This article provides a step-by-step walkthrough for building a Retrieval Augmented Generation (RAG) application from PDF documents. The process involves parsing and extracting content from PDF documents, creating a knowledge graph using Neo4j AuraDB, ingesting data into Neo4j using Python, using Neo4j’s vector index for semantic search, utilizing GenAI-Stack for fast prototyping, and incorporating OpenAI models for embedding and text generation. The article emphasizes the importance of leveraging graph databases like Neo4j for storing knowledge and highlights the benefits of the Cypher query language and complex retrieval capabilities. The author also mentions the potential for adding more features to the project in future posts.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Building a Graph+LLM powered RAG application from PDF documents | Adoption of graph-based retrieval augmented generation (RAG) applications | More widespread use of graph databases for document retrieval and generation | Increasing demand for more accurate and relevant generation of responses |
| Use of semantic-rich & schema-flexible graph databases for RAG solutions | Shift from traditional databases to graph databases | Widespread use of graph databases for knowledge storage and retrieval | Ability to store and retrieve knowledge in a more flexible and structured manner |
| PDF documents are treated as property graphs | Recognition of PDF documents as graph data | PDF documents are analyzed and stored as graphs for better retrieval | Improved indexing and retrieval of information from PDF documents |
| Integration of Neo4j AuraDB for knowledge storage | Increased use of Neo4j AuraDB for graph storage | More applications using Neo4j AuraDB for knowledge storage in the cloud | Simplified management of graph database infrastructure |
| Ingestion of PDF document contents into Neo4j using Python + Neo4j driver | Automation of data ingestion into Neo4j from PDF documents | Streamlined process for loading PDF document contents into Neo4j | Efficient and automated ingestion of data for graph-based retrieval and generation |
| Use of Neo4j vector index for semantic search | Utilization of Neo4j’s native indexes for semantic search | Improved search capabilities in Neo4j for retrieving relevant information | Enhanced searching and retrieval of information using vector indexes |
| Development of the GenAI-Stack for fast prototyping | Adoption of the GenAI-Stack for rapid development of AI applications | Increased usage of the GenAI-Stack for creating AI applications with improved accuracy | Faster development and prototyping of AI applications using pre-built environments |
| Integration of OpenAI Models for embedding and text generation | Integration of OpenAI’s embedding model and LLM GPT-4 for text generation | Enhanced text generation capabilities using OpenAI’s advanced models | Improved quality and relevance of generated text responses |
| Preparation and walkthrough of a sample project for building a graph-powered RAG application | Guidance for developers in building graph-based RAG applications | More developers able to build graph-powered RAG applications | Increased access to tools and resources for building graph-based RAG applications |
| Continued development and enhancement of the sample project | Iterative improvement and addition of new features to the sample project | More advanced and feature-rich graph-powered RAG applications | Evolution of graph-powered RAG applications through continuous development and research |
| Increased adoption of graph databases for generative AI | Adoption of graph databases for grounding generative AI models | Use of graph databases to improve generative AI model grounding | Enhanced capabilities and performance of generative AI models using graph databases |