Downloaded 11 times
![Proprietary + Confidential
Retrieval Augmented Generation - a Primer
RAG
Vector Similarity Search
GraphRAG
Vector Similarity Search
+
Graph Traversal
User question: "Apollo 11”
Vector: [0.85, 0.75]
Similarity Score Result Vector Embedding
0.99 Apollo 11 [0.1, 0.5]
0.76 Apollo 12 [0.9, 0.8]
0.5 Space Shuttle [0.3, 0.2]
Similarity search
Vector embedding](https://image.slidesharecdn.com/googlefinalslides-241217130738-6a4fc6cd/75/Smarter-Knowledge-Graphs-For-Public-Sector-10-2048.jpg)
Uploaded byNeo4j
Smarter Knowledge Graphs For Public Sector
The document discusses the integration of Neo4j and Google Cloud for creating smarter knowledge graphs aimed at enhancing public sector capabilities, including fraud detection and citizen services. It highlights challenges in extracting value from both structured and unstructured data and how generative AI can facilitate knowledge graph development and interaction. Key takeaways emphasize the accelerated building of knowledge graphs through collaborative solutions and the potential for deeper insights and improved customer service.
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- 7. Proprietary + Confidential Challengeswith optimal Knowledge Graph adoption Enterprises struggle with the challenge of extracting value from vast amounts of data. Structured data comes in many formats with well defined APIs. Unstructured data contained in documents, drawings, case sheets, financial reports, audio files and video recording can be more difficult to integrate
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- 13. Proprietary + Confidential KeyTakeaways 1 Building a Knowledge Graph for your organization can be greatly accelerated by the partnership between Neo4j and Google 2 Go to Market quicker, harnessing the power of Google Cloud’s Generative AI offerings to build and populate your Knowledge Graph 3 Unlock deeper insights and democratize access to your Knowledge Graph with the synergy between Neo4j and Google Cloud, to understand and serve your own customers better!
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