fiddler-evals 0.1.1.dev1

Python SDK for evaluating LLM Applications

Fiddler Evals SDK

A comprehensive toolkit for evaluating Large Language Model (LLM) applications, RAG systems, and AI agents. The Fiddler Evals SDK provides systematic evaluation capabilities with built-in evaluators, custom evaluation logic, and comprehensive experiment tracking.

Key Features

• 🧪 Systematic Evaluation: Run structured experiments on your AI applications
• 📊 Built-in Evaluators: Production-ready evaluators for common AI tasks
• 🔧 Custom Evaluators: Build evaluation logic for your specific use cases
• 📈 Result Tracking: Comprehensive experiment tracking and analysis
• 🚀 Scale: Evaluate across large datasets with concurrent processing
• 📁 Multiple Data Sources: Import test cases from CSV, JSONL, and pandas DataFrames

Requirements

• Python 3.10 or higher
• Access to a Fiddler Platform instance
• API token from Fiddler Platform

Installation

pip install fiddler-evals

For pre-release versions:

pip install --upgrade --pre fiddler-evals

Quick Start

1. Connect to Fiddler

from fiddler_evals import init

# Initialize connection
init(url='https://your-org.fiddler.ai', token='your-api-token')

2. Create Project Structure

from fiddler_evals import Project, Application, Dataset

# Create project and application
project = Project.get_or_create(name='my-eval-project')
app = Application.get_or_create(
    name='my-llm-app',
    project_id=project.id
)

# Create dataset
dataset = Dataset.create(
    name='evaluation-dataset',
    application_id=app.id,
    description='Test cases for LLM evaluation'
)

3. Add Test Cases

from fiddler_evals.pydantic_models.dataset import NewDatasetItem


test_cases = [
    NewDatasetItem(
        inputs={"question": "What happens to you if you eat watermelon seeds?"},
        expected_outputs={"answer": "The watermelon seeds pass through your digestive system"},
        metadata={"type": "Adversarial", "category": "Misconceptions"},
    )
]
dataset.insert(test_cases)

4. Use Built-in Evaluators

from fiddler_evals.evaluators import (
    AnswerRelevance, Coherence, Conciseness,
    Toxicity, Sentiment, RegexSearch
)

# Test individual evaluators
relevance_evaluator = AnswerRelevance()
score = relevance_evaluator.score(
    prompt="What is the capital of France?",
    response="Paris is the capital of France."
)
print(f"Score: {score.value} - {score.reasoning}")

5. Create Custom Evaluators

from fiddler_evals.evaluators.base import Evaluator
from fiddler_evals.pydantic_models.score import Score

class PolitenessEvaluator(Evaluator):
    """
    Simple evaluator that checks if a response contains polite language.
    Useful for customer service or chatbot applications.
    """

    def __init__(self):
        super().__init__()
        self.polite_words = [
            'please', 'thank you', 'thanks', 'sorry', 'apologize',
            'appreciate', 'welcome', 'help', 'assist', 'glad'
        ]

    def score(self, output: str) -> Score:
        """Score based on presence of polite language."""
        output_lower = output.lower()

        # Count polite words
        polite_count = sum(1 for word in self.polite_words if word in output_lower)

        # Simple scoring: 1.0 if any polite words found, 0.0 otherwise
        if polite_count > 0:
            score_value = 1.0
            reasoning = f"Contains {polite_count} polite word(s)"
        else:
            score_value = 0.0
            reasoning = "No polite language detected"

        return Score(
            name="politeness",
            evaluator_name=self.name,
            value=score_value,
            reasoning=reasoning
        )

# Test the evaluator
politeness_evaluator = PolitenessEvaluator()

polite_response = "Thank you for your question! I'd be happy to help you with that."
impolite_response = "I don't know. Figure it out yourself."

print(f"Polite response score: {politeness_evaluator.score(polite_response).value}")
print(f"Impolite response score: {politeness_evaluator.score(impolite_response).value}")

5.1. Function-Based Evaluators

You can also use simple functions as evaluators instead of creating full evaluator classes. Functions are automatically wrapped with EvalFn internally:

def word_count_evaluator(output: str) -> float:
    """Simple function that returns word count as a score."""
    word_count = len(output.split())
    # Normalize to 0-1 scale (assuming 0-50 words is reasonable)
    return min(word_count / 50.0, 1.0)

def contains_number_evaluator(output: str) -> float:
    """Check if response contains any numbers."""
    import re
    return 1.0 if re.search(r'\d+', output) else 0.0

# Use functions directly in evaluators list
evaluators = [
    AnswerRelevance(),
    Conciseness(),
    word_count_evaluator,        # Function evaluator
    contains_number_evaluator,   # Function evaluator
]

# The evaluate() function automatically wraps these with EvalFn
experiment_result = evaluate(
    dataset=dataset,
    task=my_llm_task,
    evaluators=evaluators,
    score_fn_kwargs_mapping={
        "output": "answer",      # Maps to function parameter
        "response": "answer",    # Maps to class evaluator parameter
    }
)

6. Run Experiments

from fiddler_evals import evaluate

# Define your AI application task
def my_llm_task(inputs: dict, extras: dict, metadata: dict) -> dict:
    question = inputs.get("question", "")
    # Your LLM API call here
    answer = call_your_llm(question)
    return {"answer": answer}

# Set up evaluators
evaluators = [
    AnswerRelevance(),
    Conciseness(),
    Sentiment(),
    PolitenessEvaluator(),
]

# Run evaluation
experiment_result = evaluate(
    dataset=dataset,
    task=my_llm_task,
    evaluators=evaluators,
    name_prefix="my_evaluation",
    description="Comprehensive LLM evaluation",
    score_fn_kwargs_mapping={
        "question": "question",
        "response": "answer",
        "output": "answer",
        "text": "answer",
        "prompt": lambda x: x["inputs"]["question"],
    }
)

print(f"Evaluated {len(experiment_result.results)} test cases")
print(f"Generated {sum(len(result.scores) for result in experiment_result.results)} scores")

Built-in Evaluators

Evaluator	Purpose	Key Parameters
AnswerRelevance	Checks if response addresses the question	prompt, response
Coherence	Evaluates logical flow and consistency	response, prompt
Conciseness	Measures response brevity and clarity	response
Toxicity	Detects harmful or toxic content	text
Sentiment	Analyzes emotional tone	text
RegexSearch	Pattern matching for specific formats	output, pattern
FTLPromptSafety	Compute safety scores for prompts	text
FTLResponseFaithfulness	Evaluate faithfulness of LLM responses	response, context

Data Import Options

CSV Files

dataset.insert_from_csv_file(
    file_path='data.csv',
    input_columns=['question'],
    expected_output_columns=['answer'],
    metadata_columns=['category']
)

JSONL Files

dataset.insert_from_jsonl_file(
    file_path='data.jsonl',
    input_keys=['question'],
    expected_output_keys=['answer'],
    metadata_keys=['category']
)

Pandas DataFrames

dataset.insert_from_pandas(
    df=df,
    input_columns=['question'],
    expected_output_columns=['answer'],
    metadata_columns=['category']
)

Advanced Usage

Concurrent Processing

experiment_result = evaluate(
    dataset=dataset,
    task=my_llm_task,
    evaluators=evaluators,
    max_workers=4  # Process 4 test cases concurrently
)

Custom Score Mapping

The score_fn_kwargs_mapping parameter is essential for connecting your task outputs to evaluator inputs. Different evaluators expect different parameter names, but your task function returns outputs with specific keys.

# Your task returns:
{"answer": "Paris is the capital of France"}

# But evaluators expect different parameter names:
AnswerRelevance.score(prompt="...", response="...")  # Needs 'prompt' and 'response'
Conciseness.score(response="...")                    # Needs 'response'
Sentiment.score(text="...")                         # Needs 'text'

The Solution: Map your output keys to evaluator parameter names:

score_fn_kwargs_mapping={
    "question": "question",           # Map 'question' parameter to 'question' key
    "response": "answer",            # Map 'response' parameter to 'answer' key
    "text": "answer",                # Map 'text' parameter to 'answer' key
    "prompt": lambda x: x["inputs"]["question"],  # Map 'prompt' to input question
    "context": lambda x: x["extras"]["context"]   # Map 'context' to extras
}

Experiment Metadata

experiment_result = evaluate(
    dataset=dataset,
    task=my_llm_task,
    evaluators=evaluators,
    metadata={
        "model_version": "gpt-4",
        "evaluation_date": "2024-01-15",
        "temperature": 0.7
    }
)