Vector search with Next.js and OpenAI

Learn how to build a ChatGPT-style doc search powered by Next.js, OpenAI, and Supabase.

While our Headless Vector search provides a toolkit for generative Q&A, in this tutorial we'll go more in-depth, build a custom ChatGPT-like search experience from the ground-up using Next.js. You will:

Convert your markdown into embeddings using OpenAI.
Store you embeddings in Postgres using pgvector.
Deploy a function for answering your users' questions.

You can read our Supabase Clippy blog post for a full example.

We assume that you have a Next.js project with a collection of .mdx files nested inside your pages directory. We will start developing locally with the Supabase CLI and then push our local database changes to our hosted Supabase project. You can find the full Next.js example on GitHub.

Create a project

Create a new project in the Supabase Dashboard.
Enter your project details.
Wait for the new database to launch.

Prepare the database

Let's prepare the database schema. We can use the "OpenAI Vector Search" quickstart in the SQL Editor, or you can copy/paste the SQL below and run it yourself.

Pre-process the knowledge base at build time

With our database set up, we need to process and store all .mdx files in the pages directory. You can find the full script here, or follow the steps below:

Generate Embeddings

Create a new file lib/generate-embeddings.ts and copy the code over from GitHub.


_10curl \
_10https://raw.githubusercontent.com/supabase-community/nextjs-openai-doc-search/main/lib/generate-embeddings.ts \
_10-o "lib/generate-embeddings.ts"

Set up environment variables

We need some environment variables to run the script. Add them to your .env file and make sure your .env file is not committed to source control! You can get your local Supabase credentials by running supabase status.


_10NEXT_PUBLIC_SUPABASE_URL=
_10NEXT_PUBLIC_SUPABASE_ANON_KEY=
_10SUPABASE_SERVICE_ROLE_KEY=
_10
_10# Get your key at https://platform.openai.com/account/api-keys
_10OPENAI_KEY=

Run script at build time

Include the script in your package.json script commands to enable Vercel to automaticall run it at build time.


_10"scripts": {
_10  "dev": "next dev",
_10  "build": "pnpm run embeddings && next build",
_10  "start": "next start",
_10  "embeddings": "tsx lib/generate-embeddings.ts"
_10},

Create text completion with OpenAI API

Anytime a user asks a question, we need to create an embedding for their question, perform a similarity search, and then send a text completion request to the OpenAI API with the query and then context content merged together into a prompt.

All of this is glued together in a Vercel Edge Function, the code for which can be found on GitHub.

Create Embedding for Question

In order to perform similarity search we need to turn the question into an embedding.


_19const embeddingResponse = await fetch('https://api.openai.com/v1/embeddings', {
_19  method: 'POST',
_19  headers: {
_19    Authorization: `Bearer ${openAiKey}`,
_19    'Content-Type': 'application/json',
_19  },
_19  body: JSON.stringify({
_19    model: 'text-embedding-ada-002',
_19    input: sanitizedQuery.replaceAll('\n', ' '),
_19  }),
_19})
_19
_19if (embeddingResponse.status !== 200) {
_19  throw new ApplicationError('Failed to create embedding for question', embeddingResponse)
_19}
_19
_19const {
_19  data: [{ embedding }],
_19} = await embeddingResponse.json()

Perform similarity search

Using the embeddingResponse we can now perform similarity search by performing an remote procedure call (RPC) to the database function we created earlier.


_10const { error: matchError, data: pageSections } = await supabaseClient.rpc(
_10  'match_page_sections',
_10  {
_10    embedding,
_10    match_threshold: 0.78,
_10    match_count: 10,
_10    min_content_length: 50,
_10  }
_10)

Perform text completion request

With the relevant content for the user's question identified, we can now build the prompt and make a text completion request via the OpenAI API.

If successful, the OpenAI API will respond with a text/event-stream response that we can simply forward to the client where we'll process the event stream to smoothly print the answer to the user.


_48const prompt = codeBlock`
_48  ${oneLine`
_48    You are a very enthusiastic Supabase representative who loves
_48    to help people! Given the following sections from the Supabase
_48    documentation, answer the question using only that information,
_48    outputted in markdown format. If you are unsure and the answer
_48    is not explicitly written in the documentation, say
_48    "Sorry, I don't know how to help with that."
_48  `}
_48
_48  Context sections:
_48  ${contextText}
_48
_48  Question: """
_48  ${sanitizedQuery}
_48  """
_48
_48  Answer as markdown (including related code snippets if available):
_48`
_48
_48const completionOptions: CreateCompletionRequest = {
_48  model: 'gpt-3.5-turbo-instruct',
_48  prompt,
_48  max_tokens: 512,
_48  temperature: 0,
_48  stream: true,
_48}
_48
_48const response = await fetch('https://api.openai.com/v1/completions', {
_48  method: 'POST',
_48  headers: {
_48    Authorization: `Bearer ${openAiKey}`,
_48    'Content-Type': 'application/json',
_48  },
_48  body: JSON.stringify(completionOptions),
_48})
_48
_48if (!response.ok) {
_48  const error = await response.json()
_48  throw new ApplicationError('Failed to generate completion', error)
_48}
_48
_48// Proxy the streamed SSE response from OpenAI
_48return new Response(response.body, {
_48  headers: {
_48    'Content-Type': 'text/event-stream',
_48  },
_48})

Display the answer on the frontend

In a last step, we need to process the event stream from the OpenAI API and print the answer to the user. The full code for this can be found on GitHub.


_62const handleConfirm = React.useCallback(
_62  async (query: string) => {
_62    setAnswer(undefined)
_62    setQuestion(query)
_62    setSearch('')
_62    dispatchPromptData({ index: promptIndex, answer: undefined, query })
_62    setHasError(false)
_62    setIsLoading(true)
_62
_62    const eventSource = new SSE(`api/vector-search`, {
_62      headers: {
_62        apikey: process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY ?? '',
_62        Authorization: `Bearer ${process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY}`,
_62        'Content-Type': 'application/json',
_62      },
_62      payload: JSON.stringify({ query }),
_62    })
_62
_62    function handleError<T>(err: T) {
_62      setIsLoading(false)
_62      setHasError(true)
_62      console.error(err)
_62    }
_62
_62    eventSource.addEventListener('error', handleError)
_62    eventSource.addEventListener('message', (e: any) => {
_62      try {
_62        setIsLoading(false)
_62
_62        if (e.data === '[DONE]') {
_62          setPromptIndex((x) => {
_62            return x + 1
_62          })
_62          return
_62        }
_62
_62        const completionResponse: CreateCompletionResponse = JSON.parse(e.data)
_62        const text = completionResponse.choices[0].text
_62
_62        setAnswer((answer) => {
_62          const currentAnswer = answer ?? ''
_62
_62          dispatchPromptData({
_62            index: promptIndex,
_62            answer: currentAnswer + text,
_62          })
_62
_62          return (answer ?? '') + text
_62        })
_62      } catch (err) {
_62        handleError(err)
_62      }
_62    })
_62
_62    eventSource.stream()
_62
_62    eventSourceRef.current = eventSource
_62
_62    setIsLoading(true)
_62  },
_62  [promptIndex, promptData]
_62)

Learn more

Want to learn more about the awesome tech that is powering this?

Read about how we built ChatGPT for the Supabase Docs.
Read the pgvector Docs for Embeddings and vector similarity
Watch Greg's video for a full breakdown:

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