OpenAI SDK Usage

BVE Gateway is fully compatible with the official OpenAI SDK. The only change is setting baseURL to https://api.bve.me/v1.

TypeScript / Node.js

Install the SDK:

npm install openai

Chat completions

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-bve-YOUR_KEY',
  baseURL: 'https://api.bve.me/v1',
});

const response = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is 2 + 2?' },
  ],
});

console.log(response.choices[0].message.content);

Streaming

const stream = await client.chat.completions.stream({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Count from 1 to 5.' }],
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '');
}

Embeddings

const response = await client.embeddings.create({
  model: 'text-embedding-3-small',
  input: 'The quick brown fox',
});

console.log(response.data[0].embedding);

List models

const models = await client.models.list();
for (const model of models.data) {
  console.log(model.id);
}

Error handling

try {
  const response = await client.chat.completions.create({
    model: 'gpt-4o',
    messages: [{ role: 'user', content: 'Hello' }],
  });
} catch (err) {
  if (err instanceof OpenAI.APIError) {
    console.error(err.status, err.message);
    // err.error?.code for the machine-readable code (e.g. 'rate_limit_exceeded')
  }
}

Python

Install the SDK:

pip install openai

Chat completions

from openai import OpenAI

client = OpenAI(
    api_key="sk-bve-YOUR_KEY",
    base_url="https://api.bve.me/v1",
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is 2 + 2?"},
    ],
)

print(response.choices[0].message.content)

Streaming

with client.chat.completions.stream(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Count from 1 to 5."}],
) as stream:
    for chunk in stream:
        print(chunk.choices[0].delta.content or "", end="", flush=True)

Embeddings

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="The quick brown fox",
)

print(response.data[0].embedding[:5])

List models

models = client.models.list()
for model in models.data:
    print(model.id)

Error handling

from openai import APIError

try:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello"}],
    )
except APIError as e:
    print(e.status_code, e.message)
    # e.code for machine-readable code (e.g. 'rate_limit_exceeded')

Environment variable pattern

Avoid hardcoding keys. Use environment variables:

// TypeScript
const client = new OpenAI({
  apiKey: process.env.BVE_API_KEY,
  baseURL: 'https://api.bve.me/v1',
});

# Python
import os

client = OpenAI(
    api_key=os.environ["BVE_API_KEY"],
    base_url="https://api.bve.me/v1",
)

See also

• Chat Completions — full request/response reference
• Streaming — SSE streaming details
• Errors — all error codes
• Security Notes — keep your keys safe