ScaNN

ScaNN (Scalable Nearest Neighbors) 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，同时也支持欧氏距离等其他距离函数。该实现针对支持 AVX2 的 x86 处理器进行了优化。更多详情请参见其 Google Research github。

您需要安装 langchain-community 才能使用此集成，请运行 pip install -qU langchain-community。

安装

通过 pip 安装 ScaNN。或者，您可以遵循 ScaNN 网站 上的说明从源代码进行安装。

%pip install --upgrade --quiet  scann

##检索演示

下面我们展示如何将 ScaNN 与 Huggingface Embeddings 结合使用。

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)


model_name = "sentence-transformers/all-mpnet-base-v2"
embeddings = HuggingFaceEmbeddings(model_name=model_name)

db = ScaNN.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)

docs[0]

API Reference:TextLoader | ScaNN | HuggingFaceEmbeddings | CharacterTextSplitter

RetrievalQA 演示

接下来，我们将演示如何结合使用 ScaNN 和 Google PaLM API。

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}),
)

API Reference:RetrievalQA | ChatGooglePalm

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.

保存及加载本地检索索引

db.save_local("/tmp/db", "state_of_union")
restored_db = ScaNN.load_local("/tmp/db", embeddings, index_name="state_of_union")

Related

• Vector store conceptual guide
• Vector store how-to guides