import re
from typing import Any, Dict, List

from app.services.vector_store.base import BaseVectorStore


def _tokenize_for_keyword_score(text: str) -> List[str]:
    """Simple multilingual tokenizer for lexical matching without extra dependencies."""
    tokens = re.findall(r"[A-Za-z0-9_]+|[\u4e00-\u9fff]", text.lower())
    return [token for token in tokens if token.strip()]


def _keyword_score(query: str, doc_text: str) -> float:
    query_terms = set(_tokenize_for_keyword_score(query))
    doc_terms = set(_tokenize_for_keyword_score(doc_text))

    if not query_terms or not doc_terms:
        return 0.0

    overlap = len(query_terms.intersection(doc_terms))
    return overlap / max(1, len(query_terms))


def hybrid_search(
    vector_store: BaseVectorStore,
    query: str,
    top_k: int = 6,
    fetch_k: int = 20,
    alpha: float = 0.65,
) -> List[Dict[str, Any]]:
    """
    Hybrid retrieval via vector candidate generation + lexical reranking.

    score = alpha * vector_rank_score + (1 - alpha) * keyword_score
    """
    raw_results = vector_store.similarity_search_with_score(query, k=fetch_k)
    if not raw_results:
        return []

    ranked: List[Dict[str, Any]] = []
    total = len(raw_results)

    for index, item in enumerate(raw_results):
        if not isinstance(item, (tuple, list)) or len(item) < 1:
            continue

        doc = item[0]
        if not hasattr(doc, "page_content"):
            continue

        rank_score = 1.0 - (index / max(1, total))
        lexical_score = _keyword_score(query, doc.page_content)
        final_score = alpha * rank_score + (1.0 - alpha) * lexical_score

        ranked.append(
            {
                "document": doc,
                "vector_rank_score": round(rank_score, 6),
                "keyword_score": round(lexical_score, 6),
                "final_score": round(final_score, 6),
            }
        )

    ranked.sort(key=lambda row: row["final_score"], reverse=True)
    return ranked[:top_k]


def format_hybrid_context(rows: List[Dict[str, Any]]) -> str:
    parts: List[str] = []

    for i, row in enumerate(rows, start=1):
        doc = row["document"]
        metadata = doc.metadata or {}
        source = metadata.get("source") or metadata.get("file_name") or "unknown"
        chunk_id = metadata.get("chunk_id") or "unknown"

        parts.append(
            (
                f"[{i}] source={source}, chunk_id={chunk_id}, "
                f"score={row['final_score']}\n"
                f"{doc.page_content.strip()}"
            )
        )

    return "\n\n".join(parts)