Ollama apis: Quickstart Guide

Base URL and Authentication

The endpoint lives at https://api.ollamaapis.com/v1. You authenticate by sending your unique API key in the Authorization header. This setup works with any OpenAI-compatible SDK because we mimic the standard interface. You do not need to install proprietary libraries or manage complex routing logic.

Sending Your First Completion

Send a standard chat completion request to the /v1/chat/completions endpoint. The model identifier is uncensored. You can adjust parameters like temperature and top_p to control creativity. This direct approach avoids the abstraction layers that many developers find tedious.

Copycurl https://api.ollamaapis.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Get me the list of available models"}]
  }'

Python SDK Integration

Use the official openai Python package to interact with the API. Set the base_url to our endpoint and provide your key. This allows you to keep your existing Python code structure while switching the underlying engine. It is the easiest way to start building with this uncensored AI.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.ollamaapis.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 OpenAI client in Node.js by overriding the base URL. This method ensures compatibility with your current JavaScript or TypeScript projects. You can pass messages and settings just like you would for any other provider. The response structure remains identical to the standard.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.ollamaapis.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 via SSE

Enable streaming by setting stream: true in your request. The API returns Server-Sent Events (SSE) containing partial tokens. You can process these chunks in real-time for a smoother user experience. Token usage details appear in the final chunk of the stream.

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

The context window supports 100,000 tokens total. You are limited to 300 requests per minute and 8 concurrent requests per key. A 401 error means your key is invalid. A 402 error indicates insufficient prepaid credit. A 429 error signals you have hit the rate limit. These constraints ensure stable performance for all users.