This is a proof of concept project showing how to use Vectors in Next.js.

It's primarily a frontend repo with two endpoints for generating embeddings using the OpenAI API.

For a real life application we'd want to connect to a Postgres database with the PGVector extension to store and query these vectors.

Alternatively we could connect to a purpose built "vector database" like Pinecone or Weaviate.

You'll need an OpenAI API key added to an .env file. You'll also need to fund your account so you can make requests. I spent $0.14 developing this application 💰

cp .env.example .env

Once complete run the development server:

npm run dev

Open http://localhost:3000 with your browser to see the result.

There is a "Build Vectors" button. First click that. It will send a request to /api/build-vectors, defined in app/api/build-vectors/route.ts.

This will create a vectors for the ten static data items. You can then type a search term and click "Search".

Performing a search will send an API request to the backend at /api/build-vector (defined in app/api/build-vector/route.ts) to create an embedding for the search term.

Once the embedding for the search term is complete we're doing a poor man's vector search in the browser.

For a real application we'd likely want to do SQL or vector queries to return the results.

In order to store vectors you can add a column and index to an existing table:

ALTER TABLE your_table
ADD COLUMN embedding vector(1536);

CREATE INDEX ON your_table USING hnsw (embedding vector_cosine_ops) 
WITH (m = 16, ef_construction = 64);

To understand why this is vector length of 1536 and hnsw please ask AI. You may want to use a different index for your use case. 1536 corresponds to the text-embedding-ada-002 embeddings OpenAI creates for us.

To create an embedding using the OpenAI API. This is in the code like:

async function generateOpenAIEmbeddings(someObject) {
        // convert the object to a string
        const textToEmbed = Object.values(someObject).join(' ');

        // create an embedding from the string using the OpenAI API
        const response = await openai.embeddings.create({
            model: 'text-embedding-ada-002',
            input: textToEmbed,
        });

        return response.data[0].embedding;
    }

Creating the embeddings can be done using Node.js, Python, PHP or your language of choice.

To store a vector embedding in the database using SQL we could do:

UPDATE <some_table> SET embedding = (ARRAY[
                -0.0029228285,
                -0.030264609,
                /* ... rest of the embedding */
    ]::vector(1536))
WHERE id = <some_id_to_update>;

Then to query for a search term you'd want to create an embedding for the search term then run a query like:

SELECT some_table.*, embedding <=> (ARRAY[
                -0.0066249488,
                -0.008707857,
                /* ... rest of the embedding */
    ]::vector(1536)) AS distance
FROM <some_table>
ORDER BY distance asc nulls last
LIMIT 100