This demo showcases how to use LLMs to turn audio files from call center conversations between customers and agents into valuable data, all in a single workflow orchestrated by MLRun.
MLRun automates the entire workflow, auto-scales resources as needed, and automatically logs and parses values between the different workflow steps.
By the end of this demo you will see the potential power of LLMs for feature extraction, and how easily you can do this with MLRun!
This demo uses:
- OpenAI's Whisper — To transcribe the audio calls into text.
- Flair and Microsoft's Presidio - To recognize PII so it can be filtered out.
- HuggingFace — 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 answer questions.
- and MLRun — as the orchestrator to operationalize the workflow.
The demo contains a single notebook that encompasses the entire demo.
Most of the functions are imported from MLRun's function hub, which contains a wide range of functions that can be used for a variety of use cases. All functions used in the demo include links to their source in the hub. All of the python source code is under /src. 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 supports Docker and there are sufficient 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.
