交叉编码器重排序器
本笔记本展示了如何使用您自己的 Hugging Face 交叉编码器模型或实现了交叉编码器功能的 Hugging Face 模型（例如：BAAI/bge-reranker-base）在检索器中实现重排序器。SagemakerEndpointCrossEncoder 使您能够使用加载到 SageMaker 上的这些 Hugging Face 模型。
这建立在上下文压缩检索器中的思想之上。本文档的整体结构来自Cohere 重排序器文档。
有关交叉编码器为何可以与嵌入结合使用作为重排序机制以实现更好检索的更多信息，请参阅Hugging Face 交叉编码器文档。
#!pip install faiss sentence_transformers
# OR (depending on Python version)
#!pip install faiss-cpu sentence_transformers
# 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)]
)
)
设置基础向量存储检索器
首先，我们初始化一个简单的向量存储检索器，并存储2023年国情咨文（分块）。我们可以将检索器设置为检索大量（20个）文档。
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
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)
embeddingsModel = HuggingFaceEmbeddings(
model_name="sentence-transformers/msmarco-distilbert-dot-v5"
)
retriever = FAISS.from_documents(texts, embeddingsModel).as_retriever(
search_kwargs={"k": 20}
)
query = "What is the plan for the economy?"
docs = retriever.invoke(query)
pretty_print_docs(docs)
使用 CrossEncoderReranker 进行重排序
现在，让我们用 ContextualCompressionRetriever 封装我们的基础检索器。CrossEncoderReranker 使用 HuggingFaceCrossEncoder 对返回的结果进行重排序。
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CrossEncoderReranker
from langchain_community.cross_encoders import HuggingFaceCrossEncoder
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.
将 Hugging Face 模型上传到 SageMaker 端点
这是一个 inference.py 示例，用于创建与 SagemakerEndpointCrossEncoder 兼容的端点。有关详细的分步指南，请参阅这篇文章。
它会即时下载 Hugging Face 模型，因此您无需将 pytorch_model.bin 等模型文件保存在 model.tar.gz 中。
import json
import logging
from typing import List
import torch
from sagemaker_inference import encoder
from transformers import AutoModelForSequenceClassification, AutoTokenizer
PAIRS = "pairs"
SCORES = "scores"
class CrossEncoder:
def __init__(self) -> None:
self.device = (
torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
)
logging.info(f"Using device: {self.device}")
model_name = "BAAI/bge-reranker-base"
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
self.model = self.model.to(self.device)
def __call__(self, pairs: List[List[str]]) -> List[float]:
with torch.inference_mode():
inputs = self.tokenizer(
pairs,
padding=True,
truncation=True,
return_tensors="pt",
max_length=512,
)
inputs = inputs.to(self.device)
scores = (
self.model(**inputs, return_dict=True)
.logits.view(
-1,
)
.float()
)
return scores.detach().cpu().tolist()
def model_fn(model_dir: str) -> CrossEncoder:
try:
return CrossEncoder()
except Exception:
logging.exception(f"Failed to load model from: {model_dir}")
raise
def transform_fn(
cross_encoder: CrossEncoder, input_data: bytes, content_type: str, accept: str
) -> bytes:
payload = json.loads(input_data)
model_output = cross_encoder(**payload)
output = {SCORES: model_output}
return encoder.encode(output, accept)