init the project

server/core/llm_provider/__init__.py

@@ -0,0 +1,5 @@
+from server.core.llm_provider.base import LLMClient
+from server.core.llm_provider.router import ModelRouter, router
+from server.core.llm_provider.mock_client import MockLLMClient
+
+__all__ = ["LLMClient", "ModelRouter", "router", "MockLLMClient"]

server/core/llm_provider/base.py

@@ -0,0 +1,145 @@
+# OpenAI-compatible LLM client with retry, token tracking, and vision support.
+
+import base64
+import logging
+import os
+import time
+from typing import Optional
+
+from openai import OpenAI
+
+logger = logging.getLogger("testflow")
+
+
+class LLMClient:
+    """Generic OpenAI-compatible LLM client.
+
+    Usage::
+
+        client = LLMClient(api_key="sk-xxx", base_url="https://api.deepseek.com/v1")
+        text = client.chat("deepseek-chat", [{"role": "user", "content": "Hello"}])
+        print(client.usage)
+    """
+
+    TIMEOUT = 120
+    MAX_RETRIES = 3
+
+    def __init__(
+        self,
+        *,
+        api_key: str = "",
+        base_url: str = "",
+        model: str = "",
+        timeout: int | None = None,
+    ):
+        if not api_key:
+            raise ValueError(f"API key is required for LLMClient")
+        self._client = OpenAI(api_key=api_key, base_url=base_url)
+        self._timeout = timeout or self.TIMEOUT
+        self._model = model
+        self._prompt_tokens = 0
+        self._completion_tokens = 0
+
+    @property
+    def usage(self) -> dict:
+        return {
+            "prompt_tokens": self._prompt_tokens,
+            "completion_tokens": self._completion_tokens,
+            "total_tokens": self._prompt_tokens + self._completion_tokens,
+        }
+
+    @staticmethod
+    def estimate_tokens(text: str) -> int:
+        cjk = sum(1 for c in text if '一' <= c <= '鿿' or '　' <= c <= '〿')
+        other = len(text) - cjk
+        return max(1, int(cjk / 1.7 + other / 3.0))
+
+    @staticmethod
+    def estimate_image_tokens() -> int:
+        return 500
+
+    def chat(
+        self,
+        model: str,
+        messages: list[dict],
+        *,
+        timeout: int | None = None,
+        response_format: dict | None = None,
+        temperature: float = 0.0,
+    ) -> str:
+        # Estimate prompt size
+        prompt_chars = sum(len(str(m.get("content", ""))) for m in messages)
+        label = f"chat({model})"
+
+        def _call():
+            t0 = time.time()
+            kwargs = dict(
+                model=model,
+                messages=messages,
+                timeout=timeout or self._timeout,
+                temperature=temperature,
+            )
+            if response_format is not None:
+                kwargs["response_format"] = response_format
+            logger.info("[LLM] → %s (prompt ~%d chars / ~%d tokens)",
+                         model, prompt_chars, prompt_chars // 3)
+            resp = self._client.chat.completions.create(**kwargs)
+            content = resp.choices[0].message.content
+            usg = resp.usage
+            if usg:
+                self._prompt_tokens += usg.prompt_tokens
+                self._completion_tokens += usg.completion_tokens
+            elapsed = time.time() - t0
+            logger.info("[LLM] ← %s: %d chars, p:%d c:%d tokens, %.1fs",
+                         model, len(content) if content else 0,
+                         usg.prompt_tokens if usg else 0,
+                         usg.completion_tokens if usg else 0,
+                         elapsed)
+            if not content:
+                raise RuntimeError("Empty response from LLM")
+            return content
+
+        return self._retry(_call, label)
+
+    def chat_with_image(
+        self,
+        model: str,
+        image_path: str,
+        prompt: str,
+        *,
+        timeout: int | None = None,
+    ) -> str:
+        """Send a prompt with an image attachment (for vision models)."""
+        with open(image_path, "rb") as f:
+            img_b64 = base64.b64encode(f.read()).decode()
+        mime = self._mime_type(image_path)
+
+        messages = [{
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{img_b64}"}},
+                {"type": "text", "text": prompt},
+            ],
+        }]
+        return self.chat(model, messages, timeout=timeout)
+
+    @staticmethod
+    def _mime_type(image_path: str) -> str:
+        ext = os.path.splitext(image_path)[1].lstrip(".").lower()
+        return {
+            "png": "image/png", "jpg": "image/jpeg", "jpeg": "image/jpeg",
+            "gif": "image/gif", "bmp": "image/bmp",
+            "webp": "image/webp", "svg": "image/svg+xml", "tiff": "image/tiff",
+        }.get(ext, "image/png")
+
+    def _retry(self, fn, label: str) -> str:
+        last_error: Optional[Exception] = None
+        for attempt in range(self.MAX_RETRIES):
+            try:
+                return fn()
+            except Exception as e:
+                last_error = e
+                logger.warning("%s error (attempt %d/%d): %s", label, attempt + 1, self.MAX_RETRIES, e)
+                if attempt < self.MAX_RETRIES - 1:
+                    time.sleep(2 ** attempt)
+        raise RuntimeError(f"{label}: all retries exhausted") from last_error

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."

server/core/llm_provider/router.py

@@ -0,0 +1,67 @@
+# Model router: creates LLM clients based on provider configuration.
+
+import logging
+
+from server.config import settings, DEEPSEEK_API_KEY, DEEPSEEK_BASE_URL, DASHSCOPE_API_KEY, DASHSCOPE_BASE_URL
+from server.core.llm_provider.base import LLMClient
+from server.core.llm_provider.mock_client import MockLLMClient
+
+logger = logging.getLogger("testflow")
+
+
+class ModelRouter:
+    """Creates and caches LLM client instances.
+
+    Two independent mock toggles:
+      - USE_MOCK      → controls pipeline (left panel): text + image clients
+      - CHAT_USE_MOCK → controls AI assistant (right panel): chat client
+    """
+
+    def __init__(self):
+        self._clients: dict[str, LLMClient] = {}
+        self._log_status()
+
+    def _log_status(self):
+        pipe = "MOCK" if settings.USE_MOCK else "REAL"
+        chat = "MOCK" if settings.CHAT_USE_MOCK else "REAL"
+        logger.info("[ROUTER] 流水线=%s | AI助手=%s (DeepSeek %s / Qwen %s)",
+                     pipe, chat, settings.TEXT_MODEL, settings.IMAGE_MODEL)
+
+    def get_text_client(self) -> LLMClient:
+        """Pipeline text client (controlled by USE_MOCK)."""
+        return self._get_or_create("text", settings.USE_MOCK, settings.TEXT_MODEL,
+                                    DEEPSEEK_API_KEY, DEEPSEEK_BASE_URL)
+
+    def get_image_client(self) -> LLMClient:
+        """Pipeline image client (controlled by USE_MOCK)."""
+        return self._get_or_create("image", settings.USE_MOCK, settings.IMAGE_MODEL,
+                                    DASHSCOPE_API_KEY, DASHSCOPE_BASE_URL)
+
+    def get_chat_client(self) -> LLMClient:
+        """AI assistant chat client (controlled by CHAT_USE_MOCK)."""
+        return self._get_or_create("chat", settings.CHAT_USE_MOCK, settings.TEXT_MODEL,
+                                    DEEPSEEK_API_KEY, DEEPSEEK_BASE_URL)
+
+    def _get_or_create(self, key: str, use_mock: bool, model: str,
+                       api_key: str, base_url: str) -> LLMClient:
+        if key not in self._clients:
+            if use_mock:
+                logger.info("[ROUTER] %s client → MOCK", key)
+                self._clients[key] = MockLLMClient(model_name=model)
+            else:
+                logger.info("[ROUTER] %s client → REAL (%s)", key, model)
+                self._clients[key] = LLMClient(
+                    api_key=api_key, base_url=base_url, model=model,
+                )
+        return self._clients[key]
+
+    @property
+    def text_model(self) -> str:
+        return settings.TEXT_MODEL
+
+    @property
+    def image_model(self) -> str:
+        return settings.IMAGE_MODEL
+
+
+router = ModelRouter()