import re
import unicodedata
from bs4 import BeautifulSoup

def preprocess(text: str, lowercase: bool = True) -> str:
    # 1. Decode HTML entities and strip tags
    text = BeautifulSoup(text, "html.parser").get_text(separator=" ")

    # 2. Normalize unicode (NFD -> NFC, remove combining chars if needed)
    text = unicodedata.normalize("NFC", text)

    # 3. Lowercase
    if lowercase:
        text = text.lower()

    # 4. Remove URLs, emails, special tokens
    text = re.sub(r"https?://\S+|www\.\S+", " ", text)
    text = re.sub(r"\S+@\S+\.\S+", " ", text)

    # 5. Collapse whitespace
    text = re.sub(r"\s+", " ", text).strip()

    return text

> Persist the preprocessing config (lowercase flag, regex patterns) alongside
> your model so training and inference use identical transformations.

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import numpy as np
import faiss
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("BAAI/bge-small-en-v1.5")

> For production, use `faiss.IndexIVFFlat` (approximate, faster) or a managed
> vector store (pgvector, Pinecone, Weaviate) rather than exact `IndexFlatIP`.

from datasets import Dataset
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    TrainingArguments,
    Trainer,
)
import torch

MODEL_ID = "distilbert-base-uncased"
LABELS = ["negative", "neutral", "positive"]
id2label = {i: l for i, l in enumerate(LABELS)}
label2id = {l: i for i, l in enumerate(LABELS)}

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(
    MODEL_ID, num_labels=len(LABELS), id2label=id2label, label2id=label2id
)

def tokenize(batch):
    return tokenizer(batch["text"], truncation=True, padding="max_length", max_length=128)

> Use `distilbert` or `roberta-base` for most classification tasks. Only
> escalate to larger models if the smaller ones underperform after fine-tuning.

import spacy
from transformers import pipeline

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from langchain.text_splitter import RecursiveCharacterTextSplitter

def chunk_document(
    text: str,
    chunk_size: int = 512,
    chunk_overlap: int = 64,
) -> list[dict]:
    """
    Recursive splitter tries paragraph -> sentence -> word boundaries in order.
    chunk_overlap ensures context continuity across chunk boundaries.
    """
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size,
        chunk_overlap=chunk_overlap,
        separators=["\n\n", "\n", ". ", " ", ""],
    )
    chunks = splitter.split_text(text)
    return [{"text": chunk, "chunk_index": i, "total_chunks": len(chunks)} for i, chunk in enumerate(chunks)]

> Rule of thumb: chunk_size 256-512 tokens for precise retrieval, 512-1024 for
> richer context. Always store chunk metadata (source doc ID, page, position)
> alongside the embedding.

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