Retrieval-Augmented Generation with LangChain
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Open the starter notebook file for Lesson 4 to try this out. The starter notebook has been modified to reflect the basic RAG implementation. It currently doesn’t use the default RAG prompt. This specific prompt conditions the response, which doesn’t help showcase what you’re trying to prove here. Because the response will be long, the response’s length is displayed instead of the response itself. Finally, the chunk_overlap has been reduced since this is a relatively small dataset. Run the code as is to see how many documents are returned based on the given query:
4
retriever = database.as_retriever(search_kwargs={"k": 1})
print(len(response))
print(response)
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor
compressor = LLMChainExtractor.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=retriever
)
compressed_docs = compression_retriever.invoke(
"How was security during the 2024 Olympics"
)
compressed_docs
[Document(metadata={'source': 'https://en.wikipedia.org/wiki/Concerns_
and_controversies_at_the_2024_Summer_Olympics', 'summary':
'Various concerns and controversies arose in relation to the
2024 Summer Olympics, including security concerns, human rights
issues, and controversy over allowing Israel to participate
amidst the Israel–Hamas war, and allowing Russian and Belarusian
athletes to compete as neutrals amidst the Russian invasion of
Ukraine. Despite the nominal Olympic Truce in place, the wars in
Ukraine and Palestine already set a more conflicted political
background to the 2024 Summer Olympics, before considering domestic
and sporting issues.\n\n', 'title': 'Concerns and controversies
at the 2024 Summer Olympics'}, page_content='Various concerns and
controversies arose in relation to the 2024 Summer Olympics,
including security concerns, human rights issues, and controversy
over allowing Israel to participate amidst the Israel–Hamas war,
and allowing Russian and Belarusian athletes to compete as neutrals
amidst the Russian invasion of Ukraine.'),
Document(metadata={'source': 'https://en.wikipedia.org/wiki/Concerns_
and_controversies_at_the_2024_Summer_Olympics', 'summary': 'Various
concerns and controversies arose in relation to the 2024 Summer
Olympics, including security concerns, human rights issues, and
controversy over allowing Israel to participate amidst the Israel–Hamas
war, and allowing Russian and Belarusian athletes to compete as
neutrals amidst the Russian invasion of Ukraine. Despite the nominal
Olympic Truce in place, the wars in Ukraine and Palestine already set
a more conflicted political background to the 2024 Summer Olympics,
before considering domestic and sporting issues.\n\n', 'title':
'Concerns and controversies at the 2024 Summer Olympics'},
page_content='In February 2024, the French government announced that,
as a security precaution, the number of spectators for the opening
ceremony along the Seine would be reduced from 600,000 to 300,000.
This plan was proposed by Minister of the Interior Gérald Darmanin
in 2022. A security perimeter around the area designated for spectator
access was planned to be erected in the days leading up to the games,
limiting access for the public. In July 2024, it was reported that
there would be an expected 220,000 spectators and 45,000 police and
security officers present.')]
Introducing Re-ranking
A popular re-ranking tool is the Cohere API. Visit the developer page at https://dashboard.cohere.com/welcome/register to obtain a free API key. Visit the API Keys page from left menu items on the dashboard, and copy the default API key under the Trials keys section. In a new cell, install Cohere or, better still, langchain-cohere:
pip install cohere
pip install langchain-cohere
import os
import getpass
os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:")
from langchain.retrievers.contextual_compression import
ContextualCompressionRetriever
from langchain_cohere import CohereRerank
from langchain_community.llms import Cohere
llm = Cohere(temperature=0)
compressor = CohereRerank(model="rerank-english-v3.0")
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=retriever
)
compressed_docs = compression_retriever.invoke(
"How was security during the 2024 Olympics"
)
compressed_docs