fix: Use the identifier column as hash if available (#9405)
* fix: Use the identifier column as hash if available * Update kb_ingest.py * [autofix.ci] apply automated fixes * Update src/backend/base/langflow/components/data/kb_ingest.py Co-authored-by: Edwin Jose <edwin.jose@datastax.com> * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Edwin Jose <edwin.jose@datastax.com>
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2 changed files with 13 additions and 7 deletions
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@ -139,8 +139,8 @@ class KBIngestionComponent(Component):
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{
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{
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"column_name": "text",
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"column_name": "text",
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"vectorize": True,
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"vectorize": True,
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"identifier": False,
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"identifier": True,
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}
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},
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],
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],
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),
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),
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IntInput(
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IntInput(
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@ -402,16 +402,22 @@ class KBIngestionComponent(Component):
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# Convert each row to a Data object
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# Convert each row to a Data object
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for _, row in df_source.iterrows():
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for _, row in df_source.iterrows():
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# Build content text from vectorized columns using list comprehension
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# Build content text from identifier columns using list comprehension
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content_parts = [str(row[col]) for col in content_cols if col in row and pd.notna(row[col])]
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identifier_parts = [str(row[col]) for col in content_cols if col in row and pd.notna(row[col])]
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page_content = " ".join(content_parts)
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# Join all parts into a single string
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page_content = " ".join(identifier_parts)
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# Build metadata from NON-vectorized columns only (simple key-value pairs)
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# Build metadata from NON-vectorized columns only (simple key-value pairs)
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data_dict = {
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data_dict = {
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"text": page_content, # Main content for vectorization
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"text": page_content, # Main content for vectorization
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}
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}
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# Add identifier columns if they exist
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if identifier_cols:
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identifier_parts = [str(row[col]) for col in identifier_cols if col in row and pd.notna(row[col])]
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page_content = " ".join(identifier_parts)
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# Add metadata columns as simple key-value pairs
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# Add metadata columns as simple key-value pairs
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for col in df_source.columns:
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for col in df_source.columns:
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if col not in content_cols and col in row and pd.notna(row[col]):
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if col not in content_cols and col in row and pd.notna(row[col]):
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