Integration Guide

This guide integrates Qevron into your application from scratch, step by step: from setup to your first call, then to a production-ready configuration. You can follow it without skipping any step.

Prerequisites

Before you start, you should have:

Requirement	Description
Qevron API key	A key starting with sk-. If you don't have one, create it from Authentication.
A programming language	This guide has Python and JavaScript examples. You can also follow along language-agnostically with curl.
Basic terminal knowledge	Running commands, setting environment variables.

The base URL is the same in every example: https://app.qevron.ai/v1

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Step 1 — Get and store the key securely

An API key is like a password. Never put it in source code; store it in an environment variable.

macOS / Linux

export QEVRON_API_KEY="sk-..."

Windows (PowerShell)

$Env:QEVRON_API_KEY = "sk-..."

.env file

# .env  (add this file to .gitignore!)
QEVRON_API_KEY=sk-...

WARNING

Don't put the key on the client side (browser/mobile app) — anyone can read it. Always proxy requests through your own server (see Step 7).

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Step 2 — Install the SDK

Because Qevron is OpenAI-compatible, you use the official openai SDK.

Python

pip install openai

JavaScript

npm install openai

If you use curl, no installation is needed.

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Step 3 — Configure the client

The only difference is base_url: point it at Qevron instead of OpenAI.

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["QEVRON_API_KEY"],
    base_url="https://app.qevron.ai/v1",
)

JavaScript

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.QEVRON_API_KEY,
  baseURL: "https://app.qevron.ai/v1",
});

---

Step 4 — First call and reading the response

Python

resp = client.chat.completions.create(
    model="verinova",
    messages=[{"role": "user", "content": "Hello! Introduce yourself in one sentence."}],
)
print(resp.choices[0].message.content)   # reply text
print(resp.usage.total_tokens)           # tokens spent

JavaScript

const resp = await client.chat.completions.create({
  model: "verinova",
  messages: [{ role: "user", content: "Hello! Introduce yourself in one sentence." }],
});
console.log(resp.choices[0].message.content);
console.log(resp.usage.total_tokens);

curl

curl https://app.qevron.ai/v1/chat/completions \
  -H "Authorization: Bearer $QEVRON_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"verinova","messages":[{"role":"user","content":"Hello!"}]}'

The reply text is in choices[0].message.content, usage info is in usage. To see which models are available, use /v1/models.

---

Step 5 — Add streaming

For chat UIs, show the response word by word:

Python

stream = client.chat.completions.create(
    model="verinova",
    messages=[{"role": "user", "content": "Explain RAG in three bullet points."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

JavaScript

const stream = await client.chat.completions.create({
  model: "verinova",
  messages: [{ role: "user", content: "Explain RAG in three bullet points." }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0].delta.content ?? "");
}

Details: Streaming.

---

Step 6 — Error handling and retries

Qevron does automatic failover at the channel level (if one provider goes down it switches to another). Still, it's good practice to retry transient errors (429, 5xx) on the client side with exponential backoff.

import time
from openai import OpenAI, APIStatusError, APIConnectionError

client = OpenAI(api_key=os.environ["QEVRON_API_KEY"], base_url="https://app.qevron.ai/v1")

def chat_with_retry(messages, model="verinova", max_retries=4):
    for attempt in range(max_retries):
        try:
            return client.chat.completions.create(model=model, messages=messages)
        except APIStatusError as e:
            # 429 (rate limit) and 5xx are retryable; 4xx (e.g. 401/404) are not
            if e.status_code in (429, 500, 502, 503) and attempt < max_retries - 1:
                time.sleep(2 ** attempt)   # 1s, 2s, 4s, 8s
                continue
            raise
        except APIConnectionError:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
                continue
            raise

print(chat_with_retry([{"role": "user", "content": "hi"}]).choices[0].message.content)

Full list of error codes: Errors.

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Step 7 — Production checklist

Before going live:

• [ ] Keep the key on the server. Don't call directly from browser/mobile; proxy the request through your own backend.
• [ ] Secret management. Store the key in an environment variable / secret manager, never commit it.
• [ ] Make model selection dynamic. Instead of hardcoding model names, verify available ones with /v1/models.
• [ ] Monitor rate limits. Track the X-RateLimit-* values in response headers and slow down if needed (see API Keys & Quota).
• [ ] Add retry logic (Step 6).
• [ ] Track usage. Monitor cost with the usage field in each response or the billing endpoints.
• [ ] Set timeouts. Give a reasonable timeout for long requests (image/video).

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For AI agents and tools

You can reach all of this documentation as raw markdown, without rendering — to feed it to an AI assistant or IDE:

# A single page: append .md to the URL
curl https://docs.qevron.ai/en/api/chat.md

# All docs in one file
curl https://docs.qevron.ai/llms-full.txt

Details: API Overview — Raw markdown access.

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Next steps

First Chatbot

Build a streaming bot that remembers history.

RAG: Embedding + Rerank

Accurate answers with your own documents.

API Reference

All endpoints and real schemas.

OpenAI Compatibility

Move your existing code to Qevron.