Serverless LLM with AWS Lambda and LangChain: A Revolution in Application Development
summary
Shows a concrete serverless RAG pipeline: S3 uploads trigger AWS Lambda to split documents, embeddings (OpenAI or Titan) land in a vector store (OpenSearch or Pinecone) and Bedrock returns answers, all wired with CDK for generative AI L3 constructs. Claims three lines of code to load split and embed, cites chunk size and overlap as key tuning, and demonstrates an end-to-end deployable stack using Amazon Bedrock knowledge base and Lambda that the demos indicate is functional and scalable.
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chapters (9)
- 0:00Serverless LLM intro
- 3:17Large language models overview
- 7:58LLM production challenges
- 9:48LangChain for LLM apps
- 12:48Building a simple LangChain chain
- 15:19Retrieval Augmented Generation
- 26:20Amazon Bedrock Knowledge Base
- 29:52CDK for GenAI infra
- 35:22Q&A: Open vs proprietary LLMs
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