50 lines
No EOL
2 KiB
Python
50 lines
No EOL
2 KiB
Python
""" Here we update semantic graph with content that classifier produced"""
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from cognitive_architecture.infrastructure.databases.graph.get_graph_client import get_graph_client, GraphDBType
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async def add_classification_nodes(graph_id, classification_data):
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graph_client = get_graph_client(GraphDBType.NETWORKX)
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await graph_client.load_graph_from_file()
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context = classification_data["context_name"]
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layer = classification_data["layer_name"]
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# Create the layer classification node ID using the context_name
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layer_classification_node_id = f"LLM_LAYER_CLASSIFICATION:{context}:{graph_id}"
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# Add the node to the graph, unpacking the node data from the dictionary
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await graph_client.add_node(layer_classification_node_id, **classification_data)
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# Link this node to the corresponding document node
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await graph_client.add_edge(graph_id, layer_classification_node_id, relationship = "classified_as")
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# Create the detailed classification node ID using the context_name
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detailed_classification_node_id = f"LLM_CLASSIFICATION:LAYER:{layer}:{graph_id}"
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# Add the detailed classification node, reusing the same node data
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await graph_client.add_node(detailed_classification_node_id, **classification_data)
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# Link the detailed classification node to the layer classification node
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await graph_client.add_edge(layer_classification_node_id, detailed_classification_node_id, relationship = "contains_analysis")
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return True
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# if __name__ == "__main__":
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# import asyncio
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# # Assuming all necessary imports and GraphDBType, get_graph_client, Document, DocumentType, etc. are defined
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# # Initialize the graph client
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# graph_client = get_graph_client(GraphDBType.NETWORKX)
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# G = asyncio.run(add_classification_nodes(graph_client, "Document:doc1", {"data_type": "text",
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# "context_name": "TEXT",
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# "layer_name": "Articles, essays, and reports"}))
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# from cognitive_architecture.utils import render_graph
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# ff = asyncio.run( render_graph(G.graph, graph_type='networkx'))
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# print(ff) |