Name: Improved Results with Vector Search in Knowledge Graphs
Start: 2024-02-08T18:00:00-06:00
End: 2024-02-08T19:30:00-06:00
Location: Keystone CoLAB

Join us at the Keystone CoLAB to connect with Kansas City's data science community. Jennifer Reif, Senior Developer Advocate at Neo4j and host of the graphstuff.fm podcast will share about improved results with vector search in knowledge graphs.

Vector search within knowledge graphs enables users to improve responses from their applications by combining the natural-language strength of an LLM and data accuracy of a graph. In this presentation, we will discuss what vector search is, what it looks like in a graph, how to add vectors to enhance the data, and how to use an LLM with graph vector search to harness relevant and contextual responses to questions.

Refreshments will be provided by Neo4j.

Nathan Smith

Mark McKelvey

Tim Chung

Aaron Moss

Data Science KC

Technology

Python

Big Data

Data Analytics

Business Intelligence

Statistical Computing

Natural Language Processing

Machine Learning

Text Analytics

Data Visualization

Predictive Analytics

Data Mining

R Project for Statistical Computing

Data Science

Applied Statistics

Artificial Intelligence Applications

Improved Results with Vector Search in Knowledge Graphs

Data Science using Python

Graph Databases

Search, Information Retrieval

Keystone CoLAB

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Improved Results with Vector Search in Knowledge Graphs

Data Science KC

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