Nevidljivi Zakoni Ljubavi Pdf May 2026

In a world that often speaks of love as a matter of luck, chemistry, or chance, the book "Nevidljivi zakoni ljubavi" (The Invisible Laws of Love) takes a radically different approach. It argues that love, much like gravity or electromagnetism, operates according to a set of unseen but deeply predictable principles.

For readers in the Balkans and beyond, this book has become a quiet bestseller—a manual for those tired of chaotic relationships and seeking a logical, almost scientific framework for emotional connection. nevidljivi zakoni ljubavi pdf

The search term "nevidljivi zakoni ljubavi pdf" is very common for two reasons. First, the book is often discussed in therapy groups and online forums, creating a demand for immediate access. Second, many of the editions are printed in small runs (primarily in Serbian, Croatian, or Bosnian), making physical copies hard to find outside of major bookstores in the region. In a world that often speaks of love

However, it is important to note that as of 2026, a legal, free PDF of the full book is generally not available for public download. Most versions are still under copyright protection. import requests from urllib

The "invisible laws of love" generally refer to the unspoken, universal principles that govern love, relationships, and emotional connections between people. These laws are not necessarily written down but are believed to influence how love works, how relationships evolve, and how individuals interact within those relationships.

pip install requests beautifulsoup4 pdfplumber openai chromadb fastapi uvicorn python-multipart aiohttp

import requests
from urllib.parse import urljoin
def download_pdf(url, dest_path):
    r = requests.get(url, stream=True, timeout=30)
    r.raise_for_status()
    with open(dest_path, 'wb') as f:
        for chunk in r.iter_content(8192):
            f.write(chunk)
    return dest_path

import pdfplumber
def extract_text_from_pdf(path):
    texts = []
    with pdfplumber.open(path) as pdf:
        for page in pdf.pages:
            texts.append(page.extract_text() or "")
    return "\n".join(texts)

import tiktoken  # optional token counting; otherwise split by chars
def chunk_text(text, max_chars=3000):
    chunks = []
    start = 0
    while start < len(text):
        chunk = text[start:start+max_chars]
        chunks.append(chunk)
        start += max_chars
    return chunks

from openai import OpenAI
import chromadb
client = OpenAI(api_key="OPENAI_KEY")
# embeddings via openai
def get_embedding(text):
    res = client.embeddings.create(model="text-embedding-3-small", input=text)
    return res.data[0].embedding
# Chroma
import chromadb
from chromadb.config import Settings
chroma_client = chromadb.Client(Settings(chroma_db_impl="duckdb+parquet", persist_directory="./chroma_db"))
col = chroma_client.create_collection("nevidljivi_zakoni")
def index_chunks(chunks, metadata_list):
    ids=[]
    embs=[]
    for i,chunk in enumerate(chunks):
        emb = get_embedding(chunk)
        ids.append(f"doc_i")
        embs.append(emb)
    col.add(ids=ids, embeddings=embs, documents=chunks, metadatas=metadata_list)
    chroma_client.persist()

from openai import OpenAI
client = OpenAI(api_key="OPENAI_KEY")
def summarize_long(text, length="short"):
    prompt_short = f"Sažmi sljedeći tekst u jednu kratku točku (20-40 riječi):\n\ntext"
    prompt_long = f"Napiši detaljan sažetak (300-500 riječi) i izdvoji 10 ključnih točaka:\n\ntext"
    prompt = prompt_short if length=="short" else prompt_long
    resp = client.chat.completions.create(model="gpt-4o-mini", messages=["role":"user","content":prompt], max_tokens=800)
    return resp.choices[0].message.content

(Napomena: prilagodite model imena prema svom pristupu.)

def retrieve(query, k=4):
    q_emb = get_embedding(query)
    res = col.query(query_embeddings=[q_emb], n_results=k, include=["documents","metadatas"])
    return res
def answer_query(query):
    hits = retrieve(query)
    context = "\n\n---\n\n".join(h['document'] for h in hits['documents'][0])
    prompt = f"Koristi sljedeći kontekst za odgovor na pitanje; navedi izvore (stranicu/segment id) ako je moguće.\n\nKontekst:\ncontext\n\nPitanje: query\nOdgovor:"
    resp = client.chat.completions.create(model="gpt-4o-mini", messages=["role":"user","content":prompt], max_tokens=500)
    return resp.choices[0].message.content