GenerateGraph

Overview

GenerateGraph is a singleton template designed to extract ontological relationships from a given context. It processes text to identify concepts and their relationships, generating a graph-like structure of related terms and their connections.

Inputs

Field	Type	Description
context	str	The context from which to extract the ontology of terms

Outputs

Field	Type	Description
graph	GraphRelation	The generated graph relation containing node_1, node_2, and edge
model	str	The AI model used for generation

GraphRelation Schema

Field	Type	Description
node_1	str	A concept from extracted ontology
node_2	str	A related concept from extracted ontology
edge	str	Relationship between the two concepts

Usage

GenerateGraph instance can be used in data generation as follows:

from dria.factory import GenerateGraph

my_dataset = DriaDataset(
    name="generate_graph",
    description="A dataset for ontology extraction",
    schema=GenerateGraph.OutputSchema,
)
generator = DatasetGenerator(dataset=my_dataset)

Expected output

{
   "graph":[
      {
         "edge":"Machine learning is a subfield within the broader field of Artificial Intelligence.",
         "node_1":"Artificial Intelligence",
         "node_2":"machine learning"
      },
      {
         "edge":"Deep learning is another subfield of Artificial Intelligence that focuses on deep neural networks.",
         "node_1":"Artificial Intelligence",
         "node_2":"deep learning"
      },
      {
         "edge":"Deep learning is a specific approach within machine learning that uses deep neural networks to model complex patterns in data.",
         "node_1":"machine learning",
         "node_2":"deep learning"
      },
      {
         "edge":"Neural networks are crucial components used in the construction of deep learning systems.",
         "node_1":"neural networks",
         "node_2":"deep learning systems"
      }
   ],
   "model":"qwen2.5:32b-instruct-fp16"
}