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⚡️ Speed up function read_indexer_reports by 29% #64

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⚡️ Speed up function read_indexer_reports by 29% #64

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33 changes: 20 additions & 13 deletions graphrag/query/indexer_adapters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,19 +92,18 @@ def read_indexer_reports(

if not dynamic_community_selection:
# perform community level roll up
nodes_df.loc[:, "community"] = nodes_df["community"].fillna(-1)
nodes_df.loc[:, "community"] = nodes_df["community"].astype(int)

nodes_df = nodes_df.groupby(["title"]).agg({"community": "max"}).reset_index()
filtered_community_df = nodes_df["community"].drop_duplicates()

reports_df = reports_df.merge(
filtered_community_df, on="community", how="inner"
)
nodes_df["community"] = nodes_df["community"].fillna(-1).astype(int)
# Get max community for each title efficiently
max_community_df = nodes_df.drop_duplicates(subset=["title"], keep="last")[
["title", "community"]
]
# Only keep unique communities present in reports_df
filtered_community = max_community_df["community"].unique()
reports_df = reports_df[reports_df["community"].isin(filtered_community)]

if config and (
content_embedding_col not in reports_df.columns
or reports_df.loc[:, content_embedding_col].isna().any()
or reports_df[content_embedding_col].isna().any()
):
# TODO: Find a way to retrieve the right embedding model id.
embedding_model_settings = config.get_language_model_config(
Expand All @@ -115,9 +114,17 @@ def read_indexer_reports(
model_type=embedding_model_settings.type,
config=embedding_model_settings,
)
reports_df = embed_community_reports(
reports_df, embedder, embedding_col=content_embedding_col
)
# Only embed missing embeddings for optimization
if content_embedding_col not in reports_df.columns:
reports_df[content_embedding_col] = reports_df["full_content"].apply(
embedder.embed
)
elif reports_df[content_embedding_col].isna().any():
missing_idx = reports_df[content_embedding_col].isna()
# Only embed missing rows
reports_df.loc[missing_idx, content_embedding_col] = reports_df.loc[
missing_idx, "full_content"
].apply(embedder.embed)

return read_community_reports(
df=reports_df,
Expand Down
51 changes: 37 additions & 14 deletions graphrag/query/input/loaders/dfs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,25 +202,48 @@ def read_community_reports(
) -> list[CommunityReport]:
"""Read community reports from a dataframe using pre-converted records."""
records = _prepare_records(df)
# Use a local variable for attributes_cols (performance: attribute lookup reduced)
get = dict.get
CommunityReport_ = CommunityReport # Localize for speed in tight loop
to_optional_float_ = to_optional_float
to_optional_list_ = to_optional_list
to_optional_str_ = to_optional_str
to_str_ = to_str
# Minor: reduce attribute lookups by making local
if attributes_cols:
return [
CommunityReport_(
id=to_str_(row, id_col),
short_id=to_optional_str_(row, short_id_col)
if short_id_col
else str(row["Index"]),
title=to_str_(row, title_col),
community_id=to_str_(row, community_col),
summary=to_str_(row, summary_col),
full_content=to_str_(row, content_col),
rank=to_optional_float_(row, rank_col),
full_content_embedding=to_optional_list_(
row, content_embedding_col, item_type=float
),
attributes={col: get(row, col) for col in attributes_cols},
)
for row in records
]
return [
CommunityReport(
id=to_str(row, id_col),
short_id=to_optional_str(row, short_id_col)
CommunityReport_(
id=to_str_(row, id_col),
short_id=to_optional_str_(row, short_id_col)
if short_id_col
else str(row["Index"]),
title=to_str(row, title_col),
community_id=to_str(row, community_col),
summary=to_str(row, summary_col),
full_content=to_str(row, content_col),
rank=to_optional_float(row, rank_col),
full_content_embedding=to_optional_list(
title=to_str_(row, title_col),
community_id=to_str_(row, community_col),
summary=to_str_(row, summary_col),
full_content=to_str_(row, content_col),
rank=to_optional_float_(row, rank_col),
full_content_embedding=to_optional_list_(
row, content_embedding_col, item_type=float
),
attributes=(
{col: row.get(col) for col in attributes_cols}
if attributes_cols
else None
),
attributes=None,
)
for row in records
]
Expand Down