"""Document ingestion pipeline — .docx files to Qdrant vectors."""

import os
import re
from docx import Document

from app.config import DOCS_DIR, CHUNK_SIZE, CHUNK_OVERLAP, COLLECTION_NAME
from app.retriever import get_retriever


def find_all_docx(root_dir: str) -> list[str]:
    """Recursively find all .docx files under root_dir."""
    docx_files = []
    for dirpath, dirnames, filenames in os.walk(root_dir):
        for fname in filenames:
            if fname.endswith(".docx") and not fname.startswith("~$"):
                docx_files.append(os.path.join(dirpath, fname))
    return docx_files


def extract_text_from_docx(filepath: str) -> str:
    """Extract all text from a .docx file."""
    doc = Document(filepath)
    paragraphs = []
    for para in doc.paragraphs:
        text = para.text.strip()
        if text:
            paragraphs.append(text)

    # Also extract text from tables
    for table in doc.tables:
        for row in table.rows:
            row_text = " | ".join(cell.text.strip() for cell in row.cells if cell.text.strip())
            if row_text:
                paragraphs.append(row_text)

    return "\n".join(paragraphs)


def clean_text(text: str) -> str:
    """Normalize and clean extracted text."""
    # Normalize whitespace
    text = re.sub(r"\s+", " ", text)
    # Remove special unicode characters
    text = text.encode("ascii", errors="ignore").decode("ascii")
    # Strip leading/trailing whitespace
    text = text.strip()
    return text


def chunk_text(text: str, chunk_size: int = CHUNK_SIZE, overlap: int = CHUNK_OVERLAP) -> list[str]:
    """Split text into overlapping chunks."""
    if not text:
        return []

    chunks = []
    start = 0
    text_len = len(text)

    while start < text_len:
        end = start + chunk_size

        # Try to break at a sentence boundary
        if end < text_len:
            # Look for sentence-ending punctuation near the chunk boundary
            search_zone = text[max(end - 80, start):end]
            last_period = search_zone.rfind(". ")
            last_newline = search_zone.rfind("\n")
            break_point = max(last_period, last_newline)

            if break_point != -1:
                end = max(end - 80, start) + break_point + 1

        chunk = text[start:end].strip()
        if chunk and len(chunk) > 30:  # Skip tiny fragments
            chunks.append(chunk)

        start = end - overlap if end < text_len else text_len

    return chunks


def build_metadata(filepath: str, docs_root: str) -> dict:
    """Build metadata dict from file path."""
    rel_path = os.path.relpath(filepath, docs_root)
    parts = rel_path.replace("\\", "/").split("/")

    source_file = parts[-1]
    folder = "/".join(parts[:-1]) if len(parts) > 1 else "root"

    # Infer department/grouping from folder structure
    department = parts[1] if len(parts) > 2 else parts[0] if len(parts) > 1 else "general"

    return {
        "source_file": source_file,
        "folder": folder,
        "department": department,
        "relative_path": rel_path,
    }


def ingest_all_documents():
    """Main ingestion pipeline: find → extract → chunk → embed → store."""
    print(f"Scanning for documents in: {DOCS_DIR}")
    docx_files = find_all_docx(DOCS_DIR)
    print(f"Found {len(docx_files)} .docx files")

    if not docx_files:
        print("No documents found. Exiting.")
        return

    retriever = get_retriever()

    # Ensure collection exists
    retriever.ensure_collection()

    # Clear existing data for clean re-ingestion
    try:
        retriever.client.delete_collection(COLLECTION_NAME)
        print(f"Cleared existing collection: {COLLECTION_NAME}")
        retriever.ensure_collection()
    except Exception:
        pass

    all_chunks = []
    total_chars = 0

    for i, filepath in enumerate(docx_files, 1):
        rel_path = os.path.relpath(filepath, DOCS_DIR)
        print(f"  [{i}/{len(docx_files)}] Processing: {rel_path}")

        try:
            raw_text = extract_text_from_docx(filepath)
            cleaned = clean_text(raw_text)
            total_chars += len(cleaned)

            if not cleaned:
                print(f"    → Skipped (empty after cleaning)")
                continue

            metadata = build_metadata(filepath, DOCS_DIR)
            chunks = chunk_text(cleaned)
            print(f"    → {len(chunks)} chunks ({len(cleaned)} chars)")

            for idx, chunk in enumerate(chunks):
                all_chunks.append({
                    "text": chunk,
                    "metadata": {**metadata, "chunk_index": idx},
                })

        except Exception as e:
            print(f"    → ERROR: {e}")

    print(f"\nTotal: {len(all_chunks)} chunks from {len(docx_files)} files ({total_chars:,} chars)")
    print("Embedding and uploading to Qdrant...")

    # Upsert all chunks
    retriever.upsert_chunks(all_chunks)

    # Verify
    info = retriever.get_collection_info()
    print(f"\nDone! Collection info: {info}")


if __name__ == "__main__":
    ingest_all_documents()