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RAGStack-Lambda

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Serverless document and media processing with AI chat.

🔥🔥🔥✓ VerifiedAccount requiredNeeds API keys
#email#automation#scraping#database#documents#cloud#devops#git

Serverless document and media processing with AI chat. Scale-to-zero architecture — no vector database fees, no idle costs. Upload documents, images, video, and audio — extract text with OCR or transcription — query using Amazon Bedrock or your AI assistant via MCP.

QUESTIONS?

Features

  • ☁️ Fully serverless architecture (Lambda, Step Functions, S3, DynamoDB)

  • 🧠 NEW Amazon Nova multimodal embeddings for text and image vectorization

  • 📄 Document processing & vectorization (PDF, images, Office docs, HTML, CSV, JSON, XML, EML, EPUB) → stored in managed knowledge base

  • 🎬 NEW Video/audio processing - transcribe speech with AWS Transcribe, searchable by timestamp

  • 💬 AI chat with retrieval-augmented context and source attribution

  • 📎 Collapsible source citations with optional document downloads

  • ⏱️ NEW Media sources with timestamp links - click to play at exact position

  • 🔍 Metadata filtering - auto-discover document metadata and filter search results

  • 🎯 Relevancy boost for filtered results - prioritize matches from metadata filters

  • 🔄 Knowledge Base reindex - regenerate metadata for existing documents with updated settings

  • 🗑️ Document management - reprocess, reindex, or delete documents from the dashboard

  • 🌐 Web component for any framework (React, Vue, Angular, Svelte)

  • 🚀 One-click deploy

  • 💰 $7-10/month (1000 docs, Textract + Haiku)

Live Demo

Environment URL Credentials Base Pipeline dhrmkxyt1t9pb.cloudfront.net [email protected] / Guest@123 Project Showcase showcase-htt.hatstack.fun Login as guest

Base Pipeline: The core document processing tool - upload, OCR, and query documents.

Project Showcase: See RAGStack powering a real application.

Web Component Integration

See RAGSTACK_CHAT.md for web component integration guide.

API Access

Server-side integrations use API key authentication. Get your key from Dashboard → Settings.

Copy & paste — that's it
curl -X POST 'YOUR_GRAPHQL_ENDPOINT' \
 -H 'x-api-key: YOUR_API_KEY' \
 -H 'Content-Type: application/json' \
 -d '{"query": "query { searchKnowledgeBase(query: \"...\") { results { content } } }"}'

Web component uses IAM auth (no API key needed - handled automatically).

Each UI tab shows server-side API examples in an expandable section.

MCP Server (AI Assistant Integration)

Use your knowledge base directly in Claude Desktop, Cursor, VS Code, Amazon Q CLI, and other MCP-compatible tools.

Copy & paste — that's it
# Install (or use uvx for zero-install)
pip install ragstack-mcp

Add to your AI assistant's MCP config:

Copy & paste — that's it
{
 "ragstack-kb": {
 "command": "uvx",
 "args": ["ragstack-mcp"],
 "env": {
 "RAGSTACK_GRAPHQL_ENDPOINT": "YOUR_ENDPOINT",
 "RAGSTACK_API_KEY": "YOUR_API_KEY"
 }
 }
}

Then ask naturally: "Search my knowledge base for authentication docs"

See MCP Server docs for full setup instructions.

Architecture

Copy & paste — that's it
Upload → OCR → Embeddings → Bedrock KB
 ↓
 Web UI (Dashboard + Chat) ←→ GraphQL API
 ↓
 Web Component ←→ AI Chat with Sources

Documentation

Development

Copy & paste — that's it
npm run check # Lint + test all (backend + frontend)

Acknowledgments

This project was inspired by: