Getting Started with TrustGraph

Welcome to TrustGraph! This section will help you get up and running quickly.

TrustGraph Fundamentals

  1. Core Concepts - Understand key TrustGraph concepts
  2. Installation - Deploy TrustGraph in the environment of your choice
  3. First Steps - Interact with your TrustGraph instance, load some data and get some results from it.

What You’ll Learn

  • What TrustGraph is, and why you would want to use it
  • Core concepts and terminology
  • How to deploy TrustGraph
  • Basic configuration and setup
  • First-hand experience of some basic usage

Quickstart with Docker Deployed Locally

Docker Compose provides the easiest way to get TrustGraph running locally with all required services orchestrated together. This deployment method is ideal for:

  • Local development and testing
  • Proof-of-concept implementations
  • Small-scale deployments
  • Learning and experimentation

System Requirements

  • Docker Engine or Podman Machine installed and running
  • Operating System: Linux or macOS (Windows deployments not tested)
  • Python 3.x for CLI tools
  • Sufficient system resources (recommended: 8GB RAM, 4 CPU cores)

Note: If using Podman, substitute podman for docker in all commands.

Configuration Setup

Create Configuration

Use the TrustGraph Configuration Builder to generate your deployment configuration:

  1. Select Deployment: Choose Docker Compose or Podman Compose
  2. Graph Store: Select Cassandra (recommended for ease of use)
  3. Vector Store: Select Qdrant (recommended for ease of use)
  4. Chunker Settings:
    • Type: Recursive
    • Chunk size: 1000
    • Overlap: 50
  5. LLM Model: Choose your preferred model:
    • Local: LMStudio or Ollama for local GPU deployment
    • Cloud: VertexAI on Google (offers free credits)
  6. Output Tokens: 2048 (safe default)
  7. Customization: Enable LLM Prompt Manager and Agent Tools
  8. Generate: Download the deployment bundle

Install CLI Tools 

python3 -m venv env
source env/bin/activate # On Windows: env\Scripts\activate
pip install trustgraph-cli

Note: Keep this virtual environment activated for all TrustGraph CLI commands.

Launch TrustGraph 

docker-compose -f docker-compose.yaml up -d

Verify TrustGraph Installation

Check Container Status

After deployment, it may take a while to pull all necessary components. Verify that TrustGraph processors have started:

tg-show-processor-state

Processors start quickly, but Pulsar and Cassandra can take up to 60 seconds to initialize.

If you’re using Docker Compose, check that containers are running:

docker ps

Any containers that have exited unexpectedly can be found with:

docker ps -a

Important: Allow the system to stabilize for 120 seconds before proceeding. Services may appear “stuck” if they didn’t have time to initialize correctly.

Verify Complete Startup

Check that all main services are running:

tg-show-flows

You should see a default flow. If you see an error, wait a moment and try again.

Load Sample Documents

Load some sample documents to get started:

tg-load-sample-documents

Access TrustGraph Interfaces

Web Workbench

Access the TrustGraph web interface at http://localhost:8888/

Verify the workbench is working:

  • Prompts page: Check that you can see system prompts
  • Library page: Verify you can see the sample documents you just loaded

Monitoring with Grafana

Access Grafana monitoring at http://localhost:3000/

  • Login: admin / admin
  • Dashboard: Select the TrustGraph dashboard
  • Skip password change or set a new password

After loading documents, you should see the processing backlog rise to a few hundred document chunks.

Process Your First Document

Load a Document via Workbench

  1. Go to the Library page in the workbench
  2. Select a document (“Beyond State Vigilance” is a good starting document)
  3. Click on the document to select it
  4. Click Submit in the action bar at the bottom
  5. Select a processing flow (use the default)
  6. Click Submit to start processing

Monitor Processing

Watch the processing progress in Grafana. You should see the backlog rise as the document is chunked and processed.

Verify Knowledge Graph Creation

Check that the knowledge graph is successfully parsing data:

tg-show-graph

The output should show semantic triples in N-Triples format:

<http://trustgraph.ai/e/enterprise> <http://trustgraph.ai/e/was-carried> "to altitude and released for a gliding approach" .
<http://trustgraph.ai/e/enterprise> <http://www.w3.org/2000/01/rdf-schema#label> "Enterprise" .
<http://trustgraph.ai/e/enterprise> <http://www.w3.org/2004/02/skos/core#definition> "A prototype space shuttle orbiter used for atmospheric flight testing" .

Explore Your Knowledge

  1. In the workbench, click the Vector Search tab
  2. Search for a term (e.g., “state”)
  3. Review the search results
  4. Click on results to explore the knowledge graph
  5. Use Graph View to visualize relationships

GraphRAG Queries

  1. In the workbench, click the Graph RAG tab
  2. Enter a question about your document: 
    What is this document about?
  3. Review the contextual response generated using your knowledge graph

CLI GraphRAG

You can also run Graph RAG queries from the command line:

tg-invoke-graph-rag "What are the main topics covered in the loaded documents?"

Shut Down TrustGraph

When you’re finished, properly shut down TrustGraph:

For Docker Compose:

docker-compose down -v -t 0

Verify cleanup:

# Check no containers are running
docker ps

# Check volumes are removed
docker volume ls

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