"""MCP tools for RAG search over legal documents and decisions."""

from __future__ import annotations

import json
import logging
from uuid import UUID

from legal_mcp.services import db, embeddings

logger = logging.getLogger(__name__)


async def search_decisions(
    query: str,
    limit: int = 10,
    section_type: str = "",
    practice_area: str = "",
    appeal_subtype: str = "",
    case_number: str = "",
) -> str:
    """חיפוש סמנטי בהחלטות קודמות ובמסמכים — מסונן לפי תחום משפטי.

    Args:
        query: שאילתת חיפוש בעברית
        limit: מספר תוצאות מקסימלי
        section_type: סינון לפי סוג סעיף (facts, legal_analysis, ...)
        practice_area: תחום משפטי לסינון (appeals_committee/national_insurance/...)
        appeal_subtype: סוג ערר לסינון (building_permit/betterment_levy/compensation_197)
        case_number: אם סופק, ה-practice_area/subtype יוסקו אוטומטית מהתיק
    """
    # Auto-resolve practice_area from case_number if available
    if case_number and not practice_area:
        case = await db.get_case_by_number(case_number)
        if case:
            practice_area = case.get("practice_area") or ""
            appeal_subtype = appeal_subtype or (case.get("appeal_subtype") or "")

    if not practice_area:
        logger.warning(
            "search_decisions called without practice_area filter — "
            "results may mix legal domains"
        )

    query_emb = await embeddings.embed_query(query)
    results = await db.search_similar(
        query_embedding=query_emb,
        limit=limit,
        section_type=section_type or None,
        practice_area=practice_area or None,
        appeal_subtype=appeal_subtype or None,
    )

    if not results:
        return "לא נמצאו תוצאות."

    formatted = []
    for r in results:
        formatted.append({
            "score": round(float(r["score"]), 4),
            "case_number": r["case_number"],
            "document": r["document_title"],
            "section": r["section_type"],
            "page": r["page_number"],
            "content": r["content"],
        })

    return json.dumps(formatted, ensure_ascii=False, indent=2)


async def search_case_documents(
    case_number: str,
    query: str,
    limit: int = 10,
) -> str:
    """חיפוש סמנטי בתוך מסמכי תיק ספציפי.

    Args:
        case_number: מספר תיק הערר
        query: שאילתת חיפוש
        limit: מספר תוצאות מקסימלי
    """
    case = await db.get_case_by_number(case_number)
    if not case:
        return f"תיק {case_number} לא נמצא."

    query_emb = await embeddings.embed_query(query)
    # Restricted to case_id — practice_area filter would be redundant.
    results = await db.search_similar(
        query_embedding=query_emb,
        limit=limit,
        case_id=UUID(case["id"]),
    )

    if not results:
        return f"לא נמצאו תוצאות בתיק {case_number}."

    formatted = []
    for r in results:
        formatted.append({
            "score": round(float(r["score"]), 4),
            "document": r["document_title"],
            "section": r["section_type"],
            "page": r["page_number"],
            "content": r["content"],
        })

    return json.dumps(formatted, ensure_ascii=False, indent=2)


async def find_similar_cases(
    description: str,
    limit: int = 5,
    practice_area: str = "",
    appeal_subtype: str = "",
    case_number: str = "",
) -> str:
    """מציאת תיקים דומים על בסיס תיאור — מסונן לפי תחום משפטי.

    Args:
        description: תיאור התיק או הנושא
        limit: מספר תוצאות מקסימלי
        practice_area: תחום משפטי לסינון
        appeal_subtype: סוג ערר לסינון
        case_number: אם סופק, ה-practice_area/subtype יוסקו אוטומטית מהתיק
    """
    if case_number and not practice_area:
        case = await db.get_case_by_number(case_number)
        if case:
            practice_area = case.get("practice_area") or ""
            appeal_subtype = appeal_subtype or (case.get("appeal_subtype") or "")

    if not practice_area:
        logger.warning(
            "find_similar_cases called without practice_area filter — "
            "results may mix legal domains"
        )

    query_emb = await embeddings.embed_query(description)
    results = await db.search_similar(
        query_embedding=query_emb,
        limit=limit * 3,  # Get more to deduplicate by case
        practice_area=practice_area or None,
        appeal_subtype=appeal_subtype or None,
    )

    if not results:
        return "לא נמצאו תיקים דומים."

    # Deduplicate by case_number, keep best score per case
    seen_cases = {}
    for r in results:
        cn = r["case_number"]
        if cn not in seen_cases or r["score"] > seen_cases[cn]["score"]:
            seen_cases[cn] = r

    # Sort by score and limit
    top_cases = sorted(seen_cases.values(), key=lambda x: x["score"], reverse=True)[:limit]

    formatted = []
    for r in top_cases:
        formatted.append({
            "score": round(float(r["score"]), 4),
            "case_number": r["case_number"],
            "document": r["document_title"],
            "relevant_section": r["content"][:500],
        })

    return json.dumps(formatted, ensure_ascii=False, indent=2)