如何使用父文档检索器

在将文档拆分以用于检索时，通常存在相互冲突的需求：

您可能希望文档较小，以便它们的嵌入能最准确地反映其含义。如果太长，嵌入可能会失去意义。
您希望文档足够长，以便保留每个分块的上下文。

The ParentDocumentRetriever 通过分割和存储小块数据来平衡。在检索过程中，它首先获取这些小块，然后查找这些块的父级 id 并返回那些更大的文档。

请注意，"父文档"指的是一个小块所源自的文档。这可以是整个原始文档，也可以是一个较大的块。

from langchain.retrievers import ParentDocumentRetriever

API 参考:父文档检索器

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:内存存储 |文本加载器 |OpenAI嵌入 |递归字符文本分割器

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

在这种模式下，我们想要检索完整的文档。因此，我们只指定了一个子分割器。

# 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']

让我们现在调用向量存储搜索功能——我们应该会看到它返回的是小块内容（因为我们存储的是小块内容）。

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.

现在让我们从整体检索器中检索。这应该返回大型文档——因为它返回包含较小分块的文档。

retrieved_docs = retriever.invoke("justice breyer")

len(retrieved_docs[0].page_content)

检索更大的块

有时，完整文档可能过大而不适合直接检索。在这种情况下，我们真正想要做的是先将原始文档拆分为较大的块，然后再将其拆分为较小的块。接着我们对这些较小的块进行索引，但在检索时却检索较大的块（但仍不是完整文档）。

# 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)

我们现在可以看到文档数量远不止两个——这些是更大的分块。

len(list(store.yield_keys()))

让我们确保底层的向量存储仍然能检索到小块内容。

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.

检索完整文档​

检索更大的块​

检索完整文档

检索更大的块