langchain/docs/docs/integrations/providers/vdms.mdx

# 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)