如何使用上下文压缩进行检索
使用检索的一个挑战是，当你将数据导入系统时，通常无法预知文档存储系统将面临的具体查询。这意味着与查询最相关的信息可能被埋藏在包含大量无关文本的文档中。将整个文档传递给你的应用程序可能导致更昂贵的大型语言模型调用以及更差的响应效果。
上下文压缩旨在解决这一问题。其思路很简单：在立即返回检索到的文档之前，可以利用给定查询的上下文对文档进行压缩，从而仅返回相关的信息。“压缩”在此既指压缩单个文档的内容，也指整体过滤掉无关的文档。
要使用上下文压缩检索器，您需要：
- 一个基础 检索器
- 文档压缩器
上下文压缩检索器将查询传递给基础检索器，获取初始文档后，再将这些文档送入文档压缩器。文档压缩器接收一个文档列表，并通过缩减文档内容或直接删除文档来缩短列表。
开始使用
# Helper function for printing docs
def pretty_print_docs(docs):
print(
f"\n{'-' * 100}\n".join(
[f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)]
)
)
使用原始向量存储检索器
让我们首先初始化一个简单的向量存储检索器，并将2023年国情咨文演讲内容（以片段形式）存储进去。我们可以看到，对于一个示例问题，检索器会返回一到两个相关文档，以及一些不相关的文档。而且，即使这些相关文档中也包含大量无关信息。
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
documents = TextLoader("state_of_the_union.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever()
docs = retriever.invoke("What did the president say about Ketanji Brown Jackson")
pretty_print_docs(docs)
Document 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.
----------------------------------------------------------------------------------------------------
Document 2:
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.
----------------------------------------------------------------------------------------------------
Document 3:
And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong.
As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential.
While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.
And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things.
So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.
First, beat the opioid epidemic.
----------------------------------------------------------------------------------------------------
Document 4:
Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers.
And as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.
That ends on my watch.
Medicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect.
We’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees.
Let’s pass the Paycheck Fairness Act and paid leave.
Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty.
Let’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.
使用上下文压缩功能，搭配 LLMChainExtractor
现在，让我们用一个 ContextualCompressionRetriever 包装我们的基础检索器。我们将添加一个 LLMChainExtractor，它将遍历最初返回的文档，并从每个文档中提取与查询相关的内容。
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
compressor = LLMChainExtractor.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=retriever
)
compressed_docs = compression_retriever.invoke(
"What did the president say about Ketanji Jackson Brown"
)
pretty_print_docs(compressed_docs)
Document 1:
I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson.
更多内置压缩器：过滤器
LLMChainFilter
LLMChainFilter 略微简单一些，但更稳健，它使用大型语言模型（LLM）链来决定应过滤掉哪些最初检索到的文档以及应返回哪些文档，而无需修改文档内容。
from langchain.retrievers.document_compressors import LLMChainFilter
_filter = LLMChainFilter.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(
base_compressor=_filter, base_retriever=retriever
)
compressed_docs = compression_retriever.invoke(
"What did the president say about Ketanji Jackson Brown"
)
pretty_print_docs(compressed_docs)
API 参考：LLMChainFilter
Document 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.
LLMListwiseRerank
LLM列表级重排序 使用 零样本列表级文档重排序，其功能类似于 LLMChainFilter，是一种更稳健但成本更高的选择。建议使用性能更强的大型语言模型。
注意，LLMListwiseRerank 需要一个实现了 with_structured_output 方法的模型。
from langchain.retrievers.document_compressors import LLMListwiseRerank
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
_filter = LLMListwiseRerank.from_llm(llm, top_n=1)
compression_retriever = ContextualCompressionRetriever(
base_compressor=_filter, base_retriever=retriever
)
compressed_docs = compression_retriever.invoke(
"What did the president say about Ketanji Jackson Brown"
)
pretty_print_docs(compressed_docs)
API 参考：LLMListwiseRerank | ChatOpenAI
Document 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.
EmbeddingsFilter
对每个检索到的文档额外调用一次大型语言模型（LLM）成本高昂且速度慢。EmbeddingsFilter 通过嵌入文档和查询，仅返回与查询嵌入足够相似的文档，提供了一种更经济、更快的替代方案。
from langchain.retrievers.document_compressors import EmbeddingsFilter
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
embeddings_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)
compression_retriever = ContextualCompressionRetriever(
base_compressor=embeddings_filter, base_retriever=retriever
)
compressed_docs = compression_retriever.invoke(
"What did the president say about Ketanji Jackson Brown"
)
pretty_print_docs(compressed_docs)
API 参考：EmbeddingsFilter | OpenAIEmbeddings
Document 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.
----------------------------------------------------------------------------------------------------
Document 2:
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.
将压缩器和文档转换器串联在一起
使用 DocumentCompressorPipeline 我们也可以轻松地按顺序组合多个压缩器。除了压缩器，我们还可以在管道中添加 BaseDocumentTransformer，它们不会执行任何上下文压缩，而只是对一组文档进行某种转换。例如，TextSplitter 可用作文档转换器，将文档拆分为更小的片段，而 EmbeddingsRedundantFilter 可用于根据文档之间的嵌入相似性过滤掉冗余文档。
下面我们通过以下步骤创建一个压缩管道：首先将文档拆分为更小的片段，然后去除重复的文档，最后根据与查询的相关性进行过滤。
from langchain.retrievers.document_compressors import DocumentCompressorPipeline
from langchain_community.document_transformers import EmbeddingsRedundantFilter
from langchain_text_splitters import CharacterTextSplitter
splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0, separator=". ")
redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)
pipeline_compressor = DocumentCompressorPipeline(
transformers=[splitter, redundant_filter, relevant_filter]
)
compression_retriever = ContextualCompressionRetriever(
base_compressor=pipeline_compressor, base_retriever=retriever
)
compressed_docs = compression_retriever.invoke(
"What did the president say about Ketanji Jackson Brown"
)
pretty_print_docs(compressed_docs)
Document 1:
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
----------------------------------------------------------------------------------------------------
Document 2:
As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential.
While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year
----------------------------------------------------------------------------------------------------
Document 3:
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
----------------------------------------------------------------------------------------------------
Document 4:
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