Michael Hunger

Aug 22, 2024 • World Congress 2024

Large Language Models ❤️ Knowledge Graphs

LLMs are trained to be helpful, not factual. Learn to stop hallucinations by grounding them in a knowledge graph, creating AI you can actually trust.

#1about 3 minutes

Addressing the key challenges of large language models

LLMs often hallucinate or lack access to private data because they are trained to be helpful, not necessarily factual.

#2about 2 minutes

Using Retrieval Augmented Generation to ground LLMs

The RAG pattern improves LLM accuracy by first retrieving relevant information from a database to provide as context for the answer.

#3about 4 minutes

Representing complex data with knowledge graphs

Knowledge graphs model data as a network of entities and relationships, making complex connections intuitive and easy to query.

#4about 3 minutes

Using LLMs to build a knowledge graph from text

LLMs can automatically extract structured entities and relationships from unstructured documents to populate a knowledge graph.

#5about 3 minutes

Demo of extracting conference data into a graph

An application ingests a conference agenda and uses an LLM to automatically build a knowledge graph of speakers and their talks.

#6about 3 minutes

Combining vector and graph search with GraphRAG

The GraphRAG pattern uses vector search to find entry points into the graph and then traverses relationships to gather richer, more relevant context.

#7about 5 minutes

Code demo of querying a graph with LangChain

A Jupyter notebook demonstrates how to use LangChain and Neo4j to execute a GraphRAG query that avoids LLM hallucinations.

#8about 3 minutes

Benefits and traceability of the GraphRAG approach

This approach provides rich context, enables explainability by tracing data sources, and allows for graph enrichment with clustering algorithms.

#9about 2 minutes

How to control and validate graph extraction quality

You can guide the LLM's extraction process with a predefined schema and validate its output against a human-created baseline for accuracy.

Andrew Comp
Berlin, Germany

Intermediate

Java

JavaScript

Addressing the key challenges of large language models

Using Retrieval Augmented Generation to ground LLMs

Representing complex data with knowledge graphs

Using LLMs to build a knowledge graph from text

Demo of extracting conference data into a graph

Combining vector and graph search with GraphRAG

Code demo of querying a graph with LangChain

Benefits and traceability of the GraphRAG approach

How to control and validate graph extraction quality

Designer

Matching moments

Understanding retrieval-augmented generation (RAG)

Exploring LLMs across clouds

Introducing retrieval-augmented generation (RAG)

Martin O'Hanlon - Make LLMs make sense with GraphRAG

Why large language models need retrieval augmented generation

Build RAG from Scratch

Introducing retrieval-augmented generation to improve accuracy

Knowledge graph based chatbot

Powering real-time AI with retrieval augmented generation

Scrape, Train, Predict: The Lifecycle of Data for AI Applications

Using RAG to enrich LLMs with proprietary data

RAG like a hero with Docling

Understanding retrieval-augmented generation systems

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment

Demonstrating GraphRAG with a practical example

Martin O'Hanlon - Make LLMs make sense with GraphRAG

Featured Partners

Related Videos

Knowledge graph based chatbot

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation

Building Blocks of RAG: From Understanding to Implementation

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

Lies, Damned Lies and Large Language Models

Creating Industry ready solutions with LLM Models

Unlocking the Power of AI: Accessible Language Model Tuning for All

Give Your LLMs a Left Brain

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