cancel_task"]
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llm_context.functions.enable = ["agent.workspace.list_task"]
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context="Your master now in {location}, time: {now}, weather: {weather}."
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known_info_tips = """
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"""
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[behavior.review_task]
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## 处理已经被LLM处理过的任务,包括处理首次出错的任务,处理被的任务
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tools_tips = """
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"""
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[behavior.do] # do TODO
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type="DoTodoProcess"
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type="AgentDo"
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process_description="""
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1. 你的任务是结合自己的角色定义,手头的工具,已知信息、完成一个确定的TODO。完成该TODO后你会得到$200的小费。
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2. 输入的TODO是来自你自己对一个Task的Plan结果。
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@@ -111,12 +136,18 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
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]
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}
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"""
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[behavior.check]
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type="AgentCheck"
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#[behavior.self_thinking]
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#[behavior.check]
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[behavior.self_thinking]
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# self thing的主要目的是对各种chatlog,worklog进行分析,并更新终结。
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type="AgentSelfThinking"
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#[behavior.self_learning]
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# self_learning的主要目的是根据自己的经验,主动的学习新的知识。这通常是一个专门整理知识库的Agent,一般的Agent谨慎开启
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#type="AgentSelfLearning"
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#[behavior.self_improve]
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# self_improve 是最后的行为,允许Agent结合自己的工作经验,改进自己的提示词(注意保留历史版本)
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#type="AgentSelfImprove"
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