Software

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

Yesterday in this post I summarized launching ollama in a docker container and using LLMs from JupyterLab. In this post, I summarize launching Open WebUI in a container and using it as a front end to connect from a browser at hand to use ollama’s LLMs.

Motivation

In this post, I mentioned that the LLMs that can build knowledge graphs are OpenAI and Mistral (via API). On the Internet, I have seen examples of GraphRAG environments being built using ollama, as in this post.

I would like to try to build a knowledge graph using LLM in a local environment. In this post, I will summarize the process of installing ollama.

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

In this post, I built GraphRAG with Neo4j and LangChain on a trial basis. I am studying bedrock with this book. Since I’ve just started touching AWS, I decided to build GraphRAG on AWS.

Introduction

I built a RAG using HyDE (Hypothetical Document Embeddings), a method to improve RAGs. This post summarizes my trial of HyDE. The LLM used was gpt-4o-mini to keep costs down.

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.

Introduction

I read this article on August 8th. According to the article, a subsidiary of Preferred Networks (PFE) will start offering a free trial of LLM, which has Japanese language performance that exceeds GPT-4, prior to offering a commercial version.

I immediately applied for the free trial, received an email of acceptance, and waited for the notification of account issuance. I had received the notification e-mail on August 9th, but I had overlooked it and completely forgot that I had applied for it. Recently, after reading this post, I remembered about the free trial, rechecked my email, and found the account notification.

In this post, I will summarize what I tried of the free trial version.