cognee/cognee/tasks/storage/index_graph_edges.py
2024-12-04 15:37:48 +01:00

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2.6 KiB
Python

import logging
from collections import Counter
from cognee.infrastructure.databases.vector import get_vector_engine
from cognee.infrastructure.databases.graph import get_graph_engine
from cognee.modules.graph.models.EdgeType import EdgeType
async def index_graph_edges():
"""
Indexes graph edges by creating and managing vector indexes for relationship types.
This function retrieves edge data from the graph engine, counts distinct relationship
types, and creates `EdgeType` pydantic objects. It ensures that vector indexes are created for
the `relationship_name` field.
Steps:
1. Initialize the vector engine and graph engine.
2. Retrieve graph edge data and count relationship types (`relationship_name`).
3. Create vector indexes for `relationship_name` if they don't exist.
4. Transform the counted relationships into `EdgeType` objects.
5. Index the transformed data points in the vector engine.
Raises:
RuntimeError: If initialization of the vector engine or graph engine fails.
Returns:
None
"""
try:
created_indexes = {}
index_points = {}
vector_engine = get_vector_engine()
graph_engine = await get_graph_engine()
except Exception as e:
logging.error("Failed to initialize engines: %s", e)
raise RuntimeError("Initialization error") from e
_, edges_data = await graph_engine.get_graph_data()
edge_types = Counter(
item.get('relationship_name')
for edge in edges_data
for item in edge if isinstance(item, dict) and 'relationship_name' in item
)
for text, count in edge_types.items():
edge = EdgeType(relationship_name=text, number_of_edges=count)
data_point_type = type(edge)
for field_name in edge._metadata["index_fields"]:
index_name = f"{data_point_type.__tablename__}.{field_name}"
if index_name not in created_indexes:
await vector_engine.create_vector_index(data_point_type.__tablename__, field_name)
created_indexes[index_name] = True
if index_name not in index_points:
index_points[index_name] = []
indexed_data_point = edge.model_copy()
indexed_data_point._metadata["index_fields"] = [field_name]
index_points[index_name].append(indexed_data_point)
for index_name, indexable_points in index_points.items():
index_name, field_name = index_name.split(".")
await vector_engine.index_data_points(index_name, field_name, indexable_points)
return None