Self-query - Supabase

This template allows natural language structured querying 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.