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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
128 lines
3.9 KiB
Markdown
128 lines
3.9 KiB
Markdown
# Self-query - Supabase
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This template allows natural language structured querying of `Supabase`.
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[Supabase](https://supabase.com/docs) is an open-source alternative to `Firebase`, built on top of [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL).
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It 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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## Usage
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To use this package, install the LangChain CLI first:
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```shell
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pip install -U langchain-cli
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```
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Create a new LangChain project and install this package as the only one:
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```shell
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langchain app new my-app --package self-query-supabase
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```
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To add this to an existing project, run:
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```shell
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langchain app add self-query-supabase
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```
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Add the following code to your `server.py` file:
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```python
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from self_query_supabase.chain import chain as self_query_supabase_chain
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add_routes(app, self_query_supabase_chain, path="/self-query-supabase")
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```
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(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.
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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 running locally at
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[http://localhost:8000](http://localhost:8000)
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You can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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Access the playground at [http://127.0.0.1:8000/self-query-supabase/playground](http://127.0.0.1:8000/self-query-supabase/playground)
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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/self-query-supabase")
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```
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TODO: Instructions to set up the Supabase database and install the package.
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