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Mar 19, 2025

Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j

Andreas Kollegger argues that AI agents shouldn't be autonomous black boxes. Discover how combining GraphRAG with local LLMs turns agents into safe, composable software design patterns.

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#1 about 4 min

Introduction to Neo4j and remote developer relations work

An overview of Neo4j's growth and the global travel patterns of their developer relations team.

#2 about 3 min

Understanding graph databases and node relationship modeling

How graph databases use nodes and relationships to connect and organize data efficiently without massive join tables.

#3 about 3 min

The fundamentals of retrieval augmented generation operations

How preparing language model prompts with external retrieved context guarantees more accurate operational answers.

#4 about 3 min

Enhancing language models with graph retrieval augmented generation

Using knowledge graphs to distill information and provide highly focused context for answering language model queries.

#5 about 2 min

Merging structured and unstructured data for business insights

Techniques for connecting fragmented text documents with existing business data records to build comprehensive query systems.

#6 about 2 min

Resources and introductory tools for learning graph technologies

Available online courses and bundled functional packages that assist developers with implementing graph knowledge representations.

#7 about 5 min

Handling messy enterprise data with evolvable graph schemas

Mapping fragmented spreadsheets and legacy documentation into incrementally refined graphs simplifies massive enterprise data migrations.

#8 about 1 min

Returning to basic text formats for machine learning

Why straightforward text formats like Markdown and CSV are preferred for feeding clean unstructured data directly to automated AI scrapers.

#9 about 2 min

Addressing data privacy concerns with local language models

Running self-hosted language models ensures corporate data governance and prevents critical proprietary information from leaking.

#10 about 3 min

The impact of open source models on industry dynamics

How advanced open source releases disrupt traditional funding incentives and democratize advanced artificial intelligence capabilities.

#11 about 3 min

Overcoming language model limitations via streaming data ingestion

Processing real-time information updates with specialized small models maintains current contextual relevance without high resource consumption.

#12 about 2 min

Embedding local language models into user operating systems

How small models operating as personal desktop agents enable advanced contextual search and independent file interactions.

#13 about 6 min

Treating artificial intelligence agents as composable software units

Reframing machine learning agents from autonomous actors into controllable software architectures managed by traditional design patterns.

#14 about 4 min

Comparing graph augmented retrieval with vector similarity search

Why basic vector matching lacks the specific contextual relationships that knowledge graph cross-linking effectively secures.

#15 about 4 min

Overcoming developer intimidation when integrating foundational graph structures

Recognizing that functional graph querying relies heavily on accessible pattern matching approaches rather than entirely native data components.

#16 about 3 min

The future potential for native graph programming languages

Exploring how contemporary coding frameworks abstract internal graph operations and the viable opportunity for newly tailored native syntax.

#17 about 2 min

Elevating developer workflows by automating repetitive foundational tasks

How automated tools securely shoulder repetitive dependency tracking constraints so programmers can target impactful software challenges.

#18 about 2 min

Implementing sensible adoption strategies for generative programming systems

Applying measured incremental implementation strategies lets teams confidently adopt emerging capabilities without enduring overwhelming complexity.

Matching moments

1:44 min

Introduction to generative AI and knowledge graphs

Michael Hunger Michael Hunger · WWC 2024

2:22 min

Unlocking generative AI capabilities using knowledge graphs

Zaid Zaim Zaid Zaim +1 · WWC Europe 2026

4:11 min

Protecting enterprise data with local models and RAG

Ash Ryan Arnwine Ash Ryan Arnwine +3 · WWC 2024

2:24 min

Transforming unstructured email data into customized user knowledge graphs

Dana Lawson Dana Lawson +1 · WWC Europe 2026

2:26 min

Automating structural graph generation using large language models

Prof Smoke Prof Smoke · WWC 2025

3:50 min

Building local RAG architectures using the Anything LLM tool

Cedric Clyburn Cedric Clyburn +1 · WWC 2025

Upcoming sessions on this topic

Open session

World Congress 2026 North America

Building Stuff with GenAI - The Open Minded Workshop beyond OpenAI

Andreas Erben

CTO for Applied AI and Metaverse at daenet

Andreas Erben
Open session

World Congress 2026 North America

Beyond SQL Generation: How to Teach Agents What Your Database Actually Means

Kris Jenkins

Lead Developer Advocate at Snowflake

Kris Jenkins
Open session

World Congress 2026 North America

DeepAgents: Build Multi-Agent AI Systems That Actually Work

Apoorva Jaiswal, Anagha Rumade, Anjana Umapathy

Apoorva Jaiswal
Anagha Rumade
Anjana Umapathy
Open session

World Congress 2026 North America

No Single Model to Rule Them All: Building Resilient AI Agents Across Open & Closed LLMs

Emmanuel Acheampong

Senior Manager Developer Relations at Crusoe AI

Emmanuel Acheampong
Open session

World Congress 2026 North America

Databases in the Agent Era

Monica Sarbu

Founder and CEO of xata.io

Monica Sarbu
Open session

World Congress 2026 North America

When Agents Became Users: Rearchitecting Identity and Permissions for AI at Scale

Dor Cohen, Yoav Gal

Dor Cohen
Yoav Gal

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