Artificial Intelligence (AI) is making strides in the data analysis sphere, with teams of researchers developing new applications to convert unstructured data into usable information. Recently, one such application was introduced, known as the Neo4j LLM Knowledge Graph Builder. This tool leverages powerful machine learning models to transform unstructured data into a comprehensive knowledge graph, furthering the possibilities of data analytics.
Neo4j LLM Knowledge Graph Builder uses robust machine learning models such as OpenAI, Gemini, Llama3, Diffbot, Claude, and Qwen, which together process various types of materials including PDFs, documents, images, web pages, and even YouTube transcript videos. Through this process, the application produces intricate networks of entities and their relationships as well as sophisticated lexical graphs that comprise texts and chunks with embeddings. All these resulting data are stored in a Neo4j database.
What sets Neo4j LLM Knowledge Graph Builder apart is its flexible configuration of extraction schema. Users can define specific nodes and relationships they want to extract, ensuring the resultant knowledge graph meets their requirements. The application also incorporates post-extraction clean-up functionalities, enhancing the accuracy and relevance of the data. Moreover, the tool is beneficial in accommodating long-form English text but may face challenges in handling tabular data and images containing complex information such as diagrams.
After construction of the knowledge graph, users can extract data using several Retrieval-Augmented Generation (RAG) techniques like GraphRAG, Vector and Text2Cypher. These methods enable advanced querying and insightful data analysis. They also illustrate how the data retrieved can be used for pertinent responses.
The Neo4j LLM Knowledge Graph Builder, featuring a Python FastAPI backend and a React-based front end, is an adaptable application. While it works soundly on Google Cloud Run, users also have the option to deploy it locally using Docker Compose. The application relies on the llm-graph-transformer module, an addition to the LangChain framework by Neo4j, crafted to amplify GraphRAG search capabilities and enable seamless integration with other LangChain modules.
Getting started with the application is simple, starting with the user uploading files to the LLM Knowledge Graph Builder, which are stored as Document nodes. Following this step, the text is chunked into manageable segments linked to their associated documents using LangChain Loaders. These sections get interconnected based on similarity to form a k-nearest Neighbours (kNN) graph. After generating the graph, entities and relationships extracted are linked back to the original graph chunks, ensuring an organized information structure that enables advanced RAG patterns and insightful data analysis.
Neo4j LLM Knowledge Graph Builder has a vital role to play in the data field, changing the landscape of data analysis by transforming unstructured data into actionable knowledge graphs. This presents a world of opportunities for improved data analysis and more informed decision-making. For data scientists and analysts aiming to maximize their data, Neo4j LLM Knowledge Graph Builder is a powerful and essential tool because of its seamless integration, customizable extraction process, and robust community support.
