Tair is a cloud native in-memory database service developed by Alibaba Cloud. It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open-source Redis. Tair also introduces persistent memory-optimized instances that are based on the new non-volatile memory (NVM) storage medium.
This notebook shows how to use functionality related to the Tair vector database. You’ll need to install langchain-community with pip install -qU langchain-community to use this integration To run, you should have a Tair instance up and running. 
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import Tair
from langchain_text_splitters import CharacterTextSplitter

from langchain_community.document_loaders import TextLoader

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)
Connect to Tair using the TAIR_URL environment variable 
export TAIR_URL="redis://{username}:{password}@{tair_address}:{tair_port}"
or the keyword argument tair_url. Then store documents and embeddings into Tair. 
tair_url = "redis://localhost:6379"

# drop first if index already exists
Tair.drop_index(tair_url=tair_url)

vector_store = Tair.from_documents(docs, embeddings, tair_url=tair_url)
Query similar documents. 
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_store.similarity_search(query)
docs[0]
Tair Hybrid Search Index build 
# drop first if index already exists
Tair.drop_index(tair_url=tair_url)

vector_store = Tair.from_documents(
    docs, embeddings, tair_url=tair_url, index_params={"lexical_algorithm": "bm25"}
)
Tair Hybrid Search 
query = "What did the president say about Ketanji Brown Jackson"
# hybrid_ratio: 0.5 hybrid search, 0.9999 vector search, 0.0001 text search
kwargs = {"TEXT": query, "hybrid_ratio": 0.5}
docs = vector_store.similarity_search(query, **kwargs)
docs[0]