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VDMS

VDMS 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​

pip install vdms

Install Database​

There are two ways to get started with VDMS:

Install VDMS on your local machine via docker​

    docker run -d -p 55555:55555 intellabs/vdms:latest

Install VDMS directly on your local machine​

Please see installation instructions.

VectorStore​

The vector store is a simple wrapper around VDMS. It provides a simple interface to store and retrieve data.

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_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

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