open_notebook/utils/text_utils.py

"""
Text utilities for Open Notebook.
Extracted from main utils to avoid circular imports.
"""

import re
import unicodedata
from typing import Tuple

from langchain_text_splitters import RecursiveCharacterTextSplitter

from .token_utils import token_count

# Patterns for matching thinking content in AI responses
# Standard pattern: <think>...</think>
THINK_PATTERN = re.compile(r"<think>(.*?)</think>", re.DOTALL)
# Pattern for malformed output: content</think> (missing opening tag)
THINK_PATTERN_NO_OPEN = re.compile(r"^(.*?)</think>", re.DOTALL)


def split_text(txt: str, chunk_size=500):
    """
    Split the input text into chunks.

    Args:
        txt (str): The input text to be split.
        chunk_size (int): The size of each chunk. Default is 500.

    Returns:
        list: A list of text chunks.
    """
    overlap = int(chunk_size * 0.15)
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size,
        chunk_overlap=overlap,
        length_function=token_count,
        separators=[
            "\n\n",
            "\n",
            ".",
            ",",
            " ",
            "\u200b",  # Zero-width space
            "\uff0c",  # Fullwidth comma
            "\u3001",  # Ideographic comma
            "\uff0e",  # Fullwidth full stop
            "\u3002",  # Ideographic full stop
            "",
        ],
    )
    return text_splitter.split_text(txt)


def remove_non_ascii(text: str) -> str:
    """Remove non-ASCII characters from text."""
    return re.sub(r"[^\x00-\x7F]+", "", text)


def remove_non_printable(text: str) -> str:
    """Remove non-printable characters from text."""
    # Replace any special Unicode whitespace characters with a regular space
    text = re.sub(r"[\u2000-\u200B\u202F\u205F\u3000]", " ", text)

    # Replace unusual line terminators with a single newline
    text = re.sub(r"[\u2028\u2029\r]", "\n", text)

    # Remove control characters, except newlines and tabs
    text = "".join(
        char for char in text if unicodedata.category(char)[0] != "C" or char in "\n\t"
    )

    # Replace non-breaking spaces with regular spaces
    text = text.replace("\xa0", " ").strip()

    # Keep letters (including accented ones), numbers, spaces, newlines, tabs, and basic punctuation
    return re.sub(r"[^\w\s.,!?\-\n\t]", "", text, flags=re.UNICODE)


def parse_thinking_content(content: str) -> Tuple[str, str]:
    """
    Parse message content to extract thinking content from <think> tags.

    Handles both well-formed tags and malformed output where the opening
    <think> tag is missing but </think> is present.

    Args:
        content (str): The original message content

    Returns:
        Tuple[str, str]: (thinking_content, cleaned_content)
            - thinking_content: Content from within <think> tags
            - cleaned_content: Original content with <think> blocks removed

    Example:
        >>> content = "<think>Let me analyze this</think>Here's my answer"
        >>> thinking, cleaned = parse_thinking_content(content)
        >>> print(thinking)
        "Let me analyze this"
        >>> print(cleaned)
        "Here's my answer"
    """
    # Input validation
    if not isinstance(content, str):
        return "", str(content) if content is not None else ""

    # Limit processing for very large content (100KB limit)
    if len(content) > 100000:
        return "", content

    # Find all well-formed thinking blocks
    thinking_matches = THINK_PATTERN.findall(content)

    if thinking_matches:
        # Join all thinking content with double newlines
        thinking_content = "\n\n".join(match.strip() for match in thinking_matches)

        # Remove all <think>...</think> blocks from the original content
        cleaned_content = THINK_PATTERN.sub("", content)

        # Clean up extra whitespace
        cleaned_content = re.sub(r"\n\s*\n\s*\n", "\n\n", cleaned_content).strip()

        return thinking_content, cleaned_content

    # Handle malformed output: content</think> (missing opening tag)
    # Some models like Nemotron output thinking without the opening <think> tag
    malformed_match = THINK_PATTERN_NO_OPEN.match(content)
    if malformed_match:
        thinking_content = malformed_match.group(1).strip()
        # Remove the thinking content and </think> tag
        cleaned_content = content[malformed_match.end():].strip()
        return thinking_content, cleaned_content

    return "", content


def clean_thinking_content(content: str) -> str:
    """
    Remove thinking content from AI responses, returning only the cleaned content.

    This is a convenience function for cases where you only need the cleaned
    content and don't need access to the thinking process.

    Args:
        content (str): The original message content with potential <think> tags

    Returns:
        str: Content with <think> blocks removed and whitespace cleaned

    Example:
        >>> content = "<think>Let me think...</think>Here's the answer"
        >>> clean_thinking_content(content)
        "Here's the answer"
    """
    _, cleaned_content = parse_thinking_content(content)
    return cleaned_content