### llm_client.py

import ollama
import numpy as np
from prompt import SYSTEM_PROMPT

class LlmClient:
    def __init__(self, model_name: str):
        self.model_name = model_name

    def generate_text(self, prompt: str) -> str:
        response = ollama.chat(
            model=self.model_name,
            messages=[
                {"role": "system", "content": SYSTEM_PROMPT.strip()},
                {"role": "user", "content": prompt.strip()}
            ]
        )
        return response['message']['content'].strip()

    def get_pseudo_embedding(self, text: str):
        # Gerçek embedding kullanılmıyor, basit temsil (ortalama ASCII)
        vec = np.array([ord(c) for c in text if ord(c) < 128], dtype=np.float32)
        padded = np.zeros(768, dtype=np.float32)
        padded[:min(768, len(vec))] = vec[:768]
        return padded