# rag_supabase This template performs RAG with Supabase. [Supabase](https://supabase.com/docs) is an open-source Firebase alternative. It is built on top of [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL), a free and open-source relational database management system (RDBMS) and uses [pgvector](https://github.com/pgvector/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](https://platform.openai.com/account/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](https://supabase.com/dashboard/project/_/settings/api). - `SUPABASE_URL` corresponds to the Project URL - `SUPABASE_SERVICE_KEY` corresponds to the `service_role` API key ```shell 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](https://supabase.com/dashboard/project/_/sql/new) and run the following script to enable `pgvector` and setup your database as a vector store: ```sql -- 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`](https://python.langchain.com/docs/integrations/vectorstores/supabase) and [`OpenAIEmbeddings`](https://python.langchain.com/docs/integrations/text_embedding/openai), we need to load their API keys. ## Usage First, install the LangChain CLI: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package rag-supabase ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-supabase ``` And add the following code to your `server.py` file: ```python 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](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/rag-supabase/playground](http://127.0.0.1:8000/rag-supabase/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-supabase") ``` TODO: Add details about setting up the Supabase database