N8n Knowledge Graph Node: Extract & Visualize Data

Welcome to Phase 2 of our n8n workflow development, where we dive deep into the fascinating world of Knowledge Graphs. If you're looking to extract structured information, understand relationships between entities, and visualize complex data directly within your n8n workflows, you're in the right place! This phase focuses on creating a robust n8n node that leverages advanced AI models to bring knowledge graphs to life. We'll walk through the objectives, essential documentation, deliverables, and the technical considerations involved in building this powerful tool. Our goal is to make sophisticated data extraction accessible and intuitive for everyone using n8n.

The Vision: Building an n8n Knowledge Graph Node

The primary objective of this phase is to develop a custom n8n node specifically designed for knowledge graph extraction from unstructured text. This isn't just about pulling out names and places; it's about understanding the connections between them. We aim to extract key entities, identify the relationships that bind them, and then construct a structured graph that represents this information. Think of it as turning a dense block of text into a clear, interconnected map of concepts. This node will be a game-changer for anyone dealing with large volumes of text data, from market research and competitive analysis to academic research and content management. The node will support multiple operations, allowing users to extract entities, identify relationships, or build the complete graph, all configurable through the intuitive n8n interface. We'll be referencing a Colab notebook (docs/colab/knowledge_graph_generation.ipynb) that contains the core logic and AI prompts, ensuring a seamless integration of cutting-edge AI capabilities into your workflows. This systematic approach guarantees that we're building on proven AI models and techniques, making the node both powerful and reliable. The ability to define specific entity types, such as characters, locations, or organizations, and to automatically detect the language of the input text further enhances the node's versatility. This makes it adaptable to a wide range of use cases and data sources, providing unparalleled flexibility in data analysis and knowledge discovery.

Essential Reading Before You Begin

Before embarking on the development of our n8n knowledge graph node, it's crucial to familiarize yourself with the foundational resources. Think of these as your blueprints and user manuals. Firstly, the Custom Node Development Guide (docs/n8n/CUSTOM_NODE_DEVELOPMENT.md) is your go-to for understanding the structure of n8n nodes, the installation process, and troubleshooting common errors. This guide is indispensable for navigating the n8n development environment. Secondly, the Colab Knowledge Graph notebook (docs/colab/knowledge_graph_generation.ipynb) contains the heart of our knowledge graph logic. It details the AI prompts, the expected output formats, and the underlying reasoning for entity and relationship extraction. Understanding this notebook is key to grasping how the node will function and why it's designed a certain way. Finally, Phase 1 (google-genai-core) (PHASE-1-CORE.md) is a critical dependency. This phase ensures that our foundational connection to the AI model is solid. You need to understand how to use the core functionalities provided by Phase 1 to make calls to the AI, handle responses, and manage potential errors. Without a working Phase 1, our knowledge graph node won't be able to communicate with the AI, rendering it non-functional. Thoroughly reviewing these documents will not only prevent common pitfalls but also equip you with the knowledge to build a high-quality, efficient, and reliable n8n node. This preparatory step is vital for ensuring the success of Phase 2 and for maintaining the integrity of our overall multimodal Gemini project. The clear instructions and code examples within these documents are designed to facilitate a smooth development process, allowing you to focus on the unique aspects of knowledge graph extraction.

Deliverables: What We're Building

Our n8n nodes for knowledge graph extraction will consist of several key components, meticulously organized to ensure clarity and maintainability. The package structure will follow a standard n8n custom node format, residing within custom-nodes/n8n-nodes-knowledge-graph/. This includes essential configuration files like package.json and tsconfig.json, which define the node's dependencies and TypeScript settings, respectively. The core logic will be housed under the nodes/KnowledgeGraph/ directory, featuring KnowledgeGraph.node.ts for the main node implementation and potentially KnowledgeGraph.node.json for UI metadata. A distinctive knowledge-graph.svg icon will make it easily identifiable in the n8n interface. The real power lies within the operations/ subdirectory, which will contain separate TypeScript files for each core function: extractEntities.ts, extractRelationships.ts, and buildGraph.ts. These modular operations ensure that each piece of functionality is well-defined and testable. Furthermore, the prompts/ directory will store the AI prompts used for entity and relationship extraction, specifically entity-extraction.txt and relationship-extraction.txt. These prompts are carefully crafted to guide the AI in producing the desired output. The final deliverable includes a comprehensive README.md file detailing how to install, configure, and use the node, complete with practical examples. This structured approach ensures that the node is not only functional but also user-friendly and well-documented, adhering to best practices in software development and n8n custom node creation. The breakdown into distinct operations allows for flexibility, enabling users to choose the specific functionality they need for their workflow, whether it's a simple entity list or a fully visualized graph.

Operation 1: Extract Entities

The first core operation of our n8n knowledge graph node is extractEntities. This function is designed to pinpoint and list specific pieces of information within a given text. Users will provide the source text they want to analyze. Crucially, they can specify the entityTypes they are interested in – options include characters, locations, organizations, or all to capture everything. The node also supports multilingual capabilities, with an language parameter allowing users to set it to auto for detection or specify a language like fr (French) or en (English). The output will be a clean JSON object. This JSON will contain a list of entities, where each entity is an object with a unique id, its name as found in the text, and its type. For example, you might see `{