# api/rag_engine.py
"""
RAG engine:
- build_rag_chunks_from_file(path, doc_type) -> List[chunk]
- retrieve_relevant_chunks(query, chunks) -> (context_text, used_chunks)

Chunk format (MVP):
{
  "text": str,
  "source_file": str,
  "section": str,
  "doc_type": str
}
"""

import os
import re
from typing import Dict, List, Tuple

from pypdf import PdfReader
from docx import Document
from pptx import Presentation


# ============================
# Token helpers (optional tiktoken)
# ============================
def _safe_import_tiktoken():
    try:
        import tiktoken  # type: ignore
        return tiktoken
    except Exception:
        return None


def _approx_tokens(text: str) -> int:
    if not text:
        return 0
    return max(1, int(len(text) / 4))


def _count_text_tokens(text: str, model: str = "") -> int:
    tk = _safe_import_tiktoken()
    if tk is None:
        return _approx_tokens(text)

    try:
        enc = tk.encoding_for_model(model) if model else tk.get_encoding("cl100k_base")
    except Exception:
        enc = tk.get_encoding("cl100k_base")

    return len(enc.encode(text or ""))


def _truncate_to_tokens(text: str, max_tokens: int, model: str = "") -> str:
    """
    Deterministic truncation. Uses tiktoken if available; otherwise approximates by char ratio.
    """
    if not text:
        return text

    tk = _safe_import_tiktoken()
    if tk is None:
        # approximate by chars
        total = _approx_tokens(text)
        if total <= max_tokens:
            return text
        ratio = max_tokens / max(1, total)
        cut = max(50, min(len(text), int(len(text) * ratio)))
        s = text[:cut]
        # tighten
        while _approx_tokens(s) > max_tokens and len(s) > 50:
            s = s[: int(len(s) * 0.9)]
        return s

    try:
        enc = tk.encoding_for_model(model) if model else tk.get_encoding("cl100k_base")
    except Exception:
        enc = tk.get_encoding("cl100k_base")

    ids = enc.encode(text or "")
    if len(ids) <= max_tokens:
        return text
    return enc.decode(ids[:max_tokens])


# ============================
# RAG hard limits
# ============================
RAG_TOPK_LIMIT = 4
RAG_CHUNK_TOKEN_LIMIT = 500
RAG_CONTEXT_TOKEN_LIMIT = 2000  # 4 * 500


# ----------------------------
# Helpers
# ----------------------------
def _clean_text(s: str) -> str:
    s = (s or "").replace("\r", "\n")
    s = re.sub(r"\n{3,}", "\n\n", s)
    return s.strip()


def _split_into_chunks(text: str, max_chars: int = 1400) -> List[str]:
    """
    Simple deterministic chunker:
    - split by blank lines
    - then pack into <= max_chars
    """
    text = _clean_text(text)
    if not text:
        return []

    paras = [p.strip() for p in text.split("\n\n") if p.strip()]
    chunks: List[str] = []
    buf = ""

    for p in paras:
        if not buf:
            buf = p
            continue

        if len(buf) + 2 + len(p) <= max_chars:
            buf = buf + "\n\n" + p
        else:
            chunks.append(buf)
            buf = p

    if buf:
        chunks.append(buf)

    return chunks


def _file_label(path: str) -> str:
    return os.path.basename(path) if path else "uploaded_file"


# ----------------------------
# Parsers
# ----------------------------
def _parse_pdf_to_text(path: str) -> List[Tuple[str, str]]:
    """
    Returns list of (section_label, text)
    section_label uses page numbers.
    """
    reader = PdfReader(path)
    out: List[Tuple[str, str]] = []
    for i, page in enumerate(reader.pages):
        t = page.extract_text() or ""
        t = _clean_text(t)
        if t:
            out.append((f"p{i+1}", t))
    return out


def _parse_docx_to_text(path: str) -> List[Tuple[str, str]]:
    doc = Document(path)
    paras = [p.text.strip() for p in doc.paragraphs if p.text and p.text.strip()]
    if not paras:
        return []
    full = "\n\n".join(paras)
    return [("docx", _clean_text(full))]


def _parse_pptx_to_text(path: str) -> List[Tuple[str, str]]:
    prs = Presentation(path)
    out: List[Tuple[str, str]] = []
    for idx, slide in enumerate(prs.slides, start=1):
        lines: List[str] = []
        for shape in slide.shapes:
            if hasattr(shape, "text") and shape.text:
                txt = shape.text.strip()
                if txt:
                    lines.append(txt)
        if lines:
            out.append((f"slide{idx}", _clean_text("\n".join(lines))))
    return out


# ----------------------------
# Public API
# ----------------------------
def build_rag_chunks_from_file(path: str, doc_type: str) -> List[Dict]:
    """
    Build RAG chunks from a local file path.
    Supports: .pdf / .docx / .pptx / .txt
    """
    if not path or not os.path.exists(path):
        return []

    ext = os.path.splitext(path)[1].lower()
    source_file = _file_label(path)

    sections: List[Tuple[str, str]] = []
    try:
        if ext == ".pdf":
            sections = _parse_pdf_to_text(path)
        elif ext == ".docx":
            sections = _parse_docx_to_text(path)
        elif ext == ".pptx":
            sections = _parse_pptx_to_text(path)
        elif ext in [".txt", ".md"]:
            with open(path, "r", encoding="utf-8", errors="ignore") as f:
                sections = [("text", _clean_text(f.read()))]
        else:
            print(f"[rag_engine] unsupported file type: {ext}")
            return []
    except Exception as e:
        print(f"[rag_engine] parse error for {source_file}: {repr(e)}")
        return []

    chunks: List[Dict] = []
    for section, text in sections:
        for j, piece in enumerate(_split_into_chunks(text), start=1):
            chunks.append(
                {
                    "text": piece,
                    "source_file": source_file,
                    "section": f"{section}#{j}",
                    "doc_type": doc_type,
                }
            )

    return chunks


def retrieve_relevant_chunks(
    query: str,
    chunks: List[Dict],
    k: int = RAG_TOPK_LIMIT,
    max_context_chars: int = 600,  # kept for backward compatibility (still used as a safety cap)
    min_score: int = 6,
    chunk_token_limit: int = RAG_CHUNK_TOKEN_LIMIT,
    max_context_tokens: int = RAG_CONTEXT_TOKEN_LIMIT,
    model_for_tokenizer: str = "",
) -> Tuple[str, List[Dict]]:
    """
    Deterministic lightweight retrieval (no embeddings):
    - score by token overlap
    - return top-k chunks concatenated as context

    Hard limits implemented:
    - top-k <= 4 (default)
    - each chunk <= 500 tokens
    - total context <= 2000 tokens (default)
    """
    query = _clean_text(query)
    if not query or not chunks:
        return "", []

    # ✅ Short query gate: avoid wasting time on RAG for greetings / tiny inputs
    q_tokens_list = re.findall(r"[a-zA-Z0-9]+", query.lower())
    if (len(q_tokens_list) < 3) and (len(query) < 20):
        return "", []

    q_tokens = set(q_tokens_list)
    if not q_tokens:
        return "", []

    scored: List[Tuple[int, Dict]] = []
    for c in chunks:
        text = (c.get("text") or "")
        if not text:
            continue
        t_tokens = set(re.findall(r"[a-zA-Z0-9]+", text.lower()))
        score = len(q_tokens.intersection(t_tokens))
        if score >= min_score:
            scored.append((score, c))

    if not scored:
        return "", []

    scored.sort(key=lambda x: x[0], reverse=True)

    # hard cap k
    k = min(int(k or RAG_TOPK_LIMIT), RAG_TOPK_LIMIT)
    top = [c for _, c in scored[:k]]

    # truncate each chunk to <= chunk_token_limit
    used: List[Dict] = []
    truncated_texts: List[str] = []
    total_tokens = 0

    for c in top:
        raw = c.get("text") or ""
        if not raw:
            continue

        t = _truncate_to_tokens(raw, max_tokens=chunk_token_limit, model=model_for_tokenizer)

        # enforce total context tokens cap
        t_tokens = _count_text_tokens(t, model=model_for_tokenizer)
        if total_tokens + t_tokens > max_context_tokens:
            remaining = max_context_tokens - total_tokens
            if remaining <= 0:
                break
            t = _truncate_to_tokens(t, max_tokens=remaining, model=model_for_tokenizer)
            t_tokens = _count_text_tokens(t, model=model_for_tokenizer)

        # legacy char cap safety (keep your previous behavior as extra guard)
        if max_context_chars and max_context_chars > 0:
            # approximate: don't let total string blow up
            current_chars = sum(len(x) for x in truncated_texts)
            if current_chars + len(t) > max_context_chars:
                t = t[: max(0, max_context_chars - current_chars)]

        t = _clean_text(t)
        if not t:
            continue

        truncated_texts.append(t)
        used.append(c)
        total_tokens += t_tokens

        if total_tokens >= max_context_tokens:
            break

    if not truncated_texts:
        return "", []

    context = "\n\n---\n\n".join(truncated_texts)
    return context, used