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code cleans. (#9916)

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### What problem does this PR solve?



### Type of change

- [x] Refactoring
- [x] Performance Improvement

Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
This commit is contained in:
湛露先生
2025-09-05 09:59:27 +08:00
committed by GitHub
parent ddaed541ff
commit b14052e5a2
4 changed files with 7 additions and 9 deletions
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6
graphrag/general/entity_embedding.py
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@ -21,7 +21,7 @@ class NodeEmbeddings:
embeddings: np.ndarray
def embed_nod2vec(
def embed_node2vec(
graph: nx.Graph | nx.DiGraph,
dimensions: int = 1536,
num_walks: int = 10,
@ -44,13 +44,13 @@ def embed_nod2vec(
return NodeEmbeddings(embeddings=lcc_tensors[0], nodes=lcc_tensors[1])
def run(graph: nx.Graph, args: dict[str, Any]) -> NodeEmbeddings:
def run(graph: nx.Graph, args: dict[str, Any]) -> dict:
"""Run method definition."""
if args.get("use_lcc", True):
graph = stable_largest_connected_component(graph)
# create graph embedding using node2vec
embeddings = embed_nod2vec(
embeddings = embed_node2vec(
graph=graph,
dimensions=args.get("dimensions", 1536),
num_walks=args.get("num_walks", 10),