### Install OpenAI Go SDK

Source: https://tokenmix.ai/docs/sdks

Install the OpenAI Go SDK using 'go get'. This is required for the Go example.

```bash
go get github.com/openai/openai-go
```

--------------------------------

### Install OpenAI Node.js SDK

Source: https://tokenmix.ai/docs/sdks

Install the OpenAI Node.js package via npm. Required before using the Node.js example.

```bash
npm install openai
```

--------------------------------

### Install Anthropic SDK (Python)

Source: https://tokenmix.ai/docs/sdks

Install the official Anthropic SDK for Python using pip. This is required before using the Anthropic SDK examples.

```bash
pip install anthropic
```

--------------------------------

### Install OpenAI SDK with pip

Source: https://tokenmix.ai/docs/quickstart

Install the official OpenAI SDK for Python. TokenMix works with any OpenAI-compatible client library.

```bash
pip install openai
```

--------------------------------

### Use API key with OpenAI Python SDK

Source: https://tokenmix.ai/docs/authentication

Set your TokenMix API key and base URL for the OpenAI Python client. Your key must start with sk-tm-. Replace the example key with your actual key.

```python
from openai import OpenAI

client = OpenAI(
    api_key="sk-tm-your-api-key-here",
    base_url="https://api.tokenmix.ai/v1"
)
```

--------------------------------

### Using Your API Key with OpenAI SDK (Python)

Source: https://tokenmix.ai/docs/authentication

Example of initializing the OpenAI client with a TokenMix API key and base URL in Python.

```APIDOC
## Using Your API Key - Python OpenAI SDK

### Description
Pass your API key as a Bearer token in the Authorization header by initializing the OpenAI client with the TokenMix API key and base URL.

### Method
SDK

### Endpoint
https://api.tokenmix.ai/v1

### Parameters
#### Client Configuration
- **api_key** (string) - Required - Your TokenMix API key, starting with sk-tm-
- **base_url** (string) - Required - The TokenMix API base URL: https://api.tokenmix.ai/v1

### Request Example
from openai import OpenAI

client = OpenAI(
    api_key="sk-tm-your-api-key-here",
    base_url="https://api.tokenmix.ai/v1"
)
```

--------------------------------

### Detail Parameter Configuration

Source: https://tokenmix.ai/docs/vision

Example of setting the detail parameter to 'high' to enable high-resolution processing.

```json
{
  "type": "image_url",
  "image_url": {
    "url": "https://example.com/detailed-chart.png",
    "detail": "high"
  }
}
```

--------------------------------

### GET /api/v1/usage

Source: https://tokenmix.ai/docs/openai-compatibility

OpenRouter-style usage alias.

```APIDOC
## GET /api/v1/usage

### Description
OpenRouter-style usage alias.

### Method
GET

### Endpoint
/api/v1/usage

```

--------------------------------

### GET /v1/models

Source: https://tokenmix.ai/docs/openai-compatibility

Lists available models.

```APIDOC
## GET /v1/models

### Description
Lists available models.

### Method
GET

### Endpoint
/v1/models

```

--------------------------------

### GET /v1/credits

Source: https://tokenmix.ai/docs/openai-compatibility

Provides an OpenRouter-like wallet credits summary.

```APIDOC
## GET /v1/credits

### Description
Provides an OpenRouter-like wallet credits summary.

### Method
GET

### Endpoint
/v1/credits

```

--------------------------------

### Example response headers

Source: https://tokenmix.ai/docs/rate-limits

Shows the HTTP response headers returned by the API to indicate current rate limit status.

```bash
# Example response headers
HTTP/2 200
x-ratelimit-limit-requests: <your RPM limit>
x-ratelimit-remaining-requests: <remaining RPM requests>
x-ratelimit-reset-requests: 12s
x-ratelimit-limit-day: <your RPD limit>
x-ratelimit-remaining-day: <remaining daily requests>
x-ratelimit-reset-day: 28800s
```

--------------------------------

### JSON Mode with Python

Source: https://tokenmix.ai/docs/structured-output

Use JSON mode to get valid JSON output. Set response_format to {"type": "json_object"} and include the word "JSON" in the system or user message to avoid whitespace generation. The example extracts product info and prints the parsed JSON.

```python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.tokenmix.ai/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "system",
            "content": "Extract product info. Respond in JSON with fields: name, price, category."
        },
        {
            "role": "user",
            "content": "The new UltraWidget Pro costs $49.99 and is in the electronics category."
        }
    ],
    response_format={"type": "json_object"}
)

import json
data = json.loads(response.choices[0].message.content)
print(data)
# {"name": "UltraWidget Pro", "price": 49.99, "category": "electronics"}
```

--------------------------------

### GET /api/v1/models

Source: https://tokenmix.ai/docs/openai-compatibility

OpenRouter-like models list; defaults to text, use output_modalities=all for all models.

```APIDOC
## GET /api/v1/models

### Description
OpenRouter-like models list; defaults to text, use output_modalities=all for all models.

### Method
GET

### Endpoint
/api/v1/models

```

--------------------------------

### Anthropic SDK Python example

Source: https://tokenmix.ai/docs/openai-compatibility

Use the official Anthropic SDK with TokenMix by setting the base URL to https://api.tokenmix.ai and providing your API key. The example creates a message with model claude-sonnet-4.6.

```python
import anthropic

client = anthropic.Anthropic(
    api_key="sk-tm-your-api-key-here",
    base_url="https://api.tokenmix.ai"
)

message = client.messages.create(
    model="claude-sonnet-4.6",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude!"}
    ]
)
print(message.content[0].text)
```

--------------------------------

### Single Image Analysis with Python

Source: https://tokenmix.ai/docs/vision

Example of sending a single image URL with a text prompt to the model using the OpenAI Python client.

```python
from openai import OpenAI
 
client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.tokenmix.ai/v1"
)
 
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"
                    }
                }
            ]
        }
    ],
    max_tokens=512
)
 
print(response.choices[0].message.content)
```

--------------------------------

### Using Environment Variables for API Key (Python)

Source: https://tokenmix.ai/docs/authentication

Example of storing the TokenMix API key in an environment variable and using it with the OpenAI SDK in Python.

```APIDOC
## Using Environment Variables - Python OpenAI SDK

### Description
For production, store your API key in environment variables instead of hardcoding it in source code.

### Method
SDK

### Endpoint
https://api.tokenmix.ai/v1

### Parameters
#### Environment Variables
- **TOKENMIX_API_KEY** (string) - Required - The TokenMix API key stored in an environment variable

#### Client Configuration
- **api_key** (string) - Required - Retrieved from os.environ["TOKENMIX_API_KEY"]
- **base_url** (string) - Required - The TokenMix API base URL: https://api.tokenmix.ai/v1

### Request Example
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["TOKENMIX_API_KEY"],
    base_url="https://api.tokenmix.ai/v1"
)
```

--------------------------------

### Anthropic SDK Streaming Example (Python)

Source: https://tokenmix.ai/docs/sdks

Streams a chat completion from the 'claude-sonnet-4.6' model using the Anthropic SDK. Prints each text chunk as it arrives.

```python
with client.messages.stream(
    model="claude-sonnet-4.6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Tell me a short story."}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)
print()
```

--------------------------------

### Create Video Response (JSON)

Source: https://tokenmix.ai/docs/videos

Example response from the create video endpoint, containing the task ID, status 'pending', model, and creation timestamp. Poll the status endpoint with this ID until status is 'completed' or 'failed'.

```json
{
  "id": "d290f1ee-6c54-4b01-90e6-d701748f0851",
  "status": "pending",
  "model": "sora-2",
  "created_at": 1710000000
}
```

--------------------------------

### Text-to-Speech (TTS) Example - Python

Source: https://tokenmix.ai/docs/audio

Generates speech from text using the tts-1 model and saves the audio to a file. Requires an OpenAI client configured with your API key and base URL.

```python
from openai import OpenAI
from pathlib import Path

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.tokenmix.ai/v1"
)

response = client.audio.speech.create(
    model="tts-1",
    voice="alloy",
    input="Hello! Welcome to TokenMix.",
    response_format="mp3",
    speed=1.0
)

speech_file = Path("output.mp3")
response.stream_to_file(speech_file)
print(f"Audio saved to {speech_file}")
```

--------------------------------

### Python Example with OpenAI SDK

Source: https://tokenmix.ai/docs/embeddings

Use the OpenAI Python SDK with a custom base URL to create embeddings. The response contains the embedding vector; print its dimensions and first five values.

```python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.tokenmix.ai/v1"
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="TokenMix provides unified API access to multiple AI models.",
    dimensions=1536
)

embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}")
print(f"First 5 values: {embedding[:5]}")
```

--------------------------------

### GET /v1/usage

Source: https://tokenmix.ai/docs/openai-compatibility

Provides account API usage summary for the authenticated API key owner.

```APIDOC
## GET /v1/usage

### Description
Provides account API usage summary for the authenticated API key owner.

### Method
GET

### Endpoint
/v1/usage

```

--------------------------------

### Anthropic SDK Chat Completion Example (Python)

Source: https://tokenmix.ai/docs/sdks

Creates an Anthropic client with the TokenMix base URL and sends a chat completion request to the 'claude-sonnet-4.6' model. Replace 'YOUR_API_KEY' with your actual API key.

```python
import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://api.tokenmix.ai"
)

message = client.messages.create(
    model="claude-sonnet-4.6",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "What is the capital of Japan?"}
    ]
)
print(message.content[0].text)
```

--------------------------------

### Define Tools with JSON Schema

Source: https://tokenmix.ai/docs/function-calling

Example of defining a tool using the tools parameter with JSON Schema, specifically for a weather retrieval function.

```json
{
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get the current weather for a given location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "City name, e.g. San Francisco, CA"
            },
            "unit": {
              "type": "string",
              "enum": ["celsius", "fahrenheit"],
              "description": "Temperature unit"
            }
          },
          "required": ["location"]
        }
      }
    }
  ]
}
```

--------------------------------

### Error response example

Source: https://tokenmix.ai/docs/authentication

The JSON structure returned when authentication fails, containing an OpenAI-compatible error object.

```json
{
  "error": {
    "message": "invalid api key",
    "type": "invalid_request_error",
    "request_id": "req_abc123"
  }
}
```

--------------------------------

### Use API key with Node.js

Source: https://tokenmix.ai/docs/authentication

Set the TokenMix API key and base URL in the Node.js OpenAI client. Replace the example key with your actual key.

```javascript
import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-tm-your-api-key-here',
  baseURL: 'https://api.tokenmix.ai/v1'
});
```

--------------------------------

### Response Format Example

Source: https://tokenmix.ai/docs/embeddings

Shows the JSON structure returned by the embeddings endpoint, including the embedding array, model, and token usage.

```json
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0091, 0.0152, -0.0033, 0.0074, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 10,
    "total_tokens": 10
  }
}
```

--------------------------------

### Example Response for JSON Schema Mode

Source: https://tokenmix.ai/docs/structured-output

Shows the expected output for the event_details schema. The model will always produce this structure when strict: true is set.

```json
{
  "event_name": "AI Summit",
  "date": "March 15, 2026",
  "location": "San Francisco",
  "attendees": 500
}
```

--------------------------------

### Generate Video from Image with Python

Source: https://tokenmix.ai/docs/videos

Python example using the requests library to generate a video from a reference image. The model wan2.6-i2v is used. The image_url must point to a reference image up to 20MB, with width and height at least 300px and aspect ratio between 1:2.5 and 2.5:1. The response contains the task ID.

```python
import requests

response = requests.post(
    "https://api.tokenmix.ai/v1/videos/generations",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json",
    },
    json={
        "model": "wan2.6-i2v",
        "prompt": "The person in the photo starts walking through a garden",
        "image_url": "https://example.com/reference-photo.jpg",
    },
)

task = response.json()
print(f"Task ID: {task['id']}")
```

--------------------------------

### Go Chat Completion Example with OpenAI SDK

Source: https://tokenmix.ai/docs/sdks

Uses the OpenAI Go SDK to send a chat completion request to the 'gpt-4o-mini' model via TokenMix's OpenAI-compatible endpoint. Replace 'YOUR_API_KEY' with your actual API key.

```go
package main

import (
	"context"
	"fmt"

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

func main() {
	client := openai.NewClient(
		option.WithAPIKey("YOUR_API_KEY"),
		option.WithBaseURL("https://api.tokenmix.ai/v1"),
	)

	response, err := client.Chat.Completions.New(
		context.Background(),
		openai.ChatCompletionNewParams{
			Model: "gpt-4o-mini",
			Messages: []openai.ChatCompletionMessageParamUnion{
				openai.SystemMessage("You are a helpful assistant."),
				openai.UserMessage("What is the capital of Japan?"),
			},
			MaxTokens: openai.Int(1024),
		},
	)
	if err != nil {
		panic(err)
	}

	fmt.Println(response.Choices[0].Message.Content)
}
```

--------------------------------

### Implement Automatic Prompt Caching with OpenAI SDK

Source: https://tokenmix.ai/docs/prompt-caching

This example demonstrates how to reuse a static system prompt across multiple user turns to trigger automatic caching. It includes logic to check for cache hits using the prompt_tokens_details field in the response usage.

```python
from openai import OpenAI
 
client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.tokenmix.ai/v1",
)
 
# Same system prompt + tool schema reused across multiple user turns.
# Supported models cache the shared prefix automatically — no markers needed.
SYSTEM_PROMPT = """You are an expert technical support assistant...
<... 1500+ tokens of product knowledge, style rules, FAQs ...>"""
 
def ask(user_question: str):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": user_question},
        ],
    )
    usage = response.usage
    # Read cache hits from the nested field:
    cached = getattr(usage, "prompt_tokens_details", None)
    if cached and cached.get("cached_tokens", 0) > 0:
        print(f"cache hit: {cached['cached_tokens']} tokens at cached input rate")
    return response.choices[0].message.content
 
# First call: cache write (billed at normal input price).
ask("How do I reset my password?")
# Second call within ~5 minutes: cache read on the shared prefix.
ask("Can I change my email?")
```

--------------------------------

### GET /api/v1/credits

Source: https://tokenmix.ai/docs/openai-compatibility

OpenRouter-style credits alias.

```APIDOC
## GET /api/v1/credits

### Description
OpenRouter-style credits alias.

### Method
GET

### Endpoint
/api/v1/credits

```

--------------------------------

### GET /api/v1/generation

Source: https://tokenmix.ai/docs/openai-compatibility

OpenRouter-style generation lookup alias.

```APIDOC
## GET /api/v1/generation

### Description
OpenRouter-style generation lookup alias.

### Method
GET

### Endpoint
/api/v1/generation

```

--------------------------------

### GET /v1/generation

Source: https://tokenmix.ai/docs/openai-compatibility

Looks up a generation by TokenMix response ID (OpenRouter-like).

```APIDOC
## GET /v1/generation

### Description
Looks up a generation by TokenMix response ID (OpenRouter-like).

### Method
GET

### Endpoint
/v1/generation

```

--------------------------------

### Enable streaming with Python (OpenAI client)

Source: https://tokenmix.ai/docs/streaming

Set stream: true in the request body to enable streaming. The example prints each content delta as it arrives. Long responses must use streaming to avoid 408 request_timeout.

```python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.tokenmix.ai/v1"
)

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    stream=True,
    max_tokens=1024
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
print()  # newline at end
```

--------------------------------

### Image response format

Source: https://tokenmix.ai/docs/images

Example of a standard image generation response containing a URL.

```json
{
  "created": 1710000000,
  "data": [
    {
      "url": "https://cdn.tokenmix.ai/images/abc123.png"
    }
  ]
}
```

--------------------------------

### GET /v1/models/{model}

Source: https://tokenmix.ai/docs/openai-compatibility

Retrieves metadata for a single model, used for SDK probes.

```APIDOC
## GET /v1/models/{model}

### Description
Retrieves metadata for a single model, used for SDK probes.

### Method
GET

### Endpoint
/v1/models/{model}

### Parameters
#### Path Parameters
- **model** (string) - Required - The model identifier.

```

--------------------------------

### OpenAI-compatible Response Usage Fields

Source: https://tokenmix.ai/docs/prompt-caching

Example of an OpenAI-compatible response showing the nested prompt_tokens_details.cached_tokens field.

```json
{
  "usage": {
    "prompt_tokens": 1500,
    "completion_tokens": 120,
    "total_tokens": 1620,
    "prompt_tokens_details": {
      "cached_tokens": 1400
    }
  }
}
```

--------------------------------

### Anthropic Response Usage Fields

Source: https://tokenmix.ai/docs/prompt-caching

Example of an Anthropic-compatible response containing cache_creation_input_tokens and cache_read_input_tokens fields.

```json
{
  "usage": {
    "prompt_tokens": 11,
    "completion_tokens": 4,
    "total_tokens": 15,
    "cache_creation_input_tokens": 1041,
    "cache_read_input_tokens": 0
  }
}
```

--------------------------------

### Generate Image-to-Image with Python

Source: https://tokenmix.ai/docs/images

Use the requests library to send a POST request to the TokenMix API with a reference image URL and a text prompt. Ensure you replace YOUR_API_KEY with your actual API key.

```python
import requests
 
response = requests.post(
    "https://api.tokenmix.ai/v1/images/generations",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json",
    },
    json={
        "model": "flux-1-kontext-pro",
        "prompt": "Transform this photo into a watercolor painting style",
        "image_url": "https://example.com/my-photo.jpg",
        "size": "1024x1024",
    },
)
 
print(response.json()["data"][0]["url"])
```

--------------------------------

### Submit video generation request with reference images (HappyHorse R2V) - Python

Source: https://tokenmix.ai/docs/videos

Sends a POST request to /videos/generations with the happyhorse-1.0-r2v model, providing two reference images. The response contains the task ID. Requires a valid API key. The example uses 720p resolution, 9:16 aspect ratio, and 7-second duration.

```python
import requests

response = requests.post(
    "https://api.tokenmix.ai/v1/videos/generations",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json",
    },
    json={
        "model": "happyhorse-1.0-r2v",
        "prompt": "Keep the product shape and logo consistent while showing a slow cinematic rotation",
        "images": [
            "https://example.com/reference-1.jpg",
            "https://example.com/reference-2.jpg",
        ],
        "resolution": "720p",
        "aspect_ratio": "9:16",
        "duration": 7,
    },
)

task = response.json()
print(f"Task ID: {task['id']}")
```

--------------------------------

### Retry-After header example

Source: https://tokenmix.ai/docs/rate-limits

The HTTP header indicating the duration to wait before retrying after a 429 error.

```bash
# The Retry-After header tells you how long to wait
HTTP/2 429
Retry-After: 2
```

--------------------------------

### GET /api/v1/models/{author}/{slug}/endpoints

Source: https://tokenmix.ai/docs/openai-compatibility

OpenRouter-like aggregate endpoint availability for one model.

```APIDOC
## GET /api/v1/models/{author}/{slug}/endpoints

### Description
OpenRouter-like aggregate endpoint availability for one model.

### Method
GET

### Endpoint
/api/v1/models/{author}/{slug}/endpoints

### Parameters
#### Path Parameters
- **author** (string) - Required - The model author.
- **slug** (string) - Required - The model slug.

```

--------------------------------

### Make First Chat Completion Request with Python

Source: https://tokenmix.ai/docs/quickstart

Send a chat completion request using the OpenAI SDK with the TokenMix base URL. Replace YOUR_API_KEY with the key you created. The response content is printed to the console.

```python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.tokenmix.ai/v1"
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is machine learning?"}
    ],
    temperature=0.7,
    max_tokens=1024
)

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

--------------------------------

### List Models with cURL

Source: https://tokenmix.ai/docs/quickstart

Retrieve the list of currently available models. Returns all enabled models with basic info. Replace YOUR_API_KEY with your actual API key.

```bash
curl https://api.tokenmix.ai/v1/models \
  -H "Authorization: Bearer YOUR_API_KEY"
```

--------------------------------

### GET /v1/models (cURL)

Source: https://tokenmix.ai/docs/sdks

Lists the available models from the TokenMix AI API using cURL.

```APIDOC
## GET /v1/models

### Description
Lists the available models from the TokenMix AI API using cURL.

### Method
GET

### Endpoint
https://api.tokenmix.ai/v1/models

### Parameters
#### Headers
- **Authorization** (string) - Required - Bearer token for authentication.

### Request Example
curl https://api.tokenmix.ai/v1/models \
  -H "Authorization: Bearer YOUR_API_KEY"

### Response
#### Success Response (200)
- **data** (array) - The list of available models.

#### Response Example
{
  "data": [
    {
      "id": "gpt-4o-mini"
    }
  ]
}
```

--------------------------------

### Function Calling Workflow in Python

Source: https://tokenmix.ai/docs/function-calling

A complete implementation showing the initial request, local function execution, and sending the tool result back to the model.

```python
from openai import OpenAI
import json
 
client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.tokenmix.ai/v1"
)
 
# Your function implementation
def get_weather(location, unit="celsius"):
    # In production, call a real weather API
    return {"temperature": 22, "unit": unit, "condition": "sunny"}
 
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["location"]
            }
        }
    }
]
 
messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]
 
# Step 1: Initial request
response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=tools
)
 
assistant_message = response.choices[0].message
messages.append(assistant_message)
 
# Step 2: Execute function
if assistant_message.tool_calls:
    for tool_call in assistant_message.tool_calls:
        args = json.loads(tool_call.function.arguments)
        result = get_weather(**args)
 
        # Step 3: Send result back
        messages.append({
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(result)
        })
 
# Final response with function results
final_response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=tools
)
 
print(final_response.choices[0].message.content)
# "The current weather in Tokyo is 22°C and sunny."
```

--------------------------------

### STT Transcription Response - JSON

Source: https://tokenmix.ai/docs/audio

Example JSON response containing the transcribed text from the speech-to-text endpoint.

```json
{
  "text": "Hello, this is a test recording for speech-to-text transcription."
}
```

--------------------------------

### Streaming Response Format

Source: https://tokenmix.ai/docs/chat-completions

Example of the Server-Sent Events (SSE) format where each event contains a delta field, concluding with [DONE].

```text
1data: {"id":"chatcmpl-abc123","choices":[{"delta":{"role":"assistant"},"index":0}]}
2 
3data: {"id":"chatcmpl-abc123","choices":[{"delta":{"content":"Once"},"index":0}]}
4 
5data: {"id":"chatcmpl-abc123","choices":[{"delta":{"content":" upon"},"index":0}]}
6 
7data: {"id":"chatcmpl-abc123","choices":[{"delta":{"content":" a"},"index":0}]}
8 
9data: [DONE]
```