Confident AI
Qu'est-ce que Confident AI ?
Confiant IA est une plateforme d'évaluation LLM tout-en-un conçue pour aider les entreprises à justifier la disponibilité en production de leurs applications de Modèles de Langage. Elle propose plus de 14 mesures, la gestion des ensembles de données, la surveillance, l'intégration des retours humains, et fonctionne avec le framework ouvert DeepEval.
Comment utiliser Confident AI ?
Utiliser Confiant IA est simple. Menez des expériences LLM, gérez les ensembles de données et surveillez les performances. Intégrez les retours humains pour des améliorations automatiques dans les applications LLM. Commencez par configurer des expériences et évaluer les résultats.
Fonctionnalités de base de Confident AI
Plus de 14 mesures pour les expériences LLM
Gestion des ensembles de données
Surveillance des performances
Intégration des retours humains
Fonctionne avec le framework DeepEval
Les cas d'utilisation de Confident AI
Les entreprises peuvent utiliser Confiant IA pour évaluer la disponibilité de leurs applications LLM pour le déploiement en production.
FAQ de Confident AI
Confiant IA peut-il être utilisé par des entreprises de toutes tailles ?
Comment Confiant IA peut-il aider à améliorer automatiquement les applications LLM ?
Société Confident AI
Confident AI Nom de l'entreprise : Confident AI Inc. .
Confident AI Tarifs
Lien de tarification Confident AI : https://www.confident-ai.com/pricing
Confident AI Github
Lien de Github Confident AI : https://github.com/confident-ai/deepeval
Confident AI Avis (0)
Analyse de Confident AI
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How to Setup DeepEval for Fast, Easy, and Powerful LLM Evaluations
Quickly get started running evals for your LLMs with Open-Source framework DeepEval. This is a quick how-to tutorial on how-to get started using the DeepEval framework. Evaluating LLMs is critical for improving performance and guaranteeing reliability for production LLM applications. At Eigen, we run evals on all our applications to ensure we’re meeting required thresholds and find areas where we can improve. If you’re looking to build production AI applications contact us at eigen.net. Follow along the Quick Introduction in the DeepEval documentation. https://docs.confident-ai.com/docs/getting-started Make sure to create a free account to view your eval results at http://confident-ai.com and run the command “deepeval login” in your terminal to automatically view your results in the web app. Here are the commands to run if you're following along: **Setup Python Virtual Environment** python3 -m venv venv source venv/bin/activate **Install DeepEval** pip install -U deepeval **Set OpenAI API Key as Env. Variable** export OPENAI_API_KEY=yourAPIkey **Create file to run test** touch test_example.py **Paste code below to test_example.py** ____________________ from deepeval import assert_test from deepeval.test_case import LLMTestCase from deepeval.metrics import AnswerRelevancyMetric def test_answer_relevancy(): answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5) test_case = LLMTestCase( input="What if these shoes don't fit?", actual_output="We offer a 30-day full refund at no extra cost." ) assert_test(test_case, [answer_relevancy_metric]) **Command For Testing First Eval** deepeval test run test_example.py
How to fine-tune an LLM? Getting started
In the first episode of the AI, LLM, and GraphRAG series, Code with Buda takes you on a hands-on journey about how to fine tune an LLM. The topics covered are: 1. How to Generate a Training Dataset 2. How to Fine-tune an LLM to Learn Cypher 3. How to Brainstorm Model Validation Techniques You can check out Marko on Github here: https://github.com/gitbuda If you are interested in the topic, learn more about GraphRAG and how companies are using it with Memgraph: 1. Memgraph Academy - Stay one node ahead: https://memgraph.com/academy/enhancing-ai-with-graph-databases-and-llms 2. Microchip Optimises LLM Chatbot with RAG and a Knowledge Graph: https://memgraph.com/webinars/microchip-optimizes-llm-chatbot-with-rag-and-a-knowledge-graph 3. Optimising Insulin Management: The Role of GraphRAG in Patient Care: https://memgraph.com/webinars/optimizing-insulin-management-the-role-of-graphrag-in-patient-care --- About Memgraph: Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently. It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, & criminal risk assessment), 360-degree data and network exploration [Identity and Access Management (IAM), Master Data Management (MDM), & Bill of Materials (BOM)], and logistics and network optimization. Website: https://www.memgraph.com Twitter: https://www.twitter.com/memgraphdb LinkedIn: https://www.linkedin.com/company/memgraph Facebook: https://www.facebook.com/memgraph --- 00:00:00 Intro 00:02:51 Star explaining setup and the work so far 00:04:27 Quick unsloth.ai explanation 00:05:09 Continue with the setup and the context 00:26:14 Centralize templates 01:18:49 Play with the base model 02:24:20 Refactor and play with the models 02:46:26 Deepevel setup (some unrelated issues) 03:11:57 Playing with deepevel (docs.confident-ai.com) 03:18:52 Setup the OpenAI 03:28:43 Pause (skip this completely) 03:37:23 Getting deepeval work with local GPU running ollama model (llama3)