community[minor]: Add TablestoreVectorStore (#25767)

Thank you for contributing to LangChain!

- [x] **PR title**:  community: add TablestoreVectorStore



- [x] **PR message**: 
    - **Description:** add TablestoreVectorStore
    - **Dependencies:** none


- [x] **Add tests and docs**: If you're adding a new integration, please
include
  1. a test for the integration: yes
  2. an example notebook showing its use: yes

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>

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

@@ -0,0 +1,93 @@
+"""Test tablestore functionality."""
+
+import os
+
+import pytest
+from langchain_core.documents import Document
+
+from langchain_community.embeddings import FakeEmbeddings
+from langchain_community.vectorstores.tablestore import TablestoreVectorStore
+
+
+def test_tablestore() -> None:
+    """Test end to end construction and search."""
+    test_embedding_dimension_size = 4
+    embeddings = FakeEmbeddings(size=test_embedding_dimension_size)
+
+    end_point = os.getenv("end_point")
+    instance_name = os.getenv("instance_name")
+    access_key_id = os.getenv("access_key_id")
+    access_key_secret = os.getenv("access_key_secret")
+    if (
+        end_point is None
+        or instance_name is None
+        or access_key_id is None
+        or access_key_secret is None
+    ):
+        pytest.skip(
+            "end_point is None or instance_name is None or "
+            "access_key_id is None or access_key_secret is None"
+        )
+    """
+        1. create vector store 
+    """
+    store = TablestoreVectorStore(
+        embedding=embeddings,
+        endpoint=end_point,
+        instance_name=instance_name,
+        access_key_id=access_key_id,
+        access_key_secret=access_key_secret,
+        vector_dimension=test_embedding_dimension_size,
+    )
+
+    """
+        2. create table and index. (only needs to be run once)
+    """
+    store.create_table_if_not_exist()
+    store.create_search_index_if_not_exist()
+
+    """
+        3. add document
+    """
+    store.add_documents(
+        [
+            Document(
+                id="1",
+                page_content="1 hello world",
+                metadata={"type": "pc", "time": 2000},
+            ),
+            Document(
+                id="2", page_content="abc world", metadata={"type": "pc", "time": 2009}
+            ),
+            Document(
+                id="3",
+                page_content="3 text world",
+                metadata={"type": "sky", "time": 2010},
+            ),
+            Document(
+                id="4", page_content="hi world", metadata={"type": "sky", "time": 2030}
+            ),
+            Document(
+                id="5", page_content="hi world", metadata={"type": "sky", "time": 2030}
+            ),
+        ]
+    )
+
+    """
+        4. delete document
+    """
+    assert store.delete(["3"])
+
+    """
+        5. get document
+    """
+    get_docs = store.get_by_ids(["1", "4"])
+    assert len(get_docs) == 2
+    assert get_docs[0].id == "1"
+    assert get_docs[1].id == "4"
+
+    """
+        6. similarity_search
+    """
+    search_result = store.similarity_search_with_score(query="hello world", k=2)
+    assert len(search_result) == 2

libs/community/tests/unit_tests/vectorstores/test_imports.py

@@ -84,6 +84,7 @@ EXPECTED_ALL = [
     "StarRocks",
     "SupabaseVectorStore",
     "SurrealDBStore",
+    "TablestoreVectorStore",
     "Tair",
     "TencentVectorDB",
     "TiDBVectorStore",

libs/community/tests/unit_tests/vectorstores/test_indexing_docs.py

@@ -88,6 +88,7 @@ def test_compatible_vectorstore_documentation() -> None:
         "SingleStoreDB",
         "SupabaseVectorStore",
         "SurrealDBStore",
+        "TablestoreVectorStore",
         "TileDB",
         "TimescaleVector",
         "TencentVectorDB",