# @awseventschannel on YouTube

- **Type:** Video
- **Original URL:** https://youtube.com/watch?v=pm1cIyQlIzo
- **Gondola URL:** https://gondola.cc/posts/59440709-awseventschannel-youtube
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/bda1a8774d.jpg
- **Posted:** 2026-03-13T05:35:58.000+00:00
- **Account Owner:** AWS Events (@awseventschannel) — https://gondola.cc/awseventschannel

## Caption

🤖 Agentic apps need databases that adapt — not rigid schemas that break when your LLM decides to add a field. 

RAG pipelines need #VectorSearch that doesn't force you into a separate data silo. Developers need tools that actually understand their #AI workflows. Amazon DocumentDB is evolving for this new era.  

Join this stream as we cover:  

✅ Flexible schema for agentic behavior — MongoDB-compatible API lets your AI agents store dynamic, evolving data structures without predefined schemas
✅ Native vector search for RAG — Store & query vectors directly in DocumentDB. No sync pipelines. No consistency drift
✅ DocumentDB 8.0 reduces index build time by up to 30x — parallel HNSW indexing means you can rebuild indexes frequently, keeping your AI answers fresh 
✅ DocumentDB MCP server — Give Kiro, Claude, Cursor & other AI tools direct access to your database through Model Context Protocol \
✅ AWS plugin integration — Estimate costs, configure instances & get database settings right from your IDE 
 
See how #AmazonDocumentDB handles vectors, flexible schemas & AI tool integration in a single service.

DocumentDB MCP Server: https://awslabs.github.io/mcp/servers/documentdb-mcp-server
Vector search samples: https://github.com/aws-samples/amazon-documentdb-samples/tree/master/samples/vector-search

## Stats

- **Views:** 902
- **Likes:** 26
- **Shares:** 0
- **Comments:** 0

## Tags

vectorsearch, amazondocumentdb, ai

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