continuous-eval

Production-Grade Evaluation for LLM-Powered Applications

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Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

README:

Production-Grade Evaluation for LLM-Powered Applications

Overview

continuous-eval is an open-source package created for granular and rigorous evaluation of LLM-powered application.

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
Retrieval	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, SQLSyntaxMatch, SQLASTSimilarity
Code Generation	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 a pipeline

Define modules in your pipeline and select corresponding metrics.

from continuous_eval.eval import Module, ModuleOutput, Pipeline, Dataset, EvaluationRunner
from continuous_eval.eval.logger import PipelineLogger
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

pipelog = PipelineLogger(pipeline=pipeline)

# now run your LLM application pipeline, and for each module, log the results:
pipelog.log(uid=sample_uid, module="module_name", value=data)

# Once you finish logging the data, you can use the EvaluationRunner to evaluate the logs
evalrunner = EvaluationRunner(pipeline)
metrics = evalrunner.evaluate(pipelog)
metrics.results() # returns a dictionary with the results

To run evaluation over an existing dataset (BYODataset), you can run the following:

dataset = Dataset(...)
evalrunner = EvaluationRunner(pipeline)
metrics = evalrunner.evaluate(dataset)

Synthetic Data Generation

Ground truth data, or reference data, is important for evaluation as it can offer a comprehensive and consistent measurement of system performance. However, it is often costly and time-consuming to manually curate such a golden dataset. We have created a synthetic data pipeline that can custom generate user interaction data for a variety of use cases such as RAG, agents, copilots. They can serve a starting point for a golden dataset for evaluation or for other training purposes. Below is an example for Coding Agents. Try out this demo: Synthetic Data Demo

💡 Contributing

Interested in contributing? Contributions to LlamaIndex core as well as contributing integrations that build on the core are both accepted and highly encouraged! See our Contribution Guide 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)
- How to Make the Most Out of LLM Production Data: Simulated User Feedback (link)
- Generate Synthetic Data to Test LLM Applications (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.

For Tasks:

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