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OpenAI SDK Usage

Mixroute Api is fully compatible with the official OpenAI SDK. Simply change the base URL to use Mixroute Api with your existing OpenAI SDK code.

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Installation

pip install openai

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Configuration

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Python

from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxxxxxxxx",
    base_url="https://console.mixroute.io/v1"
)

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Node.js / TypeScript

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-xxxxxxxxxx',
  baseURL: 'https://console.mixroute.io/v1'
});

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Go

package main

import (
    "github.com/openai/openai-go"
    "github.com/openai/openai-go/option"
)

func main() {
    client := openai.NewClient(
        option.WithAPIKey("sk-xxxxxxxxxx"),
        option.WithBaseURL("https://console.mixroute.io/v1"),
    )
}

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Chat Completions

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Basic Usage

from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxxxxxxxx",
    base_url="https://console.mixroute.io/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

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Streaming

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

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Node.js Streaming

const stream = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Tell me a story' }],
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

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Using Different Models

Mixroute Api supports models from multiple providers using the same SDK: 
# OpenAI models
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}]
)

# Anthropic Claude (via OpenAI format)
response = client.chat.completions.create(
    model="claude-3-5-sonnet-20241022",
    messages=[{"role": "user", "content": "Hello"}]
)

# Google Gemini (via OpenAI format)
response = client.chat.completions.create(
    model="gemini-1.5-pro",
    messages=[{"role": "user", "content": "Hello"}]
)

# Meta Llama
response = client.chat.completions.create(
    model="llama-3.1-70b",
    messages=[{"role": "user", "content": "Hello"}]
)

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Vision (Multimodal)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://console.mixroute.io/image.jpg"}
                }
            ]
        }
    ]
)

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Base64 Image

import base64

with open("image.jpg", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this image"},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}
                }
            ]
        }
    ]
)

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Function Calling

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools,
    tool_choice="auto"
)

# Check if model wants to call a function
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

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Embeddings

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="The quick brown fox jumps over the lazy dog"
)

embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}")

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Batch Embeddings

response = client.embeddings.create(
    model="text-embedding-3-small",
    input=[
        "First document",
        "Second document",
        "Third document"
    ]
)

for i, item in enumerate(response.data):
    print(f"Document {i}: {len(item.embedding)} dimensions")

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Image Generation

response = client.images.generate(
    model="dall-e-3",
    prompt="A serene Japanese garden with cherry blossoms",
    size="1024x1024",
    quality="standard",
    n=1
)

image_url = response.data[0].url
print(image_url)

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List Models

models = client.models.list()

for model in models.data:
    print(f"{model.id} - {model.owned_by}")

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Error Handling

from openai import OpenAI, APIError, RateLimitError, APIConnectionError

client = OpenAI(
    api_key="sk-xxxxxxxxxx",
    base_url="https://console.mixroute.io/v1"
)

try:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError:
    print("Rate limit exceeded. Please retry later.")
except APIConnectionError:
    print("Connection error. Check your network.")
except APIError as e:
    print(f"API error: {e.message}")

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Async Usage

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    api_key="sk-xxxxxxxxxx",
    base_url="https://console.mixroute.io/v1"
)

async def main():
    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello!"}]
    )
    print(response.choices[0].message.content)

asyncio.run(main())

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Environment Variables

Set environment variables instead of hardcoding: 
export OPENAI_API_KEY="sk-xxxxxxxxxx"
export OPENAI_BASE_URL="https://console.mixroute.io/v1"

from openai import OpenAI

# Automatically reads from environment variables
client = OpenAI()

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Best Practices

  • Use environment variables for API keys
  • Implement retry logic for transient errors
  • Use streaming for better UX with long responses
  • Batch requests when processing multiple items
  • Monitor usage to stay within rate limits