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Description

DeepEval offers an intuitive open-source framework designed for the assessment and testing of large language model systems, similar to what Pytest does but tailored specifically for evaluating LLM outputs. It leverages cutting-edge research to measure various performance metrics, including G-Eval, hallucinations, answer relevancy, and RAGAS, utilizing LLMs and a range of other NLP models that operate directly on your local machine. This tool is versatile enough to support applications developed through methods like RAG, fine-tuning, LangChain, or LlamaIndex. By using DeepEval, you can systematically explore the best hyperparameters to enhance your RAG workflow, mitigate prompt drift, or confidently shift from OpenAI services to self-hosting your Llama2 model. Additionally, the framework features capabilities for synthetic dataset creation using advanced evolutionary techniques and integrates smoothly with well-known frameworks, making it an essential asset for efficient benchmarking and optimization of LLM systems. Its comprehensive nature ensures that developers can maximize the potential of their LLM applications across various contexts.

Description

Pytest is an invaluable tool for enhancing your programming skills, as it simplifies the creation of both basic tests and complicated functional tests for various applications and libraries. The framework’s ability to provide detailed assertion introspection means you can rely solely on standard assert statements for all your testing needs. It offers thorough information regarding failed assertions, automatically identifies test modules and functions, and features modular fixtures that help manage both small and parameterized long-lived test resources effectively. Additionally, pytest can seamlessly execute unittest (including trial) and nose test suites, and it is compatible with Python versions 3.6 and above, as well as PyPy 3. Its rich plugin architecture boasts over 315 external plugins and is backed by a vibrant community of users. Furthermore, the maintainers of pytest, along with thousands of other packages, have partnered with Tidelift to provide commercial support and maintenance for the open-source dependencies integral to your projects. By leveraging pytest, you can save valuable time, minimize risks, and enhance the overall health of your codebase, all while ensuring that the developers of the specific dependencies you rely on are compensated for their work. This commitment to community and support truly sets pytest apart as a leader in the testing framework landscape.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Opik
Allure Report
Captain
Codecov
Coverage.py
Katalon Recorder
Katalon True Platform
KitchenAI
Launchable
Llama 2
LlamaIndex
OpenAI
Pynt
Python
Ragas
Roost.ai
TestQuality
VIKTOR
Zebrunner
pytest-cov

Integrations

Opik
Allure Report
Captain
Codecov
Coverage.py
Katalon Recorder
Katalon True Platform
KitchenAI
Launchable
Llama 2
LlamaIndex
OpenAI
Pynt
Python
Ragas
Roost.ai
TestQuality
VIKTOR
Zebrunner
pytest-cov

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Confident AI

Country

United States

Website

docs.confident-ai.com

Vendor Details

Company Name

pytest

Founded

2004

Website

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

Product Features

Product Features

Functional Testing

Automated Testing
Interface Testing
Regression Testing
Reporting / Analytics
Sanity Testing
Smoke Testing
System Testing
Unit Testing

Software Testing

Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing

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