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fix

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This commit is contained in:
João Moura
2025-03-14 07:48:39 -07:00
parent 9e68cbbb3d
commit 568aace62e
1 changed files with 35 additions and 24 deletions
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59
src/crewai_tools/tools/databricks_query_tool/databricks_query_tool.py
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@@ -345,7 +345,12 @@ class DatabricksQueryTool(BaseTool):
# Dump the raw structure of result data to help troubleshoot
if hasattr(result.result, 'data_array'):
print(f"data_array structure: {type(result.result.data_array)}")
if result.result.data_array and len(result.result.data_array) > 0:
# Add defensive check for None data_array
if result.result.data_array is None:
print("data_array is None - likely an empty result set or DDL query")
# Return empty result handling rather than trying to process null data
return "Query executed successfully (no data returned)"
elif result.result.data_array and len(result.result.data_array) > 0:
print(f"First chunk type: {type(result.result.data_array[0])}")
if len(result.result.data_array[0]) > 0:
print(f"First row type: {type(result.result.data_array[0][0])}")
@@ -354,43 +359,49 @@ class DatabricksQueryTool(BaseTool):
# IMPROVED DETECTION LOGIC: Check if we're possibly dealing with rows where each item
# contains a single value or character (which could indicate incorrect row structure)
is_likely_incorrect_row_structure = False
sample_size = min(20, len(result.result.data_array[0]))
if sample_size > 0:
single_char_count = 0
single_digit_count = 0
total_items = 0
# Only try to analyze sample if data_array exists and has content
if hasattr(result.result, 'data_array') and result.result.data_array and len(result.result.data_array) > 0 and len(result.result.data_array[0]) > 0:
sample_size = min(20, len(result.result.data_array[0]))
for i in range(sample_size):
val = result.result.data_array[0][i]
total_items += 1
if isinstance(val, str) and len(val) == 1 and not val.isdigit():
single_char_count += 1
elif isinstance(val, str) and len(val) == 1 and val.isdigit():
single_digit_count += 1
if sample_size > 0:
single_char_count = 0
single_digit_count = 0
total_items = 0
# If a significant portion of the first values are single characters or digits,
# this likely indicates data is being incorrectly structured
if total_items > 0 and (single_char_count + single_digit_count) / total_items > 0.5:
print(f"Detected potential incorrect row structure: {single_char_count} single chars, {single_digit_count} digits out of {total_items} total items")
is_likely_incorrect_row_structure = True
for i in range(sample_size):
val = result.result.data_array[0][i]
total_items += 1
if isinstance(val, str) and len(val) == 1 and not val.isdigit():
single_char_count += 1
elif isinstance(val, str) and len(val) == 1 and val.isdigit():
single_digit_count += 1
# If a significant portion of the first values are single characters or digits,
# this likely indicates data is being incorrectly structured
if total_items > 0 and (single_char_count + single_digit_count) / total_items > 0.5:
print(f"Detected potential incorrect row structure: {single_char_count} single chars, {single_digit_count} digits out of {total_items} total items")
is_likely_incorrect_row_structure = True
# Additional check: if many rows have just 1 item when we expect multiple columns
rows_with_single_item = sum(1 for row in result.result.data_array[:sample_size] if isinstance(row, list) and len(row) == 1)
if rows_with_single_item > sample_size * 0.5 and len(columns) > 1:
print(f"Many rows ({rows_with_single_item}/{sample_size}) have only a single value when expecting {len(columns)} columns")
is_likely_incorrect_row_structure = True
rows_with_single_item = 0
if hasattr(result.result, 'data_array') and result.result.data_array and len(result.result.data_array) > 0:
sample_size_for_rows = min(sample_size, len(result.result.data_array[0])) if 'sample_size' in locals() else min(20, len(result.result.data_array[0]))
rows_with_single_item = sum(1 for row in result.result.data_array[0][:sample_size_for_rows] if isinstance(row, list) and len(row) == 1)
if rows_with_single_item > sample_size_for_rows * 0.5 and len(columns) > 1:
print(f"Many rows ({rows_with_single_item}/{sample_size_for_rows}) have only a single value when expecting {len(columns)} columns")
is_likely_incorrect_row_structure = True
# Check if we're getting primarily single characters or the data structure seems off,
# we should use special handling
if is_likely_incorrect_row_structure:
if 'is_likely_incorrect_row_structure' in locals() and is_likely_incorrect_row_structure:
print("Data appears to be malformed - will use special row reconstruction")
needs_special_string_handling = True
else:
needs_special_string_handling = False
# Process results differently based on detection
if needs_special_string_handling:
if 'needs_special_string_handling' in locals() and needs_special_string_handling:
# We're dealing with data where the rows may be incorrectly structured
print("Using row reconstruction processing mode")