Obiguard provides a robust and secure gateway to facilitate the integration of various Large Language Models (LLMs) into your applications, including Groq APIs. With Obiguard, you can take advantage of features like fast AI gateway access, observability, prompt management, and more, all while ensuring the secure management of your LLM API keys through a virtual key system.
Provider Slug. groq

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Obiguard SDK Integration with Groq Models

Obiguard provides a consistent API to interact with models from various providers. To integrate Groq with Obiguard:

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1. Install the Obiguard SDK

Add the Obiguard SDK to your application to interact with Groq AI’s API through Obiguard’s gateway. 
pip install obiguard

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2. Initialize Obiguard with the Virtual Key

To use Groq with Obiguard, get your API key from here, then add it to Obiguard to create the virtual key. 
from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="sk-obg***", # Your Obiguard API key
  virtual_key="VIRTUAL_KEY" # Replace with your virtual key for Groq
)

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3. Invoke Chat Completions with Groq

Use the Obiguard instance to send requests to Groq. You can also override the virtual key directly in the API call if needed. 
completion = client.chat.completions.create(
  messages= [{"role": 'user', "content": 'Say this is a test'}],
  model= 'mistral-medium'
)

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Groq Tool Calling

Tool calling feature lets models trigger external tools based on conversation context. You define available functions, the model chooses when to use them, and your application executes them and returns results. Obiguard supports Groq Tool Calling and makes it interoperable across multiple providers.

Supported Groq Models with Tool Calling
Get Weather Tool
tools = [{
  "type": "function",
  "function": {
    "name": "getWeather",
    "description": "Get the current weather",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {"type": "string", "description": "City and state"},
        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
      },
      "required": ["location"]
    }
  }
}]

response = client.chat.completions.create(
  model="llama-3.3-70b-versatile",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What's the weather like in Delhi - respond in JSON"}
  ],
  tools=tools,
  tool_choice="auto"
)
print(response.choices[0].finish_reason)

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Groq Speech to Text (Whisper)

OpenAI’s Audio API converts speech to text using the Whisper model. It offers transcription in the original language and translation to English, supporting multiple file formats and languages with high accuracy.
Python
audio_file= open("/path/to/file.mp3", "rb")

# Transcription
transcription = client.audio.transcriptions.create(
  model="whisper-large-v3",
  file=audio_file
)
print(transcription.text)

# Translation
translation = client.audio.translations.create(
  model="whisper-large-v3",
  file=audio_file
)
print(translation.text)

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Groq Text to Speech

Groq’s Text to Speech (TTS) API converts written text into natural-sounding audio using six distinct voices. It supports multiple languages, streaming capabilities, and various audio formats for different use cases.
Python
from pathlib import Path

speech_file_path = Path(__file__).parent / "speech.mp3"
response = client.audio.speech.create(
  model="playai-tts",
  voice="Fritz-PlayAI",
  input="Today is a wonderful day to build something people love!"
)

with open(speech_file_path, "wb") as f:
  f.write(response.content)

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Next Steps

The complete list of features supported in the SDK are available on the link below.

Obiguard SDK Client

Learn more about the Obiguard SDK Client