## self-query-supabase

This templates allows natural language structured quering of Supabase.

[Supabase](https://supabase.com/docs) is an open-source alternative to Firebase, built on top of [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL).

It uses [pgvector](https://github.com/pgvector/pgvector) to store embeddings within your tables.

## Environment Setup[​](#environment-setup "Direct link to 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

```
export SUPABASE_URL=export SUPABASE_SERVICE_KEY=export OPENAI_API_KEY=
```

## Setup Supabase Database[​](#setup-supabase-database "Direct link to Setup Supabase Database")

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

1.  Head over to [https://database.new](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:
    
    ```
    -- Enable the pgvector extension to work with embedding vectorscreate extension if not exists vector;-- Create a table to store your documentscreate 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 documentscreate function match_documents (  query_embedding vector (1536),  filter jsonb default '{}') returns table (  id uuid,  content text,  metadata jsonb,  similarity float) language plpgsql as $$#variable_conflict use_columnbegin  return query  select    id,    content,    metadata,    1 - (documents.embedding <=> query_embedding) as similarity  from documents  where metadata @> filter  order by documents.embedding <=> query_embedding;end;$$;
    ```
    

## Usage[​](#usage "Direct link to 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_chainadd_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=trueexport 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:

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

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

Access the template from code with:

```
from langserve.client import RemoteRunnablerunnable = RemoteRunnable("http://localhost:8000/self-query-supabase")
```

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