from langchain.vectorstores import Qdrant
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document
import os
import fitz  # PyMuPDF
from config import EMBEDDING_MODEL,QDRANT_HOST,QDRANT_API_KEY 

embedding_model = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)

def extract_text_from_pdf(pdf_path):
    if not os.path.exists(pdf_path):
        raise FileNotFoundError(f"File not found: {pdf_path}")
    
    doc = fitz.open(pdf_path)
    text = "\n".join([page.get_text("text") for page in doc])
    return text

def load_pdf_data(pdf_path):
    text = extract_text_from_pdf(pdf_path)
    
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=2000,  
        chunk_overlap=100,  
    )

    chunks = splitter.split_text(text)
    
    documents = [
        Document(page_content=chunk, metadata={"source": pdf_path})
        for chunk in chunks
    ]
    return documents

def get_vector_db():
    qdrant_url = QDRANT_HOST  
    api_key = QDRANT_API_KEY
    collection_name = "discvr_embeddings"
    
    docs = load_pdf_data("data/Explorer.pdf")
    
    vector_db = Qdrant.from_documents(
        docs, embedding_model,
        location=qdrant_url,
        collection_name=collection_name,
        api_key=api_key,
        timeout=500
    )
    return vector_db

def retrieve_info(query, k=20):
    vector_db = get_vector_db()
    docs = vector_db.similarity_search(query, k=k)
    return "\n".join([doc.page_content for doc in docs])