"""Prepare article bodies as conversation-ready context.

Used by the briefing ``discuss-article`` flow. A raw trafilatura extraction
is often too large to drop whole into a chat history without eating the
context window, so this module runs a map-reduce step over oversized
articles and returns a compact, structured context that still preserves the
article's meaning across sections.

Small articles pass through unchanged — map-reduce only fires when the raw
body exceeds CHAR_BUDGET. The output is cached on ``rss_items.context_prepared``
by the caller, so repeat discuss-clicks on the same article skip this work
entirely.
"""

from __future__ import annotations

import asyncio
import logging
import re

from fabledassistant.services.llm import generate_completion

logger = logging.getLogger(__name__)

# ~12k tokens at 4 chars/token. Comfortably under OLLAMA_NUM_CTX=16384
# with room left for system prompt, chat history, and the assistant reply.
CHAR_BUDGET = 48_000

# Chunk size for the map step on oversized articles. Overlap preserves
# context across paragraph boundaries that happen to land mid-sentence.
CHUNK_CHARS = 8_000
CHUNK_OVERLAP = 400

_PARA_SPLIT = re.compile(r"\n\s*\n")


def _chunk_by_paragraph(body: str) -> list[str]:
    """Split ``body`` into chunks of up to CHUNK_CHARS, respecting paragraphs.

    Paragraphs longer than CHUNK_CHARS are split mid-paragraph as a last
    resort. Adjacent chunks share CHUNK_OVERLAP chars of trailing text so
    a sentence straddling the boundary stays readable on both sides.
    """
    paragraphs = [p.strip() for p in _PARA_SPLIT.split(body) if p.strip()]
    chunks: list[str] = []
    current: list[str] = []
    current_len = 0
    for para in paragraphs:
        para_len = len(para)
        if para_len > CHUNK_CHARS:
            if current:
                chunks.append("\n\n".join(current))
                current, current_len = [], 0
            for i in range(0, para_len, CHUNK_CHARS - CHUNK_OVERLAP):
                chunks.append(para[i : i + CHUNK_CHARS])
            continue
        if current_len + para_len + 2 > CHUNK_CHARS and current:
            chunks.append("\n\n".join(current))
            tail = current[-1][-CHUNK_OVERLAP:] if current else ""
            current = [tail, para] if tail else [para]
            current_len = len(tail) + para_len + (2 if tail else 0)
        else:
            current.append(para)
            current_len += para_len + 2
    if current:
        chunks.append("\n\n".join(current))
    return chunks


async def _summarize_chunk(title: str, chunk: str, index: int, total: int, model: str) -> str:
    """Map-step summary of one article chunk.

    Aims for ~300 words of dense, factual prose — not bullet points — so the
    downstream chat model can quote from it naturally.
    """
    messages = [
        {
            "role": "system",
            "content": (
                "You are summarizing one section of a larger article so a downstream "
                "conversation model can discuss the full article without having to read "
                "every word.\n\n"
                "Requirements:\n"
                "- 250–350 words of dense factual prose\n"
                "- Preserve specific claims, numbers, names, and quotes\n"
                "- Do NOT editorialize or add analysis\n"
                "- Do NOT use bullet points or headings\n"
                "- Do NOT say 'this section' or 'this article' — write content, not meta"
            ),
        },
        {
            "role": "user",
            "content": (
                f"Article: {title}\n"
                f"Section {index + 1} of {total}:\n\n{chunk}"
            ),
        },
    ]
    try:
        # Pin num_ctx — same rationale as services/research.py:66. A large
        # chunk plus system prompt can push well past the default window;
        # silent truncation here would drop the tail of the chunk without
        # any error, producing a misleading summary.
        raw = await generate_completion(
            messages, model, max_tokens=600, num_ctx=16384
        )
        return raw.strip()
    except Exception:
        logger.warning(
            "Article chunk summary failed for section %d/%d of '%s'",
            index + 1, total, title, exc_info=True,
        )
        # Fall back to the raw chunk truncated to ~1500 chars so the overall
        # pipeline still delivers something rather than dropping the section.
        return chunk[:1500]


async def prepare_article_context(
    title: str,
    url: str,
    body: str,
    model: str,
) -> str:
    """Return a conversation-ready context block for ``body``.

    - Small article (≤ CHAR_BUDGET): returns ``body`` unchanged.
    - Oversized article: runs a parallel map step over paragraph-aware
      chunks and concatenates the summaries under section headers.

    The returned string is what should go into the ``read_article`` synthetic
    tool-result in chat history. Callers are responsible for caching it to
    ``rss_items.context_prepared``.
    """
    body = body or ""
    if len(body) <= CHAR_BUDGET:
        return body

    chunks = _chunk_by_paragraph(body)
    logger.info(
        "Article '%s' is %d chars, map-reducing into %d chunks",
        title, len(body), len(chunks),
    )

    summaries = await asyncio.gather(
        *[
            _summarize_chunk(title, chunk, i, len(chunks), model)
            for i, chunk in enumerate(chunks)
        ]
    )

    header = (
        f"(This article was longer than the chat window could hold verbatim, "
        f"so the full text was split into {len(chunks)} sections and each was "
        "summarized below. Each section preserves specific claims, numbers, "
        "and quotes from the original.)\n\n"
    )
    parts = [
        f"## Section {i + 1}\n\n{summary}"
        for i, summary in enumerate(summaries)
    ]
    return header + "\n\n".join(parts)