evallm 0.1.1

A flexible task evaluation client

🧪 task-evaluator

A lightweight, pluggable Python package to evaluate tasks using different model providers like Ollama and OpenAI — with a unified interface.

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📦 Installation

pip install evallm

🚀 Usage

Using Ollama

Before using the LLMEvaluator with Ollama, ensure you have an Ollama server running. If you don't have any, you can start one in your local environment using Ollama CLI. If you don't have it installed, you can download it from here.

After that you can start a server with the following command:

ollama serve

This example assumes you are running a local Ollama server (e.g., at localhost:11434).

from evaluator import LLMEvaluator

# Initialize the Evaluator with Ollama
evaluator = LLMEvaluator(connection="ollama", model="llama3.1:8b", task="summarization")

# Evaluate a task
result = evaluator.evaluate(
    text="The quick brown fox jumps over the lazy dog. The dog was not happy about it.",
    summary="A fox jumps over a dog.",
)
print(result)

Ollama uses port 11434 by default. If you want to change the port, you can specify the full URI when initializing the LLMEvaluator. For example, if you want to use port 12345, you can do it like this:

from evaluator import LLMEvaluator
evaluator = LLMEvaluator(connection="ollama", model="llama3.1:8b", url="http://localhost:12345", task="summarization")

Using OpenAI

Since we do not deploy any LLM models globally, you can only use OpenAI models with your OpenAI API key. You can find the installation instructions here. You can set your OpenAI API key in your environment variables. For example, in a Unix-like terminal, you can do it like this:

export OPENAI_API_KEY="your_openai_api_key"

Or in a Windows terminal, you can do it like this:

set OPENAI_API_KEY="your_openai_api_key"

You can also set the OpenAI API key in your code directly, but it's not recommended for security reasons.

This example assumes you have set up your OpenAI API key in your environment variables.

from evaluator import LLMEvaluator

import os

# Initialize the Evaluator with OpenAI
evaluator = Evaluator(connection="openai", model="gpt-3.5-turbo", api_key=os.getenv("OPENAI_API_KEY"), task="summarization")

# Evaluate a task
result = evaluator.evaluate(
    text="The quick brown fox jumps over the lazy dog. The dog was not happy about it.",
    summary="A fox jumps over a dog.",
)
print(result)

🔍 Tasks

Currently, the following tasks are supported:

• 📝 Summarization: Evaluates the quality of a summary given an original text
• 🔎 NLI: Natural Language Inference to determine relationships between text pairs
• ⚖️ Pairwise: Compares two outputs to determine which one better meets specified criteria

🧪 Evaluation

The evaluation process is based on the task you choose. The LLMEvaluator class provides a unified interface for evaluating tasks using different model providers.

Summarization

The summarization task evaluates the quality of a summary given an original text. It uses the evaluate method to compare the generated summary with the original text.

from evaluator import LLMEvaluator

evaluator = LLMEvaluator(connection="ollama", model="llama3.1:8b", task="summarization")
result = evaluator.evaluate(
    text="The quick brown fox jumps over the lazy dog. The dog was not happy about it.",
    summary="A fox jumps over a dog.",
    metric="all"  # or "coherence", "fluency", "relevancy", "consistency"
)
print(result)

The evaluate method takes the following parameters:

• text: The original text to be summarized.
• summary: The generated summary to be evaluated.
• metric: The evaluation metric to be used. The default is all, which means all metrics will be used. You can also specify a single metric, such as coherence, fluency, relevancy and consistency.

The method returns a dataclass object containing the evaluation results, including the score and the explanation comes from the evaluation.

• score: The score of the evaluation (1-5).
• explanation: The explanation of the evaluation.
• metric: The metric used for the evaluation.

Note that if you use the all metric, the result will be a list of dataclass objects, each containing the score and explanation for each metric.

NLI

The nli task evaluates the relationship between two text pairs and the label of the relationship. It uses the evaluate method to compare the generated summary with the original text.

from evaluator import LLMEvaluator

evaluator = LLMEvaluator(connection="ollama", model="llama3.1:8b", task="nli")
result = evaluator.evaluate(
    premise="The quick brown fox jumps over the lazy dog.",
    hypothesis="The dog was not happy about it.",
    label="entailment",  # or "contradiction", "neutral"
)

The evaluate method takes the following parameters:

• premise: The premise of the inference.
• hypothesis: The hypothesis of the inference.
• label: The label of the inference. It should be one of these: entailment, contradiction or neutral.

The method returns a dataclass object containing the evaluation results, including the pass/fail status and the explanation comes from the evaluation.

• status: The status of the evaluation. It is True if the evaluation passed, otherwise False.
• explanation: The explanation of the evaluation.

Pairwise

The pairwise task evaluates a question and two outputs to determine which one better meets the needs in the question. It uses the evaluate method to compare the two outputs.

from evaluator import LLMEvaluator

evaluator = LLMEvaluator(connection="ollama", model="llama3.1:8b", task="pairwise")

result = evaluator.evaluate(
    question="Give me a sentence about a dog.",
    output1="The quick brown fox jumps over the lazy cat.",
    output2="The dog is barking at the cat.",
)

The evaluate method takes the following parameters:

• question: The question to be answered.
• output1: The first output to be evaluated.
• output2: The second output to be evaluated.

The method returns a dataclass object containing the evaluation results, including the choice and the explanation comes from the evaluation.

• choice: The choice of the evaluation. It is 1 if the first output is better, 2 if the second output is better.
• explanation: The explanation of the evaluation.