Add PyLucene integration and CPU/GPU end-to-end tests

README.md

@@ -20,6 +20,100 @@ mvn clean compile package
 
 The artifacts would be built and available in the target / folder.
 
+### Using with PyLucene
+
+PyLucene embeds a JVM and starts it with the classpath passed to `lucene.initVM(...)`.
+Because PyLucene's generated Python module only exposes the Java classes it was built
+to wrap, use Lucene's service provider lookup to load `cuvs-lucene` codecs from
+Python instead of importing `com.nvidia.cuvs.lucene` classes directly.
+
+Build the standard cuvs-lucene jar:
+
+```sh
+mvn clean package -DskipTests
+```
+
+Then start PyLucene with the base `cuvs-java` jar, the standard `cuvs-lucene`
+jar, and PyLucene's own Lucene classpath:
+
+```python
+import os
+from pathlib import Path
+
+import lucene
+
+cuvs_java_jar = Path(os.environ["CUVS_LUCENE_CUVS_JAVA_JAR"])
+cuvs_lucene_jar = next(
+    jar
+    for jar in Path("target").glob("cuvs-lucene-*.jar")
+    if "-jar-with-" not in jar.name
+    and not jar.name.endswith(("-sources.jar", "-javadoc.jar"))
+)
+lucene.initVM(
+    classpath=os.pathsep.join(
+        [str(cuvs_java_jar), str(cuvs_lucene_jar), lucene.CLASSPATH]
+    ),
+    vmargs=[
+        "--enable-native-access=ALL-UNNAMED",
+        "--add-modules=jdk.incubator.vector",
+    ],
+)
+
+from org.apache.lucene.codecs import Codec
+
+codec = Codec.forName("Lucene101AcceleratedHNSWCodec")
+```
+
+Use the returned `codec` with `IndexWriterConfig.setCodec(codec)`. The standard
+artifact includes `cuvs-lucene` classes and service descriptors.
+PyLucene must provide Lucene classes, and the base multi-release `cuvs-java` jar
+must be present separately on the JVM classpath. Do not use a native classifier
+`cuvs-java` jar here unless you also want to rely on its embedded native
+libraries; the base jar uses native libraries from
+`LD_LIBRARY_PATH`/`java.library.path`.
+
+To run the PyLucene pytest smoke suite against a local PyLucene environment:
+
+```sh
+./test_pylucene.sh
+```
+
+The script builds and validates the jar before invoking pytest. To invoke pytest
+directly against existing artifacts instead:
+
+```sh
+CUVS_LUCENE_JAR=/path/to/cuvs-lucene.jar \
+CUVS_LUCENE_CUVS_JAVA_JAR=/path/to/cuvs-java.jar \
+python3 -m pytest -q -s examples/Python/test_pylucene_smoke.py
+```
+
+To run an expanded GPU end-to-end pytest suite through CPU HNSW,
+CAGRA-to-HNSW, and CAGRA search paths:
+
+```sh
+./test_pylucene.sh --gpu-e2e
+```
+
+The expanded suite runs the `gpu-basic`, `gpu-segments`, `cpu-hnsw`, and
+`cagra-hnsw` case groups. The basic cases cover `hnsw`, `cagra`, `hnsw-single`,
+and `cagra-single`. The segment cases cover 1-segment indexes, 10-segment
+indexes, 10 segments force-merged to 1, and 100 segments force-merged to 10 for
+both HNSW and CAGRA. The CPU HNSW cases force the accelerated HNSW codec through
+its Lucene CPU fallback path in the same run, including 10 segments force-merged
+to 1 and 100 segments force-merged to 10. The CAGRA-to-HNSW cases explicitly
+cover one-layer and three-layer HNSW graphs built from CAGRA with NN_DESCENT,
+`graphDegree=32`, and `intermediateGraphDegree=64`. The base matrix uses 2,000
+documents and 32 dimensions; high-segment cases use at least 257 rows per
+segment to avoid expected cuVS graph-degree clamps on tiny per-segment datasets.
+The suite checks Lucene SPI discovery, jar packaging, index file suffixes
+(`.vex`/`.vem` for HNSW and `.vcag`/`.vemc` for CAGRA), indexed vector metadata,
+unfiltered KNN, filtered KNN, missing-vector documents, deletions, and force
+merge behavior. To run a subset or resize the test:
+
+```sh
+./test_pylucene.sh --gpu-e2e --cases=gpu-segments --rows=5000 --dims=64 --topk=20
+```
+
 ### Running Tests
 
 ```sh

ci/test_pylucene_smoke.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+
+# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+set -euo pipefail
+
+REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+exec "${REPO_ROOT}/test_pylucene.sh" "$@"