software

Introduction

Yesterday, I installed OpenFOAM and ParaView and tried to simulate and visualize a Poiseuille flow (Hagen-poiseuille flow). This time, I followed the book and ran “cavity” from the OpenFOAM built-in tutorial.

Motivation

I have been working on PINNs since the end of last year and have attempted two problems. I am planning to select and work on problems in the field of fluid dynamics. In fact, I am working on incompressible flow around a cylinder, but I have been stuck for a week because I can’t figure out how to relate the output of the model to the differential equations (the key part of PINNs).

I decided to learn a bit of OpenFOAM if I am going to work on the fluid dynamics area, so I installed OpenFOAM, ParaView, and tried the examples.

Introduction

Recently, I summarized the mass, spring, and damper problem with PINNs. This time, I’ve worked on the Burgers’ equation and summarized it.

Motivation

I tried PINNs (Physics-Informed Neural Networks) with NVIDIA Modulus over a year ago, After that, it was completely untouched. I have heard Riken/Matsuoka-san’s talk at a recent seminar (AI for Science; here and here), I decided to study it again and tried to implement PINNs using a physically understandable problem as an example.

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.

Motivation

I was trying a graph RAG and got an error “TypeError: ‘NoneType’ object is not iterable”. I did a lot of research, but could not find the direct cause of the error and just could not get around it. The python version of the container I was using at the time was 3.10.12. The version of python in the article I was referring to was 3.11.0, so I decided to update the python version of the container to 3.11 as a trial.

This post summarizes the creation of the JupyterLab container with a python version of 3.11.

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.