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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

Setup Supabase Database​

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

  1. Head over to 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
    return query
    1 - (documents.embedding <=> query_embedding) as similarity
    from documents
    where metadata @> filter
    order by documents.embedding <=> query_embedding;

Setup Environment Variables​

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


First, install the LangChain CLI:

pip install -U langchain-cli

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 file:

from rag_supabase.chain import chain as rag_supabase_chain

add_routes(app, rag_supabase_chain, path="/rag-supabase")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

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 We can access the playground at

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

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