OpenAI vs. Open-source Embedding Model Showdown скачать в хорошем качестве

OpenAI vs. Open-source Embedding Model Showdown

In today's video, Jacky Liang, developer advocate at Timescale, deep dives into the complex world of embedding models for AI applications, comparing OpenAI's reliable but costly models against high-performing open-source alternatives. Discover how these models measure up on tasks involving text chunks and various question types, from simple to context-based. Learn how pgai Vectorizer simplifies handling multiple models and automates embedding updates within PostgreSQL. Get detailed results from the evaluation of four popular models, along with practical recommendations to help you choose the best model for your specific needs and constraints. 🛠 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 📌 pgai Vectorizer Quick Start ⇒ https://tsdb.co/pgaivectorizer-quick-... 📌 Evaluating Open-Source vs. OpenAI Embeddings for RAG ⇒ https://tsdb.co/evaluate-oss-vs-opena... 🐯 𝗔𝗯𝗼𝘂𝘁 𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲 At Timescale, we see a world made better via innovative technologies, and we are dedicated to serving software developers and businesses worldwide, enabling them to build the next wave of computing. Timescale is a remote-first company with a global workforce backed by top-tier investors with a track record of success in the industry. 💻 𝗙𝗶𝗻𝗱 𝗨𝘀 𝗢𝗻𝗹𝗶𝗻𝗲! 🔍 Website ⇒ https://tsdb.co/homepage 🔍 Slack ⇒ https://slack.timescale.com 🔍 GitHub ⇒ https://github.com/timescale 🔍 Twitter ⇒   / timescaledb   🔍 Twitch ⇒   / timescaledb   🔍 LinkedIn ⇒   / timescaledb   🔍 Timescale Blog ⇒ https://tsdb.co/blog 🔍 Timescale Documentation ⇒ https://tsdb.co/docs 📚 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀 ⏱ 0:00 ⇒ Introduction: Choosing the Right Embedding Model ⏱ 00:25 ⇒ The Challenges of Building Your Own AI Rag App ⏱ 00:51 ⇒ Introducing PGA Vectorizer: Simplifying Embedding Management ⏱ 01:39 ⇒ Creating and Testing Embeddings with PGA Vectorizer ⏱ 02:21 ⇒ Evaluating Embedding Models: Methodology and Setup ⏱ 05:01 ⇒ Running the Evaluation: Steps and Code Walkthrough ⏱ 08:02 ⇒ Results and Analysis: Which Model Performs Best? ⏱ 08:54 Conclusion: Recommendations and Final Thoughts