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StarRocks

StarRocks is a High-Performance Analytical Database. StarRocks is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.

Usually StarRocks 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.

Here we'll show how to use the StarRocks Vector Store.

Setup​

#!pip install 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

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import StarRocks
from langchain.vectorstores.starrocks import StarRocksSettings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter, TokenTextSplitter
from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import DirectoryLoader
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader, UnstructuredMarkdownLoader

update_vectordb = False

API Reference:

    /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 "

Load docs and split them into tokens​

Load all markdown files under the docs directory

for starrocks documents, you can clone repo from https://github.com/StarRocks/starrocks, 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]
    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

Create vectordb instance​

Use StarRocks as vectordb​

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

Convert tokens into embeddings and put them into vectordb​

Here we use StarRocks as vectordb, you can configure StarRocks instance via StarRocksSettings.

Configuring StarRocks 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')
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]


    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                   |
    ----------------------------------------------------------------------------

Build QA and ask question to it​

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;`