community: Add support for SAP HANA Vector hnsw index creation (#27884)

**Issue:** Added support for creating indexes in the SAP HANA Vector
engine.
 
**Changes**: 
1. Introduced a new function `create_hnsw_index` in `hanavector.py` that
enables the creation of indexes for SAP HANA Vector.
2. Added integration tests for the index creation function to ensure
functionality.
3. Updated the documentation to reflect the new index creation feature,
including examples and output from the notebook.
4. Fix the operator issue in ` _process_filter_object` function and
change the array argument to a placeholder in the similarity search SQL
statement.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/community/tests/integration_tests/vectorstores/test_hanavector.py

@@ -1432,3 +1432,193 @@ def test_preexisting_specific_columns_for_returned_metadata_completeness(
     assert docs[0].metadata["quality"] == "good"
     assert docs[0].metadata["ready"]
     assert "NonExisting" not in docs[0].metadata.keys()
+
+
+@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed")
+def test_create_hnsw_index_with_default_values(texts: List[str]) -> None:
+    table_name = "TEST_TABLE_HNSW_INDEX_DEFAULT"
+
+    # Delete table if it exists (cleanup from previous tests)
+    drop_table(test_setup.conn, table_name)
+
+    # Create table and insert data
+    vectorDB = HanaDB.from_texts(
+        connection=test_setup.conn,
+        texts=texts,
+        embedding=embedding,
+        table_name=table_name,
+    )
+
+    # Test the creation of HNSW index
+    try:
+        vectorDB.create_hnsw_index()
+    except Exception as e:
+        pytest.fail(f"Failed to create HNSW index: {e}")
+
+    # Perform a search using the index to confirm its correctness
+    search_result = vectorDB.max_marginal_relevance_search(texts[0], k=2, fetch_k=20)
+
+    assert len(search_result) == 2
+    assert search_result[0].page_content == texts[0]
+    assert search_result[1].page_content != texts[0]
+
+
+@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed")
+def test_create_hnsw_index_with_defined_values(texts: List[str]) -> None:
+    table_name = "TEST_TABLE_HNSW_INDEX_DEFINED"
+
+    # Delete table if it exists (cleanup from previous tests)
+    drop_table(test_setup.conn, table_name)
+
+    # Create table and insert data
+    vectorDB = HanaDB.from_texts(
+        connection=test_setup.conn,
+        texts=texts,
+        embedding=embedding,
+        table_name=table_name,
+        distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
+    )
+
+    # Test the creation of HNSW index with specific values
+    try:
+        vectorDB.create_hnsw_index(
+            index_name="my_L2_index", ef_search=500, m=100, ef_construction=200
+        )
+    except Exception as e:
+        pytest.fail(f"Failed to create HNSW index with defined values: {e}")
+
+    # Perform a search using the index to confirm its correctness
+    search_result = vectorDB.max_marginal_relevance_search(texts[0], k=2, fetch_k=20)
+
+    assert len(search_result) == 2
+    assert search_result[0].page_content == texts[0]
+    assert search_result[1].page_content != texts[0]
+
+
+@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed")
+def test_create_hnsw_index_after_initialization(texts: List[str]) -> None:
+    table_name = "TEST_TABLE_HNSW_INDEX_AFTER_INIT"
+
+    drop_table(test_setup.conn, table_name)
+
+    # Initialize HanaDB without adding documents yet
+    vectorDB = HanaDB(
+        connection=test_setup.conn,
+        embedding=embedding,
+        table_name=table_name,
+    )
+
+    # Create HNSW index before adding documents
+    vectorDB.create_hnsw_index(
+        index_name="index_pre_add", ef_search=400, m=50, ef_construction=150
+    )
+
+    # Add texts after index creation
+    vectorDB.add_texts(texts=texts)
+
+    # Perform similarity search using the index
+    search_result = vectorDB.similarity_search(texts[0], k=3)
+
+    # Assert that search result is valid and has expected length
+    assert len(search_result) == 3
+    assert search_result[0].page_content == texts[0]
+    assert search_result[1].page_content != texts[0]
+
+
+@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed")
+def test_duplicate_hnsw_index_creation(texts: List[str]) -> None:
+    table_name = "TEST_TABLE_HNSW_DUPLICATE_INDEX"
+
+    # Delete table if it exists (cleanup from previous tests)
+    drop_table(test_setup.conn, table_name)
+
+    # Create table and insert data
+    vectorDB = HanaDB.from_texts(
+        connection=test_setup.conn,
+        texts=texts,
+        embedding=embedding,
+        table_name=table_name,
+    )
+
+    # Create HNSW index for the first time
+    vectorDB.create_hnsw_index(
+        index_name="index_cosine",
+        ef_search=300,
+        m=80,
+        ef_construction=100,
+    )
+
+    with pytest.raises(Exception):
+        vectorDB.create_hnsw_index(ef_search=300, m=80, ef_construction=100)
+
+
+@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed")
+def test_create_hnsw_index_invalid_m_value(texts: List[str]) -> None:
+    table_name = "TEST_TABLE_HNSW_INVALID_M"
+
+    # Cleanup: drop the table if it exists
+    drop_table(test_setup.conn, table_name)
+
+    # Create table and insert data
+    vectorDB = HanaDB.from_texts(
+        connection=test_setup.conn,
+        texts=texts,
+        embedding=embedding,
+        table_name=table_name,
+    )
+
+    # Test invalid `m` value (too low)
+    with pytest.raises(ValueError):
+        vectorDB.create_hnsw_index(m=3)
+
+    # Test invalid `m` value (too high)
+    with pytest.raises(ValueError):
+        vectorDB.create_hnsw_index(m=1001)
+
+
+@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed")
+def test_create_hnsw_index_invalid_ef_construction(texts: List[str]) -> None:
+    table_name = "TEST_TABLE_HNSW_INVALID_EF_CONSTRUCTION"
+
+    # Cleanup: drop the table if it exists
+    drop_table(test_setup.conn, table_name)
+
+    # Create table and insert data
+    vectorDB = HanaDB.from_texts(
+        connection=test_setup.conn,
+        texts=texts,
+        embedding=embedding,
+        table_name=table_name,
+    )
+
+    # Test invalid `ef_construction` value (too low)
+    with pytest.raises(ValueError):
+        vectorDB.create_hnsw_index(ef_construction=0)
+
+    # Test invalid `ef_construction` value (too high)
+    with pytest.raises(ValueError):
+        vectorDB.create_hnsw_index(ef_construction=100001)
+
+
+@pytest.mark.skipif(not hanadb_installed, reason="hanadb not installed")
+def test_create_hnsw_index_invalid_ef_search(texts: List[str]) -> None:
+    table_name = "TEST_TABLE_HNSW_INVALID_EF_SEARCH"
+
+    # Cleanup: drop the table if it exists
+    drop_table(test_setup.conn, table_name)
+
+    # Create table and insert data
+    vectorDB = HanaDB.from_texts(
+        connection=test_setup.conn,
+        texts=texts,
+        embedding=embedding,
+        table_name=table_name,
+    )
+
+    # Test invalid `ef_search` value (too low)
+    with pytest.raises(ValueError):
+        vectorDB.create_hnsw_index(ef_search=0)
+
+    # Test invalid `ef_search` value (too high)
+    with pytest.raises(ValueError):
+        vectorDB.create_hnsw_index(ef_search=100001)