Bump version: 0.9.0-beta.8 → 0.9.0-beta.9

# Conflicts:
#	python/pyproject.toml

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.5.2"
+current_version = "0.5.2-final.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/build_windows_wheel/action.yml

@@ -28,7 +28,7 @@ runs:
         args: ${{ inputs.args }}
         docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         working-directory: python
-    - uses: actions/upload-artifact@v3
+    - uses: actions/upload-artifact@v4
       with:
         name: windows-wheels
         path: python\target\wheels

.gitignore

@@ -41,5 +41,3 @@ dist
 target
 
 **/sccache.log
-
-Cargo.lock

Cargo.toml

@@ -35,7 +35,7 @@ arrow-schema = "51.0"
 arrow-arith = "51.0"
 arrow-cast = "51.0"
 async-trait = "0"
-chrono = "0.4.35"
+chrono = "=0.4.39"
 datafusion-physical-plan = "37.1"
 half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",
@@ -45,7 +45,8 @@ log = "0.4"
 object_store = "0.9.0"
 pin-project = "1.0.7"
 snafu = "0.7.4"
-url = "2"
+url = "=2.3.1"
 num-traits = "0.2"
 regex = "1.10"
 lazy_static = "1"
+napi-build = "=2.1.2"

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.5.2",
+  "version": "0.5.2-final.1",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",

nodejs/Cargo.toml

@@ -15,14 +15,15 @@ crate-type = ["cdylib"]
 arrow-ipc.workspace = true
 futures.workspace = true
 lancedb = { path = "../rust/lancedb" }
-napi = { version = "2.15", default-features = false, features = [
+napi = { version = "=2.16.1", default-features = false, features = [
     "napi7",
     "async",
 ] }
-napi-derive = "2"
+napi-derive = "=2.16.1"
+napi-build = "=2.1.2"
 
 # Prevent dynamic linking of lzma, which comes from datafusion
 lzma-sys = { version = "*", features = ["static"] }
 
 [build-dependencies]
-napi-build = "2.1"
+napi-build = "=2.1.2"

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.5.2",
+	"version": "0.5.2-final.1",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.5.2",
+	"version": "0.5.2-final.1",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

nodejs/npm/linux-arm64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.5.2",
+	"version": "0.5.2-final.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.5.2",
+	"version": "0.5.2-final.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.5.2",
+	"version": "0.5.2-final.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -10,7 +10,7 @@
     "vector database",
     "ann"
   ],
-  "version": "0.5.2",
+  "version": "0.5.2-final.1",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.9.0"
+current_version = "0.9.0-beta.9"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.9.0"
+version = "0.9.0-beta.9"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
@@ -19,6 +19,8 @@ lancedb = { path = "../rust/lancedb" }
 env_logger = "0.10"
 pyo3 = { version = "0.20", features = ["extension-module", "abi3-py38"] }
 pyo3-asyncio = { version = "0.20", features = ["attributes", "tokio-runtime"] }
+base64ct = "=1.6.0" # workaround for https://github.com/RustCrypto/formats/issues/1684
+chrono = "=0.4.39"
 
 # Prevent dynamic linking of lzma, which comes from datafusion
 lzma-sys = { version = "*", features = ["static"] }

python/pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "packaging",
     "cachetools",
     "overrides>=0.7",
+    "urllib3==1.26.19"
 ]
 description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]

python/python/lancedb/__init__.py

@@ -35,6 +35,7 @@ def connect(
     host_override: Optional[str] = None,
     read_consistency_interval: Optional[timedelta] = None,
     request_thread_pool: Optional[Union[int, ThreadPoolExecutor]] = None,
+    storage_options: Optional[Dict[str, str]] = None,
     **kwargs,
 ) -> DBConnection:
     """Connect to a LanceDB database.
@@ -70,6 +71,9 @@ def connect(
         executor will be used for making requests. This is for LanceDB Cloud
         only and is only used when making batch requests (i.e., passing in
         multiple queries to the search method at once).
+    storage_options: dict, optional
+        Additional options for the storage backend. See available options at
+        https://lancedb.github.io/lancedb/guides/storage/
 
     Examples
     --------
@@ -105,12 +109,16 @@ def connect(
             region,
             host_override,
             request_thread_pool=request_thread_pool,
+            storage_options=storage_options,
             **kwargs,
         )
 
     if kwargs:
         raise ValueError(f"Unknown keyword arguments: {kwargs}")
-    return LanceDBConnection(uri, read_consistency_interval=read_consistency_interval)
+    return LanceDBConnection(
+        uri,
+        read_consistency_interval=read_consistency_interval,
+    )
 
 
 async def connect_async(

python/python/lancedb/query.py

@@ -117,6 +117,8 @@ class Query(pydantic.BaseModel):
 
     with_row_id: bool = False
 
+    fast_search: bool = False
+
 
 class LanceQueryBuilder(ABC):
     """An abstract query builder. Subclasses are defined for vector search,
@@ -130,7 +132,9 @@ class LanceQueryBuilder(ABC):
             query: Optional[Union[np.ndarray, str, "PIL.Image.Image", Tuple]],
             query_type: str,
             vector_column_name: str,
-        ordering_field_name: str = None,
+            ordering_field_name: Optional[str] = None,
+            fts_columns: Union[str, List[str]] = [],
+            fast_search: bool = False,
     ) -> LanceQueryBuilder:
         """
         Create a query builder based on the given query and query type.
@@ -147,13 +151,18 @@ class LanceQueryBuilder(ABC):
             If "auto", the query type is inferred based on the query.
         vector_column_name: str
             The name of the vector column to use for vector search.
+        fast_search: bool
+            Skip flat search of unindexed data.
         """
-        if query is None:
+        # Check hybrid search first as it supports empty query pattern
-            return LanceEmptyQueryBuilder(table)
-
         if query_type == "hybrid":
             # hybrid fts and vector query
-            return LanceHybridQueryBuilder(table, query, vector_column_name)
+            return LanceHybridQueryBuilder(
+                table, query, vector_column_name, fts_columns=fts_columns
+            )
+
+        if query is None:
+            return LanceEmptyQueryBuilder(table)
 
         # remember the string query for reranking purpose
         str_query = query if isinstance(query, str) else None
@@ -165,12 +174,17 @@ class LanceQueryBuilder(ABC):
         )
 
         if query_type == "hybrid":
-            return LanceHybridQueryBuilder(table, query, vector_column_name)
+            return LanceHybridQueryBuilder(
+                table, query, vector_column_name, fts_columns=fts_columns
+            )
 
         if isinstance(query, str):
             # fts
             return LanceFtsQueryBuilder(
-                table, query, ordering_field_name=ordering_field_name
+                table,
+                query,
+                ordering_field_name=ordering_field_name,
+                fts_columns=fts_columns,
             )
 
         if isinstance(query, list):
@@ -180,7 +194,9 @@ class LanceQueryBuilder(ABC):
         else:
             raise TypeError(f"Unsupported query type: {type(query)}")
 
-        return LanceVectorQueryBuilder(table, query, vector_column_name, str_query)
+        return LanceVectorQueryBuilder(
+            table, query, vector_column_name, str_query, fast_search
+        )
 
     @classmethod
     def _resolve_query(cls, table, query, query_type, vector_column_name):
@@ -196,8 +212,6 @@ class LanceQueryBuilder(ABC):
         elif query_type == "auto":
             if isinstance(query, (list, np.ndarray)):
                 return query, "vector"
-            if isinstance(query, tuple):
-                return query, "hybrid"
             else:
                 conf = table.embedding_functions.get(vector_column_name)
                 if conf is not None:
@@ -224,9 +238,14 @@ class LanceQueryBuilder(ABC):
     def __init__(self, table: "Table"):
         self._table = table
         self._limit = 10
+        self._offset = 0
         self._columns = None
         self._where = None
+        self._prefilter = False
         self._with_row_id = False
+        self._vector = None
+        self._text = None
+        self._ef = None
 
     @deprecation.deprecated(
         deprecated_in="0.3.1",
@@ -337,11 +356,13 @@ class LanceQueryBuilder(ABC):
         ----------
         limit: int
             The maximum number of results to return.
-            By default the query is limited to the first 10.
+            The default query limit is 10 results.
-            Call this method and pass 0, a negative value,
+            For ANN/KNN queries, you must specify a limit.
-            or None to remove the limit.
+            Entering 0, a negative number, or None will reset
-            *WARNING* if you have a large dataset, removing
+            the limit to the default value of 10.
-            the limit can potentially result in reading a
+            *WARNING* if you have a large dataset, setting
+            the limit to a large number, e.g. the table size,
+            can potentially result in reading a
             large amount of data into memory and cause
             out of memory issues.
 
@@ -351,11 +372,33 @@ class LanceQueryBuilder(ABC):
             The LanceQueryBuilder object.
         """
         if limit is None or limit <= 0:
+            if isinstance(self, LanceVectorQueryBuilder):
+                raise ValueError("Limit is required for ANN/KNN queries")
+            else:
                 self._limit = None
         else:
             self._limit = limit
         return self
 
+    def offset(self, offset: int) -> LanceQueryBuilder:
+        """Set the offset for the results.
+
+        Parameters
+        ----------
+        offset: int
+            The offset to start fetching results from.
+
+        Returns
+        -------
+        LanceQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        if offset is None or offset <= 0:
+            self._offset = 0
+        else:
+            self._offset = offset
+        return self
+
     def select(self, columns: Union[list[str], dict[str, str]]) -> LanceQueryBuilder:
         """Set the columns to return.
 
@@ -417,6 +460,80 @@ class LanceQueryBuilder(ABC):
         self._with_row_id = with_row_id
         return self
 
+    def explain_plan(self, verbose: Optional[bool] = False) -> str:
+        """Return the execution plan for this query.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> db = lancedb.connect("./.lancedb")
+        >>> table = db.create_table("my_table", [{"vector": [99, 99]}])
+        >>> query = [100, 100]
+        >>> plan = table.search(query).explain_plan(True)
+        >>> print(plan) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
+        ProjectionExec: expr=[vector@0 as vector, _distance@2 as _distance]
+        GlobalLimitExec: skip=0, fetch=10
+          FilterExec: _distance@2 IS NOT NULL
+            SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], preserve_partitioning=[false]
+              KNNVectorDistance: metric=l2
+                LanceScan: uri=..., projection=[vector], row_id=true, row_addr=false, ordered=false
+
+        Parameters
+        ----------
+        verbose : bool, default False
+            Use a verbose output format.
+
+        Returns
+        -------
+        plan : str
+        """  # noqa: E501
+        ds = self._table.to_lance()
+        return ds.scanner(
+            nearest={
+                "column": self._vector_column,
+                "q": self._query,
+                "k": self._limit,
+                "metric": self._metric,
+                "nprobes": self._nprobes,
+                "refine_factor": self._refine_factor,
+            },
+            prefilter=self._prefilter,
+            filter=self._str_query,
+            limit=self._limit,
+            with_row_id=self._with_row_id,
+            offset=self._offset,
+        ).explain_plan(verbose)
+
+    def vector(self, vector: Union[np.ndarray, list]) -> LanceQueryBuilder:
+        """Set the vector to search for.
+
+        Parameters
+        ----------
+        vector: np.ndarray or list
+            The vector to search for.
+
+        Returns
+        -------
+        LanceQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        raise NotImplementedError
+
+    def text(self, text: str) -> LanceQueryBuilder:
+        """Set the text to search for.
+
+        Parameters
+        ----------
+        text: str
+            The text to search for.
+
+        Returns
+        -------
+        LanceQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        raise NotImplementedError
+
 
 class LanceVectorQueryBuilder(LanceQueryBuilder):
     """
@@ -445,6 +562,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             query: Union[np.ndarray, list, "PIL.Image.Image"],
             vector_column: str,
             str_query: Optional[str] = None,
+            fast_search: bool = False,
     ):
         super().__init__(table)
         self._query = query
@@ -455,13 +573,14 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._prefilter = False
         self._reranker = None
         self._str_query = str_query
+        self._fast_search = fast_search
 
-    def metric(self, metric: Literal["L2", "cosine"]) -> LanceVectorQueryBuilder:
+    def metric(self, metric: Literal["L2", "cosine", "dot"]) -> LanceVectorQueryBuilder:
         """Set the distance metric to use.
 
         Parameters
         ----------
-        metric: "L2" or "cosine"
+        metric: "L2" or "cosine" or "dot"
             The distance metric to use. By default "L2" is used.
 
         Returns
@@ -469,7 +588,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         LanceVectorQueryBuilder
             The LanceQueryBuilder object.
         """
-        self._metric = metric
+        self._metric = metric.lower()
         return self
 
     def nprobes(self, nprobes: int) -> LanceVectorQueryBuilder:
@@ -494,6 +613,28 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._nprobes = nprobes
         return self
 
+    def ef(self, ef: int) -> LanceVectorQueryBuilder:
+        """Set the number of candidates to consider during search.
+
+        Higher values will yield better recall (more likely to find vectors if
+        they exist) at the expense of latency.
+
+        This only applies to the HNSW-related index.
+        The default value is 1.5 * limit.
+
+        Parameters
+        ----------
+        ef: int
+            The number of candidates to consider during search.
+
+        Returns
+        -------
+        LanceVectorQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        self._ef = ef
+        return self
+
     def refine_factor(self, refine_factor: int) -> LanceVectorQueryBuilder:
         """Set the refine factor to use, increasing the number of vectors sampled.
 
@@ -554,15 +695,11 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             refine_factor=self._refine_factor,
             vector_column=self._vector_column,
             with_row_id=self._with_row_id,
+            offset=self._offset,
+            fast_search=self._fast_search,
+            ef=self._ef,
         )
         result_set = self._table._execute_query(query, batch_size)
-        if self._reranker is not None:
-            rs_table = result_set.read_all()
-            result_set = self._reranker.rerank_vector(self._str_query, rs_table)
-            # convert result_set back to RecordBatchReader
-            result_set = pa.RecordBatchReader.from_batches(
-                result_set.schema, result_set.to_batches()
-            )
 
         return result_set
 
@@ -756,12 +893,34 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
 
 class LanceEmptyQueryBuilder(LanceQueryBuilder):
     def to_arrow(self) -> pa.Table:
-        ds = self._table.to_lance()
+        return self.to_batches().read_all()
-        return ds.to_table(
+
+    def to_batches(self, /, batch_size: Optional[int] = None) -> pa.RecordBatchReader:
+        query = Query(
             columns=self._columns,
             filter=self._where,
-            limit=self._limit,
+            k=self._limit or 10,
+            with_row_id=self._with_row_id,
+            vector=[],
+            # not actually respected in remote query
+            offset=self._offset or 0,
         )
+        return self._table._execute_query(query)
+
+    def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
+        """Rerank the results using the specified reranker.
+
+        Parameters
+        ----------
+        reranker: Reranker
+            The reranker to use.
+
+        Returns
+        -------
+        LanceEmptyQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        raise NotImplementedError("Reranking is not yet supported.")
 
 
 class LanceHybridQueryBuilder(LanceQueryBuilder):

python/python/lancedb/remote/__init__.py

@@ -47,6 +47,8 @@ class VectorQuery(BaseModel):
 
     vector_column: str = VECTOR_COLUMN_NAME
 
+    bypass_vector_index: Optional[bool] = None
+
 
 @attrs.define
 class VectorQueryResult:

python/python/lancedb/remote/client.py

@@ -55,11 +55,13 @@ class RestfulLanceDBClient:
     region: str
     api_key: Credential
     host_override: Optional[str] = attrs.field(default=None)
+    db_prefix: Optional[str] = attrs.field(default=None)
 
     closed: bool = attrs.field(default=False, init=False)
 
     connection_timeout: float = attrs.field(default=120.0, kw_only=True)
     read_timeout: float = attrs.field(default=300.0, kw_only=True)
+    storage_options: Optional[Dict[str, str]] = attrs.field(default=None, kw_only=True)
 
     @functools.cached_property
     def session(self) -> requests.Session:
@@ -92,6 +94,18 @@ class RestfulLanceDBClient:
             headers["Host"] = f"{self.db_name}.{self.region}.api.lancedb.com"
         if self.host_override:
             headers["x-lancedb-database"] = self.db_name
+        if self.storage_options:
+            if self.storage_options.get("account_name") is not None:
+                headers["x-azure-storage-account-name"] = self.storage_options[
+                    "account_name"
+                ]
+            if self.storage_options.get("azure_storage_account_name") is not None:
+                headers["x-azure-storage-account-name"] = self.storage_options[
+                    "azure_storage_account_name"
+                ]
+        if self.db_prefix:
+            headers["x-lancedb-database-prefix"] = self.db_prefix
+
         return headers
 
     @staticmethod
@@ -158,6 +172,7 @@ class RestfulLanceDBClient:
             headers["content-type"] = content_type
         if request_id is not None:
             headers["x-request-id"] = request_id
+
         with self.session.post(
             urljoin(self.url, uri),
             headers=headers,
@@ -245,7 +260,6 @@ def retry_adapter(options: Dict[str, Any]) -> HTTPAdapter:
             connect=connect_retries,
             read=read_retries,
             backoff_factor=backoff_factor,
-            backoff_jitter=backoff_jitter,
             status_forcelist=statuses,
             allowed_methods=methods,
         )

python/python/lancedb/remote/db.py

@@ -15,7 +15,7 @@ import inspect
 import logging
 import uuid
 from concurrent.futures import ThreadPoolExecutor
-from typing import Iterable, List, Optional, Union
+from typing import Dict, Iterable, List, Optional, Union
 from urllib.parse import urlparse
 
 from cachetools import TTLCache
@@ -44,20 +44,25 @@ class RemoteDBConnection(DBConnection):
         request_thread_pool: Optional[ThreadPoolExecutor] = None,
         connection_timeout: float = 120.0,
         read_timeout: float = 300.0,
+        storage_options: Optional[Dict[str, str]] = None,
     ):
         """Connect to a remote LanceDB database."""
         parsed = urlparse(db_url)
         if parsed.scheme != "db":
             raise ValueError(f"Invalid scheme: {parsed.scheme}, only accepts db://")
         self.db_name = parsed.netloc
+        prefix = parsed.path.lstrip("/")
+        self.db_prefix = None if not prefix else prefix
         self.api_key = api_key
         self._client = RestfulLanceDBClient(
             self.db_name,
             region,
             api_key,
             host_override,
+            self.db_prefix,
             connection_timeout=connection_timeout,
             read_timeout=read_timeout,
+            storage_options=storage_options,
         )
         self._request_thread_pool = request_thread_pool
         self._table_cache = TTLCache(maxsize=10000, ttl=300)

python/python/lancedb/remote/table.py

@@ -15,13 +15,14 @@ import logging
 import uuid
 from concurrent.futures import Future
 from functools import cached_property
-from typing import Dict, Iterable, Optional, Union
+from typing import Dict, Iterable, Optional, Union, Literal
 
 import pyarrow as pa
 from lance import json_to_schema
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
 from lancedb.merge import LanceMergeInsertBuilder
+from lancedb.query import LanceQueryBuilder
 
 from ..query import LanceVectorQueryBuilder
 from ..table import Query, Table, _sanitize_data
@@ -81,6 +82,7 @@ class RemoteTable(Table):
     def create_scalar_index(
         self,
         column: str,
+        index_type: Literal["BTREE", "BITMAP", "LABEL_LIST", "scalar"] = "scalar",
     ):
         """Creates a scalar index
         Parameters
@@ -89,8 +91,6 @@ class RemoteTable(Table):
             The column to be indexed.  Must be a boolean, integer, float,
             or string column.
         """
-        index_type = "scalar"
-
         data = {
             "column": column,
             "index_type": index_type,
@@ -228,10 +228,21 @@ class RemoteTable(Table):
             content_type=ARROW_STREAM_CONTENT_TYPE,
         )
 
+    def query(
+        self,
+        query: Union[VEC, str] = None,
+        query_type: str = "vector",
+        vector_column_name: Optional[str] = None,
+        fast_search: bool = False,
+    ) -> LanceVectorQueryBuilder:
+        return self.search(query, query_type, vector_column_name, fast_search)
+
     def search(
         self,
-        query: Union[VEC, str],
+        query: Union[VEC, str] = None,
+        query_type: str = "vector",
         vector_column_name: Optional[str] = None,
+        fast_search: bool = False,
     ) -> LanceVectorQueryBuilder:
         """Create a search query to find the nearest neighbors
         of the given query vector. We currently support [vector search][search]
@@ -278,6 +289,11 @@ class RemoteTable(Table):
             - If the table has multiple vector columns then the *vector_column_name*
             needs to be specified. Otherwise, an error is raised.
 
+        fast_search: bool, optional
+            Skip a flat search of unindexed data. This may improve
+            search performance but search results will not include unindexed data.
+
+            - *default False*.
         Returns
         -------
         LanceQueryBuilder
@@ -293,7 +309,14 @@ class RemoteTable(Table):
         """
         if vector_column_name is None:
             vector_column_name = inf_vector_column_query(self.schema)
-        return LanceVectorQueryBuilder(self, query, vector_column_name)
+
+        return LanceQueryBuilder.create(
+            self,
+            query,
+            query_type,
+            vector_column_name=vector_column_name,
+            fast_search=fast_search,
+        )
 
     def _execute_query(
         self, query: Query, batch_size: Optional[int] = None

python/python/tests/test_remote_db.py

@@ -21,6 +21,7 @@ class FakeLanceDBClient:
         pass
 
     def query(self, table_name: str, query: VectorQuery) -> VectorQueryResult:
+        print(f"{query=}")
         assert table_name == "test"
         t = pa.schema([]).empty_table()
         return VectorQueryResult(t)
@@ -39,3 +40,21 @@ def test_remote_db():
     table = conn["test"]
     table.schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2))])
     table.search([1.0, 2.0]).to_pandas()
+
+
+def test_empty_query_with_filter():
+    conn = lancedb.connect("db://client-will-be-injected", api_key="fake")
+    setattr(conn, "_client", FakeLanceDBClient())
+
+    table = conn["test"]
+    table.schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2))])
+    print(table.query().select(["vector"]).where("foo == bar").to_arrow())
+
+
+def test_fast_search_query_with_filter():
+    conn = lancedb.connect("db://client-will-be-injected", api_key="fake")
+    setattr(conn, "_client", FakeLanceDBClient())
+
+    table = conn["test"]
+    table.schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2))])
+    print(table.query([0, 0], fast_search=True).select(["vector"]).where("foo == bar").to_arrow())

python/python/tests/test_table.py

@@ -735,7 +735,7 @@ def test_create_scalar_index(db):
     indices = table.to_lance().list_indices()
     assert len(indices) == 1
     scalar_index = indices[0]
-    assert scalar_index["type"] == "Scalar"
+    assert scalar_index["type"] == "BTree"
 
     # Confirm that prefiltering still works with the scalar index column
     results = table.search().where("x = 'c'").to_arrow()

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-node"
-version = "0.5.2"
+version = "0.5.2-final.1"
 description = "Serverless, low-latency vector database for AI applications"
 license.workspace = true
 edition.workspace = true

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.5.2"
+version = "0.5.2-final.1"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true