Title

AWS re:Invent 2023 - Power Amazon Bedrock applications with Neo4j knowledge graph (DAT203)

Summary

Anthony Prasad Tevraj, Senior Partner Solutions Architect at AWS, and Ben Lackey from Neo4j present an architecture for creating GenAI applications using Neo4j and Amazon Bedrock.
They discuss the limitations of out-of-the-box LLMs (Large Language Models), such as hallucinations, errors, and lack of context.
Neo4j is introduced as a solution to improve LLM accuracy through fine-tuning, few-shot prompting, grounding, and vector embedding, which helps eliminate bias and hallucinations.
The architecture includes an ingestion layer into Neo4j's knowledge graph and a consumption layer to provide outputs to users, such as chatbots or customer service representatives.
A demo is shown by Ben Lackey, where a simple chatbot application is built using Amazon SageMaker Studio, Bedrock, and Neo4j to process and query financial data from the SEC's Edgar system.
The demo illustrates how LLMs can be combined with Neo4j knowledge graphs to extract and present information in a natural language format, overcoming limitations of traditional databases.
Resources such as slides, blog posts, GitHub code, and white papers are offered for further learning, and attendees are invited to visit the Neo4j booth for more information.

The integration of Neo4j's graph database with Amazon Bedrock's LLM capabilities allows for the creation of more accurate and context-aware applications by leveraging the strengths of both technologies.
Grounding LLMs with a knowledge graph can significantly reduce the occurrence of hallucinations and biases, which are common issues with standalone LLMs.
Vector embedding in Neo4j supports both semantic and vector search, enabling more nuanced data retrieval and relationship traversal, which is particularly useful for complex queries.
The use of prompt engineering with LLMs can lead to more targeted and specific information extraction, which is beneficial for applications requiring precise data handling.
The demo showcases the practical application of these technologies in the financial sector, but the principles can be applied to various domains such as healthcare, retail, and logistics.
The approach of using GenAI to process semi-structured data represents a shift from traditional ETL processes, potentially saving development time and resources while increasing flexibility.
The combination of natural language processing with graph databases is a natural fit, as both operate on the principle of interconnected entities and relationships, which can lead to more intuitive and powerful data analysis tools.