Skip to main content

self-query-supabase

This templates allows natural language structured quering of Supabase.

Supabase is an open-source alternative to Firebase, built on top of PostgreSQL.

It uses pgvector to store embeddings within your tables.

Environment Setup​

Set the OPENAI_API_KEY environment variable to access the OpenAI models.

To get your OPENAI_API_KEY, navigate to API keys on your OpenAI account and create a new secret key.

To find your SUPABASE_URL and SUPABASE_SERVICE_KEY, head to your Supabase project's API settings.

  • SUPABASE_URL corresponds to the Project URL
  • SUPABASE_SERVICE_KEY corresponds to the service_role API key
export SUPABASE_URL=
export SUPABASE_SERVICE_KEY=
export OPENAI_API_KEY=

Setup Supabase Database​

Use these steps to setup your Supabase database if you haven't already.

  1. Head over to https://database.new to provision your Supabase database.

  2. In the studio, jump to the SQL editor and run the following script to enable pgvector and setup your database as a vector store:

    -- Enable the pgvector extension to work with embedding vectors
    create extension if not exists vector;

    -- Create a table to store your documents
    create table
      documents (
        id uuid primary key,
        content text, -- corresponds to Document.pageContent
        metadata jsonb, -- corresponds to Document.metadata
        embedding vector (1536) -- 1536 works for OpenAI embeddings, change as needed
      );

    -- Create a function to search for documents
    create function match_documents (
      query_embedding vector (1536),
      filter jsonb default '{}'
    ) returns table (
      id uuid,
      content text,
      metadata jsonb,
      similarity float
    ) language plpgsql as $$
    #variable_conflict use_column
    begin
      return query
      select
        id,
        content,
        metadata,
        1 - (documents.embedding <=> query_embedding) as similarity
      from documents
      where metadata @> filter
      order by documents.embedding <=> query_embedding;
    end;
    $$;

Usage​

To use this package, install the LangChain CLI first:

pip install -U langchain-cli

Create a new LangChain project and install this package as the only one:

langchain app new my-app --package self-query-supabase

To add this to an existing project, run:

langchain app add self-query-supabase

Add the following code to your server.py file:

from self_query_supabase.chain import chain as self_query_supabase_chain

add_routes(app, self_query_supabase_chain, path="/self-query-supabase")

(Optional) If you have access to LangSmith, configure it to help trace, monitor and debug LangChain applications. If you don't have access, skip this section.

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server running locally at http://localhost:8000

You can see all templates at http://127.0.0.1:8000/docs Access the playground at http://127.0.0.1:8000/self-query-supabase/playground

Access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/self-query-supabase")

TODO: Instructions to set up the Supabase database and install the package.