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- **Description:** Correct naming for package in README - **Issue:** README wasn't aligned with pyproject.toml, resulting in not being able to install the rag-supabase package. - **Tag maintainer:** @gregnr
133 lines
4.2 KiB
Markdown
133 lines
4.2 KiB
Markdown
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# rag_supabase
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This template performs RAG with Supabase.
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[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.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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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.
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To find your `SUPABASE_URL` and `SUPABASE_SERVICE_KEY`, head to your Supabase project's [API settings](https://supabase.com/dashboard/project/_/settings/api).
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- `SUPABASE_URL` corresponds to the Project URL
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- `SUPABASE_SERVICE_KEY` corresponds to the `service_role` API key
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```shell
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export SUPABASE_URL=
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export SUPABASE_SERVICE_KEY=
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export OPENAI_API_KEY=
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```
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## Setup Supabase Database
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Use these steps to setup your Supabase database if you haven't already.
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1. Head over to https://database.new to provision your Supabase database.
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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:
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```sql
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-- Enable the pgvector extension to work with embedding vectors
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create extension if not exists vector;
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-- Create a table to store your documents
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create table
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documents (
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id uuid primary key,
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content text, -- corresponds to Document.pageContent
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metadata jsonb, -- corresponds to Document.metadata
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embedding vector (1536) -- 1536 works for OpenAI embeddings, change as needed
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);
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-- Create a function to search for documents
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create function match_documents (
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query_embedding vector (1536),
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filter jsonb default '{}'
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) returns table (
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id uuid,
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content text,
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metadata jsonb,
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similarity float
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) language plpgsql as $$
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#variable_conflict use_column
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begin
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return query
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select
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id,
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content,
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metadata,
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1 - (documents.embedding <=> query_embedding) as similarity
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from documents
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where metadata @> filter
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order by documents.embedding <=> query_embedding;
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end;
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$$;
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```
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## Setup Environment Variables
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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.
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## Usage
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First, install the LangChain CLI:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-supabase
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-supabase
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```
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And add the following code to your `server.py` file:
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```python
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from rag_supabase.chain import chain as rag_supabase_chain
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add_routes(app, rag_supabase_chain, path="/rag-supabase")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/rag-supabase/playground](http://127.0.0.1:8000/rag-supabase/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-supabase")
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```
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TODO: Add details about setting up the Supabase database |