Groq

https://groq.com/

tip

We support ALL Groq models, just set model=groq/<any-model-on-groq> as a prefix when sending litellm requests

API Key

# env variable
os.environ['GROQ_API_KEY']

Sample Usage

from litellm import completion
import os

os.environ['GROQ_API_KEY'] = ""
response = completion(
    model="groq/llama2-70b-4096", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

Sample Usage - Streaming

from litellm import completion
import os

os.environ['GROQ_API_KEY'] = ""
response = completion(
    model="groq/llama2-70b-4096", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
    stream=True
)

for chunk in response:
    print(chunk)

Supported Models - ALL Groq Models Supported!

We support ALL Groq models, just set groq/ as a prefix when sending completion requests

Model Name	Function Call
llama3-8b-8192	completion(model="groq/llama3-8b-8192", messages)
llama3-70b-8192	completion(model="groq/llama3-70b-8192", messages)
llama2-70b-4096	completion(model="groq/llama2-70b-4096", messages)
mixtral-8x7b-32768	completion(model="groq/mixtral-8x7b-32768", messages)
gemma-7b-it	completion(model="groq/gemma-7b-it", messages)

Groq - Tool / Function Calling Example

# Example dummy function hard coded to return the current weather
import json
def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
    elif "san francisco" in location.lower():
        return json.dumps(
            {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}
        )
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})




# Step 1: send the conversation and available functions to the model
messages = [
    {
        "role": "system",
        "content": "You are a function calling LLM that uses the data extracted from get_current_weather to answer questions about the weather in San Francisco.",
    },
    {
        "role": "user",
        "content": "What's the weather like in San Francisco?",
    },
]
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                    },
                },
                "required": ["location"],
            },
        },
    }
]
response = litellm.completion(
    model="groq/llama2-70b-4096",
    messages=messages,
    tools=tools,
    tool_choice="auto",  # auto is default, but we'll be explicit
)
print("Response\n", response)
response_message = response.choices[0].message
tool_calls = response_message.tool_calls


# Step 2: check if the model wanted to call a function
if tool_calls:
    # Step 3: call the function
    # Note: the JSON response may not always be valid; be sure to handle errors
    available_functions = {
        "get_current_weather": get_current_weather,
    }
    messages.append(
        response_message
    )  # extend conversation with assistant's reply
    print("Response message\n", response_message)
    # Step 4: send the info for each function call and function response to the model
    for tool_call in tool_calls:
        function_name = tool_call.function.name
        function_to_call = available_functions[function_name]
        function_args = json.loads(tool_call.function.arguments)
        function_response = function_to_call(
            location=function_args.get("location"),
            unit=function_args.get("unit"),
        )
        messages.append(
            {
                "tool_call_id": tool_call.id,
                "role": "tool",
                "name": function_name,
                "content": function_response,
            }
        )  # extend conversation with function response
    print(f"messages: {messages}")
    second_response = litellm.completion(
        model="groq/llama2-70b-4096", messages=messages
    )  # get a new response from the model where it can see the function response
    print("second response\n", second_response)