Apache Doris is a modern data warehouse for real-time analytics. It delivers lightning-fast analytics on real-time data at scale.
Usually Apache Doris is categorized into OLAP, and it has showed excellent performance in ClickBench — a Benchmark For Analytical DBMS. Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.
You’ll need to install langchain-community with pip install -qU langchain-community to use this integration Here we’ll show how to use the Apache Doris Vector Store.

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Setup

%pip install --upgrade --quiet  pymysql
Set update_vectordb = False at the beginning. If there is no docs updated, then we don’t need to rebuild the embeddings of docs 
!pip install  sqlalchemy
!pip install langchain

from langchain.chains import RetrievalQA
from langchain_community.document_loaders import (
    DirectoryLoader,
    UnstructuredMarkdownLoader,
)
from langchain_community.vectorstores.apache_doris import (
    ApacheDoris,
    ApacheDorisSettings,
)
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import TokenTextSplitter

update_vectordb = False

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Load docs and split them into tokens

Load all markdown files under the docs directory for Apache Doris documents, you can clone repo from https://github.com/apache/doris, and there is docs directory in it. 
loader = DirectoryLoader(
    "./docs", glob="**/*.md", loader_cls=UnstructuredMarkdownLoader
)
documents = loader.load()
Split docs into tokens, and set update_vectordb = True because there are new docs/tokens. 
# load text splitter and split docs into snippets of text
text_splitter = TokenTextSplitter(chunk_size=400, chunk_overlap=50)
split_docs = text_splitter.split_documents(documents)

# tell vectordb to update text embeddings
update_vectordb = True
split_docs[-20] print(”# docs = %d, # splits = %d” % (len(documents), len(split_docs)))

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Create vectordb instance

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Use Apache Doris as vectordb

def gen_apache_doris(update_vectordb, embeddings, settings):
    if update_vectordb:
        docsearch = ApacheDoris.from_documents(split_docs, embeddings, config=settings)
    else:
        docsearch = ApacheDoris(embeddings, settings)
    return docsearch

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Convert tokens into embeddings and put them into vectordb

Here we use Apache Doris as vectordb, you can configure Apache Doris instance via ApacheDorisSettings. Configuring Apache Doris instance is pretty much like configuring mysql instance. You need to specify:
  1. host/port
  2. username(default: ‘root’)
  3. password(default: ”)
  4. database(default: ‘default’)
  5. table(default: ‘langchain’)
import os
from getpass import getpass

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

update_vectordb = True

embeddings = OpenAIEmbeddings()

# configure Apache Doris settings(host/port/user/pw/db)
settings = ApacheDorisSettings()
settings.port = 9030
settings.host = "172.30.34.130"
settings.username = "root"
settings.password = ""
settings.database = "langchain"
docsearch = gen_apache_doris(update_vectordb, embeddings, settings)

print(docsearch)

update_vectordb = False

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Build QA and ask question to it

llm = OpenAI()
qa = RetrievalQA.from_chain_type(
    llm=llm, chain_type="stuff", retriever=docsearch.as_retriever()
)
query = "what is apache doris"
resp = qa.run(query)
print(resp)