"""Recursive character chunking — the ``"R"`` strategy.

Wraps LangChain's :class:`RecursiveCharacterTextSplitter` and delivers
output rows in the LightRAG file-chunker schema. The splitter walks the
``separators`` list from longest semantic boundary (``\\n\\n`` by default)
to weakest (the empty string), recursively re-splitting any segment that
still exceeds the token cap.

Token accounting goes through the LightRAG :class:`Tokenizer` via the
``length_function`` plug-in — without that, ``chunk_size`` would be
measured in characters and ``chunk_token_size`` would lose its meaning.

Output cap is *not* enforced internally: oversized segments are produced
when no separator can break them, and
:func:`lightrag.utils.enforce_chunk_token_limit_before_embedding` does the
final hard split before embedding.
"""

from __future__ import annotations

from typing import Any

from lightrag.utils import Tokenizer, logger

try:
    from langchain_text_splitters import RecursiveCharacterTextSplitter

    _LANGCHAIN_TEXT_SPLITTERS_AVAILABLE = True
except ImportError:
    _LANGCHAIN_TEXT_SPLITTERS_AVAILABLE = False
    RecursiveCharacterTextSplitter = None  # type: ignore[assignment]


def chunking_by_recursive_character(
    tokenizer: Tokenizer,
    content: str,
    chunk_token_size: int = 1200,
    *,
    chunk_overlap_token_size: int = 100,
    separators: list[str] | None = None,
) -> list[dict[str, Any]]:
    """Recursive character splitter — the ``"R"`` chunking strategy.

    Args:
        tokenizer: LightRAG tokenizer; used as the length function so
            ``chunk_token_size`` and ``chunk_overlap_token_size`` are
            interpreted in tokens, not characters.
        content: Text to split.
        chunk_token_size: Hard target size for each chunk (tokens).
        chunk_overlap_token_size: Token overlap between adjacent chunks.
        separators: Cascade of split candidates. ``None`` defers to
            LangChain's defaults: ``["\\n\\n", "\\n", " ", ""]``.

    Returns:
        Ordered list of ``{"tokens", "content", "chunk_order_index"}``
        dicts.
    """
    if not _LANGCHAIN_TEXT_SPLITTERS_AVAILABLE:
        raise ImportError(
            "langchain-text-splitters is required for the 'R' chunking "
            "strategy; install with `pip install langchain-text-splitters>=0.3`."
        )

    if not content or not content.strip():
        return []

    splitter_kwargs: dict[str, Any] = {
        "chunk_size": max(int(chunk_token_size), 1),
        "chunk_overlap": max(int(chunk_overlap_token_size), 0),
        "length_function": lambda s: len(tokenizer.encode(s)),
    }
    if separators is not None:
        splitter_kwargs["separators"] = list(separators)

    splitter = RecursiveCharacterTextSplitter(**splitter_kwargs)

    pieces = splitter.split_text(content)
    results: list[dict[str, Any]] = []
    for piece in pieces:
        body = piece.strip()
        if not body:
            continue
        results.append(
            {
                "tokens": len(tokenizer.encode(body)),
                "content": body,
                "chunk_order_index": len(results),
            }
        )

    if not results:
        # Defensive: splitter returned only whitespace fragments. Fall
        # through with a single chunk of stripped content so downstream
        # callers always receive at least one row when input is non-empty.
        logger.warning(
            "[recursive_character] splitter produced no non-empty chunks "
            "for %d-char input; emitting single fallback chunk.",
            len(content),
        )
        body = content.strip()
        if body:
            results.append(
                {
                    "tokens": len(tokenizer.encode(body)),
                    "content": body,
                    "chunk_order_index": 0,
                }
            )

    return results