Integration guide for uncensored models

Base URL and Authentication

Point your client to https://api.unrestrictedaitextgenerator.com/v1 and pass your key in the Authorization header. The system rejects requests without valid credentials, returning a 401 error if the key is missing or incorrect.

First Request

Send a POST request to /v1/chat/completions with the model ID uncensored and your desired messages. The API returns a text response directly, filtering out standard corporate refusals for lawful adult content. Use this endpoint to verify your connectivity before integrating into larger pipelines.

Copycurl https://api.unrestrictedaitextgenerator.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Summarize the latest kernel patch notes"}]
  }'

Python SDK Integration

Configure the OpenAI Python library with the custom base URL and your API key to streamline request handling. This approach allows you to use familiar methods like chat.completions.create() while accessing our uncensored LLM capabilities. The library handles serialization automatically, reducing boilerplate code in your scripts.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.unrestrictedaitextgenerator.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

Initialize the Node.js client with the specific base URL to ensure all requests route through our servers. This setup supports standard promise-based workflows, making it easy to handle responses in asynchronous environments. You can manage tokens and errors using standard JavaScript patterns without custom parsing logic.

Copyimport OpenAI from "openai";

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

Enable streaming by setting stream: true to receive Server-Sent Events (SSE) as tokens are generated. Token usage statistics appear in the final chunk of the stream, allowing you to track costs in real-time. This method reduces perceived latency for high-volume text generation tasks.

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 and Error Handling

Monitor your request rate, as the API enforces a limit of 300 requests per minute per key. If you exceed this threshold, the system returns a 429 error until the window resets. Ensure your prepaid credit is active, as a 402 error indicates insufficient funds. The context window supports 100,000 tokens total, with a maximum output of 16,000 tokens per request.