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Apache Doris

Apache Doris 是一个现代数据仓库,用于实时分析。 它提供了对实时数据的闪电般快速的分析,能够大规模处理。

通常 Apache Doris 被归类为 OLAP,并且在 ClickBench — 一个分析型数据库基准测试 中表现出色。由于它具有超快的向量化执行引擎,它也可以用作快速的向量数据库。

您需要使用 pip install -qU langchain-community 安装 langchain-community 才能使用此集成。

在这里,我们将展示如何使用 Apache Doris 向量存储。

设置

%pip install --upgrade --quiet  pymysql

在开始时设置 update_vectordb = False。如果没有文档被更新,那么我们就不需要重建文档的嵌入。

!pip install  sqlalchemy
!pip install langchain
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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

加载文档并将其分割为标记

加载 docs 目录下的所有 markdown 文件

对于 Apache Doris 文档,您可以从 https://github.com/apache/doris 克隆仓库,里面有 docs 目录。

loader = DirectoryLoader(
"./docs", glob="**/*.md", loader_cls=UnstructuredMarkdownLoader
)
documents = loader.load()

将文档分割为标记,并设置 update_vectordb = True,因为有新的文档/标记。

# 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)))

创建 vectordb 实例

使用 Apache Doris 作为 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

将标记转换为嵌入并放入 vectordb

在这里我们使用 Apache Doris 作为向量数据库,您可以通过 ApacheDorisSettings 配置 Apache Doris 实例。

配置 Apache Doris 实例与配置 MySQL 实例非常相似。您需要指定:

  1. 主机/端口
  2. 用户名(默认:'root')
  3. 密码(默认:'')
  4. 数据库(默认:'default')
  5. 表(默认:'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

构建问答系统并向其提问

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)

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