Groq
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/llama3-8b-8192",
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/llama3-8b-8192",
messages=[
{"role": "user", "content": "hello from litellm"}
],
stream=True
)
for chunk in response:
print(chunk)
Usage with LiteLLM Proxy
1. Set Groq Models on config.yaml
model_list:
- model_name: groq-llama3-8b-8192 # Model Alias to use for requests
litellm_params:
model: groq/llama3-8b-8192
api_key: "os.environ/GROQ_API_KEY" # ensure you have `GROQ_API_KEY` in your .env
2. Start Proxy
litellm --config config.yaml
3. Test it
Make request to litellm proxy
- Curl Request
- OpenAI v1.0.0+
- Langchain
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "groq-llama3-8b-8192",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
response = client.chat.completions.create(model="groq-llama3-8b-8192", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
model = "groq-llama3-8b-8192",
temperature=0.1
)
messages = [
SystemMessage(
content="You are a helpful assistant that im using to make a test request to."
),
HumanMessage(
content="test from litellm. tell me why it's amazing in 1 sentence"
),
]
response = chat(messages)
print(response)
Supported Models - ALL Groq Models Supported!
We support ALL Groq models, just set groq/ as a prefix when sending completion requests
| Model Name | Usage |
|---|---|
| llama-3.1-8b-instant | completion(model="groq/llama-3.1-8b-instant", messages) |
| llama-3.1-70b-versatile | completion(model="groq/llama-3.1-70b-versatile", messages) |
| 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/llama3-8b-8192",
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/llama3-8b-8192", messages=messages
) # get a new response from the model where it can see the function response
print("second response\n", second_response)
Speech to Text - Whisper
os.environ["GROQ_API_KEY"] = ""
audio_file = open("/path/to/audio.mp3", "rb")
transcript = litellm.transcription(
model="groq/whisper-large-v3",
file=audio_file,
prompt="Specify context or spelling",
temperature=0,
response_format="json"
)
print("response=", transcript)