Amazon Web Services & Neo4j AI Use Cases

This repository contains worked examples for ai applications with different source data types and use cases. Examples leverage Amazon Bedrock.

1. Mixed Structured Data (Finserv): Demonstrates how to build an AI apps on top of mixed data - where documents are connected by structured data. This is akin to structured tables linking to unstructured text fields. Leverages United States Security & Exchange Commission (SEC) company filings data. Semi-structured data comes from form13 Asset manager ownership data while unstructured text comes from form10k sections.
2. Document Data (Commercial Contracts): Demonstrates how to build agentic AI apps on top of documents with repeated lexical structure - i.e. documents sharing similar hierarchical/section structure, components, and/or conceptual breakdown. Applicable to documents like legal contracts, product catalogs, user manuals and more. This example uses commercial contracts to demonstrate.
3. Unstructured Text Data (Medical Research): Demonstrates how to build AI apps on top of highly unstructured text documents. Leveraged Named Entity Recognition (NER) to build a knowledge graph for GraphRAG. Applicable to some forms of internal documentation,research papers, and other text where there is no consistent structure. Leverages Named Entity Recognition (NER) to build a knowledge graph.

Prerequisites for All Examples

1. Follow this guide to configure your environment to use the Bedrock API.
2. Install requirements.txt