Deno Fresh OpenAI Doc Search Starter

Template for building your own custom ChatGPT style doc search powered by Fresh, Deno, OpenAI, and Supabase.

This starter takes all the .mdx files in the docs directory and processes them to use as custom context within OpenAI Text Completion prompts.

Run locally

Prerequisites

• Deno
• Docker
• Git
• A free Supabase account
• Supabase CLI
• An OpenAI Account

Setup env vars:

cp .env.example .env

Set the required env vars as outlined in the file.

Start the project:

supabase start
deno task embeddings
deno task start

This will watch the project directory and restart as necessary.

Deploy

Push local migrations to Supabase

1. Create a new project on Supabase
2. Link your project: supabase link --project-ref=your-project-ref
3. Push up migration: supabase db push

Setup GitHub Action

We're using a GitHub Action to generate the embeddings whenever we merge into the main branch.

1. Get SUPABASE_URL and SUPABASE_SERVICE_ROLE_KEY from your Supabase Studio and set them as Actions secrets in GitHub.
2. Set OPENAI_KEY as Actions secrets in GitHub.
3. Push or merge into main to kick off the GitHub action.

Automatic Deployment with Deno Deploy

These steps show you how to deploy your app close to your users at the edge with Deno Deploy.

1. Clone this repository to your GitHub account.

2. Sign into Deno Deploy with your GitHub account.

3. Select your GitHub organization or user, repository, and branch

4. Select "Automatic" deployment mode and main.ts as the entry point

5. Click "Link", which will start the deployment.

6. Once the deployment is complete, click on "Settings" and add the production environmental variables, then hit "Save"

Voila, you've got your own custom ChatGPT!

Technical Details

Building your own custom ChatGPT involves four steps:

1. [⚡️ GitHub Action] Pre-process the knowledge base (your .mdx files in your docs folder).
2. [⚡️ GitHub Action] Store embeddings in Postgres with pgvector.
3. [🏃 Runtime] Perform vector similarity search to find the content that's relevant to the question.
4. [🏃 Runtime] Inject content into OpenAI GPT-3 text completion prompt and stream response to the client.

⚡️ GitHub Action

Step 1. and 2. happen via a GitHub Action anytime we make changes to the main branch. During this time the generate-embeddings script is being executed which performs the following tasks:

sequenceDiagram
    participant GitHub Action
    participant DB (pgvector)
    participant OpenAI (API)
    loop 1. Pre-process the knowledge base
        GitHub Action->>GitHub Action: Chunk .mdx files into sections
        loop 2. Create & store embeddings
            GitHub Action->>OpenAI (API): create embedding for page section
            OpenAI (API)->>GitHub Action: embedding vector(1536)
            GitHub Action->>DB (pgvector): store embedding for page section
        end
    end

In addition to storing the embeddings, this script generates a checksum for each of your .mdx files and stores this in another database table to make sure the embeddings are only regenerated when the file has changed.

🏃 Runtime

Step 3. and 4. happen at runtime, anytime the user submits a question. When this happens, the following sequence of tasks is performed:

sequenceDiagram
    participant Client
    participant Edge Function
    participant DB (pgvector)
    participant OpenAI (API)
    Client->>Edge Function: { query: lorem ispum }
    critical 3. Perform vector similarity search
        Edge Function->>OpenAI (API): create embedding for query
        OpenAI (API)->>Edge Function: embedding vector(1536)
        Edge Function->>DB (pgvector): vector similarity search
        DB (pgvector)->>Edge Function: relevant docs content
    end
    critical 4. Inject content into prompt
        Edge Function->>OpenAI (API): completion request prompt: query + relevant docs content
        OpenAI (API)-->>Client: text/event-stream: completions response
    end

The relevant files for this are the SearchDialog (Client) component and the vector-search (Edge Function).

The initialization of the database, including the setup of the pgvector extension is stored in the supabase/migrations folder which is automatically applied to your local Postgres instance when running supabase start.

Learn More

• Read the blogpost on how we built ChatGPT for the Supabase Docs.
• [Docs] pgvector: Embeddings and vector similarity
• Watch Greg's "How I built this" video on the Rabbit Hole Syndrome YouTube Channel: