Ragstack MCP-Server | PlugMCP
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Dokumentation
☁️ 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
Search, chat, upload, and scrape a serverless RAGStack knowledge base on AWS.
Beliebiges Tool in beliebigen Agenten. MCP-Verzeichnis für Leute, die keine Server schreiben.
Produkt
Verzeichnis
Entwickler
Unternehmen
RAGSTACK_GRAPHQL_ENDPOINT, RAGSTACK_API_KEY 2026-11-09
📎 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
Base Pipeline : The core document processing tool - upload, OCR, and query documents.
Project Showcase : See RAGStack powering a real application.
Quick Start
Option 1: One-Click Deploy (AWS Marketplace) REPO IS IN ACTIVE DEVELOPMENT AND WILL CHANGE OFTEN
Deploy directly from the AWS Console - no local setup required:
Check your email for the temporary password (from Cognito)
Go to CloudFormation → your stack → Outputs tab to find the Dashboard URL (UIUrl)
Option 2: Deploy from Source For customization or development:
AWS Account with admin access
Python 3.13+, Node.js 24+
uv (Python package manager)AWS CLI, SAM CLI (configured)
Docker (for Lambda layer builds)
git clone https://github.com/HatmanStack/RAGStack-Lambda.git
cd RAGStack-Lambda
# Install dependencies
uv sync
# Deploy (defaults to us-east-1 for Nova Multimodal Embeddings)
python publish.py \
--stack-name my-docs \
--admin-email [email protected]
Option 3: Nested Stack Deployment Resources:
RAGStack:
Type: AWS::CloudFormation::Stack
Properties:
TemplateURL: https://ragstack-quicklaunch-public.s3.us-east-1.amazonaws.com/ragstack-template.yaml
Parameters:
StackPrefix: 'my-app-ragstack' # Required: lowercase prefix
AdminEmail: [email protected]
Web Component Integration
API Access Server-side integrations use API key authentication. Get your key from Dashboard → Settings.
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.
# Install (or use uvx for zero-install)
pip install ragstack-mcp
Add to your AI assistant's MCP config:
{
"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"
Architecture Upload → OCR → Embeddings → Bedrock KB
↓
Web UI (Dashboard + Chat) ←→ GraphQL API
↓
Web Component ←→ AI Chat with Sources
Usage
Documents Upload documents in various formats. Auto-detection routes to optimal processor:
Processing time: UPLOADED → PROCESSING → INDEXED (typically 1-5 min for text, 2-15 min for OCR, 5-20 min for media)
Images Upload JPG, PNG, GIF, WebP with captions. Both visual content and caption text are searchable.
Web Scraping
Video & Audio Upload MP4, WebM, MP3, WAV, M4A, OGG, or FLAC files. Speech is transcribed using AWS Transcribe and segmented into 30-second chunks for search. Sources include timestamps (e.g., "1:30-2:00") with clickable links that play at the exact position.
Speaker diarization (identify who said what)
Configurable language (30+ languages supported)
Timestamp-linked sources in chat responses
Chat Ask questions about your content. Sources show where answers came from.
Documentation
Development npm run check # Lint + test all (backend + frontend)
Deployment Options
Direct Deployment # Full deployment (defaults to us-east-1)
python publish.py --stack-name myapp --admin-email [email protected]
# Skip dashboard build (still builds web component)
python publish.py --stack-name myapp --admin-email [email protected] --skip-ui
# Skip ALL UI builds (dashboard and web component)
python publish.py --stack-name myapp --admin-email [email protected] --skip-ui-all
# Enable demo mode (rate limits: 5 uploads/day, 30 chats/day; disables reindex/reprocess/delete)
python publish.py --stack-name myapp --admin-email [email protected] --demo-mode
Publish to AWS Marketplace (Maintainers) To update the one-click deploy template:
python publish.py --publish-marketplace
This packages the application and uploads to S3 for one-click deployment.
Note: Currently requires us-east-1 (Nova Multimodal Embeddings). When available in other regions, use --region <region>.
Acknowledgments This project was inspired by:
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Wie installiere ich Ragstack? Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
Benötigt Ragstack einen API-Schlüssel? Der Herausgeber nennt diese Umgebungsvariablen: RAGSTACK_GRAPHQL_ENDPOINT, RAGSTACK_API_KEY.
Welchen Transport verwendet Ragstack? Ragstack läuft lokal über stdio.
Wann wurde Ragstack zuletzt geprüft? Zuletzt geprüft: 2026-11-09. Search, chat, upload, and scrape a serverless RAGStack knowledge base on AWS.
azure-compute Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine l Tools haben Beschreibungen 0 von 6
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