RAGStack FAQs

Question 1

How does RAGStack provide AI chat and context?

Accepted Answer

RAGStack integrates Amazon Nova multimodal embeddings for text and image vectorization and uses AWS Bedrock for AI chat. It offers retrieval-augmented context, meaning the AI assistant pulls information directly from your processed documents and media, providing source attribution for verifiable answers.

Question 2

What types of documents and media can RAGStack process?

Accepted Answer

RAGStack handles a wide array of formats, including PDFs, images, Office documents (DOCX, XLSX), HTML, CSV, JSON, XML, EML, EPUB, and even video/audio files (MP4, MP3, WAV). It leverages OCR for images/scanned PDFs and AWS Transcribe for searchable audio/video transcripts.

Question 3

What is RAGStack and its primary benefit?

Accepted Answer

RAGStack is a fully serverless platform designed for processing diverse documents and media, enabling AI chat capabilities. Its primary benefit is providing cost-effective, 'scale-to-zero' operations by eliminating idle costs and vector database fees, ensuring you only pay for what you use.

Question 4

What AWS services does RAGStack utilize for its serverless architecture?

Accepted Answer

RAGStack is built entirely on a serverless AWS architecture, leveraging services like Lambda, Step Functions, S3, DynamoDB, Bedrock, Transcribe, and Cognito. This design ensures high scalability, reliability, and cost efficiency without managing servers.

Question 5

Can RAGStack be integrated with existing applications or AI assistants?

Accepted Answer

Yes, RAGStack offers flexible integration options. It provides a web component for embedding AI chat into any frontend framework (React, Vue, Angular), a GraphQL API for server-side integrations, and compatibility with MCP-enabled AI assistants like Claude Desktop and Amazon Q CLI.

RAGStack

RAGStack

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主要功能

使用案例