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Tair

Tair 是由 Alibaba Cloud 开发的云原生内存数据库服务。 它提供丰富的数据模型和企业级能力,以支持您的实时在线场景,同时保持与开源 Redis 的完全兼容。Tair 还引入了基于新型非易失性存储介质 (NVM) 的持久内存优化实例。

本笔记本展示了如何使用与 Tair 向量数据库相关的功能。

您需要使用 pip install -qU langchain-community 安装 langchain-community 才能使用此集成。

要运行,您应该有一个正在运行的 Tair 实例。

<!--IMPORTS:[{"imported": "FakeEmbeddings", "source": "langchain_community.embeddings.fake", "docs": "https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.fake.FakeEmbeddings.html", "title": "Tair"}, {"imported": "Tair", "source": "langchain_community.vectorstores", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.tair.Tair.html", "title": "Tair"}, {"imported": "CharacterTextSplitter", "source": "langchain_text_splitters", "docs": "https://python.langchain.com/api_reference/text_splitters/character/langchain_text_splitters.character.CharacterTextSplitter.html", "title": "Tair"}]-->
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import Tair
from langchain_text_splitters import CharacterTextSplitter
<!--IMPORTS:[{"imported": "TextLoader", "source": "langchain_community.document_loaders", "docs": "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.text.TextLoader.html", "title": "Tair"}]-->
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)

使用 TAIR_URL 环境变量连接到 Tair

export TAIR_URL="redis://{username}:{password}@{tair_address}:{tair_port}"

或关键字参数 tair_url

然后将文档和嵌入存储到 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)

查询相似文档。

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

Tair 混合搜索索引构建

# 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 混合搜索

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]

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