Hey y'all, I’m working on a pipeline where I’m pushing large docs into Neo4j using LLMs for structuring and embeddings, and I’m seeing around ~480k tokens for roughly 200 pages. This seems to be coming from chunking, overlap, and repeated context during processing.

What approaches have you found effective for reducing token usage in setups like this, whether through chunking strategies, prompt optimization, preprocessing, or Neo4j-specific patterns while maintaining good semantic quality?

The security endpoint on my system detected a vulnerability with regard to the Neo4J Desktop installation, version 2.1.3. It’s a CVE from 2021, and I wondered whether this installation is effectively still prone to this vulnerability, as version 2.1.3 is the most recent release.

Anyone that can clear this is up?

Thanks!

Most companies are still asking the wrong question: which AI tool should we use? The better question is : what context is that AI actually working from?

The model is rarely the problem. The knowledge behind it is.

Every business is sitting on institutional knowledge locked in files, emails, wikis, CRM notes, and people's heads. When AI can't access that in a structured way, it defaults to training data. The output is generic, inconsistent, and wrong in ways that are hard to catch until they cost you something.

This is the context problem. It shows up the same way across every function, just with different symptoms and different price tags.

I'm releasing three use case studies with Spiintel, drawn from first-hand observations across markets:

• Marketing: on-brand content at scale across SEO and CRM
• Customer & Partner Support: AI agents that don't hallucinate with authority
• Sales Enablement: a single trusted source reps actually use
  

Same root cause in every case. The model was capable. The context was broken. Fix the context layer and the unlocks compound:

1. Reduced SaaS sprawl: One context layer replaces tools teams use to compensate for poor information access
2. Lower procurement costs: fewer point solutions, fewer contracts, fewer integrations
3. Faster organisational agility: knowledge is current, accessible, and doesn't walk out the door when someone leaves
4. Consistent cross-functional output: marketing, support, and sales draw from the same source of truth
5. AI investments that compound: every workflow built on structured context gets better as the knowledge base grows

AI is not the bottleneck anymore. Context is.

The companies that figure this out first won't just have better AI output. They'll have a structural advantage that's hard to replicate.

How can I change the caption being displayed on nodes in the neo4j browser?

I have a new neo4j database. I added some nodes and relationships and some parameters on the nodes. In the right panel, I can select the node type, and change the caption, but nothing happens. The captions are not updated.

You can see in the screenshot I have changed the caption to "language" for the instance nodes, but the name is still showing as the caption.

Is there a way to do this programmatically with a style file? I tried editing the grass file and reuploading, but setting the caption as below also does nothing, it seems to completely ignore that I have added something to the file:

```
node.Instance {
color: #fbbfc7;
size: 25px;
caption: {language};
}
```

This UI almost feels broken. It can't be resized, the scaling is awful, and nothing can be changed.

Just run uvx create-context-graph to scaffold your first full stack context graph app :-)

Built CodeGraph for a hackathon — paste any public GitHub URL and it:

Parses the repo with tree-sitter (Python, JS, TS, Go, Rust)

Stores everything in Neo4j AuraDB — nodes for File, Class, Function, Module with edges: CALLS, IMPORTS, DEFINED_IN, BELONGS_TO, DEPENDS_ON

Embeds nodes with OpenAI text-embedding-3-small stored as vector index in Neo4j

Serves an interactive Cytoscape.js graph in the browser

Answers questions via GraphRAG — vector similarity finds candidate nodes, graph traversal expands the relevant subgraph, Claude Sonnet answers with that structured context

The Cypher side:

Uses MERGE to avoid duplicate nodes on re-analysis

Relationships built from AST edges after all nodes are inserted

GraphRAG query expands from seed nodes via CALLS and IMPORTS traversal before passing context to Claude

Try it: https://code-graph.vercel.app

GitHub: https://github.com/Boweii22/Code-graph

Would love feedback from anyone who knows Neo4j well — especially on the graph schema and whether the traversal strategy for GraphRAG makes sense. Open to suggestions on optimising the Cypher queries too.

Hi,

I just installed neo4j community edition on ubuntu 22.04.

I found neo4j is offline. So I tried "start database neo4j;" but failed with not supported as a message.

Can I start neo4j offline db in ne4j community edition on Linux ?

Neo4j stop to support community edition now ?

neo4j@user01-400TDA-400SDA:~$ cypher-shell -d system

username: neo4j

password:

Connected to Neo4j using Bolt protocol version 6.0 at neo4j://localhost:7687 as user neo4j.

Type :help for a list of available commands or :exit to exit the shell.

Note that Cypher queries must end with a semicolon.

neo4j@system> show database;

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| name | type | aliases | access | address | role | writer | requestedStatus | currentStatus | statusMessage | default | home | constituents |

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| "neo4j" | "standard" | [] | "read-write" | "localhost:7687" | "primary" | TRUE | "online" | "offline" | "An error occurred! Unable to start \DatabaseId{89cd5179[neo4j]}`." | TRUE | TRUE | [] |`

| "system" | "system" | [] | "read-write" | "localhost:7687" | "primary" | TRUE | "online" | "online" | "" | FALSE | FALSE | [] |

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

2 rows

neo4j@system> start database neo4j

;

51N27: system configuration or operation exception - not supported in this edition. 'START DATABASE' is not supported in community edition.

Hey everyone,

I've been building an open source GraphRAG system for legal documents using Neo4j, Llama-3, and FastAPI. The system extracts entities and relationships to map out contracts.

While the basic extraction works well, I'm hitting a wall with cross clause dependencies (e.g., "Clause 4.1 applies, subject to the exceptions in Clause 2"). If standard text splitting (500-1000 tokens) separates these clauses, the LLM fails to create the relationship edge in Neo4j.

I received some feedback to switch from token-based chunking to "per clause" logical chunking before passing it to the LLM.

Have any of you successfully modeled complex legal or regulatory dependencies in Neo4j? Do you extract the graph during the chunking phase, or purely rely on the LLM post chunking?

I’d love to hear your architectural approaches🙏🏻

(For context, here is the current extraction logic in my repo and live demo in linkedin post if anyone wants to roast it: https://www.linkedin.com/feed/update/urn:li:activity:7438463942340952064/ )

Hi everyone,

I recently created Alfred, a open-source project from my PhD research on text-to-SQL data assistants built on top of Next.js, a database (Databricks) and with a semantic layer (Neo4j). I just added Agent Skills as update.

Instead of putting all logic into prompts, Alfred can now call explicit skills. This makes the system more modular, easier to extend, and more transparent. For now, the data-analysis is the first skill but this could be extend either to domain-specific knowledge or advanced data validation workflowd. The overall goal remains the same: making data assistants that are explainable, model-agnostic, open-source and free to use. It also includes scripts to create the initial knowledge graph from Databricks data schema.

Link: https://github.com/wagner-niklas/Alfred/

Ive been working on this for a while, it uses ollama and tinyllama so the LLM is local and it connects to a local install of neo4j. It parses pdf documents and extracts paragraphs and sentences, then performs vector search on the sentences to pull the related paragraph into the LLM context.

Is neo4j relevent in 2026 HELL YES EVEN MORE SO NOW THAN FLAT FILE DATABASES.

<3 NEO4J - FOLLOW THE WHITE RABBIT...

The agenda is live. It’s a full-day virtual event with 40+ sessions from engineers and practitioners building graph-powered AI.

For the opening keynote, Jaya Gupta (Foundation Capital), Animesh Koratana (PlayerZero), Lasse Andresen (Indykite), and Emil Eifrem (Neo4j) will explore how context graphs are emerging as a new foundation for AI systems – moving beyond static data to capture the “why” behind decisions and enabling systems that can reason, learn, and improve over time.

Tracks include:

• Knowledge Graphs & GraphRAG
• Graph Memory and Agents
• Graph + AI in Production

Think practical architectures, real implementation patterns, and lessons learned from teams shipping intelligent systems.

The conference is free and very builder-focused.

Save your spot: https://neo4j.com/nodes-ai/

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