Utils

Utility modules for gllm-datastore.

QueryFilter

Bases: BaseModel

Model for query filters.

Attributes:

Name	Type	Description
conditions	dict[str, Any]	The conditions for filtering the query.

Example

QueryFilter(conditions={"column1": "value1", "column2": "value2"})

QueryOptions

Bases: BaseModel

Model for query options.

Attributes:

Name	Type	Description
columns	Sequence[str] | None	The columns to include in the query result. Defaults to None.
fields	Sequence[str] | None	The fields to include in the query result. Defaults to None.
order_by	str | None	The column to order the query result by. Defaults to None.
order_desc	bool	Whether to order the query result in descending order. Defaults to False.
limit	int | None	The maximum number of rows to return. Defaults to None.

Example

QueryOptions(fields=["field1", "field2"], order_by="column1", order_desc=True, limit=10)

convert_ttl_to_seconds(ttl)

Convert TTL (time-to-live) string with time units to seconds.

Supported units: s (seconds), m (minutes), h (hours), d (days), w (weeks), y (years).

Examples:

"2m" -> 120 (2 minutes in seconds) "1h" -> 3600 (1 hour in seconds) "1y" -> 31536000 (1 year in seconds) 300 -> 300 (numeric input returned as is)

Parameters:

Name	Type	Description	Default
ttl	str | int	Time to live value with optional unit suffix (e.g., "2m", "1h", "1y") or numeric value in seconds.	required

Returns:

Name	Type	Description
int	int	TTL converted to seconds.

Raises:

Type	Description
ValueError	If the input format is invalid.

flatten_dict(nested_dict, parent_key='', sep='.')

Flatten a nested dictionary into a single level dictionary.

Parameters:

Name	Type	Description	Default
nested_dict	dict[str, Any]	The nested dictionary to flatten.	required
parent_key	str	The parent key to prepend to the keys in the flattened dictionary. Defaults to empty string.	''
sep	str	The separator to use between the parent key and the child key. Defaults to ".".	'.'

Returns:

Type	Description
dict[str, Any]	dict[str, Any]: The flattened dictionary.

Examples:

nested = {"a": {"b": 1, "c": 2}, "d": 3}
flattened = flatten_dict(nested)
# Result: {"a.b": 1, "a.c": 2, "d": 3}

from_langchain(doc, score=None)

Create a standardized Chunk from a LangChain Document.

Parameters:

Name	Type	Description	Default
doc	Document	The document to create a Chunk from.	required
score	float | None	The score to assign to the Chunk. Defaults to None, in which case it will attempt to get the score from the score metadata.	None

Returns:

Name	Type	Description
Chunk	Chunk	The standardized Chunk object.