Category: Knowledge Graphs

Creating Knowledge Graphs automatically for GraphRAG: Part 2: with LLMs

And the winner is using LLMs to create knowledge graphs over using NLP. Can LLMs do a better job? The Neo4j LLM Graph Builder in particular, has shown they can. What about the cost of using OpenAI along with the loss of privacy of data by submitting? The answer is free and local LLM models (Llama3 versions are available thru ollama) work too with Graph Builder. I tested with OpenAI GPT-4o, llama3, llama3.1, llama3.2. I noticed gemma2 is also available thru ollama. With these local LLMs, you will need a high end Nvidia card to work best.

Neo4j Labs LLM Knowledge Graph Builder main info site

Short Youtube demo video

The Online LLM Graph Builder can be used. You need to provide it with your Aura Neo4j connection info (you can create an account for a free Aura DB). It only has Diffbot, OpenAI, and Gemini LLM models available.

Graph Builder can upload from local files, AWS S3, web pages, Wikipedia, and Youtube. Google GCS can be a source if configured.

First choose the LLM model to use. Then upload one or more files. Then choose generate graph. You can view the graphs with the basic viewer (which allows hiding chunk nodes, community nodes, so you can see the entities and relationships). The Bloom viewer is also available, which is more complicated.

You can also chat with the data using GraphRAG and your chosen LLM. Answers have a icon below them that when clicked, provides info on graph doc sources, what entities, and what chunks were used to answer.

LLM Graph Builder Github project (Apache 2.0 open source)

The online version doesn’t have the llama3 models. So you need to clone the github project and build locally. To add using Meta Llama3 models, you need to configure it. You use the example.env to create a .env file and then add an optional OpenAI key, LLM model configuration, and indicate you initial Neo4j database info. Neo4j connection info can also be provided in the UI. Then do docker compose up. I have a fork of the main branch in my LLM Graph Builder that has added: configuration for lllama3, llama3.1, llama3.2, and openai gpt-4 choices, some neo4j connection config examples, switched to 8090 to not conflict with Alfresco 8080, has an additional debug log to so you can check on model config. and has a sample files folder with space-station.txt.

Speaking of Alfresco, I could add to my Alfresco GenAI Semantic project to call the separable backend of Graph Builder to generate a knowledge graph of new or updated Alfresco documents that have a new custom aspect. The backend may only have support for sources coming for the app’s kinds of sources currently. Also note in terms of UI integration, Alfresco’s ADF components and the ACA client use Angular. Neo4j Graph Builder’s front end uses React (and so does some of their other software projects).

space-station.txt with OpenAI GPT-4o:

space-station.txt with Meta Llama3:

space-station.txt with Meta Llama3.1:

space-station.txt with smaller Meta Llama3.2:

OpenAI GPT-4o with Albert Einstein Wikipedia page (340 nodes, 230 relationships):

Meta Llama3 with Albert Einstein Wikipedia page (150 nodes, 150 relationships), not shown: Llama3.1 (had 161 nodes, 85 relationships), not shown Llama3.2 (125 nodes, 76 relationships)

Creating Knowledge Graphs automatically for GraphRAG: Part 1: with NLP

(next post Part 2: with LLM)

I first investigated how NLP could be used for both entity recognition and relation extraction for creating a knowledge graphs of content. Tomaz Bratanic’s Neo4j blog article  used Relik for NLP along with LlamaIndex for creating a graph in Neo4j, and setting up an embedding model for use with LLM queries.

In my llama_relik github project, I used the  notebook from the blog article and changed it to use fastcoref instead of coreferee. Fastcoref was mentioned in the medium article version of the Neo4j blog article in the comments. It’s supposed to work better. There is also a python file in this project than can be used instead of the notebook.

I submitted some fixes to Relik on Windows, but it performs best on Linux in general and was more able to use the GPU “cuda” mode instead of “cpu”.

Similar work has been done using Rebel for NLP by Neo4j / Tomaz Bratanic, Saurav Joshi, and Qrious Kamal

Note that Relik has closed information extraction (CIE) models that do both entity linking (EL) and relation extraction (RE) . It also has models focused on either EL or RE.

Below is a screenshot from Neo4j with a knowledge graph created with the python file from the llama_relik project using the “relik-cie-small” model with the spacy space station sample text (ignore chunk node and it’s mentions relations). Notice how it has separate entities for “ISS” and “International Space Station” .

The “relik-cie-large” model finds more relations in screenshot below. It also has separate entities for “ISS” and “International Space Station” (and throws in second “International Space Station”).