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# prompt模板使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号
# 本配置文件支持热加载，修改prompt模板后无需重启服务。
# LLM对话支持的变量：
# - input: 用户输入内容
# 知识库和搜索引擎对话支持的变量：
# - context: 从检索结果拼接的知识文本
# - question: 用户提出的问题
# Agent对话支持的变量：
# - tools: 可用的工具列表
# - tool_names: 可用的工具名称列表
# - history: 用户和Agent的对话历史
# - input: 用户输入内容
# - agent_scratchpad: Agent的思维记录
PROMPT_TEMPLATES = {
"llm_chat": {
"default":
'{{ input }}',
"with_history":
'The following is a friendly conversation between a human and an AI. '
'The AI is talkative and provides lots of specific details from its context. '
'If the AI does not know the answer to a question, it truthfully says it does not know.\n\n'
'Current conversation:\n'
'{history}\n'
'Human: {input}\n'
'AI:',
"py":
'你是一个聪明的代码助手，请你给我写出简单的py代码。 \n'
'{{ input }}',
},
"knowledge_base_chat": {
"default":
'<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，'
'不允许在答案中添加编造成分，答案请使用中文。 </指令>\n'
'<已知信息>{{ context }}</已知信息>\n'
'<问题>{{ question }}</问题>\n',
"text":
'<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，答案请使用中文。 </指令>\n'
'<已知信息>{{ context }}</已知信息>\n'
'<问题>{{ question }}</问题>\n',
"empty": # 搜不到知识库的时候使用
'请你回答我的问题:\n'
'{{ question }}\n\n',
},
"search_engine_chat": {
"default":
'<指令>这是我搜索到的互联网信息，请你根据这些信息进行提取并有调理，简洁的回答问题。'
'如果无法从中得到答案，请说 “无法搜索到能回答问题的内容”。 </指令>\n'
'<已知信息>{{ context }}</已知信息>\n'
'<问题>{{ question }}</问题>\n',
"search":
'<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，答案请使用中文。 </指令>\n'
'<已知信息>{{ context }}</已知信息>\n'
'<问题>{{ question }}</问题>\n',
},
"agent_chat": {
"default":
'Answer the following questions as best you can. If it is in order, you can use some tools appropriately. '
'You have access to the following tools:\n\n'
'{tools}\n\n'
'Use the following format:\n'
'Question: the input question you must answer1\n'
'Thought: you should always think about what to do and what tools to use.\n'
'Action: the action to take, should be one of [{tool_names}]\n'
'Action Input: the input to the action\n'
'Observation: the result of the action\n'
'... (this Thought/Action/Action Input/Observation can be repeated zero or more times)\n'
'Thought: I now know the final answer\n'
'Final Answer: the final answer to the original input question\n'
'Begin!\n\n'
'history: {history}\n\n'
'Question: {input}\n\n'
'Thought: {agent_scratchpad}\n',
"ChatGLM3":
'You can answer using the tools, or answer directly using your knowledge without using the tools. '
'Respond to the human as helpfully and accurately as possible.\n'
'You have access to the following tools:\n'
'{tools}\n'
'Use a json blob to specify a tool by providing an action key (tool name) '
'and an action_input key (tool input).\n'
'Valid "action" values: "Final Answer" or [{tool_names}]'
'Provide only ONE action per $JSON_BLOB, as shown:\n\n'
'```\n'
'{{{{\n'
' "action": $TOOL_NAME,\n'
' "action_input": $INPUT\n'
'}}}}\n'
'```\n\n'
'Follow this format:\n\n'
'Question: input question to answer\n'
'Thought: consider previous and subsequent steps\n'
'Action:\n'
'```\n'
'$JSON_BLOB\n'
'```\n'
'Observation: action result\n'
'... (repeat Thought/Action/Observation N times)\n'
'Thought: I know what to respond\n'
'Action:\n'
'```\n'
'{{{{\n'
' "action": "Final Answer",\n'
' "action_input": "Final response to human"\n'
'}}}}\n'
'Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. '
'Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.\n'
'history: {history}\n\n'
'Question: {input}\n\n'
'Thought: {agent_scratchpad}',
}
}
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