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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 -qU dingodb # or install latest: %pip install -qU 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)

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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!"])

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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")
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