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Integrations

Every "linked thing" on an Open Register object is a leaf. Meetings, contacts, files, wiki pages, kanban cards, chat threads — each is a leaf with the same provider contract on the backend and the same registry surface on the frontend. Plus a separate set of integrations for LLMs and automation engines.

Leaf integrations​

Open Register ships 18 leaves plus the 5 always-available built-ins. Every leaf surfaces as a sidebar tab on linked objects, a dashboard widget in four surfaces, and an admin row with health status. See Leaf integration system for the architecture and Pluggable integration registry for the full ADR-019 contract.

Always-available built-ins​

  • Files — files attached to an object (magic-column).
  • Notes — free-form notes (link-table). Documented under the pluggable integration registry.
  • Tags — system tags (link-table).
  • Tasks — to-dos (link-table).
  • Audit trail — every change on the object (query-time).

NC-native leaves​

GroupLeaf
CoreShares
CommsCalendar · Contacts · Email · Talk
DocsBookmarks · Collectives · Maps · Photos
WorkflowActivity · Analytics · Cospend · Deck · Flow · Forms · Polls · Time tracker

External leaves (OpenConnector-routed)​

  • xWiki — link xWiki pages with breadcrumb + text preview.
  • OpenProject — link OpenProject work packages.

OpenRegister's own push events​

OpenRegister itself emits notify_custom events on every object lifecycle change so consumers can subscribe and refresh without polling.

  • OpenRegister Push Events — or-object-{uuid} and or-collection-{register}-{schema} event reference, fan-out semantics, batch mode, subscription examples.

LLM hosting platforms​

Services that host and run large language models locally.

  • Ollama — simple native API for running LLMs.
  • Hugging Face — TGI / vLLM with an OpenAI-compatible API.

LLM models​

Specific models you can plug in.

  • Mistral — high-performance 7B model.
  • Dolphin — document parsing and OCR.

Entity extraction​

  • Presidio — Microsoft's PII detector.

Automation platforms​

Integration Architecture​

The following diagram shows how all integrations work together in OpenRegister:

Integration Flow​

1. Text Extraction Pipeline​

2. Chat & RAG Pipeline​

3. Automation Pipeline​

Integration Comparison​

LLM Hosting Platforms​

PlatformAPI TypeSetup DifficultyPerformanceBest For
OllamaNative⭐⭐⭐⭐⭐ Easy⚡⚡⚡ GoodDevelopment, simple setup
TGIOpenAI-Compatible⭐⭐⭐ Medium⚡⚡ FastProduction, optimized
vLLMOpenAI-Compatible⭐⭐⭐ Medium⚡⚡⚡ Very FastHigh throughput

LLM Models​

ModelSizeUse CaseHosting
Mistral 7B7BChat, RAG, general purposeOllama, TGI, vLLM
Dolphin0.3BDocument parsing, OCRCustom container

Entity Extraction​

ServiceAccuracyLanguagesBest For
Presidio90-95%50+GDPR compliance, production
MITIE75-85%LimitedFast local processing
LLM-based92-98%AllHighest accuracy

Automation Platforms​

PlatformLanguageUse CaseBest For
n8nVisual/JSWorkflow automationNon-developers
WindmillPython/TS/Go/BashScript executionDevelopers
Custom WebhooksAnyCustom integrationsFull control

Quick Start Guide​

For AI Chat & RAG​

  1. Choose LLM Hosting: Start with Ollama for easiest setup
  2. Pull Model: Download Mistral or Llama 3.2
  3. Configure: Set up in OpenRegister Settings → LLM Configuration
  4. Enable RAG: Vectorize your objects and files

For Document Processing​

  1. Deploy Dolphin: Start Dolphin container for OCR
  2. Configure: Set Dolphin as extraction method
  3. Process Files: Upload documents for automatic processing

For GDPR Compliance​

  1. Start Presidio: Presidio is included in docker-compose
  2. Configure: Enable entity extraction in settings
  3. Monitor: Track PII in GDPR register

For Automation​

  1. Choose Platform: n8n for workflows or Windmill for scripts
  2. Set Up Webhooks: Register webhook endpoints
  3. Create Workflows: Build automation for your use cases

Integration Requirements​

Minimum Requirements​

  • CPU: 4+ cores recommended
  • RAM: 16GB minimum (32GB recommended for larger models)
  • Storage: 50GB+ for models and data
  • GPU: Optional but recommended (8GB+ VRAM for LLMs)

Docker Requirements​

  • Docker 20.10+
  • Docker Compose 2.0+
  • NVIDIA Docker runtime (for GPU support)

Configuration Overview​

LLM Configuration​

# docker-compose.yml
services:
  ollama:
    image: ollama/ollama:latest
    # ... configuration
  
  tgi-mistral:
    image: ghcr.io/huggingface/text-generation-inference:latest
    # ... configuration

Entity Extraction Configuration​

services:
  presidio-analyzer:
    image: mcr.microsoft.com/presidio-analyzer:latest
    # ... configuration

Document Processing Configuration​

services:
  dolphin-vlm:
    build: ./docker/dolphin
    # ... configuration

Best Practices​

1. Start Simple​

Begin with Ollama for LLM hosting - it's the easiest to set up and configure.

2. Use GPU When Available​

GPU acceleration provides 10-100x performance improvement for LLMs and document processing.

3. Choose Right Model Size​

  • Development: Use smaller models (3B-7B) for faster iteration
  • Production: Use larger models (7B-13B) for better quality

4. Monitor Resource Usage​

Keep an eye on:

  • Memory usage (models can be memory-intensive)
  • GPU utilization
  • API response times

5. Implement Fallbacks​

Always have fallback options:

  • LLPhant for text extraction if Dolphin unavailable
  • MITIE for entity extraction if Presidio unavailable
  • Database search if vector search unavailable

Troubleshooting​

Common Issues​

  1. Container Communication: Always use container names, not localhost
  2. Model Not Found: Ensure model names include version tags
  3. Out of Memory: Reduce model size or increase available RAM
  4. Slow Performance: Enable GPU acceleration

Getting Help​

  • Check individual integration documentation
  • Review Development Guides
  • Open GitHub issues for bugs
  • Check Docker logs for errors

Next Steps​