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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

Ragas is a comprehensive open-source framework aimed at testing and evaluating applications that utilize Large Language Models (LLMs). It provides automated metrics to gauge performance and resilience, along with the capability to generate synthetic test data that meets specific needs, ensuring quality during both development and production phases. Furthermore, Ragas is designed to integrate smoothly with existing technology stacks, offering valuable insights to enhance the effectiveness of LLM applications. The project is driven by a dedicated team that combines advanced research with practical engineering strategies to support innovators in transforming the landscape of LLM applications. Users can create high-quality, diverse evaluation datasets that are tailored to their specific requirements, allowing for an effective assessment of their LLM applications in real-world scenarios. This approach not only fosters quality assurance but also enables the continuous improvement of applications through insightful feedback and automatic performance metrics that clarify the robustness and efficiency of the models. Additionally, Ragas stands as a vital resource for developers seeking to elevate their LLM projects to new heights.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

LangChain
Llama 2
OpenAI
Opik
Athina AI
Claude
Codestral
Gemini
Gemini 1.5 Flash
Gemini 1.5 Pro
Gemini 2.0
Gemini Nano
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Mathstral
Ministral 3B
Mistral Small

Integrations

LangChain
Llama 2
OpenAI
Opik
Athina AI
Claude
Codestral
Gemini
Gemini 1.5 Flash
Gemini 1.5 Pro
Gemini 2.0
Gemini Nano
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Mathstral
Ministral 3B
Mistral Small

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
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

Ragas

Country

United States

Website

www.ragas.io

Product Features

Product Features

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