import os
import fitz  # PyMuPDF
from qdrant_client import QdrantClient
from qdrant_client.http import models
import requests

LOCALAI_URL = os.getenv("LOCALAI_URL", "http://localhost:8500")
QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333")

COLLECTION_NAME = "planning_docs"

qdrant = QdrantClient(url=QDRANT_URL)

def embed_text(text: str):
    r = requests.post(
        f"{LOCALAI_URL}/v1/embeddings",
        json={"model": "nomic-embed-text", "input": text}
    )
    resp = r.json()
    if "data" not in resp:
        print("Embedding error:", resp)
        raise ValueError(f"Embedding failed: {resp}")
    return resp["data"][0]["embedding"]

def chunk_text(text, chunk_size=500, overlap=50):
    words = text.split()
    chunks = []
    for i in range(0, len(words), chunk_size - overlap):
        chunk = " ".join(words[i:i+chunk_size])
        chunks.append(chunk)
    return chunks

def extract_text_from_pdf(pdf_path):
    doc = fitz.open(pdf_path)
    text = ""
    for page in doc:
        text += page.get_text("text") + "\n"
    return text

def ensure_collection():
    if COLLECTION_NAME not in [c.name for c in qdrant.get_collections().collections]:
        qdrant.create_collection(
            collection_name=COLLECTION_NAME,
            vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE)
        )

def ingest_pdfs(pdf_folder):
    ensure_collection()
    for filename in os.listdir(pdf_folder):
        if not filename.endswith(".pdf"):
            continue
        path = os.path.join(pdf_folder, filename)
        print(f"Processing {path}...")
        text = extract_text_from_pdf(path)
        chunks = chunk_text(text)

        payloads = []
        vectors = []
        for idx, chunk in enumerate(chunks):
            embedding = embed_text(chunk)
            payloads.append({
                "source": f"{filename} (chunk {idx})",
                "text": chunk
            })
            vectors.append(embedding)

        qdrant.upsert(
            collection_name=COLLECTION_NAME,
            points=models.Batch(
                ids=[f"{filename}-{i}" for i in range(len(chunks))],
                vectors=vectors,
                payloads=payloads
            )
        )

if __name__ == "__main__":
    ingest_pdfs("./pdfs")