Building J.A.R.V.I.S-Like Chatbot Using GROQ and Python

Let's build our own Jarvis, a smart assistant that communicates with you in real time. We'll use Groq, a cutting-edge and extremely fast technology, to achieve this. This guide will walk you through creating a simple web app that converts your voice to text, processes it with Groq for understanding and response, and then reads the reply back to you.

Why is Groq So Fast?

Groq leverages Tensor Streaming Processor (TSP) architecture, designed specifically for high-speed AI tasks. This architecture enables Groq to perform complex computations with remarkable speed and efficiency, making it ideal for AI applications. In our tests, Groq completed intricate tasks even before GPT had begun.

Why Choose Deepgram?

Deepgram excels at converting speech to text and text to speech quickly and accurately. It is trusted by a wide range of users, from small startups to major organizations like NASA. Deepgram's ability to understand various languages and accents makes it highly suitable for applications requiring accurate speech recognition and synthesis. Additionally, its speed complements Groq perfectly, enabling seamless interaction.

Using Flask

Flask is a lightweight and straightforward Python framework for building web applications. It provides all the necessary tools to get started quickly.

With Flask, you can create a webpage where users can record their voice. This recording is sent to your Flask app, processed, and then converted back to speech for the user to hear. This functionality makes it easy to interact with the app using voice commands.

In this video, we'll use Groq to create our own version of Jarvis from the ground up.

Who am I speaking with? You are speaking with Jarvis, sir.

What is your purpose? My purpose is to teach developers about large language models.

We'll make a web app that records your voice, converts the recording into text, and sends it to Groq for processing. Groq processes the data super fast and sends back the results, which we then turn into speech.

What is Groq?

Groq is similar to GPT but with incredibly faster processing capabilities. For example, Groq completes a 500-word poem before GPT even starts.

Why is Groq So Fast?

Comparing CPUs, GPUs, and Groq chips is like comparing a skilled worker, a big team, and a precise military operation. Think of a CPU as a very smart and flexible employee—it can do many things but only one at a time with each core. Even a powerful CPU like the Intel i9, with its 24 cores, can only handle 24 tasks simultaneously.

Using the RTX 4080 graphics card as an example, this GPU has over 9,000 CUDA cores. Historically, it was limited to gaming and graphics rendering.

With the emergence of large language models (LLMs), GPUs found a new use case. For instance, to use the Llama 2 model with 70 billion parameters, you might start with at least two RTX 4080 graphics cards. Services like RunPod allow you to rent GPU instances for LLMs at around 50 cents per hour. But even with strong GPUs, why would we need technology like Groq?

GPUs are designed to handle many tasks in parallel, excelling at rendering multiple pixels in a game. However, LLMs process information sequentially, posing a challenge for GPUs. While GPUs are powerful, they're not optimized for the sequential and interconnected workloads of LLMs.

Groq chips, however, are tailored for large language models. They focus on one task at a time but work together, sharing memory for fast and accurate language tasks with minimal delay.

Let's Start Building Jarvis with Groq

First, go to the Deepgram homepage and sign up or log in using your Gmail or by creating a free account. You'll receive $200 worth of credits.

Next, navigate to the documentation and the getting started guide under the text-to-speech section. You'll find a sample Python code snippet for converting text to speech using the Deepgram SDK. Create a new file, texttospeech.py, and paste the code. Simplify it by inputting the API key directly in the console, eliminating the need for the dotenv library. Change the main function name to text_to_speech and modify it to accept a text parameter. This method will use the provided text parameter for speech conversion and return the generated sound file's name.

Call the new method with the sentence "This is a test." Before testing, set up the environment by creating a virtual environment and installing the Deepgram SDK. Fetch the API key from the Deepgram website, name it, and set the permission. Copy and store it as an environment variable.

Test the setup by running the Python script. You'll see a new file created—listen to it to confirm it works.

Next, for speech-to-text, create a new file named speechtotext.py. Refer to the Deepgram documentation for transcribing a local sound file. Copy and paste the code, remove parts related to dotenv, and clean up the code. Rename the method to speech_to_text, allowing it to accept an audio file parameter for transcription. Test it with a sample pizza ordering sound file. Print the result to see the transcription.

Now that we can hear and speak, let's start working on our web app. Create a new file named app.py and import the necessary Flask functionalities. Install Flask and import tempfile for creating temporary files. Initialize a Flask app, define a route that delivers an index page using Flask's render_template method, and start the server in debug mode on port 880.

Create a templates folder and an index.html file inside it. Paste the prepared code for the index page, which includes a recording indicator and start/stop buttons for initiating and ending recordings. Create a static folder with a js subfolder and paste the prepared JavaScript code. This script manages the recording process and sends the audio data to the server.

Start the server and check the index page. You’ll see a simple page with a black button and a microphone icon. Now, create an endpoint that receives sound data, converts it to text, and possibly translates it before turning it back into speech. Test the setup to ensure it works.

Finally, leverage the power of Groq for tasks like translation or answering questions. Visit groq.com and log in using your Gmail account. Try out Groq and get direct Python code examples. For instance, ask Groq to write a poem about LLMs. Copy the Python code example and create a Groq service Python file. Set up an API key for Groq and install Groq with pip. Test it and wrap everything inside an execute function.

Use the execute function in your Flask app to process transcriptions. Test it by translating text to German and confirming the accuracy. Adjust the prompt to create an improved version of Siri or Alexa that can answer questions using its vast knowledge.

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• Date: 23 Jun 2024