langchain/templates/self-query-supabase/self_query_supabase/chain.py
2023-10-26 19:44:30 -07:00

55 lines
1.7 KiB
Python

import os
from langchain.chains.query_constructor.base import AttributeInfo
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms.openai import OpenAI
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
from langchain.vectorstores.supabase import SupabaseVectorStore
from supabase.client import create_client
supabase_url = os.environ.get("SUPABASE_URL")
supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
supabase = create_client(supabase_url, supabase_key)
embeddings = OpenAIEmbeddings()
vectorstore = SupabaseVectorStore(
client=supabase,
embedding=embeddings,
table_name="documents",
query_name="match_documents",
)
# Adjust this based on the metadata you store in the `metadata` JSON column
metadata_field_info = [
AttributeInfo(
name="genre",
description="The genre of the movie",
type="string or list[string]",
),
AttributeInfo(
name="year",
description="The year the movie was released",
type="integer",
),
AttributeInfo(
name="director",
description="The name of the movie director",
type="string",
),
AttributeInfo(
name="rating", description="A 1-10 rating for the movie", type="float"
),
]
# Adjust this based on the type of documents you store
document_content_description = "Brief summary of a movie"
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)
chain = RunnableParallel({"query": RunnablePassthrough()}) | retriever