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Groq

https://groq.com/

We support ALL Groq models, just set groq/ as a prefix when sending completion 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 NameFunction Call
llama3-8b-8192completion(model="groq/llama3-8b-8192", messages)
llama3-70b-8192completion(model="groq/llama3-70b-8192", messages)
llama2-70b-4096completion(model="groq/llama2-70b-4096", messages)
mixtral-8x7b-32768completion(model="groq/mixtral-8x7b-32768", messages)
gemma-7b-itcompletion(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)