Vector search with Next.js and OpenAI

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:

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

You can read our Zuvo 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 Zuvo CLI and then push our local database changes to our hosted Zuvo project. You can find the full Next.js example on GitHub.

Create a project

1. Create a new project in the Zuvo Studio.
2. Enter your project details.
3. Wait for the new database to launch.

Prepare the database

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.

    supabase init

      supabase migration new init

      -- Enable pgvector extension
      create extension if not exists vector with schema public;

    -- Stores the checksum of our pages.
    -- This ensures that we only regenerate embeddings
    -- when the page content has changed.
    create table "public"."nods_page" (
      id bigserial primary key,
      parent_page_id bigint references public.nods_page,
      path text not null unique,
      checksum text,
      meta jsonb,
      type text,
      source text
    );

    -- Grant the privileges the roles need
    GRANT SELECT ON public.nods_page TO anon;

    alter table "public"."nods_page"
      enable row level security;

    create policy "Allow public read access to nods_page"
      on public.nods_page
      for select
      to anon
      using (true);

    -- Stores the actual embeddings with some metadata
    create table "public"."nods_page_section" (
      id bigserial primary key,
      page_id bigint not null references public.nods_page on delete cascade,
      content text,
      token_count int,
      embedding extensions.vector(1536),
      slug text,
      heading text
    );

    -- Grant the privileges the roles need
    GRANT SELECT ON public.nods_page_section TO anon;

    alter table "public"."nods_page_section"
      enable row level security;

    create policy "Allow public read access to nods_page_section"
      on public.nods_page_section
      for select
      to anon
      using (true);

    -- Create embedding similarity search functions
    create or replace function match_page_sections(
        embedding extensions.vector(1536),
        match_threshold float,
        match_count int,
        min_content_length int
    )
    returns table (
        id bigint,
        page_id bigint,
        slug text,
        heading text,
        content text,
        similarity float
    )
    language plpgsql
    as $$
    #variable_conflict use_variable
    begin
      return query
      select
        nods_page_section.id,
        nods_page_section.page_id,
        nods_page_section.slug,
        nods_page_section.heading,
        nods_page_section.content,
        (nods_page_section.embedding <#> embedding) * -1 as similarity
      from nods_page_section

      -- We only care about sections that have a useful amount of content
      where length(nods_page_section.content) >= min_content_length

      -- The dot product is negative because of a Postgres limitation, so we negate it
      and (nods_page_section.embedding <#> embedding) * -1 > match_threshold

      -- OpenAI embeddings are normalized to length 1, so
      -- cosine similarity and dot product will produce the same results.
      -- Using dot product which can be computed slightly faster.
      --
      -- For the different syntaxes, see https://github.com/pgvector/pgvector
      order by nods_page_section.embedding <#> embedding

      limit match_count;
    end;
    $$;

      supabase start

      supabase link --project-ref=your-project-ref

      supabase db push

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:

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

      NEXT_PUBLIC_SUPABASE_URL=
      NEXT_PUBLIC_SUPABASE_PUBLISHABLE_KEY=
      SUPABASE_SECRET_KEY=

      # Get your key at https://platform.openai.com/account/api-keys
      OPENAI_API_KEY=

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

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.

      const embeddingResponse = await fetch('https://api.openai.com/v1/embeddings', {
        method: 'POST',
        headers: {
          Authorization: `Bearer ${openAiKey}`,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({
          model: 'text-embedding-ada-002',
          input: sanitizedQuery.replaceAll('\n', ' '),
        }),
      })

      if (embeddingResponse.status !== 200) {
        throw new ApplicationError('Failed to create embedding for question', embeddingResponse)
      }

      const {
        data: [{ embedding }],
      } = await embeddingResponse.json()

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

      const prompt = codeBlock`
        ${oneLine`
          You are a very enthusiastic Zuvo representative who loves
          to help people! Given the following sections from the Zuvo
          documentation, answer the question using only that information,
          outputted in markdown format. If you are unsure and the answer
          is not explicitly written in the documentation, say
          "Sorry, I don't know how to help with that."
        `}

        Context sections:
        ${contextText}

        Question: """
        ${sanitizedQuery}
        """

        Answer as markdown (including related code snippets if available):
      `

      const completionOptions: CreateCompletionRequest = {
        model: 'gpt-3.5-turbo-instruct',
        prompt,
        max_tokens: 512,
        temperature: 0,
        stream: true,
      }

      const response = await fetch('https://api.openai.com/v1/completions', {
        method: 'POST',
        headers: {
          Authorization: `Bearer ${openAiKey}`,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify(completionOptions),
      })

      if (!response.ok) {
        const error = await response.json()
        throw new ApplicationError('Failed to generate completion', error)
      }

      // Proxy the streamed SSE response from OpenAI
      return new Response(response.body, {
        headers: {
          'Content-Type': 'text/event-stream',
        },
      })

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.

const handleConfirm = React.useCallback(
  async (query: string) => {
    setAnswer(undefined)
    setQuestion(query)
    setSearch('')
    dispatchPromptData({ index: promptIndex, answer: undefined, query })
    setHasError(false)
    setIsLoading(true)

    const eventSource = new SSE(`api/vector-search`, {
      headers: {
        apikey: process.env.NEXT_PUBLIC_SUPABASE_PUBLISHABLE_KEY ?? '',
        Authorization: `Bearer ${process.env.NEXT_PUBLIC_SUPABASE_PUBLISHABLE_KEY}`,
        'Content-Type': 'application/json',
      },
      payload: JSON.stringify({ query }),
    })

    function handleError<T>(err: T) {
      setIsLoading(false)
      setHasError(true)
      console.error(err)
    }

    eventSource.addEventListener('error', handleError)
    eventSource.addEventListener('message', (e: any) => {
      try {
        setIsLoading(false)

        if (e.data === '[DONE]') {
          setPromptIndex((x) => {
            return x + 1
          })
          return
        }

        const completionResponse: CreateCompletionResponse = JSON.parse(e.data)
        const text = completionResponse.choices[0].text

        setAnswer((answer) => {
          const currentAnswer = answer ?? ''

          dispatchPromptData({
            index: promptIndex,
            answer: currentAnswer + text,
          })

          return (answer ?? '') + text
        })
      } catch (err) {
        handleError(err)
      }
    })

    eventSource.stream()

    eventSourceRef.current = eventSource

    setIsLoading(true)
  },
  [promptIndex, promptData]
)

Learn more

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

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