chore: upgrade to lance 0.10.1 (#1034)

upgrade to lance 0.10.1 and update doc string to reflect dynamic
projection options

python/python/lancedb/query.py

@@ -16,7 +16,7 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
 from pathlib import Path
-from typing import TYPE_CHECKING, List, Literal, Optional, Tuple, Type, Union
+from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple, Type, Union
 
 import deprecation
 import numpy as np
@@ -93,7 +93,7 @@ class Query(pydantic.BaseModel):
     metric: str = "L2"
 
     # which columns to return in the results
-    columns: Optional[List[str]] = None
+    columns: Optional[Union[List[str], Dict[str, str]]] = None
 
     # optional query parameters for tuning the results,
     # e.g. `{"nprobes": "10", "refine_factor": "10"}`
@@ -321,20 +321,27 @@ class LanceQueryBuilder(ABC):
             self._limit = limit
         return self
 
-    def select(self, columns: list) -> LanceQueryBuilder:
+    def select(self, columns: Union[list[str], dict[str, str]]) -> LanceQueryBuilder:
         """Set the columns to return.
 
         Parameters
         ----------
-        columns: list
-            The columns to return.
+        columns: list of str, or dict of str to str default None
+            List of column names to be fetched.
+            Or a dictionary of column names to SQL expressions.
+            All columns are fetched if None or unspecified.
 
         Returns
         -------
         LanceQueryBuilder
             The LanceQueryBuilder object.
         """
-        self._columns = columns
+        if isinstance(columns, list):
+            self._columns = columns
+        elif isinstance(columns, dict):
+            self._columns = list(columns.items())
+        else:
+            raise ValueError("columns must be a list or a dictionary")
         return self
 
     def where(self, where: str, prefilter: bool = False) -> LanceQueryBuilder:
@@ -392,7 +399,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
     >>> (table.search([0.4, 0.4])
     ...       .metric("cosine")
     ...       .where("b < 10")
-    ...       .select(["b"])
+    ...       .select(["b", "vector"])
     ...       .limit(2)
     ...       .to_pandas())
        b      vector  _distance

python/python/lancedb/table.py

@@ -159,7 +159,7 @@ class Table(ABC):
 
     Can query the table with [Table.search][lancedb.table.Table.search].
 
-    >>> table.search([0.4, 0.4]).select(["b"]).to_pandas()
+    >>> table.search([0.4, 0.4]).select(["b", "vector"]).to_pandas()
        b      vector  _distance
     0  4  [0.5, 1.3]       0.82
     1  2  [1.1, 1.2]       1.13
@@ -435,7 +435,7 @@ class Table(ABC):
         >>> query = [0.4, 1.4, 2.4]
         >>> (table.search(query)
         ...     .where("original_width > 1000", prefilter=True)
-        ...     .select(["caption", "original_width"])
+        ...     .select(["caption", "original_width", "vector"])
         ...     .limit(2)
         ...     .to_pandas())
           caption  original_width           vector  _distance
@@ -1264,7 +1264,7 @@ class LanceTable(Table):
         >>> query = [0.4, 1.4, 2.4]
         >>> (table.search(query)
         ...     .where("original_width > 1000", prefilter=True)
-        ...     .select(["caption", "original_width"])
+        ...     .select(["caption", "original_width", "vector"])
         ...     .limit(2)
         ...     .to_pandas())
           caption  original_width           vector  _distance

python/python/tests/test_query.py

@@ -87,7 +87,7 @@ def test_query_builder(table):
     rs = (
         LanceVectorQueryBuilder(table, [0, 0], "vector")
         .limit(1)
-        .select(["id"])
+        .select(["id", "vector"])
         .to_list()
     )
     assert rs[0]["id"] == 1