Confident AI
Was ist Confident AI?
Confident AI ist eine All-in-One LLM Evaluierungsplattform, die entwickelt wurde, um Unternehmen bei der Rechtfertigung der Produktionsbereitschaft ihrer Sprachmodellanwendungen zu helfen. Es bietet 14+ Metriken, Dataset-Verwaltung, Monitoring, Integration von menschlichem Feedback und arbeitet mit dem DeepEval Open Framework.
Wie benutzt man Confident AI?
Die Verwendung von Confident AI ist unkompliziert. Führen Sie LLM-Experimente durch, verwalten Sie Datensätze und überwachen Sie die Leistung. Integrieren Sie menschliches Feedback für automatische Verbesserungen in LLM-Anwendungen. Beginnen Sie mit dem Einrichten von Experimenten und der Auswertung der Ergebnisse.
Confident AI's Hauptmerkmale
14+ Metriken für LLM-Experimente
Dataset-Verwaltung
Leistungsüberwachung
Integration von menschlichem Feedback
Arbeitet mit dem DeepEval-Framework
Confident AI's Anwendungsfälle
Unternehmen können Confident AI nutzen, um die Einsatzbereitschaft ihrer LLM-Anwendungen für die Produktionsbereitstellung zu bewerten.
FAQ von Confident AI
Kann Confident AI für Unternehmen jeder Größe genutzt werden?
Wie kann Confident AI automatisch zur Verbesserung von LLM-Apps beitragen?
Confident AI Unternehmen
Confident AI Firmenname: Confident AI Inc. .
Confident AI Preise
Confident AI Preislink: https://www.confident-ai.com/pricing
Confident AI Github
Confident AI Github link: https://github.com/confident-ai/deepeval
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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)