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94 lines
3.1 KiB
Markdown
94 lines
3.1 KiB
Markdown
# Visualizing Knowledge Graphs for Complex Topics
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In this guide, you'll discover how to visualize a detailed knowledge graph for understanding complex topics, in this case, quantum mechanics. We leverage OpenAI's API and the Graphviz library to bring structure to intricate subjects.
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!!! tips "Motivation"
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Knowledge graphs offer a visually appealing and coherent way to understand complicated topics like quantum mechanics. By generating these graphs automatically, you can accelerate the learning process and make it easier to digest complex information.
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## Defining the Structures
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Let's model a knowledge graph with **`Node`** and **`Edge`** objects. **`Node`** objects represent key concepts or entities, while **`Edge`** objects indicate the relationships between them.
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```python
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from pydantic import BaseModel, Field
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from typing import List
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class Node(BaseModel):
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id: int
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label: str
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color: str
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class Edge(BaseModel):
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source: int
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target: int
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label: str
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color: str = "black"
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class KnowledgeGraph(BaseModel):
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nodes: List[Node] = Field(..., default_factory=list)
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edges: List[Edge] = Field(..., default_factory=list)
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```
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## Generating Knowledge Graphs
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The **`generate_graph`** function leverages OpenAI's API to generate a knowledge graph based on the input query.
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```python
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from openai import OpenAI
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import instructor
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# Adds response_model to ChatCompletion
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# Allows the return of Pydantic model rather than raw JSON
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client = instructor.patch(OpenAI())
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def generate_graph(input) -> KnowledgeGraph:
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return client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[
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{
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"role": "user",
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"content": f"Help me understand the following by describing it as a detailed knowledge graph: {input}",
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}
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],
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response_model=KnowledgeGraph,
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) # type: ignore
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```
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## Visualizing the Graph
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The **`visualize_knowledge_graph`** function uses the Graphviz library to render the generated knowledge graph.
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```python
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from graphviz import Digraph
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def visualize_knowledge_graph(kg: KnowledgeGraph):
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dot = Digraph(comment="Knowledge Graph")
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# Add nodes
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for node in kg.nodes:
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dot.node(str(node.id), node.label, color=node.color)
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# Add edges
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for edge in kg.edges:
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dot.edge(str(edge.source), str(edge.target), label=edge.label, color=edge.color)
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# Render the graph
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dot.render("knowledge_graph.gv", view=True)
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```
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## Putting It All Together
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Execute the code to generate and visualize a knowledge graph for understanding quantum mechanics.
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```python
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graph: KnowledgeGraph = generate_graph("Teach me about quantum mechanics")
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visualize_knowledge_graph(graph)
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```
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This will produce a visual representation of the knowledge graph, stored as "knowledge_graph.gv". You can open this file to explore the key concepts and their relationships in quantum mechanics.
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By leveraging automated knowledge graphs, you can dissect complex topics into digestible pieces, making the learning journey less daunting and more effective.
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