"""Refinement prompt for the writing editor — natural language only.

The model refines exactly ONE section of a document the user is writing. The
rest of the document travels as prefix/suffix context so the section stays
coherent with its surroundings, but the model regenerates ONLY the section —
a large document is never re-emitted whole (FIM-style).

The base texts (persona, rules, framings) are admin-editable: they come from
`app/prompts/overrides.py::get_prompt`, which returns a DB override when one
exists and the code default (`app/prompts/defaults.py`) otherwise.
"""

from app.llm.client import ChatMessage
from app.prompts.overrides import get_prompt


def render_refine_prompt(
    section: str,
    *,
    prefix: str,
    suffix: str,
    persona: str | None,
    hint: str | None,
    context: str | None = None,
    knowledge: list[str] | None = None,
) -> list[ChatMessage]:
    # A template may carry its own persona; otherwise the admin-editable default.
    system_parts = [persona.strip() if persona else get_prompt("refine_persona")]
    if hint:
        system_parts.append(f"What this section should convey: {hint}")
    if context:
        # Background from the chat this capture came from, so the refinement
        # is on-topic — but only as orientation, never a source of new facts.
        system_parts.append(
            f"Background (the conversation this document came from, for "
            f"orientation only — do not invent facts from it): {context}"
        )
    system_parts.append(get_prompt("refine_rules"))

    # The document being edited leads the user turn (prefix/suffix/section);
    # the retrieved knowledge trails it, because it changes on every call and
    # keeping it last leaves the stable prompt prefix reusable between calls.
    user_parts: list[str] = []
    if prefix.strip():
        user_parts.append(
            f"Text before the section (context only, do not repeat it):\n{prefix}"
        )
    if suffix.strip():
        user_parts.append(
            f"Text after the section (context only, do not repeat it):\n{suffix}"
        )
    user_parts.append(f"Refine only this section:\n{section}")
    if knowledge:
        # What the company has already documented elsewhere. It is grounding,
        # not source material: it keeps terminology and facts consistent and
        # lets the section point at related documents, but it must not be
        # copied in or become a way to add facts the section's notes do not
        # support.
        joined = "\n\n".join(knowledge)
        user_parts.append(f"{get_prompt('grounding_framing')}\n{joined}")

    return [
        {"role": "system", "content": "\n\n".join(system_parts)},
        {"role": "user", "content": "\n\n".join(user_parts)},
    ]


def render_topic_summary_prompt(transcript: str) -> list[ChatMessage]:
    """Condense a conversation into a short search topic (a few words)."""
    return [
        {"role": "system", "content": get_prompt("topic_summary")},
        {"role": "user", "content": f"Conversation:\n{transcript}"},
    ]


def render_title_prompt(content_md: str) -> list[ChatMessage]:
    """Suggest a concise document title from its written content."""
    return [
        {"role": "system", "content": get_prompt("title")},
        {"role": "user", "content": f"Document:\n{content_md}"},
    ]