
# 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

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 $$
   #variable_conflict use_column
   begin
     return query
     select
       id,
       content,
       metadata,
       1 - (documents.embedding <=> query_embedding) as similarity
     from documents
     where metadata @> filter
     order by documents.embedding <=> query_embedding;
   end;
   $$;
   ```

## Usage

To use this package, install the LangChain CLI first:

```shell
pip install -U langchain-cli
```

Create a new LangChain project and install this package as the only one:

```shell
langchain app new my-app --package self-query-supabase
```

To add this to an existing project, run:

```shell
langchain app add self-query-supabase
```

Add the following code to your `server.py` file:
```python
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.

```shell
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:

```shell
langchain serve
```

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:

```python
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.
