149 lines
6.2 KiB
Python
149 lines
6.2 KiB
Python
""" Here we update semantic graph with content that classifier produced"""
|
|
import uuid
|
|
import json
|
|
from datetime import datetime
|
|
from enum import Enum, auto
|
|
from typing import Type, Optional, Any
|
|
from pydantic import BaseModel
|
|
from cognitive_architecture.infrastructure.databases.graph.get_graph_client import get_graph_client
|
|
from cognitive_architecture.modules.cognify.llm.content_to_propositions import generate_graph
|
|
from cognitive_architecture.shared.data_models import GraphDBType, DefaultGraphModel, Document, DocumentType, Category, \
|
|
Relationship, UserProperties, UserLocation, KnowledgeGraph, TextSubclass, TextContent, DefaultContentPrediction
|
|
|
|
|
|
async def add_propositions(G, category_name, subclass_content, layer_description, new_data, layer_uuid,
|
|
layer_decomposition_uuid):
|
|
""" Add nodes and edges to the graph for the given LLM knowledge graph and the layer"""
|
|
|
|
# Find the node ID for the subclass within the category
|
|
await G.load_graph_from_file()
|
|
|
|
subclass_node_id = None
|
|
for node, data in G.graph.nodes(data=True):
|
|
if subclass_content in node:
|
|
subclass_node_id = node
|
|
|
|
print(subclass_node_id)
|
|
|
|
if not subclass_node_id:
|
|
print(f"Subclass '{subclass_content}' under category '{category_name}' not found in the graph.")
|
|
return G
|
|
|
|
# Mapping from old node IDs to new node IDs
|
|
node_id_mapping = {}
|
|
|
|
# Add nodes from the Pydantic object
|
|
for node in new_data.nodes:
|
|
unique_node_id = uuid.uuid4()
|
|
new_node_id = f"{node.description} - {str(layer_uuid)} - {str(layer_decomposition_uuid)} - {str(unique_node_id)}"
|
|
await G.add_node(new_node_id,
|
|
created_at=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
|
updated_at=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
|
description=node.description,
|
|
category=node.category,
|
|
memory_type=node.memory_type,
|
|
layer_uuid=str(layer_uuid),
|
|
layer_description=str(layer_description),
|
|
layer_decomposition_uuid=str(layer_decomposition_uuid),
|
|
unique_id=str(unique_node_id),
|
|
type='detail')
|
|
await G.add_edge(subclass_node_id, new_node_id, relationship='detail')
|
|
|
|
# Store the mapping from old node ID to new node ID
|
|
node_id_mapping[node.id] = new_node_id
|
|
|
|
# Add edges from the Pydantic object using the new node IDs
|
|
for edge in new_data.edges:
|
|
# Use the mapping to get the new node IDs
|
|
source_node_id = node_id_mapping.get(edge.source)
|
|
target_node_id = node_id_mapping.get(edge.target)
|
|
|
|
if source_node_id and target_node_id:
|
|
await G.add_edge(source_node_id, target_node_id, description=edge.description, relationship='relation')
|
|
else:
|
|
print(f"Could not find mapping for edge from {edge.source} to {edge.target}")
|
|
|
|
return G
|
|
|
|
async def append_to_graph(layer_graphs, required_layers, G):
|
|
# Generate a UUID for the overall layer
|
|
layer_uuid = uuid.uuid4()
|
|
decomposition_uuids = set()
|
|
# Extract category name from required_layers data
|
|
category_name = required_layers.dict()['label']['type']
|
|
|
|
# Extract subgroup name from required_layers data
|
|
# Assuming there's always at least one subclass and we're taking the first
|
|
subgroup_name = required_layers.dict()['label']['subclass'][0].value # Access the value of the enum
|
|
|
|
for layer_ind in layer_graphs:
|
|
|
|
for layer_json, knowledge_graph in layer_ind.items():
|
|
# Decode the JSON key to get the layer description
|
|
layer_description = json.loads(layer_json)
|
|
|
|
# Generate a UUID for this particular layer decomposition
|
|
layer_decomposition_uuid = uuid.uuid4()
|
|
decomposition_uuids.add(layer_decomposition_uuid)
|
|
# Assuming append_data_to_graph is defined elsewhere and appends data to G
|
|
# You would pass relevant information from knowledge_graph along with other details to this function
|
|
F = await add_propositions(G, category_name, subgroup_name, layer_description, knowledge_graph,
|
|
layer_uuid, layer_decomposition_uuid)
|
|
|
|
# Print updated graph for verification (assuming F is the updated NetworkX Graph)
|
|
print("Updated Nodes:", F.graph.nodes(data=True))
|
|
|
|
return F, decomposition_uuids
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import asyncio
|
|
|
|
|
|
# Assuming all necessary imports and GraphDBType, get_graph_client, Document, DocumentType, etc. are defined
|
|
|
|
# Initialize the graph client
|
|
graph_client = get_graph_client(GraphDBType.NETWORKX)
|
|
|
|
from typing import List, Type
|
|
|
|
|
|
# Assuming generate_graph, KnowledgeGraph, and other necessary components are defined elsewhere
|
|
|
|
async def generate_graphs_for_all_layers(text_input: str, layers: List[str], response_model: Type[BaseModel]):
|
|
tasks = [generate_graph(text_input, "generate_graph_prompt.txt", {'layer': layer}, response_model) for layer in
|
|
layers]
|
|
return await asyncio.gather(*tasks)
|
|
|
|
|
|
input_article_one= "The quick brown fox jumps over the lazy dog"
|
|
|
|
|
|
# Execute the async function and print results for each set of layers
|
|
async def async_graph_per_layer(text_input: str, cognitive_layers: List[str]):
|
|
graphs = await generate_graphs_for_all_layers(text_input, cognitive_layers, KnowledgeGraph)
|
|
# for layer, graph in zip(cognitive_layers, graphs):
|
|
# print(f"{layer}: {graph}")
|
|
return graphs
|
|
|
|
cognitive_layers_one = ['Structural Layer', 'Semantic Layer',
|
|
'Syntactic Layer',
|
|
'Discourse Layer',
|
|
'Pragmatic Layer',
|
|
'Stylistic Layer',
|
|
'Referential Layer',
|
|
'Citation Layer',
|
|
'Metadata Layer']
|
|
required_layers_one = DefaultContentPrediction(
|
|
label=TextContent(
|
|
type='TEXT',
|
|
subclass=[TextSubclass.ARTICLES]
|
|
)
|
|
)
|
|
# Run the async function for each set of cognitive layers
|
|
level_1_graph = asyncio.run( async_graph_per_layer(input_article_one, cognitive_layers_one))
|
|
|
|
G = asyncio.run(append_to_graph(level_1_graph, required_layers_one, graph_client))
|
|
|
|
|
|
|