langchain/templates/rag-supabase

rag_supabase

This template performs RAG with Supabase.

Supabase is an open-source Firebase alternative. It is built on top of PostgreSQL, a free and open-source relational database management system (RDBMS) and 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 $$
   #variable_conflict use_column
   begin
     return query
     select
       id,
       content,
       metadata,
       1 - (documents.embedding <=> query_embedding) as similarity
     from documents
     where metadata @> filter
     order by documents.embedding <=> query_embedding;
   end;
   $$;
   

Setup Environment Variables

Since we are using SupabaseVectorStore and OpenAIEmbeddings, we need to load their API keys.

Usage

First, install the LangChain CLI:

pip install -U "langchain-cli[serve]"

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package rag_supabase

If you want to add this to an existing project, you can just run:

langchain app add rag_supabase

And add the following code to your server.py file:

from dotenv import load_dotenv

load_dotenv()

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can 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 is running locally at http://localhost:8000

We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/rag_supabase/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag_supabase")

TODO: Add details about setting up the Supabase database