I'm so happy and proud of this new Aura Analytics offering. It's serverless and "pas as you go" (or prepaid if you want that) and it allows you to run sophisticated graph algos like pagerank, pathfinding, link predication on top of ANY enterprise data. Yes, even data outside a Neo4j database! Very cool stuff. Graph analytics for everyone!
🎊 The release of Neo4j 5.26 as our Long-Term Support (LTS) has arrived!
During these last years, Neo4j 5 has included:
🤩 Many features and enhancements across multiple dimensions
🤩 Performance and scalability
🤩 Operational capabilities
🤩 Security improvements
🤩 AI readiness
Take a look at these details and migrate to Neo4j 5.26 LTS! https://t.co/6eCuJbRctA
#Neo4j #Neo4j5 #graphdatabase
Dramatically improve the accuracy of your knowledge graph applications by applying agentic strategies with LlamaIndex workflows!
In this comprehensive post by Tomaz Bratanic of @neo4j, he builds up slowly from a naive text2cypher implementation to an agentic approach with error checking, retries and correction, and he has the benchmarks to prove it's a better strategy!
➡️ Implement agentic strategies for text2cypher using LlamaIndex Workflows
➡️ Explore multi-step approaches with retry and self-correction mechanisms
➡️ Understand the benefits of iterative planning for complex queries
➡️ Gain insights on benchmarking and real-world deployment considerations
Check out the full guest post on our blog here:
https://t.co/4AYhI7LGfQ
📅 It's the 350th day of the year, time to celebrate the #A350!
Here are some Family Facts about the #LongRangeLeader:
✈️ Designed to fly up to 9700nm
🌍 Operated on +1250 routes
🌐 On +1.6M flights
Flying +420M passengers around the world in its beautiful #AirspaceCabin.
🎊 Thrilled to announce a major milestone in #Neo4j: the general availability of Change Data Capture (CDC) in Aura Virtual Dedicated Cloud (Enterprise) and Neo4j Enterprise subscriptions and the Neo4j Connector for Confluent and Apache Kafka v5.1 with support for CDC.
Key features, uses and more:
https://t.co/8hgC7i4YSM
Jerry said it really well. I think of it in two ways:
1. GraphRAG is a superset of vector-only RAG. It's not graphs INSTEAD OF vectors. It's graph AND vectors.
2. As an industry, we already converged on the best way to do Retrieval for the web. The key to a good R was graph algorithms (specifically PageRank). That innovation created a trillion dollar company.
a) Retrieve the relevant documents through keyword / vector search.
b) Rank them in the graph to get the "top ten blue links."
Vector-only RAG is Altavista. 🔍
GraphRAG is Google. 🚀
Ok, this is pretty crazy.
SQL has been the lingua franca of database querying since the dawn of time.
But for the first time in over three decades (!), ISO just published a NEW database query language called GQL -- the Graph Query Language!
Thanks to @MGazanayi , the @Docker compose repository for the @neo4j SSO with @keycloak example is upgraded to KC20+ along with many improvements ! https://t.co/pj3vI9fquQ
Just read this while reviewing a paper: "Allow the data model to evolve according to the needs of the domain, not the database."
This is such a pithy but incredibly important observation. This is why graph databases exist.
Wow, in Jensen's keynote yesterday at #GTC24, he calls out three sources of data to integrate with LLMs: 1) vector databases, 2) ERP / CRM and 3) *knowledge graphs*!
There's this increasing realization that LLMs and Knowledge Graphs are match made in heaven. Higher accuracy, completeness of answer, explainability.
Left brain, right brain indeed.
https://t.co/GSvFGE1X00
🤖JSON-based Agents With Ollama & LangChain
Learn to implement a Mixtral agent that interacts with a graph database Neo4j through a semantic layer
This work by @tb_tomaz is great for a few reasons:
- Shows how to build an agent with an OSS model
- Shows how to build and use a graph database
- Shows the power of creating a semantic layer over that graph database
Read his detailed blog here: https://t.co/s7G0XF0BUR
Notebook: https://t.co/DrXBDIGkTE
Template: https://t.co/1UYUKZeHqa