cat-cafe-client 0.0.1

Python client for CAT Cafe - Continuous Alignment Testing platform for LLM observability

CAT Cafe SDK

Python SDK for CAT Cafe - Continuous Alignment Testing platform for LLM observability.

Installation

pip install cat-cafe-client

Quick Start

from cat.cafe.client import CATCafeClient, DatasetImport, DatasetExample

# Initialize the client
client = CATCafeClient(base_url="http://localhost:8000")

# Create a dataset
dataset = DatasetImport(
    name="My Test Dataset",
    description="Sample dataset for testing",
    examples=[
        DatasetExample(
            input={"messages": [{"role": "user", "content": "What's the weather?"}]},
            output={"messages": [{"role": "assistant", "content": "Weather info"}]},
            metadata={"tags": ["weather"]},
        )
    ]
)

# Import the dataset
result = client.import_dataset(dataset)
dataset_id = result["dataset"]["id"]

# Define a test function
def my_test_function(example):
    # Your AI system logic here
    messages = example.input.get("messages", []) if isinstance(example.input, dict) else []
    user_question = messages[-1]["content"] if messages else ""
    return f"Response to: {user_question}"

# Define evaluators
def accuracy_evaluator(actual_output, reference_output):
    # Your evaluation logic here
    expected_messages = reference_output.get("messages", []) if isinstance(reference_output, dict) else []
    score = 0.8  # Example score
    reason = "Good response"
    return score, reason

# Run tests on the dataset
experiment_id = client.run_test_on_dataset(
    dataset=dataset_id,
    test_function=my_test_function,
    evaluators=[accuracy_evaluator],
    name="My Experiment",
    description="Testing my AI system"
)

print(f"Experiment completed: {experiment_id}")

Core Classes

CATCafeClient

The main client for interacting with CAT Cafe:

client = CATCafeClient(base_url="http://localhost:8000")

Dataset Models

• DatasetImport: For creating new datasets with examples
• DatasetExample: Individual examples in a dataset
• Dataset: Structured dataset object returned from API
• Example: Individual example object returned from API

Experiment Models

• Experiment: Experiment configuration
• ExperimentResult: Results from running experiments

Key Methods

Dataset Operations

# Import a complete dataset
result = client.import_dataset(dataset_import)

# Fetch dataset as structured object
dataset = client.fetch_dataset(dataset_id)

# Find dataset by name
dataset = client.fetch_dataset_by_name("My Dataset")

# List all datasets
datasets = client.list_datasets()

Experiment Operations

# Run tests on a dataset (all-in-one)
experiment_id = client.run_test_on_dataset(
    dataset=dataset_id,
    test_function=my_test_func,
    evaluators=[evaluator1, evaluator2],
    name="My Test Run"
)

# Manual experiment workflow
experiment_id = client.start_experiment(experiment_config)
client.submit_results(experiment_id, results)
client.complete_experiment(experiment_id)

Test Functions

Test functions receive an Example object and should return a string output:

def my_test_function(example: Example) -> str:
    # Access the input messages
    messages = example.input.get("messages", []) if isinstance(example.input, dict) else list(example.input)
    user_message = messages[-1]["content"] if messages else ""
    
    # Your AI system logic here
    response = call_my_ai_system(user_message)
    
    return response

Evaluators

Evaluators receive the actual output and reference output payload, returning a score and reason:

def my_evaluator(actual_output: str, reference_output: list) -> tuple[float, str]:
    # Your evaluation logic
    if "correct_info" in actual_output:
        return 1.0, "Contains correct information"
    else:
        return 0.0, "Missing correct information"

Advanced Usage

Experiment Runner

Experiment runner orchestration (listeners, tracing, caching, etc.) now lives in the cat-experiments package. Install and import cat.experiments if you need the higher-level runner utilities; this SDK now focuses on the core client and evaluation primitives.

Async Test Functions

async def async_test_function(example: Example) -> str:
    # Async AI system call
    response = await my_async_ai_system(example.input)
    return response

# Note: Async functions work but have limitations in certain contexts
experiment_id = client.run_test_on_dataset(
    dataset=dataset_id,
    test_function=async_test_function,
    name="Async Test"
)

Custom Metadata

def metadata_function(example: Example, output: str) -> dict:
    return {
        "response_length": len(output),
        "example_tags": example.tags
    }

experiment_id = client.run_test_on_dataset(
    dataset=dataset_id,
    test_function=my_test_function,
    metadata_function=metadata_function,
    name="Test with Metadata"
)

Manual Experiment Control

# Create experiment configuration
experiment_config = Experiment(
    name="Manual Experiment",
    description="Step-by-step experiment",
    dataset_id=dataset_id,
    tags=["manual", "testing"]
)

# Start experiment
experiment_id = client.start_experiment(experiment_config)

# Run your tests and collect results
results = []
dataset = client.fetch_dataset(dataset_id)

for example in dataset.examples:
    output = my_test_function(example)
    
    result = ExperimentResult(
        example_id=example.id,
        input_data={"input": example.input},
        output=dict(example.output),
        actual_output=output,
        evaluation_scores={"manual_score": 0.8}
    )
    results.append(result)

# Submit results
client.submit_results(experiment_id, results)

# Complete experiment
client.complete_experiment(experiment_id, {"total_examples": len(results)})

Requirements

• Python 3.12+
• httpx
• CAT Cafe server running (default: http://localhost:8000)

License

MIT License