mirror of
https://github.com/hwchase17/langchain
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51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
import os
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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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 RunnableParallel, RunnablePassthrough
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from langchain.vectorstores.supabase import SupabaseVectorStore
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from supabase.client import create_client
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supabase_url = os.environ.get("SUPABASE_URL")
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supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
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supabase = create_client(supabase_url, supabase_key)
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embeddings = OpenAIEmbeddings()
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vectorstore = SupabaseVectorStore(
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client=supabase,
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embedding=embeddings,
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table_name="documents",
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query_name="match_documents",
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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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