README.md

Discontinued, not hosted anymore.

Replicate SupportGPT

Make ChatGPT answer questions based on your documentation.

How it works

The goal is to make ChatGPT answer questions within a limited context, where the context is a relevant section of a larger documentation. To do this we use embeddings. In short, embeddings are tokens converted into vectors that can be used to calculate how closely related two strings are. If we split the documentation into chunks and encode them as embeddings in a vector database, we can query relevant documentation chunks later if we use the same encoding on questions. The relevant documentation chunks will then be used as context for a ChatGPT session.

This app is powered by:

⚡️ Supabase, for vector based database.

▲ Vercel, a platform for running web apps.

⚡️ Nuxt.js server-side API handlers, for talking to the Supabase database.

📦 Vuetify, a Vue.js component framework for the browser UI.

Setup Supabase

Supabase supports vectors in their PostgreSQL database. Create an account and execute the following queries:

Enable the vector extension:

create extension vector;

Create a table for the documentation chunks:

create table documents (
  id bigserial primary key,
  content text,
  url text,
  embedding vector (1536)
);

Create a PostgreSQL function that uses the <=> cosine distance operator to get similar documentation chunks.

create or replace function match_documents (
  query_embedding vector(1536),
  similarity_threshold float,
  match_count int
)
returns table (
  id bigint,
  content text,
  url text,
  similarity float
)
language plpgsql
as $$
begin
  return query
  select
    documents.id,
    documents.content,
    documents.url,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where 1 - (documents.embedding <=> query_embedding) > similarity_threshold
  order by similarity desc
  limit match_count;
end;
$$;

Run it locally

You need an OpenAI API key, a Supabase URL and Supabase API key (you can find these in the Supabase web portal under Project → API). Copy the contents of .example.env into a new file in the root of your directory called .env and insert the API keys there, like this:

NUXT_OPENAI_API_KEY=<your OpenAI API key here>
NUXT_SUPABASE_URL=<your Supabase URL here>
NUXT_SUPABASE_KEY=<your Supabase API key here>

Then, install the dependencies and run the local development server:

npm install
npm run dev

Open http://localhost:3000 in your web browser. Done!

Populate vector database with embeddings

Modify the scripts/create-embeddings.js script to include URLs of the documentation to create embeddings of:

// Add documentation URLs to be fetched here
const urls = [
  'https://replicate.com/home',
  'https://replicate.com/docs',
  'https://replicate.com/docs/get-started/python'
  // ...
]

And run it:

node scripts/create-embeddings.js

One-click deploy

Deploy this project using Vercel: