CLI Configuration Tool

Description

Generate TrustGraph deployment configurations from the command line without using a browser

Difficulty

Beginner

Duration

10 min

You will need
  • Node.js and npm installed
  • Basic command-line familiarity
Goal

Generate a complete TrustGraph deployment configuration by answering a series of questions in your terminal.

Overview

Elsewhere we describe how to use the Configuration Portal to build your TrustGraph deployment configuration. This page describes the same thing in command-line form. This is an optional guide - if you’re happy with the browser experience you can stick with that.

The TrustGraph CLI Configuration Tool provides the same functionality as the TrustGraph Configuration Builder web interface, but runs entirely in your terminal. Answer around a dozen questions about your deployment preferences and the tool generates a complete configuration bundle ready to deploy.

This is useful when:

  • You prefer working in a terminal
  • You’re on a headless server without a browser
  • You want to script configuration generation
  • You’re working in an environment with restricted web access

Running the Tool

Run the configuration tool using npx:

npx @trustgraph/config

The first time you run it, npm will prompt to install the package:

Need to install the following packages:
@trustgraph/config@1.0.0
Ok to proceed? (y)

Press y to continue. The tool will launch an interactive configuration wizard.

Configuration Questions

The tool guides you through a series of questions to configure your deployment:

1. TrustGraph Version 

◆  Which TrustGraph version?
│  ○ TrustGraph 2.0
│  ○ TrustGraph 1.9
│  ● TrustGraph 1.8 (Recommended)
│  ○ TrustGraph 1.7
│  ○ TrustGraph 1.6

Use the latest stable version (selected by default) unless you want to experiment with something cutting edge.

Remember the version number you select - you’ll need it later when installing CLI tools to ensure version compatibility.

2. Platform 

◆  Which platform?
│  ● Docker Compose (Recommended)
│  ○ Podman Compose
│  ○ Minikube
│  ○ Google Kubernetes Engine (GKE)
│  ○ AWS EKS
│  ○ Azure AKS
│  ○ Scaleway Kubernetes
│  ○ OVHCloud Kubernetes

Choose your deployment platform:

  • Docker Compose - Easy to install on macOS and Linux. Great for evaluation and learning. May not be well suited to production deployments.
  • Podman Compose - Open-source alternative to Docker with similar ease of use.
  • Minikube - Local Kubernetes for development and testing.
  • Cloud Kubernetes - GKE, EKS, AKS, Scaleway, and OVHCloud options for production deployments.

3. Graph Store 

◆  Which graph store?
│  ● Apache Cassandra (Recommended)
│  ○ Neo4j
│  ○ Memgraph
│  ○ FalkorDB

Cassandra isn’t strictly a graph database, but it covers all the functionality TrustGraph needs - TrustGraph layers a graph schema on top of it. If you have a preference for one of the dedicated graph databases, use that instead.

4. Vector Store 

◆  Which vector database?
│  ● Qdrant (Recommended)
│  ○ Milvus
│  ○ Pinecone

Qdrant is recommended - it offers high requests-per-second, minimal latency, and fast indexing with accuracy control.

5. Object Store 

◆  Which object store?
│  ● Apache Cassandra

Currently there is only one option for object storage. Select Cassandra to continue.

6. LLM Provider

You need to have access to an LLM. TrustGraph can work with many different kinds of LLM. You can use a cloud-hosted service, or have an LLM hosted locally on your device or network. TrustGraph can work with small models which you can run on standard home/office equipment, but small models are still demanding on resources. A 16GB laptop is able to run an LLM but likely not at the same time as running all the containers which make up a TrustGraph deployment.

Here are some example ways to get an LLM to run:

Provider Description Access type
Google Cloud VertexAI This is a subscription-based service which is part of Google Cloud. The Gemini models are good and cost-effective. There are free credits for new users. Cloud subscription
AWS Bedrock Amazon’s managed LLM service with Claude, Mistral, and other models available. Running Claude on Bedrock is a good option. Cloud subscription
Azure Microsoft’s cloud subscription services include Machine Learning Services (MLS) and Cognitive Services (CS). The TrustGraph Azure integration can use the MLS service, while Azure OpenAI can use CS models. Cloud subscription
Anthropic Claude Integrates with Anthropic’s APIs directly for access to the Claude models. Claude models are very capable. API subscription
Mistral AI Integrates with Mistral’s APIs directly for access to the Mistral models. API subscription
OpenAI Integrates with OpenAI’s API for GPT models API subscription
Ollama Run models locally on your machine. Supports Llama, Mistral, and many others. Self-hosted
vLLM The most comprehensive self-hosted model engine Self-hosted
LMStudio Desktop application for running local LLMs with an OpenAI-compatible API. LMStudio is a very user-friendly experience, which makes it easier to diagnose and solve hosting problems. Note: LMStudio is free, but only for non-work-related use. Self-hosted

Using a cloud-hosted service is a good starting point - you will need a subscription, but no extra hardware. If you do want to run an LLM locally, you will need a device with a good GPU, and likely some experience of running this yourself as you may need to debug model / hosting issues.

◆  How will you run the LLM?
│  ● Ollama (Recommended)
│  ○ Llamafile
│  ○ LM Studio
│  ○ OpenAI
│  ○ Claude (Anthropic)
│  ○ Mistral
│  ○ Cohere
│  ○ Azure AI
│  ○ Azure OpenAI
│  ○ Amazon Bedrock
│  ○ Google AI Studio
│  ○ Vertex AI
│  ○ vLLM
│  ○ Text Generation Inference (TGI)

7. Maximum Output Tokens 

◆  Maximum output tokens?
│  4096

This sets the maximum number of tokens the LLM can generate in a single response. Choose based on your hosting capability:

  • 2048 - Suitable for low-end or resource-constrained hosting
  • 4096 - A good default for medium-capacity setups
  • 8192 or more - For high-compute hosting such as large cloud models

8. OCR Processing 

◆  Enable OCR processing?
│  ○ Yes / ● No

The default PDF processing extracts text from PDFs that contain structured text. If you need to process scanned documents or images, enable OCR processing. Selecting Yes reveals additional options:

◆  Which OCR engine?
│  ● PDF Decode (Recommended)
│  ○ Tesseract
│  ○ Mistral
  • PDF Decode - Default configuration for PDFs with structured text (no OCR)
  • Tesseract - Open-source OCR engine for scanned documents
  • Mistral - Uses Mistral’s vision capabilities for OCR

9. Embeddings Engine 

◆  Configure embeddings engine?
│  ○ Yes / ● No

The default embeddings engine is FastEmbed. If you want to use a different engine, select Yes to reveal the options:

◆  Which embeddings engine?
│  ● FastEmbed (Recommended)
│  ○ HuggingFace sentence-transformers
│  ○ Ollama
  • FastEmbed - Lightweight, fast Python library for embedding generation with a small container image and quick start time
  • HuggingFace sentence-transformers - Access to a wider range of embedding models
  • Ollama - Use Ollama for embeddings if you’re already running it for your LLM

Configuration Summary

After answering all questions, the tool displays a summary of your selections:

●  Configuration Summary:
│    Which TrustGraph version? TrustGraph 1.8
│    Which platform? Docker Compose
│    Which graph store? Apache Cassandra
│    Which vector database? Qdrant
│    Which object store? Apache Cassandra
│    How will you run the LLM? Ollama
│    Maximum output tokens? 4096
│    Enable OCR processing? No
│    Configure embeddings engine? No
│
◇  Configuration generated
│
◆  Save deployment package as:
│  deploy.zip

Review the summary to confirm your choices. Enter a filename for the deployment package (or accept the default deploy.zip) and press Enter.

◇  Saved to deploy.zip
│
◇  Installation guide generated
│
◆  Save installation guide as:
│  INSTALLATION.md

The tool also generates an installation guide tailored to your configuration. Enter a filename (or accept the default INSTALLATION.md) and press Enter.

◇  Saved to INSTALLATION.md
│
└  Done!

Output Files

The tool generates two files:

  • deploy.zip - A deployment bundle containing your docker-compose.yaml (or Kubernetes manifests) and configuration files for TrustGraph, Grafana, Prometheus, and other components.
  • INSTALLATION.md - A tailored installation guide with step-by-step instructions for your specific configuration.

Next Steps

  1. Read the generated INSTALLATION.md for instructions specific to your setup
  2. Unpack the deployment bundle and follow the installation guide
  3. For additional details, see the platform-specific guides: