import numpy as np
import json
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')

def load_knowledge():
    embeddings = np.load("knowledge/embeddings.npy")
    with open("knowledge/chunks.json", "r", encoding="utf-8") as f:
        chunks = json.load(f)
    return embeddings, chunks

def find_relevant_context(query, top_k=3):
    emb, chunks = load_knowledge()
    query_emb = model.encode([query])
    scores = np.dot(emb, query_emb.T).flatten()
    best_idx = np.argsort(scores)[-top_k:][::-1]
    context = ""
    for i in best_idx:
        if scores[i] > 0.2:
            context += f"[{chunks[i]['source']}] {chunks[i]['title']}: {chunks[i]['text']}\n\n"
    return context.strip()