mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-07 08:26:06 +00:00
### 1. Agent Wrapper(统一 Agent 后端抽象) - **`base_agent_wrapper.py`**:`reply()` 返回值从 `tuple[str, Any]` 改为 `dict`(含 `session_id` / `last_message` / `result` / 可选 `structured_output`);`reply_stream()` 改为产出统一的 `StreamChunk`。废弃 `add_tools()`,改为 `add_job_tools(names: list[str])`(按名解析 BaseJob)与 `add_skills()`;新增 `_resolve_job_tools()`、`_merged_kwargs()`、`_chunk()` 辅助方法及 `project_path` / `project_skills_root` 属性。 - **`as_agent_wrapper.py`(AgentScope 后端)**: - 会话持久化重写:`session_path` 落地到 `<vault>/<session_dir>/agentscope/`,`_load_state` 支持 `resume` / `session_id` / `fork_session`,并做 UUID 校验(`_validate_session_id`);`_cleanup_expired_sessions` 按天数清理过期会话。 - 新增内置工具集(`BypassAnalysisBash` + Edit/Glob/Grep/Read/Write),`BypassAnalysisBash` 绕过 AgentScope 自带 Bash 静态分析以让 permission_mode 生效;`_resolve_skills()` 把配置的 skill 暴露给后端,`_load_tool_env()` 注入项目 `.env`。 - `_event_to_chunk()` 把 20+ 种 AgentScope 事件(Reply/Text/Thinking/Data/ToolCall/ToolResult/ModelCall/ExceedMaxIters)归一化为 `StreamChunk`。 - **`cc_agent_wrapper.py`(Claude Code SDK 后端,+551 行)**: - 新增 `_CcFileSessionStore`:基于 vault 的文件型会话存储,实现 append(按 uuid 去重)/ load / list / delete / list_subkeys,并对路径做 `_safe_parts` + `resolve()` 防越界校验。 - `_build_options()`:统一构建 `ClaudeAgentOptions`,处理 skills、disallowed_tools(默认禁 `WebSearch`)、`.env` 注入、Claude Code 的 API 凭据解析(`_claude_code_api_env`,多级 base_url/api_key 回退)、`CLAUDE_CONFIG_DIR` 设置、skill 目录软链接(`_ensure_claude_skill_dir`)。 - `_raw_event_to_chunk()` / `_message_content_to_chunks()`:把 Anthropic 流式事件(message_start/delta/stop、content_block_*)与 SDK 消息块(AssistantMessage/UserMessage/ResultMessage/RateLimitEvent)转换为统一 `StreamChunk`;跟踪 block_id/block_type/tool_call_name 做关联;处理尾部 `"success"` 误报异常的吞掉逻辑。 ### 2. 统一流式协议(StreamChunk / ChunkEnum) - **`stream_chunk.py`**:`StreamChunk` 扩展为承载 AS + CC 双后端完整信息的统一结构,新增 `session_id` / `block_id` / `tool_call_id` / `tool_call_name` / `media_type` / `input_tokens` / `output_tokens` 等字段,纯文本流仍保持轻量。 - **`chunk_enum.py`**:补全生命周期标记 `REPLY_START` / `REPLY_END`,并文档化两套后端事件 → ChunkEnum 的映射。 ### 3. Index 模块重构(变化批次化 + dispatch) - 新增 `_change_batch.py`:`coalesce_changes()` 把同路径多次事件折叠为最终状态(结合 path 存在性判定),`bucket_changes()` 按 watchfiles.Change 分桶。 - 新增 `init_changes.py`(`InitChangesStep`):一次性扫描,对比 file_store / file_catalog 已索引节点计算 added/modified/deleted,写入 `context["changes"]` 后 dispatch。 - 新增 `update_changes.py`:抽象基类 `ChangeApplyStep` 统一 added/modified/deleted 处理与错误收集;`UpdateCatalogStep`(写 file_catalog)、`UpdateIndexStep`(写 file_store,含按后缀解析 chunker)。 - **`watch_changes.py`**:改用 `dispatch_step_specs`(基类提供的 `dispatch_steps()`),每批先 `coalesce_changes` 再 dispatch;默认参数调整(debounce 5000ms / step 1000ms / poll 5000ms)并暴露常量。 - 删除旧步骤:`clear_and_scan` / `foreach_dispatch` / `scan_changes` / `update_catalog`(旧) / `update_index`(旧);`clear_store.py` 取代 clear_and_scan。 ### 4. Evolve / Dream 模块(拆分为多步 pipeline) - 删除旧的单体 `auto_dream.py` / `dream.py` / `dream.yaml`,新增 `dream/` 子包,按 5 个步骤组织: - **`extract.py`**:扫描当日 day-index + daily 笔记,对比 file_catalog 找出 changed/deleted,调用 LLM 全局抽取 `units`(procedure/personal/wiki 三桶)与 `topics`,路径与桶做清洗/路由。 - **`integrate.py`**:逐个 unit 调用 LLM 写入 digest,结构化输出 `IntegrateOutcome`(CREATE/CORROBORATE/REFINE/CORRECT),失败 unit/路径收集回写。 - **`topics.py`**:写 `daily/<date>/interests.yaml`,结合当天已有 + 近 N 天做去重(`normalize_topic`),可走 LLM 或纯规则去重两条路径。 - **`proactive.py`**:读取当日 `interests.yaml`,作为主动推荐话题的入口。 - **`finish.py`**:把变更路径落盘到 dream file_catalog(checkpoint),渲染最终汇总摘要。 - 新增 `schema.py`(`DreamState` 等跨步骤共享状态与结构化输出模型)与 `utils.py`(状态存取、扫描打包、YAML 读写、结构化回复解析等公共函数)。 - `evolve/__init__.py` 导出全部新 step。 ### 5. auto_memory / auto_resource(适配新 Agent API) - **`auto_memory.py`**:会话路径迁移到 `<session_dir>/dialog/<session_id>.jsonl`;改用 `job_tools`;新增 `source_conversation` frontmatter 反向链接(`_session_link`);执行后刷新 day 索引(`refresh_day_index`),并对 session_id 做合法性校验。 - **`auto_resource.py`**:资源改用「同名 daily note」方案(`_compute_note_stem` 取文件 stem);批量处理 `changes: list[dict]`(`_handle_change` 逐项处理,返回逐项结果摘要);agent 会话 id 用稳定的 `uuid5`;同样刷新 day 索引。 ### 6. BaseStep 基类增强 - 新增 `dispatch_steps` / `dispatch_step_specs` 机制:`_resolve_dispatch_step()` 支持字符串或 dict 形式的 step spec,`dispatch_steps()` 复用当前 context 调用下游 step。 - 新增 `config_value()`:按 key 取 app config,缺失时回退 `ApplicationConfig` 默认值。 - 小幅清理:`language` 初始化、`copy()`、`Ref.__init__` 签名精简。 ### 7. Components 改动 - **`file_store/local_file_store.py`**:持久化改用 zstd 压缩(`.jsonl.zst`,通过新 `utils/jsonl_zst.py`);upsert 时先删除旧 chunk 的 keyword 文档;embedding 复用改为 `(text, embedding)` 键控,要求文本一致才复用;新增 `_matches_search_filter()` 对 vector/keyword 搜索做 path/path_prefix/metadata 的统一后过滤。 - **`keyword_index/bm25_index.py`**:索引文件名加入组件名 + tokenizer 指纹(sha256 前 12 位),快照/恢复时校验指纹防配置漂移;空索引 dump 时删除文件,加载失败抛错而非静默。 - **`file_chunker/markdown_file_chunker.py`**:弃用 `python-frontmatter`,改用内置 YAML 解析(非法 YAML 不阻断正文索引),并修正因 frontmatter 占用行号导致的 AST 行号偏移(`line_offset`)。 - **`cron_job.py`**:大幅简化(-187 行),由原来「dispatch 外部 job/step + 多种调度模式」改为「在自身 steps 上跑 cron 表达式」;`Application` 启动顺序随之调整为 base > stream > background > cron。 - 其余小调整:service(base/http/mcp)、file_graph、file_catalog、as_llm、as_embedding、tokenizer、prompt_handler、base_component 的签名/接口微调。 ### 8. Application 生命周期 - `_start()` 启动顺序明确为 components → base → stream → background → cron,启动失败会触发 `_close()` 回滚并 re-raise(不再吞异常)。 - 启动时创建 `session_dir` 目录;新增 `update_component()`(按类型/名就地更新已存在组件,不存在则报错)。 ### 9. File IO / 路径安全 - **`_path.py`**:`resolve_path` 增加 vault 越界防护(`is_relative_to` 校验),禁止 `.` / `..` 路径分量,支持 `allow_empty`。 - **`read.py`**:大文件(超过 `MAX_FILE_READ_BYTES`)走按行读取 `read_file_lines_safe`,避免一次性载入内存。 - **`_file_io.py` / `_daily_index.py` / `_path.py`** 等支持函数补齐(如 `refresh_day_index`、`read_file_lines_safe`)。 - **`env_utils.py`**:新增 `parse_env_file()`,`load_env()` 返回加载到的键值、支持 `override`、对无路径调用做幂等缓存。 ### 10. Config - `ApplicationConfig` 新增 `session_dir`(默认 `reme_session`)。 - `config_parser.py`:环境变量展开后做类型转换(`_convert_value`)、dot-notation 与 key=value 参数校验更严格、配置文件路径支持相对 `_CONFIG_DIR` 查找、根非 dict 报错。 - `default.yaml`:作业编排改用 `init_changes_step` + `dispatch_steps`(index/resource/digest 三个 watch loop 与 reindex);新增 `auto_dream`(4 步)、`proactive` 作业,移除旧 `dream`;file_catalog 增配 `resource` / `digest` / `dream` 实例;LLM 默认值与 Claude Code 凭据配置调整(tool_result_limit 50000、thinking_enable=false 等)。 ### 11. 其它 - 新增 `steps/common/add.py`(`AddStep` 算术 demo)、`channel/__init__.py` 与 common `__init__` 导出整理。 - 新增 4 篇文档:`docs4/auto_dream_logic_and_step_refactor.md`、`docs4/watch_loop_step_refactor_plan.md`、`docs4/todo.md`,以及 `reme_design.md` 更新。 **
249 lines
8.7 KiB
Python
249 lines
8.7 KiB
Python
"""Common utilities: hashing, async stream task execution, HTTP helpers."""
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import asyncio
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import hashlib
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import json
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import socket
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import subprocess
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import sys
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import time
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from collections.abc import AsyncGenerator, Callable
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from contextlib import asynccontextmanager
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from typing import Any, Literal
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from .logger_utils import get_logger
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from ..constants import REME_DEFAULT_HOST, REME_DEFAULT_PORT
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from ..enumeration import ChunkEnum
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from ..schema import StreamChunk
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def hash_text(text: str, encoding: str = "utf-8") -> str:
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"""Return SHA-256 hex digest of text."""
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return hashlib.sha256(text.encode(encoding)).hexdigest()
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def _format_chunk(
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chunk: StreamChunk,
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output_format: Literal["str", "bytes", "chunk"],
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) -> str | bytes | StreamChunk:
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"""Render a StreamChunk in the requested transport format."""
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if output_format == "chunk":
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return chunk
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data = "data:[DONE]\n\n" if chunk.done else f"data:{chunk.model_dump_json()}\n\n"
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return data.encode() if output_format == "bytes" else data
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async def execute_stream_task(
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stream_queue: asyncio.Queue[StreamChunk],
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task: asyncio.Task[Any],
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task_name: str | None = None,
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output_format: Literal["str", "bytes", "chunk"] = "str",
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) -> AsyncGenerator[str | bytes | StreamChunk, None]:
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"""Yield chunks from stream_queue while monitoring task; cancels task on exit.
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output_format: "str"/"bytes" emit SSE frames, "chunk" emits raw StreamChunk.
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"""
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logger = get_logger()
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consumer: asyncio.Task[StreamChunk] | None = None
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try:
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while True:
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consumer = get_chunk = asyncio.create_task(stream_queue.get())
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done, _pending = await asyncio.wait({get_chunk, task}, return_when=asyncio.FIRST_COMPLETED)
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# Producer still running — relay the next chunk and continue.
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if task not in done:
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chunk = get_chunk.result()
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yield _format_chunk(chunk, output_format)
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if chunk.done:
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return
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continue
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# Producer finished. Capture any pending chunk, then stop the consumer wait
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# so we can inspect task state safely.
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pending_chunk: StreamChunk | None = None
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if get_chunk in done:
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pending_chunk = get_chunk.result()
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else:
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get_chunk.cancel()
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try:
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await get_chunk
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except asyncio.CancelledError:
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pass
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# Surface task failure first — an exception trumps trailing data.
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if task.cancelled():
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msg = f"Task cancelled: {task_name}" if task_name else "Task cancelled"
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raise asyncio.CancelledError(msg)
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exc = task.exception()
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if exc is not None:
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log_msg = f"Task error in {task_name}: {exc}" if task_name else f"Task error: {exc}"
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logger.error(log_msg, exc_info=exc)
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raise exc
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# Producer ended cleanly — flush pending + drain queue so no chunk is lost,
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# then emit the terminal sentinel.
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if pending_chunk is not None:
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yield _format_chunk(pending_chunk, output_format)
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if pending_chunk.done:
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return
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while not stream_queue.empty():
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chunk = stream_queue.get_nowait()
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yield _format_chunk(chunk, output_format)
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if chunk.done:
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return
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yield _format_chunk(StreamChunk(chunk_type=ChunkEnum.DONE, chunk="", done=True), output_format)
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return
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finally:
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# Cancel consumer wait if still pending (e.g. on consumer aclose).
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if consumer is not None and not consumer.done():
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consumer.cancel()
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try:
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await consumer
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except asyncio.CancelledError:
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pass
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# Cancel producer task if still running to avoid resource leaks.
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if not task.done():
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task.cancel()
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try:
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await task
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except asyncio.CancelledError:
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pass
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def _pick_free_port(host: str = REME_DEFAULT_HOST) -> int:
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"""Bind to port 0 and return the OS-assigned free port."""
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s.bind((host, 0))
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return s.getsockname()[1]
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async def _wait_reme_ready(host: str, port: int, timeout: float) -> None:
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"""Poll find_reme until it reports 'reme' or timeout elapses."""
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from .service_utils import find_reme
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deadline = time.time() + timeout
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while time.time() < deadline:
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status = await find_reme(host, port)
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if status == "reme":
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return
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await asyncio.sleep(0.2)
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raise TimeoutError(f"ReMe service did not become ready at {host}:{port} within {timeout}s")
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@asynccontextmanager
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async def mock_reme_server(
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host: str = REME_DEFAULT_HOST,
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port: int | None = None,
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config: str | None = None,
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extra_args: list[str] | None = None,
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startup_timeout: float = 120.0,
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shutdown_timeout: float = 10.0,
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log_to_file: bool = False,
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enable_logo: bool = False,
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):
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"""Spawn `reme start` as a subprocess and yield (host, port) once ready.
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Auto-picks a free port when port is None. Subprocess is terminated on exit.
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"""
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logger = get_logger()
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if port is None:
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port = _pick_free_port(host)
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cmd: list[str] = [
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sys.executable,
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"-m",
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"reme4.reme",
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"start",
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f"service.host={host}",
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f"service.port={port}",
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f"log_to_file={'true' if log_to_file else 'false'}",
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f"enable_logo={'true' if enable_logo else 'false'}",
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]
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if config:
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cmd.append(f"config={config}")
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if extra_args:
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cmd.extend(extra_args)
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logger.info(f"Launching mock reme server: {' '.join(cmd)}")
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proc = subprocess.Popen(
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cmd,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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text=True,
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)
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try:
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await _wait_reme_ready(host, port, startup_timeout)
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yield host, port
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except Exception:
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# Capture early-exit output for diagnostics.
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if proc.poll() is not None and proc.stdout is not None:
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tail = proc.stdout.read()
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logger.error(f"reme server exited early. output:\n{tail}")
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raise
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finally:
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if proc.poll() is None:
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proc.terminate()
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try:
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proc.wait(timeout=shutdown_timeout)
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except subprocess.TimeoutExpired:
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logger.warning("reme server did not terminate gracefully, killing")
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proc.kill()
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proc.wait(timeout=shutdown_timeout)
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if proc.stdout is not None:
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try:
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proc.stdout.close()
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except Exception:
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pass
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async def call_action(
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action: str,
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host: str = REME_DEFAULT_HOST,
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port: int = REME_DEFAULT_PORT,
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timeout: float = 30.0,
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**kwargs,
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) -> dict | str:
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"""POST to /{action}; return parsed JSON (dict) for JSON endpoints, raw text for SSE."""
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from ..components.client.http_client import HttpClient
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pieces: list[str] = []
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async with HttpClient(host=host, port=port, timeout=timeout) as client:
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async for chunk in client.stream_chunks(action, **kwargs):
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payload = chunk.chunk
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pieces.append(payload if isinstance(payload, str) else json.dumps(payload, ensure_ascii=False))
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raw = "".join(pieces)
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try:
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return json.loads(raw)
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except (ValueError, json.JSONDecodeError):
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return raw
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async def call_and_check(
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action: str,
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host: str = REME_DEFAULT_HOST,
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port: int = REME_DEFAULT_PORT,
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validator: Callable[[Any], bool] | None = None,
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expected: Any = None,
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timeout: float = 30.0,
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**kwargs,
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) -> Any:
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"""Call action and verify response. Raises AssertionError on mismatch.
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- validator(result) -> bool: custom predicate.
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- expected: deep-equality target (compared to result, or to result[key] when expected is dict).
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"""
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result = await call_action(action, host=host, port=port, timeout=timeout, **kwargs)
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if validator is not None and not validator(result):
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raise AssertionError(f"validator rejected response for action={action!r}: {result!r}")
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if expected is not None:
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if isinstance(expected, dict) and isinstance(result, dict):
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for k, v in expected.items():
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if result.get(k) != v:
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raise AssertionError(
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f"action={action!r} expected {k}={v!r}, got {result.get(k)!r} (full: {result!r})",
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)
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elif result != expected:
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raise AssertionError(f"action={action!r} expected {expected!r}, got {result!r}")
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return result
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