Quickstart: Integrate the Uncensored Model

Authentication & Base URL

Connect your client to our API using the standard base URL and an API key. Your requests authenticate via the Authorization header with a Bearer token. The base URL is https://api.bestuncensoredaimodel.com/v1. This ensures compatibility with existing OpenAI SDKs and tools. Generate your key on the dashboard and keep it secure.

Sending Requests: Chat Completions

Send text in and get text out via the chat completions endpoint. Use the model ID uncensored for unrestricted responses. Your prompts stay yours without hidden training. Set your system instructions to define the tone. The API handles the rest. See the example below.

Copycurl https://api.bestuncensoredaimodel.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Summit starts at 6am tomorrow."}]
  }'

Python SDK Integration

Use the official OpenAI Python library to interact with the API. Configure the base URL and API key in the client initialization. This approach requires minimal boilerplate. You can send messages and receive structured outputs. It works seamlessly with our uncensored model. Here is the Python implementation.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.bestuncensoredaimodel.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 SDK Integration

Integrate using the JavaScript SDK for server-side applications. Import the client and set the environment variables. This method supports modern async patterns. You can handle errors and responses efficiently. The setup mirrors the Python approach. See the Node.js code below.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.bestuncensoredaimodel.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 & Token Usage

Enable streaming to receive tokens as they generate. This reduces perceived latency for long outputs. The API uses Server-Sent Events (SSE) for streaming. Token usage details appear in the final chunk. This is ideal for real-time user interfaces. Check the streaming example.

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)

Limits, Errors, and Context Window

Monitor your usage against strict rate limits. You get 300 requests per minute and 8 concurrent requests. The context window supports 100,000 tokens total. Errors like 401 indicate an invalid key. A 402 error means you need more credit. Handle 429s with exponential backoff. Your prepaid credit never expires.