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vector embeddings
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Classroom Contents
Vectoring Into The Future: AWS Empowered RAG Systems for LLMs
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- 1 intro
- 2 preamble
- 3 agenda
- 4 why foundation models?
- 5 generative ai can be used for a wide range of use cases
- 6 aws offers a broad choice of generative ai capabilities
- 7 limitations of llms
- 8 vector embeddings
- 9 vector databases
- 10 enabling vector search across aws services
- 11 amazon autota with postgresql compatibility
- 12 using pgvector in aws
- 13 amazon opensearch service
- 14 using opensearch in aws
- 15 amazon documentdb
- 16 amazon memorydb
- 17 amazon neptune analytics
- 18 amazon bedrock
- 19 knowledge bases for amazon bedrock
- 20 vector databases for amazon bedrock
- 21 retrieve and generate api
- 22 demo time
