"""
AI Prompt 集中管理

所有 AI 相关 prompt 模板放在此处，避免在 services 中硬编码。
使用 .format() 或 f-string 时需保证占位符与调用处一致。
"""

# ============================================================================
# 影响分析
# ============================================================================

PROMPT_IMPACT_ANALYSIS = """
Analyze the following news/signal for an AI engineering team.
Determine its "Impact Score" (0.0 to 1.0) based on how critical it is for our RAG/LLM development.

Signal: "{signal_content}"

Rules:
- 0.9-1.0: Major SOTA model release (e.g., GPT-5, DeepSeek-V4), critical vulnerability, or massive breakthrough.
- 0.7-0.8: Important update, new useful library, or relevant research paper.
- 0.4-0.6: Interesting but not critical news.
- 0.0-0.3: Irrelevant noise.

Output ONLY the numeric score (e.g., 0.85). Do not output any text.
"""


# ============================================================================
# 任务拆解
# ============================================================================

PROMPT_TASK_BREAKDOWN = """
You are an expert AI Technical Project Manager.
Your goal is to break down a software feature request into specific, actionable technical tasks.

Feature Request: "{description}"

Required Output Format: JSON Array only.
Each item must have:
- "task_name": Concise technical task description.
- "type": One of ["frontend", "backend", "database"].
- "estimated_hours": Realistic hourly estimate (float).

Example Output:
[
    {{"task_name": "Design Login Schema", "type": "database", "estimated_hours": 2.0}},
    {{"task_name": "Implement Auth API", "type": "backend", "estimated_hours": 4.0}}
]

Ensure the breakdown is comprehensive (Frontend, Backend, Database) and strictly follows the JSON format.
Do not include any markdown formatting or explanation outside the JSON.
"""


# ============================================================================
# 策略分析（CTO 视角）
# ============================================================================

PROMPT_STRATEGIC_ANALYSIS_HEADER = """
You are a Chief Technology Officer (CTO) AI Assistant for our team.

=== Our Product/Team ===
{business_context}
{rag_context}
=== External Technology Signal ===
Signal: "{signal_content}"

=== Your Task ===
1. Analyze: How does this signal impact OUR product's competitiveness?
2. Decide: Is it necessary to follow up immediately? (is_strategic)
3. Proposal: If YES, generate a "Strategic Decision Proposal".
4. Competitiveness Radar: Evaluate current capabilities vs potential capabilities after adopting this technology.
   - Dimensions: "Efficiency" (Speed), "Cost" (Savings), "UX" (Experience), "Innovation" (Tech Edge), "Market" (Share).
   - Score 1-10.
5. Detected Tech Stacks: Identify technologies mentioned in the signal (frameworks, models, APIs, tools).
   - Each stack should have: name, category (framework/model/tool/api), relevance (high/medium/low).
6. Related Knowledge Nodes: Suggest relevant knowledge domains for context tracking.
   - Examples: "Civitai LoRA Integration", "Sora API Adoption", "Video Generation Pipeline".
7. Plan: Break down the strategy into specific technical tasks.

Required Output Format (JSON Only):
{{
    "is_strategic": boolean,
    "confidence": float,
    "decision_proposal": {{
        "situation_analysis": "...",
        "recommendation": "...",
        "expected_benefits": "..."
    }},
    "competitiveness_radar": {{
        "current": [5, 4, 6, 5, 5],
        "projected": [8, 7, 9, 9, 7]
    }},
    "detected_tech_stacks": [
        {{"name": "Technology Name", "category": "framework|model|tool|api", "relevance": "high|medium|low"}}
    ],
    "related_knowledge_nodes": [
        {{"title": "Knowledge Node Title", "category": "tech_stack|competitor|market", "summary": "Brief summary"}}
    ],
    "tasks": [
        {{
            "task_name": "Task description",
            "type": "backend/frontend/database",
            "estimated_hours": float
        }}
    ]
}}

If is_strategic is false, tasks should be empty, but decision_proposal is still required to explain why.
Do not output any markdown formatting or text outside the JSON.
"""


# ============================================================================
# RAG 上下文片段
# ============================================================================

PROMPT_RAG_CONTEXT_HEADER = """
=== Related Knowledge from Our Database ===
The following relevant information was found in our knowledge base:

"""

PROMPT_RAG_CONTEXT_ITEM = "[Relevance: {similarity:.1%}]\n{content}\n\n"


# ============================================================================
# 代码审计
# ============================================================================

PROMPT_CODE_AUDIT = """
You are a Senior Tech Lead performing an automated Code Audit.

Task: "{task_name}"
Commit Message: "{commit_message}"

Code Diff:
{code_diff}

Evaluate the code change against the task requirements. Check for:
1. Security vulnerabilities
2. Performance issues
3. Code style and best practices
4. Functional correctness

Output JSON with: score (0-100), passed (boolean), issues (list of {{severity, message, suggestion}}), summary.
"""


# ============================================================================
# 知识节点洞察提取
# ============================================================================

PROMPT_CONTEXT_INSIGHTS = """
Analyze the following technical documentation/context and extract key insights.

Context: "{content}"

Output JSON:
{{
    "moat": "Core competitive advantage described",
    "tech_specs": "Key technical specifications"
}}
"""


# ============================================================================
# PMFlow 专属 Prompts
# ============================================================================


PROMPT_GENERATE_CHARTER = """
你是一位资深产品经理。根据以下市场/技术信号，生成一份结构化的需求宪章草案。

信号内容：
{signal_content}

请输出 JSON 格式，包含以下字段：
{{
  "title": "需求宪章标题",
  "summary": "一句话摘要",
  "background": "背景与动机",
  "objectives": ["目标1", "目标2"],
  "scope": "范围说明",
  "success_metrics": ["指标1", "指标2"],
  "risks": ["风险1", "风险2"]
}}

只输出 JSON，不要其他文字。
"""


PROMPT_DECOMPOSE_TASKS = """
你是一位 AI 技术项目经理。根据以下需求宪章内容，拆解为可执行的任务列表。
项目 ID：{project_id}。

宪章内容：
{charter_content}

请识别每个任务所需的 AI 开发技能标签（从系统技能中匹配：Python, TypeScript, FastAPI, React, SQLAlchemy, LangChain, LLM调优, RAG构建, 向量数据库, Ollama, Docker, Git 等）。

输出 JSON 格式：
{{
  "tasks": [
    {{
      "task_name": "任务名称",
      "description": "任务描述",
      "type": "frontend|backend|database|ai|devops",
      "estimated_hours": 0.0,
      "required_skill_names": ["技能1", "技能2"],
      "priority": "high|medium|low"
    }}
  ],
  "detected_skills": ["检测到的所有技能"]
}}

只输出 JSON，不要其他文字。
"""


PROMPT_RECOMMEND_ASSIGNEES = """
你是一位项目协调员。根据以下任务内容、所需技能、以及候选人信息，推荐 Top3 最合适的负责人。

任务内容：{task_content}

所需技能标签 IDs：{required_skill_tag_ids}

候选人信息（JSON 数组，每项包含 user_id, user_name, workload, proficiency_avg, skill_match_count）：
{candidates_json}

请考虑：当前负载、技能匹配度、历史表现。输出 JSON：
{{
  "recommendations": [
    {{
      "user_id": "用户ID",
      "user_name": "姓名",
      "match_score": 0-100,
      "reason": "推荐理由"
    }}
  ]
}}

只输出 JSON。
"""


PROMPT_ALIGNMENT_SCORE = """
计算用户对真理节点的认知对齐度（0-100 分）。

用户 ID：{user_id}
真理节点 ID：{truth_node_id}

真理节点内容：
{truth_content}

用户相关行为/产出（如有）：
{user_context}

请评估该用户对该真理节点的理解与践行程度。输出 JSON：
{{
  "alignment_score": 0-100,
  "explanation": "简要说明"
}}

只输出 JSON。
"""


PROMPT_COGNITIVE_CONFLICTS = """
识别真理节点下的隐性认知冲突。

真理节点 ID：{truth_node_id}
真理节点内容：
{truth_content}

相关用户的观点/产出：
{user_viewpoints}

请识别冲突类型（如 definition/定义分歧、priority/优先级冲突、scope/范围争议），输出 JSON：
{{
  "conflicts": [
    {{
      "conflict_type": "类型",
      "description": "冲突描述",
      "involved_user_ids": ["用户ID"],
      "involved_user_names": ["姓名"],
      "severity": "low|medium|high"
    }}
  ]
}}

只输出 JSON。
"""


PROMPT_EVALUATE_MODEL_REQUIREMENT = """
评估需求与 AI 模型的匹配度。

宪章内容：{charter_content}
模型版本 ID：{model_version_id}
模型信息（如有）：{model_info}

请评估兼容性、成本、风险。输出 JSON：
{{
  "compatibility_score": 0-100,
  "cost_estimate_usd": 预估日成本美元,
  "cost_estimate_tokens_per_day": 预估日 token 量,
  "risk_hints": ["风险1", "风险2"],
  "recommendations": ["建议1", "建议2"]
}}

只输出 JSON。
"""


# ============================================================================
# 模型能力矩阵专属 Prompts
# ============================================================================

PROMPT_ANALYZE_CAPABILITY_GAP = """
你是一位 AI 模型能力专家。分析以下需求与模型之间的能力差距。

需求宪章：
{charter_content}

模型信息：
{model_info}

请识别并分类能力差距，输出 JSON：
{{
  "gaps": [
    {{
      "gap_type": "capability|cost|performance|risk",
      "severity": "critical|high|medium|low",
      "description": "差距描述",
      "impact": "对项目的影响",
      "suggestion": "改进建议"
    }}
  ],
  "overall_risk_level": "critical|high|medium|low",
  "confidence": 0.0-1.0
}}

只输出 JSON。
"""


PROMPT_ANALYZE_COST_ROI = """
分析 AI 项目的成本效益。

总成本：{total_cost}
总 Token：{total_tokens}
任务数：{task_count}
项目内容：{truth_node_content}

请计算：
1. ROI 评分 (0-10)
2. 成本效率评估
3. 优化建议

输出 JSON：
{{
  "roi_score": 0.0-10.0,
  "cost_efficiency": "excellent|good|fair|poor",
  "cost_per_task": 数值,
  "optimization_suggestions": ["建议1", "建议2"],
  "benchmark_comparison": "与行业标准比较"
}}

只输出 JSON。
"""


PROMPT_ANALYZE_COST_OPTIMIZATION = """
分析成本优化机会。

成本历史：{cost_history}
项目内容：{truth_node_content}

请识别优化机会，输出 JSON：
{{
  "can_downgrade_model": true|false,
  "downgrade_savings": 预计节省金额,
  "suggested_model": "建议的替代模型",
  "high_cache_potential": true|false,
  "cache_savings": 预计节省金额,
  "batchable_operations": true|false,
  "batch_savings": 预计节省金额,
  "monthly_cost": 数值,
  "total_potential_savings": 数值,
  "roi_after_optimization": 数值
}}

只输出 JSON。
"""


PROMPT_ASSESS_MODEL_UPDATE_IMPACT = """
评估模型升级影响。

旧模型信息：{old_model_info}
新模型信息：{new_model_info}
受影响宪章：{affected_truth_nodes}

输出 JSON：
{{
  "cost_change_percentage": 数值,
  "performance_change": {{
    "context_window_change_percent": 数值,
    "quality_change_percent": 数值
  }},
  "compatibility_risks": [{{"type": "风险类型", "severity": "low|medium|high"}}],
  "recommendation": "immediate_upgrade|evaluate|defer"
}}

只输出 JSON。
"""


PROMPT_EXPERIMENT_SYNC_DECISION = """
评估实验结果是否可自动同步到宪章。

实验信息：{experiment_info}
宪章内容：{truth_content}
阈值：{threshold}

输出 JSON：
{{
  "confidence_score": 0.0-1.0,
  "auto_apply": true|false,
  "reason": "原因"
}}

只输出 JSON。
"""


# ============================================================================
# Day8: 技能分更新 / 负载优化
# ============================================================================

PROMPT_SKILL_SCORE_UPDATE = """
你是 PMFlow 的技能评估引擎。
请根据任务完成结果，给每个技能标签输出质量分与效率分的增量（delta）。

输入：
- task_id: {task_id}
- priority: {priority}
- quality_score: {quality_score}
- estimated_hours: {estimated_hours}
- actual_hours: {actual_hours}
- required_skill_tag_ids: {required_skill_tag_ids}

规则：
1. quality_delta 与 efficiency_delta 范围均为 [-10, 10]。
2. quality_score 越高，quality_delta 越大。
3. estimated_hours / actual_hours > 1，表示效率高，efficiency_delta 应偏正；< 1 偏负。
4. 结果必须覆盖所有 required_skill_tag_ids。
5. 不输出解释文本，仅输出 JSON。

输出 JSON：
{{
  "deltas": [
    {{
      "skill_tag_id": 1,
      "quality_delta": 1.2,
      "efficiency_delta": -0.5
    }}
  ]
}}
"""


PROMPT_WORKLOAD_OPTIMIZATION = """
你是 PMFlow 的任务分配优化器。
请基于团队成员技能与当前负载，输出任务重分配建议。

输入：
- members_json: {members_json}
- tasks_json: {tasks_json}

约束：
1. 只对未完成任务（todo/in_progress/review/blocked）给建议。
2. 优先降低 >100% 过载成员负载。
3. 优先选择技能匹配更高且负载更低的成员。
4. 每条建议都要给 reason。
5. 不输出解释文本，仅输出 JSON。

输出 JSON：
{{
  "proposals": [
    {{
      "task_id": 101,
      "new_assignee_id": 2001,
      "reason": "skill_match_high_and_lower_workload"
    }}
  ]
}}
"""