Baidu VectorDB

Baidu VectorDB is a robust, enterprise-level distributed database service, meticulously developed and fully managed by Baidu Intelligent Cloud. It stands out for its exceptional ability to store, retrieve, and analyze multi-dimensional vector data. At its core, VectorDB operates on Baidu's proprietary "Mochow" vector database kernel, which ensures high performance, availability, and security, alongside remarkable scalability and user-friendliness.

This database service supports a diverse range of index types and similarity calculation methods, catering to various use cases. A standout feature of VectorDB is its capacity to manage an immense vector scale of up to 10 billion, while maintaining impressive query performance, supporting millions of queries per second (QPS) with millisecond-level query latency.

This notebook shows how to use functionality related to the Baidu VectorDB.

To run, you should have a Database instance..

!pip3 install pymochow

from langchain.text_splitter import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import BaiduVectorDB
from langchain_community.vectorstores.baiduvectordb import ConnectionParams

loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = FakeEmbeddings(size=128)

conn_params = ConnectionParams(
    endpoint="http://192.168.xx.xx:xxxx", account="root", api_key="****"
)

vector_db = BaiduVectorDB.from_documents(
    docs, embeddings, connection_params=conn_params, drop_old=True
)

query = "What did the president say about Ketanji Brown Jackson"
docs = vector_db.similarity_search(query)
docs[0].page_content

vector_db = BaiduVectorDB(embeddings, conn_params)
vector_db.add_texts(["Ankush went to Princeton"])
query = "Where did Ankush go to college?"
docs = vector_db.max_marginal_relevance_search(query)
docs[0].page_content