trustgraph/README.md

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.

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 graph.

Getting started

TBD