如何使用 Parent Document Retriever

在拆分文档进行检索时，经常存在相互冲突的愿望：

1. 您可能希望拥有小文档，以便它们的嵌入可以最 准确地反映了它们的含义。如果太长，则嵌入向量可以 失去意义。
2. 您希望拥有足够长的文档，每个块的上下文为 保留。

这ParentDocumentRetriever通过拆分和存储来取得平衡 小块数据。在检索过程中，它首先获取小块 但随后查找这些块的父 ID 并返回那些较大的 文件。

请注意，“父文档”是指一小块 源于。这可以是整个原始文档，也可以是更大的 块。

from langchain.retrievers import ParentDocumentRetriever

API 参考：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

API 参考：InMemoryStore | 文本加载器 | OpenAI 嵌入 | 递归角色文本拆分器

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，我们想要检索完整的文档。因此，我们只指定一个子 splitter。

# This text splitter is used to create the child documents
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# The vectorstore to use to index the child chunks
vectorstore = Chroma(
    collection_name="full_documents", embedding_function=OpenAIEmbeddings()
)
# The storage layer for the parent documents
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())

['9a63376c-58cc-42c9-b0f7-61f0e1a3a688',
 '40091598-e918-4a18-9be0-f46413a95ae4']

现在让我们调用向量存储搜索功能 - 我们应该看到它返回小块（因为我们存储的是小块）。

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.

现在，让我们从整个检索器中检索。这应该返回大型文档 - 因为它返回较小数据块所在的文档。

retrieved_docs = retriever.invoke("justice breyer")

len(retrieved_docs[0].page_content)

38540

检索较大的块

有时，完整的文档可能太大，以至于无法按原样检索它们。在这种情况下，我们真正想做的是首先将原始文档拆分为较大的块，然后将其拆分为较小的块。然后，我们索引较小的块，但在检索时，我们检索较大的块（但仍然不是完整的文档）。

# This text splitter is used to create the parent documents
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
# This text splitter is used to create the child documents
# It should create documents smaller than the parent
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# The vectorstore to use to index the child chunks
vectorstore = Chroma(
    collection_name="split_parents", embedding_function=OpenAIEmbeddings()
)
# The storage layer for the parent documents
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不止两个文档 - 这些是更大的块。

len(list(store.yield_keys()))

66

让我们确保底层 vector store 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)

1849

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.