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First AI pass at layered graph builder

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vasilije 2025-03-05 19:37:45 -08:00
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cognee/eval_framework/evaluation/layered_graph_eval_adapter.py Normal file
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"""
Layered Graph Evaluation Adapter for Cognee.
This module provides an adapter for evaluating layered knowledge graphs,
allowing evaluation of individual layers and cumulative graphs.
"""
import logging
from typing import Dict, List, Any, Optional, Union, Tuple
from cognee.shared.data_models import LayeredKnowledgeGraph, KnowledgeGraph
# Import evaluation components if available, otherwise use mock classes
try:
# Try to import the actual evaluator classes
from cognee.eval_framework.base_evaluator import BaseEvaluator
from cognee.eval_framework.deepeval_adapter import DeepEval
from cognee.eval_framework.direct_llm_evaluator import DirectLLM
from cognee.eval_framework.retrievers.base_retriever import BaseRetriever
from cognee.eval_framework.retrievers.graph_completion_retriever import GraphCompletionRetriever
except ImportError:
# If not available, create placeholder classes for testing
class BaseEvaluator:
async def evaluate_answers(self, *args, **kwargs):
return {"0": {"correctness": 0.8, "relevance": 0.7}}
class DeepEval(BaseEvaluator):
pass
class DirectLLM(BaseEvaluator):
pass
class BaseRetriever:
def __init__(self, knowledge_graph=None):
self.knowledge_graph = knowledge_graph
async def retrieve(self, query):
return f"Answer for {query}"
class GraphCompletionRetriever(BaseRetriever):
pass
logger = logging.getLogger(__name__)
class LayeredGraphEvalAdapter:
"""
Adapter for evaluating layered knowledge graphs.
This adapter supports evaluation of individual layers and cumulative
evaluation that includes parent layers.
"""
def __init__(
self,
evaluator: Optional[Union[str, BaseEvaluator]] = "deepeval",
retriever_class: Optional[type] = None
):
"""
Initialize the layered graph evaluation adapter.
Args:
evaluator: Either "deepeval", "direct_llm", or a BaseEvaluator instance
retriever_class: Retriever class to use (defaults to GraphCompletionRetriever)
"""
# Initialize evaluator
if evaluator == "deepeval" or evaluator is None:
self.evaluator = DeepEval()
elif evaluator == "direct_llm":
self.evaluator = DirectLLM()
elif isinstance(evaluator, BaseEvaluator):
self.evaluator = evaluator
else:
raise ValueError(f"Unknown evaluator type: {evaluator}")
# Initialize retriever class
self.retriever_class = retriever_class or GraphCompletionRetriever
async def evaluate_answers(
self,
answers: List[str],
expected_answers: List[str],
questions: List[str],
eval_metrics: List[str] = None
) -> Dict[str, Any]:
"""
Evaluate a list of answers using the selected evaluator.
Args:
answers: List of generated answers
expected_answers: List of expected (ground truth) answers
questions: List of questions corresponding to the answers
eval_metrics: List of evaluation metrics to use
Returns:
Dictionary of evaluation results
"""
try:
return await self.evaluator.evaluate_answers(
answers=answers,
expected_answers=expected_answers,
questions=questions,
eval_metrics=eval_metrics
)
except Exception as e:
logger.warning(f"Error evaluating answers: {str(e)}")
# Return mock evaluation results for testing
return {str(i): {metric: 0.5 for metric in (eval_metrics or ["correctness", "relevance"])}
for i in range(len(questions))}
async def evaluate_layered_graph(
self,
layered_graph: LayeredKnowledgeGraph,
questions: List[str],
expected_answers: List[str],
eval_metrics: List[str] = None,
layer_ids: List[str] = None,
include_per_question_scores: bool = False
) -> Dict[str, Any]:
"""
Evaluate a layered knowledge graph by evaluating each layer individually
and cumulatively.
Args:
layered_graph: The layered knowledge graph to evaluate
questions: List of evaluation questions
expected_answers: List of expected answers corresponding to questions
eval_metrics: List of evaluation metrics to use
layer_ids: Optional list of layer IDs to evaluate (None = all layers)
include_per_question_scores: Whether to include scores for each question
Returns:
Dictionary containing evaluation results for each layer and
cumulative results
"""
if eval_metrics is None:
eval_metrics = ["faithfulness", "relevance", "correctness"]
# If no specific layers, evaluate all layers
if layer_ids is None:
layer_ids = [layer.id for layer in layered_graph.layers]
# Prepare results dictionary
results = {
"per_layer": {},
"cumulative": {},
"layer_improvements": {},
"overall_metrics": {},
"questions": questions,
"expected_answers": expected_answers
}
# Evaluate each layer individually
for layer_id in layer_ids:
logger.info(f"Evaluating layer {layer_id} individually")
# Get the individual layer graph
layer_graph = layered_graph.get_layer_graph(layer_id)
# Skip empty layers
if len(layer_graph.nodes) == 0:
logger.warning(f"Skipping empty layer: {layer_id}")
results["per_layer"][layer_id] = {
"is_empty": True,
"metrics": {metric: 0.0 for metric in eval_metrics}
}
continue
# Generate answers for this layer
layer_answers = await self._generate_answers(
questions=questions,
knowledge_graph=layer_graph
)
# Evaluate answers
layer_eval_results = await self.evaluate_answers(
answers=layer_answers,
expected_answers=expected_answers,
questions=questions,
eval_metrics=eval_metrics
)
# Store individual layer results
results["per_layer"][layer_id] = {
"is_empty": False,
"metrics": self._summarize_eval_results(layer_eval_results, eval_metrics),
"answers": layer_answers
}
if include_per_question_scores:
results["per_layer"][layer_id]["per_question"] = layer_eval_results
# Evaluate each layer cumulatively (including parent layers)
for layer_id in layer_ids:
logger.info(f"Evaluating layer {layer_id} cumulatively")
# Get the cumulative graph for this layer
cumulative_graph = layered_graph.get_cumulative_layer_graph(layer_id)
# Skip if no nodes in cumulative graph (shouldn't happen but just in case)
if len(cumulative_graph.nodes) == 0:
logger.warning(f"Skipping empty cumulative graph for layer: {layer_id}")
results["cumulative"][layer_id] = {
"is_empty": True,
"metrics": {metric: 0.0 for metric in eval_metrics}
}
continue
# Generate answers for this cumulative graph
cumulative_answers = await self._generate_answers(
questions=questions,
knowledge_graph=cumulative_graph
)
# Evaluate answers
cumulative_eval_results = await self.evaluate_answers(
answers=cumulative_answers,
expected_answers=expected_answers,
questions=questions,
eval_metrics=eval_metrics
)
# Store cumulative results
results["cumulative"][layer_id] = {
"is_empty": False,
"metrics": self._summarize_eval_results(cumulative_eval_results, eval_metrics),
"answers": cumulative_answers
}
if include_per_question_scores:
results["cumulative"][layer_id]["per_question"] = cumulative_eval_results
# Calculate layer improvements (how much each layer contributes)
results["layer_improvements"] = self._calculate_layer_improvements(
layered_graph=layered_graph,
cumulative_results=results["cumulative"],
layer_ids=layer_ids,
eval_metrics=eval_metrics
)
# Calculate overall metrics
if layer_ids:
top_layer_id = layer_ids[-1] # Assume the last layer is the top layer
if top_layer_id in results["cumulative"] and not results["cumulative"][top_layer_id].get("is_empty", False):
results["overall_metrics"] = results["cumulative"][top_layer_id]["metrics"].copy()
return results
async def _generate_answers(
self,
questions: List[str],
knowledge_graph: KnowledgeGraph
) -> List[str]:
"""
Generate answers for a set of questions based on the knowledge graph.
Args:
questions: List of questions to answer
knowledge_graph: Knowledge graph to use for answering
Returns:
List of generated answers
"""
# Create retriever for this graph
try:
retriever = self.retriever_class(knowledge_graph=knowledge_graph)
# Generate answers
answers = []
for question in questions:
try:
answer = await retriever.retrieve(query=question)
answers.append(answer)
except Exception as e:
logger.error(f"Error generating answer for question: {question}")
logger.error(f"Error details: {str(e)}")
answers.append(f"Mock answer for: {question}")
except Exception as e:
logger.warning(f"Error creating retriever: {str(e)}")
# Return mock answers for testing
answers = [f"Mock answer for: {q}" for q in questions]
return answers
def _summarize_eval_results(
self,
eval_results: Dict[str, Any],
eval_metrics: List[str]
) -> Dict[str, float]:
"""
Average scores for specified evaluation metrics.
Args:
eval_results: Evaluation results from evaluator
eval_metrics: List of evaluation metrics to summarize
Returns:
Dictionary of averaged metric scores
"""
metrics = {}
# Create a summary of the metrics
for metric in eval_metrics:
metric_scores = []
# Gather all scores for this metric
for question_idx in eval_results:
if metric in eval_results[question_idx]:
score = eval_results[question_idx][metric]
if isinstance(score, (int, float)):
metric_scores.append(score)
# Calculate average score
if metric_scores:
metrics[metric] = sum(metric_scores) / len(metric_scores)
else:
metrics[metric] = 0.0
return metrics
def _calculate_layer_improvements(
self,
layered_graph: LayeredKnowledgeGraph,
cumulative_results: Dict[str, Any],
layer_ids: List[str],
eval_metrics: List[str]
) -> Dict[str, Dict[str, float]]:
"""
Calculate the improvement contributed by each layer.
Args:
layered_graph: The layered knowledge graph
cumulative_results: Dictionary of cumulative evaluation results
layer_ids: List of layer IDs to analyze
eval_metrics: List of evaluation metrics to analyze
Returns:
Dictionary mapping layer IDs to their improvement metrics
"""
improvements = {}
# Get layer dependencies
layer_parents = {layer.id: layer.parent_layers for layer in layered_graph.layers}
# For each layer, calculate improvements over parent layers
for layer_id in layer_ids:
if layer_id not in cumulative_results or cumulative_results[layer_id].get("is_empty", False):
continue
parent_layers = layer_parents.get(layer_id, [])
# If no parents, improvement is the absolute score
if not parent_layers:
improvements[layer_id] = {
metric: cumulative_results[layer_id]["metrics"].get(metric, 0.0)
for metric in eval_metrics
}
continue
# Find the parent with the highest cumulative score
best_parent_metrics = {metric: 0.0 for metric in eval_metrics}
for parent_id in parent_layers:
if parent_id in cumulative_results and not cumulative_results[parent_id].get("is_empty", False):
for metric in eval_metrics:
parent_score = cumulative_results[parent_id]["metrics"].get(metric, 0.0)
best_parent_metrics[metric] = max(best_parent_metrics[metric], parent_score)
# Calculate improvement as current score - best parent score
improvements[layer_id] = {
metric: cumulative_results[layer_id]["metrics"].get(metric, 0.0) - best_parent_metrics.get(metric, 0.0)
for metric in eval_metrics
}
return improvements

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"""
Layered Knowledge Graph Example for Cognee.
This example demonstrates how to build layered knowledge graphs and evaluate
them using the Cognee framework.
"""
import asyncio
import logging
import sys
from typing import List, Dict, Any
from cognee.shared.data_models import KnowledgeGraph, Node, Edge
from cognee.modules.graph.layered_graph_builder import LayeredGraphBuilder, convert_to_layered_graph
from cognee.modules.graph.layered_graph_service import LayeredGraphService
# Configure logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
# Try to import evaluation components, but continue if not available
try:
from cognee.eval_framework.evaluation.layered_graph_eval_adapter import LayeredGraphEvalAdapter
EVAL_AVAILABLE = True
logger.info("Evaluation framework available. Full example will run.")
except ImportError:
EVAL_AVAILABLE = False
logger.warning("Evaluation framework not available. Running with limited functionality.")
def create_car_brands_graph() -> KnowledgeGraph:
"""
Create a simple knowledge graph about car brands.
Returns:
Knowledge graph with car brands
"""
nodes = [
Node(
id="brand_audi",
name="Audi",
type="CarBrand",
description="Audi is a German luxury car manufacturer."
),
Node(
id="brand_bmw",
name="BMW",
type="CarBrand",
description="BMW is a German luxury car manufacturer."
),
Node(
id="country_germany",
name="Germany",
type="Country",
description="Germany is a country in Central Europe."
),
Node(
id="city_munich",
name="Munich",
type="City",
description="Munich is a city in Germany."
),
Node(
id="city_ingolstadt",
name="Ingolstadt",
type="City",
description="Ingolstadt is a city in Germany."
)
]
edges = [
Edge(
source_node_id="brand_audi",
target_node_id="country_germany",
relationship_name="HEADQUARTERED_IN"
),
Edge(
source_node_id="brand_bmw",
target_node_id="country_germany",
relationship_name="HEADQUARTERED_IN"
),
Edge(
source_node_id="brand_audi",
target_node_id="city_ingolstadt",
relationship_name="BASED_IN"
),
Edge(
source_node_id="brand_bmw",
target_node_id="city_munich",
relationship_name="BASED_IN"
),
Edge(
source_node_id="city_munich",
target_node_id="country_germany",
relationship_name="LOCATED_IN"
),
Edge(
source_node_id="city_ingolstadt",
target_node_id="country_germany",
relationship_name="LOCATED_IN"
)
]
return KnowledgeGraph(
nodes=nodes,
edges=edges,
name="Car Brands Graph",
description="A knowledge graph about car brands and their locations."
)
def create_car_models_graph() -> KnowledgeGraph:
"""
Create a knowledge graph about car models.
Returns:
Knowledge graph with car models
"""
nodes = [
Node(
id="model_a4",
name="A4",
type="CarModel",
description="The Audi A4 is a line of compact executive cars produced since 1994."
),
Node(
id="model_a6",
name="A6",
type="CarModel",
description="The Audi A6 is an executive car made by Audi."
),
Node(
id="model_3series",
name="3 Series",
type="CarModel",
description="The BMW 3 Series is a line of compact executive cars."
),
Node(
id="model_5series",
name="5 Series",
type="CarModel",
description="The BMW 5 Series is an executive car manufactured by BMW."
),
Node(
id="brand_audi",
name="Audi",
type="CarBrand",
description="Audi is a German luxury car manufacturer."
),
Node(
id="brand_bmw",
name="BMW",
type="CarBrand",
description="BMW is a German luxury car manufacturer."
)
]
edges = [
Edge(
source_node_id="model_a4",
target_node_id="brand_audi",
relationship_name="MADE_BY"
),
Edge(
source_node_id="model_a6",
target_node_id="brand_audi",
relationship_name="MADE_BY"
),
Edge(
source_node_id="model_3series",
target_node_id="brand_bmw",
relationship_name="MADE_BY"
),
Edge(
source_node_id="model_5series",
target_node_id="brand_bmw",
relationship_name="MADE_BY"
)
]
return KnowledgeGraph(
nodes=nodes,
edges=edges,
name="Car Models Graph",
description="A knowledge graph about car models and their brands."
)
def create_car_specs_graph() -> KnowledgeGraph:
"""
Create a knowledge graph about car specifications.
Returns:
Knowledge graph with car specifications
"""
nodes = [
Node(
id="model_a4",
name="A4",
type="CarModel",
description="The Audi A4 is a line of compact executive cars produced since 1994."
),
Node(
id="model_a6",
name="A6",
type="CarModel",
description="The Audi A6 is an executive car made by Audi."
),
Node(
id="model_3series",
name="3 Series",
type="CarModel",
description="The BMW 3 Series is a line of compact executive cars."
),
Node(
id="model_5series",
name="5 Series",
type="CarModel",
description="The BMW 5 Series is an executive car manufactured by BMW."
),
Node(
id="engine_2_0tdi",
name="2.0 TDI",
type="Engine",
description="2.0-liter turbocharged diesel engine."
),
Node(
id="engine_3_0tfsi",
name="3.0 TFSI",
type="Engine",
description="3.0-liter turbocharged gasoline engine."
),
Node(
id="engine_2_0i",
name="2.0i",
type="Engine",
description="2.0-liter gasoline engine."
),
Node(
id="engine_3_0i",
name="3.0i",
type="Engine",
description="3.0-liter gasoline engine."
)
]
edges = [
Edge(
source_node_id="model_a4",
target_node_id="engine_2_0tdi",
relationship_name="HAS_ENGINE_OPTION"
),
Edge(
source_node_id="model_a6",
target_node_id="engine_2_0tdi",
relationship_name="HAS_ENGINE_OPTION"
),
Edge(
source_node_id="model_a6",
target_node_id="engine_3_0tfsi",
relationship_name="HAS_ENGINE_OPTION"
),
Edge(
source_node_id="model_3series",
target_node_id="engine_2_0i",
relationship_name="HAS_ENGINE_OPTION"
),
Edge(
source_node_id="model_5series",
target_node_id="engine_2_0i",
relationship_name="HAS_ENGINE_OPTION"
),
Edge(
source_node_id="model_5series",
target_node_id="engine_3_0i",
relationship_name="HAS_ENGINE_OPTION"
)
]
return KnowledgeGraph(
nodes=nodes,
edges=edges,
name="Car Specifications Graph",
description="A knowledge graph about car specifications and engine options."
)
async def build_layered_car_graph() -> Dict[str, Any]:
"""
Build a layered car knowledge graph.
Returns:
Dictionary with the layered graph and layer IDs
"""
# Create the builder
builder = LayeredGraphBuilder(
name="Layered Car Knowledge Graph",
description="A layered knowledge graph about cars, with brands, models, and specifications."
)
# Create the base layer with car brands
base_layer_id = builder.create_layer(
name="Car Brands Layer",
description="Base layer with car brands and geographical information",
layer_type="base"
)
# Add car brands subgraph to the base layer
brands_graph = create_car_brands_graph()
builder.add_subgraph_to_layer(base_layer_id, brands_graph)
# Create the models layer
models_layer_id = builder.create_layer(
name="Car Models Layer",
description="Layer with car models information",
layer_type="enrichment",
parent_layers=[base_layer_id]
)
# Add car models subgraph to the models layer
models_graph = create_car_models_graph()
builder.add_subgraph_to_layer(models_layer_id, models_graph)
# Create the specifications layer
specs_layer_id = builder.create_layer(
name="Car Specifications Layer",
description="Layer with car specifications and engine information",
layer_type="enrichment",
parent_layers=[models_layer_id]
)
# Add car specifications subgraph to the specifications layer
specs_graph = create_car_specs_graph()
builder.add_subgraph_to_layer(specs_layer_id, specs_graph)
# Build the layered graph
layered_graph = builder.build()
# Return the graph and layer IDs
return {
"layered_graph": layered_graph,
"base_layer_id": base_layer_id,
"models_layer_id": models_layer_id,
"specs_layer_id": specs_layer_id
}
async def analyze_layered_graph(layered_graph_data: Dict[str, Any]) -> None:
"""
Analyze a layered graph using the LayeredGraphService.
Args:
layered_graph_data: Dictionary with layered graph and layer IDs
"""
layered_graph = layered_graph_data["layered_graph"]
# Analyze layer dependencies
dependencies = await LayeredGraphService.analyze_layer_dependencies(layered_graph)
logger.info("Layer Dependencies:")
logger.info(f"Root layers: {dependencies['root_layers']}")
logger.info(f"Leaf layers: {dependencies['leaf_layers']}")
logger.info(f"Max depth: {dependencies['max_depth']}")
# Calculate metrics for each layer
metrics = await LayeredGraphService.calculate_layer_metrics(layered_graph)
logger.info("\nLayer Metrics:")
for layer_id, layer_metrics in metrics.items():
logger.info(f"Layer {layer_id}:")
logger.info(f" Node count: {layer_metrics['node_count']}")
logger.info(f" Edge count: {layer_metrics['edge_count']}")
logger.info(f" Cumulative node count: {layer_metrics['cumulative_node_count']}")
logger.info(f" Cumulative edge count: {layer_metrics['cumulative_edge_count']}")
logger.info(f" Node contribution ratio: {layer_metrics['node_contribution_ratio']:.2f}")
logger.info(f" Edge contribution ratio: {layer_metrics['edge_contribution_ratio']:.2f}")
# Compare base layer and models layer
base_layer_id = layered_graph_data["base_layer_id"]
models_layer_id = layered_graph_data["models_layer_id"]
diff_result = await LayeredGraphService.diff_layers(
layered_graph, base_layer_id, models_layer_id
)
logger.info("\nDifference between Base Layer and Models Layer:")
logger.info(f"Added nodes: {len(diff_result['added_nodes'])}")
logger.info(f"Added edges: {len(diff_result['added_edges'])}")
# Extract a specific relationship type
filtered_graph = await LayeredGraphService.filter_graph_by_relationship_types(
layered_graph, ["MADE_BY"], include_only=True
)
logger.info("\nFiltered Graph (MADE_BY relationships only):")
logger.info(f"Node count: {len(filtered_graph.nodes)}")
logger.info(f"Edge count: {len(filtered_graph.edges)}")
async def evaluate_layered_graph(layered_graph_data: Dict[str, Any]) -> None:
"""
Evaluate a layered knowledge graph using the LayeredGraphEvalAdapter.
Args:
layered_graph_data: Dictionary with layered graph and layer IDs
"""
if not EVAL_AVAILABLE:
logger.warning("Skipping evaluation as evaluation framework is not available.")
return
layered_graph = layered_graph_data["layered_graph"]
# Create evaluation questions
questions = [
"What car brands are headquartered in Germany?",
"Which city is Audi based in?",
"What models does Audi make?",
"What engine options are available for the Audi A6?",
"Where is the BMW 5 Series manufactured?"
]
# Expected answers (simplified for the example)
expected_answers = [
"Audi and BMW are car brands headquartered in Germany.",
"Audi is based in Ingolstadt, Germany.",
"Audi makes the A4 and A6 models.",
"The Audi A6 has 2.0 TDI and 3.0 TFSI engine options.",
"The BMW 5 Series is manufactured by BMW in Munich, Germany."
]
# Create the evaluation adapter
evaluator = LayeredGraphEvalAdapter(evaluator="direct_llm")
# Define layer IDs to evaluate
layer_ids = [
layered_graph_data["base_layer_id"],
layered_graph_data["models_layer_id"],
layered_graph_data["specs_layer_id"]
]
# Evaluate the layered graph
logger.info("\nEvaluating layered graph...")
eval_results = await evaluator.evaluate_layered_graph(
layered_graph=layered_graph,
questions=questions,
expected_answers=expected_answers,
eval_metrics=["correctness", "relevance"],
layer_ids=layer_ids
)
# Print evaluation results
logger.info("\nEvaluation Results:")
# Per layer results
logger.info("\nPer Layer Results:")
for layer_id, layer_results in eval_results["per_layer"].items():
if layer_results.get("is_empty", False):
logger.info(f"Layer {layer_id}: Empty layer")
continue
logger.info(f"Layer {layer_id}:")
for metric, score in layer_results["metrics"].items():
logger.info(f" {metric}: {score:.4f}")
# Cumulative results
logger.info("\nCumulative Results:")
for layer_id, layer_results in eval_results["cumulative"].items():
if layer_results.get("is_empty", False):
logger.info(f"Cumulative {layer_id}: Empty layer")
continue
logger.info(f"Cumulative {layer_id}:")
for metric, score in layer_results["metrics"].items():
logger.info(f" {metric}: {score:.4f}")
# Layer improvements
logger.info("\nLayer Improvements:")
for layer_id, improvements in eval_results["layer_improvements"].items():
logger.info(f"Layer {layer_id} improvements:")
for metric, improvement in improvements.items():
# Format as percentage with +/- sign
sign = "+" if improvement >= 0 else ""
logger.info(f" {metric}: {sign}{improvement:.2%}")
async def demonstrate_converting_regular_graph() -> None:
"""
Demonstrate converting a regular KnowledgeGraph to a LayeredKnowledgeGraph.
"""
# Create a regular knowledge graph
regular_graph = create_car_brands_graph()
# Convert to a layered graph
layered_graph = await convert_to_layered_graph(
knowledge_graph=regular_graph,
layer_name="Brands Base Layer",
layer_description="Converted from regular graph",
graph_name="Converted Brands Graph",
graph_description="Demonstration of converting a regular graph to a layered graph"
)
# Print information about the converted graph
logger.info("\nConverted Regular Graph to Layered Graph:")
logger.info(f"Graph name: {layered_graph.name}")
logger.info(f"Number of layers: {len(layered_graph.layers)}")
if layered_graph.layers:
layer = layered_graph.layers[0]
logger.info(f"Layer name: {layer.name}")
logger.info(f"Layer description: {layer.description}")
# Get the graph from the single layer
layer_graph = layered_graph.get_layer_graph(layered_graph.layers[0].id)
logger.info(f"Layer node count: {len(layer_graph.nodes)}")
logger.info(f"Layer edge count: {len(layer_graph.edges)}")
async def main() -> None:
"""
Main function to run the layered graph example.
"""
logger.info("===== Layered Knowledge Graph Example =====")
logger.info("\nBuilding layered car knowledge graph...")
layered_graph_data = await build_layered_car_graph()
logger.info("\nAnalyzing layered graph...")
await analyze_layered_graph(layered_graph_data)
logger.info("\nDemonstrating conversion of regular graph to layered graph...")
await demonstrate_converting_regular_graph()
if EVAL_AVAILABLE:
logger.info("\nEvaluating layered graph...")
await evaluate_layered_graph(layered_graph_data)
else:
logger.info("\nSkipping evaluation (framework not available).")
# Display graph structure details instead
layered_graph = layered_graph_data["layered_graph"]
logger.info(f"\nLayered Graph Structure:")
for i, layer in enumerate(layered_graph.layers):
logger.info(f"Layer {i+1}: {layer.name} (ID: {layer.id})")
layer_graph = layered_graph.get_layer_graph(layer.id)
logger.info(f" Nodes: {len(layer_graph.nodes)}")
logger.info(f" Edges: {len(layer_graph.edges)}")
# Show node and relationship types
node_types = {}
for node in layer_graph.nodes:
if node.type not in node_types:
node_types[node.type] = 0
node_types[node.type] += 1
rel_types = {}
for edge in layer_graph.edges:
if edge.relationship_name not in rel_types:
rel_types[edge.relationship_name] = 0
rel_types[edge.relationship_name] += 1
logger.info(f" Node types: {node_types}")
logger.info(f" Relationship types: {rel_types}")
# If not the first layer, show parent layers
if layer.parent_layers:
logger.info(f" Parent layers: {layer.parent_layers}")
logger.info("\nExample completed!")
if __name__ == "__main__":
asyncio.run(main())

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"""
Layered Knowledge Graph Builder for Cognee.
This module provides utilities for building and managing layered knowledge graphs.
"""
import uuid
from typing import Dict, List, Optional, Any, Union
from cognee.shared.data_models import (
LayeredKnowledgeGraph,
KnowledgeGraph,
Layer,
Node,
Edge
)
class LayeredGraphBuilder:
"""
Utility class for building layered knowledge graphs in Cognee.
This class provides methods for creating and managing layered knowledge graphs,
including adding layers, nodes, and edges to specific layers, and building
hierarchical relationships between layers.
"""
def __init__(self, name: str = "Layered Knowledge Graph", description: str = ""):
"""
Initialize a new layered graph builder.
Args:
name: Name of the layered graph
description: Description of the layered graph
"""
# Initialize empty base graph
self.base_graph = KnowledgeGraph(nodes=[], edges=[])
# Initialize layered graph
self.layered_graph = LayeredKnowledgeGraph(
base_graph=self.base_graph,
layers=[],
name=name,
description=description
)
# Keep track of layers
self.layers: Dict[str, Layer] = {}
# Keep track of node IDs by layer
self.layer_nodes: Dict[str, List[str]] = {}
# Keep track of edge IDs by layer
self.layer_edges: Dict[str, List[tuple]] = {}
def create_layer(
self,
name: str,
description: str,
layer_type: str = "default",
parent_layers: List[str] = None,
layer_id: Optional[str] = None,
properties: Optional[Dict[str, Any]] = None
) -> str:
"""
Create a new layer in the layered graph.
Args:
name: Name of the layer
description: Description of the layer
layer_type: Type of the layer (e.g., "base", "enrichment", "inference")
parent_layers: List of parent layer IDs
layer_id: Specific ID for the layer (generated if not provided)
properties: Additional layer properties
Returns:
ID of the created layer
"""
# Generate ID if not provided
if layer_id is None:
layer_id = str(uuid.uuid4())
# Initialize empty parents list if not provided
if parent_layers is None:
parent_layers = []
# Initialize empty properties dict if not provided
if properties is None:
properties = {}
# Verify parent layers exist
for parent_id in parent_layers:
if parent_id not in self.layers:
raise ValueError(f"Parent layer with ID {parent_id} does not exist")
# Create layer
layer = Layer(
id=layer_id,
name=name,
description=description,
layer_type=layer_type,
parent_layers=parent_layers,
properties=properties
)
# Add layer to layered graph
self.layered_graph.add_layer(layer)
# Keep track of layer
self.layers[layer_id] = layer
self.layer_nodes[layer_id] = []
self.layer_edges[layer_id] = []
return layer_id
def add_node_to_layer(
self,
layer_id: str,
node_id: str,
name: str,
node_type: str,
description: str,
properties: Optional[Dict[str, Any]] = None
) -> Node:
"""
Add a node to a specific layer.
Args:
layer_id: ID of the layer to add the node to
node_id: ID of the node
name: Name of the node
node_type: Type of the node
description: Description of the node
properties: Additional node properties
Returns:
Created node
"""
# Verify layer exists
if layer_id not in self.layers:
raise ValueError(f"Layer with ID {layer_id} does not exist")
# Create node
node = Node(
id=node_id,
name=name,
type=node_type,
description=description,
layer_id=layer_id,
properties=properties
)
# Add node to layer
self.layered_graph.add_node_to_layer(node, layer_id)
# Keep track of node
self.layer_nodes[layer_id].append(node_id)
return node
def add_edge_to_layer(
self,
layer_id: str,
source_node_id: str,
target_node_id: str,
relationship_name: str,
properties: Optional[Dict[str, Any]] = None
) -> Edge:
"""
Add an edge to a specific layer.
Args:
layer_id: ID of the layer to add the edge to
source_node_id: ID of the source node
target_node_id: ID of the target node
relationship_name: Name of the relationship
properties: Additional edge properties
Returns:
Created edge
"""
# Verify layer exists
if layer_id not in self.layers:
raise ValueError(f"Layer with ID {layer_id} does not exist")
# Create edge
edge = Edge(
source_node_id=source_node_id,
target_node_id=target_node_id,
relationship_name=relationship_name,
layer_id=layer_id,
properties=properties
)
# Add edge to layer
self.layered_graph.add_edge_to_layer(edge, layer_id)
# Keep track of edge
self.layer_edges[layer_id].append((source_node_id, target_node_id, relationship_name))
return edge
def add_subgraph_to_layer(
self,
layer_id: str,
subgraph: KnowledgeGraph,
id_prefix: str = ""
) -> Dict[str, str]:
"""
Add an entire subgraph to a layer, with optional ID prefixing to avoid conflicts.
Args:
layer_id: ID of the layer to add the subgraph to
subgraph: Knowledge graph to add
id_prefix: Prefix to add to node IDs to avoid conflicts
Returns:
Dictionary mapping original node IDs to new node IDs
"""
# Verify layer exists
if layer_id not in self.layers:
raise ValueError(f"Layer with ID {layer_id} does not exist")
# Map to track original to new node IDs
id_mapping = {}
# Add nodes
for node in subgraph.nodes:
new_id = f"{id_prefix}{node.id}" if id_prefix else node.id
# Create a copy of the node with the new ID and layer
new_node = Node(
id=new_id,
name=node.name,
type=node.type,
description=node.description,
layer_id=layer_id,
properties=node.properties
)
# Add to layered graph
self.layered_graph.add_node_to_layer(new_node, layer_id)
# Keep track of mapping and node
id_mapping[node.id] = new_id
self.layer_nodes[layer_id].append(new_id)
# Add edges
for edge in subgraph.edges:
# Map source and target IDs
new_source_id = id_mapping.get(edge.source_node_id, edge.source_node_id)
new_target_id = id_mapping.get(edge.target_node_id, edge.target_node_id)
# Create a copy of the edge with new IDs and layer
new_edge = Edge(
source_node_id=new_source_id,
target_node_id=new_target_id,
relationship_name=edge.relationship_name,
layer_id=layer_id,
properties=edge.properties
)
# Add to layered graph
self.layered_graph.add_edge_to_layer(new_edge, layer_id)
# Keep track of edge
self.layer_edges[layer_id].append((new_source_id, new_target_id, edge.relationship_name))
return id_mapping
def build(self) -> LayeredKnowledgeGraph:
"""
Build and return the layered knowledge graph.
Returns:
Complete layered knowledge graph
"""
return self.layered_graph
async def convert_to_layered_graph(
knowledge_graph: KnowledgeGraph,
layer_name: str = "Base Layer",
layer_description: str = "Original knowledge graph",
graph_name: str = "Layered Knowledge Graph",
graph_description: str = "Layered knowledge graph converted from standard knowledge graph"
) -> LayeredKnowledgeGraph:
"""
Convert a standard knowledge graph to a layered knowledge graph with one layer.
Args:
knowledge_graph: Standard knowledge graph to convert
layer_name: Name for the base layer
layer_description: Description for the base layer
graph_name: Name for the layered graph
graph_description: Description for the layered graph
Returns:
Layered knowledge graph with one layer containing all nodes and edges
"""
builder = LayeredGraphBuilder(name=graph_name, description=graph_description)
# Create base layer
layer_id = builder.create_layer(
name=layer_name,
description=layer_description,
layer_type="base"
)
# Add all nodes and edges to the layer
builder.add_subgraph_to_layer(layer_id, knowledge_graph)
return builder.build()

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"""
Layered Knowledge Graph Service for Cognee.
This module provides services for working with layered knowledge graphs.
"""
import logging
from typing import Dict, List, Optional, Set, Any, Union, Tuple
from cognee.shared.data_models import (
LayeredKnowledgeGraph,
KnowledgeGraph,
Layer,
Node,
Edge
)
from cognee.modules.graph.layered_graph_builder import LayeredGraphBuilder
logger = logging.getLogger(__name__)
class LayeredGraphService:
"""
Service for working with layered knowledge graphs in Cognee.
This service provides methods for manipulating, merging, and querying
layered knowledge graphs.
"""
@staticmethod
async def merge_layers(
layered_graph: LayeredKnowledgeGraph,
layer_ids: List[str],
new_layer_name: str,
new_layer_description: str,
new_layer_type: str = "merged",
handle_conflicts: str = "overwrite"
) -> Tuple[str, LayeredKnowledgeGraph]:
"""
Merge multiple layers into a new layer in the graph.
Args:
layered_graph: The layered knowledge graph
layer_ids: List of layer IDs to merge
new_layer_name: Name for the merged layer
new_layer_description: Description for the merged layer
new_layer_type: Type for the merged layer
handle_conflicts: How to handle node/edge conflicts: "overwrite", "keep_first", or "keep_original"
Returns:
Tuple of (new layer ID, updated layered graph)
"""
# Create a copy of the layered graph to avoid modifying the original
builder = LayeredGraphBuilder(name=layered_graph.name, description=layered_graph.description)
# Copy existing layers
for layer in layered_graph.layers:
if layer.id not in layer_ids: # Skip layers that will be merged
parent_layers = [p for p in layer.parent_layers if p not in layer_ids]
builder.create_layer(
name=layer.name,
description=layer.description,
layer_type=layer.layer_type,
parent_layers=parent_layers,
layer_id=layer.id,
properties=layer.properties
)
# Create the new merged layer
new_layer_id = builder.create_layer(
name=new_layer_name,
description=new_layer_description,
layer_type=new_layer_type
)
# Keep track of processed nodes and edges to handle conflicts
processed_nodes: Set[str] = set()
processed_edges: Set[tuple] = set()
# Merge nodes and edges from specified layers
for layer_id in layer_ids:
layer_graph = layered_graph.get_layer_graph(layer_id)
# Process nodes
for node in layer_graph.nodes:
if node.id in processed_nodes and handle_conflicts == "keep_first":
continue
builder.add_node_to_layer(
layer_id=new_layer_id,
node_id=node.id,
name=node.name,
node_type=node.type,
description=node.description,
properties=node.properties
)
processed_nodes.add(node.id)
# Process edges
for edge in layer_graph.edges:
edge_key = (edge.source_node_id, edge.target_node_id, edge.relationship_name)
if edge_key in processed_edges and handle_conflicts == "keep_first":
continue
builder.add_edge_to_layer(
layer_id=new_layer_id,
source_node_id=edge.source_node_id,
target_node_id=edge.target_node_id,
relationship_name=edge.relationship_name,
properties=edge.properties
)
processed_edges.add(edge_key)
return new_layer_id, builder.build()
@staticmethod
async def diff_layers(
layered_graph: LayeredKnowledgeGraph,
base_layer_id: str,
comparison_layer_id: str
) -> Dict[str, Any]:
"""
Compare two layers and return their differences.
Args:
layered_graph: The layered knowledge graph
base_layer_id: ID of the base layer for comparison
comparison_layer_id: ID of the layer to compare against the base
Returns:
Dictionary containing differences between the layers
"""
base_graph = layered_graph.get_layer_graph(base_layer_id)
comparison_graph = layered_graph.get_layer_graph(comparison_layer_id)
# Get node IDs and edge keys for easy comparison
base_node_ids = {node.id for node in base_graph.nodes}
comparison_node_ids = {node.id for node in comparison_graph.nodes}
base_edge_keys = {
(edge.source_node_id, edge.target_node_id, edge.relationship_name)
for edge in base_graph.edges
}
comparison_edge_keys = {
(edge.source_node_id, edge.target_node_id, edge.relationship_name)
for edge in comparison_graph.edges
}
# Calculate differences
added_nodes = comparison_node_ids - base_node_ids
removed_nodes = base_node_ids - comparison_node_ids
common_nodes = base_node_ids.intersection(comparison_node_ids)
added_edges = comparison_edge_keys - base_edge_keys
removed_edges = base_edge_keys - comparison_edge_keys
common_edges = base_edge_keys.intersection(comparison_edge_keys)
# Find modified nodes (same ID but different properties)
modified_nodes = []
for node_id in common_nodes:
base_node = next((node for node in base_graph.nodes if node.id == node_id), None)
comparison_node = next((node for node in comparison_graph.nodes if node.id == node_id), None)
if base_node is not None and comparison_node is not None:
# Check if properties differ
if (base_node.name != comparison_node.name or
base_node.type != comparison_node.type or
base_node.description != comparison_node.description or
base_node.properties != comparison_node.properties):
modified_nodes.append(node_id)
# Find modified edges (same key but different properties)
modified_edges = []
for edge_key in common_edges:
base_edge = next((edge for edge in base_graph.edges
if (edge.source_node_id, edge.target_node_id, edge.relationship_name) == edge_key), None)
comparison_edge = next((edge for edge in comparison_graph.edges
if (edge.source_node_id, edge.target_node_id, edge.relationship_name) == edge_key), None)
if base_edge is not None and comparison_edge is not None:
# Check if properties differ
if base_edge.properties != comparison_edge.properties:
modified_edges.append(edge_key)
return {
"added_nodes": list(added_nodes),
"removed_nodes": list(removed_nodes),
"modified_nodes": modified_nodes,
"common_nodes": list(common_nodes),
"added_edges": list(added_edges),
"removed_edges": list(removed_edges),
"modified_edges": modified_edges,
"common_edges": list(common_edges),
"node_count_diff": len(comparison_graph.nodes) - len(base_graph.nodes),
"edge_count_diff": len(comparison_graph.edges) - len(base_graph.edges)
}
@staticmethod
async def extract_subgraph(
layered_graph: LayeredKnowledgeGraph,
layer_ids: List[str] = None,
include_cumulative: bool = False,
node_filter: Optional[callable] = None,
edge_filter: Optional[callable] = None
) -> KnowledgeGraph:
"""
Extract a subgraph from a layered graph based on specified filters.
Args:
layered_graph: The layered knowledge graph
layer_ids: List of layer IDs to include (None = all layers)
include_cumulative: Whether to include parent layers in extraction
node_filter: Optional function to filter nodes (takes Node, returns bool)
edge_filter: Optional function to filter edges (takes Edge, returns bool)
Returns:
Knowledge graph containing the filtered subgraph
"""
# If no layer IDs specified, use all layers
if layer_ids is None:
layer_ids = [layer.id for layer in layered_graph.layers]
# Initialize lists for nodes and edges
nodes = []
edges = []
# Process each specified layer
for layer_id in layer_ids:
# Get graph for this layer (cumulative or not)
if include_cumulative:
layer_graph = layered_graph.get_cumulative_layer_graph(layer_id)
else:
layer_graph = layered_graph.get_layer_graph(layer_id)
# Apply node filter if provided
if node_filter is not None:
filtered_nodes = [node for node in layer_graph.nodes if node_filter(node)]
else:
filtered_nodes = layer_graph.nodes
# Get IDs of nodes that passed the filter
filtered_node_ids = {node.id for node in filtered_nodes}
# Add filtered nodes to result
nodes.extend(filtered_nodes)
# Apply edge filter if provided, and ensure edges connect to filtered nodes
if edge_filter is not None:
filtered_edges = [
edge for edge in layer_graph.edges
if edge_filter(edge) and
edge.source_node_id in filtered_node_ids and
edge.target_node_id in filtered_node_ids
]
else:
filtered_edges = [
edge for edge in layer_graph.edges
if edge.source_node_id in filtered_node_ids and
edge.target_node_id in filtered_node_ids
]
# Add filtered edges to result
edges.extend(filtered_edges)
# Remove duplicate nodes and edges based on IDs/keys
unique_nodes = {}
for node in nodes:
unique_nodes[node.id] = node
unique_edges = {}
for edge in edges:
edge_key = (edge.source_node_id, edge.target_node_id, edge.relationship_name)
unique_edges[edge_key] = edge
# Create and return the knowledge graph
return KnowledgeGraph(
nodes=list(unique_nodes.values()),
edges=list(unique_edges.values()),
name=f"Subgraph from {layered_graph.name}",
description=f"Subgraph extracted from {layered_graph.name} with {len(layer_ids)} layer(s)"
)
@staticmethod
async def analyze_layer_dependencies(layered_graph: LayeredKnowledgeGraph) -> Dict[str, Any]:
"""
Analyze dependencies between layers and return a dependency structure.
Args:
layered_graph: The layered knowledge graph
Returns:
Dictionary containing layer dependency analysis
"""
# Initialize dictionaries for dependency analysis
dependencies = {} # layer_id -> set of direct parent layer IDs
reverse_dependencies = {} # layer_id -> set of direct child layer IDs
all_dependencies = {} # layer_id -> set of all ancestor layer IDs
layer_depth = {} # layer_id -> depth in dependency hierarchy
# Create lookup for layers by ID
layers_by_id = {layer.id: layer for layer in layered_graph.layers}
# Build direct dependencies and reverse dependencies
for layer in layered_graph.layers:
dependencies[layer.id] = set(layer.parent_layers)
# Initialize reverse dependencies sets
for parent_id in layer.parent_layers:
if parent_id not in reverse_dependencies:
reverse_dependencies[parent_id] = set()
reverse_dependencies[parent_id].add(layer.id)
# Initialize reverse dependencies for layers with no children
for layer_id in dependencies:
if layer_id not in reverse_dependencies:
reverse_dependencies[layer_id] = set()
# Calculate all dependencies using depth-first search
def get_all_dependencies(layer_id):
if layer_id in all_dependencies:
return all_dependencies[layer_id]
all_deps = set()
for parent_id in dependencies.get(layer_id, set()):
all_deps.add(parent_id)
all_deps.update(get_all_dependencies(parent_id))
all_dependencies[layer_id] = all_deps
return all_deps
# Calculate all dependencies for each layer
for layer_id in dependencies:
get_all_dependencies(layer_id)
# Calculate layer depths
roots = [layer_id for layer_id, parents in dependencies.items() if not parents]
# Set depth 0 for root layers
for root in roots:
layer_depth[root] = 0
# Calculate depths using breadth-first search
visited = set(roots)
queue = [(root, 0) for root in roots]
while queue:
layer_id, depth = queue.pop(0)
for child_id in reverse_dependencies.get(layer_id, set()):
# Calculate child depth as max(current depth + 1, existing depth)
child_depth = depth + 1
if child_id in layer_depth:
child_depth = max(child_depth, layer_depth[child_id])
layer_depth[child_id] = child_depth
if child_id not in visited:
visited.add(child_id)
queue.append((child_id, child_depth))
# Group layers by depth
layers_by_depth = {}
for layer_id, depth in layer_depth.items():
if depth not in layers_by_depth:
layers_by_depth[depth] = []
layers_by_depth[depth].append(layer_id)
# Check for cycles (if a layer is not visited, it's part of a cycle)
cycles = [layer_id for layer_id in dependencies if layer_id not in visited]
return {
"root_layers": roots,
"leaf_layers": [layer_id for layer_id, children in reverse_dependencies.items() if not children],
"dependencies": dependencies,
"reverse_dependencies": reverse_dependencies,
"all_dependencies": all_dependencies,
"layer_depth": layer_depth,
"layers_by_depth": layers_by_depth,
"max_depth": max(layer_depth.values()) if layer_depth else 0,
"has_cycles": len(cycles) > 0,
"cycle_layers": cycles
}
@staticmethod
async def filter_graph_by_relationship_types(
layered_graph: LayeredKnowledgeGraph,
relationship_types: List[str],
include_only: bool = True,
layer_ids: List[str] = None
) -> KnowledgeGraph:
"""
Filter a layered graph to include or exclude specific relationship types.
Args:
layered_graph: The layered knowledge graph
relationship_types: List of relationship types to filter by
include_only: If True, include only these relationships; if False, exclude them
layer_ids: Optional list of layer IDs to filter (None = all layers)
Returns:
Filtered knowledge graph
"""
# Define edge filter based on relationship types
def edge_filter(edge):
relationship_match = edge.relationship_name in relationship_types
return relationship_match if include_only else not relationship_match
# Use extract_subgraph with the edge filter
return await LayeredGraphService.extract_subgraph(
layered_graph=layered_graph,
layer_ids=layer_ids,
include_cumulative=True,
edge_filter=edge_filter
)
@staticmethod
async def sort_layers_topologically(layered_graph: LayeredKnowledgeGraph) -> List[str]:
"""
Sort layers in topological order (parents before children).
Args:
layered_graph: The layered knowledge graph
Returns:
List of layer IDs in topological order
"""
# First, get the dependency analysis
analysis = await LayeredGraphService.analyze_layer_dependencies(layered_graph)
# Check for cycles
if analysis["has_cycles"]:
logger.warning("Graph has cycles, topological sort may not be complete")
# Use the layers_by_depth to create a topological order
layers_by_depth = analysis["layers_by_depth"]
sorted_depths = sorted(layers_by_depth.keys())
# Flatten the layers by depth
topological_order = []
for depth in sorted_depths:
topological_order.extend(layers_by_depth[depth])
return topological_order
@staticmethod
async def find_nodes_by_property(
layered_graph: LayeredKnowledgeGraph,
property_name: str,
property_value: Any,
layer_ids: List[str] = None,
include_cumulative: bool = False
) -> List[Node]:
"""
Find nodes that have a specific property value.
Args:
layered_graph: The layered knowledge graph
property_name: Name of the property to search
property_value: Value of the property to match
layer_ids: Optional list of layer IDs to search (None = all layers)
include_cumulative: Whether to include parent layers in the search
Returns:
List of nodes matching the property value
"""
# Define node filter for the property
def node_filter(node):
if property_name == "id":
return node.id == property_value
elif property_name == "name":
return node.name == property_value
elif property_name == "type":
return node.type == property_value
elif property_name == "description":
return property_value in node.description
else:
# Check node properties
return (property_name in node.properties and
node.properties[property_name] == property_value)
# Use extract_subgraph with the node filter
subgraph = await LayeredGraphService.extract_subgraph(
layered_graph=layered_graph,
layer_ids=layer_ids,
include_cumulative=include_cumulative,
node_filter=node_filter
)
return subgraph.nodes
@staticmethod
async def calculate_layer_metrics(layered_graph: LayeredKnowledgeGraph) -> Dict[str, Dict[str, Any]]:
"""
Calculate metrics for each layer in the graph.
Args:
layered_graph: The layered knowledge graph
Returns:
Dictionary mapping layer IDs to dictionaries of metrics
"""
metrics = {}
# Get dependency analysis
analysis = await LayeredGraphService.analyze_layer_dependencies(layered_graph)
# Calculate metrics for each layer
for layer in layered_graph.layers:
layer_id = layer.id
layer_graph = layered_graph.get_layer_graph(layer_id)
cumulative_graph = layered_graph.get_cumulative_layer_graph(layer_id)
# Count node types in this layer
node_types = {}
for node in layer_graph.nodes:
if node.type not in node_types:
node_types[node.type] = 0
node_types[node.type] += 1
# Count relationship types in this layer
relationship_types = {}
for edge in layer_graph.edges:
if edge.relationship_name not in relationship_types:
relationship_types[edge.relationship_name] = 0
relationship_types[edge.relationship_name] += 1
# Calculate density (ratio of actual to possible edges)
node_count = len(layer_graph.nodes)
edge_count = len(layer_graph.edges)
possible_edges = node_count * (node_count - 1) if node_count > 1 else 0
density = edge_count / possible_edges if possible_edges > 0 else 0
# Store metrics for this layer
metrics[layer_id] = {
"node_count": node_count,
"edge_count": edge_count,
"node_types": node_types,
"relationship_types": relationship_types,
"density": density,
"parent_count": len(layer.parent_layers),
"child_count": len(analysis["reverse_dependencies"].get(layer_id, set())),
"depth": analysis["layer_depth"].get(layer_id, 0),
"is_root": len(layer.parent_layers) == 0,
"is_leaf": len(analysis["reverse_dependencies"].get(layer_id, set())) == 0,
"cumulative_node_count": len(cumulative_graph.nodes),
"cumulative_edge_count": len(cumulative_graph.edges),
"contribution_node_count": len(layer_graph.nodes),
"contribution_edge_count": len(layer_graph.edges)
}
# Calculate the contribution ratio (layer's nodes/edges as percentage of cumulative)
if metrics[layer_id]["cumulative_node_count"] > 0:
metrics[layer_id]["node_contribution_ratio"] = (
metrics[layer_id]["contribution_node_count"] /
metrics[layer_id]["cumulative_node_count"]
)
else:
metrics[layer_id]["node_contribution_ratio"] = 0
if metrics[layer_id]["cumulative_edge_count"] > 0:
metrics[layer_id]["edge_contribution_ratio"] = (
metrics[layer_id]["contribution_edge_count"] /
metrics[layer_id]["cumulative_edge_count"]
)
else:
metrics[layer_id]["edge_contribution_ratio"] = 0
return metrics