Bump version: 0.9.0-beta.9 → 0.9.0-beta.10

.gitignore

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

Cargo.toml

@@ -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"

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"

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.9.0-beta.8"
+current_version = "0.9.0-beta.10"
 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-beta.8"
+version = "0.9.0-beta.10"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/python/lancedb/query.py

@@ -119,6 +119,8 @@ class Query(pydantic.BaseModel):
 
     fast_search: bool = False
 
+    bypass_vector_index: Optional[bool] = None
+
 
 class LanceQueryBuilder(ABC):
     """An abstract query builder. Subclasses are defined for vector search,
@@ -127,14 +129,15 @@ class LanceQueryBuilder(ABC):
 
     @classmethod
     def create(
-            cls,
-            table: "Table",
-            query: Optional[Union[np.ndarray, str, "PIL.Image.Image", Tuple]],
-            query_type: str,
-            vector_column_name: str,
-            ordering_field_name: Optional[str] = None,
-            fts_columns: Union[str, List[str]] = [],
-            fast_search: bool = False,
+        cls,
+        table: "Table",
+        query: Optional[Union[np.ndarray, str, "PIL.Image.Image", Tuple]],
+        query_type: str,
+        vector_column_name: str,
+        ordering_field_name: Optional[str] = None,
+        fts_columns: Union[str, List[str]] = [],
+        fast_search: bool = False,
+        bypass_vector_index: Optional[bool] = None,
     ) -> LanceQueryBuilder:
         """
         Create a query builder based on the given query and query type.
@@ -153,6 +156,8 @@ class LanceQueryBuilder(ABC):
             The name of the vector column to use for vector search.
         fast_search: bool
             Skip flat search of unindexed data.
+        bypass_vector_index: Optional[bool]
+            Bypass the vector index and use a brute force search.
         """
         # Check hybrid search first as it supports empty query pattern
         if query_type == "hybrid":
@@ -195,7 +200,12 @@ class LanceQueryBuilder(ABC):
             raise TypeError(f"Unsupported query type: {type(query)}")
 
         return LanceVectorQueryBuilder(
-            table, query, vector_column_name, str_query, fast_search
+            table,
+            query,
+            vector_column_name,
+            str_query,
+            fast_search,
+            bypass_vector_index,
         )
 
     @classmethod
@@ -557,12 +567,13 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
     """
 
     def __init__(
-            self,
-            table: "Table",
-            query: Union[np.ndarray, list, "PIL.Image.Image"],
-            vector_column: str,
-            str_query: Optional[str] = None,
-            fast_search: bool = False,
+        self,
+        table: "Table",
+        query: Union[np.ndarray, list, "PIL.Image.Image"],
+        vector_column: str,
+        str_query: Optional[str] = None,
+        fast_search: bool = False,
+        bypass_vector_index: Optional[bool] = None,
     ):
         super().__init__(table)
         self._query = query
@@ -574,6 +585,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._reranker = None
         self._str_query = str_query
         self._fast_search = fast_search
+        self._bypass_vector_index = bypass_vector_index
 
     def metric(self, metric: Literal["L2", "cosine", "dot"]) -> LanceVectorQueryBuilder:
         """Set the distance metric to use.
@@ -697,6 +709,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             with_row_id=self._with_row_id,
             offset=self._offset,
             fast_search=self._fast_search,
+            bypass_vector_index=self._bypass_vector_index,
             ef=self._ef,
         )
         result_set = self._table._execute_query(query, batch_size)
@@ -728,7 +741,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         return self
 
     def rerank(
-            self, reranker: Reranker, query_string: Optional[str] = None
+        self, reranker: Reranker, query_string: Optional[str] = None
     ) -> LanceVectorQueryBuilder:
         """Rerank the results using the specified reranker.
 
@@ -947,7 +960,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
     def _validate_fts_index(self):
         if self._table._get_fts_index_path() is None:
             raise ValueError(
-                "Please create a full-text search index " "to perform hybrid search."
+                "Please create a full-text search index to perform hybrid search."
             )
 
     def _validate_query(self, query):

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/table.py

@@ -234,8 +234,11 @@ class RemoteTable(Table):
         query_type: str = "vector",
         vector_column_name: Optional[str] = None,
         fast_search: bool = False,
+        bypass_vector_index: Optional[bool] = None,
     ) -> LanceVectorQueryBuilder:
-        return self.search(query, query_type, vector_column_name, fast_search)
+        return self.search(
+            query, query_type, vector_column_name, fast_search, bypass_vector_index
+        )
 
     def search(
         self,
@@ -243,6 +246,7 @@ class RemoteTable(Table):
         query_type: str = "vector",
         vector_column_name: Optional[str] = None,
         fast_search: bool = False,
+        bypass_vector_index: Optional[bool] = None,
     ) -> LanceVectorQueryBuilder:
         """Create a search query to find the nearest neighbors
         of the given query vector. We currently support [vector search][search]
@@ -294,6 +298,15 @@ class RemoteTable(Table):
             search performance but search results will not include unindexed data.
 
             - *default False*.
+
+        bypass_vector_index: bool, optional
+            If True, the query will bypass the vector index and perform a full scan.
+            An exhaustive (flat) search will be performed.  The query vector will
+            be compared to every vector in the table.  At high scales this can be
+            expensive.  However, this is often still useful.  For example, skipping
+            the vector index can give you ground truth results which you can use to
+            calculate your recall to select an appropriate value for nprobes.
+
         Returns
         -------
         LanceQueryBuilder
@@ -316,6 +329,7 @@ class RemoteTable(Table):
             query_type,
             vector_column_name=vector_column_name,
             fast_search=fast_search,
+            bypass_vector_index=bypass_vector_index,
         )
 
     def _execute_query(
@@ -377,9 +391,9 @@ class RemoteTable(Table):
         params["on"] = merge._on[0]
         params["when_matched_update_all"] = str(merge._when_matched_update_all).lower()
         if merge._when_matched_update_all_condition is not None:
-            params[
-                "when_matched_update_all_filt"
-            ] = merge._when_matched_update_all_condition
+            params["when_matched_update_all_filt"] = (
+                merge._when_matched_update_all_condition
+            )
         params["when_not_matched_insert_all"] = str(
             merge._when_not_matched_insert_all
         ).lower()
@@ -387,9 +401,9 @@ class RemoteTable(Table):
             merge._when_not_matched_by_source_delete
         ).lower()
         if merge._when_not_matched_by_source_condition is not None:
-            params[
-                "when_not_matched_by_source_delete_filt"
-            ] = merge._when_not_matched_by_source_condition
+            params["when_not_matched_by_source_delete_filt"] = (
+                merge._when_not_matched_by_source_condition
+            )
 
         self._conn._client.post(
             f"/v1/table/{self._name}/merge_insert/",

python/python/tests/test_remote_db.py

@@ -57,4 +57,23 @@ def test_fast_search_query_with_filter():
 
     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())
+    print(
+        table.query([0, 0], fast_search=True)
+        .select(["vector"])
+        .where("foo == bar")
+        .to_arrow()
+    )
+
+
+def test_bypass_vector_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], bypass_vector_index=True)
+        .select(["vector"])
+        .where("foo == bar")
+        .to_arrow()
+    )