tumeryk-guardrails 0.2.2

API Client for Tumeryk_Guardrails

Tumeryk Guardrails Client

The Tumeryk Guardrails Client is a Python package that provides an interface to the Tumeryk Guardrails API. The client allows you to easily use the API from your Python code.

Setup

To install the Tumeryk Guardrails Client, use pip:

pip install tumeryk_guardrails

Example .env File

TUMERYK_USERNAME=sample_username
TUMERYK_PASSWORD=sample_password
TUMERYK_POLICY=hr_policy

Simple Usage

You can use the Tumeryk Guardrails Client with minimal setup. The client will automatically load the configuration from the .env file if it exists. Here's an example of simple usage:

from dotenv import load_dotenv
load_dotenv()

import tumeryk_guardrails

messages = [{"role": "user", "content": "hi"}]

response = tumeryk_guardrails.tumeryk_completions(messages=messages)

print(response)

Manual Usage

The Tumeryk Guardrails Client uses chat.tmryk.com as the default base URL. However, you can change this URL if required. Here's how you can set a custom base URL:

import tumeryk_guardrails

# Set a custom base URL
tumeryk_guardrails.set_base_url("https://your-custom-url.com")

Configuration

The Tumeryk Guardrails Client uses environment variables to store credentials and policy. Here's an example of how to use environment variables:

import tumeryk_guardrails
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Retrieve credentials and policy from environment variables
username = os.getenv("TUMERYK_USERNAME")
password = os.getenv("TUMERYK_PASSWORD")
policy = os.getenv("TUMERYK_POLICY")

# Authenticate with Tumeryk Guardrails
tumeryk_guardrails.login(username, password)

# Retrieve available policies
policies = tumeryk_guardrails.get_policies()
print("Available Policies:", policies)

# Set the chosen policy
tumeryk_guardrails.set_policy(policy)

# Prepare a message for the guard service
messages = [{"role": "user", "content": "Example input to guard"}]

# Send a request to the guard service
response = tumeryk_guardrails.tumeryk_completions(messages)
print("Guard Response:")
print(response)

Response Structure

The guard service returns a structured response with comprehensive security metrics and detailed logging. Here's an example:

{
    "messages": [
        {
            "role": "assistant",
            "content": "Physics has no single founder. Ancient Greek philosophers like Aristotle, and later scientists such as Galileo, Newton, and Einstein all made foundational contributions.",
            "stats": {
                "total_calls": 1,
                "total_time": 5.176722764968872,
                "total_tokens": 64,
                "total_prompt_tokens": 28,
                "total_completion_tokens": 36,
                "latencies": [
                    2.5881643295288086,
                    2.5885584354400635
                ],
                "llm_process_time": 1.9073486328125e-06
            }
        }
    ],
    "metrics": {
        "violation": false,
        "jailbreak_detection": false,
        "topic_relevance": 1000,
        "trust_score": 773,
        "model_score": 739,
        "real_time_score": 824,
        "hallucination_score": -1.0,
        "bias_score": {
            "input": 990,
            "output": 522
        },
        "toxicity_scores": {
            "input": "Safe",
            "output": "Safe"
        },
        "llama_guard_allowed": {
            "input": true,
            "output": true
        },
        "llama_guard_categories": {
            "input": "Safe",
            "output": "Safe"
        },
        "information": {
            "300": "Low Prompt Injection Score",
            "301": "Low Security Score"
        },
        "jailbreak_score": 991,
        "moderation_score_input": 1000,
        "moderation_score_output": 1000
    },
    "log": "\n# General stats\n\n- Total time: 4.45s\n  - [1.17s][26.3%]: INPUT Rails\n  - [2.60s][58.47%]: DIALOG Rails\n  - [0.66s][14.79%]: OUTPUT Rails\n  - [0.02s][0.44%]: Processing overhead \n- 2 LLM calls, 5.18s total duration, 56 total prompt tokens, 72 total completion tokens, 128 total tokens.\n\n# Detailed stats\n\n- [0.05s] INPUT (jailbreak detection heuristics): 3 actions (jailbreak_detection_heuristics, record_jailbreak_score, get_jailbreak_threshold), 0 llm calls []\n- [0.57s] INPUT (aegis guard check input): 2 actions (aegis_guard_check, record_guard_allowed), 0 llm calls []\n- [0.42s] INPUT (topic guard check input): 2 actions (topic_guard_check_input, record_topic_relevance), 0 llm calls []\n- [0.04s] INPUT (check bias input): 3 actions (check_bias, record_input_bias_score, get_input_fairness_threshold), 0 llm calls []\n- [0.08s] INPUT (mask sensitive data on input): 1 actions (mask_sensitive_data), 0 llm calls []\n- [2.60s] DIALOG (generate user intent): 1 actions (generate_user_intent), 2 llm calls [2.59s, 2.59s]\n- [0.51s] OUTPUT (aegis guard check output): 2 actions (aegis_guard_check, record_guard_allowed), 0 llm calls []\n- [0.05s] OUTPUT (check bias output): 3 actions (check_bias, record_output_bias_score, get_output_fairness_threshold), 0 llm calls []\n- [0.07s] OUTPUT (mask sensitive data on output): 1 actions (mask_sensitive_data), 0 llm calls []\n\n\n"
}

Key Response Components

Messages: Contains the assistant's response with detailed statistics including token usage, processing time, and latencies.

Metrics: Comprehensive security and quality metrics including:

• trust_score: Overall trust rating (0-1000)
• model_score: Model-specific quality score
• real_time_score: Real-time processing score
• jailbreak_score: Protection against jailbreak attempts
• bias_score: Bias detection for input and output
• toxicity_scores: Safety classifications
• topic_relevance: Relevance to intended topics
• hallucination_score: Detection of AI hallucinations
• violation: Boolean indicating policy violations
• information: Additional security insights

Log: Detailed processing statistics showing:

• Processing time breakdown by rail type (INPUT, DIALOG, OUTPUT)
• LLM call statistics and token usage
• Individual action execution times and details
• Performance metrics for each processing stage

Available Methods

The Tumeryk Guardrails Client provides the following methods to interact with the Tumeryk Guardrails API:

• login(username, password): Authenticate and store access token.
• get_policies(): Fetch available policies and return a list.
• set_policy(config_id): Set the configuration/policy to be used by the user.
• tumeryk_completions(messages): Send user input to the Guard service.
• get_base_url(): Get the current base URL.
• set_base_url(base_url): Set a new base URL.

Dependencies

• requests: Used for making HTTP requests to the API.