Build RAG Without a Separate Vector DB — Amazon DocumentDB | Databases for AI скачать в хорошем качестве

Build RAG Without a Separate Vector DB — Amazon DocumentDB | Databases for AI

📚 Your team has thousands of pages of docs. Your developers still can't find the right answer. Join us to learn how to simplify search. Blogs, PDFs, FAQs, developer guides—the knowledge exists. It's just buried. So your devs spend hours searching instead of minutes building. The fix sounds obvious: build a chatbot over it. But then you look at the typical RAG architecture & see two databases, a sync layer & a pile of glue code. There's a simpler path. Amazon DocumentDB stores your vectors & your source content in the same document—so retrieval is one query to one place. No separate vector DB. No cross-database orchestration. We walk through the full build: ⚡ Parse & embed PDFs, blogs & FAQs using Amazon SageMaker & #AmazonBedrock ⚡ Store embeddings alongside source data in DocumentDB—single collection, single document ⚡ Demo a working chatbot that handles everything from "show me the aggregation syntax" to "how do I migrate my Oracle workload to a document database?" Collapse your #RAG architecture & give your devs a knowledge assistant they'll actually use. That's the power of #VectorSearch in a document database.