Python's summary and comparison of processing solutions for different text lengths

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')  # 384-dimensional small model
def process_short(text):
    """Direct full text encoding"""
    return (text, convert_to_tensor=True)

# Exampleshort_text = "The basic concept of natural language processing"  # Length about 15 tokensvector = process_short(short_text)

from langchain_text_splitters import RecursiveCharacterTextSplitter

def process_medium(text):
    """Overporous blocking strategy"""
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,
        chunk_overlap=50,
        separators=["\n\n", "\n", "。", "！", "？"]
    )
    chunks = splitter.split_text(text)
    return [(chunk) for chunk in chunks]

# Examplemedium_text = "History of development of machine learning... (about 1500 words)"  # About 1800 tokenschunk_vectors = process_medium(medium_text)

import spacy

def process_long(text):
    """Semantic chunking + summary enhancement"""
    # Loading semantic segmentation model    nlp = ("zh_core_web_sm") 
    doc = nlp(text)
    
    # Split by paragraph    chunks = [ for sent in ]
    
    # Generate a chapter summary    summary_model = SentenceTransformer('uer/sbert-base-chinese-nli')
    summaries = [summary_model.encode(chunk[:200]) for chunk in chunks]
    
    return chunks, summaries

# Examplelong_text = "Artificial Intelligence Technology White Paper... (about 20,000 words)"  # About 20,000 tokenstext_chunks, summary_vecs = process_long(long_text)

import faiss
import numpy as np

class HierarchicalIndex:
    def __init__(self):
        # Two-level index structure        self.summary_index = faiss.IndexFlatL2(384)
        self.chunk_index = (
            faiss.IndexFlatL2(384), 384, 100, 16, 8
        )
         = []

    def add_document(self, text):
        # Generate paragraph level summary        chunks, summaries = process_long(text)
        
        # Build index        summary_vecs = (summaries).astype('float32')
        chunk_vecs = ([(c) for c in chunks]).astype('float32')
        
        self.summary_index.add(summary_vecs)
        self.chunk_index.add(chunk_vecs)
        (chunks)

    def search(self, query, k=5):
        # Search the summary layer first        query_vec = (query).astype('float32')
        _, sum_indices = self.summary_index.search(([query_vec]), 10)
        
        # Search related blocks        target_chunks = [self.chunk_index.reconstruct(i) for i in sum_indices]
        target_chunks = (target_chunks).astype('float32')
        _, chunk_indices = self.chunk_index.search(target_chunks, k)
        
        return [[i] for i in chunk_indices]

#User Examplehindex = HierarchicalIndex()
hindex.add_document("Technical documents in a certain field... (about 150,000 words)")  # About 200,000 tokensresults = ("The application of deep learning in medical imaging")

import  as dd
from  import Client

def process_extreme(file_path):
    """Distributed Processing Solution"""
    client = Client(n_workers=4)  # Start Dask cluster    
    # chunked reading    df = dd.read_parquet(file_path, chunksize=100000)  
    
    # Parallel encoding    df['vector'] = df['text'].map_partitions(
        lambda s: (),
        meta=('vector', object)
    )
    
    # Build a distributed index    df.to_parquet("encoded_data.parquet", engine="pyarrow")
    
# Example (processing 1 million texts)process_extreme("massive_data.parquet")