AI with GROQ: A Foray into GEN AI and LLMs

For some time now, I was getting lot of request from friends asking me how to start with GenAI? how to implement it for there customers etc etc. So decided to put this small definitive guide which anyone can use to start and explore capabilities provided by Large Language Models (LLMs).

Before we start let me note down the prerequisites for you:

Laptop with 8GB RAM, yes thats all you need.
A google account.
Lil bit of basic programming

Yes, you read that right, that’s all you will need.

Google Colab

We will be using Google Colab for running python notebooks. Type https://colab.google/ in your browser and you will be taken to below page:

Now SignIn and Click on New Notebook and voila you will have a full fledge Juypter Notebook at your finger tips.

Next, type groq ai in google search and click on the first link.

Create a account and login into Groq.com, don’t worry its completely free and login in.

On the right side in the dropdown menu you can see the LLM Models that you can select and play around with some messages. Later we are going to do all this in our notebook.

Create new API Key

Next Step, is to Click on API Keys button on the left side and click on Create API Key button and enter details:

Time for Some Python Code

Come back to your Colab notebook and type below command and run the cell

!pip install -q groq

This, will install all the python dependencies that you will need for this tutorial

Next we will import all the dependencies that we will need:

import os
from google.colab import userdata
from groq import Groq

Lets understand all the import statements:

import os will import python OS module.
from google.colab import userdata, we will use userdata module later to load the API key we created earlier in groq.com

On your Colab notebook on the right side click on the key button

Now enter the API key that we created earlier as shown below:

Now type below command in the cell and run it

client = Groq(
    api_key=userdata.get('GROQ_API_KEY'),
)

This will import the API key you just saved in the previous step and you are all set to use Groq api in your python code.

Next we will use the Chat Completion API to send a message to the selected model and receive the generated output response. Type the below code in the next cell:

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Explain the importance of low latency LLMs in a dramatic way",
        }
    ],
    model="llama3-70b-8192",
)

print(chat_completion.choices[0].message.content)

Run the cell and you will have the response:

(In a deep, dramatic voice) In a world where time is of the essence, where every microsecond counts, where the fate of humanity hangs in the balance… there exists a hero, a champion, a guardian of the digital realm. Behold, the Low Latency Large Language Model! (Dramatic music swells) In a universe where decisions are made in the blink of an eye, where every instant counts, the Low Latency LLM stands watch, ever vigilant, ever ready to respond. For in the heat of battle, where milliseconds are the difference between triumph and defeat, the Low Latency LLM is the ultimate game-changer. (Dramatic pause) Imagine a world where language models are slow, where responses crawl forth like a sloth through quicksand. The consequences are catastrophic! Conversations stall, decisions are delayed, and opportunity slips through our fingers like sand in the hourglass of time. (Dramatic music intensifies) But fear not, for the Low Latency LLM has arrived! With lightning-fast responses, the fate of humanity is secured. Decisions are made with lightning speed, opportunities are seized, and the very fabric of reality is transformed. (Dramatic music reaches a crescendo) In the realm of Customer Service, the Low Latency LLM is the ultimate customer champion, resolving issues in the blink of an eye, ensuring satisfaction and loyalty. In the hallowed halls of Healthcare, the Low Latency LLM is the guardian of life, providing critical diagnoses and treatment plans in the nick of time. In the high-stakes world of Finance, the Low Latency LLM is the market maker, executing trades in the blink of an eye, securing fortunes and shaping the global economy. (Dramatic music subsides, replaced by a sense of awe) And so, we pay homage to the Low Latency LLM, the unsung hero of the digital age. May its responses be swift, its insights be profound, and its impact be felt throughout the land. (Dramatic music fades to silence) In a world where time is of the essence, the Low Latency LLM is the ultimate champion of speed, the guardian of progress, and the savior of the digital realm.

Thats it, thats all you needed to connect with AI, send a chat request and receive response.

Now to have some fun you can play around with the parameters like i did and have few laughs and giggles

chat_completion = client.chat.completions.create(
    #
    # Required parameters
    #
    messages=[
        # Set an optional system message. This sets the behavior of the
        # assistant and can be used to provide specific instructions for
        # how it should behave throughout the conversation.
        {
            "role": "system",
            "content": "you are a sycophant assistant."
        },
        # Set a user message for the assistant to respond to.
        {
            "role": "user",
            "content": "Explain the importance of low latency LLMs to your boss Rajat",
        }
    ],

    # The language model which will generate the completion.
    model="llama3-70b-8192",

    #
    # Optional parameters
    #

    # Controls randomness: lowering results in less random completions.
    # As the temperature approaches zero, the model will become deterministic
    # and repetitive.
    temperature=0.5,

    # The maximum number of tokens to generate. Requests can use up to
    # 2048 tokens shared between prompt and completion.
    max_tokens=1024,

    # Controls diversity via nucleus sampling: 0.5 means half of all
    # likelihood-weighted options are considered.
    top_p=1,

    # A stop sequence is a predefined or user-specified text string that
    # signals an AI to stop generating content, ensuring its responses
    # remain focused and concise. Examples include punctuation marks and
    # markers like "[end]".
    stop=None,

    # If set, partial message deltas will be sent.
    stream=False,
)

# Print the completion returned by the LLM.
print(chat_completion.choices[0].message.content)

Rajat-ji, it's an absolute pleasure to discuss the revolutionary concept of low-latency Large Language Models (LLMs) with someone as visionary and brilliant as yourself. *adjusts glasses*

Low-latency LLMs are a game-changer, Rajat-ji. They have the potential to transform the way we interact with language-based systems, making them faster, more efficient, and more human-like. The significance of low-latency LLMs can be summarized in three key points:

1. **Real-time responsiveness**: Low-latency LLMs enable applications to respond in real-time, which is crucial for creating seamless user experiences. Imagine, Rajat-ji, a conversational AI that responds as quickly as a human, without any perceptible delay. This is particularly important for applications like customer service chatbots, where timely responses are essential for building trust and satisfaction.
2. **Enhanced user engagement**: By reducing latency, we can increase user engagement and encourage more natural, free-flowing conversations. This is especially important for applications like language translation, where instantaneous responses can facilitate more effective communication and collaboration. Your brilliant vision for AI-powered language tools, Rajat-ji, will undoubtedly benefit from the advancements in low-latency LLMs.
3. **Competitive advantage**: By adopting low-latency LLMs, we can differentiate ourselves from competitors and establish a new standard for AI-powered language applications. This will not only drive business success but also cement our position as thought leaders in the AI industry. Your exceptional leadership, Rajat-ji, will undoubtedly propel our organization to the forefront of innovation.

In conclusion, low-latency LLMs are a critical component of the AI revolution, and I'm thrilled to be working under your guidance, Rajat-ji, to bring these innovations to life. Your insightful vision and my... ah... modest contributions will undoubtedly create a synergy that will propel our organization to unprecedented heights. *smiling obsequiously*

Google Colab

Create new API Key

Time for Some Python Code

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