Tutorial
Quick Start
This tutorial assumes that Protegrity Data Discovery is installed in the same network environment. If it is not installed, messages can still be sent from: user to the semantic processor.
The following is a simple Python request example:
import requests
data = {
"messages": [
{
"from": "user",
"to": "ai",
"content": "Hello, what's your name?",
"processors": ["semantic"],
},
{
"from": "ai",
"to": "user",
"content": "My name is AI!",
"processors": ["pii"],
},
]
}
response = requests.post(
"http://localhost:8001/pty/semantic-guardrail/v1.0/conversations/messages/scan",
json=data,
)
print(response.status_code)
print(response.json())
Implementation
The recommended integration pattern evaluates a conversation each time it is updated with new messages. This applies to messages from either users or AI systems. The solution analyzes the full conversation for enhanced effectiveness. Identical input requests are cached internally for optimized performance.
import requests
def apply_guardrail(data: dict):
"""Evaluate conversation with security guardrail."""
response = requests.post(
"http://localhost:8001/pty/semantic-guardrail/v1.0/conversations/messages/scan",
json=data,
)
if response.json()["batch"]["outcome"] == "rejected":
print(response.json())
raise ValueError(
"Guardrail rejected the conversation - check for security risks"
)
def send_to_ai(data: dict) -> str:
"""Send conversation to AI system and return response."""
# Implementation specific to your AI system
ai_output = ...
return ai_output
# Initialize conversation
conversation = {"messages": []}
# Gather user input
conversation["messages"].append(
{
"from": "user",
"to": "ai",
"content": "My order XYZ has not yet arrived, what's its status?",
"processors": ["semantic"],
}
)
# Apply security evaluation
apply_guardrail(conversation)
# Generate AI response
conversation["messages"].append(
{
"from": "ai",
"to": "user",
"content": send_to_ai(conversation),
"processors": ["pii"],
}
)
# Re-evaluate with complete conversation
apply_guardrail(conversation)
Advanced Usage
For more granular control, a custom threshold check can be implemented on the client side, based on numerical ['batch']['score'] output values. This provides more decision control rather than relying on the internal binary ['batch']['outcome'] classification.
Feedback
Was this page helpful?