AWS Developers | Amazon Neptune: Simplifying Graph Queries With LLMs and LangChain @awsdevelopers | Uploaded 1 year ago | Updated 1 hour ago
Using graphs with Amazon Neptune just got a whole lot simpler with Langchain and LLMs! Customers use graphs with Amazon Neptune for use cases like detecting fraud, customer 360, and security posture assessment. They also use Amazon Neptune to build knowledge graphs that connect disparate data sources.
In this session, you will learn how you can quickly create and connect to a Neptune cluster, load a graph, and use the Neptune workbench notebooks to query using natural language. You will also learn how to setup the Langchain integration with a few lines of code, chose from different LLMs, and use prompt engineering to refine your results.
***To learn more about the services featured in this talk, please visit: aws.amazon.com/neptune
#AWS #amazonneptune #langchain
Using graphs with Amazon Neptune just got a whole lot simpler with Langchain and LLMs! Customers use graphs with Amazon Neptune for use cases like detecting fraud, customer 360, and security posture assessment. They also use Amazon Neptune to build knowledge graphs that connect disparate data sources.
In this session, you will learn how you can quickly create and connect to a Neptune cluster, load a graph, and use the Neptune workbench notebooks to query using natural language. You will also learn how to setup the Langchain integration with a few lines of code, chose from different LLMs, and use prompt engineering to refine your results.
***To learn more about the services featured in this talk, please visit: aws.amazon.com/neptune
#AWS #amazonneptune #langchain
