init the project

server/core/llm_provider/mock_client.py

@@ -0,0 +1,463 @@
+# Mock LLM client for offline development/testing.
+
+import json
+import logging
+
+from server.core.llm_provider.base import LLMClient
+
+logger = logging.getLogger("testflow")
+
+MOCK_IR_YAML = """meta:
+  prd_title: ZeekerWatchman 产品需求文档
+  extraction_date: "2026-05-22"
+  skill_used: default
+
+features:
+  - module: PRD管理
+    feature_name: PRD文件上传
+    description: 支持拖拽上传或文本粘贴，解析.md/.txt/.docx/.pdf格式文件
+    inputs:
+      - PRD文件 (.md/.txt/.docx/.pdf)
+    outputs:
+      - PRD_Version记录
+      - 纯文本摘要
+    preconditions:
+      - 用户已登录平台
+    constraints:
+      - 文件大小不超过10MB
+      - 仅支持指定格式
+    priority: P0
+    dependencies: []
+
+  - module: PRD管理
+    feature_name: PRD版本快照
+    description: 上传后即时保存为不可变版本，支持历史回溯
+    inputs:
+      - 已上传的PRD
+    outputs:
+      - 版本快照记录
+    preconditions:
+      - PRD已成功上传
+    constraints:
+      - 版本不可修改
+    priority: P1
+    dependencies:
+      - PRD文件上传
+
+  - module: IR引擎
+    feature_name: IR生成
+    description: 调用LLM利用Skill的extract_ir_prompt将PRD转化为符合IR Schema的YAML
+    inputs:
+      - PRD文本
+      - Skill名称
+    outputs:
+      - IR YAML
+    preconditions:
+      - PRD解析完成
+      - Skill已选择
+    constraints:
+      - 必须符合ir_schema.json
+      - 必须满足principles.yaml约束
+    priority: P0
+    dependencies:
+      - PRD文件上传
+
+  - module: IR引擎
+    feature_name: IR可视化确认
+    description: 双栏展示，左侧YAML编辑器，右侧思维导图实时渲染
+    inputs:
+      - IR YAML
+    outputs:
+      - 用户确认/编辑后的IR
+    preconditions:
+      - IR已生成
+    constraints:
+      - 支持实时编辑保存
+    priority: P1
+    dependencies:
+      - IR生成
+
+  - module: 用例引擎
+    feature_name: 测试用例生成
+    description: 基于确认后的IR调用gen_cases_prompt生成JSON格式用例集
+    inputs:
+      - 最终IR YAML
+      - Skill的gen_cases_prompt
+    outputs:
+      - TestCase_Set
+    preconditions:
+      - IR已确认
+    constraints:
+      - P0功能必须覆盖异常和边界场景
+      - 用例标题使用Given-When-Then结构
+    priority: P0
+    dependencies:
+      - IR生成
+
+  - module: 用例引擎
+    feature_name: 测试用例导出
+    description: 支持一键导出为YAML/CSV/XMind格式
+    inputs:
+      - TestCase_Set
+      - 目标格式
+    outputs:
+      - 下载文件
+    preconditions:
+      - 用例已生成
+    constraints:
+      - YAML用于自动化，CSV用于评审，XMind用于展示
+    priority: P1
+    dependencies:
+      - 测试用例生成"""
+
+MOCK_TESTCASES = [
+    {
+        "id": "TC-ZWM-001",
+        "module": "PRD管理",
+        "feature": "PRD文件上传",
+        "case_title": "正向-上传md格式PRD成功",
+        "preconditions": "用户已登录，文件为有效md格式",
+        "steps": "Given 用户在主页面\nWhen 拖拽或选择一个.md文件上传\nThen 系统解析成功并显示PRD摘要",
+        "expected_result": "返回PRD ID，状态为ready，显示文本摘要",
+        "priority": "P0",
+        "tags": ["正向", "冒烟"]
+    },
+    {
+        "id": "TC-ZWM-002",
+        "module": "PRD管理",
+        "feature": "PRD文件上传",
+        "case_title": "异常-上传不支持的文件格式",
+        "preconditions": "用户已登录",
+        "steps": "Given 用户在主页面\nWhen 上传一个.exe文件\nThen 系统返回错误提示",
+        "expected_result": "返回400错误，提示仅支持.md/.txt/.docx/.pdf",
+        "priority": "P0",
+        "tags": ["异常"]
+    },
+    {
+        "id": "TC-ZWM-003",
+        "module": "PRD管理",
+        "feature": "PRD文件上传",
+        "case_title": "边界-上传超过10MB的文件",
+        "preconditions": "用户已登录",
+        "steps": "Given 用户在主页面\nWhen 上传一个11MB的md文件\nThen 系统拒绝并提示文件过大",
+        "expected_result": "返回错误，提示文件大小不超过10MB",
+        "priority": "P1",
+        "tags": ["边界"]
+    },
+    {
+        "id": "TC-ZWM-004",
+        "module": "IR引擎",
+        "feature": "IR生成",
+        "case_title": "正向-从PRD生成IR成功",
+        "preconditions": "PRD已解析，Skill已选择",
+        "steps": "Given PRD文本可用\nWhen 调用IR生成接口\nThen 返回符合IR Schema的YAML",
+        "expected_result": "生成包含features列表的YAML，功能点齐全",
+        "priority": "P0",
+        "tags": ["正向"]
+    },
+    {
+        "id": "TC-ZWM-005",
+        "module": "IR引擎",
+        "feature": "IR生成",
+        "case_title": "异常-空PRD文本生成IR",
+        "preconditions": "PRD文本为空",
+        "steps": "Given PRD内容为空\nWhen 调用IR生成接口\nThen 返回错误提示",
+        "expected_result": "返回400错误，提示PRD无文本内容",
+        "priority": "P0",
+        "tags": ["异常"]
+    },
+    {
+        "id": "TC-ZWM-006",
+        "module": "用例引擎",
+        "feature": "测试用例生成",
+        "case_title": "正向-基于IR生成测试用例",
+        "preconditions": "IR已确认",
+        "steps": "Given IR YAML可用\nWhen 调用用例生成接口\nThen 返回JSON格式的用例集",
+        "expected_result": "用例集包含P0功能的正向和异常用例，步骤为Given-When-Then格式",
+        "priority": "P0",
+        "tags": ["正向"]
+    },
+    {
+        "id": "TC-ZWM-007",
+        "module": "用例引擎",
+        "feature": "测试用例导出",
+        "case_title": "正向-导出用例为YAML格式",
+        "preconditions": "用例集已生成",
+        "steps": "Given 用例集可用\nWhen 选择YAML格式导出\nThen 下载YAML文件",
+        "expected_result": "下载的YAML文件包含所有用例及元数据",
+        "priority": "P1",
+        "tags": ["正向"]
+    },
+    {
+        "id": "TC-ZWM-008",
+        "module": "用例引擎",
+        "feature": "测试用例导出",
+        "case_title": "正向-导出用例为CSV格式",
+        "preconditions": "用例集已生成",
+        "steps": "Given 用例集可用\nWhen 选择CSV格式导出\nThen 下载CSV文件",
+        "expected_result": "CSV文件包含用例ID、模块、标题、步骤、预期结果等列",
+        "priority": "P1",
+        "tags": ["正向"]
+    }
+]
+
+
+MOCK_SEMANTIC_INDEX = {
+    "feature_name": "ZeekerWatchman 测试用例智能管理平台",
+    "concepts": [
+        {"name": "PRD", "aliases": ["产品需求文档", "需求文档"], "defined_in": ["1"]},
+        {"name": "IR", "aliases": ["中间表示", "Intermediate Representation"], "defined_in": ["1", "4.2.2"]},
+        {"name": "Skill", "aliases": ["技能包", "测试方法论"], "defined_in": ["4.1"]},
+        {"name": "TestCase", "aliases": ["测试用例", "用例"], "defined_in": ["4.2.3"]},
+    ],
+    "function_units": [
+        {
+            "unit_id": "FU-001",
+            "name": "PRD文件上传与解析",
+            "description": "用户上传.md/.txt/.docx/.pdf格式的PRD文件，系统调用解析器提取纯文本和图片，生成版本快照",
+            "sources": [{"section": "4.2.1 PRD 输入与解析", "type": "para", "text_snippet": "支持拖拽上传或直接粘贴文本。服务端调用LLM或本地解析库提取纯文本"}],
+        },
+        {
+            "unit_id": "FU-002",
+            "name": "IR生成",
+            "description": "基于PRD文本和选定的Skill，调用LLM生成符合IR Schema的YAML中间表示",
+            "sources": [{"section": "4.2.2 中间表示 IR 生成与确认", "type": "para", "text_snippet": "reasoning模块调用llm_provider，利用Skill的extract_ir_prompt.j2生成符合ir_schema.json的YAML"}],
+        },
+        {
+            "unit_id": "FU-003",
+            "name": "IR验证与自检",
+            "description": "对生成的IR进行JSON Schema校验和Principles规则检查，输出审核意见",
+            "sources": [{"section": "4.2.2 中间表示 IR 生成与确认", "type": "para", "text_snippet": "LangGraph节点会校验IR是否满足schema和soul/principles.yaml"}],
+        },
+        {
+            "unit_id": "FU-004",
+            "name": "IR人工确认与编辑",
+            "description": "用户可在双栏界面编辑YAML并保存，保存触发新版本生成",
+            "sources": [{"section": "4.2.2 中间表示 IR 生成与确认", "type": "para", "text_snippet": "用户可直接编辑YAML并保存，保存操作触发新IR_Version生成"}],
+        },
+        {
+            "unit_id": "FU-005",
+            "name": "测试用例生成",
+            "description": "基于确认后的IR调用gen_cases_prompt生成JSON格式用例集，P0功能覆盖正向和异常场景",
+            "sources": [{"section": "4.2.3 测试用例生成与导出", "type": "para", "text_snippet": "基于确认后的最终IR，调用gen_cases_prompt.j2生成JSON格式的用例集"}],
+        },
+        {
+            "unit_id": "FU-006",
+            "name": "测试用例导出",
+            "description": "支持将用例集导出为YAML/CSV/XMind三种格式",
+            "sources": [{"section": "4.2.3 测试用例生成与导出", "type": "para", "text_snippet": "YAML带语法高亮的代码预览和文件下载，CSV文件，XMind服务端生成.xmind文件下载"}],
+        },
+    ],
+}
+
+MOCK_IR_RULES = [
+    {
+        "description": "用户上传PRD文件时，系统检查文件格式(.md/.txt/.docx/.pdf)和大小(≤10MB)，通过后调用解析器提取纯文本并创建不可变版本快照",
+        "priority": "P0",
+        "sources": [{"type": "para", "section": "4.2.1 PRD 输入与解析", "text_snippet": "支持拖拽上传或直接粘贴文本"}],
+        "precondition": {"app_type": "Web应用", "app_state": "已登录"},
+        "trigger": {
+            "operator": "AND",
+            "conditions": [
+                {"signal": "文件格式", "operator": "in", "value": [".md", ".txt", ".docx", ".pdf"]},
+                {"signal": "文件大小", "operator": "<=", "value": 10, "unit": "MB"},
+            ],
+        },
+        "actions": [
+            {"type": "system", "description": "保存原始文件"},
+            {"type": "system", "description": "调用解析器提取纯文本"},
+            {"type": "system", "description": "创建PRD_Version快照"},
+        ],
+    },
+    {
+        "description": "上传不支持的文件格式时，系统拒绝并返回错误提示，仅支持.md/.txt/.docx/.pdf",
+        "priority": "P0",
+        "sources": [{"type": "para", "section": "4.2.1", "text_snippet": "支持拖拽上传"}],
+        "precondition": {},
+        "trigger": {
+            "operator": "OR",
+            "conditions": [
+                {"signal": "文件格式", "operator": "not_in", "value": [".md", ".txt", ".docx", ".pdf"]},
+                {"signal": "文件大小", "operator": ">", "value": 10, "unit": "MB"},
+            ],
+        },
+        "actions": [
+            {"type": "user_interaction", "description": "显示错误提示", "content": "不支持的文件格式，仅支持.md/.txt/.docx/.pdf，且不超过10MB"},
+        ],
+    },
+    {
+        "description": "基于PRD文本和选定Skill调用DeepSeek生成IR YAML，结果需符合ir_schema.json和principles.yaml",
+        "priority": "P0",
+        "sources": [{"type": "para", "section": "4.2.2", "text_snippet": "reasoning模块调用llm_provider生成IR"}],
+        "precondition": {"app_state": "PRD已解析"},
+        "trigger": {
+            "operator": "AND",
+            "conditions": [
+                {"signal": "PRD文本", "operator": "exists", "value": True},
+                {"signal": "Skill", "operator": "selected", "value": True},
+            ],
+        },
+        "actions": [
+            {"type": "system", "description": "加载Skill的extract_ir_prompt.j2模板"},
+            {"type": "system", "description": "调用LLM生成IR YAML"},
+            {"type": "system", "description": "持久化IR_Version"},
+        ],
+    },
+    {
+        "description": "IR生成后自动进行JSON Schema校验和Principles规则检查，输出审核意见包含error和warning",
+        "priority": "P1",
+        "sources": [{"type": "para", "section": "4.2.2", "text_snippet": "LangGraph节点会校验IR"}],
+        "precondition": {"app_state": "IR已生成"},
+        "trigger": {"operator": "AND", "conditions": [{"signal": "IR", "operator": "exists", "value": True}]},
+        "actions": [
+            {"type": "system", "description": "JSON Schema校验"},
+            {"type": "system", "description": "Principles规则检查"},
+            {"type": "user_interaction", "description": "展示审核意见列表"},
+        ],
+    },
+    {
+        "description": "基于确认后的IR生成JSON格式测试用例集，每个P0功能点至少包含正向和异常各1条用例，使用Given-When-Then结构",
+        "priority": "P0",
+        "sources": [{"type": "para", "section": "4.2.3", "text_snippet": "基于确认后的最终IR生成JSON格式的用例集"}],
+        "precondition": {"app_state": "IR已确认"},
+        "trigger": {"operator": "AND", "conditions": [{"signal": "IR已确认", "operator": "==", "value": True}]},
+        "actions": [
+            {"type": "system", "description": "加载Skill的gen_cases_prompt.j2模板"},
+            {"type": "system", "description": "调用LLM生成用例JSON"},
+            {"type": "system", "description": "创建TestCase_Set并关联IR_Version"},
+        ],
+    },
+    {
+        "description": "用例导出功能：YAML格式用于自动化执行，CSV用于Excel评审归档",
+        "priority": "P1",
+        "sources": [{"type": "para", "section": "4.2.3", "text_snippet": "支持一键导出YAML/CSV/XMind"}],
+        "precondition": {"app_state": "用例已生成"},
+        "trigger": {"operator": "OR", "conditions": [
+            {"signal": "导出格式", "operator": "==", "value": "yaml"},
+            {"signal": "导出格式", "operator": "==", "value": "csv"},
+            {"signal": "导出格式", "operator": "==", "value": "xmind"},
+        ]},
+        "actions": [
+            {"type": "system", "description": "格式化用例为指定格式"},
+            {"type": "user_interaction", "description": "触发文件下载"},
+        ],
+    },
+]
+
+
+def _mock_chat_reply(prompt: str) -> str:
+    """Generate a mock chat reply based on user message content."""
+    # Extract the last user message
+    user_msg = ""
+    for line in prompt.split("\n"):
+        if line.startswith("## 当前上下文"):
+            break
+        if line and not line.startswith("#") and not line.startswith("```"):
+            user_msg += line + " "
+
+    if "分析" in user_msg or "功能点" in user_msg:
+        return (
+            "根据 PRD 内容分析，我发现了以下功能点：\n\n"
+            "1. **用户登录** - 支持邮箱/手机号登录，包含密码验证和错误锁定机制\n"
+            "2. **用户注册** - 新用户注册流程，需要邮箱验证\n"
+            "3. **密码重置** - 通过邮箱验证码重置密码\n\n"
+            "建议操作：\n"
+            "- 点击「生成 IR」将这些功能点转换为结构化 IR\n"
+            "- 我可以帮你检查是否有遗漏的功能点"
+        )
+    if "用例" in user_msg or "测试" in user_msg:
+        return (
+            "当前测试用例包含以下覆盖：\n\n"
+            "- P0 正向用例：覆盖核心登录、注册流程\n"
+            "- P0 异常用例：密码错误、账号锁定\n"
+            "- P1 边界用例：连续错误锁定 15 分钟\n\n"
+            "建议：增加并发登录和 Token 过期测试"
+        )
+    return (
+        "你好！我是 ZeekerWatchman 助手。我可以帮你：\n\n"
+        "- 分析 PRD 文档，提取功能点\n"
+        "- 审查和修改 IR（中间表示）\n"
+        "- 生成和优化测试用例\n"
+        "- 导出测试用例为 YAML/CSV 格式\n\n"
+        "请上传一个 PRD 文档开始，或者告诉我你需要什么帮助。"
+    )
+
+
+class MockLLMClient(LLMClient):
+    """Mock client that returns realistic dummy responses for demo/testing."""
+
+    def __init__(self, model_name: str = "mock"):
+        self._client = None
+        self._timeout = 60
+        self._model = model_name
+        self._prompt_tokens = 0
+        self._completion_tokens = 0
+
+    def chat(
+        self,
+        model: str,
+        messages: list[dict],
+        *,
+        timeout: int | None = None,
+        response_format: dict | None = None,
+        temperature: float = 0.0,
+    ) -> str:
+        # Check both system prompt and last message for signal phrases
+        full_prompt = ""
+        sys_prompt = ""
+        last_msg = ""
+        if messages:
+            for m in messages:
+                content = m.get("content", "")
+                content = content if isinstance(content, str) else str(content)
+                full_prompt += content + "\n"
+                if m.get("role") == "system":
+                    sys_prompt = content
+            last_msg = messages[-1].get("content", "")
+            last_msg = last_msg if isinstance(last_msg, str) else str(last_msg)
+
+        # Detect mode: check system prompt first, then last user message
+        is_chat = (
+            "测试用例管理助手" in full_prompt
+            or "## 当前上下文" in full_prompt
+        )
+        is_semantic = (
+            "语义索引" in full_prompt
+            or "function_units" in full_prompt
+            or "semantic" in full_prompt.lower()
+        )
+        is_tc = (
+            "生成完整的测试用例集" in full_prompt
+            or "Given-When-Then" in full_prompt
+            or "## IR 内容" in full_prompt
+        )
+        is_stage2 = (
+            "精准上下文包" in full_prompt
+            or "IR Schema" in full_prompt
+            or "unit_id" in full_prompt
+        )
+
+        if is_chat:
+            result = _mock_chat_reply(last_msg)
+        elif is_semantic:
+            result = json.dumps(MOCK_SEMANTIC_INDEX, ensure_ascii=False)
+        elif is_stage2:
+            result = json.dumps(MOCK_IR_RULES, ensure_ascii=False)
+        elif is_tc:
+            result = json.dumps(MOCK_TESTCASES, ensure_ascii=False)
+        else:
+            result = MOCK_IR_YAML
+
+        logger.info("[MOCK] → %d chars (mode=%s)", len(result),
+                     "chat" if is_chat else "semantic" if is_semantic else "stage2" if is_stage2 else "testcases" if is_tc else "ir_yaml")
+        return result
+
+    def chat_with_image(
+        self,
+        model: str,
+        image_path: str,
+        prompt: str,
+        *,
+        timeout: int | None = None,
+    ) -> str:
+        return "type: other\nMock image analysis - this is a demo response."