Search the top K semantically similar records
Search the top K semantically similar records
If you have defined indices, you can query Epsilla with natural language questions. The question will be embedded using the same model as the index definition:
status_code, response = db.query(
table_name="MyTable", # The name of the table to query against.
query_index="Index", # The index name to query against.
query_text="Where can I find a serene environment, ideal for relaxation and introspection?",
# The query question.
limit=2, # The top K result to return.
response_fields=["Doc"], # (Optional) which fields to be included in
# the response. If not provided, will include
# all fields in the table.
filter="ID < 6 AND Doc <> 'London'", # (Optional) a boolean expression for filtering
# out the results.
with_distance=True # (Optional) include the distance or not in
# the response. Default is False. When given
# as True, each matched record will have a
# @distance field returned.
)
print(response)Response example:
{
'message': 'Query search successfully.',
'result': [
{
'@distance': 0.4707561135292053,
'Doc': 'The library was a quiet haven, filled with the scent of old books and the soft rustling of pages.',
'ID': 3
},
{
'@distance': 0.47871750593185425,
'Doc': 'High in the mountains, the air was crisp and clear, revealing breathtaking views of the valley below.',
'ID': 4
}
],
'statusCode': 200
}If there is only one embedding field, the query field parameter can be omitted. Below is an example with minimum required parameters:
status_code, response = db.query(
table_name="MyTable",
query_text="Where can I find a serene environment, ideal for relaxation and introspection?",
limit=2
)If you brought in your own embedding vectors via vector fields, you can query by providing the target vector:
status_code, response = db.query(
table_name="MyTable", # The name of the table to query against.
query_field="Embedding", # The embedding field name to query against.
query_vector=[0.35, 0.55, 0.47, 0.94], # The embedded vector from the question.
limit=2, # The top K result to return.
response_fields=["Doc"], # (Optional) which fields to be included in
# the response. If not provided, will include
# all fields in the table.
filter="ID < 6 AND Doc <> 'London'", # (Optional) a boolean expression for filtering
# out the results.
with_distance=True # (Optional) include the distance or not in
# the response. Default is False. When given
# as True, each matched record will have a
# @distance field returned.
)
print(response)Response example:
{
'statusCode': 200,
'message': 'Query search successfully.',
'result': [
{
'@distance': 0.0001000004049274139,
'Doc': 'The library was a quiet haven, filled with the scent of old books and the soft rustling of pages.'
},
{
'@distance': 0.2176999747753143,
'Doc': 'The garden was blooming with vibrant flowers, attracting butterflies and bees with their sweet nectar.'
}
]
}Filter Expression
Epsilla supports filter expression in search that is compatible with SQL. The following operators are supported:
+
Add
-
Subtract
*
Multiply
/
Divide
%
Modulo
=
Equal to
>
Greater than
<
Less than
>=
Greater than or equal to
<=
Less than or equal to
<>
Not equal to
AND
TRUE if all the conditions separated by AND is TRUE
NOT
TRUE if the condition afterward is NOT TRUE
OR
TRUE if any of the conditions separated by OR is TRUE
A string literal needs to be surrounded by single quotes.
Refer to a field of the record directly by the field name.
Providing an empty filter is same as not providing the filter at all (all records will pass the evaluation).
Filter on @distance
Epsilla supports filter the result by the distance. For example, use the following filter condition to only return the matching results that have a semantic similarity distance less than 0.5:
"filter": "@distance < 0.5"@distance can be combined and nested with all the other filter condition operators.
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