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scikit-learn 是一个开源机器学习算法集合,其中包含 k 近邻 的一些实现。SKLearnVectorStore 封装了这一实现,并增加了将向量存储以 json、bson(二进制 json)或 Apache Parquet 格式持久化的功能。
本 notebook 展示了如何使用 SKLearnVectorStore 向量数据库。 使用此集成需要通过 pip install -qU langchain-community 安装 langchain-community
pip install -qU  scikit-learn

# # if you plan to use bson serialization, install also:
pip install -qU  bson

# # if you plan to use parquet serialization, install also:
pip install -qU  pandas pyarrow
要使用 OpenAI 嵌入,您需要一个 OpenAI API 密钥。可以在 platform.openai.com/account/api-keys 获取,也可以使用任何其他嵌入模型。
import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass("Enter your OpenAI key:")

基本用法

加载示例文档语料库

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import SKLearnVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

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 = OpenAIEmbeddings()

创建 SKLearnVectorStore,索引文档语料库并运行示例查询

import tempfile

persist_path = os.path.join(tempfile.gettempdir(), "union.parquet")

vector_store = SKLearnVectorStore.from_documents(
    documents=docs,
    embedding=embeddings,
    persist_path=persist_path,  # persist_path and serializer are optional
    serializer="parquet",
)

query = "What did the president say about Ketanji Brown Jackson"
docs = vector_store.similarity_search(query)
print(docs[0].page_content)
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you're at it, pass the Disclose Act so Americans can know who is funding our elections.

Tonight, I'd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation's top legal minds, who will continue Justice Breyer's legacy of excellence.

保存和加载向量存储

vector_store.persist()
print("Vector store was persisted to", persist_path)
Vector store was persisted to /var/folders/6r/wc15p6m13nl_nl_n_xfqpc5c0000gp/T/union.parquet
vector_store2 = SKLearnVectorStore(
    embedding=embeddings, persist_path=persist_path, serializer="parquet"
)
print("A new instance of vector store was loaded from", persist_path)
A new instance of vector store was loaded from /var/folders/6r/wc15p6m13nl_nl_n_xfqpc5c0000gp/T/union.parquet
docs = vector_store2.similarity_search(query)
print(docs[0].page_content)
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you're at it, pass the Disclose Act so Americans can know who is funding our elections.

Tonight, I'd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation's top legal minds, who will continue Justice Breyer's legacy of excellence.

清理

os.remove(persist_path)