Dolphin ai chat API Quickstart Guide

SDK Installation & Configuration

Start by installing the official client library for your language. Our API uses the standard OpenAI package structure, so you likely already have it. Configure the base URL to point to our endpoint and supply your API key.

Use the environment variable method to keep credentials secure. The base URL is HTTPS://api.dolphinaichat.com/v1. This works with any OpenAI-compatible client by changing base_url and the API key.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.dolphinaichat.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)

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.dolphinaichat.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);

Making Your First POST Request

Send a simple chat completion request to test connectivity. Use the model ID "uncensored" for the open-weight model tuned for unrestricted responses.

Copycurl https://api.dolphinaichat.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Summit the daily server load statistics for the last week"}]
  }'

Handling Streaming Responses (SSE)

Enable streaming to receive tokens in real-time. The API supports Server-Sent Events (SSE) for progressive output. 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)

Understanding Context Windows & Tokens

The model supports a 100,000 token context window for prompt and completion combined. Max output is 16,000 tokens per request. Default max_tokens is 2,048 if not set. Pricing is $0.15 per 1M input tokens and $0.50 per 1M output tokens.

Error Codes & Rate Limiting

Handle standard HTTP errors gracefully. 401 indicates an invalid key. 402 means you have no credit. 429 signals rate limiting. Limits are 300 requests per minute per key and 8 concurrent requests. Request body max is 8 MB.

JSON Mode & Function Calling Setup

Use response_format: {"type": "json_object"} for structured outputs. Enable function calling with tools and tool_choice. Parameters like temperature and top_p are fully supported.