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41 lines
1.9 KiB
Python
41 lines
1.9 KiB
Python
# flake8: noqa
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from langchain.prompts.prompt import PromptTemplate
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_DEFAULT_ENTITY_EXTRACTION_TEMPLATE = """You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.
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The conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.
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Return the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).
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EXAMPLE
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Conversation history:
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Person #1: how's it going today?
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AI: "It's going great! How about you?"
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Person #1: good! busy working on Langchain. lots to do.
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AI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"
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Last line:
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Person #1: i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.
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Output: Langchain
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END OF EXAMPLE
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EXAMPLE
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Conversation history:
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Person #1: how's it going today?
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AI: "It's going great! How about you?"
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Person #1: good! busy working on Langchain. lots to do.
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AI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"
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Last line:
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Person #1: i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I'm working with Person #2.
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Output: Langchain, Person #2
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END OF EXAMPLE
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Conversation history (for reference only):
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{history}
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Last line of conversation (for extraction):
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Human: {input}
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Output:"""
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ENTITY_EXTRACTION_PROMPT = PromptTemplate(
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input_variables=["history", "input"], template=_DEFAULT_ENTITY_EXTRACTION_TEMPLATE
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)
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