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分类目录：《自然语言处理从入门到应用》总目录
Vectorstores是构建索引的最重要组件之一。本文展示了与VectorStores相关的基本功能。在使用VectorStores时，创建要放入其中的向量是一个关键部分，通常通过嵌入来创建。
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chromawith open('../../state_of_the_union.txt') as f:state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_texts(texts, embeddings)query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
日志输出：
Running Chroma using direct local API. Using DuckDB in-memory for database. Data will be transient.
输入：
print(docs[0].page_content)
输出：
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. We cannot let this happen. 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.
添加文本
我们可以使用add_texts方法轻松地将文本添加到VectorStore中。它将返回一个文档ID的列表（以防我们需要在下游使用它们）。
docsearch.add_texts(["Ankush went to Princeton"])
输出：
['a05e3d0c-ab40-11ed-a853-e65801318981']
输入：
query = "Where did Ankush go to college?"
docs = docsearch.similarity_search(query)
docs[0]
Document(page_content='Ankush went to Princeton', lookup_str='', metadata={}, lookup_index=0)
从文档初始化
我们还可以直接从文档初始化一个Vectorstore。当我们在文本分割器上使用该方法直接获取文档时，这非常有用（当原始文档具有相关联的元数据时非常方便）。
documents = text_splitter.create_documents([state_of_the_union], metadatas=[{"source": "State of the Union"}])
docsearch = Chroma.from_documents(documents, embeddings)query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
日志输出：
Running Chroma using direct local API. Using DuckDB in-memory for database. Data will be transient.
输入：
print(docs[0].page_content)
输出：
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. We cannot let this happen. 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.
参考文献：
[1] LangChain官方网站：https://www.langchain.com/
[2] LangChain 🦜️🔗 中文网，跟着LangChain一起学LLM/GPT开发：https://www.langchain.com.cn/
[3] LangChain中文网 - LangChain 是一个用于开发由语言模型驱动的应用程序的框架：http://www.cnlangchain.com/