test(review): 新增 pytest 离线回归网，锁定 dwg_review 既有行为
在改动审查流水线（确定性检查前置 / 结论结构化 / 证据交叉校验）之前， 先立一张离线、确定性、秒级的回归网。 - tests/fakes.py：假 LLM + 假 MCP 基座 + 响应构造器（ok_text/ok_image/err） + make_ctx/make_evidence，绝不触网、不读密钥 - 10 个模块共 134 用例： mcp_client / llm_client / cad_tools / checklist / config / planning / evidence / review / report / annotator - pytest.ini（testpaths=tests）+ requirements-dev.txt + 根 conftest.py （sys.path + RENDER_WAIT=0 / REPORT_DIR=tmp fixture）
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"""离线测试假件：假 LLM、假 MCP 基座、响应构造器。
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设计约束（踩过的坑，务必遵守）：
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- 绝不构造真实的 `LlmClient`——没有 API Key 时它的 `__init__` 直接抛 `LlmError`。
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- 绝不无参构造 `CadTools()` / `ReviewContext.create()`——二者会立刻调用
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`list_tools()` 去连 127.0.0.1:8080。一律用 `make_ctx()` 直接拼装。
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本文件名不带 `test_` 前缀，故不会被 pytest 收集为用例。
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"""
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import json
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from dwg_review.cad_tools import CadTools
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from dwg_review.checklist import DEFAULT_CHECKLIST
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from dwg_review.context import ReviewContext
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from dwg_review.mcp_client import McpClient, McpError, McpToolError
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__all__ = [
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"FakeLlmClient",
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"FakeMcpClient",
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"McpError",
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"McpToolError",
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"STANDARD_TOOLS",
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"err",
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"frame",
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"llm_calls",
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"llm_text",
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"make_ctx",
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"make_evidence",
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"ok_image",
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"ok_mixed",
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"ok_text",
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"rpc_error",
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"tool_schema",
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]
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# ---------------------------------------------------------------------------
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# OpenAI 风格响应对象（模仿 openai SDK 的 choices[0].message 结构）
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# ---------------------------------------------------------------------------
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class _Fn:
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"""tool_call.function"""
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def __init__(self, name, arguments):
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self.name = name
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self.arguments = arguments
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class _TC:
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"""一个 tool_call。"""
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def __init__(self, call_id, name, args):
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self.id = call_id
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self.type = "function"
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self.function = _Fn(
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name, args if isinstance(args, str) else json.dumps(args, ensure_ascii=False)
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)
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class _Msg:
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"""assistant 消息。"""
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def __init__(self, content=None, tool_calls=None):
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self.role = "assistant"
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self.content = content
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self.tool_calls = tool_calls or []
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class _Choice:
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def __init__(self, message):
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self.message = message
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class _Resp:
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def __init__(self, message):
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self.choices = [_Choice(message)]
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def llm_text(text):
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"""构造一个「纯文本回复」的 LLM 响应。"""
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return _Resp(_Msg(content=text))
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def llm_calls(pairs, content=None):
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"""构造一个「请求调用工具」的 LLM 响应。
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pairs: [(工具名, 参数字典), ...]
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content: 可选的附带文本。
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"""
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tool_calls = [
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_TC(str(i + 1), name, args) for i, (name, args) in enumerate(pairs)
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]
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return _Resp(_Msg(content=content, tool_calls=tool_calls))
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# ---------------------------------------------------------------------------
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# 假 LLM 客户端
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# ---------------------------------------------------------------------------
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class FakeLlmClient:
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"""按脚本回放 LLM 响应，并记录每次调用。
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- `script` ：`chat()` 依次弹出的响应队列；弹空后回落到 `text`。
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- `text` ：`chat()` / `chat_text()` 的兜底文本。
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- `text_script` ：`chat_text()` 专用队列（便于同时喂 summarize 与 extract_issues）。
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"""
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def __init__(self, script=None, text="", text_script=None, model="fake-model"):
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self.script = list(script or [])
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self.text = text
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self.text_script = list(text_script or [])
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self.model = model
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self.calls = []
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def chat(self, messages, *, tools=None, timeout=None, retries=None):
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self.calls.append({"messages": list(messages), "tools": tools})
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if self.script:
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return self.script.pop(0)
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return llm_text(self.text)
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def chat_text(self, system, user, **kwargs):
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self.calls.append({"system": system, "user": user})
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if self.text_script:
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return self.text_script.pop(0)
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return self.text
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# ---------------------------------------------------------------------------
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# JSON-RPC / MCP 响应构造器
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# ---------------------------------------------------------------------------
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def ok_text(obj, *, req_id=1):
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"""成功响应，content 为一段文本（非字符串会先 JSON 序列化）。"""
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text = obj if isinstance(obj, str) else json.dumps(obj, ensure_ascii=False)
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return {
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"jsonrpc": "2.0",
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"id": req_id,
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"result": {"content": [{"type": "text", "text": text}]},
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}
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def ok_image(b64, mime="image/png", *, req_id=1):
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"""成功响应，content 为一张图片。"""
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return {
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"jsonrpc": "2.0",
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"id": req_id,
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"result": {"content": [{"type": "image", "data": b64, "mimeType": mime}]},
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}
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def ok_mixed(objs, images=(), *, req_id=1):
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"""成功响应，content 同时含文本与图片。"""
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content = []
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for obj in objs:
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text = obj if isinstance(obj, str) else json.dumps(obj, ensure_ascii=False)
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content.append({"type": "text", "text": text})
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for b64, mime in images:
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content.append({"type": "image", "data": b64, "mimeType": mime})
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return {"jsonrpc": "2.0", "id": req_id, "result": {"content": content}}
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def err(text, *, req_id=1):
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"""工具执行错误：result.isError == true。"""
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return {
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"jsonrpc": "2.0",
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"id": req_id,
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"result": {"content": [{"type": "text", "text": text}], "isError": True},
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}
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def rpc_error(code, message, *, req_id=1):
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"""协议级错误：JSON-RPC 顶层 error。"""
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return {"jsonrpc": "2.0", "id": req_id, "error": {"code": code, "message": message}}
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# ---------------------------------------------------------------------------
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# 工具 schema
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# ---------------------------------------------------------------------------
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def tool_schema(name, description="", properties=None, required=None):
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"""构造一条 tools/list 条目。"""
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return {
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"name": name,
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"description": description,
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"inputSchema": {
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"type": "object",
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"properties": properties or {},
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"required": required or [],
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},
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}
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STANDARD_TOOLS = [
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tool_schema("get_drawing_info", "图纸信息"),
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tool_schema("get_layers", "图层列表"),
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tool_schema("get_layouts", "布局列表"),
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tool_schema("detect_drawing_frames", "识别图框"),
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tool_schema("zoom_to_window", "缩放到窗口"),
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tool_schema("zoom_to_entity", "缩放到实体"),
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tool_schema("query_entities", "查询图元"),
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tool_schema("get_text_content", "文字内容"),
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tool_schema("get_dimensions", "标注"),
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tool_schema("get_block_references", "块参照"),
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tool_schema("get_polylines_info", "多段线"),
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tool_schema("get_viewport_screenshot", "视口截图"),
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tool_schema("get_entity_detail", "实体详情"),
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tool_schema("find_duplicate_entities", "重复实体"),
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tool_schema("find_zero_length", "零长度实体"),
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tool_schema("add_leader_annotation", "添加引线批注"),
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tool_schema("highlight_entities", "高亮实体"),
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tool_schema("delete_entities", "删除实体"),
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]
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# ---------------------------------------------------------------------------
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# 假 MCP 客户端
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# ---------------------------------------------------------------------------
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def _as_result(value, arguments):
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"""把注入值解释为「客户端返回值」或「异常」。"""
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if isinstance(value, Exception):
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raise value
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if callable(value):
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value = value(arguments)
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if isinstance(value, Exception):
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raise value
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# 原始 JSON-RPC 响应 → 走真实客户端的归一化（error → McpError，isError → McpToolError）
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if isinstance(value, dict) and ("result" in value or "error" in value):
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return McpClient._normalize(value)
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return value
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class FakeMcpClient:
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"""假 MCP 基座客户端。
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- `tools` ：tools/list 返回的工具列表（None = STANDARD_TOOLS）。
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- `responses` ：工具名 → 注入值。注入值可以是
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* 原始响应 dict（用 ok_text/ok_image/err/rpc_error 构造）——按真实客户端语义归一化；
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* 异常实例（McpError/McpToolError）——直接抛出；
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* 可调用对象 f(arguments)——返回上面任意一种。
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未命中的工具默认返回 `ok_text({})`。
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"""
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def __init__(self, tools=None, responses=None, available=True):
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self._tools = list(STANDARD_TOOLS if tools is None else tools)
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self.responses = dict(responses or {})
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self.available = available
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self.calls = [] # call_tool 调用日志
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self.raw_calls = [] # raw_rpc 调用日志
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# -- 客户端 API --------------------------------------------------------
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def list_tools(self, *, use_cache=True):
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return list(self._tools)
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def call_tool(self, name, arguments=None, *, timeout=None, retries=None, is_write=False):
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self.calls.append({"name": name, "arguments": arguments, "is_write": is_write})
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if name not in self.responses:
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return ok_text({})
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return _as_result(self.responses[name], arguments)
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def raw_rpc(self, method, params=None, *, timeout=None, retries=None):
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self.raw_calls.append({"method": method, "params": params})
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if method in self.responses:
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return _as_result(self.responses[method], params)
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return {"jsonrpc": "2.0", "id": 1, "result": {}}
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def is_available(self, timeout=1.5):
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return self.available
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# -- 断言辅助 ----------------------------------------------------------
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def called(self, name):
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return [c for c in self.calls if c["name"] == name]
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def called_names(self):
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return [c["name"] for c in self.calls]
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def arguments_of(self, name):
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"""返回某工具**最后一次**调用的参数。"""
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hits = self.called(name)
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return hits[-1]["arguments"] if hits else None
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# ---------------------------------------------------------------------------
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# 组合辅助
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# ---------------------------------------------------------------------------
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def make_ctx(client=None, llm=None, checklist=None, **over):
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"""直接拼装一个 ReviewContext（不触碰网络、不读密钥）。"""
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client = client if client is not None else FakeMcpClient()
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params = dict(
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client=client,
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tools=CadTools(client),
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llm=llm if llm is not None else FakeLlmClient(),
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checklist=checklist if checklist is not None else DEFAULT_CHECKLIST,
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max_frames=0,
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frame_limit=200,
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max_iters=4,
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)
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params.update(over)
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return ReviewContext(**params)
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def frame(bbox, kind="title_block", sheet_size="A1", layer="0", confidence=0.9, **extra):
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"""构造一个图框描述（detect_drawing_frames 返回的 items 元素）。"""
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data = {
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"bbox": list(bbox),
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"kind": kind,
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"sheet_size": sheet_size,
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"layer": layer,
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"confidence": confidence,
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}
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data.update(extra)
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return data
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def make_evidence(
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index=1,
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bbox=(0.0, 0.0, 100.0, 50.0),
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image_b64=None,
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image_mime=None,
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**over,
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):
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"""构造 collect_frame 的返回值（evidence）。"""
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ev = {
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"index": index,
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"frame": frame(bbox),
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"window": {
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"min_x": float(bbox[0]),
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"min_y": float(bbox[1]),
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"max_x": float(bbox[2]),
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"max_y": float(bbox[3]),
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},
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"entities": {"total": 0, "items": []},
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"texts": {"total": 0, "items": []},
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"dimensions": {"total": 0, "items": []},
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"blocks": {"total": 0, "items": []},
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"polylines": {"total": 0, "items": []},
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"image_b64": image_b64,
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"image_mime": image_mime,
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}
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ev.update(over)
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return ev
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