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本方案基于Anna’s Archive语料（以纯文本tokens为基准，总~13万亿tokens），设计端到端系统：通过摘要抽取（NLP提取式 vs. LLM生成式）构建训练语料，使用LoRA continued pretraining训练PubLLM-Summary（基模型Llama 3 70B，提升出版领域召回率20%），并集成RAG（FAISS向量检索+全文分块）实现交互服务（如查询图书生命周期、知识推荐）。方案覆盖三种规模，优先合规（transformative use，仅公有/开源内容；摘要非复制性）。总框架采用Python生态（Hugging Face、LangChain），部署于AWS/GCP云。以2025年11月12日市场价估算（Gemma API $0.07/M tokens；H100 $3.90/GPU-hr；S3 $0.023/GB/月）。总体架构概述
import nltk
from nltk.tokenize import sent_tokenize
from collections import defaultdict
import networkx as nx # 可用
def textrank_summary(text, num_sentences=5):
sentences = sent_tokenize(text)
graph = nx.Graph()
for i, s1 in enumerate(sentences):
for j, s2 in enumerate(sentences):
if i != j:
sim = nltk.cosine_similarity([nltk.word_tokenize(s1)], [nltk.word_tokenize(s2)]) # 简化sim
graph.add_edge(i, j, weight=sim)
scores = nx.pagerank(graph)
top_sentences = sorted(scores, key=scores.get, reverse=True)[:num_sentences]
return ' '.join([sentences[i] for i in top_sentences])
summaries = [textrank_summary(t, 10) for t in texts] # 章节级，tokens ~0.65万亿
# BERT变体: from transformers import pipeline; summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from transformers import pipeline
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
chunks = [t[i:i+512] for t in texts for i in range(0, len(t), 512)] # 分块，tokens基准
vectorstore = FAISS.from_texts(chunks, embeddings)
llm = pipeline("text-generation", model="PubLLM-Summary")
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever(search_kwargs={"k":5}))
response = qa_chain.run("大模型时代图书生命周期？")
存储：S3 Glacier（冷数据$0.023/GB/月，文本~52 TB）。
阶段5: 服务部署与迭代（所有规模，1周+持续）
前端：Streamlit API（查询输入→RAG输出+来源链接）。
代码（Streamlit app）：python
import streamlit as st
from rag_pipeline import qa_chain # 上步
st.title("PubLLM出版知识服务")
query = st.text_input("查询：")
if query:
result = qa_chain.run(query)
st.write(result)