🔍 Looking for alternatives to OpenAI's embedding models?
In this hands-on technical deep dive, Jacky Liang, developer advocate at Timescale shows you how to evaluate open-source embedding models that can match or even outperform proprietary solutions.
In this video developers will learn:
- How to benchmark embedding models using real-world text and questions
- A practical evaluation workflow you can replicate in your projects
- Performance insights across different query types (from simple lookups to complex contextual searches)
- Using pgai Vectorizer to automate embedding management in PostgreSQL
- Actual benchmarks comparing popular models like BGE-M3, mxbai-embed-large, and nomic-embed-text
Whether you're building a production RAG application or just exploring embeddings, this video will give you the data and tools to make informed decisions about your embedding infrastructure.
🛠 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...
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