StarRocks

StarRocks 是一个高性能分析数据库。StarRocks 是一款面向全场景分析的下一代亚秒级 MPP 数据库，包括多维分析、实时分析和即席查询。

通常，StarRocks 被归类为 OLAP，并且在ClickBench — 分析型 DBMS 基准测试中表现出卓越的性能。由于它拥有超快的向量化执行引擎，因此也可以用作快速的向量数据库。

在这里，我们将展示如何使用 StarRocks 向量存储。

设置

%pip install --upgrade --quiet  pymysql langchain-community

在开始时设置 update_vectordb = False。如果没有文档更新，则不需要重建文档的嵌入。

from langchain.chains import RetrievalQA
from langchain_community.document_loaders import (
    DirectoryLoader,
    UnstructuredMarkdownLoader,
)
from langchain_community.vectorstores import StarRocks
from langchain_community.vectorstores.starrocks import StarRocksSettings
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import TokenTextSplitter

update_vectordb = False

API 参考：RetrievalQA | DirectoryLoader | UnstructuredMarkdownLoader | StarRocks | StarRocksSettings | OpenAI | OpenAIEmbeddings | TokenTextSplitter

/Users/dirlt/utils/py3env/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.7) or chardet (5.1.0)/charset_normalizer (2.0.9) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "

加载文档并将其拆分为标记

加载 docs 目录下的所有 Markdown 文件

对于 StarRocks 文档，您可以从https://github.com/StarRocks/starrocks克隆存储库，其中包含 docs 目录。

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

将文档拆分为标记，并设置 update_vectordb = True，因为存在新的文档/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]

Document(page_content='Compile StarRocks with Docker\n\nThis topic describes how to compile StarRocks using Docker.\n\nOverview\n\nStarRocks provides development environment images for both Ubuntu 22.04 and CentOS 7.9. With the image, you can launch a Docker container and compile StarRocks in the container.\n\nStarRocks version and DEV ENV image\n\nDifferent branches of StarRocks correspond to different development environment images provided on StarRocks Docker Hub.\n\nFor Ubuntu 22.04:\n\n| Branch name | Image name              |\n  | --------------- | ----------------------------------- |\n  | main            | starrocks/dev-env-ubuntu:latest     |\n  | branch-3.0      | starrocks/dev-env-ubuntu:3.0-latest |\n  | branch-2.5      | starrocks/dev-env-ubuntu:2.5-latest |\n\nFor CentOS 7.9:\n\n| Branch name | Image name                       |\n  | --------------- | ------------------------------------ |\n  | main            | starrocks/dev-env-centos7:latest     |\n  | branch-3.0      | starrocks/dev-env-centos7:3.0-latest |\n  | branch-2.5      | starrocks/dev-env-centos7:2.5-latest |\n\nPrerequisites\n\nBefore compiling StarRocks, make sure the following requirements are satisfied:\n\nHardware\n\n', metadata={'source': 'docs/developers/build-starrocks/Build_in_docker.md'})

print("# docs  = %d, # splits = %d" % (len(documents), len(split_docs)))

# docs  = 657, # splits = 2802

创建向量数据库实例

使用 StarRocks 作为向量数据库

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

将标记转换为嵌入并将其放入向量数据库

这里我们使用 StarRocks 作为向量数据库，您可以通过 StarRocksSettings 配置 StarRocks 实例。

配置 StarRocks 实例非常类似于配置 MySQL 实例。您需要指定

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

embeddings = OpenAIEmbeddings()

# configure starrocks settings(host/port/user/pw/db)
settings = StarRocksSettings()
settings.port = 41003
settings.host = "127.0.0.1"
settings.username = "root"
settings.password = ""
settings.database = "zya"
docsearch = gen_starrocks(update_vectordb, embeddings, settings)

print(docsearch)

update_vectordb = False

Inserting data...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2802/2802 [02:26<00:00, 19.11it/s]
``````output
zya.langchain @ 127.0.0.1:41003

username: root

Table Schema:
----------------------------------------------------------------------------
|name                    |type                    |key                     |
----------------------------------------------------------------------------
|id                      |varchar(65533)          |true                    |
|document                |varchar(65533)          |false                   |
|embedding               |array<float>            |false                   |
|metadata                |varchar(65533)          |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 = "is profile enabled by default? if not, how to enable profile?"
resp = qa.run(query)
print(resp)

 No, profile is not enabled by default. To enable profile, set the variable `enable_profile` to `true` using the command `set enable_profile = true;`

相关

• 向量存储 概念指南
• 向量存储 操作指南