community[minor]: Add VDMS vectorstore (#19551)

- **Description:** Add support for Intel Lab's [Visual Data Management
System (VDMS)](https://github.com/IntelLabs/vdms) as a vector store
- **Dependencies:** `vdms` library which requires protobuf = "4.24.2".
There is a conflict with dashvector in `langchain` package but conflict
is resolved in `community`.
- **Contribution maintainer:** [@cwlacewe](https://github.com/cwlacewe)
- **Added tests:**
libs/community/tests/integration_tests/vectorstores/test_vdms.py
- **Added docs:** docs/docs/integrations/vectorstores/vdms.ipynb
- **Added cookbook:** cookbook/multi_modal_RAG_vdms.ipynb

---------

Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>

docs/docs/integrations/providers/vdms.mdx

@@ -0,0 +1,62 @@
+# VDMS
+
+> [VDMS](https://github.com/IntelLabs/vdms/blob/master/README.md) is a storage solution for efficient access
+> of big-”visual”-data that aims to achieve cloud scale by searching for relevant visual data via visual metadata
+> stored as a graph and enabling machine friendly enhancements to visual data for faster access.
+
+## Installation and Setup
+
+### Install Client
+
+```bash
+pip install vdms
+```
+
+### Install Database
+
+There are two ways to get started with VDMS:
+
+#### Install VDMS on your local machine via docker
+```bash
+    docker run -d -p 55555:55555 intellabs/vdms:latest
+```
+
+#### Install VDMS directly on your local machine
+Please see [installation instructions](https://github.com/IntelLabs/vdms/blob/master/INSTALL.md).
+
+
+
+## VectorStore
+
+The vector store is a simple wrapper around VDMS. It provides a simple interface to store and retrieve data.
+
+```python
+from langchain_community.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+
+loader = TextLoader("./state_of_the_union.txt")
+documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+docs = text_splitter.split_documents(documents)
+
+from langchain_community.vectorstores import VDMS
+from langchain_community.vectorstores.vdms import VDMS_Client
+from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
+
+client = VDMS_Client("localhost", 55555)
+vectorstore = VDMS.from_documents(
+    docs,
+    client=client,
+    collection_name="langchain-demo",
+    embedding_function=HuggingFaceEmbeddings(),
+    engine="FaissFlat"
+    distance_strategy="L2",
+)
+
+query = "What did the president say about Ketanji Brown Jackson"
+results = vectorstore.similarity_search(query)
+```
+
+For a more detailed walkthrough of the VDMS wrapper, see [this notebook](/docs/integrations/vectorstores/vdms)
+
+

docs/docs/modules/data_connection/indexing.ipynb

@@ -60,7 +60,7 @@
     "   * document addition by id (`add_documents` method with `ids` argument)\n",
     "   * delete by id (`delete` method with `ids` argument)\n",
     "\n",
-    "Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`.\n",
+    "Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`.\n",
     "  \n",
     "## Caution\n",
     "\n",