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https://github.com/supabase-community/nextjs-openai-doc-search?og=v2

Template for building your own custom ChatGPT style doc search powered by Next.js, OpenAI, and Supabase.
https://github.com/supabase-community/nextjs-openai-doc-search?og=v2

ai chatgpt nextjs openai postgres supabase template vector-search

Last synced: 22 days ago
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Template for building your own custom ChatGPT style doc search powered by Next.js, OpenAI, and Supabase.

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# Next.js OpenAI Doc Search Starter



This starter takes all the `.mdx` files in the `pages` directory and processes them to use as custom context within [OpenAI Text Completion](https://platform.openai.com/docs/guides/completion) prompts.



## Deploy



Deploy this starter to Vercel. The Supabase integration will automatically set the required environment variables and configure your [Database Schema](./supabase/migrations/20230406025118_init.sql). All you have to do is set your `OPENAI_KEY` and you're ready to go!



[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=Next.js%20OpenAI%20Doc%20Search%20Starter&demo-description=Template%20for%20building%20your%20own%20custom%20ChatGPT%20style%20doc%20search%20powered%20by%20Next.js%2C%20OpenAI%2C%20and%20Supabase.&demo-url=https%3A%2F%2Fsupabase.com%2Fdocs&demo-image=%2F%2Fimages.ctfassets.net%2Fe5382hct74si%2F1OntM6THNEUvlUsYy6Bjmf%2F475e39dbc84779538c8ed47c63a37e0e%2Fnextjs_openai_doc_search_og.png&project-name=Next.js%20OpenAI%20Doc%20Search%20Starter&repository-name=nextjs-openai-doc-search-starter&repository-url=https%3A%2F%2Fgithub.com%2Fsupabase-community%2Fnextjs-openai-doc-search%2F&from=github&integration-ids=oac_VqOgBHqhEoFTPzGkPd7L0iH6&env=OPENAI_KEY&envDescription=Get%20your%20OpenAI%20API%20key%3A&envLink=https%3A%2F%2Fplatform.openai.com%2Faccount%2Fapi-keys&teamCreateStatus=hidden&external-id=https%3A%2F%2Fgithub.com%2Fsupabase-community%2Fnextjs-openai-doc-search%2Ftree%2Fmain)



## Technical Details



Building your own custom ChatGPT involves four steps:



1. [👷 Build time] Pre-process the knowledge base (your `.mdx` files in your `pages` folder).

2. [👷 Build time] Store embeddings in Postgres with [pgvector](https://supabase.com/docs/guides/database/extensions/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.



## 👷 Build time



Step 1. and 2. happen at build time, e.g. when Vercel builds your Next.js app. During this time the [`generate-embeddings`](./lib/generate-embeddings.ts) script is being executed which performs the following tasks:



```mermaid

sequenceDiagram

    participant Vercel

    participant DB (pgvector)

    participant OpenAI (API)

    loop 1. Pre-process the knowledge base

        Vercel->>Vercel: Chunk .mdx pages into sections

        loop 2. Create & store embeddings

            Vercel->>OpenAI (API): create embedding for page section

            OpenAI (API)->>Vercel: embedding vector(1536)

            Vercel->>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:



```mermaid

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)](./components/SearchDialog.tsx) component and the [`vector-search` (Edge Function)](./pages/api/vector-search.ts).



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



## Local Development



### Configuration



- `cp .env.example .env`

- Set your `OPENAI_KEY` in the newly created `.env` file.

- Set `NEXT_PUBLIC_SUPABASE_ANON_KEY` and `SUPABASE_SERVICE_ROLE_KEY` run:

  > Note: You have to run supabase to retrieve the keys.



### Start Supabase



Make sure you have Docker installed and running locally. Then run



```bash

supabase start

```



To retrieve `NEXT_PUBLIC_SUPABASE_ANON_KEY` and `SUPABASE_SERVICE_ROLE_KEY` run:



```bash

supabase status

```



### Start the Next.js App



In a new terminal window, run



```bash

pnpm dev

```



### Using your custom .mdx docs



1. By default your documentation will need to be in `.mdx` format. This can be done by renaming existing (or compatible) markdown `.md` file.

2. Run `pnpm run embeddings` to regenerate embeddings.

   > Note: Make sure supabase is running. To check, run `supabase status`. If is not running run `supabase start`.

3. Run `pnpm dev` again to refresh NextJS localhost:3000 rendered page.



## Learn More



- Read the blogpost on how we built [ChatGPT for the Supabase Docs](https://supabase.com/blog/chatgpt-supabase-docs).

- [[Docs] pgvector: Embeddings and vector similarity](https://supabase.com/docs/guides/database/extensions/pgvector)

- Watch [Greg's](https://twitter.com/ggrdson) "How I built this" [video](https://youtu.be/Yhtjd7yGGGA) on the [Rabbit Hole Syndrome YouTube Channel](https://www.youtube.com/@RabbitHoleSyndrome):



[![Video: How I Built Supabase’s OpenAI Doc Search](https://img.youtube.com/vi/Yhtjd7yGGGA/0.jpg)](https://www.youtube.com/watch?v=Yhtjd7yGGGA)



## Licence



Apache 2.0