Design specialized agents that identify the userâs goal, and suggest what nodes and relationships to extract from relevant structured and unstructured data.
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Instructor: Andreas Kollegger
Design specialized agents that identify the userâs goal, and suggest what nodes and relationships to extract from relevant structured and unstructured data.
Implement the multi-agent system using Googleâs ADK to orchestrate the specialized agents and output the graph schemas for structured and unstructured data.
Construct the graphs based on the proposed schemas and connect them to obtain the complete knowledge graph.
Learn to automate the construction of knowledge graphs using agents in Agentic Knowledge Graph Construction, taught by Andreas Kollegger, Developer Evangelist for Generative AI at Neo4j.
In traditional RAG systems, documents are split into chunks stored in a vector database. In a knowledge graph, chunks are additionally placed in a graph that better represents relationships within your data. For example, a chunk representing a product review can be connected in a graph to another node representing the product that was mentioned in the review. Manually constructing knowledge graphs can be a lot of work. In this course, youâll learn how to use collaborative agents to generate the construction plan for your knowledge graph.
Youâll implement an agentic system using Googleâs Agent Development Kit (ADK), to build a knowledge graph that helps you find the root cause of product issues. Youâll work with structured data consisting of product and supplier information, and unstructured data consisting of product reviews.
Youâll design agents that suggest how to transform your structured and unstructured data into graphs. For example, from each CSV file, you can either extract a node representing a product, a part of a product or a supplier, or you can extract a relationship: a product contains this part, a part is provided by this supplier. From each review chunk, you can extract what product and issues were mentioned. Finally, you will construct the graphs based on the plans provided by the agents and connect them in a complete knowledge graph.
In detail, youâll:
By the end of this course, youâll know how to use agents to model and construct knowledge graphs that can enhance the accuracy of your retrieval systems.
If youâve worked with RAG systems and want to go a step further, or if youâre curious about agentic design and knowledge graph databases, this course is for you. You must be familiar with Python. Knowledge of basic Cypher queries can be helpful, but not required.
Introduction
What is a Knowledge Graph?
Architecture of the Multi-Agent System
Introduction to Google's ADK - Part I
Introduction to Google's ADK â Part II
Understanding User Intent
File Suggestions
Schema Proposal for Structured Data
Schema Proposal for UnStructured Data
Knowledge Graph Construction - Part I
Knowledge Graph Construction â Part II
Conclusion
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