A guide to implementing a pure front-end AI chat program
Deep Chat's Web Modeling feature makes it possible to run AI on the browser side, with specific implementation paths:
- Model Selection: Support for lightweight models such as RedPajama/TinyLlama via the
npm install deep-chat-web-llmmounting - local inference: Configuration
webModelAfter the properties, the model weights are automatically downloaded and cached in IndexedDB - Resource control: Built-in models take up about 300MB-2GB of storage space and automatically handle memory allocation
- Functional limitations: Suitable for simple QA scenarios, complex tasks still need to connect to cloud APIs
Deployment process::
- Adding static HTML to the
<script src="deepChat.bundle.js"></script> - herald
<deep-chat webModel='{"model":"TinyLlama"}'></deep-chat> - Improve loading speed by pre-caching model files with Service Worker
- utilization
onMessageInterceptor handles special response formats for local models
caveat: First time loading requires downloading the model file, it is recommended to add a loading progress prompt. For devices with poor performance, thequantizationparameter enables the 4-bit quantization version.
This answer comes from the articleDeep Chat: an AI chat component for quick website integrationThe
May not be reproduced without permission:AI productivity tools " How to implement the AI chat feature to run in a serverless environment?
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