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Masterclasses led by Ben Gardner, Martin O’Hanlon, Paco Nathan and Amy Hodler covering ontology-based data management, multimodal GraphRAG and building high-quality knowledge graphs.

We're excited to invite you to our next Meetup, co-hosted with the London Java Community!

Join fellow graph enthusiasts, Neo4j developers, and members of the Java community as we explore how graphs, knowledge graphs, and context engineering can unlock smarter applications and better answers. We’ll also uncover how AI-generated code can be weaponized, exposing hidden vulnerabilities in the software we trust. Whether you’re curious about building with graph-powered AI or safeguarding your projects from new threats, this meetup will give you insights you can put into practice.

Session 1: Black Friday Brilliance: Managing a Billion Transactions with Tech, Tactics, and Teamwork by Jamie Colmans

The Black Friday and Cyber Monday period is one of the busiest times in the retail calendar, both in stores and online, and here at Loqate our customers rely on our infrastructure to support their businesses at this crucial time. Over the four-day BFCM period in 2023 we processed over 1 billion requests to our APIs, and we managed this with greater than 99.99% availability! We've seen our request volume increase over 100x in the last 10 years, and managing this requires the right technologies, careful planning, and a great team of people.

With insightful commentary from a cross-section of our brilliant Dev team, I’ll talk you through how we scale our infrastructure to support these increases in traffic, and some of the technologies and processes we use. I’ll also give some insights into how the team works together over this busy period to keep everything running smoothly.

Session 2: WhatsThat? Using graphs and AI to make sense of your friends WhatsApp group by Martin O'Hanlon

Communication, messaging, and memory are complicated. How are you supposed to keep up with it all? It's difficult for you to keep up with the constant stream of messages you get. How can we expect an LLM to do the same? Let's look at how we can use graphs and AI to summarise the complex streams of messages you get in that oddly titled WhatsApp group.

We look forward to connecting with the community to exchange ideas on graphs, tech, and AI - join the discussion!

Register: https://luma.com/p7w9uqtz?tk=XlH6v1

Vector Space Day is bringing together engineers, researchers, and AI builders to explore the cutting edge of retrieval, vector search infrastructure, and agentic AI.

We’re excited that Martin O'Hanlon from Neo4j will be speaking!

His session, Hands On with GraphRAG, is a practical exploration of how graphs strengthen RAG. Attendees will learn how to: -Build a Knowledge Graph from unstructured data -Use GraphRAG to provide grounding and context for generative AI -Improve vector retrieval with graph structures -Integrate GraphRAG tools into a LangChain agent

Join us in Berlin for a hands-on look at how Knowledge Graphs + RAG create more reliable and context-aware AI applications:

In this module you will learn how to write Cypher code to retrieve data from the graph. You will learn how to:- Retrieve nodes from the graph.- Retrieve nodes with a particular label.- Filter the retrieval by a property value.- Return property values.- Retrieve nodes and relationships from the graph using patterns in the graph.- Filter queriesUsing the Movies example dataset, you will create and execute Cypher code to find actors and movies in our graph.

Presenter: Martin O'Hanlon

Full Course: https://graphacademy.neo4j.com/courses/cypher-fundamentals/

In this module you will learn how to write Cypher code to retrieve data from the graph. You will learn how to:- Retrieve nodes from the graph.- Retrieve nodes with a particular label.- Filter the retrieval by a property value.- Return property values.- Retrieve nodes and relationships from the graph using patterns in the graph.- Filter queriesUsing the Movies example dataset, you will create and execute Cypher code to find actors and movies in our graph.

Presenter: Martin O'Hanlon

Full Course: https://graphacademy.neo4j.com/courses/cypher-fundamentals/

In this module you will learn how to write Cypher code to retrieve data from the graph. You will learn how to:- Retrieve nodes from the graph.- Retrieve nodes with a particular label.- Filter the retrieval by a property value.- Return property values.- Retrieve nodes and relationships from the graph using patterns in the graph.- Filter queriesUsing the Movies example dataset, you will create and execute Cypher code to find actors and movies in our graph.

Presenter: Martin O'Hanlon

Full Course: https://graphacademy.neo4j.com/courses/cypher-fundamentals/

In this module you will learn how to write Cypher code to retrieve data from the graph. You will learn how to:- Retrieve nodes from the graph.- Retrieve nodes with a particular label.- Filter the retrieval by a property value.- Return property values.- Retrieve nodes and relationships from the graph using patterns in the graph.- Filter queriesUsing the Movies example dataset, you will create and execute Cypher code to find actors and movies in our graph.

Presenter: Martin O'Hanlon

Full Course: https://graphacademy.neo4j.com/courses/cypher-fundamentals/

Discover the power of integrating Neo4j with Generative AI models through Langchain.

Learn how to harness graph databases to enhance the accuracy and reliability of Large Language Models (LLMs) by grounding them with factual information, effectively preventing misinformation or 'hallucinations.' We go hands-on using Langchain and Python to seamlessly connect an LLM with Neo4j, leveraging Cypher and Vector Indexes for robust AI applications. Although our focus is on OpenAI models, Langchain's versatility allows for the exploration of various LLMs.

Presenter: Martin O'Hanlon

Full Course: https://graphacademy.neo4j.com/courses/llm-fundamentals/

Discover the power of integrating Neo4j with Generative AI models through Langchain.

Learn how to harness graph databases to enhance the accuracy and reliability of Large Language Models (LLMs) by grounding them with factual information, effectively preventing misinformation or 'hallucinations.' We go hands-on using Langchain and Python to seamlessly connect an LLM with Neo4j, leveraging Cypher and Vector Indexes for robust AI applications. Although our focus is on OpenAI models, Langchain's versatility allows for the exploration of various LLMs.

Presenter: Martin O'Hanlon

Full Course: https://graphacademy.neo4j.com/courses/llm-fundamentals/

Discover the power of integrating Neo4j with Generative AI models through Langchain.

Learn how to harness graph databases to enhance the accuracy and reliability of Large Language Models (LLMs) by grounding them with factual information, effectively preventing misinformation or 'hallucinations.' We go hands-on using Langchain and Python to seamlessly connect an LLM with Neo4j, leveraging Cypher and Vector Indexes for robust AI applications. Although our focus is on OpenAI models, Langchain's versatility allows for the exploration of various LLMs.

Presenter: Martin O'Hanlon

Full Course: https://graphacademy.neo4j.com/courses/llm-fundamentals/

Discover the power of integrating Neo4j with Generative AI models through Langchain.

Learn how to harness graph databases to enhance the accuracy and reliability of Large Language Models (LLMs) by grounding them with factual information, effectively preventing misinformation or 'hallucinations.' We go hands-on using Langchain and Python to seamlessly connect an LLM with Neo4j, leveraging Cypher and Vector Indexes for robust AI applications. Although our focus is on OpenAI models, Langchain's versatility allows for the exploration of various LLMs.

Presenter: Martin O'Hanlon

Full Course: https://graphacademy.neo4j.com/courses/llm-fundamentals/