LanceDB integration update (#22869)

Added : 

- [x] relevance search (w/wo scores)
- [x] maximal marginal search
- [x] image ingestion
- [x] filtering support
- [x] hybrid search w reranking 

make test, lint_diff and format checked.

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

@@ -11,7 +11,7 @@ def import_lancedb() -> Any:
         import lancedb
     except ImportError as e:
         raise ImportError(
-            "Could not import pinecone lancedb package. "
+            "Could not import lancedb package. "
             "Please install it with `pip install lancedb`."
         ) from e
     return lancedb
@@ -56,3 +56,51 @@ def test_lancedb_add_texts() -> None:
     result = store.similarity_search("text 2")
     result_texts = [doc.page_content for doc in result]
     assert "text 2" in result_texts
+
+
+@pytest.mark.requires("lancedb")
+def test_mmr() -> None:
+    embeddings = FakeEmbeddings()
+    store = LanceDB(embedding=embeddings)
+    store.add_texts(["text 1", "text 2", "item 3"])
+    result = store.max_marginal_relevance_search(query="text")
+    result_texts = [doc.page_content for doc in result]
+    assert "text 1" in result_texts
+
+    result = store.max_marginal_relevance_search_by_vector(
+        embeddings.embed_query("text")
+    )
+    result_texts = [doc.page_content for doc in result]
+    assert "text 1" in result_texts
+
+
+@pytest.mark.requires("lancedb")
+def test_lancedb_delete() -> None:
+    embeddings = FakeEmbeddings()
+
+    store = LanceDB(embedding=embeddings)
+    store.add_texts(["text 1", "text 2", "item 3"])
+    store.delete(filter="text = 'text 1'")
+    assert store.get_table().count_rows() == 2
+
+
+@pytest.mark.requires("lancedb")
+def test_lancedb_all_searches() -> None:
+    embeddings = FakeEmbeddings()
+
+    store = LanceDB(embedding=embeddings)
+    store.add_texts(["text 1", "text 2", "item 3"])
+    result_1 = store.similarity_search_with_relevance_scores(
+        "text 1", distance="cosine"
+    )
+    assert len(result_1[0]) == 2
+    assert "text 1" in result_1[0][0].page_content
+
+    result_2 = store.similarity_search_by_vector(embeddings.embed_query("text 1"))
+    assert "text 1" in result_2[0].page_content
+
+    result_3 = store.similarity_search_by_vector_with_relevance_scores(
+        embeddings.embed_query("text 1")
+    )
+    assert len(result_3[0]) == 2  # type: ignore
+    assert "text 1" in result_3[0][0].page_content  # type: ignore