mirror of
https://github.com/hwchase17/langchain
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52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
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from operator import itemgetter
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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.pydantic_v1 import BaseModel
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import ConfigurableField, RunnableParallel
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from neo4j_advanced_rag.retrievers import (
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hypothetic_question_vectorstore,
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parent_vectorstore,
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summary_vectorstore,
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typical_rag,
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)
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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model = ChatOpenAI()
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retriever = typical_rag.as_retriever().configurable_alternatives(
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ConfigurableField(id="strategy"),
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default_key="typical_rag",
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parent_strategy=parent_vectorstore.as_retriever(),
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hypothetical_questions=hypothetic_question_vectorstore.as_retriever(),
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summary_strategy=summary_vectorstore.as_retriever(),
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)
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chain = (
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RunnableParallel(
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{
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"context": itemgetter("question") | retriever,
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"question": itemgetter("question"),
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}
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)
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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question: str
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chain = chain.with_types(input_type=Question)
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