RAG系统中的“幕后英雄”：重排器如何提升信息检索的精准度？ 原创

from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import FlashrankRerank
from langchain_openai import ChatOpenAI
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)
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pip install -U ragatouille
from ragatouille import RAGPretrainedModel
from langchain.retrievers import ContextualCompressionRetriever
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])
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pip install --upgrade --quiet  rank_llm
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_community.document_compressors.rankllm_rerank import RankLLMRerank
compressor = RankLLMRerank(top_n=3, model="zephyr")
compression_retriever = ContextualCompressionRetriever(
   base_compressor=compressor, base_retriever=retriever
)
compressed_docs = compression_retriever.invoke(query)
pretty_print_docs(compressed_docs)
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import openai
# Set your OpenAI API key
openai.api_key = 'YOUR_API_KEY'
def pointwise_rerank(query, document):
   prompt = f"Rate the relevance of the following document to the query on a scale from 1 to 10:\n\nQuery: {query}\nDocument: {document}\n\nRelevance Score:"
   response = openai.ChatCompletion.create(
       model="gpt-4-turbo",
       messages=[{"role": "user", "content": prompt}]
   )
   return response['choices'][0]['message']['content'].strip()
def listwise_rerank(query, documents):
   # Use a sliding window approach to rerank documents
   window_size = 5
   reranked_docs = []
   for i in range(0, len(documents), window_size):
       window = documents[i:i + window_size]
       prompt = f"Given the query, please rank the following documents:\n\nQuery: {query}\nDocuments: {', '.join(window)}\n\nRanked Document Identifiers:"
       response = openai.ChatCompletion.create(
           model="gpt-4-turbo",
           messages=[{"role": "user", "content": prompt}]
       )
       ranked_ids = response['choices'][0]['message']['content'].strip().split(', ')
       reranked_docs.extend(ranked_ids)
   return reranked_docs
def pairwise_rerank(query, documents):
   scores = {}
   for i in range(len(documents)):
       for j in range(i + 1, len(documents)):
           doc1 = documents[i]
           doc2 = documents[j]
           prompt = f"Which document is more relevant to the query?\n\nQuery: {query}\nDocument 1: {doc1}\nDocument 2: {doc2}\n\nAnswer with '1' for Document 1, '2' for Document 2:"
           response = openai.ChatCompletion.create(
               model="gpt-4-turbo",
               messages=[{"role": "user", "content": prompt}]
           )
           winner = response['choices'][0]['message']['content'].strip()
           if winner == '1':
               scores[doc1] = scores.get(doc1, 0) + 1
               scores[doc2] = scores.get(doc2, 0)
           elif winner == '2':
               scores[doc2] = scores.get(doc2, 0) + 1
               scores[doc1] = scores.get(doc1, 0)
   # Sort documents based on scores
   ranked_docs = sorted(scores.items(), key=lambda item: item[1], reverse=True)
   return [doc for doc, score in ranked_docs]
# Example usage
query = "What are the benefits of using LLMs for document reranking?"
documents = [
   "LLMs can process large amounts of text quickly.",
   "They require extensive fine-tuning for specific tasks.",
   "LLMs can generate human-like text responses.",
   "They are limited by their training data and may produce biased results."
]
# Pointwise Reranking
for doc in documents:
   score = pointwise_rerank(query, doc)
   print(f"Document: {doc} - Relevance Score: {score}")
# Listwise Reranking
reranked_listwise = listwise_rerank(query, documents)
print(f"Listwise Reranked Documents: {reranked_listwise}")
# Pairwise Reranking
reranked_pairwise = pairwise_rerank(query, documents)
print(f"Pairwise Reranked Documents: {reranked_pairwise}")
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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
)
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