import asyncio
from reme_ai import ReMeApp

async def main():
    async with ReMeApp(
        "llm.default.model_name=qwen3-30b-a3b-thinking-2507",
        "embedding_model.default.model_name=text-embedding-v4",
        "vector_store.default.backend=memory"
    ) as app:
        # 经验总结：从执行轨迹中学习
        result = await app.async_execute(
            name="summary_task_memory",
            workspace_id="task_workspace",
            trajectories=[
                {
                    "messages": [
                        {"role": "user", "content": "Help me create a project plan"}
                    ],
                    "score": 1.0
                }
            ]
        )
        print(result)

        # 记忆检索：获取相关经验
        result = await app.async_execute(
            name="retrieve_task_memory",
            workspace_id="task_workspace",
            query="How to efficiently manage project progress?",
            top_k=1
        )
        print(result)

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

# 经验总结：从执行轨迹中学习
curl -X POST http://localhost:8002/summary_task_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "trajectories": [
      {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
    ]
  }'

# 记忆检索：获取相关经验
curl -X POST http://localhost:8002/retrieve_task_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "query": "How to efficiently manage project progress?",
    "top_k": 1
  }'

import asyncio
from reme_ai import ReMeApp

async def main():
    async with ReMeApp(
        "llm.default.model_name=qwen3-30b-a3b-thinking-2507",
        "embedding_model.default.model_name=text-embedding-v4",
        "vector_store.default.backend=memory"
    ) as app:
        # 记忆整合：从用户交互中学习
        result = await app.async_execute(
            name="summary_personal_memory",
            workspace_id="task_workspace",
            trajectories=[
                {
                    "messages": [
                        {"role": "user", "content": "I like to drink coffee while working in the morning"},
                        {"role": "assistant",
                         "content": "I understand, you prefer to start your workday with coffee to stay energized"}
                    ]
                }
            ]
        )
        print(result)

        # 记忆检索：获取个人记忆片段
        result = await app.async_execute(
            name="retrieve_personal_memory",
            workspace_id="task_workspace",
            query="What are the user's work habits?",
            top_k=5
        )
        print(result)

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

# 记忆整合：从用户交互中学习
curl -X POST http://localhost:8002/summary_personal_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "trajectories": [
      {"messages": [
        {"role": "user", "content": "I like to drink coffee while working in the morning"},
        {"role": "assistant", "content": "I understand, you prefer to start your workday with coffee to stay energized"}
      ]}
    ]
  }'

# 记忆检索：获取个人记忆片段
curl -X POST http://localhost:8002/retrieve_personal_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "query": "What are the user'\''s work habits?",
    "top_k": 5
  }'

import asyncio
from reme_ai import ReMeApp

async def main():
    async with ReMeApp(
        "llm.default.model_name=qwen3-30b-a3b-thinking-2507",
        "embedding_model.default.model_name=text-embedding-v4",
        "vector_store.default.backend=memory"
    ) as app:
        # 记录工具调用结果
        result = await app.async_execute(
            name="add_tool_call_result",
            workspace_id="tool_workspace",
            tool_call_results=[
                {
                    "create_time": "2025-10-21 10:30:00",
                    "tool_name": "web_search",
                    "input": {"query": "Python asyncio tutorial", "max_results": 10},
                    "output": "Found 10 relevant results...",
                    "token_cost": 150,
                    "success": True,
                    "time_cost": 2.3
                }
            ]
        )
        print(result)

        # 从历史生成使用指南
        result = await app.async_execute(
            name="summary_tool_memory",
            workspace_id="tool_workspace",
            tool_names="web_search"
        )
        print(result)

        # 在使用前检索工具指南
        result = await app.async_execute(
            name="retrieve_tool_memory",
            workspace_id="tool_workspace",
            tool_names="web_search"
        )
        print(result)

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

# 记录工具调用结果
curl -X POST http://localhost:8002/add_tool_call_result \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "tool_workspace",
    "tool_call_results": [
      {
        "create_time": "2025-10-21 10:30:00",
        "tool_name": "web_search",
        "input": {"query": "Python asyncio tutorial", "max_results": 10},
        "output": "Found 10 relevant results...",
        "token_cost": 150,
        "success": true,
        "time_cost": 2.3
      }
    ]
  }'

# 从历史生成使用指南
curl -X POST http://localhost:8002/summary_tool_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "tool_workspace",
    "tool_names": "web_search"
  }'

# 在使用前检索工具指南
curl -X POST http://localhost:8002/retrieve_tool_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "tool_workspace",
    "tool_names": "web_search"
  }'

import asyncio
from reme_ai import ReMeApp


async def main():
    async with ReMeApp(
        "llm.default.model_name=qwen3-30b-a3b-thinking-2507",
        "embedding_model.default.model_name=text-embedding-v4",
        "vector_store.default.backend=memory"
    ) as app:
        # 对长对话 / 长流程的工作记忆进行压缩与总结
        result = await app.async_execute(
            name="summary_working_memory",
            messages=[
                {
                    "role": "system",
                    "content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
                },
                {
                    "role": "user",
                    "content": "搜索下reme项目的的README内容"
                },
                {
                    "role": "assistant",
                    "content": "",
                    "tool_calls": [
                        {
                            "index": 0,
                            "id": "call_6596dafa2a6a46f7a217da",
                            "function": {
                                "arguments": "{\"query\": \"readme\"}",
                                "name": "web_search"
                            },
                            "type": "function"
                        }
                    ]
                },
                {
                    "role": "tool",
                    "content": "ultra large context , over 50000 tokens......"
                },
                {
                    "role": "user",
                    "content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
                }
            ],
            working_summary_mode="auto",
            compact_ratio_threshold=0.75,
            max_total_tokens=20000,
            max_tool_message_tokens=2000,
            group_token_threshold=4000,
            keep_recent_count=2,
            store_dir="test_working_memory",
            chat_id="demo_chat_id",
        )
        print(result)


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

curl -X POST http://localhost:8002/summary_working_memory \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
      },
      {
        "role": "user",
        "content": "搜索下reme项目的的README内容"
      },
      {
        "role": "assistant",
        "content": "",
        "tool_calls": [
          {
            "index": 0,
            "id": "call_6596dafa2a6a46f7a217da",
            "function": {
              "arguments": "{\"query\": \"readme\"}",
              "name": "web_search"
            },
            "type": "function"
          }
        ]
      },
      {
        "role": "tool",
        "content": "ultra large context , over 50000 tokens......"
      },
      {
        "role": "user",
        "content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
      }
    ],
    "working_summary_mode": "auto",
    "compact_ratio_threshold": 0.75,
    "max_total_tokens": 20000,
    "max_tool_message_tokens": 2000,
    "group_token_threshold": 4000,
    "keep_recent_count": 2,
    "store_dir": "test_working_memory",
    "chat_id": "demo_chat_id"
  }'

import asyncio
from reme_ai import ReMeApp

async def main():
    async with ReMeApp(
        "llm.default.model_name=qwen3-30b-a3b-thinking-2507",
        "embedding_model.default.model_name=text-embedding-v4",
        "vector_store.default.backend=memory"
    ) as app:
        # 加载内置记忆
        result = await app.async_execute(
            name="vector_store",
            workspace_id="appworld",
            action="load",
            path="./docs/library/"
        )
        print(result)

        # 查询相关记忆
        result = await app.async_execute(
            name="retrieve_task_memory",
            workspace_id="appworld",
            query="How to navigate to settings and update user profile?",
            top_k=1
        )
        print(result)

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