向量存储
info
请参阅集成，了解与第三方向量存储的内置集成的文档。
存储和搜索非结构化数据的最常见方法之一是将其嵌入并存储生成的嵌入向量，然后在查询时嵌入非结构化查询并检索与嵌入查询“最相似”的嵌入向量。向量存储负责存储嵌入数据并为您执行向量搜索。
入门
本演示介绍与向量存储相关的基本功能。使用向量存储的关键部分是创建要放入其中的向量，通常通过嵌入来创建。因此，建议在深入研究之前先熟悉文本嵌入模型接口。
本教程使用 FAISS 向量数据库，该数据库利用了 Facebook AI Similarity Search（FAISS）库。
pip install faiss-cpu
我们想要使用 OpenAIEmbeddings，所以我们需要获取 OpenAI API 密钥。
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
raw_documents = TextLoader('../../../state_of_the_union.txt').load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
db = FAISS.from_documents(documents, OpenAIEmbeddings())
Similarity search
query = "What did the president say about Ketanji Brown Jackson"
docs = db.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.
通过向量进行相似性搜索 (Similarity search by vector)
还可以使用 similarity_search_by_vector 来搜索与给定嵌入向量相似的文档，该函数接受一个嵌入向量作为参数，而不是一个字符串。
embedding_vector = embeddings.embed_query(query)
docs = db.similarity_search_by_vector(embedding_vector)
异步操作
向量存储通常作为一个独立的服务运行，需要进行一些IO操作，因此可能会被异步调用。这样可以提高性能，因为您不必浪费时间等待外部服务的响应。如果您使用的是异步框架，如FastAPI，这可能也很重要。
LangChain支持对向量存储的异步操作。所有的方法都可以使用它们的异步对应方法调用，前缀为a，表示async。
Qdrant是一个向量存储，支持所有的异步操作，因此在本教程中将使用它。
pip install qdrant-client
from langchain.vectorstores import Qdrant
异步创建向量存储
db = await Qdrant.afrom_documents(documents, embeddings, "http://localhost:6333")
相似度搜索
query = "What did the president say about Ketanji Brown Jackson"
docs = await db.asimilarity_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.
通过向量进行相似度搜索
embedding_vector = embeddings.embed_query(query)
docs = await db.asimilarity_search_by_vector(embedding_vector)
最大边际相关搜索 (MMR)
最大边际相关搜索优化了与查询的相似度和所选文档之间的多样性。也支持异步API。
query = "What did the president say about Ketanji Brown Jackson"
found_docs = await qdrant.amax_marginal_relevance_search(query, k=2, fetch_k=10)
for i, doc in enumerate(found_docs):
print(f"{i + 1}.", doc.page_content, "\n")
1. 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.
2. We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together.
I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera.
They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun.
Officer Mora was 27 years old.
Officer Rivera was 22.
Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers.
I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.
I’ve worked on these issues a long time.
I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.