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Build a real-time graph retrieval-augmented generation (RAG) application called Graphy, utilizing a combination of closed-source technology including an LLM (GPT-4) for processing and Neo4j as a cloud database. The application will allow users to upload PDF files, which are then processed and turned into a graph database structure that captures relationships among entities such as patients and their medications. A demonstration of how to set up the application, configure keys, and process queries will also be included, with a version two planned that will employ open-source tools.
Introduction to building Graphy as a real-time RAG application.
Future Graphy V2 will utilize open-source tools and self-host Neo4j.
Use of closed-source technologies like LLM, Neo4j, and LangChain for orchestration.
Growing demand for graph-based RAG applications and their benefits.
Code walkthrough for uploading and processing PDF files for the graph database.
The integration of graph databases with LLMs represents a significant advancement in contextual understanding. By leveraging Neo4j and OpenAI's GPT-4, developers can create applications that not only retrieve but also reason about data relationships. This approach is particularly beneficial in fields like healthcare, where understanding complex interactions among patient data can lead to better decision-making. As these technologies evolve, they promise to enhance the accuracy and efficacy of AI applications significantly.
The transition from closed-source to open-source technologies in AI development is a critical trend that promotes accessibility and transparency. However, it is important to ensure that responsible AI practices are adhered to, especially in sensitive domains such as healthcare data management. Structuring databases that effectively represent human conditions demands a focus on ethical data use to prevent biases in model outputs. This movement towards open-source platforms can facilitate more collaborative and ethical development practices.
The application processes uploaded PDFs and retrieves information based on relationships mapped in the graph database.
It is essential for the application to process relationships among various data entities.
It is used for processing user queries and understanding context in the stored graph data.
Its architecture facilitates efficient querying and organization of large datasets, which is essential in Graphy's functionality.
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OpenAI's models enable advanced natural language understanding, crucial for dialog systems in applications like Graphy.
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Gao Dalie (高達烈) 15month
BALA GOPAL REDDY PEDDIREDDY 9month
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