百度向量数据库
百度向量数据库 是一个强大的企业级分布式数据库服务，由百度智能云精心开发并全面管理。它以卓越的多维向量数据存储、检索和分析能力而脱颖而出。VectorDB 的核心基于百度自主研发的 "Mochow" 向量数据库内核，确保高性能、高可用性和安全性，同时具备出色的可扩展性和用户友好性。
该数据库服务支持多种索引类型和相似性计算方法，满足各种使用场景。VectorDB 的一大亮点是其管理高达 100 亿的巨大向量规模的能力，同时保持出色的查询性能，支持每秒数百万次查询 (QPS)，查询延迟在毫秒级别。
您需要使用 pip install -qU langchain-community 安装 langchain-community 才能使用此集成。
本笔记本展示了如何使用与百度向量数据库相关的功能。
要运行，您应该拥有一个 数据库实例。。
!pip3 install pymochow
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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
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../how_to/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
