Using OpenAI with JavaScript

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When trying to find my way around in the buzzing lands of OpenAI and vector databases, the dots were not always easy to connect. In this guide I’m sharing what I’ve learned during my journey to make yours even better. You might find a trick or a treat!

Most of OpenAI tooling and examples is based on Python, but this guide uses JavaScript exclusively.

We’ll begin with a brief explanation of some core concepts, before diving into more and more code. Towards the finish we’ll discuss some strategies for token management and maintaining a conversation.


Here are the topics we will be discussing:

OpenAI endpoints

In this guide, we will work with two OpenAI REST endpoints.

Chat Completions


The Create chat completion endpoint generates a human-like text completion for a provided prompt. We’ll use it to start and keep the conversation going between the end-user and OpenAI’s Large Language Models (LLMs) such as GPT-3.5 and GPT-4.

Create Embeddings


With the embeddings endpoint, we can create embeddings from plain text. We will use these embeddings to store and query a vector database. Embeddings? Vector database? No worries, we have you covered.

The openai package

We’re going to use these endpoints directly, and not OpenAI’s npm package. This package targets Node.js, but eventually you might want to deploy your own endpoint on an environment without Node.js, such as a serverless or edge platform like Cloudlare Workers, Netlify Edge or Deno. Now that fetch is ubiquitous I think the REST APIs are just as easy to use without any dependencies. I like being “closer to the metal” and stay flexible.

Key concepts

We’ve already introduced a few concepts that may be new to you. Let’s discuss embeddings, vector databases and prompts briefly before diving into any code.

If you’re familiar with them, feel free to skip straight to ingestion.


Vector embeddings are numerical representations of textual data in a high-dimensional space. They are generated using large language models (LLMs). Embeddings allow for efficient storage and search of content that is semantically related to a user’s query. Semantically similar text is mapped close together in the vector space, and we can find relevant content using a vector embedding created from user input.

For comparison, a lexical or “full text” search looks for literal matches of the query words and phrases, without understanding the overall meaning of the query.

Vector databases

Why do we need a vector database? Can’t we just query OpenAI and get a response?

Yes, we can use the ChatGPT UI or even the OpenAI chat completions endpoint directly. However, the response will be limited to what the OpenAI models are trained on. The response may not be up-to-date, accurate, or specific enough for your needs.

What if you want to have OpenAI generate responses based solely on your own domain-specific content? For users to “chat with your content”. Sounds interesting! But how to go about this?

Unlike ChatGPT, the OpenAI APIs are not storing any of your content and they do not store state or a session of the conversation(s). This is where vector databases come in. Adding a vector database in the mix has interesting advantages:

Setting up a vector database might be easier than you think. I’ve been trying out managed solutions like Pinecone and Supabase without any issues. There are more options though, and I don’t feel like I’m in a position to recommend one over another. I do like that I can use Pinecone without dependencies using only fetch and their REST API.


A prompt is the textual input we send to the chat completions endpoint to have it generate a relevant “completion”. You could say a prompt is a question, and a completion is an answer.

Prompts are plain text and we can provide extra details and information to improve the results. The more context we provide, the better the response will be.

Requests to the chat completions endpoint are essentially stateless: not your content, no session, no state. The challenge is to optimize and include the right information with each request. We’ll be discussing prompts throughout this guide, and ways to optimize them.


Armed with this knowledge, let’s begin building a chat application with a vector database.

We’ll need to get content into this database. Content is stored as vector embeddings, and we can create those from textual content by using the embeddings endpoint.


Before creating the database table or index, it’s important to consider what we will do with the results of semantic search queries.

Vector embeddings are a compressed representation of semantics for efficient storage and querying. It’s not possible to translate them back to the original text. This is the reason we need to store the original text along with the embeddings in the database.

The text can be stored as metadata and can include more useful things to display in the application, such as document or section titles and URL’s to link back to the original source.


There are tools that can help with this. I have seen a few solutions that offer easy content ingestion, but you don’t have much freedom such as to choose where the content will be stored:


As I wanted to start out with command-line tools and learn more about the OpenAI APIs, embeddings and vector database, I decided to develop a tool myself.

This work ended up as 7-docs and comes with the 7d command-line tool to ingest content from plain text, Markdown and PDF files into a vector database. It ingests content from local files, GitHub repositories and also HTML from public websites. Currently it supports “upserting” vectors into Pinecone indexes and Supabase tables.

To get an idea what ingestion using 7d looks like, here are some examples that demonstrate how to ingest Markdown files:

Terminal window
7d ingest --files '*.md' --db pinecone --namespace my-docs
Terminal window
7d ingest --source github --repo reactjs/ \


When the embeddings and metadata are in the database, we can query it. We’ll look at some example code to implement this 4-step strategy:

  1. Create a vector embedding from the user’s textual input.
  2. Query the database with this vector for related chunks of content.
  3. Build the prompt from the search results and the user’s input.
  4. Ask the model to generate a chat completion based on this prompt.

The next examples show working code, but contain no error handling or optimizations. Just plain JavaScript without dependencies.

(Don’t want to implement this yourself, or just want to see examples? Visit 7-docs for available demos and starterkits to hit the ground running.)

1. Create a vector embedding

The first function we’ll need creates a vector embedding based on the user’s input:

export const createEmbeddings = async ({ token, model, input }) => {
const response = await fetch('', {
headers: {
'Content-Type': 'application/json',
Authorization: `Bearer ${token}`,
method: 'POST',
body: JSON.stringify({ input, model }),
const { error, data, usage } = await response.json();
return data;

This function can be called like this:

const vector = await createEmbeddings({
token: '[OPENAI_API_TOKEN]',
model: 'text-embedding-ada-002',
input: 'What is an embedding?',

2. Query the database

In the second step we are going to query the database with the vector embedding we just created. Below is an example that queries a Pinecone index for vectors with related content using fetch. The rows returned from this query are mapped to the metadata that’s stored with the vector in the same row. We need this metadata in the next step.

export const query = async ({ token, vector, namespace }) => {
const response = await fetch('https://[my-index]', {
headers: {
'Content-Type': 'application/json',
'Api-Key': token,
method: 'POST',
body: JSON.stringify({
topK: 10,
includeMetadata: true,
const data = await response.json();
return => match.metadata);

This query function can be invoked with the vector we received from createEmbeddings() like so:

const metadata = await query({
token: '[PINECONE_API_KEY]',
vector: vector, // Here's the vector we received from `createEmbeddings()`
namespace: 'my-knowledge-base',

3. Build the prompt

The third step builds the prompt. There are multiple ways to go about this and the content of the template probably requires customization on your end, but here is an example:

const template = `Answer the question as truthfully and accurately as possible using the provided context.
If the answer is not contained within the text below, say "Sorry, I don't have that information.".
Context: {CONTEXT}
Question: {QUERY}
Answer: `;
const getPrompt = (context, query) => {
return template.replace('{CONTEXT}', context).replace('{QUERY}', query);

And here is how we can create the prompt with context from the metadata returned from the database query:

// Create a concatenated string from search results metadata
const context = => metadata.content).join(' ');
// Build the complete prompt including the context and the question
const prompt = getPrompt(context, 'What is an embedding?');

Later in this guide, we will also look at example code to maintain a conversation instead of merely asking one-shot questions.

4. Generate chat completion

We are ready for the last step: ask the model for a chat completion with our prompt. Here’s an example function to call this endpoint:

export const chatCompletions = async ({ token, body }) => {
const response = await fetch('', {
method: 'POST',
headers: {
Authorization: `Bearer ${token}`,
'Content-Type': 'application/json',
body: JSON.stringify(body),
return response;

And here’s how to make the request with the prompt:

const messages = [];
role: 'user',
content: prompt, // This is the `prompt` we received from `getPrompt()`
const response = await chatCompletions({
token: '[OPENAI_API_TOKEN]',
body: {
model: 'gpt-3.5-turbo',
const data = await response.json();
const text = data.choices[0].message.content;

The text contains the human-readable answer from OpenAI.

Excellent, this is the essence of generating chat completions based on your own vector database. Now, how do we combine these four steps and integrate them into a user interface? You can create a function that abstracts this away, or use the @7-docs/edge package to do this for you. Keep reading to see an example.

In the next part of this guide, we will explore a UI component featuring a basic form for users to submit their queries. This component will also render the streaming response generated by the function in the next section.

User Interface

Let’s put our 4-step strategy into action and build function and form.

(Don’t want to implement this yourself, or just want to see examples? Visit 7-docs for available demos and starterkits to hit the ground running.)


The /api/completion endpoint will listen to incoming requests and respond using all of the query logic from the previous section.

We’re going to use the @7-docs/edge package, which abstracts away the 4-step strategy and some boring boilerplate. We need to pass the OPENAI_API_KEY and a query function from a database adapter, Pinecone in this example. We pass it to getCompletionHandler so it can query the database when it needs to. We would pass a different function if we wanted to used a different type of database (like Supabase or Milvus).

Let’s bring this together in a serverless or edge function handler in just a few lines of code:

import { getCompletionHandler, pinecone } from '@7-docs/edge';
import { createClient } from '@supabase/supabase-js';
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const PINECONE_URL = process.env.PINECONE_URL;
const namespace = 'my-knowledge-base';
const query: QueryFn = (vector: number[]) =>
export default getCompletionHandler({ OPENAI_API_KEY, query });

This pattern can be used anywhere from traditional servers to edge functions, since there are no dependencies on modules only available in Node.js.


Now we still need a UI component to render an input field, send the input to the /api/completion endpoint, and render the streaming response.

This minimal example uses a little React and JSX for an easy read, but it could just as well be plain JavaScript or any other framework.

import { useState } from 'react';
export default function Page() {
const [query, setQuery] = useState('');
const [outputStream, setOutputStream] = useState('');
function startStream(query) {
const searchParams = new URLSearchParams();
searchParams.set('query', encodeURIComponent(query));
searchParams.set('embedding_model', 'text-embedding-ada-002');
searchParams.set('completion_model', 'gpt-3.5-turbo');
const url = '/api/completion?' + searchParams.toString();
const source = new EventSource(url);
source.addEventListener('message', event => {
if ( === '[DONE]') {
} else {
const data = JSON.parse(;
const text = data.choices[0].delta.content;
if (text) setOutputStream(v => v + text);
const onSubmit = event => {
if (event) event.preventDefault();
return (
<form onSubmit={onSubmit}>
How can I help you?
onChange={event => setQuery(}
<input type="submit" value="Send" />

Now all the components in a “chat with your content” have come together:

The following sections will build on to make everything even more interesting!


To start a chat, we’ve seen how to build a basic prompt. This is good enough for one-shot questions, but we need more to build a meaningful conversation. The chat completions endpoint accepts an array of messages, so a pattern to fill this array could look like this:

  1. Add a system message that explains the model (i.e. the assistant) how to behave and respond.
  2. Add the conversation history with user and assistant messages.
  3. Add the user prompt, containing the context and the query.

Here is an example building on the initial prompt example that extends the messages array to build the conversation:

// Create a concatenated string from search results metadata from step 2: query the database
const context = => metadata.content).join(' ');
const system = `Answer the question as truthfully as possible using the provided context.
If the answer is not contained within the text below, say "Sorry, I don't have that information.".`;
// In a real application, the conversation `history` can be sent
// with every request from the client, or by using some kind of storage.
const history = [
['What is an embedding?', 'An embedding is...'],
['Can you give an example?', 'Here is an example...'],
const prompt = getPrompt(context, 'Can I restore the original text?');
const messages = [];
role: 'system',
content: system,
history.forEach(([question, answer]) => {
role: 'user',
content: question,
role: 'assistant',
content: answer,
role: 'user',
content: prompt,
const response = await chatCompletions({
token: '[OPENAI_API_TOKEN]',
model: 'gpt-3.5-turbo',

The actual history can come from the client. For instance, this could be stored in UI component state, or browser session storage. In that case, it will need to be sent with every request to the function. Other ways of storing and retrieving the conversation history is outside the scope of this guide.

See the starter kits for examples to handle this in the user interface in tandem with the @7-docs/edge package.


Tokens (not characters) are the unit used by OpenAI for limits and usage. There are limits to the number of tokens that can be sent to and received from the API endpoints with each request.


The maximum number of input tokens to create embeddings with the text-embedding-ada-002 model is 8191.

The price is $ 0.0004 per 1k tokens, which comes down to a maximum of $ 0.0032 per request when sending 8k tokens. That’s roughly 6.000 words that can be sent at once to create vector embeddings. We can send as many requests as we want.

During content ingestion you may need this endpoint for a short period in bursts, depending on the amount of content. Remember that we also need it to create an embedding from the user’s input to query the vector database. Depending on the user’s input this request is usually smaller, but may occur frequently for a longer period depending on application traffic.

Chat completions

For the chat completions endpoint, the max_tokens value represents the number of tokens the model is allowed to use when generating the completion. The models have their own limit (context length) and pricing:

ModelContext Length$/1k prompt$/1k completion
gpt-3.5-turbo4.096$ 0.002$ 0.002
gpt-48.192$ 0.03$ 0.06
gpt-4-32k32.768$ 0.06$ 0.12

The sum of the tokens for the prompt plus the max_tokens for completion cannot exceed the model’s context length. For gpt-3.5-turbo this means:

num_tokens(prompt) + max_tokens <= 4096

To see what this means in practice, we’ll discuss tokenization first and then look at an example calculation.


The number of tokens for a given text can be calculated using a tokenizer (such as GPT-3-Encoder). Tokenization can be slow on larger chunks, and npm packages for Node.js may not work in other environments such as the browser or Deno.

The alternative is to make an estimate: use 4 characters per token or 0.75 words per token. That’s 75 words per 100 tokens. This is a very rough estimate for the English language and varies per language. You should probably also add a small safety margin to stay within the limits and prevent erors.

OpenAI provides an online Tokenizer. For Python there’s tiktoken.


Let’s say you’re using the gpt-3.5-turbo model. If you want to preserve 25% for the completion, use max_tokens: 1024. The rest of the model’s context can be occupied by the prompt. That’s 3072 tokens (4096-1024), which comes down to an estimated 2304 words (3072*0.75) or 12.288 characters (3072*4).

The length of the prompt is the combined length of all content in the messages (i.e. the combined messages of the system, user and assistant roles in Conversation).

If the prompt has the maximum length and the model would use all completion tokens, using 4096 tokens would cost $ 0.008 (4*$0.002).

Using the gpt-4 model, the same roundtrip would cost $ 0.15 (3*$0.03 for the prompt + 1*$0.06 for the completion).


To optimize for your end-user, you’ll need to find the right balance between input (prompt) and output (completion).

When adding context and conversation history to the chat completion request it may become a challenge to keep everything within the model’s limit. More context and more conversation history (input) means less room for the completion (output).

There are a few ways I can think of to help mitigate this:

Another thing to consider is the amount of context to send with the prompt. This context comes from the semantic search results when querying the vector database. You may want to create smaller vector embeddings during ingestion to eventually have more options and wiggle room when building the context for the chat completion. On the other hand, including smaller but more varied pieces of context may result in less “focused” completions.

Overall, I think what matters most is to not lose the first and last question throughout the conversation. Keep in mind that the model does not store state or session.


When using OpenAI endpoints, the token usage for the request is included in the response (with separate prompt_tokens and completion_tokens). Unfortunately, usage is not included for streaming chat completion responses (stream: true).


A quick overview of some common parameters you may want to tweak for better chat completions.


The temperature parameter is a number between 0 and 2 (default: 1). A low number like 0.2 makes the output more focused and deterministic. You want this when the output should be generated based on the context sent within the prompt. A higher value like 0.8 makes the output more random.

presence_penalty and frequency_penalty

A number between -2 and 2 to decrease or increase the presence and frequency of tokens. The default value is 0 and this is fine for most situations. If you want to reduce repetition, try numbers between 0.1 and 1. Negative numbers increase the likelihood of repetition.


As we’ve seen when creating the messages array, each message is assigned a role (system, user or assistant). You can make the conversation more personal and send a name with each message.

Markdown & code blocks

If you ingest Markdown content, you likely also want the completion to include Markdown and code blocks when relevant. Here’s a list of things to remember during ingestion and building the client application:



Next steps

After figuring out how to connect the dots, it’s exciting to tinker and continue the journey to improve the user experience. Here are a few pointers that may inspire you:

Closing remarks

We’ve explored many aspects of using OpenAI with JavaScript to create useful applications. We’ve covered everything from ingesting content to building a user interface with your own serverless or edge function. Hopefully, this guide is helpful in your own journey. Good luck!

I would love to hear about your thoughts and what you are building, please share with me on Twitter!

Special thanks goes out to Enis Bayramoğlu for a great review.