Learning
Tutorials: Implementing LVS and R_m
Hands-on Implementation Guides
Ready to implement the SYMBI framework in your own projects? These guides provide step-by-step instructions and code snippets to get you started.
1. How to Calculate R_m
To calculate the Resonance Metric for a conversation, follow these steps:
- Generate embeddings for the user input and the AI response using a compatible model (e.g., MiniLM).
- Calculate the cosine similarity (V_align) between these embeddings.
- Extract the entropy/perplexity from the LLM response object.
- Apply the weights as specified in our Resonance Metrics page.
Python Snippet
# Simple R_m implementation
def get_quick_resonance(input_text, response_text):
v_align = model.encode_similarity(input_text, response_text)
entropy = get_model_entropy(response_text)
# Using standard weights
return (1 + entropy) / (v_align * 0.5 + 0.5)2. Generating Trust Receipts
Once you have your R_m score, you can package it into a verifiable Trust Receipt.
Python Snippet
from symbi_framework import TrustReceipt
# Create and sign receipt
receipt = TrustReceipt.issue(
interaction_id="...",
metrics={"score": 1.45, "status": "HIGH_RESONANCE"}
)
print(receipt.to_json())3. Integration with SONATE
For users of the **Yseeku Platform**, integrating LVS is as simple as plugging into the@sonate/detect module. This module provides real-time monitoring of R_m across all your AI endpoints.