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 ‚Äî 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‚Äã#!pip install pymysqlSet update_vectordb = False at the beginning. If there is no docs updated, then we don't need to rebuild the embeddings of docsfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import StarRocksfrom langchain.vectorstores.starrocks import StarRocksSettingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitter, TokenTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import VectorDBQAfrom langchain.document_loaders import DirectoryLoaderfrom langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoader, UnstructuredMarkdownLoaderupdate_vectordb = False    /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‚ÄãLoad all markdown files under the docs directoryfor 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 texttext_splitter = TokenTextSplitter(chunk_size=400,
StarRocks is a High-Performance Analytical Database.
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 ‚Äî 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‚Äã#!pip install pymysqlSet update_vectordb = False at the beginning. If there is no docs updated, then we don't need to rebuild the embeddings of docsfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import StarRocksfrom langchain.vectorstores.starrocks import StarRocksSettingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitter, TokenTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import VectorDBQAfrom langchain.document_loaders import DirectoryLoaderfrom langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoader, UnstructuredMarkdownLoaderupdate_vectordb = False    /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‚ÄãLoad all markdown files under the docs directoryfor 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 texttext_splitter = TokenTextSplitter(chunk_size=400,