%pip install --upgrade --quiet  cohere

from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_cohere import CohereRerank
from langchain_community.llms import Cohere
from langchain.chains import RetrievalQA

llm = Cohere(temperature=0)
compressor = CohereRerank(model="rerank-english-v3.0")
compression_retriever = ContextualCompressionRetriever(
   base_compressor=compressor, base_retriever=retriever
)
chain = RetrievalQA.from_chain_type(
   llm=Cohere(temperature=0), retriever=compression_retriever
)
# 假設(shè) retriever 已經(jīng)定義好
# chain.invoke({'query': 'What did the president say about Ketanji Brown Jackson'})

{'query': 'What did the president say about Ketanji Brown Jackson',
'result': " The president speaks highly of Ketanji Brown Jackson, stating that she
 is one of the nation's top legal minds, and will continue the legacy of excellence
 of Justice Breyer. The president also mentions that he worked with her family and
 that she comes from a family of public school educators and police officers. Since
 her nomination, she has received support from various groups, including the
 Fraternal Order of Police and judges from both major political parties. \n\nWould
 you like me to extract another sentence from the provided text? "}

from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CrossEncoderReranker
from langchain_community.cross_encoders import HuggingFaceCrossEncoder

# 假設(shè) retriever 已經(jīng)定義好
model = HuggingFaceCrossEncoder(model_name="BAAI/bge-reranker-base")
compressor = CrossEncoderReranker(model=model, top_n=3)
compression_retriever = ContextualCompressionRetriever(
   base_compressor=compressor, base_retriever=retriever
)

# compressed_docs = compression_retriever.invoke("What is the plan for the economy?")
# pretty_print_docs(compressed_docs)

Document 1:
More infrastructure and innovation in America.
More goods moving faster and cheaper in America.
More jobs where you can earn a good living in America.
And instead of relying on foreign supply chains, let’s make it in America.
Economists call it “increasing the productive capacity of our economy.”
I call it building a better America.
My plan to fight inflation will lower your costs and lower the deficit.

----------------------------------------------------------------------------------------------------

Document 2:

Second – cut energy costs for families an average of $500 a year by combatting
climate change.

Let’s provide investments and tax credits to weatherize your homes and businesses to
be energy efficient and you get a tax credit; double America’s clean energy
production in solar, wind, and so much more;  lower the price of electric vehicles,
saving you another $80 a month because you’ll never have to pay at the gas pump
again.

----------------------------------------------------------------------------------------------------

Document 3:

Look at cars.
Last year, there weren’t enough semiconductors to make all the cars that people
wanted to buy.
And guess what, prices of automobiles went up.
So—we have a choice.
One way to fight inflation is to drive down wages and make Americans poorer.
I have a better plan to fight inflation.
Lower your costs, not your wages.
Make more cars and semiconductors in America.
More infrastructure and innovation in America.
More goods moving faster and cheaper in America.

%pip install --upgrade --quiet  voyageai
%pip install --upgrade --quiet  langchain-voyageai

import os
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain.retrievers import ContextualCompressionRetriever
from langchain_openai import OpenAI
from langchain_voyageai import VoyageAIRerank
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_voyageai import VoyageAIEmbeddings

# 假設(shè) State of the Union 文本文件在正確路徑
# documents = TextLoader("../../how_to/state_of_the_union.txt").load()
# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
# texts = text_splitter.split_documents(documents)
# retriever = FAISS.from_documents(
#    texts, VoyageAIEmbeddings(model="voyage-law-2")
# ).as_retriever(search_kwargs={"k": 20})

# llm = OpenAI(temperature=0)
# compressor = VoyageAIRerank(
# model="rerank-lite-1", voyageai_api_key=os.environ["VOYAGE_API_KEY"], top_k=3
# )
# 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:

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:

So let’s not abandon our streets. Or choose between safety and equal justice.
Let’s come together to protect our communities, restore trust, and hold law
enforcement accountable.
That’s why the Justice Department required body cameras, banned chokeholds, and
restricted no-knock warrants for its officers.

----------------------------------------------------------------------------------------------------

Document 3:

I spoke with their families and told them that we are forever in debt for their
sacrifice, and we will carry on their mission to restore the trust and safety every
community deserves.

I’ve worked on these issues a long time.

I know what works: Investing in crime prevention and community police officers
who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and
safety.

So let’s not abandon our streets. Or choose between safety and equal justice.

from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import JinaEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter

# 假設(shè) State of the Union 文本文件在正確路徑
# documents = TextLoader(
#    "../../how_to/state_of_the_union.txt",
# ).load()
# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
# texts = text_splitter.split_documents(documents)

# embedding = JinaEmbeddings(model_name="jina-embeddings-v2-base-en")
# retriever = FAISS.from_documents(texts, embedding).as_retriever(search_kwargs={"k": 20})

# query = "What did the president say about Ketanji Brown Jackson"
# docs = retriever.get_relevant_documents(query)

# Doing Reranking with Jina
from langchain.retrievers import ContextualCompressionRetriever
from langchain_community.document_compressors import JinaRerank

# compressor = JinaRerank()
# compression_retriever = ContextualCompressionRetriever(
#    base_compressor=compressor, base_retriever=retriever
# )
# compressed_docs = compression_retriever.get_relevant_documents(
#    "What did the president say about Ketanji Jackson Brown"
# )
# pretty_print_docs(compressed_docs)

Document 1:

So let’s not abandon our streets. Or choose between safety and equal justice.
Let’s come together to protect our communities, restore trust, and hold law
enforcement accountable.
That’s why the Justice Department required body cameras, banned chokeholds, and
restricted no-knock warrants for its officers.

----------------------------------------------------------------------------------------------------

Document 2:

I spoke with their families and told them that we are forever in debt for their
sacrifice, and we will carry on their mission to restore the trust and safety every
community deserves.
I’ve worked on these issues a long time.
I know what works: Investing in crime prevention and community police officers
who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and
safety.
So let’s not abandon our streets. Or choose between safety and equal justice.

pip install -U ragatouille

from ragatouille import RAGPretrainedModel
from langchain.retrievers import ContextualCompressionRetriever

# 假設(shè) retriever 已經(jīng)定義好
# RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
# compression_retriever = ContextualCompressionRetriever(
#     base_compressor=RAG.as_langchain_document_compressor(), base_retriever=retriever
# )
# compressed_docs = compression_retriever.invoke(
#     "What animation studio did Miyazaki found"
# )
# print(compressed_docs[0])

Document(page_cnotallow='In June 1985, Miyazaki, Takahata, Tokuma and Suzuki founded
the animation production company Studio Ghibli, with funding from Tokuma Shoten.
Studio Ghibli\'s first film, Laputa: Castle in the Sky (1986), employed the same
production crew of Nausica?. Miyazaki\'s designs for the film\'s setting were
inspired by Greek architecture and "European urbanistic templates". Some of the
architecture in the film was also inspired by a Welsh mining town; Miyazaki
witnessed the mining strike upon his first', metadata={'relevance_score':
26.5194149017334})

from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import FlashrankRerank
from langchain_openai import ChatOpenAI

# 假設(shè) retriever 已經(jīng)定義好
# llm = ChatOpenAI(temperature=0)
# compressor = FlashrankRerank()
# compression_retriever = ContextualCompressionRetriever(
#    base_compressor=compressor, base_retriever=retriever
# )
# compressed_docs = compression_retriever.invoke(
#    "What did the president say about Ketanji Jackson Brown"
# )
# print([doc.metadata["id"] for doc in compressed_docs])
# pretty_print_docs(compressed_docs)

[0, 5, 3]

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. One of our nation’s top legal minds, who will continue Justice
Breyer’s legacy of excellence.
----------------------------------------------------------------------------------------------------

Document 2:

He met the Ukrainian people.
From President Zelenskyy to every Ukrainian, their fearlessness, their courage,
their determination, inspires the world.
Groups of citizens blocking tanks with their bodies. Everyone from students to
retirees teachers turned soldiers defending their homeland.
In this struggle as President Zelenskyy said in his speech to the European
Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United
States is here tonight.
----------------------------------------------------------------------------------------------------

Document 3:

And tonight, I’m announcing that the Justice Department will name a chief prosecutor
for pandemic fraud.
By the end of this year, the deficit will be down to less than half what it was
before I took office.
The only president ever to cut the deficit by more than one trillion dollars in a
single year.
Lowering your costs also means demanding more competition.
I’m a capitalist, but capitalism without competition isn’t capitalism
It’s exploitation—and it drives up prices.

!pip install mxbai_rerank

from mxbai_rerank import MxbaiRerankV2

# Load the model, here we use our base sized model
model = MxbaiRerankV2("mixedbread-ai/mxbai-rerank-base-v2")

# Example query and documents
query = "Who wrote To Kill a Mockingbird?"

documents = ["To Kill a Mockingbird is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.","The novel Moby-Dick was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.","Harper Lee, an American novelist widely known for her novel To Kill a Mockingbird, was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.","Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.","The Harry Potter series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.",

 "The Great Gatsby, a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan."
]

# Calculate the scores
results = model.rank(query, documents)
print(results)

[RankResult(index=0, score=9.847987174987793, document='To Kill a Mockingbird is a
novel by Harper Lee published in 1960. It was immediately successful, winning the
Pulitzer Prize, and has become a classic of modern American literature.'),

RankResult(index=2, score=8.258672714233398, document='Harper Lee, an American
novelist widely known for her novel To Kill a Mockingbird, was born in 1926 in
Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.'),

RankResult(index=3, score=3.579845428466797, document='Jane Austen was an English
novelist known primarily for her six major novels, which interpret, critique and
comment upon the British landed gentry at the end of the 18th century.'),

RankResult(index=4, score=2.716982841491699, document='The Harry Potter series,
which consists of seven fantasy novels written by British author J.K. Rowling, is
among the most popular and critically acclaimed books of the modern era.'),

RankResult(index=1, score=2.233165740966797, document='The novel Moby-Dick was
written by Herman Melville and first published in 1851. It is considered a
masterpiece of American literature and deals with complex themes of obsession,
revenge, and the conflict between good and evil.'),

RankResult(index=5, score=1.8150043487548828, document='The Great Gatsby, a novel
written by American author F. Scott Fitzgerald, was published in 1925. The story is
set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit
of Daisy Buchanan.')]