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ScaNN

ScaNN（可扩展最近邻）是一种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修剪和量化，还支持其他距离函数，例如欧几里得距离。该实现针对支持 AVX2 的 x86 处理器进行了优化。有关更多详细信息，请参阅其 Google Research github。

您需要安装langchain-community跟pip install -qU langchain-community使用此集成

安装

通过 pip 安装 ScaNN。或者，您可以按照 ScaNN 网站上的说明从源进行安装。

%pip install --upgrade --quiet  scann

检索演示

下面我们展示了如何将 ScaNN 与 Huggingface 嵌入结合使用。

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]

RetrievalQA 演示

接下来，我们将演示如何将 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")