API 文档：快速开始

基础 URL 和认证

向我们的特定基础 URL 发送请求。在 Authorization header。无效密钥会立即返回 401 错误。

聊天完成端点

使用标准的聊天补全端点进行文本生成。将您的模型 ID 作为 uncensored系统接受用于内容控制的标准参数。

以下是开始使用的基本请求：

Copycurl https://api.deepseekapiprice.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Check the current rate for deepseek r1 per token."}]
  }'

可用参数和设置

使用标准参数控制输出风格。设置 temperature 用于随机性和 top_p 用于核采样。您还可以定义 stop 序列或强制执行 JSON 模式。这些设置允许对无审查 LLM 行为进行精确控制。

示例 Python 集成：

Copyfrom openai import OpenAI

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

Copyimport OpenAI from "openai";

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

流式传输

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)