First thing first, clone the repository.
git clone https://github.com/neural-maze/ava-whatsapp-agent-course.git
cd ava-whatsapp-agent-course
Instead of pip or poetry, we are using uv as the Python package manager.
To install uv, simply follow this instructions.
Once uv is intalled, you can install the project dependencies. First of all, let's create a virtual environment.
uv venv .venv
# macOS / Linux
. .venv/bin/activate # or source .venv/bin/activate
# Windows
. .\.venv\Scripts\Activate.ps1 # or .\.venv\Scripts\activate
uv pip install -e .Just to make sure that everything is working, simply run the following command:
uv run python --versionThe Python version should be Python 3.12.8.
Now that all the dependencies are installed, it's time to populate the .env file with the correct values.
To help you with this, we have created a .env.example file that you can use as a template.
cp .env.example .env
Now, you can open the .env file with your favorite text editor and set the correct values for the variables.
You'll notice there are a lot of variables that need to be set.
GROQ_API_KEY=""
ELEVENLABS_API_KEY=""
ELEVENLABS_VOICE_ID=""
TOGETHER_API_KEY=""
QDRANT_URL=""
QDRANT_API_KEY=""
WHATSAPP_PHONE_NUMBER_ID = ""
WHATSAPP_TOKEN = ""
WHATSAPP_VERIFY_TOKEN = ""
In this doc, we will show you how to get the values for all of these variables, except for the WhatsApp ones. That's something we will cover in a dedicated lesson, so don't worry about it for now, you can leave the WhatsApp variables empty.
To create the GROQ_API_KEY, and be able to interact with Groq models, you just need to follow this instructions.
Once you have created the API key, you can copy it and paste it into an .env file (following the same format as the .env.example file).
To create the ELEVENLABS_API_KEY you need to create an account in ElevenLabs. After that, go to your account settings and create the API key.
As for the voice ID, you can check the available voices and select the one you prefer! We'll cover this in a dedicated lesson.
Log in to Together AI and, inside your account settings, create the API key.
As we did with the previous API keys, copy the value and paste it into your own .env file.
This project uses Qdrant both locally (you don't need to do anything) and in the cloud (you need to create an account in Qdrant Cloud).
Once you are inside the Qdrant Cloud dashboard, create your API key here:
You also need a QDRANT_URL, which is the URL of your Qdrant Cloud instance. You can find it here:
Copy both values and paste them into your own .env file.
This is everything you need to get the project up and running.
Once you have everything set up, it's time to run the project locally. This is the best way to check that everything is working before starting the course.
To run the project locally, we have created a Makefile. Use the command ava-run to start the project.
make ava-runTo run the project locally using windows command prompt, you can use this code
run.bat ava-runTo run the project locally using windows powershell, you can use this code
.\run.ps1 ava-runOne of the above command will start a Docker Compose application with three services:
- A Qdrant Database (http://localhost:6333/dashboard)
- A Chainlit interface (http://localhost:8000)
- A FastAPI application (http://localhost:8080/docs)
The FastAPI application is necessary for the WhatsApp integration, but that's something we will cover in Lesson 6. So, for now, you can ignore it. Simply click the link to the Chainlit interface to start interacting with Ava.
You should see something like this:
Now that we have verified that everything is working, it's time to move on to the Course Syllabus and start the first lesson!
If you want to clean up the docker compose application and all the related local folders, you can run
make ava-delete. For more info, check the Makefile.