import asyncio

from agentscope.message import Msg
from reme.reme_light import ReMeLight


async def main():
    reme = ReMeLight(
        working_dir=".reme",  # Memory file storage directory
        max_input_length=128000,  # Model context window (tokens)
        memory_compact_ratio=0.7,  # Trigger compaction when reaching max_input_length * 0.7
        language="zh",  # Summary language (zh / "")
        tool_result_threshold=1000,  # Auto-save tool outputs exceeding this character count
        retention_days=7,  # tool_result/ file retention days
    )
    await reme.start()

    messages = [...]

    # 1. Compact oversized tool outputs (prevent tool results from overflowing context)
    messages = await reme.compact_tool_result(messages)

    # 2. Compact history to structured summary (trigger: context approaching limit), can pass previous summary for incremental update
    summary = await reme.compact_memory(messages=messages, previous_summary="")

    # 3. Submit async summary task in background (non-blocking, writes to memory/YYYY-MM-DD.md)
    reme.add_async_summary_task(messages=messages)

    # 4. Semantic memory search (Vector + BM25 hybrid retrieval)
    result = await reme.memory_search(query="Python version preference", max_results=5)

    # 5. Get in-memory instance (ReMeInMemoryMemory, manages single conversation context) AgentScope InMemoryMemory
    memory = reme.get_in_memory_memory()
    token_stats = await memory.estimate_tokens()
    print(f"Current context usage: {token_stats['context_usage_ratio']:.1f}%")
    print(f"Message tokens: {token_stats['messages_tokens']}")
    print(f"Estimated total tokens: {token_stats['estimated_tokens']}")

    # 6. Wait for background tasks before closing
    summary_result = await reme.await_summary_tasks()

    # Close ReMeLight
    await reme.close()


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

import asyncio

from reme import ReMe


async def main():
    # Initialize ReMe
    reme = ReMe(
        working_dir=".reme",
        default_llm_config={
            "backend": "openai",
            "model_name": "qwen3.5-plus",
        },
        default_embedding_model_config={
            "backend": "openai",
            "model_name": "text-embedding-v4",
            "dimensions": 1024,
        },
        default_vector_store_config={
            "backend": "local",  # Supports local/chroma/qdrant/elasticsearch
        },
    )
    await reme.start()

    messages = [
        {"role": "user", "content": "Help me write a Python script", "time_created": "2026-02-28 10:00:00"},
        {"role": "assistant", "content": "Sure, I'll help you write it", "time_created": "2026-02-28 10:00:05"},
    ]

    # 1. Summarize memory from conversation (auto-extract user preferences, task experience, etc.)
    result = await reme.summarize_memory(
        messages=messages,
        user_name="alice",  # Personal memory
        # task_name="code_writing",  # Task memory
    )
    print(f"Summarize result: {result}")

    # 2. Retrieve relevant memory
    memories = await reme.retrieve_memory(
        query="Python programming",
        user_name="alice",
        # task_name="code_writing",
    )
    print(f"Retrieve result: {memories}")

    # 3. Manually add memory
    memory_node = await reme.add_memory(
        memory_content="User prefers concise code style",
        user_name="alice",
    )
    print(f"Added memory: {memory_node}")
    memory_id = memory_node.memory_id

    # 4. Get single memory by ID
    fetched_memory = await reme.get_memory(memory_id=memory_id)
    print(f"Fetched memory: {fetched_memory}")

    # 5. Update memory content
    updated_memory = await reme.update_memory(
        memory_id=memory_id,
        user_name="alice",
        memory_content="User prefers concise, well-commented code style",
    )
    print(f"Updated memory: {updated_memory}")

    # 6. List all memories for user (with filtering and sorting)
    all_memories = await reme.list_memory(
        user_name="alice",
        limit=10,
        sort_key="time_created",
        reverse=True,
    )
    print(f"User memory list: {all_memories}")

    # 7. Delete specified memory
    await reme.delete_memory(memory_id=memory_id)
    print(f"Deleted memory: {memory_id}")

    # 8. Delete all memories (use with caution)
    # await reme.delete_all()

    await reme.close()


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