Chat Completions - PIPELLM Docs

认证和模型列表请参考概述。

端点

POST https://api.pipellm.ai/v1/chat/completions

这条 free route 只接受 OpenAI 兼容格式请求，并只路由到 OpenAI 兼容平台。如果你想保留 OpenAI 格式去调用 Anthropic 或 Gemini 模型，请使用 OpenAI Format Converter。

代码示例

cURL
Python
Node.js
Go

curl https://api.pipellm.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $PIPELLM_API_KEY" \
  -d '{
    "model": "gpt-4o",
    "max_completion_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": "Why is the sky blue?"
      }
    ]
  }'

import os
from openai import OpenAI

client = OpenAI(
  api_key=os.getenv('PIPELLM_API_KEY'),
  base_url='https://api.pipellm.ai/v1'
)

response = client.chat.completions.create(
  model='gpt-4o',
  messages=[
    {
      'role': 'user',
      'content': 'Why is the sky blue?'
    }
  ]
)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.PIPELLM_API_KEY,
  baseURL: 'https://api.pipellm.ai/v1'
});

const response = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [
    {
      role: 'user',
      content: 'Why is the sky blue?'
    }
  ]
});

package main

import (
  "context"
  "os"
  openai "github.com/sashabaranov/go-openai"
)

func main() {
  config := openai.DefaultConfig(os.Getenv("PIPELLM_API_KEY"))
  config.BaseURL = "https://api.pipellm.ai/v1"
  client := openai.NewClientWithConfig(config)

  resp, _ := client.CreateChatCompletion(
    context.Background(),
    openai.ChatCompletionRequest{
      Model: "gpt-4o",
      Messages: []openai.ChatCompletionMessage{
        {
          Role:    openai.ChatMessageRoleUser,
          Content: "Why is the sky blue?",
        },
      },
    },
  )
}

请求参数

参数	类型	必需	描述
`model`	string	是	模型 ID（如 `gpt-4o`、`grok-2`）
`messages`	array	是	消息对象数组
`max_tokens`	integer	否	最大生成 token 数
`temperature`	number	否	采样温度（0-2）
`stream`	boolean	否	启用流式响应

响应格式

{
  "id": "chatcmpl-xxx",
  "object": "chat.completion",
  "created": 1234567890,
  "model": "gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The sky appears blue because..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 50,
    "total_tokens": 60
  }
}

Function Calling

Function Calling 允许模型生成结构化的 JSON 来调用代码中的函数。

Python
Node.js

tools = [
  {
    "type": "function",
    "function": {
      "name": "get_weather",
      "description": "获取指定地点的当前天气",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {"type": "string", "description": "城市名称"}
        },
        "required": ["location"]
      }
    }
  }
]

response = client.chat.completions.create(
  model="gpt-4.1",
  messages=[{"role": "user", "content": "东京现在天气怎么样？"}],
  tools=tools,
  tool_choice="auto"
)

if response.choices[0].message.tool_calls:
  tool_call = response.choices[0].message.tool_calls[0]
  # 执行你的函数并返回结果

const tools = [
  {
    type: "function",
    function: {
      name: "get_weather",
      description: "获取指定地点的当前天气",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string", description: "城市名称" }
        },
        required: ["location"]
      }
    }
  }
];

const response = await client.chat.completions.create({
  model: 'gpt-4.1',
  messages: [{ role: 'user', content: '东京现在天气怎么样？' }],
  tools: tools,
  tool_choice: 'auto'
});

if (response.choices[0].message.tool_calls) {
  const toolCall = response.choices[0].message.tool_calls[0];
  // 执行你的函数并返回结果
}

Function Calling 官方文档

完整的函数定义和响应处理指南