mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 16:36:21 +02:00
104 lines
4.8 KiB
Markdown
104 lines
4.8 KiB
Markdown
|
|
# TrustGraph
|
|
|
|
## Introduction
|
|
|
|
TrustGraph is a true end-to-end (e2e) knowledge pipeline that performs a `naive extraction` on a text corpus
|
|
to build a RDF style knowledge graph coupled with a `RAG` service compatible with cloud LLMs and open-source
|
|
SLMs (Small Language Models).
|
|
|
|
The pipeline processing components are interconnected with a pub/sub engine to
|
|
maximize modularity and enable new knowledge processing functions. The core processing components decode documents,
|
|
chunk text, perform embeddings, apply a local SLM/LLM, call a LLM API, and generate LM predictions.
|
|
|
|
The processing showcases the reliability and efficiences of Graph RAG algorithms which can capture
|
|
contextual language flags that are missed in conventional RAG approaches. Graph querying algorithms enable retrieving
|
|
not just relevant knowledge but language cues essential to understanding semantic uses unique to a text corpus.
|
|
|
|
Processing modules are executed in containers. Processing can be scaled-up
|
|
by deploying multiple containers.
|
|
|
|
### Features
|
|
|
|
- PDF decoding
|
|
- Text chunking
|
|
- Inference of LMs deployed with [Ollama](https://ollama.com)
|
|
- Inference of LLMs: Claude, VertexAI and AzureAI serverless endpoints
|
|
- Application of a [HuggingFace](https://hf.co) embeddings models
|
|
- [RDF](https://www.w3.org/TR/rdf12-schema/)-aligned Knowledge Graph extraction
|
|
- Graph edge loading into [Apache Cassandra](https://github.com/apache/cassandra)
|
|
- Storing embeddings in [Milvus](https://github.com/milvus-io/milvus)
|
|
- Embedding query service
|
|
- Graph RAG query service
|
|
- All procesing integrates with [Apache Pulsar](https://github.com/apache/pulsar/)
|
|
- Containers, so can be deployed using Docker Compose or Kubernetes
|
|
- Plug'n'play architecture: switch different LLM modules to suit your needs
|
|
|
|
## Architecture
|
|
|
|
|
|
|
|
TrustGraph is designed to be modular to support as many Language Models and environments as possible. A natural
|
|
fit for a modular architecture is to decompose functions into a set modules connected through a pub/sub backbone.
|
|
[Apache Pulsar](https://github.com/apache/pulsar/) serves as this pub/sub backbone. Pulsar acts as the data broker
|
|
managing inputs and outputs between modules.
|
|
|
|
**Pulsar Workflows**:
|
|
- For processing flows, Pulsar accepts the output of a processing module
|
|
and queues it for input to the next subscribed 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 then delivered to a separate queue where a client
|
|
subscriber can request that output.
|
|
|
|
The entire architecture, the pub/sub backbone and set of modules, is bundled into a single Python package. A container image with the
|
|
package installed can also run the entire architecture.
|
|
|
|
## Core Modules
|
|
|
|
- `chunker-recursive` - Accepts text documents and uses LangChain recursive
|
|
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-ollama` - A service which analyses text and returns a vector
|
|
embedding using an Ollama embeddings model.
|
|
- `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.
|
|
- `triples-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.
|
|
- `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.
|
|
- `ge-write-milvus` - Takes graph embeddings mappings and records them
|
|
in the vector embeddings store.
|
|
|
|
## LM Specific Modules
|
|
|
|
- `text-completion-azure` - Sends request to AzureAI serverless endpoint
|
|
- `text-completion-claude` - Sends request to Anthropic's API
|
|
- `text-completion-ollama` - Sends request to LM running using Ollama
|
|
- `text-completion-vertexai` - Sends request to model available through VertexAI API
|
|
|
|
## Quickstart Guide
|
|
|
|
See [Quickstart on Docker Compose](docs/README.quickstart-docker-compose.md)
|
|
|
|
## Development Guide
|
|
|
|
See [Development on trustgraph](docs/README.development.md)
|
|
|
|
|
|
|