mirror of
https://github.com/hwchase17/langchain
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48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_community.vectorstores import Lantern
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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CONNECTION_STRING = "postgresql://postgres:postgres@localhost:5432"
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COLLECTION_NAME = "documents"
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DB_NAME = "postgres"
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embeddings = OpenAIEmbeddings()
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vectorstore = Lantern(
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collection_name=COLLECTION_NAME,
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connection_string=CONNECTION_STRING,
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embedding_function=embeddings,
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)
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retriever = vectorstore.as_retriever()
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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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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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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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__root__: str
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chain = chain.with_types(input_type=Question)
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