mirror of
https://github.com/hwchase17/langchain
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55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
import os
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from langchain.chains.query_constructor.base import AttributeInfo
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.llms.openai import OpenAI
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from langchain.retrievers.self_query.base import SelfQueryRetriever
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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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# Adjust this based on the metadata you store in the `metadata` JSON column
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metadata_field_info = [
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AttributeInfo(
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name="genre",
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description="The genre of the movie",
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type="string or list[string]",
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),
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AttributeInfo(
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name="year",
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description="The year the movie was released",
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type="integer",
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),
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AttributeInfo(
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name="director",
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description="The name of the movie director",
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type="string",
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),
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AttributeInfo(
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name="rating", description="A 1-10 rating for the movie", type="float"
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),
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]
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# Adjust this based on the type of documents you store
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document_content_description = "Brief summary of a movie"
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llm = OpenAI(temperature=0)
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retriever = SelfQueryRetriever.from_llm(
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llm, vectorstore, document_content_description, metadata_field_info, verbose=True
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)
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chain = RunnableParallel({"query": RunnablePassthrough()}) | retriever
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