Documentation Index
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Module
Column wrapper for type-safe filtering and sorting.
Column
Wrapper for ColumnInfo that provides type-safe filtering and sorting.
This class validates filter and sort operations based on the column’s
data_type and sortable attributes, ensuring only valid operations are performed.
Examples
# Access columns from a log stream
log_stream = LogStream.get(name="Production Logs", project_name="My AI Project")
# Filter and sort using columns
traces = log_stream.get_traces(
filters=[
log_stream.trace_columns["input"].contains("hello"),
log_stream.trace_columns["created_at"].after("2024-01-01")
],
sort=log_stream.trace_columns["created_at"].descending()
)
after
def after(self, value: str | datetime.datetime) -> LogRecordsDateFilter
value: The threshold date (string or datetime).
ascending
def ascending(self) -> LogRecordsSortClause
Returns
LogRecordsSortClause: A configured sort clause.
Raises
ValidationError: If the column is not sortable.
before
def before(self, value: str | datetime.datetime) -> LogRecordsDateFilter
value: The threshold date (string or datetime).
between
def between(self,
min_value: int | float,
max_value: int | float) -> LogRecordsNumberFilter
min_value: The minimum value (inclusive).max_value: The maximum value (inclusive).
contains
def contains(self, value: str, case_sensitive: bool=True) -> LogRecordsTextFilter
value: The substring to search for.case_sensitive: Whether the search should be case-sensitive. Default is True.
descending
def descending(self) -> LogRecordsSortClause
Returns
LogRecordsSortClause: A configured sort clause.
Raises
ValidationError: If the column is not sortable.
equals
def equals(self,
value: str | bool,
case_sensitive: bool=True) -> LogRecordsTextFilter | LogRecordsBooleanFilter
value: The value to match (string for text columns, boolean for boolean columns).case_sensitive: Whether text matching should be case-sensitive. Default is True.
Ignored for boolean columns.
greater_than
def greater_than(self, value: int | float) -> LogRecordsNumberFilter
value: The threshold value (exclusive).
greater_than_or_equal
def greater_than_or_equal(self, value: int | float) -> LogRecordsNumberFilter
value: The threshold value (inclusive).
is_false
def is_false(self) -> LogRecordsBooleanFilter
log_stream.session_columns["has_error"].is_false()
is_true
def is_true(self) -> LogRecordsBooleanFilter
log_stream.session_columns["has_error"].is_true()
less_than
def less_than(self, value: int | float) -> LogRecordsNumberFilter
value: The threshold value (exclusive).
less_than_or_equal
def less_than_or_equal(self, value: int | float) -> LogRecordsNumberFilter
value: The threshold value (inclusive).
not_equals
def not_equals(self, value: str, case_sensitive: bool=True) -> LogRecordsTextFilter
value: The text value to exclude.case_sensitive: Whether the match should be case-sensitive. Default is True.
not_in
def not_in(self,
values: list[str],
case_sensitive: bool=True) -> LogRecordsTextFilter
values: List of text values to exclude.case_sensitive: Whether the match should be case-sensitive. Default is True.
on_or_after
def on_or_after(self, value: str | datetime.datetime) -> LogRecordsDateFilter
value: The threshold date (string or datetime).
on_or_before
def on_or_before(self, value: str | datetime.datetime) -> LogRecordsDateFilter
value: The threshold date (string or datetime).
one_of
def one_of(self,
values: list[str],
case_sensitive: bool=True) -> LogRecordsTextFilter
values: List of text values to match.case_sensitive: Whether the match should be case-sensitive. Default is True.
ColumnCollection
A dictionary-like collection of Column objects for easy access by column ID.
This class provides convenient access to columns using dictionary syntax,
while also supporting iteration and other collection operations.
Inherits from Mapping[str, Column] to provide a read-only mapping interface.
Examples
log_stream = LogStream.get(name="Production Logs", project_name="My AI Project")
# Access a column by ID
input_column = log_stream.trace_columns["input"]
# Iterate over column IDs
for column_id in log_stream.trace_columns:
print(column_id)
# Iterate over Column objects
for column in log_stream.trace_columns.values():
print(column)
# Check if a column exists
if "input" in log_stream.trace_columns:
print("Input column exists")
