In this demo we will be showcasing how we used LLMs to turn call center conversation audio files of customers and agents into valueable data in a single workflow orchastrated by MLRun.
MLRun will be automating the entire workflow, auto-scale resources as needed and automatically log and parse values between the workflow different steps.
By the end of this demo you will see the potential power of LLMs for feature extraction, and how easy it is being done using MLRun!
We will use:
- OpenAI's Whisper - To transcribe the audio calls into text.
- Flair and Microsoft's Presidio - To recognize PII for filtering it out.
- HuggingFace - as the main machine learning framework to get the model and tokenizer for the features extraction. The demo uses tiiuae/falcon-40b-instruct as the LLM to asnwer questions.
- and MLRun - as the orchastraitor to operationalize the workflow.
The demo contains a single notebook that covers the entire demo.
Most of the functions are being imported from MLRun's hub - a wide range of functions that can be used for a variety of use cases. You can find all the python source code under /src and links to the used functions from the hub in the notebook.
Enjoy!
This project can run in different development environments:
- Local computer (using PyCharm, VSCode, Jupyter, etc.)
- Inside GitHub Codespaces
- Other managed Jupyter environments
To get started, fork this repo into your GitHub account and clone it into your development environment.
To install the package dependencies (not required in GitHub codespaces) use:
make install-requirements
If you prefer to use Conda use this instead (to create and configure a conda env):
make conda-env
Make sure you open the notebooks and select the
mlrunconda environment
The MLRun service and computation can run locally (minimal setup) or over a remote Kubernetes environment.
If your development environment support docker and have enough CPU resources run:
make mlrun-docker
MLRun UI can be viewed in: http://localhost:8060
If your environment is minimal, run mlrun as a process (no UI):
[conda activate mlrun &&] make mlrun-api
For MLRun to run properly you should set your client environment, this is not required when using codespaces, the mlrun conda environment, or iguazio managed notebooks.
Your environment should include MLRUN_ENV_FILE=<absolute path to the ./mlrun.env file> (point to the mlrun .env file
in this repo), see mlrun client setup instructions for details.
Note: You can also use a remote MLRun service (over Kubernetes), instead of starting a local mlrun, edit the mlrun.env and specify its address and credentials