父文档检索器

在拆分文档以进行检索时，通常存在相互矛盾的需求：

您可能希望拥有小型文档，以便它们的嵌入可以最准确地反映其含义。如果太长，嵌入可能会失去含义。
您希望有足够长的文档，以保留每个块的上下文。

ParentDocumentRetriever 通过拆分和存储小型数据块来实现这种平衡。在检索过程中，它首先获取小块，然后查找这些块的父 ID 并返回那些较大的文档。

请注意，“父文档”指的是小块来源的文档。这可以是整个原始文档，也可以是较大的块。

from langchain.retrievers import ParentDocumentRetriever

from langchain.storage import InMemoryStore
from langchain_chroma import Chroma
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

loaders = [
    TextLoader("../../paul_graham_essay.txt"),
    TextLoader("../../state_of_the_union.txt"),
]
docs = []
for loader in loaders:
    docs.extend(loader.load())

检索完整文档

在此模式下，我们希望检索完整文档。因此，我们只指定一个子分割器。

# 此文本分割器用于创建子文档
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# 用于索引子块的向量存储
vectorstore = Chroma(
    collection_name="full_documents", embedding_function=OpenAIEmbeddings()
)
# 父文档的存储层
store = InMemoryStore()
retriever = ParentDocumentRetriever(
    vectorstore=vectorstore,
    docstore=store,
    child_splitter=child_splitter,
)

retriever.add_documents(docs, ids=None)

这应该会产生两个键，因为我们添加了两个文档。

list(store.yield_keys())

['cfdf4af7-51f2-4ea3-8166-5be208efa040',
 'bf213c21-cc66-4208-8a72-733d030187e6']

让我们现在调用向量存储搜索功能 - 我们应该看到它返回小块（因为我们存储了小块）。

sub_docs = vectorstore.similarity_search("justice breyer")

print(sub_docs[0].page_content)

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.

让我们现在从整体检索器中检索。这应该返回大文档 - 因为它返回了包含小块的文档。

retrieved_docs = retriever.invoke("justice breyer")

len(retrieved_docs[0].page_content)

检索较大块

有时，完整文档可能太大，因此不希望按原样检索它们。在这种情况下，我们真正想要做的是首先将原始文档拆分为较大块，然后将其拆分为较小块。然后我们对小块进行索引，但在检索时我们检索较大块（但仍然不是完整文档）。

# 用于创建父文档的文本分割器
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
# 用于创建子文档的文本分割器
# 它应该创建比父文档小的文档
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# 用于索引子块的向量存储
vectorstore = Chroma(
    collection_name="split_parents", embedding_function=OpenAIEmbeddings()
)
# 父文档的存储层
store = InMemoryStore()

retriever = ParentDocumentRetriever(
    vectorstore=vectorstore,
    docstore=store,
    child_splitter=child_splitter,
    parent_splitter=parent_splitter,
)

retriever.add_documents(docs)

我们可以看到现在有远远不止两个文档 - 这些是较大的块。

len(list(store.yield_keys()))

让我们确保底层向量存储仍然检索小块。

sub_docs = vectorstore.similarity_search("justice breyer")

print(sub_docs[0].page_content)

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.

retrieved_docs = retriever.invoke("justice breyer")

len(retrieved_docs[0].page_content)

print(retrieved_docs[0].page_content)

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.

A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.

And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.

We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.

We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.

We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.

We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.

多向量（MultiVector）自查询（SelfQuery）