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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 Β· World Congress 2024

2:22 min

Unlocking generative AI capabilities using knowledge graphs

Zaid Zaim Zaid Zaim +1 Β· World Congress 2026 Europe

2:30 min

Building a scalable context graph for software agents

Michael Angelo Rivera Michael Angelo Rivera +2 Β· World Congress 2026 North America

4:11 min

Protecting enterprise data with local models and RAG

Ash Ryan Arnwine Ash Ryan Arnwine +3 Β· World Congress 2024

2:24 min

Transforming unstructured email data into customized user knowledge graphs

Dana Lawson Dana Lawson +1 Β· World Congress 2026 Europe

2:26 min

Automating structural graph generation using large language models

Prof Smoke Prof Smoke Β· World Congress 2025

Upcoming sessions on this topic

Open session

Thinking in Graphs, Reasoning with Gemini!

  • Bhavesh Bhatt

    N/A

    Lead AI Engineer

Open session

Build a Zero-Cost AI Agent in Your Browser

  • Aishwarya Mathuria

    Adobe

    Computer Scientist

  • Nishant Thakur

    Adobe

    Senior Computer Scientist

Open session

Architecting Trusted AI: Coupling Multi-Agent Systems with dbt Semantic Store for Zero-Hallucination

  • Pushkar Mishra

    J.P. Morgan

    Senior Vice President

Open session

Teaching AI Coding Agents to Build It Right the First Time

  • Sajeetharan Sinnathurai

    Microsoft

    Principal Product Manager

Open session

Building World-Aware Physical AI with Agent Memory and Context Graphs

  • Vasundhara Shukla

    Neo4j

    Developer Advocate

  • Zaid Zaim

    Neo4j

    Developer Advocate | Microsoft AI MVP

Open session

Agentic Forecasting with sktime: Towards Autonomous Time Series Analysis

  • Franz Kiraly

    sktime

    Director

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