Understanding Embedding Vectors and the RAG Pattern in AI Solutions
summary
Shows how OpenAI embeddings (1,536 dims) convert text to normalized vectors (magnitude 1) and uses cosine similarity via dot product to rank documents; demo runs a Python notebook with 10 fictitious city descriptions, returns top-k results (best match 0.8226 for “Auroria”), then builds a RAG flow by injecting the top 3 docs into Azure + ChatGPT‑4‑Turbo prompts. Covers vector stores vs in‑memory search, managed identity auth, and when to use custom GPTs.
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chapters (4)
- 0:00Introduction
- 0:33Session "Understanding Embedding Vectors and the RAG Pattern in AI Solutions" by Rainer Stropek
- 42:00Rainer answers questions from the audience
- 47:43Outro
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