legal-ai/mcp-server/src/legal_mcp/services/chunker.py

"""Legal document chunker - splits text into sections and chunks for RAG."""

from __future__ import annotations

import re
from dataclasses import dataclass, field

from legal_mcp import config

# Hebrew legal section headers.
# Covers both appeals committee decisions and external court rulings —
# court rulings use slightly different vocabulary (פסק דין, נימוקים, סוף דבר).
SECTION_PATTERNS = [
    (r"רקע\s*עובדתי|רקע\s*כללי|העובדות|הרקע", "facts"),
    (r"טענות\s*העוררי[םן]|טענות\s*המערערי[םן]|עיקר\s*טענות\s*העוררי[םן]", "appellant_claims"),
    (r"טענות\s*המשיבי[םן]|תשובת\s*המשיבי[םן]|עיקר\s*טענות\s*המשיבי[םן]", "respondent_claims"),
    (r"דיון\s*והכרעה|דיון|הכרעה|ניתוח\s*משפטי|המסגרת\s*המשפטית|נימוקים", "legal_analysis"),
    (r"מסקנ[הות]|סיכום|סוף\s*דבר", "conclusion"),
    (r"פסק[- ]?דין|החלטה|לפיכך\s*אני\s*מחליט|התוצאה", "ruling"),
    (r"מבוא|פתיחה|לפניי", "intro"),
]


@dataclass
class Chunk:
    content: str
    section_type: str = "other"
    page_number: int | None = None
    chunk_index: int = 0


def chunk_document(
    text: str,
    chunk_size: int = config.CHUNK_SIZE_TOKENS,
    overlap: int = config.CHUNK_OVERLAP_TOKENS,
) -> list[Chunk]:
    """Split a legal document into chunks, respecting section boundaries."""
    if not text.strip():
        return []

    sections = _split_into_sections(text)
    chunks: list[Chunk] = []
    idx = 0

    for section_type, section_text in sections:
        section_chunks = _split_section(section_text, chunk_size, overlap)
        for chunk_text in section_chunks:
            chunks.append(Chunk(
                content=chunk_text,
                section_type=section_type,
                chunk_index=idx,
            ))
            idx += 1

    return chunks


def _split_into_sections(text: str) -> list[tuple[str, str]]:
    """Split text into (section_type, text) pairs based on Hebrew headers."""
    # Find all section headers and their positions
    markers: list[tuple[int, str]] = []

    for pattern, section_type in SECTION_PATTERNS:
        for match in re.finditer(pattern, text):
            markers.append((match.start(), section_type))

    if not markers:
        # No sections found - treat as single block
        return [("other", text)]

    markers.sort(key=lambda x: x[0])

    sections: list[tuple[str, str]] = []

    # Text before first section
    if markers[0][0] > 0:
        intro_text = text[: markers[0][0]].strip()
        if intro_text:
            sections.append(("intro", intro_text))

    # Each section
    for i, (pos, section_type) in enumerate(markers):
        end = markers[i + 1][0] if i + 1 < len(markers) else len(text)
        section_text = text[pos:end].strip()
        if section_text:
            sections.append((section_type, section_text))

    return sections


def _split_section(text: str, chunk_size: int, overlap: int) -> list[str]:
    """Split a section into overlapping chunks by paragraphs.

    Uses approximate token counting (Hebrew ~1.5 chars per token).
    """
    if not text.strip():
        return []

    paragraphs = [p.strip() for p in text.split("\n") if p.strip()]
    chunks: list[str] = []
    current: list[str] = []
    current_tokens = 0

    for para in paragraphs:
        para_tokens = _estimate_tokens(para)

        if current_tokens + para_tokens > chunk_size and current:
            chunks.append("\n".join(current))
            # Keep overlap
            overlap_paras: list[str] = []
            overlap_tokens = 0
            for p in reversed(current):
                pt = _estimate_tokens(p)
                if overlap_tokens + pt > overlap:
                    break
                overlap_paras.insert(0, p)
                overlap_tokens += pt
            current = overlap_paras
            current_tokens = overlap_tokens

        current.append(para)
        current_tokens += para_tokens

    if current:
        chunks.append("\n".join(current))

    return chunks


def _estimate_tokens(text: str) -> int:
    """Rough token estimate for Hebrew text (~1.5 chars per token)."""
    return max(1, len(text) // 2)