[python] Bump version: 0.6.3 → 0.6.4

Updating package-lock.json
Bump version: 0.4.12 → 0.4.13
2025-12-25 22:29:58 +00:00 · 2024-03-16 18:59:14 +00:00 · 2024-03-16 05:21:58 +00:00 · 2024-03-16 05:21:44 +00:00 · 2024-03-15 22:21:04 -07:00 · 2024-03-15 14:16:05 -07:00
15 changed files with 76 additions and 95 deletions
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.4.12
+current_version = 0.4.13
 commit = True
 message = Bump version: {current_version} → {new_version}
 tag = True
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -14,10 +14,10 @@ keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]

 [workspace.dependencies]
-lance = { "version" = "=0.10.2", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.10.2" }
-lance-linalg = { "version" = "=0.10.2" }
-lance-testing = { "version" = "=0.10.2" }
+lance = { "version" = "=0.10.4", "features" = ["dynamodb"] }
+lance-index = { "version" = "=0.10.4" }
+lance-linalg = { "version" = "=0.10.4" }
+lance-testing = { "version" = "=0.10.4" }
 # Note that this one does not include pyarrow
 arrow = { version = "50.0", optional = false }
 arrow-array = "50.0"
--- a/docs/src/concepts/index_ivfpq.md
+++ b/docs/src/concepts/index_ivfpq.md
@@ -31,7 +31,7 @@ As an example, consider starting with 128-dimensional vector consisting of 32-bi

 While PQ helps with reducing the size of the index, IVF primarily addresses search performance. The primary purpose of an inverted file index is to facilitate rapid and effective nearest neighbor search by narrowing down the search space.

-In IVF, the PQ vector space is divided into *Voronoi cells*, which are essentially partitions that consist of all the points in the space that are within a threshold distance of the given region's seed point. These seed points are used to create an inverted index that correlates each centroid with a list of vectors in the space, allowing a search to be restricted to just a subset of vectors in the index.
+In IVF, the PQ vector space is divided into *Voronoi cells*, which are essentially partitions that consist of all the points in the space that are within a threshold distance of the given region's seed point. These seed points are initialized by running K-means over the stored vectors. The centroids of K-means turn into the seed points which then each define a region. These regions are then are used to create an inverted index that correlates each centroid with a list of vectors in the space, allowing a search to be restricted to just a subset of vectors in the index.

 ![](../assets/ivfpq_ivf_desc.webp)

--- a/node/package-lock.json
+++ b/node/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "vectordb",
-  "version": "0.4.12",
+  "version": "0.4.13",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "vectordb",
-      "version": "0.4.12",
+      "version": "0.4.13",
      "cpu": [
        "x64",
        "arm64"
@@ -52,11 +52,11 @@
        "uuid": "^9.0.0"
      },
      "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.4.12",
-        "@lancedb/vectordb-darwin-x64": "0.4.12",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.4.12",
-        "@lancedb/vectordb-linux-x64-gnu": "0.4.12",
-        "@lancedb/vectordb-win32-x64-msvc": "0.4.12"
+        "@lancedb/vectordb-darwin-arm64": "0.4.13",
+        "@lancedb/vectordb-darwin-x64": "0.4.13",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.4.13",
+        "@lancedb/vectordb-linux-x64-gnu": "0.4.13",
+        "@lancedb/vectordb-win32-x64-msvc": "0.4.13"
      },
      "peerDependencies": {
        "@apache-arrow/ts": "^14.0.2",
@@ -333,66 +333,6 @@
        "@jridgewell/sourcemap-codec": "^1.4.10"
      }
    },
-    "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.4.12",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.4.12.tgz",
-      "integrity": "sha512-38/rkJRlWXkPWXuj9onzvbrhnIWcIUQjgEp5G9v5ixPosBowm7A4j8e2Q8CJMsVSNcVX2JLqwWVldiWegZFuYw==",
-      "cpu": [
-        "arm64"
-      ],
-      "optional": true,
-      "os": [
-        "darwin"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.4.12",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.4.12.tgz",
-      "integrity": "sha512-psE48dztyO450hXWdv9Rl9aayM2HQ1uF9wErfC0gKmDUh1N0NdVq2viDuFpZxnmCis/nvGwKlYiYT9OnYNCJ9g==",
-      "cpu": [
-        "x64"
-      ],
-      "optional": true,
-      "os": [
-        "darwin"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.4.12",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.4.12.tgz",
-      "integrity": "sha512-xwkgF6MiF5aAdG9JG8v4ke652YxUJrhs9z4OrsEfrENnvsIQd2C5UyKMepVLdvij4BI/XPFRFWXdjPvP7S9rTA==",
-      "cpu": [
-        "arm64"
-      ],
-      "optional": true,
-      "os": [
-        "linux"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.4.12",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.4.12.tgz",
-      "integrity": "sha512-gJqYR0aymrS+C60xc4EQPzmQ5/69XfeFv2ofBvAj7qW+c6BcnoAcfVl+7s1IrcWeGz251sm5cD5Lx4AzJd89dA==",
-      "cpu": [
-        "x64"
-      ],
-      "optional": true,
-      "os": [
-        "linux"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.4.12",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.4.12.tgz",
-      "integrity": "sha512-LhCzpyEeBUyO6L2fuVqeP3mW8kYDryyU9PNqcM01m88sZB1Do6AlwiM+GjPRQ0SpzD0LK9oxQqSmJrdcNGqjbw==",
-      "cpu": [
-        "x64"
-      ],
-      "optional": true,
-      "os": [
-        "win32"
-      ]
-    },
    "node_modules/@neon-rs/cli": {
      "version": "0.0.160",
      "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.160.tgz",
--- a/node/package.json
+++ b/node/package.json
@@ -1,6 +1,6 @@
 {
  "name": "vectordb",
-  "version": "0.4.12",
+  "version": "0.4.13",
  "description": " Serverless, low-latency vector database for AI applications",
  "main": "dist/index.js",
  "types": "dist/index.d.ts",
@@ -88,10 +88,10 @@
    }
  },
  "optionalDependencies": {
-    "@lancedb/vectordb-darwin-arm64": "0.4.12",
-    "@lancedb/vectordb-darwin-x64": "0.4.12",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.4.12",
-    "@lancedb/vectordb-linux-x64-gnu": "0.4.12",
-    "@lancedb/vectordb-win32-x64-msvc": "0.4.12"
+    "@lancedb/vectordb-darwin-arm64": "0.4.13",
+    "@lancedb/vectordb-darwin-x64": "0.4.13",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.4.13",
+    "@lancedb/vectordb-linux-x64-gnu": "0.4.13",
+    "@lancedb/vectordb-win32-x64-msvc": "0.4.13"
  }
 }
--- a/python/.bumpversion.cfg
+++ b/python/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.6.3
+current_version = 0.6.4
 commit = True
 message = [python] Bump version: {current_version} → {new_version}
 tag = True
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -1,9 +1,9 @@
 [project]
 name = "lancedb"
-version = "0.6.3"
+version = "0.6.4"
 dependencies = [
    "deprecation",
-    "pylance==0.10.2",
+    "pylance==0.10.4",
    "ratelimiter~=1.0",
    "retry>=0.9.2",
    "tqdm>=4.27.0",
@@ -81,6 +81,7 @@ embeddings = [
    "awscli>=1.29.57",
    "botocore>=1.31.57",
 ]
+azure = ["adlfs>=2024.2.0"]

 [tool.maturin]
 python-source = "python"
--- a/python/python/lancedb/query.py
+++ b/python/python/lancedb/query.py
@@ -271,8 +271,7 @@ class LanceQueryBuilder(ABC):
        and also the "_distance" column which is the distance between the query
        vector and the returned vectors.
        """
-        # raise NotImplementedError
-        self.to_arrow()
+        raise NotImplementedError

    def to_list(self) -> List[dict]:
        """
@@ -435,12 +434,12 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        self._vector_column = vector_column
        self._prefilter = False

-    def metric(self, metric: Literal["L2", "cosine", "dot"]) -> LanceVectorQueryBuilder:
+    def metric(self, metric: Literal["L2", "cosine"]) -> LanceVectorQueryBuilder:
        """Set the distance metric to use.

        Parameters
        ----------
-        metric: "L2" or "cosine" or "dot"
+        metric: "L2" or "cosine"
            The distance metric to use. By default "L2" is used.

        Returns
--- a/python/python/lancedb/remote/table.py
+++ b/python/python/lancedb/remote/table.py
@@ -296,7 +296,6 @@ class RemoteTable(Table):
        return LanceVectorQueryBuilder(self, query, vector_column_name)

    def _execute_query(self, query: Query) -> pa.Table:
-        print("query metric", query.metric)
        if (
            query.vector is not None
            and len(query.vector) > 0
--- a/python/python/lancedb/table.py
+++ b/python/python/lancedb/table.py
@@ -1522,7 +1522,7 @@ class LanceTable(Table):

    def _execute_query(self, query: Query) -> pa.Table:
        ds = self.to_lance()
-        print("metric:", query.metric)  
+
        return ds.to_table(
            columns=query.columns,
            filter=query.filter,
--- a/python/python/lancedb/util.py
+++ b/python/python/lancedb/util.py
@@ -26,6 +26,18 @@ import pyarrow as pa
 import pyarrow.fs as pa_fs


+def safe_import_adlfs():
+    try:
+        import adlfs
+
+        return adlfs
+    except ImportError:
+        return None
+
+
+adlfs = safe_import_adlfs()
+
+
 def get_uri_scheme(uri: str) -> str:
    """
    Get the scheme of a URI. If the URI does not have a scheme, assume it is a file URI.
@@ -92,6 +104,17 @@ def fs_from_uri(uri: str) -> Tuple[pa_fs.FileSystem, str]:
        path = get_uri_location(uri)
        return fs, path

+    elif get_uri_scheme(uri) == "az" and adlfs is not None:
+        az_blob_fs = adlfs.AzureBlobFileSystem(
+            account_name=os.environ.get("AZURE_STORAGE_ACCOUNT_NAME"),
+            account_key=os.environ.get("AZURE_STORAGE_ACCOUNT_KEY"),
+        )
+
+        fs = pa_fs.PyFileSystem(pa_fs.FSSpecHandler(az_blob_fs))
+
+        path = get_uri_location(uri)
+        return fs, path
+
    return pa_fs.FileSystem.from_uri(uri)


--- a/python/python/lancedb/utils/events.py
+++ b/python/python/lancedb/utils/events.py
@@ -69,7 +69,7 @@ class _Events:
        self.throttled_event_names = ["search_table"]
        self.throttled_events = set()
        self.max_events = 5  # max events to store in memory
-        self.rate_limit = 60.0 * 5  # rate limit (seconds)
+        self.rate_limit = 60.0 * 60.0  # rate limit (seconds)
        self.time = 0.0

        if is_git_dir():
--- a/python/python/tests/test_io.py
+++ b/python/python/tests/test_io.py
@@ -16,16 +16,35 @@ import os
 import lancedb
 import pytest

+# AWS:
 # You need to setup AWS credentials an a base path to run this test. Example
 #    AWS_PROFILE=default TEST_S3_BASE_URL=s3://my_bucket/dataset pytest tests/test_io.py
+#
+# Azure:
+# You need to setup Azure credentials an a base path to run this test. Example
+#   export AZURE_STORAGE_ACCOUNT_NAME="<account>"
+#   export AZURE_STORAGE_ACCOUNT_KEY="<key>"
+#   export REMOTE_BASE_URL=az://my_blob/dataset
+#   pytest tests/test_io.py
+
+
+@pytest.fixture(autouse=True, scope="module")
+def setup():
+    yield
+
+    if remote_url := os.environ.get("REMOTE_BASE_URL"):
+        db = lancedb.connect(remote_url)
+
+        for table in db.table_names():
+            db.drop_table(table)


@pytest.mark.skipif(
-    (os.environ.get("TEST_S3_BASE_URL") is None),
-    reason="please setup s3 base url",
+    (os.environ.get("REMOTE_BASE_URL") is None),
+    reason="please setup remote base url",
 )
-def test_s3_io():
-    db = lancedb.connect(os.environ.get("TEST_S3_BASE_URL"))
+def test_remote_io():
+    db = lancedb.connect(os.environ.get("REMOTE_BASE_URL"))
    assert db.table_names() == []

    table = db.create_table(
--- a/rust/ffi/node/Cargo.toml
+++ b/rust/ffi/node/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-node"
-version = "0.4.12"
+version = "0.4.13"
 description = "Serverless, low-latency vector database for AI applications"
 license.workspace = true
 edition.workspace = true
--- a/rust/lancedb/Cargo.toml
+++ b/rust/lancedb/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.4.12"
+version = "0.4.13"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
Author	SHA1	Message	Date
Lance Release	81f2cdf736	[python] Bump version: 0.6.3 → 0.6.4	2024-03-16 18:59:14 +00:00
Lance Release	d404a3590c	Updating package-lock.json	2024-03-16 05:21:58 +00:00
Lance Release	e688484bd3	Bump version: 0.4.12 → 0.4.13	2024-03-16 05:21:44 +00:00
Weston Pace	3bcd61c8de	feat: bump lance to 0.10.4 (#1123 )	2024-03-15 22:21:04 -07:00
vincent d warmerdam	c76ec48603	Explain vonoroi seed initalisation (#1114 ) This PR fixes https://github.com/lancedb/lancedb/issues/1112. It turned out that K-means is currently used internally, so I figured adding that context to the docs would be nice.	2024-03-15 14:16:05 -07:00
Christian Di Lorenzo	d974413745	fix(python): Add python azure blob read support (#1102 ) I know there's a larger effort to have the python client based on the core rust implementation, but in the meantime there have been several issues (#1072 and #485) with some of the azure blob storage calls due to pyarrow not natively supporting an azure backend. To this end, I've added an optional import of the fsspec implementation of azure blob storage [`adlfs`](https://pypi.org/project/adlfs/) and passed it to `pyarrow.fs`. I've modified the existing test and manually verified it with some real credentials to make sure it behaves as expected. It should be now as simple as: ```python import lancedb db = lancedb.connect("az://blob_name/path") table = db.open_table("test") table.search(...) ``` Thank you for this cool project and we're excited to start using this for real shortly! 🎉 And thanks to @dwhitena for bringing it to my attention with his prediction guard posts. Co-authored-by: christiandilorenzo <christian.dilorenzo@infiniaml.com>	2024-03-15 14:15:41 -07:00
Weston Pace	ec4f2fbd30	feat: update lance to v0.10.3 (#1094 )	2024-03-15 08:50:28 -07:00
Ayush Chaurasia	6375ea419a	chore(python): Increase event interval for telemetry (#1108 ) Increasing event reporting interval from 5mins to 60mins	2024-03-15 17:04:43 +05:30