Can someone explain how JEV is different from a simple embeddings model?

How is JEV any different from using an embeddings model? I really will appreciate if someone can explain this to me - because I have yet to see the difference.

I'll even give you my JEV server for free! It uses ollama, you install \ollama pull nomic-embed-text:latest\.

% python3 ./jev_embedding.py "How high is the sky?"
find_phone: 0.38
volume: 0.41
calendar: 0.44
tell_the_time: 0.49
weather: 0.53

% python3 ./jev_embedding.py "I had this thing on my anus. The doctor burned it off with a laser."
weather: 0.35
tell_the_time: 0.36
calendar: 0.37
volume: 0.38
find_phone: 0.43

% python3 ./jev_embedding.py "can you help me locate my phone."
volume: 0.38
weather: 0.40
calendar: 0.43
tell_the_time: 0.53
find_phone: 0.89

% python3 ./jev_embedding.py "Hello Cleveland! I can't HEAR you"
weather: 0.37
calendar: 0.40
tell_the_time: 0.44
find_phone: 0
volume: 0.56

#!/usr/bin/env python3
"""
jev_embedding.py — minimal showcase of the embedding-based intent router,
excised from jarvis_workflow.py.

Given a phrase on the command line, it embeds the phrase and every example
utterance (via the local Ollama embedding model), then prints the cosine
similarity of the phrase to each intent — the raw routing signal — instead of
running a handler and speaking an answer.

python3 jev_embedding.py "How high is the sky?"
"""

import sys
import requests

# --- Config (same endpoint/model as jarvis_workflow.py) ---
OLLAMA_EMBED_URL = "http://localhost:11434/api/embeddings"
INTENT_EMBED_MODEL = "nomic-embed-text"

# --- The five cases to detect ---
# label -> example utterances, matched by similarity.
INTENTS = {
"volume": [
"turn the volume up",
"make it quieter",
"set the volume to seven",
],
"tell_the_time": [
"what time is it",
"can you tell me the time",
],
"weather": [
"how's the weather going to be today",
"will it rain today",
"do I need a raincoat",
],
"find_phone": [
"find my phone",
"where's my phone",
"ring my phone",
],
"calendar": [
"when is my next meeting",
"what's coming up on the calendar tomorrow",
],
}
def _embed(text):
"""Return a unit-normalised embedding (list of floats) from the Ollama model."""
r = requests.post(OLLAMA_EMBED_URL,
json={"model": INTENT_EMBED_MODEL, "prompt": text},
timeout=10)
vec = r.json().get("embedding")
if not vec:
raise RuntimeError("no embedding returned")
norm = (sum(x * x for x in vec)) ** 0.5 or 1.0
return [x / norm for x in vec]


def _cosine(a, b):
"""Cosine of two unit vectors is their dot product."""
return sum(x * y for x, y in zip(a, b))


def score_intents(text):
"""Best cosine similarity of text to each intent's example utterances."""
q = _embed(text)
return {label: max(_cosine(q, _embed(ex)) for ex in examples)
for label, examples in INTENTS.items()}


if __name__ == "__main__":
if len(sys.argv) < 2:
print('Usage: python3 jev_embedding.py "your phrase"')
sys.exit(1)

phrase = " ".join(sys.argv[1:])
scores = score_intents(phrase)
for label, score in sorted(scores.items(), key=lambda kv: kv[1]):
print(f"{label}: {score:.2f}")