Javier- Build a personalized Commute virtual assistant in Python w/ Hopsworks & LLM Function Calling

LLM function calling enables virtual assistants to understand user intent and execute specific functions from a predefined set to retrieve external data or perform operations

The project demonstrates building a commute assistant using:

• Hopsworks AI lake house as the feature store
• Public transport API data from Stockholm
• Functions for predicting delays and retrieving historical departure data
• LLM (fine-tuned Mistral model) for natural language understanding

The FDI pipeline architecture consists of:

• Feature pipeline for raw data processing
• Training pipeline for model training/fine-tuning
• Inference pipeline for real-time predictions

Hopsworks provides key abstractions:

• Feature groups for organizing related features
• Feature views for low-latency feature vector retrieval
• Model registry and serving capabilities
• Streaming data processing with Quick Streams

LLM function calling workflow:

1. User sends question to assistant
2. LLM determines which function(s) to call
3. System executes functions to get external data
4. Data is incorporated into prompt
5. LLM generates final response

The system addresses LLM limitations by:

• Adding real-time context to static model knowledge
• Using function calling for current data retrieval
• Maintaining conversation history
• Breaking complex queries into function-specific steps

Implementation includes:

• Prompt templates for function calling
• JSON Function definitions with parameters
• Feature pipelines for real-time and batch processing
• Streaming aggregations for historical analysis