continuous-eval 0.3.1

Open-Source Evaluation for GenAI Application Pipelines.

Open-Source Evaluation for GenAI Application Pipelines

Overview

continuous-eval is an open-source package created for granular and holistic evaluation of GenAI application pipelines.

How is continuous-eval different?

• Modularized Evaluation: Measure each module in the pipeline with tailored metrics.

• Comprehensive Metric Library: Covers Retrieval-Augmented Generation (RAG), Code Generation, Agent Tool Use, Classification and a variety of other LLM use cases. Mix and match Deterministic, Semantic and LLM-based metrics.

• Leverage User Feedback in Evaluation: Easily build a close-to-human ensemble evaluation pipeline with mathematical guarantees.

• Synthetic Dataset Generation: Generate large-scale synthetic dataset to test your pipeline.

Getting Started

This code is provided as a PyPi package. To install it, run the following command:

python3 -m pip install continuous-eval

if you want to install from source:

git clone https://github.com/relari-ai/continuous-eval.git && cd continuous-eval
poetry install --all-extras

To run LLM-based metrics, the code requires at least one of the LLM API keys in .env. Take a look at the example env file .env.example.

Run a single metric

Here's how you run a single metric on a datum. Check all available metrics here: link

from continuous_eval.metrics.retrieval import PrecisionRecallF1

datum = {
    "question": "What is the capital of France?",
    "retrieved_context": [
        "Paris is the capital of France and its largest city.",
        "Lyon is a major city in France.",
    ],
    "ground_truth_context": ["Paris is the capital of France."],
    "answer": "Paris",
    "ground_truths": ["Paris"],
}

metric = PrecisionRecallF1()

print(metric(**datum))

Available Metrics

Module	Category	Metrics
Retrieval	Deterministic	PrecisionRecallF1, RankedRetrievalMetrics
	LLM-based	LLMBasedContextPrecision, LLMBasedContextCoverage
Text Generation	Deterministic	DeterministicAnswerCorrectness, DeterministicFaithfulness, FleschKincaidReadability
	Semantic	DebertaAnswerScores, BertAnswerRelevance, BertAnswerSimilarity
	LLM-based	LLMBasedFaithfulness, LLMBasedAnswerCorrectness, LLMBasedAnswerRelevance, LLMBasedStyleConsistency
Classification	Deterministic	ClassificationAccuracy
Code Generation	Deterministic	CodeStringMatch, PythonASTSimilarity
	LLM-based	LLMBasedCodeGeneration
Agent Tools	Deterministic	ToolSelectionAccuracy
Custom		Define your own metrics

To define your own metrics, you only need to extend the Metric class implementing the __call__ method. Optional methods are batch (if it is possible to implement optimizations for batch processing) and aggregate (to aggregate metrics results over multiple samples_).

Run evaluation on pipeline modules

Define modules in your pipeline and select corresponding metrics.

from continuous_eval.eval import Module, ModuleOutput, Pipeline, Dataset
from continuous_eval.metrics.retrieval import PrecisionRecallF1, RankedRetrievalMetrics
from continuous_eval.metrics.generation.text import DeterministicAnswerCorrectness
from typing import List, Dict

dataset = Dataset("dataset_folder")

# Simple 3-step RAG pipeline with Retriever->Reranker->Generation
retriever = Module(
    name="Retriever",
    input=dataset.question,
    output=List[str],
    eval=[
        PrecisionRecallF1().use(
            retrieved_context=ModuleOutput(),
            ground_truth_context=dataset.ground_truth_context,
        ),
    ],
)

reranker = Module(
    name="reranker",
    input=retriever,
    output=List[Dict[str, str]],
    eval=[
        RankedRetrievalMetrics().use(
            retrieved_context=ModuleOutput(),
            ground_truth_context=dataset.ground_truth_context,
        ),
    ],
)

llm = Module(
    name="answer_generator",
    input=reranker,
    output=str,
    eval=[
        FleschKincaidReadability().use(answer=ModuleOutput()),
        DeterministicAnswerCorrectness().use(
            answer=ModuleOutput(), ground_truth_answers=dataset.ground_truths
        ),
    ],
)

pipeline = Pipeline([retriever, reranker, llm], dataset=dataset)
print(pipeline.graph_repr()) # optional: visualize the pipeline

Now you can run the evaluation on your pipeline

eval_manager.start_run()
  while eval_manager.is_running():
    if eval_manager.curr_sample is None:
      break
    q = eval_manager.curr_sample["question"] # get the question or any other field
    # run your pipeline ...
    eval_manager.next_sample()

To log the results you just need to call the eval_manager.log method with the module name and the output, for example:

eval_manager.log("answer_generator", response)

The evaluator manager also offers

• eval_manager.run_metrics() to run all the metrics defined in the pipeline
• eval_manager.run_tests() to run the tests defined in the pipeline (see the documentation docs for more details)

Resources

• Docs: link
• Examples Repo: end-to-end example repo
• Blog Posts:
  • Practical Guide to RAG Pipeline Evaluation: Part 1: Retrieval, Part 2: Generation
  • How important is a Golden Dataset for LLM evaluation? (link)
  • How to evaluate complex GenAI Apps: a granular approach (link)
• Discord: Join our community of LLM developers Discord
• Reach out to founders: Email or Schedule a chat

License

This project is licensed under the Apache 2.0 - see the LICENSE file for details.

Open Analytics

We monitor basic anonymous usage statistics to understand our users' preferences, inform new features, and identify areas that might need improvement. You can take a look at exactly what we track in the telemetry code

To disable usage-tracking you set the CONTINUOUS_EVAL_DO_NOT_TRACK flag to true.