ScaNN
ScaNN（可扩展最近邻）是一种用于大规模高效向量相似性搜索的方法。
ScaNN 包括用于最大内积搜索的搜索空间剪枝和量化，并且支持其他距离函数，例如欧几里得距离。该实现经过优化，适用于支持 AVX2 的 x86 处理器。有关更多详细信息，请参阅其 Google Research github。
您需要使用 pip install -qU langchain-community 安装 langchain-community 才能使用此集成。
安装
通过 pip 安装 ScaNN。或者，您可以按照 ScaNN 网站 上的说明从源代码安装。
%pip install --upgrade --quiet scann
检索演示
以下是如何将 ScaNN 与 Huggingface Embeddings 结合使用的示例。
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import ScaNN
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings()
db = ScaNN.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
docs[0]
RetrievalQA 演示
接下来，我们演示如何将 ScaNN 与 Google PaLM API 结合使用。
您可以从 https://developers.generativeai.google/tutorials/setup 获取 API 密钥。
from langchain.chains import RetrievalQA
from langchain_community.chat_models.google_palm import ChatGooglePalm
palm_client = ChatGooglePalm(google_api_key="YOUR_GOOGLE_PALM_API_KEY")
qa = RetrievalQA.from_chain_type(
llm=palm_client,
chain_type="stuff",
retriever=db.as_retriever(search_kwargs={"k": 10}),
)
print(qa.run("What did the president say about Ketanji Brown Jackson?"))
The president said that Ketanji Brown Jackson is one of our nation's top legal minds, who will continue Justice Breyer's legacy of excellence.
print(qa.run("What did the president say about Michael Phelps?"))
The president did not mention Michael Phelps in his speech.
保存和加载本地检索索引
db.save_local("/tmp/db", "state_of_union")
restored_db = ScaNN.load_local("/tmp/db", embeddings, index_name="state_of_union")