mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.schema import AgentAction, AgentFinish
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template = """You are a helpful assistant. Help the user answer any questions.
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You have access to the following tools:
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{tools}
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In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>
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For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
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<tool>search</tool><tool_input>weather in SF</tool_input>
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<observation>64 degrees</observation>
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When you are done, you can respond as normal to the user.
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Example 1:
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Human: Hi!
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Assistant: Hi! How are you?
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Human: What is the weather in SF?
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Assistant: <tool>search</tool><tool_input>weather in SF</tool_input>
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<observation>64 degrees</observation>
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It is 64 degress in SF
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Begin!""" # noqa: E501
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conversational_prompt = ChatPromptTemplate.from_messages(
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[
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("system", template),
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MessagesPlaceholder(variable_name="chat_history"),
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("user", "{question}"),
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("ai", "{agent_scratchpad}"),
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]
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)
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def parse_output(message):
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text = message.content
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if "</tool>" in text:
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tool, tool_input = text.split("</tool>")
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_tool = tool.split("<tool>")[1]
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_tool_input = tool_input.split("<tool_input>")[1]
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if "</tool_input>" in _tool_input:
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_tool_input = _tool_input.split("</tool_input>")[0]
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return AgentAction(tool=_tool, tool_input=_tool_input, log=text)
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else:
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return AgentFinish(return_values={"output": text}, log=text)
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