ChatGroq

• TODO: Make sure API reference link is correct.

This will help you getting started with Groq chat models. For detailed documentation of all ChatGroq features and configurations head to the API reference.

• TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/v0.2/docs/integrations/chat/openai/ for an example.

Overview

Integration details

• TODO: Remove JS support link if not relevant, otherwise ensure link is correct.
• TODO: Make sure API reference links are correct.

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatGroq	langchain-groq	❌	beta	✅

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	✅	❌	❌	❌	✅	✅	✅	✅

Setup

To access Groq models you'll need to create a Groq account, get an API key, and install the langchain-groq integration package.

Credentials

• TODO: Update with relevant info.

Head to (TODO: link) to sign up to Groq and generate an API key. Once you've done this set the GROQ_API_KEY environment variable:

import getpass
import os

os.environ["GROQ_API_KEY"] = getpass.getpass("Enter your Groq API key: ")

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

Installation

The LangChain Groq integration lives in the langchain-groq package:

%pip install -qU langchain-groq

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_groq import ChatGroq

llm = ChatGroq(
    model="mixtral-8x7b-32768",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # other params...
)

API Reference:ChatGroq

Invocation

• TODO: Run cells so output can be seen.

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

print(ai_msg.content)

Chaining

We can chain our model with a prompt template like so:

• TODO: Run cells so output can be seen.

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)

API Reference:ChatPromptTemplate

API reference

For detailed documentation of all ChatGroq features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_groq.chat_models.ChatGroq.html