Getting Started

tip

Before launching TrustGraph, be sure to have the Docker Engine or Podman Machine installed and running on the host machine.

note

TrustGraph has been tested on Linux and MacOS with Docker and Podman. Windows deployments have not been tested.

tip

If using Podman, the only change will be to substitute podman instead of docker in all commands.

All TrustGraph components are deployed through a Docker Compose file. There are 16 Docker Compose files to choose from, depending on the desired model deployment and choosing between the graph stores Cassandra or Neo4j:

AzureAI serverless endpoint for deployed models in Azure
Bedrock API for models deployed in AWS Bedrock
Claude through Anthropic's API
Cohere through Cohere's API
Mix for mixed model deployments
Ollama for local model deployments
OpenAI for OpenAI's API
VertexAI for models deployed in Google Cloud

Docker Compose enables the following functions:

Run the required components for full end-to-end Graph RAG knowledge pipeline
Inspect processing logs
Load text corpus and begin knowledge extraction
Verify extracted Graph Edges
Model agnostic, Graph RAG

Preparing TrustGraph

Below is a step-by-step guide to deploy TrustGraph, extract knowledge from a PDF, build the vector and graph stores, and finally generate responses with Graph RAG.

Install requirements

python3 -m venv env
. env/bin/activate
pip3 install pulsar-client
pip3 install cassandra-driver
export PYTHON_PATH=.

Clone the GitHub Repo

git clone https://github.com/trustgraph-ai/trustgraph trustgraph
cd trustgraph

TrustGraph as Docker Compose Files

Launching TrustGraph is a simple as running a single Docker Compose file. There are Docker Compose files for each possible model deployment and graph store configuration. Depending on your chosen model ang graph store deployment, chose one of the following launch files:

Model Deployment	Graph Store	Launch File
AWS Bedrock	Cassandra	`tg-launch-bedrock-cassandra.yaml`
AWS Bedrock	Neo4j	`tg-launch-bedrock-neo4j.yaml`
AzureAI Serverless Endpoint	Cassandra	`tg-launch-azure-cassandra.yaml`
AzureAI Serverless Endpoint	Neo4j	`tg-launch-azure-neo4j.yaml`
Anthropic API	Cassandra	`tg-launch-claude-cassandra.yaml`
Anthropic API	Neo4j	`tg-launch-claude-neo4j.yaml`
Cohere API	Cassandra	`tg-launch-cohere-cassandra.yaml`
Cohere API	Neo4j	`tg-launch-cohere-neo4j.yaml`
Mixed Depoloyment	Cassandra	`tg-launch-mix-cassandra.yaml`
Mixed Depoloyment	Neo4j	`tg-launch-mix-neo4j.yaml`
Ollama	Cassandra	`tg-launch-ollama-cassandra.yaml`
Ollama	Neo4j	`tg-launch-ollama-neo4j.yaml`
OpenAI	Cassandra	`tg-launch-openai-cassandra.yaml`
OpenAI	Neo4j	`tg-launch-openai-neo4j.yaml`
VertexAI	Cassandra	`tg-launch-vertexai-cassandra.yaml`
VertexAI	Neo4j	`tg-launch-vertexai-neo4j.yaml`

caution

All tokens, paths, and authentication files must be set PRIOR to launching a Docker Compose file.

Chunking

Extraction performance can vary signficantly with chunk size. The default chunk size is 2000 characters using a recursive method. Decreasing the chunk size may increase the amount of extracted graph edges at the cost of taking longer to complete the extraction process. The chunking method and sizes can be adjusted in the selected YAML file. In the selected YAML file, find the section for chunker. Under the commands list, modify the follwing parameters:

- "chunker-recursive" # recursive text splitter in characters
- "chunker-token" # recursive style token splitter
- "--chunk-size"
- "<number-of-characters/tokens-per-chunk>"
- "--chunk-overlap"
- "<number-of-characters/tokens-to-overlap-per-chunk>"

Model Parameters

Most configurations allow adjusting some model parameters. For configurations with adjustable parameters, the temperature and max_output tokens can be set in the selected YAML file:

- "-x"
- <max_model_output_tokens>
- "-t"
- <model_temperature>

tip

The default temperature in TrustGraph is set to 0.0. Even for models with long input contexts, the max output might only be 2048 (like some intances of Llama3.1). Make sure max_output is not set higher than allowed for a given model.

Choose a TrustGraph Configuration

Choose one of the Docker Compose files that meets your preferred model and graph store deployments. Each deployment will require setting some environment variables and commands in the chosen YAML file. All variables and commands must be set prior to running the chosen Docker Compose file.

AWS Bedrock API

export AWS_ID_KEY=<ID-KEY-HERE>
export AWS_SECRET_KEY=<TOKEN-GOES-HERE>
docker compose -f tg-launch-bedrock-cassandra.yaml up -d # Using Cassandra as the graph store
docker compose -f tg-launch-bedrock-neo4j.yaml up -d # Using Neo4j as the graph store

note

The current defaults for AWS Bedrock are Mistral Large 2 (24.07) in US-West-2.

To change the model and region, go the sections for text-completion and text-completion-rag in the tg-launch-bedrock.yaml file. Add the following lines under the command section:

- "-r"
- "<"us-east-1" or "us-west-2">
- "-m"
- "<bedrock-api-model-name-here>

tip

Having two separate modules for text-completion and text-completion-rag allows for using one model for extraction and a different model for RAG.

AzureAI Serverless Model Deployment

export AZURE_ENDPOINT=<https://ENDPOINT.HOST.GOES.HERE/>
export AZURE_TOKEN=<TOKEN-GOES-HERE>
docker compose -f tg-launch-azure-cassandra.yaml up -d # Using Cassandra as the graph store
docker compose -f tg-launch-azure-neo4j.yaml up -d # Using Neo4j as the graph store

Claude through Anthropic API

export CLAUDE_KEY=<TOKEN-GOES-HERE>
docker compose -f tg-launch-claude-cassandra.yaml up -d # Using Cassandra as the graph store
docker compose -f tg-launch-claude-neo4j.yaml up -d # Using Neo4j as the graph store

Cohere API

export COHERE_KEY=<TOKEN-GOES-HERE>
docker compose -f tg-launch-cohere-cassandra.yaml up -d # Using Cassandra as the graph store
docker compose -f tg-launch-cohere-neo4j.yaml up -d # Using Neo4j as the graph store

Ollama Hosted Model Deployment

tip

The power of Ollama is the flexibility it provides in Language Model deployments. Being able to run LMs with Ollama enables fully secure AI TrustGraph pipelines that aren't relying on any external APIs. No data is leaving the host environment or network. More information on Ollama deployments can be found here.

note

The current default model for an Ollama deployment is Gemma2:9B.

export OLLAMA_HOST=<hostname> # Set to location of machine running Ollama such as http://localhost:11434
docker compose -f tg-launch-ollama-cassandra.yaml up -d # Using Cassandra as the graph store
docker compose -f tg-launch-ollama-neo4j.yaml up -d # Using Neo4j as the graph store

note

On MacOS, if running Ollama locally set OLLAMA_HOST=http://host.docker.internal:11434.

To change the Ollama model, first make sure the desired model has been pulled and fully downloaded. In the YAML file, go to the section for text-completion and text-completion-rag. Under commands, add the following two lines:

- "-m"
- "<model-name-here>"

OpenAI API

export OPENAI_KEY=<TOKEN-GOES-HERE>
docker compose -f tg-launch-openai-cassandra.yaml up -d # Using Cassandra as the graph store
docker compose -f tg-launch-openai-neo4j.yaml up -d # Using Neo4j as the graph store

VertexAI through GCP

mkdir -p vertexai
cp <your config> vertexai/private.json
docker compose -f tg-launch-vertexai-cassandra.yaml up -d # Using Cassandra as the graph store
docker compose -f tg-launch-vertexai-neo4j.yaml up -d # Using Neo4j as the graph store

tip

If you're running SELinux on Linux you may need to set the permissions on the VertexAI directory so that the key file can be mounted on a Docker container using the following command:

chcon -Rt svirt_sandbox_file_t vertexai/

Mixing Models

One of the most powerful features of TrustGraph is the ability to use one model deployment for the Naive Extraction process and a different model for RAG. Since the Naive Extraction can be a one time process, it makes sense to use a more performant model to generate the most comprehensive set of graph edges and embeddings as possible. With a high-quality extraction, it's possible to use a much smaller model for RAG and still achieve "big" model performance.

A "split" model deployment uses tg-launch-mix.yaml. There are two modules: text-completion and text-completion-rag. The text-completion module is called only for extraction while text-completion-rag is called only for RAG.

Choosing Model Deployments

Before launching the Docker Compose file, the desired model deployments must be specified. The options are:

text-completion-azure
text-completion-bedrock
text-completion-claude
text-completion-cohere
text-completion-ollama
text-completion-openai
text-completion-vertexai

For the text-completion and text-completion-rag modules in the tg-launch-mix.yamlfile, choose one of the above deployment options and enter that line as the first line under command for each text-completion and text-completion-rag module. Depending on the model deployment, other variables such as endpoints, keys, and model names must specified under the command section as well. Once all variables and commands have been set, the mix deployment can be lauched with:

docker compose -f tg-launch-mix-cassandra.yaml up -d # Using Cassandra as the graph store
docker compose -f tg-launch-mix-neo4j.yaml up -d # Using Neo4j as the graph store

tip

Any of the YAML files can be modified for a "split" deployment by adding the text-completion-rag module.

Running TrustGraph

After running the chosen Docker Compose file, all TrustGraph services will launch and be ready to run Naive Extraction jobs and provide RAG responses using the extracted knowledge.

Verify TrustGraph Containers

On first running a Docker Compose file, it may take a while (depending on your network connection) to pull all the necessary components. Once all of the components have been pulled, check that the TrustGraph containers are running:

docker ps

Any containers that have exited unexpectedly can be found by checking the STATUS field using the following:

docker ps -a

tip

Before proceeding, allow the system to stabilize. A safe warm up period is 120 seconds. If services seem to be "stuck", it could be because services did not have time to initialize correctly and are trying to restart. Waiting 120 seconds before launching any scripts should provide much more reliable operation.

Load a Text Corpus

Create a sources directory and get a test PDF file. To demonstrate the power of TrustGraph, the provided script loads a PDF of the public Roger's Commision Report from the NASA Challenger disaster. This PDF includes complex formatting, unique terms, complex concepts, unique concepts, and information not commonly found in public knowledge sources.

mkdir sources
curl -o sources/Challenger-Report-Vol1.pdf https://sma.nasa.gov/SignificantIncidents/assets/rogers_commission_report.pdf

Load the file for knowledge extraction:

scripts/load-pdf -f sources/Challenger-Report-Vol1.pdf

note

To load a text file, use the following script:

scripts/load-text -f sources/<txt-file.txt>

The console output File loaded. indicates the text corpus has been sucessfully loaded to the processing queues and extraction will begin.

Processing Logs

At this point, many processing services are running concurrently. You can check the status of these processes with the following logs:

PDF Decoder:

docker logs trustgraph-pdf-decoder-1

Output should look:

Decoding 1f7b7055...
Done.

Chunker:

docker logs trustgraph-chunker-1

The output should be similiar to the output of the Decode, except it should be a sequence of many entries.

Vectorizer:

docker logs trustgraph-vectorize-1

Similar output to above processes, except many entries instead.

Language Model Inference:

docker logs trustgraph-text-completion-1

Output should be a sequence of entries:

Handling prompt fa1b98ae-70ef-452b-bcbe-21a867c5e8e2...
Send response...
Done.

Knowledge Graph Definitions:

docker logs trustgraph-kg-extract-definitions-1

Output should be an array of JSON objects with keys entity and definition:

Indexing 1f7b7055-p11-c1...
[
    {
        "entity": "Orbiter",
        "definition": "A spacecraft designed for spaceflight."
    },
    {
        "entity": "flight deck",
        "definition": "The top level of the crew compartment, typically where flight controls are located."
    },
    {
        "entity": "middeck",
        "definition": "The lower level of the crew compartment, used for sleeping, working, and storing equipment."
    }
]
Done.

Knowledge Graph Relationshps:

docker logs trustgraph-kg-extract-relationships-1

Output should be an array of JSON objects with keys subject, predicate, object, and object-entity:

Indexing 1f7b7055-p11-c3...
[
    {
        "subject": "Space Shuttle",
        "predicate": "carry",
        "object": "16 tons of cargo",
        "object-entity": false
    },
    {
        "subject": "friction",
        "predicate": "generated by",
        "object": "atmosphere",
        "object-entity": true
    }
]
Done.

Graph Parsing

To check that the knowledge graph is successfully parsing data:

scripts/graph-show

The output should be a set of 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 and landing at the Mojave Desert test center.
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.

Number of Graph Edges

N-Triples format is not particularly human readable. It's more useful to know how many graph edges have successfully been extracted from the text corpus:

scripts/graph-show  | wc -l

The Challenger report has a long introduction with quite a bit of adminstrative text commonly found in official reports. The first few hundred graph edges mostly capture this document formatting knowledge. To fully test the ability to extract complex knowledge, wait until at least 1000 graph edges have been extracted. The full extraction for this PDF will extract many thousand graph edges.

RAG Test Query

scripts/query-graph-rag -q "Give me 20 facts about the space shuttle Challenger"

This script forms a LM prompt asking for 20 facts regarding the Challenger disaster. Depending on how many graph edges have been extracted, the response will be similar to:

Here are 20 facts from the provided knowledge graph about the Space Shuttle disaster:

**Space Shuttle Challenger was a Space Shuttle spacecraft.**
**The third Spacelab mission was carried by Orbiter Challenger.**
**Francis R. Scobee was the Commander of the Challenger crew.**
**Earth-to-orbit systems are designed to transport payloads and humans from Earth's surface into orbit.**
**The Space Shuttle program involved the Space Shuttle.**
**Orbiter Challenger flew on mission 41-B.**
**Orbiter Challenger was used on STS-7 and STS-8 missions.**
**Columbia completed the orbital test.**
**The Space Shuttle flew 24 successful missions.**
**One possibility for the Space Shuttle was a winged but unmanned recoverable liquid-fuel vehicle based on the Saturn 5 rocket.**
**A Commission was established to investigate the space shuttle Challenger accident.**
**Judit h Arlene Resnik was Mission Specialist Two.**
**Mission 51-L was originally scheduled for December 1985 but was delayed until January 1986.**
**The Corporation's Space Transportation Systems Division was responsible for the design and development of the Space Shuttle Orbiter.**
**Michael John Smith was the Pilot of the Challenger crew.**
**The Space Shuttle is composed of two recoverable Solid Rocket Boosters.**
**The Space Shuttle provides for the broadest possible spectrum of civil/military missions.**
**Mission 51-L consisted of placing one satellite in orbit, deploying and retrieving Spartan, and conducting six experiments.**
**The Space Shuttle became the focus of NASA's near-term future.**
**The Commission focused its attention on safety aspects of future flights.** 

For any errors with the RAG proces, check the following log:

docker logs -f trustgraph-graph-rag-1

Custom RAG Requests

At any point, a RAG request can be generated and run with the following script:

scripts/query-graph-rag -q "RAG request here"

Shutting Down TrustGraph

When shutting down TrustGraph, it's best to shut down all Docker containers and volumes. Run the docker compose down command that corresponds to your model and graph store deployment:

docker compose -f tg-launch-<model-deployment>-<graph-store>.yaml down -v

tip

To confirm all Docker containers have been shut down, check that the following list is empty:

docker ps

To confirm all Docker volumes have been removed, check that the following list is empty:

docker volume ls

Getting Started

Preparing TrustGraph​

Install requirements​

Clone the GitHub Repo​

TrustGraph as Docker Compose Files​

Chunking​

Model Parameters​

Choose a TrustGraph Configuration​

AWS Bedrock API​

AzureAI Serverless Model Deployment​

Claude through Anthropic API​

Cohere API​

Ollama Hosted Model Deployment​

OpenAI API​

VertexAI through GCP​

Mixing Models​

Choosing Model Deployments​

Running TrustGraph​

Verify TrustGraph Containers​

Load a Text Corpus​

Processing Logs​

Graph Parsing​

Number of Graph Edges​

RAG Test Query​

Custom RAG Requests​

Shutting Down TrustGraph​

Preparing TrustGraph

Install requirements

Clone the GitHub Repo

TrustGraph as Docker Compose Files

Chunking

Model Parameters

Choose a TrustGraph Configuration

AWS Bedrock API

AzureAI Serverless Model Deployment

Claude through Anthropic API

Cohere API

Ollama Hosted Model Deployment

OpenAI API

VertexAI through GCP

Mixing Models

Choosing Model Deployments

Running TrustGraph

Verify TrustGraph Containers

Load a Text Corpus

Processing Logs

Graph Parsing

Number of Graph Edges

RAG Test Query

Custom RAG Requests

Shutting Down TrustGraph