mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-11-02 07:13:50 +00:00
ffb4d08c4f
* feat(file_io): add daily_write step for creating daily notes with conversation metadata - Add DailyWriteStep class that delegates to write job for creating daily notes - Register daily_write job in default configuration with proper parameters - Include validation for name and session_id path components - Add test coverage for daily_write functionality including metadata handling - Preserve existing job execution method in application.py after repositioning - Update base_step.py to use positional-only parameter syntax for job methods - Import and expose DailyWriteStep in file_io module initialization - Override reserved metadata keys (name, description, session_id, source_conversation) with fixed values - Refresh daily index after successful write operation - Generate proper source conversation links in markdown format * feat(daily): refactor daily note system with enhanced metadata handling - Introduce validate_filename_component function and export it - Add _INDEX_HIDDEN_METADATA_KEYS to hide conversation metadata from index - Update scan_notes to exclude hidden metadata keys from index rendering - Modify auto_memory to use daily_write tool and manage session frontmatter - Implement session note lookup and renaming based on frontmatter name - Update daily_list to return flattened note metadata including session info - Change daily_write to dispatch write step instead of running job - Add test cases for updated daily note functionality and metadata handling - Update version from 0.4.0.2 to 0.4.0.3 * fix(evolve): correct metadata update in auto memory response - Fixed trailing comma issue in metadata dictionary update - Ensured proper formatting of response metadata structure - Maintained existing functionality while fixing syntax error * refactor(auto_resource): replace daily_create with dynamic note management - Remove DailyCreateStep and related exports from file_io module - Replace static daily note creation with dynamic resource-linked card system - Implement LLM-suggested naming with frontmatter-driven file management - Add source_resource linking for tracking original files - Introduce collision handling with hash-based suffixes - Update documentation to reflect new resource card workflow - Modify auto_resource prompts to use write/edit tools instead of daily_create - Adjust test fixture comments to match new agent behavior - Update framework diagrams and quick start examples accordingly * feat(app): add version info to app initialization and update auto-memory logic - Include version number in application startup logging - Remove tool result truncation logic from auto-memory step - Update auto-memory to exclude tool_result blocks from saved history - Add test case to verify tool results are filtered out from message saving - Update YAML prompts to clarify filename naming rules without dates - Modify configuration to support new dispatch steps format with persistence control * feat(auto_memory): add note modification tracking and optimize frontmatter updates - Add _note_bytes and _note_modified methods to track actual file changes - Optimize frontmatter updates by checking existing metadata before update - Add modified flag to response metadata indicating actual note changes - Update logging to include modified status in various operations - Add comprehensive tests for modified/unmodified detection scenarios - Enhance result hook logic to skip when no actual changes occur - Refactor metadata handling to properly track creation vs modification status
208 lines
4.6 KiB
Markdown
208 lines
4.6 KiB
Markdown
# 快速开始
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## 安装
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ReMe 要求 Python 3.11+。
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从 pip 安装：
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```bash
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pip install "reme-ai[core]"
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```
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从源码安装：
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```bash
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git clone https://github.com/agentscope-ai/ReMe.git
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cd ReMe
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pip install -e ".[core]"
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```
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`core` extra 建议安装：当前代码会导入 AgentScope wrapper，自进化记忆也依赖它。
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如果要使用 `auto_memory`、`auto_resource`、`auto_dream` 这类 Agent 流程，再配置 LLM：
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```bash
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cat > .env <<'EOF'
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LLM_BACKEND=openai
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LLM_MODEL_NAME=qwen3.7-plus
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LLM_API_KEY=your_api_key
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LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
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EOF
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```
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只跑基础文件读写和 BM25 检索，可以先不配。
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---
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## 启动
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```bash
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reme start
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```
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默认服务地址是 `127.0.0.1:2333`。如果端口被占用：
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```bash
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reme start service.port=8181
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```
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```bash
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reme version
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reme health_check
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reme list
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```
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`reme list` 会列出服务端 action。普通命令会通过 HTTP 调用服务端 Job。
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---
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## Workspace 目录
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默认 workspace 是当前目录下的 `.reme/`，启动时会自动创建：
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```text
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.reme/
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├── metadata/ # 索引、图谱、catalog 等持久状态
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├── session/ # Agent session 与原始对话
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├── resource/ # 外部资料
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├── daily/ # daily note
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└── digest/ # 长期记忆
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```
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目录分层、Markdown frontmatter 和 wikilink 语义见 [Memory as File](./memory_as_file.md)。
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也可以启动时指定：
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```bash
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reme start workspace_dir=/tmp/reme-demo service.port=8181
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```
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---
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## 写入、索引、检索
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```bash
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reme write \
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path=digest/wiki/quick-start-demo \
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name="Quick Start Demo" \
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description="快速开始示例记忆" \
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content="# Quick Start Demo
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ReMe 会索引 daily、digest 和 resource 目录中的 Markdown。
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相关链接：[[digest/wiki/search-demo.md]]"
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```
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`path` 是 workspace 内路径；没有后缀时会自动补 `.md`；Markdown 文件会写入 `name` 和 `description` front matter。
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后台 watcher 会自动建索引；也可以手动重建：
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```bash
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reme reindex
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```
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搜索：
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```bash
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reme search query="快速开始 示例 记忆" limit=5
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```
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读取：
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```bash
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reme read path=digest/wiki/quick-start-demo start_line=1 end_line=20
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```
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默认配置下，检索主要是 BM25 + wikilink 图谱扩展；向量检索能力在代码中支持，但默认未启用 embedding store。完整检索流程见
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[Memory Search](./memory_search.md)。
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---
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## 文件与 Daily Note
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```bash
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reme stat path=digest/wiki/quick-start-demo
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reme edit path=digest/wiki/quick-start-demo old="会索引" new="会持续索引"
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reme frontmatter_read path=digest/wiki/quick-start-demo
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reme frontmatter_update path=digest/wiki/quick-start-demo metadata='{"tags":["demo"]}'
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```
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`list` 这个名字在 CLI 中用于 action 列表，所以文件列表 Job 需要用 HTTP 调：
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```bash
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curl -s http://127.0.0.1:2333/list \
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-H 'Content-Type: application/json' \
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-d '{"path":"digest","recursive":true,"limit":50}'
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```
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Daily note：
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```bash
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reme write path=daily/2026-06-20/demo-session.md name=demo-session description="Demo session" content="记录内容"
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reme daily_list
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reme daily_reindex
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```
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`write` 可直接创建 daily note；需要刷新当天索引时运行 `daily_reindex`。
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---
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## 自动记忆
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```bash
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reme auto_memory \
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session_id=chat-demo \
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messages='[{"role":"user","content":"我偏好把项目经验沉淀成 Markdown。"},{"role":"assistant","content":"已记录。"}]' \
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memory_hint="记录用户偏好"
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```
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外部资料放入 `resource/YYYY-MM-DD/` 后，默认后台会监听 `md/txt/json/jsonl/csv/yaml/html`。也可以手动触发：
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```bash
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reme auto_resource changes='[{"path":"resource/2026-06-20/report.md","change":"added"}]'
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```
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把 daily 整理到长期 digest：
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```bash
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reme auto_dream date=2026-06-20
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reme proactive date=2026-06-20
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```
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这些流程需要可用 LLM；未配置 LLM 时请先使用 `write/read/search` 这类基础能力。
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更多细节见 [Auto Memory](./auto_memory.md)、[Auto Resource](./auto_resource.md)、[Auto Dream](./auto_dream.md) 和
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[Proactive](./proactive.md)。
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---
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## HTTP 与配置
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每个可服务 Job 都暴露为 `POST /<job>`：
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```bash
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curl -s http://127.0.0.1:2333/version \
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-H 'Content-Type: application/json' \
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-d '{}'
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curl -s http://127.0.0.1:2333/search \
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-H 'Content-Type: application/json' \
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-d '{"query":"快速开始","limit":5}'
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```
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默认配置来自 `reme/config/default.yaml`。启动时可以用 dot notation 覆盖：
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```bash
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reme start \
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workspace_dir=/tmp/reme-demo \
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service.host=127.0.0.1 \
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service.port=8181 \
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enable_logo=false
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```
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也可以指定 YAML/JSON 配置文件：
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```bash
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reme start config=/path/to/custom.yaml
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```
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