API Documentation

Base URL and Authentication

Connect to the API by updating your client configuration. Use the base URL https://api.gptchatcc.com/v1 and pass your key in the Authorization header. This standard setup ensures your existing OpenAI SDKs route requests to our uncensored model immediately.

• Base URL: https://api.gptchatcc.com/v1
• Header: Authorization: Bearer YOUR_API_KEY

Your First Request

Send a simple chat completion to verify connectivity. The endpoint accepts standard parameters like model, messages, and max_tokens. Our system returns text responses without the refusals common in other models.

Here is a basic cURL example to test your connection.

Copycurl https://api.gptchatcc.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "What is the capital of France?"}]
  }'

Python SDK Integration

Use the official openai Python package to interact with the API. Change the base_url and api_key in your initialization block. This allows you to keep your existing logic while switching the underlying model.

The following code demonstrates how to initialize the client and send a request.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.gptchatcc.com/v1", api_key="YOUR_KEY")

resp = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Summarise this thread without softening it."}],
)
print(resp.choices[0].message.content)

Node.js SDK Integration

Integrate the API into your JavaScript or TypeScript projects using the openai Node package. Pass your credentials during client creation. The SDK handles serialization and error parsing automatically.

Use this snippet to configure the client in your Node environment.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.gptchatcc.com/v1", apiKey: process.env.API_KEY });

const resp = await client.chat.completions.create({
  model: "uncensored",
  messages: [{ role: "user", content: "Draft a villain monologue for my game." }],
});
console.log(resp.choices[0].message.content);

Streaming Responses

Enable real-time token delivery by setting stream: true. The API returns Server-Sent Events (SSE) for low-latency applications. Parse the chunks to display text as it generates, improving the user experience for long conversations.

This example shows how to handle streaming responses in Python.

Copystream = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Tell the story in second person."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Rate Limits and Constraints

Monitor your usage to avoid service interruptions. You are limited to 300 requests per minute and 8 concurrent requests per key. The context window supports 100,000 tokens total, with a maximum output of 16,000 tokens. Errors return 401 for invalid keys, 402 for insufficient credit, and 429 for rate limits.