Australian National University

Wenyi Pi

Neo4j APOC Library Use Case

 Author

Wenyi Pi (

 ORCID:

0009–0002–2884–277)

 Introduction

In the realm of Neo4j, the APOC (Awesome Procedures on Cypher) library stands as a powerful tool. Previously, We have talked about the importance of APOC in optimising Cypher queries and improving query efficiency in our article Exploring Methods of Cypher Query Optimisations.

Introduction to APOC: Enhancing Neo4j Capabilities

Xuzeng He

Latest findings in multiple research directions for handling graph prediction and optimization Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) A graph, in short, is a description of items linked by relations, where the items of a graph are called

 nodes

(or vertices) and their relations are called

 edges

(or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge

Recent Advances in using Machine Learning with Graphs

Vaibhav Khobragade

Generative AI entails a credit–blame asymmetry

Exploring AI’s Ethical Terrain: Addressing Bias, Security, and Beyond

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Large language models (LLMs) like OpenAI’s GPT-4, Meta’s LLaMA, and Google Gemini (previously called Bard) have showcased their vast capabilities, from passing bar exams and crafting articles to generating images and website code.

Ethics and AI: Confronting the Challenges Ahead

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large language models (LLMs) are becoming increasingly popular in natural language processing for their superior competence in various applications.

Enhancing Language Models: The Role of Knowledge Graph Augmentation in Overcoming LLM Challenges

Enhancing Data Interactivity with LLMs and Neo4j Knowledge Graphs Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Since OpenAI launched ChatGPT, a

 large language model (LLM)

based chatbot, in 2023, it has set off a technological wave.

The integration of large language models (LLMs) with Neo4j-based knowledge graphs

An Introduction to RA-CM3, MuRAG and RACE Author  Xuzeng He (ORCID: 0009-0005-7317-7426)  Generative Artificial Intelligence (GAI) has demonstrated impressive performances in tasks such as text generation and text-to-image generation.

Recent Advances in using Retrieving Multimodal Information for Augmented Generation

Understanding Sequential Data Modelling with Keras for Time Series Prediction

 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP). We will walk through a complete example of using RNNs for time series prediction, covering

Beginner’s Guide to Recurrent Neural Networks (RNNs) with Keras

Amanda Kau

Unlocking the Power of Questions — A deep dive into Question Answering Systems

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Virtual assistants have popped up on numerous websites over the years.

Harnessing The Power of Knowledge Graphs in Question Answering

Qingqin Fang

An Introduction to Retrieval Augmented Generation (RAG) and Knowledge Graph

Author Qingqin Fang (ORCID: 0009–0003–5348–4264)

 Introduction

Large Language Models (LLMs) have transformed the landscape of natural language processing, demonstrating exceptional proficiency in generating text that closely resembles human language.

Hallucination in Large Language Models and Two Effective Alleviation Pathways

How to efficiently retrieve information for different applications

Author Wenyi Pi (ORCID: 0009-0002-2884-2771) This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system. The rationale behind utilising RAG as well as potential ways in which it can be employed effectively will be covered.

Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG)

Aishwarya Nambissan

Swinburne University of Technology

Amir Aryani

A brief overview of different types of clustering techniques and their algorithms. Authors Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Amir Aryani (ORCID: 0000-0002-4259-9774)

 Background

Clustering is a fascinating technique used in machine learning, where patterns or data points are grouped based on their similarities. It’s like finding hidden connections among different data points without predefined labels.

19 Clustering Techniques

Exploring the potentials and limitations of Vision Language Models

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

The human brain is more extraordinary than any machine we could build. From an early age, many of us gain the ability to comprehend what our eyes tell us and articulate it. Furthermore, we combine evidence from all our senses to reason.

How Much Can Vision Language Models Really “See”?

Yunzhong Zhang

Efficient creation of a stoplight report with data dashboard images

Author: Yunzhong Zhang (ORCID: 0009–0002–8177–419X) Comparing data dashboards is crucial for understanding trends and performance differences. Traditionally, this task required manual effort, which was slow and sometimes inaccurate. Now, thanks to OpenAI’s GPT-4 with Vision (GPT-4V), we are able to automate and improve this process.

How to use GPT-4V For Stoplight Report

Dhruv Gupta

Improving the performance of Large Language Models Author  Dhruv Gupta (ORCID: 0009-0004-7109-5403)  ChatGPT, which first came out in late 2022, took the world by storm. Since then, various LLM models and LLM based products such as Meta’s Llama and Google’s Gemini have emerged, demonstrating the power of LLMs.

RAG: The next big thing after LLMs?

Harvesting the Power and Knowledge of Large Language Models


 Author

Amanda Kau

 (ORCID:

0009–0004–4949–9284

 )

Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

Automated Knowledge Graph Construction with Large Language Models

Nakul Nambiar

Zhuochen Wu

Authors:  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Zhuochen Wu (ORCID: 0009-0000-5642-5348)  Research Graph is a structured representation of research objects that captures information about entities and the relationships between Researcher, Organisation, Publication, Grant and Research Data.

How to use GPT API to export a research graph from PDF publications

Recent Advances in Using Machine Learning with Graphs — Part 2 Latest findings in multiple research directions for handling graph construction and network security issues Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of

Recent Advances in Using Machine Learning with Graphs — Part 2

Dhruv Gupta

Prompt Engineering — Part 2


 Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering

Author Dhruv Gupta

 (ORCID:

0009–0004–7109–5403

 )

Introduction In the previous article we discussed what prompt engineering and some of the techniques used for prompt engineering.

Prompt Engineering — Part 2

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and Knowledge Graphs


 How knowledge-enhanced language models and knowledge graphs are advancing open-domain conversational question answering

Author:

 Wenyi Pi (ORCID:


 0009-0002-2884-2771


 )

When searching for information on the website, it is common to come across a flood of

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and…

Incorporating Knowledge Graphs to explain reasoning processes


 Author

Amanda Kau (

 ORCID:

0009–0004–4949–9284) Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more.

Combining Knowledge Graphs With Language Models for Interpretability

Unlocking the power of language models: A deep dive into BERT


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Language Models: Deep Dive into BERT

Large Language Models for Fake News Generation and Detection Author Amanda Kau (

 ORCID

: 0009–0004–4949–9284) Introduction In recent years, fake news has become an increasing concern for many, and for good reason. Newspapers, which we once trusted to deliver credible news through accountable journalists, are vanishing en masse along with their writers.

Are Large Language Models Our Allies or Enemies in the Fight Against Fake News?

Attention mechanism not getting enough attention Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction As discussed in this article, RNNs were incapable of learning long-term dependencies. To solve this issue both LSTMs and GRUs were introduced. However, even though LSTMs and GRUs did a fairly decent job for textual data they did not perform well.

Transformers Models in NLP

From Naive to Modular: Tracing the Evolution of Retrieval-Augmented Generation

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large Language Models (LLMs) have achieved remarkable success.

Three Paradigms of RAG

An introduction to tools supporting PubMed Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) PubMed is a free Web literature search service developed and maintained by the National Center for Biotechnology Information (

 NCBI

), and it is also a part of NCBI’s Entrez retrieval system that provides access to a diverse set of 38 databases.

Enhancing PubMed: from a Medical Database to beyond

Aland Astudillo

How to use GROBID to extract text from PDF  Author  Aland Astudillo (ORCID: 0009-0008-8672-3168)  GROBID is a powerful and useful tool based on machine learning that can extract text information from PDF files and other files to a structured format. One of the key challenges in knowledge mining from academic articles is reading the content of PDF files.

How to use GROBID

Using Mistral for Data tagging Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Data tagging, in simple terms, is the process of assigning labels or tags to your data so that they are easier to retrieve or analyse.

How to use Large Language Models to tag your data: A complete tutorial

Integrating temporal data into static knowledge graphs

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular. In KGs, entities and concepts are represented as nodes, while the relationships between nodes are depicted as edges.

Dynamic Knowledge Graphs: A Next Step For Data Representation?

Transformative Advances in Language Models through External Knowledge Integration

Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction

In the dynamic field of natural language processing, the integration of external knowledge has emerged as a pivotal strategy for enhancing the performance of language models.

Deeper understanding of In-Context Retrieval-Augmented Language Models

Amir Aryani

Author  Amir Aryani (ORCID: 0000-0002-4259-9774) Introduction   In this article we look at Research Graph as an information model , and an approach to connect and capture the connections between research outputs, researchers and research activities. We explore the metadata model, and we discuss how to capture this graph in a Neo4j Graph Database.

Research Graph 101

Latest findings in multiple research directions for tackling reasoning and common sense challenges Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Knowledge Graphs, such as Wikidata, contain rich relational information between entities and have been widely used as a structured format for storing and representing relational information.

Large Language Models and Knowledge Graphs: Ways to combine them

Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering


 Author


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Introduction Large Language Models (LLMs) have become the new normal in the field of Natural Language Processing (NLP). With their improved performance and generative power, people around the world are relying on it for

Prompt Engineering

The AI Helper Turning Mountains of Data into Bite-Sized Instructions

Author Aland Astudillo (ORCID: 0009-0008-8672-3168) LLMs have been changing the way the entire world deals with problems and day-by-day tasks. To make them better for specific applications, they need huge amounts of data and complex and expensive approaches to training them.

What is LLMLingua?

Boosting Performance for Knowledge Graphs with Neo4j APOC Library

 Author

Wenyi Pi (ORCID: 0009–0002–2884–2771) Introduction A

 knowledge graph

(graph database) captures information about main entities in a domain and the relationships between them. It was an augmented feature store for connected data which gave access to compute, access and operationalise structure features.

Exploring Methods of Cypher Query Optimisations

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large Language Models   Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI Systems Author  Vaibhav Khobragade (ORCID: 0009–0009–8807–5982) Introduction   Large Language Models (LLMs) are artificial intelligence models that are trained on massive amounts of text data in order to generate human-like language and produce coherent

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large…

Authors  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Amir Aryani (ORCID: 0000-0002-4259-9774)  Knowledge graphs, which offer a structured representation of data and its relationships, are revolutionising how we organise and access information.

Mini RAG using Neo4j

Tools and Platform for Integration of Knowledge Graph with RAG pipelines.

Authors Aland Astudillo (ORCID: 0009-0008-8672-3168) Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Many users of chatbots such as ChatGPT, have encountered the problem of receiving inappropriate or incompatible responses. There are several reasons why this might happen.

Unveiling the Synergy: Retrieval Augmented Generation (RAG) Meets Knowledge Graphs

Author  Amir Aryani: (ORCID: 0000-0002-4259-9774) Definition   A research collaboration network is a group of researchers, and practitioners, or both, working together on joint research activities. These networks often span across disciplines, geographic boundaries, and sectors, enabling participants to share resources, expertise, and data to address common research goals more effectively than they could individually.

Research Collaboration Network

A novel approach to improving the efficiency of text search in graph databases utilizing Neo4j, OpenAI, and Typesense. Authors  Nakul Nambiar (ORCID: 0009–0009–9720–9233) Aishwarya Nambissan (ORCID: 0009–0003–3823–6609)  The ability to use cutting-edge tools and frameworks is essential for staying ahead in the ever-changing field of technology.

Typesense and Neo4j in a hybrid information retrieval solution

Improving the performance and application of Large Language Models

Author Amanda Kau (ORCID: 0009-0004-4949-9284) Large language models (LLMs) like GPT-4, the engine of products like ChatGPT, have taken centre stage in recent years due to their astonishing capabilities. Yet, they are far from perfect.

Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models

Exploring the Potential of Temporal Feature-Logic Embedding (TFLEX) in Complex Query Resolution

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Artificial intelligence (AI) and knowledge representation in the field of temporal knowledge graphs are rapidly gaining interest.

Harnessing Temporal Dynamics: Advanced Reasoning using Temporal Knowledge Graphs

Exploring innovative Strategies in Combating Misinformation with Enhanced Multimodal Understanding Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Misinformation refers to false or inaccurate information that is often given to someone in a deliberate attempt to make them believe something that is not true. This has a significantly negative impact on public health, political stability and social trust and harmony.

Multimodal Large Language Models for Misinformation Detection and Reasoning

The Three Oldest Pillars of NLP

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Natural Language Processing (NLP) has almost become synonymous with Large Language Models (LLMs), Generative AI, and fancy chatbots. With the ever-increasing amount of textual data and exponential growth in computational knowledge, these models are improving every day.

RNNs vs GRUs vs LSTMs

A Unified and Collaborative Framework for LLM Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In today’s rapidly evolving field of artificial intelligence, large language models (LLMs) are demonstrating unprecedented potential. Particularly, the Retrieval-Augmented Generation (RAG) architecture has become a hot topic in AI technology due to its unique technical capabilities.

RAG 2.0 is Coming?

Bridging Human Perception and AI’s Future: The Convergence of Visual Understanding and Semantic Networks Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

The fusion of Vision-Language Models (

 VLMs

), Generative Models, and Knowledge Graphs (

 KGs

) is reshaping how artificial intelligence (AI) understands and interacts with the world.

Tuning Vision-Language Models and Generative Models with Knowledge Graph

Understanding the Power and Applications of Natural Language Processing

 Author

Dhruv Gupta (

 ORCID:

0009–0004–7109–5403) Introduction We are living in the era of generative AI. In an era where you can ask AI models almost anything, they will most certainly have an answer to the query. With the increased computational power and the amount of textual data, these models are bound to improve their performance.

NLU vs. NLG: Unveiling the Two Sides of Natural Language Processing

Understanding how RNNs work and its applications


 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress. Researchers and enthusiasts alike have tirelessly worked across numerous aspects of this field, bringing about amazing advancements.

An Introduction to Recurrent Neural Networks (RNNs)

Exploring the Boundaries of Creativity and Responsibility in the Age of AI-Driven Media

 Author

Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

In 2024, the discipline of Generative AI takes a big step forward with the launch of revolutionary models that convert text into dynamic films, altering the landscape of digital content creation.

Generative AI’s Leap into Video in 2024 and its Ethical Horizon

Addressing Misinformation, Bias, and Privacy Concerns

Author:

 Wenyi Pi (ORCID:


 0009–0002–2884–2771


 )

Recently, the spread of false or misinformation has become an important concern for people all over the world, especially in the era of big data and multimedia, where people are faced with a flood of information that makes it difficult to determine its authenticity.

The Ethical and Social Impact of Artificial Intelligence

Exploring the OpenAlex Data Structure and Visualization Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction to OpenAlex

In today’s world, the realm of research papers is brimming with countless hot topics, and the sheer volume of publications can be overwhelming.

Unveiling Research Trends through OpenAlex Visualization

Latest findings for the use of Knowledge Graph in the field of QA in multiple research directions Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Question Answering (QA), the ability to interact with data using natural language questions and obtaining accurate results, has been a long-standing challenge in computer science dating back to the 1960s.

Question Answering enhanced by Knowledge Graph

Unlocking the power of knowledge graphs in research catalogues: A deep dive into OpenAlex


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Knowledge Graphs: Deep Dive into OpenAlex

Techniques to integrate Knowledge Graphs into Language Models

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Both knowledge graphs (KGs) and pre-trained language models (PLMs) have gained popularity due to their ability to comprehend world knowledge and their broad applicability.

A Deep Dive Into Knowledge Graph Enhanced Pre-trained Language Models

Automated Knowledge Graph Construction with Large Language Models — Part 2

 Harvesting the Power and Knowledge of Large Language Models

Author Amanda Kau (

 ORCID

:

 0009–0004–4949–9284

) Introduction Knowledge graphs (KGs) are a structured representation of data in a graphical format, in which entities are represented by nodes and are connected by edges representing relationships

Automated Knowledge Graph Construction with Large Language Models — Part 2

Understanding the Evolutionary Journey of LLMs Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction When we talk about large language models (LLMs), we are actually referring to a type of advanced software that can communicate in a human-like manner. These models have the amazing ability to understand complex contexts and generate content that is coherent and has a human feel.

Brief Introduction to the History of Large Language Models (LLMs)

An improvement architecture superior to the Transformer, proposed by Meta Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Recently, researchers from Meta and the University of Southern California have introduced a model called Megalodon. They claim that this model can expand the context window of language models to handle millions of tokens without overwhelming your memory.

The longer the context, the better? Unlimited Context Length in Megalodon

Latest findings in pre-training graphs and using them for link recommendation Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge graphs (e.g. Wikipedia). In Instagram

Working with Graphs: Pre-training and Application

Unlocking the Future of AI: The Transformative Journey of Large Language Models Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced AI algorithms.

The Journey of Large Language Models: Evolution, Application, and Limitations

Understanding Knowledge Networks From A Graph Perspective


 Author:


 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Since 2020, over ten million scholarly articles have been published annually. To put that into perspective, say all ten million articles were released on the first day of the year.

Uncovering The Secrets Behind The Most Influential Scholarly Publications With AceMap

Understanding the Balance between Internal Knowledge and External Sources

Author Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Previous research often emphasized the limitations of LLM’s information acquisition pathways, focusing on enhancing its capabilities in this regard.

When RAG and LLM Conflict: Who Will AI Listen to?

Solutions to Enhance LLM Performance in Long Contexts Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In the era of AI breakthroughs, large language models (LLMs) are not just advancements; they are revolutions, transforming how we interact with technology, from casual conversations with chatbots to the intricate mechanisms behind sophisticated data analysis tools.

Navigating the Long Context Conundrum: Challenges in Language Models’ Information Processing

Supervised Fine-tuning, Reinforcement Learning from Human Feedback and the latest SteerLM Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Large Language Models (LLMs), usually trained with extensive text data, can demonstrate remarkable capabilities in handling various tasks with state-of-the-art performance. However, people nowadays typically want something more personalised instead of a general solution.

Fine-tuning Large Language Models: A Brief Introduction

An Overview of Constructing a Knowledge Graph Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) 1. Introduction 1.1 What is a Knowledge Graph Knowledge Graphs are structured semantic knowledge bases used to rapidly describe concepts and their relationships in the physical world.

Unlocking Intelligence: The Journey from Data to Knowledge Graph

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Large Language Models (LLMs) have become the new face of Natural language processing (NLP). With their generative power and ability to comprehend human language, the human reliance on these models is increasing every day. However, the LLMs have been known to hallucinate and thus produce wrong outputs.

Prompt engineering: A Way to Smartly Use AI

Latest effort in assessing the security of the code generated by large language models Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction With the surge of Large Language Models (LLMs) nowadays, there is a rising trend among developers to use Large Language Models to assist their daily code writing. Famous products include GitHub Copilot or simply ChatGPT.

Large Language Models for Code Writing: Security Assessment

Stories by Research Graph on Medium

#### Neo4j APOC Library Use Case

<figure>
<img src="https://cdn-images-1.medium.com/max/978/0*rIcW439yt0M1I_TQ" />
<figcaption>Image created in <a
href="https://www.canva.com/">https://www.canva.com/</a></figcaption>
</figure>

### **Author**

- Wenyi Pi (**ORCID:**
 [0009--0002--2884--277](https://orcid.org/0009-0002-2884-2771))

### **Introduction**

In the realm of Neo4j, the APOC (Awesome Procedures on Cypher) library
stands as a powerful tool. Previously, We have talked about the
importance of APOC in optimising Cypher queries and improving query
efficiency in our article [Exploring Methods of Cypher Query
Optimisations](https://medium.com/@researchgraph/exploring-methods-of-cypher-query-optimisations-7b03e08b24c4).
This article delves deeper into some important functions available
within the APOC library.

Developed as a core library supported by Neo4j, APOC serves as a
standard utility library, offering access to various common procedures
and functions. Beyond simply improving query efficiency, APOC extends
Cypher queries' scope into broader areas such as data integration, graph
algorithms, and data conversion.

### **Installation**

1. Neo4j Desktop

after creating a Project and a DBMS(Database Management System), click
on the DBMS and you will find the **Plugins** tab. Look for the
**APOC **tab

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*B0JC1RggnURFLJnO" />
</figure>

Then click on APOC to expand more and click the install button

<figure>
<img src="https://cdn-images-1.medium.com/max/692/0*VZazDX5Y7UllMOQ4" />
</figure>

2\. Neo4j Server

APOC depends on the internal API for Neo4j. In Neo4j, you need to use
the **matching APOC version** to ensure that the first two version
numbers between Neo4j and APOC match.

<figure>
<img src="https://cdn-images-1.medium.com/max/924/0*GXKgOzgnfw1uE_kG" />
</figure>

Choose the right version and install.

For more details, please check the [APOC
Installation](https://neo4j.com/labs/apoc/4.1/installation/?utm_source=Google&utm_medium=PaidSearch&utm_campaign=Evergreenutm_content=APAC-Search-SEMCE-DSA-None-SEM-SEM-NonABM&utm_term=&utm_adgroup=DSA-use-cases&gad_source=1&gclid=Cj0KCQjwqdqvBhCPARIsANrmZhNexhzMDuQzOnqxnhd9EluIJm1SCZsVDgw1UCTZTHPeU8_uA63GVpcaAgQDEALw_wcB)
documentation.

### **Common features**

With over 450 standard procedures, APOC provides rich functionality
covering utilities, conversions, graph updates, and more. These
procedures are well-supported and highly deployable, whether used as
separate functions or integrated into Cypher queries.

You can find an introduction guide in [APOC User
Guide](https://neo4j.com/docs/apoc/current/) documentation

#### **Import and export**

The APOC library supports importing various data formats including JSON,
HTML, XML, CSV, and XLS to the Neo4j database. It also supports
exporting data into various data formats, including JSON, CSV, GraphML,
Excel, Gephi, and Cypher script.

#### **Data integration**

The APOC library supports integration with other databases, including
relational databases (via JDBC ***apoc.load.jdbc***), MongoDB, Elastic,
and Couchbase. It also supports importing data from LDAP directories
(via ***apoc.load.ldap***) and executing queries against other Neo4j
databases such as bolt procedures via bolt protocol. It also allows for
database modelling (via ***apoc.model.jdbc***) to extract metadata
information by any JDBC compatible database.

#### **Data structure**

The APOC library provides functions and procedures for operating data
structures, which are mainly divided into three types, namely Conversion
Functions, Map Functions, and Collection Functions. The conversion
function is used to cast the "Any" value to a specific type, it is
mainly located under the ***apoc.convert*** package.

The mapping function is used to operate the map type, it mainly resides
in the ***apoc.map*** package. The collection function is used to
operate collections and lists, mainly under the ***apoc.coll*** package.

#### **Temporal (Date Time)**

APOC library adds support for formatting time types, timestamps, and
date string values, mainly under the ***apoc.temporal*** and
***apoc.date*** packages.

#### **Mathematical**

APOC provides functions and procedures for mathematical operations,
including math functions (including rounding, maximum and minimum
values, etc.), exact math, number format conversion functions, and
bitwise operation functions. The mathematical operation function is
mainly located in the ***apoc.math*** package, the exact math
calculation function is located in the ***apoc.number.exact*** package,
the number format conversion function is located in the
***apoc.number*** package, and the bitwise operation is located in the
***apoc.bitwise*** package

#### **Advanced Graph Querying**

The advanced graph query provided by APOC library includes: path
expander (***apoc.path****)*, neighbour function (***apoc.neighbors***),
path manipulation (***apoc.path****)*, relationship querying
(***apoc.rel****)*, node querying (***apoc.nodes***), parallel node
search (***apoc.search****)*, etc.

#### **Cypher Execution**

- **Running Cypher fragments**: provide a safe, graph-aware, partially
 compiled scripting Cypher language within APOC (***apoc.cypher***)
- **Conditional Cypher Execution**: simulate an if / else structure.
- **Timeboxed Cypher statements**: terminate a cypher statement with a
 given threshold.
- **Run multiple Statements**: run each semicolon separated statement
- **Run Cypher Script Files**: run each statement in the file / each
 file, separated by semicolons
- **Parallel Cypher Execution**: provide parallel execution of Cypher
 statements

### **Examples**

The APOC library showcases its adaptability and effectiveness in real
world application. From node and relationship creation to batch
transactions, APOC offers indispensable functionalities for optimising
workflows and maximising efficiency.

#### **Help Command**

To access the APOC supported procedures and functions：

 call apoc.help('apoc')

#### **Create Nodes and Relationships**

1. Use ***apoc.create*** to create nodes.

- Create a node with dynamic labels:

**apoc.create.node**(\['Label'\], {key:value,...​})

- For example:



 CALL apoc.create.node(["Person", "Actor"], {name: "Tom Hanks"});

<figure>
<img src="https://cdn-images-1.medium.com/max/610/0*Z00M16_STJWDA15s" />
<figcaption>Create a Node Result</figcaption>
</figure>

This will create a **Node** with **Label** "*Person*" and "*Actor*",
**Attribute** "*name*" : "*Tom Hanks*".

- Create multiple node with dynamic labels:

**apoc.create.nodes**(\['Label'\],
\[{key:value,...​},{key:value,...}...\])

2\. Use ***apoc.create.relationship*** to create relationships.

- Create relationship with dynamic rel-type:

**apoc.create.relationship**(person1,'KNOWS',{key:value,...​}, person2)

- For example:



 MATCH (p:Person {name: "Tom Hanks"})
 MATCH (m:Movie {title:"You've Got Mail"})
 CALL apoc.create.relationship(p, "ACTED_IN", {roles:['Joe Fox']}, m)
 YIELD rel
 RETURN rel;

<figure>
<img src="https://cdn-images-1.medium.com/max/692/0*X7y8TtzArKlpjnPU" />
<figcaption>Create Relationship Result</figcaption>
</figure>

This will create a relationship of type "**ACTED_IN**" between the node
representing Tom Hanks (matched as a **Person** node with the name "*Tom
Hanks*") and the movie "*You've Got Mail*" (matched as a **Movie** node
with the title "*You've Got Mail"*). This relationship means that *Tom
Hanks* acted in the movie "*You've Got Mail*" and played the role of
"*Joe Fox*".

#### **Merge Nodes and Relationships**

1. Use ***apoc.merge*** to create nodes.

- Merge nodes with dynamic labels:

**apoc.merge.node**(\['Label'\], identProps:{key:value, ...​},
onCreateProps:{key:value,...​}, onMatchProps:{key:value,...​}})

- For example:



 CALL apoc.merge.node(
 ["Person", "Actor"],
 {name: "Tom Hanks"},
 {created: datetime()},
 {lastSeen: datetime()}
 );

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*Nqo_IDU3GnMsPkX2" />
<figcaption>Merge Result to Create node</figcaption>
</figure>

This will merge an existing node labelled as "*Person*" and "*Actor*"
with the name "*Tom Hanks*" and add properties created and lastseen time
or create a new node with these labels and properties if one does not
already exist.

2\. Use ***apoc.create.relationship*** to merge relationships.

- Create relationship with dynamic rel-type:

**apoc.merge.relationship**(startNode, relType,
identProps:{key:value, ...​}, onCreateProps:{key:value, ...​}, endNode,
onMatchProps:{key:value, ...​})

- For example:



 MATCH (p:Person {name: "Tom Hanks"})
 MATCH (m:Movie {title:"You've Got Mail"})
 CALL apoc.merge.relationship(p, "ACTED_IN",
 {roles:['Joe Fox']},
 {created: datetime()},
 m,
 {lastSeen: datetime()}
 )
 YIELD rel
 RETURN rel;

<figure>
<img src="https://cdn-images-1.medium.com/max/888/0*Uod7ExxBNvpqMfnE" />
</figure>

<figure>
<img src="https://cdn-images-1.medium.com/max/570/0*Mt_1DFB0GdmCFVBi" />
<figcaption>Merge Result with added properties</figcaption>
</figure>

This will merge a relationship between the node representing *Tom Hanks*
and the movie "*You've Got Mail*" with the relationship type
"**ACTED_IN**'' and the role "*Joe Fox* '', adding properties created
and lastseen time.

3\. Use ***apoc.refactor.mergeNodes***

- Merge nodes and relationships together:

**apoc.refactor.mergeNodes**(nodes LIST\<NODE\>, config MAP\<STRING,
ANY\>)

\- Merges the given **LIST\<NODE\>** onto the first **NODE** in the
**LIST\<NODE\>**. All **RELATIONSHIP** values are merged onto that
**NODE** as well.

Supported config properties:

- **Discard**: If a node's property is already set, no changes are made.
 If the node's property is not set yet, the first property in the list
 is written.
- **Override / Overwrite**: The last property in the list is written
 regardless of whether the node's property is already set, overriding
 any existing value.
- **Combine**: If there is only one property in the list, it is set as
 the node's property. If there are multiple properties in the list, an
 array is created, and an attempt is made to combine these values
 together. This may involve value transformation or type coercion.
- **mergeRels**: This parameter controls whether to allow merging
 relationships with the same type and direction. If set to true,
 relationships with the same type and direction will be merged. If set
 to false, these relationships will not be merged.
- **produceSelfRel:** If set to true (default value), any new
 self-relationship will be inserted into the target node. If set to
 false, no new self-relationship will be inserted. Note that this
 parameter is independent of the **mergeRels** configuration and does
 not affect existing self-relationships.
- **preserveExistingSelfRels**: This parameter is only effective when
 mergeRels is set to true. If set to true (default value), existing
 self-relationships in the target node will be preserved; otherwise,
 they will be deleted.
- **singleElementAsArray**: If set to false (default value) and the type
 is combine, setting to true ensures that the result remains as an
 array when merging two arrays, while setting to false makes the result
 a single value when the array size is 1.

#### **Batch Transactions**

1. Use ***apoc.periodic*** to perform periodic or batch operations.

- Run the second statement for each item returned by the first
 statement:

**apoc.periodic.iterate**('statement returning items', 'statement per
item',
{batchSize:1000,iterateList:true,parallel:false,params:{},concurrency:50,retries:0})
YIELD batches, total

- For example:



 CALL apoc.periodic.iterate(
 "MATCH (p:Person) WHERE (p)-[:ACTED_IN]->() RETURN p",
 "SET p:Actor",
 {batchSize:10000, parallel:true})

This will iterate over batches of nodes labelled as "*Person*" who have
acted in any movie, adding the label "*Actor*" to each node in batches
of 10,000 nodes and processing them in parallel.

2\. Use ***apoc.periodic.commit*** to execute a write operation.

- runs the given statement in separate transactions until it returns 0:

**apoc.periodic.commit**(statement,params)

- For example:



 CALL apoc.periodic.commit(
 "MATCH (person:Person)
 WHERE exists(person.city)
 WHERE person limit $limit
 MERGE (person:City {name:person.city})
 MERGE (person)-[:LIVES_IN]->(city)
 REMOVE person.city
 RETURN count(*)",
 {limit:1000});

This will merge a **City** node for each **Person** node with a city
property, create a **LIVES_IN** relationship, remove the city property
from each **Person** node, and return the count of processed nodes,
executed in batches of 1000 nodes until completion.

### **Practical Use Case**

I will use the APOC library to merge duplicate nodes.

Firstly create nodes and relationships:

 CALL apoc.create.nodes(["Dataset"], [{doi:'10.1001/965111',key:'orcid/001',title:'Environmental Impact on Agriculture',source:'orcid.org'},{doi:'10.1001/965111',key:'orcid/002',title:'Environmental Impact on Agriculture',source:'orcid.org'},{doi:'10.1001/965111',key:'orcid/003',title:'Environmental Impact on Agriculture',source:'orcid.org'}]);
 CALL apoc.create.node(["Researcher"], {name: "Amy Jamison"});
 CALL apoc.create.node(["Publication"], {title: "Environment"});
 CALL apoc.create.node(["Grant"], {title: "Research Institution"});

 MATCH (d1:Dataset {key:'orcid/001'})
 MATCH (g:Grant {title: "Research Institution"})
 CALL apoc.create.relationship(d1, "HAS", null, g)
 YIELD rel
 RETURN rel;

 MATCH (d2:Dataset {key:'orcid/002'})
 MATCH (g:Grant {title: "Research Institution"})
 CALL apoc.create.relationship(d2, "HAS", null, g)
 YIELD rel
 RETURN rel;

 MATCH (d3:Dataset {key:'orcid/003'})
 MATCH (g:Grant {title: "Research Institution"})
 CALL apoc.create.relationship(d3, "HAS", null, g)
 YIELD rel
 RETURN rel;

 MATCH (p:Publication {title: "Environment"})
 MATCH (d1:Dataset {key:'orcid/001'})
 CALL apoc.create.relationship(p, "PUBLICATED", null, d1)
 YIELD rel
 RETURN rel;

 MATCH (p:Publication {title: "Environment"})
 MATCH (d2:Dataset {key:'orcid/002'})
 CALL apoc.create.relationship(p, "PUBLICATED", null, d2)
 YIELD rel
 RETURN rel;

 MATCH (p:Publication {title: "Environment"})
 MATCH (d3:Dataset {key:'orcid/003'})
 CALL apoc.create.relationship(p, "PUBLICATED", null, d3)
 YIELD rel
 RETURN rel;

 MATCH (r:Researcher {name: "Amy Jamison"})
 MATCH (d1:Dataset {key:'orcid/001'})
 CALL apoc.create.relationship(r, "OWNED", null, d1)
 YIELD rel
 RETURN rel;

 MATCH (r:Researcher {name: "Amy Jamison"})
 MATCH (d2:Dataset {key:'orcid/002'})
 CALL apoc.create.relationship(r, "OWNED", null, d2)
 YIELD rel
 RETURN rel;

 MATCH (r:Researcher {name: "Amy Jamison"})
 MATCH (d3:Dataset {key:'orcid/003'})
 CALL apoc.create.relationship(r, "OWNED", null, d3)
 YIELD rel
 RETURN rel;

This creates six nodes with three **Dataset** nodes having the same
**doi**, title, and source but different key, along with one
**Researcher**, one **Publication**, and one **Grant** node, and
establishes nine relationships between them.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*o8xNhjMjJ_des8og" />
<figcaption>Create Node and Relationship</figcaption>
</figure>

Then use the ***apoc.refactor.mergeNodes*** to merge the duplicate
nodes:

 MATCH (n:Dataset)
 WITH n.doi AS doi, collect(n) as nodes
 CALL apoc.refactor.mergeNodes(nodes, {properties: {
 title:'combine',
 source:'combine',
 key:'combine',
 `.*`: 'discard'
 },mergeRels:TRUE})
 YIELD node
 RETURN node

This merges nodes based on their **doi** property while combining
specified properties and discarding all other properties. Any
relationships associated with the merged nodes will also be merged

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*fcozFqWDrTlWJHy2" />
<figcaption>Merge Node and Relationship</figcaption>
</figure>

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*YCa-tZ6IjqgnrsI2" />
<figcaption>Merge table</figcaption>
</figure>

Since I set the key attribute property as combine, that if multiple
nodes being merged have different values for the key property, those
values will be combined into an array and as set **mergeRels** to true
that relationships of the same type and direction will be merged.

If **mergeRels** not set to *True*, the result is like this:

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*0_uNfqOYUD_rbUtZ" />
<figcaption>Merge Result without mergeRels</figcaption>
</figure>

### **Conclusion**

In summary, the APOC library is the cornerstone of Neo4j users, whether
optimising queries, integrating data sources, or performing advanced
graphical analysis. APOC is an important asset in every Neo4j
practitioner's toolkit. It greatly simplifies the development process
and saves a lot of time and effort, providing users with more
flexibility and scalability.

### **References**

- APOC User Guide for neo4j 5 --- APOC documentation (2024) Neo4j Graph
 Data Platform. Available at: <https://neo4j.com/docs/apoc/current/>
- Introduction --- APOC documentation (2024) Neo4j Graph Data Platform.
 Available at: <https://neo4j.com/docs/apoc/current/introduction/>
- Neo4j-contrib (2024) NEO4J-contrib/NEO4J-APOC-procedures: Awesome
 procedures on Cypher for Neo4j --- codenamed 'APOC' . Available at:
 <https://github.com/neo4j-contrib/neo4j-apoc-procedures>.
- Merge nodes --- APOC documentation (2024) Neo4j Graph Data Platform.
 Available at:
 <https://neo4j.com/docs/apoc/current/graph-refactoring/merge-nodes/#_examples> .

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=dab999c490b8){width="1"
height="1"}

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*Gzmbpq5qJUz2W1zpHd1XGA.png" />
<figcaption>Created using DALL-E on 28 Jan 2024</figcaption>
</figure>

### Authors

* Nakul Nambiar (ORCID: [0009-0009-9720-9233](https://orcid.org/0009-0009-9720-9233))
* Amir Aryani (ORCID: [0000-0002-4259-9774](https://orcid.org/0000-0002-4259-9774))

Knowledge graphs, which offer a structured representation of data and
its relationships, are revolutionising how we organise and access
information. With large amounts of data, it sometimes becomes difficult
to draw insights from it. This blog article examines how to combine
Neo4j, a graph database, with OpenAI's Retrieval-Augmented Generation
(RAG) model to build a robust knowledge management system.

LLMs are pre-trained models and generate outputs based on the prompts we
pass them. Sometimes, the LLM results are precisely what you need, but
other times, they just return random facts from their training data.
Retrieval-augmented generation (RAG) is an AI framework that tries to
improve the response of the LLM models by providing context knowledge
from external sources. Additional benefits of RAG include:

- Enables getting insights into private data without sharing or using it
 for training.
- Reduces the chances of 'Hallucination' and incorrect responses.
- Reduces the need to train the model on new data continuously.

### Sample Data

For our exercise, we will use OpenAI, combined with the Australian
National Graph Database, to create an RAG pipeline.

The [Australian National Graph
Database](https://researchgraph.org/national-graph/) is a vast knowledge
graph with around 11 million nodes. We can't possibly fit all the data
into the OpenAI context. So, in the first phase, we will retrieve a
subset of the graph and then use this graph as a context for the OpenAI
and to answer prompts.

We use a Virtual Machine (VM) with a Neo4j server running the graph
database as a Neo4j server. The [graph
schema](https://researchgraph.org/schema/) includes five primary node
types that describe the research ecosystem: Researchers, Publications,
Datasets, Grants and Organisations.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*UocexTQHeuX3_VAwSJ03CQ.png"
alt="Research Graph Schema: https://researchgraph.org/schema/" />
<figcaption>Research Graph Schema. <a
href="https://researchgraph.org/schema/">https://researchgraph.org/schema/</a></figcaption>
</figure>

### Step 1

Firstly, we must establish the connection to the Neo4j database. Also,
you might notice that we use an OpenAI API key. This key can be
generated at the OpenAI portal. A tutorial is available
[here](https://elephas.app/blog/how-to-create-openai-api-keys-cl5c4f21d281431po7k8fgyol0).

 import os
 import json
 import pandas as pd
 import configparser
 from neo4j import GraphDatabase

 config = configparser.ConfigParser()
 config.read('config.ini')

 # Set the OpenAI API key env variable manually
 os.environ["OPENAI_API_KEY"] = config['DEFAULT']['openai-key']

 # DB credentials
 url = config['DEFAULT']['url']
 username = config['DEFAULT']['username']
 password = config['DEFAULT']['password']

### Step 2

We get a subset of the graph from the larger dataset by querying Neo4j.
The output of the graph is retrieved in JSON format.

 def run_query(cypher_query):
 with GraphDatabase.driver(url, auth=(username, password)) as driver:
 with driver.session() as session:
 result = session.run(cypher_query)
 return result.data()

 cypher_query = """
 MATCH (n:organisation)-[r]-(g:grant)
 where g.source='arc.gov.au' RETURN * LIMIT 100
 """

 result = run_query(cypher_query)

Note: We have used a limit of 100 nodes in the response to limit the
size of the returning JSON file. This would impact the LLM functionality
if it exceeds the context window size.

We create a system prompt by providing background knowledge about the
graph database and the task. Along with it, we pass the JSON subgraph
as context.

 system_prompt = f'''
 You are a helpful agent designed to fetch information from a graph database.
 Data
 {json.dumps(result)}

 This is the output of the Neo4j cypher which returns nodes and relationships. n is the organisations, g is the grants
 and r is the relationships between the grants and organisations.

 Based on this data answer the user prompts and return the output as json.

 '''

Users can then enter the prompt to get insights from the subgraph. For
example, in the prompt below, we are trying to find the grant with the
most relationship to organisations.

 prompt = "Help me find the grants which is related to the most organisations"

### Step 3

We pass the system and user prompts to the OpenAI model. In this case,
we use "gpt-4--1106-preview". More information is available on the
[OpenAI page on Models](https://platform.openai.com/docs/models).

 from openai import OpenAI
 client = OpenAI()

 def define_query(prompt, model="gpt-4-1106-preview"):
 completion = client.chat.completions.create(
 model=model,
 temperature=0,
 response_format= {
 "type": "json_object"
 },
 messages=[
 {
 "role": "system",
 "content": system_prompt
 },
 {
 "role": "user",
 "content": prompt
 }
 ]
 )
 return completion.choices[0].message.content

OpenAI provides the grant details with the most connections to
organisations as a JSON output.

 print(define_query(prompt))

### Output

This is the output provided by OpenAI RAG.

 {
 "grants": [
 {
 "arc_for_primary": "1117",
 "local_id": "LP200301123",
 "start_year": "2023",
 "purl": "http://purl.org/au-research/grants/arc/LP200301123",
 "source": "arc.gov.au",
 "type": "grant",
 "arc_scheme_name": "Linkage Projects",
 "title": "Making Australia resilient to airborne infection transmission.",
 "arc_announcement_administering_organisation": "Queensland University of Technology",
 "url": "http://dataportal.arc.gov.au/NCGP/API/grants/LP200301123",
 "arc_funding_at_announcement": "898013",
 "arc_grant_status": "Active",
 "arc_for": "040101,1117,111705,120404",
 "r_number": "158880661",
 "end_year": "2026",
 "participant_list": "Lidia Morawska,Duzgun Agdas,Laurie Buys,Keith Grimwood,Graham Johnson,Guy Marks,Alexander Paz,Thomas Rainey,Zoran Ristovski,Kirsten Spann,Bo Xia,Patrick Chambers,Shaun Clough,Stephen Corbett,Charles dePuthod,Neal Durant,Travis Kline,Peter McGarry,Ross Mensforth,Clive Paige,John Penny,Brad Prezant,Uma Rajappa,David Ward,Scott Bell,Giorgio Buonanno,Mark Jermy,Jose Jimenez,Claire Wainwright,Sandra Glaister,greg Bell,Louisa O'Toole",
 "key": "arc/LP200301123",
 "related_organisations_count": 24
 }
 ]
 }

We can compare it visually with the graph from Neo4j and see that RAG
provided the correct output.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*PkIQl8fupR-TdiwppqdJkg.png" />
</figure>

We have tested this mini pipeline with other examples, and the
simplicity of the pipeline makes it functional as long as the size of
the subgraph produced in Step 2 does not exceed the context window of
the OpenAI model.

### Conclusion

The integration of OpenAI RAG and Neo4j paves the way for innovative
solutions in knowledge graph applications, and recommendation systems.
RAG is efficient in grounding the LLM on the most recent data, and
verified information and reduces the expense of retraining the model
when new data is available.

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=3a183df816ce){width="1"
height="1"}

A novel approach to improving the efficiency of text search in graph
databases utilizing Neo4j, OpenAI, and Typesense.

### Authors

* Nakul Nambiar (ORCID: [0009–0009–9720–9233](https://orcid.org/0009-0009-9720-9233))
* Aishwarya Nambissan (ORCID: [0009–0003–3823–6609](https://orcid.org/0009-0003-3823-6609))

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*3_mdx5SSgImdy1c2RZzl1A.png" />
<figcaption>Created using DALL-E</figcaption>
</figure>

The ability to use cutting-edge tools and frameworks is essential for
staying ahead in the ever-changing field of technology. When it comes to
natural language processing, search capabilities, and graph databases,
**LLM** (Large Language Models), **Typesense**, and **Neo4j** work
together as a powerful trio that offers a cohesive and effective
solution.

As we explore further, we will learn about the unique advantages of
Typesense, Neo4j, and LLM and how their combined powers may open up a
new world of possibilities. This blog is your resource for
comprehending, putting into practice, and optimizing the potential of
this influential trio, whether you're a developer looking for creative
solutions, a data scientist seeking improvements in analytics, or a
business professional trying to maximize information retrieval.

### What is Neo4j and Typesense?

Neo4j is a well-known graph database that specializes in managing
densely linked data. Because of its superior relationship management
capabilities, it is a well-known option for applications with complex
data models. Conversely, Typesense is an extremely quick, open-source
search engine that can quickly search through and retrieve a lot of
text-based data.

While Neo4j is a powerful tool for traversing relationships and querying
graph data, its built-in text search features are not effective for
handling large amounts of text. Here, we will use Typesense to enhance
Neo4j's text search capabilities by using a customized solution for
effective and quick search operations.

### Text Search Challenges in Neo4j

**Memory**: Neo4j relies heavily on memory. Thus, it may consume a lot
of memory for intensive text search queries.

**Lack of Specialised Text Search Features**: Neo4j is a graph database,
and its main function is to handle and store graph-based data. As
compared to other search tools, it lacks some specialized text search
features, which may have a negative impact on the result.

In this article, we are using a Neo4j Graph database that uses this
[schema](https://researchgraph.org/schema) to demonstrate the challenges
for text search and potentially a working solution. The graph includes
five primary objects including research publications, researchers,
organizations, academic grants, and research data collections, capturing
the collaborative network of academic works.

The following is a subset of the schema that contains information
relevant to this article.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*6l7kfZAin3Bwp_gZdU6U1g.jpeg" />
<figcaption>Publication and grant schema from <a
href="https://researchgraph.org/schema/">Research Graph
Meta Model</a></figcaption>
</figure>

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*lrtN5375h6OAuwtGN9KNYA.jpeg" />
<figcaption>Example of the Research Graph schema</figcaption>
</figure>

To illustrate the difficulty at hand, if we wish to find research grants
and publications associated with 'Artificial Intelligence', we will
encounter the following issues:

$a$ We will need to find all alternative or relevant terms (e.g. AI,
ML, \...)

$b$ We will need to have a (computationally) expensive text index
search across Neo4j nodes as Neo4j does not particularly perform well
for text search.

To address this issue we are employing a text-based search engine called
Typesense. But first, let\'s look at the advantages and capabilities of
both tools in our toolbox.

### Advantages of Neo4j

**Flexibility:** The graph model is adaptable and may quickly change to
reflect new relationships and data structures.

**Cypher Query Language:** Neo4j uses cypher query language, which is
expressive, readable, and more suitable for graph databases. It makes
retrieving complex patterns from graph databases easier.

**Performance:** Neo4j's underlying architecture is optimized to query
nodes and relationships in a graph efficiently, thus improving the
performance.

**Scalability:** Neo4j supports horizontal scalability. It lets you add
more machines to handle large datasets when needed.

**Transactional Support:** Neo4j ensures data consistency and integrity
in transactional operations by adhering to the ACID (Atomicity,
Consistency, Isolation, Durability) standards.

### Advantages of Typesense

**Speed**: As a search engine designed just for text queries, Typesense
performs very well in terms of speed. We can offload the text-search
tasks to Typesense and increase the speed of the application.

**Rich Text Search**: Typesense provides various advanced text search
features, typo tolerance, faceted, hybrid, and fuzzy search. Hence, it
can accommodate typos and potential errors in user inputs.

**Scalable**: Typesense can scale horizontally. Thus, it can handle
large datasets and a huge volume of queries.

**Easy Integration**: Typesense has simple APIs and connectors for
different programming languages, making integrating with other platforms
and software accessible.

### Vector Search

Typesense can use various small LLM models to create embeddings for the
text and then use these embeddings to do a nearest neighbor
search (KNN).

An embedding for a document is an alternate representation of data as
floating point values. These embeddings are generated using Machine
Learning models. In principle, two similar documents would have
embeddings "closer" to each other.

For example, using OpenAI, we can convert text to embeddings using
OpenAI APIs as below:

 from openai import OpenAI
 client = OpenAI()

 response = client.embeddings.create(
 input="Your text string goes here",
 model="text-embedding-ada-002"
 )

 print(response.data[0].embedding)

The generated embeddings will be as follows:

 {
 "data": [
 {
 "embedding": [
 -0.006929283495992422,
 -0.005336422007530928,
 ...
 -4.547132266452536e-05,
 -0.024047505110502243
 ],
 "index": 0,
 "object": "embedding"
 }
 ],
 "model": "text-embedding-ada-002",
 "object": "list",
 "usage": {
 "prompt_tokens": 5,
 "total_tokens": 5
 }
 }
 

Typesense converts text in each document using the LLM model mentioned
in the schema.

Here are some common models you can use to generate these document
embeddings:

- [Sentence-BERT](https://www.sbert.net/)
- [E-5](https://medium.com/@hansahettiarachchi/unleashing-the-potential-of-embedding-model-e5-revolutionizing-natural-language-comprehension-3f1516489048)
- [CLIP](https://github.com/openai/CLIP)
- [OpenAI's Text Embeddings
 model](https://platform.openai.com/docs/api-reference/embeddings/create)
- [Google's
 PaLM API](https://developers.generativeai.google/products/makersuite)
- [Google's
 Vertex API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings)

You can find the list of built-in models supported by Typesense
[here](https://huggingface.co/typesense/models/tree/main).

For example, below is a schema for a collection of grants that creates
embeddings for the attribute "grant-summary" using the
"all-MiniLM-L12-v2" LLM model for creating the embeddings. The created
embeddings are stored as a new attribute called embedding in the same
document.

 grant_schema = {
 "name": "grant",
 "fields": [
 {"name": "grant-summary", "type": "string"},
 {"name": "flat-field-of-research", "type": "string"},
 {
 "name": "embedding",
 "type": "float[]",
 "embed": {
 "from": [
 "grant-summary"
 ],
 "model_config": {
 "model_name": "ts/all-MiniLM-L12-v2"
 }
 }
 }
 ]
 }

### Demo

We will make use of OpenAI, Typesense, and Neo4j to get relevant data
for a keyword from the Neo4j graph database.

**Resources**: We have a Virtual Machine (VM) with a Neo4j server
running with the grants data embedded on the "grant-summary" attribute.
Typesense is running on the same VM on port 8108. Lastly, we need an
OpenAI API key that can be generated from [OpenAI](https://openai.com/).

In this example, we will search for a keyword and all related related
terms. We will use OpenAI to generate the related words to the keyword.
We will then use these related words to do a semantic search in the
Typesense database to get a list of IDs. Using this list of IDs, we can
then query the Neo4j database to get the graph.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*5ng10X_cLfH_1o2rvK-cwQ.jpeg" />
<figcaption>Pipeline steps for OpenAI-Typesense-Neo4j integrated
search</figcaption>
</figure>

1. First, we accept the user input, which is the keyword that needs to
 be searched.

2\. Then, this keyword is passed to OpenAI, which generates a set of 5
keywords that are related to the given keyword.

 prompt = "We are searching academic articles for research related to a
 keyword presented by the user. Recommend five alternative terms 
 that find related papers to the given keyword. Only create keywords 
 and no other description in your response. Produce CSV"

 user_prompt = "The keyword I want search for articles is" + search_query_term


 import openai

 completion = openai.ChatCompletion.create(
 model="gpt-4-1106-preview",
 messages=[ 
 {"role": "system", "content": prompt},
 {"role": "user", "content": user_prompt}
 ]
 )

For the keyword "Artificial Intelligence", OpenAI generated the
following output below:

 {
 "role": "assistant",
 "content": "\"machine learning\", \"neural networks\", \"cognitive computing\", \"natural language processing\", \"robotics\""
 }

3\. The generated keywords are then searched in Typesense to get a set
of DOIs related to the keywords.

 import typesense

 client = typesense.Client({
 'nodes': [{
 'host': 'localhost', 
 'port': '8108', 
 'protocol': 'http'
 }],
 'api_key': api_key,
 'connection_timeout_seconds': 300
 })

 search_parameters = {
 'searches': [],
 
 }

 for term in completion.choices[0].message["content"].split(','):
 search_object = {
 'q': term.strip(),
 'query_by': 'embedding',
 'exclude_fields': 'embedding',
 'limit': 2,
 'collection': 'publications'
 }
 search_parameters["searches"].append(search_object)

 common_search_params={}
 data = client.multi_search.perform(search_parameters, common_search_params);
 dois = []
 for search_result in data['results']:
 for doc in search_result['hits']:
 dois.append(doc['document']['DOI'])
 print(doc['document']['DOI'] , ' ' , doc['document']['title'])

4\. DOIs can then be easily queried from the Neo4j database to get the
related data and graph.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*27ltmtCCpQPnPvkj6uuetw.png" />
<figcaption>Graph for the keyword “Artificial Intelligence”</figcaption>
</figure>

### Conclusion

To sum up, the integration of Neo4j, OpenAI, and Typesense is a
combination that tackles the difficulties related to text search in
graph databases.

With this integration, developers can take advantage of the benefits of
each of the platforms, making text search more effective, scalable, and
feature-rich. For those looking to improve the search capabilities of
their apps, the Neo4j-OpenAI-Typesense combination seems to be a strong
option as businesses struggle with ever-more complicated data models.

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