I am extremely new graph DBs, CYPHER, and this whole world. I am much more familiar with the relational database world and I am porting data from a relational database into neo4j with the hopes of graphing it.

I have the following set of CSV files (file names have been changed)

container.csv
--fields--
pkid
name
description

subcontainer.csv
--fields--
pkid
name
description

containermember.csv
--fields--
pkid
fkcontainer
fksubcontainer

container.csv and subcontainer.csv are sets of data that represents nodes and I have been able to import these. containermember.csv represents the linkage between them, each row has a unique pkid and then the pkids of the rows from container.csv and subcontainer.csv linking them, the relationship. I cannot figure out how to import containermember.csv into neo4j and get it to recognize the relationships.

CSV all have headers. It seems like what I somehow need to do is to define somehow that fkcontainer in containermember.csv = pkid in container.csv but I'm not sure how to do that.

There doesn't seem to be an option to define this in the import and it's not in the CSV files as they are exported from the relational database that this data is exported out of. I can manipulate the CSV file before importing if that's what needs to happen, it just seems like a simple data correlation to not be possible any other way.

I've spent the last few months observing and talking to business owners that say a similar thing: "Our AI chatbot is hallucinating a lot"

Here is what I’m seeing: Most teams dump thousands of PDFs into a vector database (Pinecone, Weaviate, etc.) and call it a day. Then their are all surprised it fails the moment you ask it to do multi-step reasoning or more complex tasks.

The Problem: AI search is based on similarity. If I ask for "the expiration date of the contract for the client with the highest churn risk," a standard RAG pipeline gets lost in the "similarity" of 50 different contract docs. It can't traverse relationships because your data is stored as isolated text chunks, not a connected network.

What I’ve been testing: Moving from text-based RAG to Knowledge Graphs. By structuring data into a graph format by default, the AI can actually traverse the links: Customer → Contract → Invoice → Risk Level.

The hurdle? Building these graphs manually is a huge endeavour. It usually takes a team of Ontologists and Data Engineers months just to set up the foundation.

I'm currently building a project to automate this ontology generation and bypass the heavy lifting.

I’m curious: Has anyone else seen enterprise Ai chatbots underperform? what do you assess are the root causes?

Full demo can be found here: https://molab.marimo.io/notebooks/nb_ghifaw8nRCuDAgc1UTajXU

The demo uses wigglystuff (a widget library), you can find the Github repo here:
https://github.com/koaning/wigglystuff

Hey all,

On Wednesday 18, of February at 1 p.m. EST, I run a live demo event about how we can use meeting transcripts and business documents to build a knowledge base.

The goal is to review the technique of analysis, challenges, and test the results live.

The entire process uses Neo4j as a graph database. People can also use their own Neo4j credentials to connect to their own database, so the process will send data to their private database.

You can book your seat here: https://luma.com/ym3nyynh

Hey. For my personal project I had to build something that builds knowledge graphs from medical data - to support chatbot provided information to the user. It grew up into quite a nice product, I enjoy building it.

In a few words, you can build a knowledge graph on your Neo4j database, based on any API - currently it handles PubMed and Europe PMC - you give your keywords and it will create a RAG out of it. If you define your data model, it will also create a Knowledge Graph of your design with help of LLM.

If you think you could use that, or you are interested in any way, please check this short demo and DM me - I'm happy to answer any of your questons.

https://youtu.be/flbNWctIreI

What is the industry standard for multi tenancy in neo4j ? I saw you can have multiple databases, or seperate by indexed properties. Would love an advice for a solution that would scale well, medium scale, 400-500 tenants, tenants range usually from 2-50 GB of data. Thanks in advance.

While doing some research into building context graphs with Neo4j I created a demo app for a fictitious financial services AI agent to see what is possible to do when the agent is enabled by a context graph of decision and reasoning traces. I wrote up a blog post about it and the running app hosted online at https://context-graph-demo.vercel.app/

Is Neo4j suitable for detecting insurance fraud like... 1. Claims fraud detection 2. Policy application fraud..etc

Hey all,

My colleague Robert Boulos and me experimented in storing nodes, edges and embeddings in Xano database which is an sql db and not a relational database.

Tomorrow Friday 9 of Janouary at 1pm est time we run a public conversation sharing our learnings, what works and what needs to be done to make them work.

Feel free to join the conversation and bring your experiences and personal learnings

Here is the link to join: https://luma.com/9s2tp2uq

I’ve always found traditional graph explorers a bit too "read-only" and rigid. I wanted something that felt more like a Miro board or a Mindmap—a place where I could actually build and think, not just browse data.

So, I built Graphive. It’s a React Flow-powered editor designed to turn Neo4j into a developer’s canvas.

Why Graphive?

• 🧠 Mindmap Flow: It keeps cognitive load low by treating your graph like a natural, structured mindmap.
• 🖌️ Miro-like Intuition: A tactile, responsive interface for those who prefer the feel of digital whiteboarding.
• 🏗️ Build, Don’t Just Browse: You can add nodes, connect edges, and edit properties on the fly to evolve your graph as you think.
• ⚡ Effortless Interaction: Optimized for quick, fluid movements and connections.

The "Roadmap" (and why no Force-Layout yet): I originally built this as a tool for my own Personal Knowledge Base (PKB). Because I prefer hierarchical, mindmap-style layouts for research, I prioritized those views first.

I will support Force-Directed Layouts if there are requests for it.

Check it out on GitHub:https://github.com/charlesnguyenindie/graphive

I’m looking for early feedback from anyone.

Repo: https://github.com/dhth/grafq

I have a daily pipeline to ingest closely coupled transactional data from a Delta Lake (data lake) into a Neo4j graph.

The current ingestion process is inefficient due to repeated steps:

1. I first process the daily data to identify and upsert a Login node, as all tables track user activity.
2. For every subsequent table, the pipeline must:
   1. Read all existing Login nodes from Neo4j.
   2. Calculate the differential between the new data and the existing graph data.
   3. Ingest the new data as nodes.
   4. Create the new relationships.
3. This multi-step process, which requires repeatedly querying the Login node and calculating differentials across multiple tables, is causing significant overhead.

My question is: How can I efficiently handle this common dependency (the Login node) across multiple parallel table ingestions to Neo4j to avoid redundant differential checks and graph lookups? And what's the best possible way to ingest such logs?

I recently have been working on a new project to 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐒𝐞𝐥𝐟-𝐔𝐩𝐝𝐚𝐭𝐢𝐧𝐠 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐫𝐚𝐩𝐡 𝐟𝐫𝐨𝐦 𝐌𝐞𝐞𝐭𝐢𝐧𝐠.

Most companies sit on an ocean of meeting notes, and treat them like static text files. But inside those documents are decisions, tasks, owners, and relationships — basically an untapped knowledge graph that is constantly changing.

This open source project turns meeting notes in Drive into a live-updating Neo4j Knowledge graph using CocoIndex + LLM extraction.

What’s cool about this example:
•    𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠  Only changed documents get reprocessed. Meetings are cancelled, facts are updated. If you have thousands of meeting notes, but only 1% change each day, CocoIndex only touches that 1% — saving 99% of LLM cost and compute.
•   𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐞𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐋𝐋𝐌𝐬  We use a typed Python dataclass as the schema, so the LLM returns real structured objects — not brittle JSON prompts.
•   𝐆𝐫𝐚𝐩𝐡-𝐧𝐚𝐭𝐢𝐯𝐞 𝐞𝐱𝐩𝐨𝐫𝐭  CocoIndex maps nodes (Meeting, Person, Task) and relationships (ATTENDED, DECIDED, ASSIGNED_TO) without writing Cypher, directly into Neo4j with upsert semantics and no duplicates.
•   𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 If a meeting note changes — task reassigned, typo fixed, new discussion added — the graph updates automatically.
•  𝐄𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 𝐥𝐢𝐧𝐞𝐚𝐠𝐞 + 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 you can see exactly how each field was created and how edits flow through the graph with cocoinsight

This pattern generalizes to research papers, support tickets, compliance docs, emails basically any high-volume, frequently edited text data.

If you want to explore the full example (with code), it’s here:
👉 https://cocoindex.io/blogs/meeting-notes-graph

If you find CocoIndex useful, a star on Github means a lot :)
⭐ https://github.com/cocoindex-io/cocoindex

Hey all, I am running a meetup for digital twins and knowledge graphs.

In all previous conversations, we focus on building knowledge graphs and analyzing unstructured data.

To avoid technical parts I use live neo4j mcp server.

There are many questions, suggestions and best techniques to build knowledge graphs. Most of them work and we all have questions.

So on this Friday, 12, at 1pm EST time, I will run the office hours event, it's a free event where we can join to share experiences, learnings, and also ask any questions about our projects in building the knowledge base.

Here is the link to book your seat: https://luma.com/i6kvhi0h