проверь код: import models
from agent import AgentConfig
import requests

def initialize():
    
    # основной чат-модель, используемая агентами (умнее, точнее)
    #chat_llm = models.get_openai_chat(model_name="gpt-4o-mini", temperature=0)
    # chat_llm = models.get_ollama_chat(model_name="gemma2:latest", temperature=0)
    # chat_llm = models.get_lmstudio_chat(model_name="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF", temperature=0)
    # chat_llm = models.get_openrouter_chat(model_name="mattshumer/reflection-70b:free")
    # chat_llm = models.get_azure_openai_chat(deployment_name="gpt-4o-mini", temperature=0)
    # chat_llm = models.get_anthropic_chat(model_name="claude-3-5-sonnet-20240620", temperature=0)
    # chat_llm = models.get_google_chat(model_name="gemini-1.5-flash", temperature=0)
    chat_llm = models.get_groq_chat(model_name="llama-3.1-70b-versatile", temperature=0)
    
    # служебная модель, используемая для вспомогательных функций (дешевле, быстрее)
    utility_llm = chat_llm  # изменить, если хотите использовать другую служебную модель

    # модель встраивания, используемая для памяти
    #embedding_llm = models.get_openai_embedding(model_name="text-embedding-3-small")
    # embedding_llm = models.get_ollama_embedding(model_name="nomic-embed-text")
    # embedding_llm = models.get_huggingface_embedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
    # embedding_llm = models.get_lmstudio_embedding(model_name="nomic-ai/nomic-embed-text-v1.5-GGUF")

    # Запрос к openrouter.ai
    response = requests.post("https://openrouter.ai/api/v1/chat/completions", json={
        "model": "huggingfaceh4/zephyr-7b-beta:free",
        "messages": [
            {"role": "user", "content": "What is the meaning of life?"},
        ],
    }, headers={
        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
        "HTTP-Referer": f"{YOUR_SITE_URL}",  # Optional, for including your app on openrouter.ai rankings.
        "X-Title": f"{YOUR_SITE_NAME}",  # Optional. Shows in rankings on openrouter.ai.
        "Content-Type": "application/json"
    })

    # Обработка ответа
    if response.status_code == 200:
        embedding_llm = response.json()
    else:
        raise Exception(f"Failed to fetch embedding model: {response.status_code}")

    # конфигурация агента
    config = AgentConfig(
        chat_model = chat_llm,
        utility_model = utility_llm,
        embeddings_model = embedding_llm,
        # prompts_subdir = "",
        # memory_subdir = "",
        # knowledge_subdir: str = ""
        auto_memory_count = 0,
        # auto_memory_skip = 2,
        # rate_limit_seconds = 60,
        rate_limit_requests = 15,
        # rate_limit_input_tokens = 0,
        # rate_limit_output_tokens = 0,
        # msgs_keep_max = 25,
        # msgs_keep_start = 5,
        # msgs_keep_end = 10,
        max_tool_response_length = 3000,
        # response_timeout_seconds = 60,
        code_exec_docker_enabled = True,
        # code_exec_docker_name = "agent-zero-exe",
        # code_exec_docker_image = "frdel/agent-zero-exe:latest",
        # code_exec_docker_ports = { "22/tcp": 50022 }
        # code_exec_docker_volumes = { files.get_abs_path("work_dir"): {"bind": "/root", "mode": "rw"} }
        code_exec_ssh_enabled = True,
        # code_exec_ssh_addr = "localhost",
        # code_exec_ssh_port = 50022,
        # code_exec_ssh_user = "root",
        # code_exec_ssh_pass = "toor",
        # additional = {},
    )

    # возвращаем объект конфигурации
    return config