TrustGraph

Introduction

TrustGraph provides a means to run a pipeline of flexible AI processing components in a flexible means to achieve a processing pipeline.

The processing components are interconnected with a pub/sub engine to make it easier to switch different procesing components in and out, or to construct different kinds of processing. The processing components do things like, decode documents, chunk text, perform embeddings, apply a local SLM/LLM, call an LLM API, and invoke LLM predictions.

The processing showcases Graph RAG algorithms which can be used to produce a knowledge graph from documents, which can then be queried by a Graph RAG query service.

Processing items are executed in containers. Processing can be scaled-up by deploying multiple containers.

Features

• PDF decoding
• Text chunking
• Invocation of LLMs hosted in Ollama
• Invocation of LLMs: Claude, VertexAI and Azure serverless endpoints
• Application of a HuggingFace embeddings algorithm
• Knowledge graph extraction
• Graph edge loading into Cassandra
• Storing embeddings in Milvus
• Embedding query service
• Graph RAG query service
• All procesing integrates with Apache Pulsar
• Containers, so can be deployed using Docker Compose or Kubernetes
• Plug'n'play, switch different LLM modules to suit your LLM options

Architecture

A set of modules are executed which use Apache Pulsar as a pub/sub system. This means that Pulsar provides input the modules and accept output.

Pulsar provides two types of connectivity:

• For processing flows, Pulsar accepts the output of a processing module and queues it for input to the next module.
• For services such as LLMs and embeddings, Pulsar provides a client/server model. A Pulsar queue is used as the input to the service. When processed, the output is delivered to a separate queue so that the caller can collect the data.

All the code is bundled into a single Python package which can be used to use all the functionality. There is also a container image with the package installed which can be used to run everything.

Included modules

• chunker-recursive - Accepts text documents and uses LangChain recurse chunking algorithm to produce smaller text chunks.
• embeddings-hf - A service which analyses text and returns a vector embedding using one of the HuggingFace embeddings models.
• embeddings-vectorize - Uses an embeddings service to get a vector embedding which is added to the processor payload.
• graph-rag - A query service which applies a Graph RAG algorithm to provide a response to a text prompt.
• graph-write-cassandra - Takes knowledge graph edges and writes them to a Cassandra store.
• kg-extract-definitions - knowledge extractor - examines text and produces graph edges. describing discovered terms and also their defintions. Definitions are derived using the input documents.
• kg-extract-relationships - knowledge extractor - examines text and produces graph edges describing the relationships between discovered terms.
• llm-azure-text - An LLM service which uses an Azure serverless endpoint to answer prompts.
• llm-claude-text - An LLM service which uses Anthropic Claude to answer prompts.
• llm-ollama-text - An LLM service which uses an Ollama service to answer prompts.
• llm-vertexai-text - An LLM service which uses VertexAI to answer prompts.
• loader - Takes a document and loads into the processing pipeline. Used e.g. to add PDF documents.
• pdf-decoder - Takes a PDF doc and emits text extracted from the document. Text extraction from PDF is not a perfect science as PDF is a printable format. For instance, the wrapping of text between lines in a PDF document is not semantically encoded, so the decoder will see wrapped lines as space-separated.
• vector-write-milvus - Takes vector-entity mappings and records them in the vector embeddings store.

Getting started

A good starting point is to try to run one of the Docker Compose files. This can be run on Linux or a Macbook (maybe Windows - not tested).

There are 4 docker compose files to get you started with one of the following LLM types:

• VertexAI on Google Cloud
• Claud Anthropic
• Azure serverless endpoint
• An Ollama-hosted LLM for an LLM running on local hardware

Using the Docker Compose you should be able to...

• Run enough components to start a Graph RAG indexing pipeline. This includes stores, LLM interfaces and processing components.
• Check the logs to ensure that things started up correctly
• Load some test data and starting indexing
• Check the graph to see that some data has started to load
• Run a query which uses the vector and graph stores to produce a prompt which is answered using an LLM.

If you get a Graph RAG response to the query, everything is working.

Docker compose files

There are 4 docker compose files to choose from depending on the LLM you wish to use:

• docker-compose-azure.yaml. This is for a serverless AI endpoint hosted on Azure. Set AZURE_TOKEN to the secret token and AZURE_ENDPOINT to the endpoint address.
• docker-compose-claude.yaml. This is for using Anthropic Claude LLM. Set CLAUDE_KEY to the API key.
• docker-compose-ollama.yaml. This is for a local LLM - gemma2 hosted using Ollama. Set OLLAMA_HOST to the host running Ollama (e.g. localhost to talk to a locally hosted Ollama.
• docker-compose-vertexai.yaml. This is for using Google Cloud VertexAI. You need a private.json authentication file for your Google Cloud. Should be at path vertexai/private.json.

docker-compose-azure.yaml

export AZURE_ENDPOINT=https://ENDPOINT.HOST.GOES.HERE/
export AZURE_TOKEN=TOKEN-GOES-HERE
docker-compose -f docker-compose-azure.yaml up -d

docker-compose-claude.yaml

export CLAUDE_KEY=TOKEN-GOES-HERE
docker-compose -f docker-compose-claude.yaml up -d

docker-compose-ollama.yaml

export OLLAMA_HOST=localhost # Set to hostname of Ollama host
docker-compose -f docker-compose-ollama.yaml up -d

docker-compose-azure.yaml

mkdir -p vertexai
cp {whatever} vertexai/private.json
docker-compose -f docker-compose-vertexai.yaml up -d

On Linux if running SELinux you may need to set the permissions on the VertexAI directory so that the key file can be mounted on a docker container...

chcon -Rt svirt_sandbox_file_t vertexai/