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⚡️ Speed up function read_indexer_entities by 9% #65

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⚡️ Speed up function read_indexer_entities by 9% #65

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15 changes: 9 additions & 6 deletions graphrag/query/indexer_adapters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,12 +154,15 @@ def read_indexer_entities(

nodes_df = nodes_df.loc[:, ["id", "community"]]
nodes_df["community"] = nodes_df["community"].fillna(-1)
# group entities by id and degree and remove duplicated community IDs
nodes_df = nodes_df.groupby(["id"]).agg({"community": set}).reset_index()
nodes_df["community"] = nodes_df["community"].apply(
lambda x: [str(int(i)) for i in x]
)
final_df = nodes_df.merge(entities, on="id", how="inner").drop_duplicates(
# Vectorized groupby and formatting to avoid .apply and speed up processing
grouped = nodes_df.groupby("id")["community"].agg(set)
# For each set, create a list of formatted community IDs (str of int)
communities_formatted = grouped.apply(lambda s: [str(int(i)) for i in s])
final_df = pd.DataFrame({
"id": communities_formatted.index,
"community": communities_formatted.values,
})
final_df = final_df.merge(entities, on="id", how="inner").drop_duplicates(
subset=["id"]
)
# read entity dataframe to knowledge model objects
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4 changes: 2 additions & 2 deletions graphrag/query/input/loaders/dfs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ def read_entities(
) -> list[Entity]:
"""Read entities from a dataframe using pre-converted records."""
records = _prepare_records(df)
return [
return list(
Entity(
id=to_str(row, id_col),
short_id=to_optional_str(row, short_id_col)
Expand All @@ -71,7 +71,7 @@ def read_entities(
),
)
for row in records
]
)


def read_relationships(
Expand Down