- 💻 Device side
we seamlessly integrate various user data types, including live information, location data, and inputs in multiple formats, such as images, text, audio, JSON, and video saved in the vector database. These inputs undergo embedding searches for relevant information and are then processed within our Flow Client before being transmitted to the MEC as inputs.
- 🌐 MEC side
We leverage MEC APIs and local vector databases, each housing data in diverse formats. These rich local data, along with the user inputs from the device side, empower our Large Language Models with user-specific insights and abundant local knowledge. We are also facilitating seamless streaming audio interactions through our streaming ASR TTS services, which operate on the MEC.
- 🔧 Edge Computing Front
We are striving to maximize the potential of edge resources, including computing, storage, and server engines, which opens up opportunities in the future such as federated learning on the edge. This approach enables us to deliver highly customized and localized services, ultimately shaping the future of our platform.
pip install -r requirements.txt create a file named ".env" and add OPENAI_API_KEY=YOUR OPENAI API KEYrun app.py