FasTrack to Production with Amazon Bedrock and Neo4j

Introduction

• The world is changing, and organizations have a tremendous amount of data.
• The challenge is how to get more interesting and important insights from this data and turn it into actionable knowledge.
• Generative AI is racing towards over a trillion dollars in revenue, but most organizations are still in the pilot or proof-of-concept phase.
• Graphs are not just solving today's difficult problems, but also providing the foundation for tomorrow's solutions.

The Rise of Generative AI

• Generative AI has revolutionized how we interact with information, with amazing capabilities in chatbots and image generators.
• However, generative AI alone is not a business information system and has limitations in providing accurate, reliable, and explainable answers.

Introducing Graph RAG (Retrieval Augmented Generation)

• Graph RAG combines the creativity of generative AI with the knowledge and context provided by a knowledge graph.
• It involves generating vector embeddings, querying the knowledge graph, and expanding the context to provide more relevant and accurate answers.

Benefits of Graph RAG

1. Higher Accuracy: Research shows significant improvements in accuracy when using a graph RAG approach compared to a baseline vector-based approach.
2. Easier Development: Knowledge graphs provide a more intuitive and transparent representation of the data, making it easier for developers to understand and debug their applications.
3. Explainability and Governance: Knowledge graphs are transparent and allow for better explainability and governance, ensuring the context provided to the language model is accurate and secure.

Key Considerations for Building a Graph RAG Application

1. Understanding Data Sources: Identify the existing data sources, whether structured or unstructured, that will be used to build the knowledge graph.
2. Extraction and Ingestion: Leverage tools like Amazon Bedrock to extract and ingest data into the knowledge graph, including handling data deduplication and entity resolution.
3. Knowledge Graph Storage and Management: Use a database like Neo4j that supports both graph and vector search, along with tools for managing and exploring the knowledge graph.
4. Serving the Graph RAG Pattern: Implement the graph RAG pattern, where a question is used to generate vector embeddings, query the knowledge graph, and expand the context to provide a more relevant answer.
5. Building Applications: Leverage the graph RAG pattern to build various applications, such as enterprise search, FAQ bots, and discovery tools, that can benefit from the richer and more accurate information provided by the knowledge graph.

Conclusion

• Graph RAG provides a powerful approach to leveraging generative AI and knowledge graphs to solve real-world business challenges.
• By considering the key areas outlined, organizations can effectively build and deploy graph RAG applications that deliver higher accuracy, easier development, and better explainability and governance.