Neo4j

Introduction

It has already been a week since I created the knowledge graph in my local environment and started building the GraphRAG environment. It is finally in a decent working condition. This post is a summary of GraphRAC using Ollama and neo4j that I built in my local environment.

はじめに

In this post, I created a knowledge graph as a starting point for building a RAG system. After trying several different LLMs, only gpt-4o and gpt-4o-mini worked properly. In a recent this post, I summarized the installation and activation of ollama.

I created a knowledge graph using ollama this time, and I summarize its contents here. I hope this will be helpful, as I got into a bit of trouble in some areas.

Introduction.

Continuing with this post from yesterday, I summarized the construction of a GraphRAG system using hybrid search.

I had imagined connecting pipes “|” like LangChain’s LCEL, but it was not what I had imagined. Please check it out below.

Introduction.

In this post, I created a knowledge graph using neo4j and built a RAG system using the knowledge graph as external information.

In this post, I have tried to build a RAG system easily using the neo4j library.

Motivation

Half a month ago in this post, I tried six LLMs for using the Knowledge Graph. As I wrote there, the LLMs available at this time are OpenAI and Mistral.　So, I tried to run MistarlAI’s LLM on my PC (local environment). In fact, I found that it is not usable for the knowledge graph. In this post, I tried to use MistaralAI via Langchain via API to see if it can be used in the knowledge graph.

Introduction

A while ago in this post, I described how I installed neo4j in a local environment (as a docker container) in order to use the knowledge graph.

In this post, I would like to summarize the contents of the simple knowledge graph that I built and used as a RAG, referring to an article on the Internet. I titled this post as first steps because I did exactly what the article on the internet said.

Motivation

So far, we have built RAG system using FAISS and BM25. Although vector search is relatively easy to construct, there are cases where the necessary information is not found in “k” documents, and I was looking for ways to improve the accuracy. I happened to read this article and became interested in the knowledge graph and decided to try it myself.

In this post, I will summarize the process of installing nao4j in my local environment and trying to use it from a browser in order to use the knowledge graph.