mirror of
https://github.com/hwchase17/langchain
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ebf998acb6
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>
104 lines
3.1 KiB
Markdown
104 lines
3.1 KiB
Markdown
# 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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Use this package to host a retrieval augment generation (RAG) API using LangServe + Supabase.
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## Install Package
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From within your `langservehub` project run:
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```shell
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poetry run poe add rag-supabase
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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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Create a `.env` file in the root of your project:
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_.env_
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```shell
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SUPABASE_URL=
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SUPABASE_SERVICE_KEY=
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OPENAI_API_KEY=
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```
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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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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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Add this file to your `.gitignore` if it isn't already there (so that we don't commit secrets):
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_.gitignore_
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```
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.env
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```
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Install [`python-dotenv`](https://github.com/theskumar/python-dotenv) which we will use to load the environment variables into the app:
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```shell
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poetry add python-dotenv
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```
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Finally, call `load_dotenv()` in `server.py`.
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_app/server.py_
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```python
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from dotenv import load_dotenv
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load_dotenv()
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```
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