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交叉编码器重排序器

该笔记本展示了如何使用你自己来自Hugging Face交叉编码器模型或实现交叉编码器功能的Hugging Face模型（示例：BAAI/bge-reranker-base）在检索器中实现重排序。使用SagemakerEndpointCrossEncoder，你可以加载部署在Sagemaker上的这些HuggingFace模型。

此内容基于 ContextualCompressionRetriever 中的思想构建。本文档的整体结构来自 Cohere Reranker 文档。

有关为何交叉编码器可与嵌入结合用作重排序机制以实现更优检索的更多信息，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​

让我们从初始化一个简单的向量存储检索器开始，并存储2023年的国情咨文演讲（分块）。我们可以将检索器设置为检索大量的文档（20份）。

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 来包装我们的基础检索器。CrossEncoderReranker 使用 HuggingFaceCrossEncoder 对返回的结果进行重新排序。

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.

上传Hugging Face模型到SageMaker端点​

这里是创建与 SagemakerEndpointCrossEncoder 协同工作的端点的示例 inference.py。如需逐步详细说明，请参考本文。

它会动态下载 Hugging Face 模型，因此您无需在 model.tar.gz 中保留诸如 pytorch_model.bin 之类的模型文件。

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)