Metadata filtering with RetrievalQA on Supabase vectorstore

[raunov]

I'm trying to do a retrievalQA on Supabase documents table. I have the match_documents function set up, and it works well, however the metadata filtering doesn't seem to work. Anyone can point me to right direction? Thanks!

from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.vectorstores import SupabaseVectorStore
from supabase.client import Client,create_client

from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate
)

import os
from dotenv import load_dotenv, find_dotenv

load_dotenv(find_dotenv())

def query_podcast(query,model="gpt-3.5",metadata=None):
    if model == "gpt-3.5":
        llm = ChatOpenAI(temperature=0,
                        model_name="gpt-3.5-turbo-16k",
                        request_timeout = 180
                        )
    elif model == "gpt-4":
        llm = ChatOpenAI(temperature=0,
                        model_name="gpt-4-0613",
                        request_timeout = 360
                        )

    supabase: Client = create_client(os.environ["SUPABASE_URL"], os.environ["SUPABASE_SERVICE_KEY"])


    embeddings = OpenAIEmbeddings()

    docsearch = SupabaseVectorStore(table_name=os.environ['VECTOR_TABLE'], client=supabase,embedding=embeddings)

    system_template = """
    <<my prompt here>>
    ----------------
    {context}"""

    messages = [
        SystemMessagePromptTemplate.from_template(system_template),
        HumanMessagePromptTemplate.from_template("{question}"),
    ]

    CHAT_PROMPT = ChatPromptTemplate.from_messages(messages)

    filters={"filter_by_some_id":metadata["some_id"]}
    
    qa = RetrievalQA.from_chain_type(llm=llm,
                                    chain_type="stuff",
                                    retriever=docsearch.as_retriever(k=6,filters=filters),
                                    chain_type_kwargs = {
                                        'verbose': False,
                                        'prompt': CHAT_PROMPT
                                    })

    return qa.run(query)

[shyeetsao]

seems there is not yet an implementation of metadata filters in SupabaseVectorStore while there is one in PGVector we can refer to