Source code for arborist.document

"""Document and Chunk dataclasses + canonical chunkers.

Every Document carries a URI (identification, backtrack, cross-link) and content.
Chunkers split content into byte-determined chunks before Merkle hashing. The
chunker's name is committed in chunking_version — changing chunker invalidates
all prior cache records under v9.8 admissibility.
"""

from __future__ import annotations

import re
import unicodedata
from dataclasses import dataclass, field
from typing import Protocol




@dataclass(frozen=True)
class Edge:
    """A cross-link from this document to another."""

    edge_type: str            # wikilink | citation | derived_from | ...
    dst_uri: str              # always present
    dst_root: str | None = None  # filled in later if/when target is ingested
    anchor: str | None = None    # optional fragment / chunk index





@dataclass
class Document:
    """An ingestable document: URI + content + outbound edges."""

    uri: str
    content: str                            # normalized text
    source_type: str                        # wikipedia_xml | html | git | ...
    title: str | None = None
    edges: list[Edge] = field(default_factory=list)
    extra: dict = field(default_factory=dict)  # source-specific metadata





def canonicalize(text: str) -> str:
    """Stable text normalization. Bumping this requires CANONICALIZATION_VERSION bump."""
    # NFC unicode, normalize whitespace runs to single spaces, strip ends.
    text = unicodedata.normalize("NFC", text)
    text = re.sub(r"[\r\n\t\f\v]+", "\n", text)
    text = re.sub(r"[ ]{2,}", " ", text)
    return text.strip()





class Chunker(Protocol):
    """A chunker splits canonicalized text into ordered chunks."""

    name: str



    def split(self, text: str) -> list[str]: ...






class TokenChunker:
    """512-token chunker (whitespace-tokenized, byte-deterministic).

    "Token" here means whitespace-separated unit, NOT a model BPE token. This
    avoids tokenizer-version drift in the chunking_version.
    """

    name = "tok-512-v1"

    def __init__(self, tokens_per_chunk: int = 512):
        self.tokens_per_chunk = tokens_per_chunk



    def split(self, text: str) -> list[str]:
        if not text:
            return []
        tokens = text.split()
        if not tokens:
            return []
        chunks: list[str] = []
        for start in range(0, len(tokens), self.tokens_per_chunk):
            chunks.append(" ".join(tokens[start : start + self.tokens_per_chunk]))
        return chunks






class SentenceChunker:
    """Sentence-aligned chunker (better for short docs like 2003 Wikipedia)."""

    name = "sent-v1"

    _split_re = re.compile(r"(?<=[.!?])\s+(?=[A-Z0-9])")



    def split(self, text: str) -> list[str]:
        if not text:
            return []
        # Naive but deterministic.
        sentences = [s.strip() for s in self._split_re.split(text) if s.strip()]
        return sentences or ([text] if text else [])






def get_chunker(name: str | None = None) -> Chunker:
    """Lookup chunker by name. Default = TokenChunker."""
    if name is None or name == TokenChunker.name:
        return TokenChunker()
    if name == SentenceChunker.name:
        return SentenceChunker()
    raise ValueError(f"unknown chunker: {name}")