RAGStack Lambda

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 | guest@hatstack.fun / 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.

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:

1. Subscribe to RAGStack on AWS Marketplace (free, not required - If subscribed Lambda roles auto-accept Bedrock model agreements on first invocation)
2. Click here to deploy
3. Enter a stack name (lowercase only, e.g., "my-docs") and your admin email
4. Click Create Stack (deployment takes ~10 minutes)

After deployment:

• 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:

Prerequisites:

• 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 admin@example.com

Option 3: Nested Stack Deployment

Deploy RAGStack as part of a larger CloudFormation stack. See [Nested Stack Deployment Guide](docs/NESTEDSTACKDEPLOYMENT.md) for details.

Quick example:

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: admin@example.com

Web Component Integration

See [RAGSTACKCHAT.md](docs/RAGSTACKCHAT.md) for web component integration guide.

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"

See [MCP Server docs](src/ragstack-mcp/README.md) for full setup instructions.

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:

| Type | Formats | Processing | |------|---------|------------| | Text | HTML, TXT, CSV, JSON, XML, EML, EPUB, DOCX, XLSX | Direct extraction with smart analysis | | OCR | PDF, JPG, PNG, TIFF, GIF, BMP, WebP, AVIF | Textract or Bedrock vision OCR (WebP/AVIF require Bedrock) | | Media | MP4, WebM, MP3, WAV, M4A, OGG, FLAC | AWS Transcribe → 30s segments → searchable with timestamps | | Passthrough | Markdown (.md) | Direct copy |

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

Scrape websites into the knowledge base. See [Web Scraping](docs/WEB_SCRAPING.md).

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.

Features:

• Speaker diarization (identify who said what)
• Configurable language (30+ languages supported)
• Timestamp-linked sources in chat responses

See [Configuration](docs/CONFIGURATION.md#media-processing-videoaudio) for language and speaker settings.

Chat

Ask questions about your content. Sources show where answers came from.

Documentation

• [Configuration](docs/CONFIGURATION.md) - Settings, quotas, API keys & document management
• [Nested Stack Deployment](docs/NESTEDSTACKDEPLOYMENT.md) - Deploy as part of larger CloudFormation stack
• [Image Upload](docs/IMAGE_UPLOAD.md) - Image upload and captioning
• [Web Scraping](docs/WEB_SCRAPING.md) - Scrape websites
• [Metadata Filtering](docs/METADATA_FILTERING.md) - Auto-discover metadata and filter results
• [Chat Component](docs/RAGSTACK_CHAT.md) - Embed chat anywhere
• [API Reference](docs/API_REFERENCE.md) - GraphQL API documentation
• [Architecture](docs/ARCHITECTURE.md) - System design & API reference
• [Development](docs/DEVELOPMENT.md) - Local dev
• [Migration](docs/MIGRATION.md) - Version migration guide
• [Troubleshooting](docs/TROUBLESHOOTING.md) - Common issues
• [Library Reference](docs/LIBRARYREFERENCE.md) - Public API for lib/ragstackcommon

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 admin@example.com

# Skip dashboard build (still builds web component)
python publish.py --stack-name myapp --admin-email admin@example.com --skip-ui

# Skip ALL UI builds (dashboard and web component)
python publish.py --stack-name myapp --admin-email admin@example.com --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 admin@example.com --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 .

Acknowledgments

This project was inspired by:

• Accelerated Intelligent Document Processing on AWS - AWS Solutions Library reference architecture
• docs-mcp-server - MCP server for documentation search

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: HatmanStack
• Source: HatmanStack/RAGStack-Lambda
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.