DingoDB is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.
You’ll need to install langchain-community with pip install -qU langchain-community to use this integration This notebook shows how to use functionality related to the DingoDB vector database. To run, you should have a DingoDB instance up and running. 
%pip install --upgrade --quiet  dingodb
# or install latest:
%pip install --upgrade --quiet  git+https://git@github.com/dingodb/pydingo.git
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key. 
import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

OpenAI API Key:········

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Dingo
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

from langchain_community.document_loaders import TextLoader

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

from dingodb import DingoDB

index_name = "langchain_demo"

dingo_client = DingoDB(user="", password="", host=["127.0.0.1:13000"])
# First, check if our index already exists. If it doesn't, we create it
if (
    index_name not in dingo_client.get_index()
    and index_name.upper() not in dingo_client.get_index()
):
    # we create a new index, modify to your own
    dingo_client.create_index(
        index_name=index_name, dimension=1536, metric_type="cosine", auto_id=False
    )

# The OpenAI embedding model `text-embedding-ada-002 uses 1536 dimensions`
docsearch = Dingo.from_documents(
    docs, embeddings, client=dingo_client, index_name=index_name
)

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Dingo
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)

print(docs[0].page_content)

​
Adding More Text to an Existing Index

More text can embedded and upserted to an existing Dingo index using the add_texts function 
vectorstore = Dingo(embeddings, "text", client=dingo_client, index_name=index_name)

vectorstore.add_texts(["More text!"])

​
Maximal Marginal Relevance Searches

In addition to using similarity search in the retriever object, you can also use mmr as retriever. 
retriever = docsearch.as_retriever(search_type="mmr")
matched_docs = retriever.invoke(query)
for i, d in enumerate(matched_docs):
    print(f"\n## Document {i}\n")
    print(d.page_content)
Or use max_marginal_relevance_search directly: 
found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)
for i, doc in enumerate(found_docs):
    print(f"{i + 1}.", doc.page_content, "\n")