ScaNN
ScaNN（可擴展最近鄰演算法）是一種用於大規模高效向量相似度搜尋的方法。
ScaNN 包含搜尋空間剪枝和量化功能，以用於最大內積搜尋，同時也支援其他距離函數，例如歐幾里得距離。此實作針對支援 AVX2 的 x86 處理器進行了最佳化。請參閱其 Google Research github 以取得更多詳細資訊。
您需要使用 `pip install -qU langchain-community` 安裝 `langchain-community` 才能使用此整合
安裝
透過 pip 安裝 ScaNN。或者，您可以按照 ScaNN 網站上的指示從原始碼安裝。
%pip install --upgrade --quiet scann
檢索示範
以下我們展示如何將 ScaNN 與 Huggingface Embeddings 結合使用。
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]
檢索問答示範
接下來，我們示範如何將 ScaNN 與 Google PaLM API 結合使用。
您可以從 https://developers.generativeai.google/tutorials/setup 取得 API 金鑰
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 參考文件：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.
儲存和載入本地檢索索引
db.save_local("/tmp/db", "state_of_union")
restored_db = ScaNN.load_local("/tmp/db", embeddings, index_name="state_of_union")