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feat(docs): update README with new pre_reasoning_hook method and enhanced examples
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2 changed files with 72 additions and 36 deletions
54
README.md
54
README.md
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@ -67,14 +67,15 @@ working_dir/
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capabilities for AI Agents:
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| Method | Function | Key Components |
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|------------------------|------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
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|------------------------|------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|
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| `start` | 🚀 Start memory system | Initialize file store, file watcher, Embedding cache; clean up expired tool result files |
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| `close` | 📕 Close and clean up | Clean tool result files, stop file watcher, save Embedding cache |
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| `compact_memory` | 📦 Compact history to summary | [Compactor](reme/memory/file_based/compactor.py) — ReActAgent generates structured context checkpoint |
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| `summary_memory` | 📝 Write important memory to files | [Summarizer](reme/memory/file_based/summarizer.py) — ReActAgent + file tools (read / write / edit) |
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| `compact_tool_result` | ✂️ Compact oversized tool output | [ToolResultCompactor](reme/memory/file_based/tool_result_compactor.py) — Truncate and save to `tool_result/`, keep file reference in message |
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| `pre_reasoning_hook` | 🔄 Pre-reasoning hook | Auto compact tool results + generate summary + async trigger memory summarization task |
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| `memory_search` | 🔍 Semantic memory search | [MemorySearch](reme/memory/tools/chunk/memory_search.py) — Vector + BM25 hybrid retrieval |
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| `get_in_memory_memory` | 🗂️ Create in-memory instance | [ReMeInMemoryMemory](reme/memory/file_based/reme_in_memory_memory.py) — Token-aware memory management, supports compression summary and state serialization |
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| `get_in_memory_memory` | 🗂️ Create in-memory instance | [ReMeInMemoryMemory](reme/memory/file_based/reme_in_memory_memory.py) — Token-aware memory management, supports compression summary and state serialization (static method) |
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---
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@ -108,38 +109,52 @@ from reme.reme_light import ReMeLight
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async def main():
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reme = ReMeLight(
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working_dir=".reme", # Memory file storage directory
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max_input_length=128000, # Model context window (tokens)
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memory_compact_ratio=0.7, # Trigger compaction when reaching max_input_length * 0.7
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language="zh", # Summary language (zh / "")
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tool_result_threshold=1000, # Auto-save tool outputs exceeding this character count
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retention_days=7, # tool_result/ file retention days
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)
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# Initialize ReMeLight
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reme = ReMeLight()
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await reme.start()
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messages = [...]
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messages = [...] # Conversation message list
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# 1. Compact oversized tool outputs (prevent tool results from overflowing context)
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messages = await reme.compact_tool_result(messages)
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# 2. Compact history to structured summary (trigger: context approaching limit), can pass previous summary for incremental update
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summary = await reme.compact_memory(messages=messages, previous_summary="")
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# 2. Compact history to structured summary (can pass previous summary for incremental update)
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summary = await reme.compact_memory(
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messages=messages,
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previous_summary="",
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max_input_length=128000, # Model context window (tokens)
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compact_ratio=0.7, # Trigger compaction when reaching max_input_length * 0.7
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language="zh", # Summary language (zh / "")
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)
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# 3. Submit async summary task in background (non-blocking, writes to memory/YYYY-MM-DD.md)
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reme.add_async_summary_task(messages=messages)
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# 4. Semantic memory search (Vector + BM25 hybrid retrieval)
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# 4. Pre-reasoning hook (auto compact tool results + generate summary)
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processed_messages, compressed_summary = await reme.pre_reasoning_hook(
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messages=messages,
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system_prompt="You are a helpful AI assistant.",
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compressed_summary="",
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max_input_length=128000,
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compact_ratio=0.7,
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memory_compact_reserve=10000,
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enable_tool_result_compact=True,
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tool_result_compact_keep_n=3,
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)
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# 5. Semantic memory search (Vector + BM25 hybrid retrieval)
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result = await reme.memory_search(query="Python version preference", max_results=5)
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# 5. Get in-memory instance (ReMeInMemoryMemory, manages single conversation context) AgentScope InMemoryMemory
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memory = reme.get_in_memory_memory()
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token_stats = await memory.estimate_tokens()
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# 6. Get in-memory instance (static method, manages single conversation context)
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memory = ReMeLight.get_in_memory_memory()
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for msg in messages:
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await memory.add(msg)
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token_stats = await memory.estimate_tokens(max_input_length=128000)
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print(f"Current context usage: {token_stats['context_usage_ratio']:.1f}%")
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print(f"Message tokens: {token_stats['messages_tokens']}")
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print(f"Estimated total tokens: {token_stats['estimated_tokens']}")
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# 6. Wait for background tasks before closing
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# 7. Wait for background tasks before closing
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summary_result = await reme.await_summary_tasks()
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# Close ReMeLight
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@ -150,6 +165,9 @@ if __name__ == "__main__":
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asyncio.run(main())
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```
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> 📂 Full example code: [test_reme_light.py](tests/light/test_reme_light.py)
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> 📋 Example output: [test_reme_light.log](tests/light/test_reme_light.log) (223,838 tokens → 1,105 tokens, 99.5% compression ratio)
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### File-Based ReMeLight Memory System Architecture
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[CoPaw MemoryManager](https://github.com/agentscope-ai/CoPaw/blob/main/src/copaw/agents/memory/memory_manager.py)
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54
README_ZH.md
54
README_ZH.md
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@ -66,9 +66,10 @@ working_dir/
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| `close` | 📕 关闭并清理 | 清理工具结果文件、停止文件监控、保存 Embedding 缓存 |
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| `compact_memory` | 📦 压缩历史对话为摘要 | [Compactor](reme/memory/file_based/compactor.py) — ReActAgent 生成结构化上下文检查点 |
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| `summary_memory` | 📝 将重要记忆写入文件 | [Summarizer](reme/memory/file_based/summarizer.py) — ReActAgent + 文件工具（read / write / edit） |
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| `compact_tool_result` | ✂️ 压缩超长工具输出 | [ToolResultCompactor](reme/memory/file_based/tool_result_compactor.py) — 截断并转存到 `tool_result/`，消息中保留文件引用 | |
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| `compact_tool_result` | ✂️ 压缩超长工具输出 | [ToolResultCompactor](reme/memory/file_based/tool_result_compactor.py) — 截断并转存到 `tool_result/`，消息中保留文件引用 |
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| `pre_reasoning_hook` | 🔄 推理前预处理钩子 | 自动压缩工具结果 + 生成摘要 + 异步触发记忆总结任务 |
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| `memory_search` | 🔍 语义搜索记忆 | [MemorySearch](reme/memory/tools/chunk/memory_search.py) — 向量 + BM25 混合检索 |
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| `get_in_memory_memory` | 🗂️ 创建会话内存实例 | [ReMeInMemoryMemory](reme/memory/file_based/reme_in_memory_memory.py) — Token 感知的内存管理，支持压缩摘要和状态序列化 |
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| `get_in_memory_memory` | 🗂️ 创建会话内存实例 | [ReMeInMemoryMemory](reme/memory/file_based/reme_in_memory_memory.py) — Token 感知的内存管理，支持压缩摘要和状态序列化（静态方法） |
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---
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@ -102,38 +103,52 @@ from reme.reme_light import ReMeLight
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async def main():
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reme = ReMeLight(
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working_dir=".reme", # 记忆文件存储目录
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max_input_length=128000, # 模型上下文窗口（tokens）
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memory_compact_ratio=0.7, # 达到 max_input_length * 0.7 时触发压缩
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language="zh", # 摘要语言（zh / ""）
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tool_result_threshold=1000, # 超过此字符数的工具输出自动转存
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retention_days=7, # tool_result/ 文件保留天数
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)
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# 初始化 ReMeLight
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reme = ReMeLight()
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await reme.start()
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messages = [...]
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messages = [...] # 对话消息列表
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# 1. 压缩超长工具输出（防止工具结果撑爆上下文）
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messages = await reme.compact_tool_result(messages)
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# 2. 将历史对话压缩为结构化摘要（触发时机：上下文接近上限），可传入上轮摘要，实现增量更新
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summary = await reme.compact_memory(messages=messages, previous_summary="")
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# 2. 将历史对话压缩为结构化摘要（可传入上轮摘要，实现增量更新）
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summary = await reme.compact_memory(
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messages=messages,
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previous_summary="",
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max_input_length=128000, # 模型上下文窗口（tokens）
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compact_ratio=0.7, # 达到 max_input_length * 0.7 时触发压缩
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language="zh", # 摘要语言（zh / ""）
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)
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# 3. 后台异步提交摘要任务（不阻塞对话，摘要写入 memory/YYYY-MM-DD.md）
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reme.add_async_summary_task(messages=messages)
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# 4. 语义搜索记忆（向量 + BM25 混合检索）
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# 4. 推理前预处理钩子（自动压缩工具结果 + 生成摘要）
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processed_messages, compressed_summary = await reme.pre_reasoning_hook(
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messages=messages,
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system_prompt="你是一个有帮助的 AI 助手。",
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compressed_summary="",
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max_input_length=128000,
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compact_ratio=0.7,
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memory_compact_reserve=10000,
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enable_tool_result_compact=True,
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tool_result_compact_keep_n=3,
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)
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# 5. 语义搜索记忆（向量 + BM25 混合检索）
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result = await reme.memory_search(query="Python 版本偏好", max_results=5)
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# 5. 获取会话内存实例（ReMeInMemoryMemory，管理单次对话的上下文）AgentScope InMemoryMemory
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memory = reme.get_in_memory_memory()
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token_stats = await memory.estimate_tokens()
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# 6. 获取会话内存实例（静态方法，管理单次对话的上下文）
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memory = ReMeLight.get_in_memory_memory()
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for msg in messages:
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await memory.add(msg)
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token_stats = await memory.estimate_tokens(max_input_length=128000)
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print(f"当前上下文使用率: {token_stats['context_usage_ratio']:.1f}%")
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print(f"消息 Token 数: {token_stats['messages_tokens']}")
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print(f"预估总 Token 数: {token_stats['estimated_tokens']}")
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# 6. 关闭前等待后台任务完成
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# 7. 关闭前等待后台任务完成
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summary_result = await reme.await_summary_tasks()
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# 关闭 ReMeLight
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@ -144,6 +159,9 @@ if __name__ == "__main__":
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asyncio.run(main())
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```
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> 📂 完整示例代码：[test_reme_light.py](tests/light/test_reme_light.py)
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> 📋 运行结果示例：[test_reme_light.log](tests/light/test_reme_light.log)（223,838 tokens → 1,105 tokens，压缩率 99.5%）
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### 基于文件的 ReMeLight 记忆系统架构
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[CoPaw MemoryManager](https://github.com/agentscope-ai/CoPaw/blob/main/src/copaw/agents/memory/memory_manager.py) 继承
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