참고소스 수정본

참고/instructor-main/docs/examples/building_knowledge_graphs.md

@@ -0,0 +1,98 @@
+---
+title: Building Knowledge Graphs from Text
+description: Learn to construct knowledge graphs from textual data using OpenAI's API and Pydantic in this comprehensive tutorial.
+---
+
+## See Also
+
+- [Knowledge Graph](./knowledge_graph.md) - Visualize knowledge graphs
+- [Entity Resolution](./entity_resolution.md) - Identify and resolve entities
+- [Document Segmentation](./document_segmentation.md) - Break down documents for analysis
+- [Nested Structures](../learning/patterns/nested_structure.md) - Complex hierarchical models
+
+# Building Knowledge Graphs from Textual Data
+
+In this tutorial, we will explore the process of constructing knowledge graphs from textual data using OpenAI's API and Pydantic. This approach is crucial for efficiently automating the extraction of structured information from unstructured text.
+
+```python
+from typing import List
+from pydantic import BaseModel, Field
+import instructor
+
+
+class Node(BaseModel):
+    id: int
+    label: str
+    color: str = "blue"  # Default color set to blue
+
+
+class Edge(BaseModel):
+    source: int
+    target: int
+    label: str
+    color: str = "black"  # Default color for edges
+
+
+class KnowledgeGraph(BaseModel):
+    nodes: List[Node] = Field(default_factory=list)
+    edges: List[Edge] = Field(default_factory=list)
+
+
+# Patch the OpenAI client to add response_model support
+client = instructor.from_provider("openai/gpt-5-nano")
+
+
+def generate_graph(input_text: str) -> KnowledgeGraph:
+    """Generates a knowledge graph from the input text."""
+    return client.create(
+        model="gpt-4o-mini",
+        messages=[
+            {
+                "role": "user",
+                "content": f"Help me understand the following by describing it as a detailed knowledge graph: {input_text}",
+            }
+        ],
+        response_model=KnowledgeGraph,
+    )
+
+
+if __name__ == "__main__":
+    input_text = "Jason is Sarah's friend and he is a doctor"
+    graph = generate_graph(input_text)
+    print(graph.model_dump_json(indent=2))
+    """
+    {
+      "nodes": [
+        {
+          "id": 1,
+          "label": "Jason",
+          "color": "blue"
+        },
+        {
+          "id": 2,
+          "label": "Sarah",
+          "color": "blue"
+        },
+        {
+          "id": 3,
+          "label": "Doctor",
+          "color": "blue"
+        }
+      ],
+      "edges": [
+        {
+          "source": 1,
+          "target": 2,
+          "label": "is a friend of",
+          "color": "black"
+        },
+        {
+          "source": 1,
+          "target": 3,
+          "label": "is a",
+          "color": "black"
+        }
+      ]
+    }
+    """
+```