Templates (#12294)

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Jacob Lee <jacoblee93@gmail.com>

templates/self-query-supabase/README.md

@@ -0,0 +1,103 @@
+# Self-querying 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.
+
+Use this package to host a LangServe API that can [self-query](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query) Supabase. You'll be able to use natural language to generate a structured query against the database.
+
+## Install Package
+
+From within your `langservehub` project run:
+
+```shell
+poetry run poe add self-query-supabase
+```
+
+## 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.
+
+Create a `.env` file in the root of your project:
+
+_.env_
+
+```shell
+SUPABASE_URL=
+SUPABASE_SERVICE_KEY=
+OPENAI_API_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
+
+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.
+
+Add this file to your `.gitignore` if it isn't already there (so that we don't commit secrets):
+
+_.gitignore_
+
+```
+.env
+```
+
+Install [`python-dotenv`](https://github.com/theskumar/python-dotenv) which we will use to load the environment variables into the app:
+
+```shell
+poetry add python-dotenv
+```
+
+Finally, call `load_dotenv()` in `server.py`.
+
+_app/server.py_
+
+```python
+from dotenv import load_dotenv
+
+load_dotenv()
+```