quotientai 0.4.7

Python library for tracing, logging, and detecting problems with AI Agents

quotientai

Overview

quotientai is an SDK and CLI for logging data to Quotient, and running hallucination and document attribution detections for retrieval and search-augmented AI systems.

Installation

pip install quotientai

Usage

Create an API key on Quotient and set it as an environment variable called QUOTIENT_API_KEY. Then follow the examples below or see our docs for a more comprehensive walkthrough.

Send your first log and detect hallucinations. Run the code below and see your Logs and Detections on your Quotient Dashboard.

from quotientai import QuotientAI
from quotientai.types import DetectionType

quotient = QuotientAI()
quotient.logger.init(
    # Required
    app_name="my-app",
    environment="dev",
    # dynamic labels for slicing/dicing analytics e.g. by customer, feature, etc
    tags={"model": "gpt-4o", "feature": "customer-support"},
    detections=[DetectionType.HALLUCINATION, DetectionType.DOCUMENT_RELEVANCY],
    detection_sample_rate=1.0,
)

log_id = quotient.log(
    user_query="How do I cook a goose?",
    model_output="The capital of France is Paris",
    documents=["Here is an excellent goose recipe..."]
)

print(log_id)

You can also use the async client if you need to create logs asynchronously.

from quotientai import AsyncQuotientAI
from quotientai.types import DetectionType
import asyncio

quotient = AsyncQuotientAI()

quotient.logger.init(
    # Required
    app_name="my-app",
    environment="dev",
    # dynamic labels for slicing/dicing analytics e.g. by customer, feature, etc
    tags={"model": "gpt-4o", "feature": "customer-support"},
    detections=[DetectionType.HALLUCINATION, DetectionType.DOCUMENT_RELEVANCY],
    detection_sample_rate=1.0,
)


async def main():
    # Mock retrieved documents
    retrieved_documents = [{"page_content": "Sample document"}]

    log_id = await quotient.log(
        user_query="Sample input",
        model_output="Sample output",
        # Page content from Documents from your retriever used to generate the model output
        documents=[doc["page_content"] for doc in retrieved_documents],
        # Message history from your chat history
        message_history=[
            {"role": "system", "content": "You are an expert on geography."},
            {"role": "user", "content": "What is the capital of France?"},
            {"role": "assistant", "content": "The capital of France is Paris"},
        ],
        # Instructions for the model to follow
        instructions=[
            "You are a helpful assistant that answers questions about the world.",
            "Answer the question in a concise manner. If you are not sure, say 'I don't know'.",
        ],
        # Tags can be overridden at log time
        tags={"model": "gpt-4o-mini", "feature": "customer-support"},
    )

    print(log_id)


# Run the async function
asyncio.run(main())