"""
"Built on Rock" — Pinecone Ingestion Script
──────────────────────────────────────────────────────────────────────────────
Run this ONCE after setting up your Pinecone index to populate it with
content from "Built on Rock" by Iyanuoluwa Joseph Akinnusi.

Prerequisites:
  1. Create a Pinecone index named 'venthrive-bff' with:
     - Dimensions: 1024
     - Metric: Cosine
     - Cloud: AWS us-east-1 (Serverless, free tier)
  2. Set PINECONE_API_KEY in backend/.env
  3. Ensure the PDF is in the APEX folder (path configured below)

Usage (from the VenThrive/backend/ directory):
    python scripts/ingest_book.py

No local ML library needed — embeddings are generated by Pinecone's
hosted multilingual-e5-large model (1024 dimensions).
"""

import os
import sys
import time

# Load backend env
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from dotenv import load_dotenv
load_dotenv(os.path.join(os.path.dirname(os.path.dirname(__file__)), ".env"))

from pinecone import Pinecone
import pypdf

# ─── Config ───────────────────────────────────────────────────────────────────
# Absolute path — the PDF lives in the sibling APEX project folder
_BASE = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
PDF_PATH = os.path.join(_BASE, "APEX", "Built On Rock by Iyanuoluwa Joseph Akinnusi-1.pdf")
CHUNK_WORDS   = 400
OVERLAP_WORDS = 50
BATCH_SIZE    = 90   # Pinecone Inference allows up to 96 inputs per request
EMBED_MODEL   = "multilingual-e5-large"


def extract_pdf_text(path: str) -> str:
    if not os.path.exists(path):
        raise FileNotFoundError(
            f"\n[ERR] PDF not found: {path}\n"
            "Ensure 'Built On Rock by Iyanuoluwa Joseph Akinnusi-1.pdf' "
            "is in the APEX folder."
        )
    reader = pypdf.PdfReader(path)
    return "\n".join(page.extract_text() or "" for page in reader.pages)


def chunk_text(text: str, chunk_words: int, overlap_words: int) -> list[dict]:
    words  = text.split()
    chunks = []
    i = 0
    while i < len(words):
        chunk_text_str = " ".join(words[i : i + chunk_words])
        chunks.append({
            "id":   f"chunk_{len(chunks):04d}",
            "text": chunk_text_str,
            "meta": {
                "source":     "built_on_rock",
                "chunk_id":   len(chunks),
                "word_start": i,
                "word_end":   min(i + chunk_words, len(words)),
            },
        })
        i += chunk_words - overlap_words
    return chunks


def main():
    api_key    = os.environ.get("PINECONE_API_KEY", "")
    index_name = os.environ.get("PINECONE_INDEX_NAME", "venthrive-bff")

    if not api_key:
        print("[ERR] PINECONE_API_KEY not set in backend/.env")
        return

    # ── Extract text ──────────────────────────────────────────────────────────
    print(f"[PDF] Reading: {os.path.basename(PDF_PATH)}")
    text = extract_pdf_text(PDF_PATH)
    words = len(text.split())
    print(f"   {words:,} words extracted from PDF")

    # ── Chunk ─────────────────────────────────────────────────────────────────
    chunks = chunk_text(text, CHUNK_WORDS, OVERLAP_WORDS)
    print(f"   {len(chunks)} chunks ({CHUNK_WORDS} words each, {OVERLAP_WORDS} overlap)")

    # ── Connect to Pinecone ───────────────────────────────────────────────────
    print(f"\n[PIN] Connecting to Pinecone index '{index_name}'...")
    pc    = Pinecone(api_key=api_key)
    index = pc.Index(index_name)

    stats = index.describe_index_stats()
    existing = stats.get("total_vector_count", 0)
    print(f"   Existing vectors: {existing}")

    if existing > 0:
        print(f"   Index already has {existing} vectors.")
        ans = input("   Clear and re-ingest? (y/N): ").strip().lower()
        if ans == 'y':
            index.delete(delete_all=True)
            print("   ✓ Cleared")
        else:
            print("   Skipping ingestion.")
            return

    # ── Embed + upsert in batches ─────────────────────────────────────────────
    print(f"\n[AI] Embedding + uploading {len(chunks)} chunks via Pinecone Inference...")
    print("   (Model: multilingual-e5-large, 1024 dims — no local ML needed)")

    for i in range(0, len(chunks), BATCH_SIZE):
        batch = chunks[i : i + BATCH_SIZE]
        texts = [c["text"] for c in batch]

        # Generate embeddings via Pinecone Inference API
        embedding_result = pc.inference.embed(
            model=EMBED_MODEL,
            inputs=texts,
            parameters={"input_type": "passage", "truncate": "END"},
        )
        embeddings = [e.values for e in embedding_result]

        # Upsert vectors
        vectors = [
            {
                "id":       chunk["id"],
                "values":   embeddings[j],
                "metadata": {**chunk["meta"], "text": chunk["text"]},
            }
            for j, chunk in enumerate(batch)
        ]
        index.upsert(vectors=vectors)

        done = min(i + BATCH_SIZE, len(chunks))
        print(f"   {done}/{len(chunks)} chunks uploaded")

        # Small pause to stay within Pinecone rate limits
        if done < len(chunks):
            time.sleep(0.5)

    # ── Done ──────────────────────────────────────────────────────────────────
    final = index.describe_index_stats()
    total = final.get("total_vector_count", "?")
    print(f"\n[OK] Ingestion complete! Index '{index_name}' now has {total} vectors.")
    print("   VenThrive AI will now draw from 'Built on Rock' for every relevant query.")


if __name__ == "__main__":
    main()