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What is pytest?

Pytest serves as an essential resource for improving your coding abilities, enabling the easy development of both simple tests and more intricate functional tests across a variety of applications and libraries. The framework excels in offering comprehensive assertion introspection, allowing you to depend solely on standard assert statements for all your testing requirements. It provides extensive insights into failed assertions, automatically detects test modules and functions, and includes versatile fixtures that efficiently manage both small and parameterized long-lived test resources. Moreover, pytest can effortlessly run unittest (including trial) and nose test suites, and it supports Python versions 3.6 and later, as well as PyPy 3. Its extensive plugin ecosystem includes over 315 external plugins, alongside a dynamic community of users contributing to its growth. Additionally, the maintainers of pytest, in collaboration with Tidelift, offer commercial support and maintenance for the open-source dependencies crucial to your projects, further enhancing its value. By integrating pytest into your workflow, you can streamline your testing process, reduce potential risks, and improve the overall quality of your codebase, all while ensuring fair compensation for the developers of the dependencies you utilize. This dedication to community engagement and support distinctly positions pytest as a frontrunner in the realm of testing frameworks, making it a preferred choice among developers.

What is DeepEval?

DeepEval presents an accessible open-source framework specifically engineered for evaluating and testing large language models, akin to Pytest, but focused on the unique requirements of assessing LLM outputs. It employs state-of-the-art research methodologies to quantify a variety of performance indicators, such as G-Eval, hallucination rates, answer relevance, and RAGAS, all while utilizing LLMs along with other NLP models that can run locally on your machine. This tool's adaptability makes it suitable for projects created through approaches like RAG, fine-tuning, LangChain, or LlamaIndex. By adopting DeepEval, users can effectively investigate optimal hyperparameters to refine their RAG workflows, reduce prompt drift, or seamlessly transition from OpenAI services to managing their own Llama2 model on-premises. Moreover, the framework boasts features for generating synthetic datasets through innovative evolutionary techniques and integrates effortlessly with popular frameworks, establishing itself as a vital resource for the effective benchmarking and optimization of LLM systems. Its all-encompassing approach guarantees that developers can fully harness the capabilities of their LLM applications across a diverse array of scenarios, ultimately paving the way for more robust and reliable language model performance.

Media

Media

Integrations Supported

Opik
Allure Report
Captain
Codecov
Coverage.py
Katalon Recorder
Launchable
Python
Qualflare
TestQuality
Testmo
VIKTOR
Zebrunner

Integrations Supported

Opik
Hugging Face
KitchenAI
Llama 2
LlamaIndex
OpenAI
Ragas
Weaviate

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

pytest

Date Founded

2004

Company Website

docs.pytest.org/en/6.2.x/

Company Facts

Organization Name

Confident AI

Company Location

United States

Company Website

docs.confident-ai.com

Categories and Features

Functional Testing

Not specified

Software Testing

Not specified

Unit Testing

Not specified

Categories and Features

LLM Evaluation

Not specified

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