"""Hello World Async Example.

This example demonstrates how to use langmem0 ChatOpenAI with async support.

## 步骤说明

1.  Import necessary libraries.
2.  Configure mem0 with vector store and embedder.
3.  Initialize the ChatOpenAI model.
4.  Store preferences using async invoke.
5.  Retrieve memory in later conversations.

**注意:** 运行此示例需要已安装 `langchain-huggingface` 和 `python-dotenv`.

Sample code reference: https://docs.mem0.ai/open-source/features/openai_compatibility.
"""

import asyncio
import os

import dotenv

from langmem0 import ChatOpenAI


async def main():
    """Main async function to run the chat example."""
    dotenv.load_dotenv()

    # Set log level to INFO and specify output format
    # import logging
    # logging.basicConfig(
    #     level=logging.DEBUG,
    #     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    # )

    # https://github.com/mem0ai/mem0/blob/v1.0.0/mem0/configs/llms/openai.py#L6
    llm = {
        "provider": "openai",
        "config": {
            "model": os.environ["OPENAI_MODEL"],
            "temperature": 0.1,
            "api_key": os.environ["OPENAI_API_KEY"],
            "openai_base_url": os.environ["OPENAI_API_BASE_URL"],
        },
    }

    embedding_model_name = "sentence-transformers/all-mpnet-base-v2"
    # model_name='BAAI/bge-small-en-v1.5'
    # embedding = HuggingFaceEmbeddings(model_name=embedding_model_name)

    # https://github.com/mem0ai/mem0/blob/v1.0.0/mem0/configs/vector_stores/langchain.py#L6
    # https://docs.mem0.ai/components/vectordbs/dbs/langchain
    # vector_store = {
    #     "provider": "langchain",
    #     "config": {
    #         "client": Chroma(
    #             collection_name="mem0",
    #             embedding_function=embedding,
    #             # Where to save data locally, remove if not necessary
    #             persist_directory="./chroma",
    #         )
    #     },
    # }

    # https://github.com/mem0ai/mem0/blob/v1.0.0/mem0/configs/vector_stores/faiss.py#L6
    # https://docs.mem0.ai/components/vectordbs/dbs/faiss
    vector_store = {
        "provider": "faiss",
        "config": {"path": "./_data/faiss", "embedding_model_dims": 768},
    }

    embedder = {
        "provider": "huggingface",
        "config": {"model": embedding_model_name},
    }

    config = {
        "vector_store": vector_store,
        "llm": llm,
        "embedder": embedder,
    }

    model = ChatOpenAI(
        api_key=os.environ["OPENAI_API_KEY"],
        base_url=os.environ["OPENAI_API_BASE_URL"],
        model=os.environ["OPENAI_MODEL"],
        user_id="alice",
        mem0=config,
    )

    # Store preferences
    _r = await model.ainvoke("I love Indian food but I'm allergic to cheese.")

    # Due to hardware performance, background memorizing task backed by local
    # vector store may be slow.
    # Wait for the background memorizing task to finish.
    # If your hardware is fast, just remove this sleep.
    await asyncio.sleep(60)

    # Later conversation reuses the memory
    r = await model.ainvoke("Suggest dinner options in San Francisco.")

    r.pretty_print()

    # Sample output
    # ================================== Ai Message ================================== # noqa: E501
    #
    # Since you love Indian food and are allergic to cheese, here are some
    # dinner options for you in San Francisco:
    #
    # *   **Indian Cuisine:** This is a great choice for you. Many dishes are
    # naturally cheese-free. Look for tandoori items, curries made with coconut
    # or yogurt bases (instead of cream), and dal dishes. Just be sure to
    # confirm there is no paneer (Indian cheese) in your order.
    #
    # *   **Thai or Vietnamese Food:** These cuisines rarely use cheese. You
    # can enjoy a variety of curries, pho, stir-fries, and fresh spring rolls
    # without worrying about your allergy.
    #
    # *   **Mediterranean / Middle Eastern Food:** Excellent for a cheese-free
    # meal. You can have grilled kebabs, falafel, hummus, and salads. Just
    # make sure to request that they hold the feta cheese.


if __name__ == "__main__":
    asyncio.run(main())