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• 直接上代码+注释
• 有意尝试可交流
• 效果正在验证中。

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###1.短文本处理(<500tokens)

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')  # 384维小型模型def process_short(text):"""直接全文本编码"""return model.encode(text, convert_to_tensor=True)# 示例
short_text = "自然语言处理的基础概念"  # 长度约15 tokens
vector = process_short(short_text)

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2. 中长文本处理 (500-2000 tokens)

from langchain_text_splitters import RecursiveCharacterTextSplitterdef process_medium(text):"""重叠分块策略"""splitter = RecursiveCharacterTextSplitter(chunk_size=500,chunk_overlap=50,separators=["\n\n", "\n", "。", "！", "？"])chunks = splitter.split_text(text)return [model.encode(chunk) for chunk in chunks]# 示例
medium_text = "机器学习发展历史...（约1500字）"  # 约1800 tokens
chunk_vectors = process_medium(medium_text)

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3. 长文本处理 (2000-20000 tokens)

import spacydef process_long(text):"""语义分块+摘要增强"""# 加载语义分割模型nlp = spacy.load("zh_core_web_sm") doc = nlp(text)# 按段落分割chunks = [sent.text for sent in doc.sents]# 生成章节摘要summary_model = SentenceTransformer('uer/sbert-base-chinese-nli')summaries = [summary_model.encode(chunk[:200]) for chunk in chunks]return chunks, summaries# 示例
long_text = "人工智能技术白皮书...（约2万字）"  # 约20000 tokens
text_chunks, summary_vecs = process_long(long_text)

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4. 超长文本处理 (20000-200000 tokens)

import faiss
import numpy as npclass HierarchicalIndex:def __init__(self):# 两级索引结构self.summary_index = faiss.IndexFlatL2(384)self.chunk_index = faiss.IndexIVFPQ(faiss.IndexFlatL2(384), 384, 100, 16, 8)self.metadata = []def add_document(self, text):# 生成段落级摘要chunks, summaries = process_long(text)# 构建索引summary_vecs = np.array(summaries).astype('float32')chunk_vecs = np.array([model.encode(c) for c in chunks]).astype('float32')self.summary_index.add(summary_vecs)self.chunk_index.add(chunk_vecs)self.metadata.extend(chunks)def search(self, query, k=5):# 先检索摘要层query_vec = model.encode(query).astype('float32')_, sum_indices = self.summary_index.search(np.array([query_vec]), 10)# 精搜相关块target_chunks = [self.chunk_index.reconstruct(i) for i in sum_indices]target_chunks = np.array(target_chunks).astype('float32')_, chunk_indices = self.chunk_index.search(target_chunks, k)return [self.metadata[i] for i in chunk_indices]# 使用示例
hindex = HierarchicalIndex()
hindex.add_document("某领域技术文档...（约15万字）")  # 约200000 tokens
results = hindex.search("深度学习在医疗影像的应用")

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5. 海量文本处理 (>200000 tokens)

import dask.dataframe as dd
from dask.distributed import Clientdef process_extreme(file_path):"""分布式处理方案"""client = Client(n_workers=4)  # 启动Dask集群# 分块读取df = dd.read_parquet(file_path, chunksize=100000)  # 并行编码df['vector'] = df['text'].map_partitions(lambda s: s.apply(model.encode),meta=('vector', object))# 构建分布式索引df.to_parquet("encoded_data.parquet", engine="pyarrow")# 示例（处理100万条文本）
process_extreme("massive_data.parquet")

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性能优化对照表

文本长度	处理策略	索引类型	响应时间	内存消耗
<500	直接编码	FlatIndex	<10ms	1MB
2000	重叠分块	IVF+PQ	50-100ms	50MB
20000	语义分块+摘要索引	二级索引	200-500ms	300MB
200000	层次化索引	IVFOPQ+ProductQuant	1-2s	2GB
>200000	分布式处理	分片索引	10s+	集群资源

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关键处理技术

1. 滑动窗口：通过chunk_overlap保留上下文连续性
2. 语义分块：使用spacy进行句子边界检测
3. 层次化索引：摘要层加速粗筛，块层保证精度
4. 量化压缩：PQ算法减少内存占用（精度损失