feat(python): allow explicit hybrid search query pattern in SaaS (feat parity) (#1698)

-  fixes https://github.com/lancedb/lancedb/issues/1697.
- unifies vector column inference logic for remote and local table to
prevent future disparities.
- Updates docstring in RemoteTable to specify empty queries are not
supported

python/python/lancedb/remote/table.py

@@ -26,7 +26,7 @@ from lancedb.embeddings import EmbeddingFunctionRegistry
 
 from ..query import LanceVectorQueryBuilder, LanceQueryBuilder
 from ..table import Query, Table, _sanitize_data
-from ..util import inf_vector_column_query, value_to_sql
+from ..util import value_to_sql, infer_vector_column_name
 from .arrow import to_ipc_binary
 from .client import ARROW_STREAM_CONTENT_TYPE
 from .db import RemoteDBConnection
@@ -266,7 +266,7 @@ class RemoteTable(Table):
 
     def search(
         self,
-        query: Union[VEC, str],
+        query: Union[VEC, str] = None,
         vector_column_name: Optional[str] = None,
         query_type="auto",
         fts_columns: Optional[Union[str, List[str]]] = None,
@@ -305,8 +305,6 @@ class RemoteTable(Table):
             - *default None*.
             Acceptable types are: list, np.ndarray, PIL.Image.Image
 
-            - If None then the select/where/limit clauses are applied to filter
-            the table
         vector_column_name: str, optional
             The name of the vector column to search.
 
@@ -329,11 +327,15 @@ class RemoteTable(Table):
             - and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
-        if vector_column_name is None and query is not None and query_type != "fts":
+        # empty query builder is not supported in saas, raise error
-            try:
+        if query is None and query_type != "hybrid":
-                vector_column_name = inf_vector_column_query(self.schema)
+            raise ValueError("Empty query is not supported")
-            except Exception as e:
+        vector_column_name = infer_vector_column_name(
-                raise e
+            schema=self.schema,
+            query_type=query_type,
+            query=query,
+            vector_column_name=vector_column_name,
+        )
 
         return LanceQueryBuilder.create(
             self,

python/python/lancedb/table.py

@@ -50,7 +50,7 @@ from .query import (
 from .util import (
     fs_from_uri,
     get_uri_scheme,
-    inf_vector_column_query,
+    infer_vector_column_name,
     join_uri,
     safe_import_pandas,
     safe_import_polars,
@@ -1630,13 +1630,12 @@ class LanceTable(Table):
             and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
-        if (
+        vector_column_name = infer_vector_column_name(
-            vector_column_name is None and query is not None and query_type != "fts"
+            schema=self.schema,
-        ) or (vector_column_name is None and query_type == "hybrid"):
+            query_type=query_type,
-            try:
+            query=query,
-                vector_column_name = inf_vector_column_query(self.schema)
+            vector_column_name=vector_column_name,
-            except Exception as e:
+        )
-                raise e
 
         return LanceQueryBuilder.create(
             self,

python/python/lancedb/util.py

@@ -9,7 +9,7 @@ import pathlib
 import warnings
 from datetime import date, datetime
 from functools import singledispatch
-from typing import Tuple, Union
+from typing import Tuple, Union, Optional, Any
 from urllib.parse import urlparse
 
 import numpy as np
@@ -212,6 +212,23 @@ def inf_vector_column_query(schema: pa.Schema) -> str:
     return vector_col_name
 
 
+def infer_vector_column_name(
+    schema: pa.Schema,
+    query_type: str,
+    query: Optional[Any],  # inferred later in query builder
+    vector_column_name: Optional[str],
+):
+    if (vector_column_name is None and query is not None and query_type != "fts") or (
+        vector_column_name is None and query_type == "hybrid"
+    ):
+        try:
+            vector_column_name = inf_vector_column_query(schema)
+        except Exception as e:
+            raise e
+
+    return vector_column_name
+
+
 @singledispatch
 def value_to_sql(value):
     raise NotImplementedError("SQL conversion is not implemented for this type")