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Thu Vu - build a knowledge graph from unstructured text using Python, Langchain, and Neo4j, with the

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Thu Vu - build a knowledge graph from unstructured text using Python, Langchain, and Neo4j, with the

https://www.youtube.com/watch?v=O-T_6KOXML4 This video by Thu Vu demonstrates how to build a knowledge graph from unstructured text using Python, Langchain, and Neo4j, with the help of Large Language Models (LLMs). The author, Thu Vu, walks through the process step-by-step, covering: 1. Understanding Knowledge Graphs:

  • A knowledge graph is a structured representation of entities and their relationships.
  • It provides a bird’s-eye view of how everything is connected, helping to understand complex topics and relationships.
  • Unlike traditional databases (tables, columns, rows), knowledge graphs represent data in a network of nodes and edges.

2. Building Knowledge Graphs with LLMs:

  • Traditionally, building knowledge graphs from unstructured text was a labor-intensive process.
  • However, LLMs, especially models like GPT-4o and open-source LLMs, can automate this by extracting entities and relationships.
  • The video highlights how LLMs can extract relevant information and represent it in a structured format, which can then be visualized as a graph.

3. Applications of Knowledge Graphs:

  • Understanding Complex Data: They help in visualizing relationships between various entities, making complex data more understandable.
  • Improving Search Results: Knowledge graphs enhance search relevance by understanding the context and relationships between keywords.
  • Fraud Detection: They can identify suspicious patterns and relationships in financial transactions that might be missed by traditional methods.
  • Drug Research & Discovery: They aid in analyzing complex relationships between genes, chemical compounds, diseases, and symptoms.
  • Learning & Study: They transform text into interactive learning experiences, making complex subjects easier to grasp.

4. Technical Implementation: starts at 10:43

  • The video demonstrates the use of Langchain’s LLMGraphTransformer and Pyvis for visualizing the graph.
  • It involves setting up a Python environment, installing necessary libraries (Langchain, OpenAI, Python-dotenv, Pyvis), and obtaining an OpenAI API key.
  • The process involves defining allowed_nodes and allowed_relationships to filter the data extraction.
  • The code snippet shows how to load the API key, initialize the LLM, and then use the LLMGraphTransformer to convert text documents into graph data.
  • Finally, it demonstrates how to visualize the generated knowledge graph using Pyvis, creating an interactive HTML file.

5. Author’s Background:

  • Thu Vu has 7 years of experience as a data analyst and data scientist at a Big4 company.
  • She creates YouTube videos about data science and AI.
  • Her goal is to teach Python for data science and AI projects, making them accessible even to those without a technical background.

The video provides a practical demonstration of how to use these tools to create a knowledge graph from a given text, showcasing the extracted nodes and relationships in a visual format. It aims to empower viewers to build similar projects and apply these techniques in their own work.

Graph View