community[minor]: Add Initial Support for TiDB Vector Store (#15796)

This pull request introduces initial support for the TiDB vector store.
The current version is basic, laying the foundation for the vector store
integration. While this implementation provides the essential features,
we plan to expand and improve the TiDB vector store support with
additional enhancements in future updates.

Upcoming Enhancements:
* Support for Vector Index Creation: To enhance the efficiency and
performance of the vector store.
* Support for max marginal relevance search. 
* Customized Table Structure Support: Recognizing the need for
flexibility, we plan for more tailored and efficient data store
solutions.

Simple use case exmaple

```python
from typing import List, Tuple
from langchain.docstore.document import Document
from langchain_community.vectorstores import TiDBVectorStore
from langchain_openai import OpenAIEmbeddings

db = TiDBVectorStore.from_texts(
    embedding=embeddings,
    texts=['Andrew like eating oranges', 'Alexandra is from England', 'Ketanji Brown Jackson is a judge'],
    table_name="tidb_vector_langchain",
    connection_string=tidb_connection_url,
    distance_strategy="cosine",
)

query = "Can you tell me about Alexandra?"
docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
    print("-" * 80)
    print("Score: ", score)
    print(doc.page_content)
    print("-" * 80)
```

docs/docs/integrations/document_loaders/tidb.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# TiDB\n",
     "\n",
-    "> [TiDB](https://github.com/pingcap/tidb) is an open-source, cloud-native, distributed, MySQL-Compatible database for elastic scale and real-time analytics.\n",
+    "> [TiDB Cloud](https://tidbcloud.com/), is a comprehensive Database-as-a-Service (DBaaS) solution, that provides dedicated and serverless options. TiDB Serverless is now integrating a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly develop AI applications using TiDB Serverless without the need for a new database or additional technical stacks. Be among the first to experience it by joining the waitlist for the private beta at https://tidb.cloud/ai.\n",
     "\n",
     "This notebook introduces how to use `TiDBLoader` to load data from TiDB in langchain."
    ]

docs/docs/integrations/memory/tidb_chat_message_history.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# TiDB\n",
     "\n",
-    "> [TiDB](https://github.com/pingcap/tidb) is an open-source, cloud-native, distributed, MySQL-Compatible database for elastic scale and real-time analytics.\n",
+    "> [TiDB Cloud](https://tidbcloud.com/), is a comprehensive Database-as-a-Service (DBaaS) solution, that provides dedicated and serverless options. TiDB Serverless is now integrating a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly develop AI applications using TiDB Serverless without the need for a new database or additional technical stacks. Be among the first to experience it by joining the waitlist for the private beta at https://tidb.cloud/ai.\n",
     "\n",
     "This notebook introduces how to use TiDB to store chat message history. "
    ]

libs/community/langchain_community/vectorstores/__init__.py

@@ -436,6 +436,12 @@ def _import_tencentvectordb() -> Any:
     return TencentVectorDB
 
 
+def _import_tidb_vectorstore() -> Any:
+    from langchain_community.vectorstores.tidb_vector import TiDBVectorStore
+
+    return TiDBVectorStore
+
+
 def _import_tiledb() -> Any:
     from langchain_community.vectorstores.tiledb import TileDB
 
@@ -651,6 +657,8 @@ def __getattr__(name: str) -> Any:
         return _import_tair()
     elif name == "TencentVectorDB":
         return _import_tencentvectordb()
+    elif name == "TiDBVectorStore":
+        return _import_tidb_vectorstore()
     elif name == "TileDB":
         return _import_tiledb()
     elif name == "Tigris":
@@ -746,6 +754,7 @@ def __getattr__(name: str) -> Any:
     "SupabaseVectorStore",
     "SurrealDBStore",
     "Tair",
+    "TiDBVectorStore",
     "TileDB",
     "Tigris",
     "TimescaleVector",