GenerateGraph

GenerateGraph is a Singleton task that generates a graph of concepts and their relationships from a given context.

Inputs

• context (str): The context from which to extract the ontology of terms.

Outputs

• graph (str): A JSON-like string containing nodes and edges representing concepts and their relationships.
• model (str): The name of the model used for generation.

Example

Generate a graph of concepts and their relationships based on a given context. This example uses the default model configured in the Dria client.

import os
import asyncio
from dria.factory import GenerateGraph
from dria.models import Model
from dria.client import Dria
from dria.models import Task

dria = Dria(rpc_token=os.environ["DRIA_RPC_TOKEN"])

async def evaluate():
    generate_graph = GenerateGraph()
    context = "Artificial Intelligence is a broad field that includes machine learning and deep learning. Neural networks are a key component of deep learning systems."
    res = await dria.execute(
        Task(
            workflow=generate_graph.workflow(context=context),
            models=[Model.GEMMA2_9B_FP16],
        ),
        timeout=75,
    )
    return generate_graph.parse_result(res)

def main():
    result = asyncio.run(evaluate())
    print(result)

if __name__ == "__main__":
    main()

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"
}