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User seeks to differentiate JEV intent routing from basic embedding models

The user is seeking clarification on the distinction between JEV (Jarvis Workflow) and standard embedding models, particularly in the context of intent recognition. They provided a Python script that uses Ollama's `nomic-embed-text` model to calculate cosine similarities between a given phrase and example utterances for various intents. The script demonstrates how these similarities can be used as a raw routing signal, prompting the user to ask for a more detailed explanation of JEV's unique aspects compared to a basic embedding approach. AI

IMPACT Clarifies the functional difference between a specific intent routing system and general embedding models for AI practitioners.

RANK_REASON User question seeking clarification on a technical topic, not a new release or significant industry event.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

User seeks to differentiate JEV intent routing from basic embedding models

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User question seeking clarification on a technical topic, not a new release or significant industry event.
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/spammmmmmmmy ·

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

    <!-- SC_OFF --><div class="md"><p>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.</p> <p>I'll even give you my JEV server for free! It uses ollama, you install `ollama …