Skip to main content

Integrations Overview

OpenRegister integrates with various external services and models to provide powerful AI capabilities, automation workflows, and advanced text processing. This page provides an overview of all available integrations and how they work together.

Integration Categories​

OpenRegister integrations fall into three main categories:

1. LLM Hosting Platforms​

Services that host and run Large Language Models locally:

  • Ollama - Simple, native API for running LLMs
  • Hugging Face - TGI/vLLM with OpenAI-compatible API

2. LLM Models​

Specific language models that can be used:

  • Mistral - High-performance 7B model
  • Dolphin - Document parsing and OCR model

3. Entity Extraction Services​

Services for detecting and extracting entities from text:

  • Presidio - Microsoft's PII detection service

4. Automation Platforms​

Workflow automation and integration 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​