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如何使用上下文压缩进行检索

检索的一个挑战是，您通常不知道在将数据引入系统时，文档存储系统将面临哪些具体查询。这意味着与查询最相关的信息可能隐藏在包含大量不相关文本的文档中。通过应用程序传递该完整文档可能会导致更昂贵的 LLM 调用和更差的响应。

上下文压缩旨在解决这个问题。这个想法很简单：您可以使用给定查询的上下文来压缩它们，以便仅返回相关信息，而不是立即按原样返回检索到的文档。这里的 “压缩” 是指压缩单个文档的内容和批量过滤掉文档。

要使用上下文压缩检索器，您需要：

Contextual Compression Retriever 将查询传递给基本检索器，获取初始文档并通过 Document Compressor 传递它们。Document Compressor 获取文档列表，并通过减少文档内容或完全删除文档来缩短它。

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)]
        )
    )

使用原版矢量存储检索器

让我们首先初始化一个简单的向量存储检索器并存储 2023 年国情咨文演讲（以块为单位）。我们可以看到，给定一个示例问题，我们的检索器返回一两个相关文档和一些不相关的文档。甚至相关的文档也有很多不相关的信息。

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LLMChainExtractor​

现在让我们用ContextualCompressionRetriever.我们将添加一个LLMChainExtractor，它将迭代最初返回的文档，并仅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：滤波器

LLMChainFilter​

这LLMChainFilter是稍微简单但更强大的压缩器，它使用 LLM 链来决定要过滤掉哪些最初检索的文档，以及要返回哪些文档，而无需处理文档内容。

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​

LLMListwiseRerank 使用零样本列表文档重新排序，其功能类似于LLMChainFilter作为一种强大但更昂贵的选择。建议使用更强大的 LLM。

请注意，LLMListwiseRerank需要实现 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)
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进行额外的 LLM 调用既昂贵又缓慢。这EmbeddingsFilter通过嵌入 documents 和 query，并且仅返回那些与查询具有足够相似嵌入的文档，提供了一种更便宜、更快速的选项。

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)
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.

将压缩机和文档变压器串在一起

使用DocumentCompressorPipeline我们还可以轻松地按顺序组合多个压缩器。除了压缩器，我们还可以添加BaseDocumentTransformers 添加到我们的管道中，它不执行任何上下文压缩，而只是对一组文档执行一些转换。例如TextSplitter可以用作文档转换器，将文档拆分为更小的部分，而EmbeddingsRedundantFilter可用于根据文档之间的嵌入相似性筛选掉多余的文档。

下面我们创建一个压缩器管道，首先将文档拆分为较小的块，然后删除多余的文档，然后根据与查询的相关性进行筛选。

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