mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
fa5d49f2c1
ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
import os
|
|
|
|
from langchain.chains.query_constructor.base import AttributeInfo
|
|
from langchain.retrievers.self_query.base import SelfQueryRetriever
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
|
from langchain_community.llms.openai import OpenAI
|
|
from langchain_community.vectorstores.supabase import SupabaseVectorStore
|
|
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
|
|
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
|