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ebf998acb6
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Lance Martin <lance@langchain.dev> Co-authored-by: Jacob Lee <jacoblee93@gmail.com>
42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
from langchain.pydantic_v1 import BaseModel
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from typing import List, Optional
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from langchain.utils.openai_functions import convert_pydantic_to_openai_function
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from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
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import json
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template = """A article will be passed to you. Extract from it all papers that are mentioned by this article.
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Do not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list.
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Do not make up or guess ANY extra information. Only extract what exactly is in the text."""
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prompt = ChatPromptTemplate.from_messages([
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("system", template),
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("human", "{input}")
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])
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# Function output schema
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class Paper(BaseModel):
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"""Information about papers mentioned."""
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title: str
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author: Optional[str]
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class Info(BaseModel):
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"""Information to extract"""
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papers: List[Paper]
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# Function definition
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model = ChatOpenAI()
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function = [convert_pydantic_to_openai_function(Info)]
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chain = prompt | model.bind(
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functions=function, function_call={"name": "Info"}
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) | (lambda x: json.loads(x.additional_kwargs['function_call']['arguments'])['papers'])
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# chain = prompt | model.bind(
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# functions=function, function_call={"name": "Info"}
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# ) | JsonKeyOutputFunctionsParser(key_name="papers")
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