1486 lines
49 KiB
Python
1486 lines
49 KiB
Python
"""
|
||
Skill 管理 API 路由
|
||
|
||
提供：
|
||
1. Skill 的 CRUD 操作
|
||
2. Agent-Skill 生命周期端点
|
||
3. AI 辅助 Skill 创建（完整流程）
|
||
4. Skill 选择和路由（LLM 与 Skills 结合）
|
||
5. Agent 工作流配置
|
||
6. 文档驱动的 Skill 生成（新增）
|
||
"""
|
||
from datetime import datetime
|
||
from fastapi import APIRouter, Depends, HTTPException, status
|
||
from typing import List, Optional, Dict, Any
|
||
from pydantic import BaseModel
|
||
|
||
from app.models.skill import (
|
||
Skill,
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||
SkillCreate,
|
||
SkillUpdate,
|
||
SkillConfigUpdate,
|
||
SkillTestRequest,
|
||
SkillTestResponse,
|
||
SkillGenerateRequest,
|
||
SkillGenerateResponse,
|
||
# 新增模型
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||
DocFetchRequest,
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||
GitHubDocFetchRequest,
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||
SkillGenerateFromDocsRequest,
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||
SkillGenerateFromDocsResponse,
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||
SkillPreviewRequest,
|
||
SkillPreviewResponse,
|
||
SkillSaveFromPreviewRequest,
|
||
SkillRefineRequest,
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||
SkillRefineResponse,
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||
)
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from app.models.skill_integration import (
|
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SkillMetadata,
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SkillTriggerType,
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||
SkillStructure,
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||
SkillGenerationRequest,
|
||
SkillGenerationResponse,
|
||
SkillAnalysis,
|
||
SkillPlanning,
|
||
SkillRefinementRequest,
|
||
SkillRefinementResponse,
|
||
SkillSelectionCriteria,
|
||
SkillSelectionResult,
|
||
SkillExecutionContext,
|
||
SkillExecutionResponse,
|
||
AgentWorkflowConfig,
|
||
AgentWorkflowStep,
|
||
)
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||
from app.core.skills.skill_manager import get_skill_manager, SkillManager
|
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from app.core.llm.glm_client import get_glm_client, GLMClient
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||
from app.utils.logger import get_logger
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||
|
||
logger = get_logger(__name__)
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||
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||
router = APIRouter(prefix="/skills", tags=["Skill管理"])
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||
|
||
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# ============================================================================
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||
# 基础 CRUD 端点
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||
# ============================================================================
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||
|
||
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@router.get("/", response_model=List[Skill])
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||
async def list_skills(
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||
skill_type: Optional[str] = None,
|
||
category: Optional[str] = None,
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skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
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||
列出所有 Skills
|
||
|
||
- **skill_type**: 筛选类型 (builtin/user)
|
||
- **category**: 筛选分类
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||
"""
|
||
skills = await skill_manager.list_skills(skill_type=skill_type, category=category)
|
||
return skills
|
||
|
||
|
||
@router.get("/{skill_id}", response_model=Skill)
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||
async def get_skill(
|
||
skill_id: str,
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skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""获取 Skill 详情"""
|
||
skill = await skill_manager.load_skill(skill_id)
|
||
if not skill:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail=f"Skill 不存在: {skill_id}"
|
||
)
|
||
return skill
|
||
|
||
|
||
@router.post("/", response_model=Skill, status_code=status.HTTP_201_CREATED)
|
||
async def create_skill(
|
||
skill_data: SkillCreate,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
|
||
创建新的用户 Skill
|
||
|
||
如果不提供 ID，系统将自动生成：
|
||
- 使用 UUID 作为唯一标识
|
||
- 基于名称生成可读 ID（kebab-case）
|
||
"""
|
||
try:
|
||
# 如果没有提供 ID，自动生成
|
||
if not skill_data.id:
|
||
import uuid
|
||
import re
|
||
|
||
# 生成基于名称的 kebab-case ID
|
||
base_id = skill_data.name.lower()
|
||
base_id = re.sub(r'[^\w\s-]', '', base_id) # 移除特殊字符
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||
base_id = re.sub(r'\s+', '-', base_id) # 空格替换为连字符
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||
base_id = re.sub(r'-{2,}', '-', base_id) # 多个连字符合并为一个
|
||
|
||
# 如果生成结果为空，使用名称的拼音或默认前缀
|
||
if not base_id or len(base_id) < 3:
|
||
base_id = f"skill-{uuid.uuid4().hex[:8]}"
|
||
|
||
# 确保 ID 唯一（添加随机后缀）
|
||
unique_id = f"{base_id}-{uuid.uuid4().hex[:6]}"
|
||
|
||
# 创建新的 SkillCreate 对象，使用自动生成的 ID
|
||
skill_data_with_id = skill_data.copy()
|
||
skill_data_with_id.id = unique_id
|
||
|
||
skill = await skill_manager.create_user_skill(skill_data_with_id)
|
||
else:
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||
skill = await skill_manager.create_user_skill(skill_data)
|
||
|
||
return skill
|
||
except Exception as e:
|
||
logger.error(f"创建 Skill 失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"创建 Skill 失败: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.put("/{skill_id}", response_model=Skill)
|
||
async def update_skill(
|
||
skill_id: str,
|
||
skill_data: SkillUpdate,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""更新 Skill 内容"""
|
||
skill = await skill_manager.update_user_skill(skill_id, skill_data)
|
||
if not skill:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail=f"Skill 不存在或不允许更新: {skill_id}"
|
||
)
|
||
return skill
|
||
|
||
|
||
@router.put("/{skill_id}/config", response_model=Skill)
|
||
async def update_skill_config(
|
||
skill_id: str,
|
||
config_update: SkillConfigUpdate,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""更新 Skill 配置"""
|
||
skill = await skill_manager.load_skill(skill_id)
|
||
if not skill:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail=f"Skill 不存在: {skill_id}"
|
||
)
|
||
|
||
# 更新配置
|
||
if config_update.preset is not None:
|
||
skill.config.preset = config_update.preset
|
||
if config_update.parameters is not None:
|
||
skill.config.parameters.update(config_update.parameters)
|
||
if config_update.weights is not None:
|
||
skill.config.weights = config_update.weights
|
||
|
||
return skill
|
||
|
||
|
||
@router.post("/{skill_id}/test", response_model=SkillTestResponse)
|
||
async def test_skill(
|
||
skill_id: str,
|
||
test_request: SkillTestRequest,
|
||
skill_manager: SkillManager = Depends(get_skill_manager),
|
||
glm_client: GLMClient = Depends(get_glm_client)
|
||
):
|
||
"""
|
||
测试 Skill
|
||
|
||
使用 Skill 的行为指导来处理测试输入，返回 LLM 的响应
|
||
"""
|
||
skill = await skill_manager.load_skill(skill_id)
|
||
if not skill:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail=f"Skill 不存在: {skill_id}"
|
||
)
|
||
|
||
try:
|
||
# 使用 GLM 客户端的 chat_with_skill 方法
|
||
response = await glm_client.chat_with_skill(
|
||
skill_behavior=skill.behavior_guide,
|
||
user_input=test_request.test_input,
|
||
context=test_request.context or {},
|
||
temperature=test_request.temperature
|
||
)
|
||
|
||
return SkillTestResponse(
|
||
skill_id=skill_id,
|
||
skill_name=skill.name,
|
||
response=response
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"测试 Skill 失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"测试 Skill 失败: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.delete("/{skill_id}", status_code=status.HTTP_204_NO_CONTENT)
|
||
async def delete_skill(
|
||
skill_id: str,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""删除用户 Skill"""
|
||
success = await skill_manager.delete_user_skill(skill_id)
|
||
if not success:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail=f"Skill 不存在或不允许删除: {skill_id}"
|
||
)
|
||
return None
|
||
|
||
|
||
@router.post("/{skill_id}/reload", response_model=Skill)
|
||
async def reload_skill(
|
||
skill_id: str,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""重新加载 Skill（清除缓存）"""
|
||
skill = await skill_manager.reload_skill(skill_id)
|
||
if not skill:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail=f"Skill 不存在: {skill_id}"
|
||
)
|
||
return skill
|
||
|
||
|
||
@router.get("/categories/list", response_model=List[str])
|
||
async def list_categories(
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""列出所有 Skill 分类"""
|
||
skills = await skill_manager.list_skills()
|
||
categories = set(skill.category for skill in skills)
|
||
return sorted(list(categories))
|
||
|
||
|
||
@router.post("/generate", response_model=SkillGenerateResponse)
|
||
async def generate_skill_with_ai(
|
||
request: SkillGenerateRequest,
|
||
skill_manager: SkillManager = Depends(get_skill_manager),
|
||
glm_client: GLMClient = Depends(get_glm_client)
|
||
):
|
||
"""
|
||
使用 AI 生成 Skill（类似 Claude Code 的 skill-creator）
|
||
|
||
流程：
|
||
1. 加载 skill-creator 的行为指导
|
||
2. 将用户需求和 skill-creator 标准一起发送给 GLM-4.7
|
||
3. AI 生成符合标准的 Skill 内容
|
||
|
||
Args:
|
||
request: 包含用户描述、分类、标签的请求
|
||
|
||
Returns:
|
||
生成的 Skill 内容和建议信息
|
||
"""
|
||
try:
|
||
# 1. 加载 skill-creator 的行为指导
|
||
skill_creator = await skill_manager.load_skill("skill-creator")
|
||
if not skill_creator:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail="skill-creator 未找到，请确保内置 Skills 正确安装"
|
||
)
|
||
|
||
# 2. 构建提示词
|
||
user_requirements = f"""用户想要创建的 Skill 描述：
|
||
{request.description}
|
||
"""
|
||
|
||
if request.category:
|
||
user_requirements += f"\n指定分类：{request.category}"
|
||
|
||
if request.tags:
|
||
user_requirements += f"\n指定标签：{', '.join(request.tags)}"
|
||
|
||
system_prompt = f"""你是一个专业的 Skill 创建专家。
|
||
|
||
以下是 skill-creator 的行为指导（关于如何创建有效 Skill 的指南）：
|
||
{'━' * 60}
|
||
{skill_creator.behavior_guide}
|
||
{'━' * 60}
|
||
|
||
你的任务是根据用户的需求，创建一个符合上述标准的 Skill。
|
||
|
||
**重要要求**：
|
||
1. SKILL.md 必须以 YAML frontmatter 开始，包含 name 和 description 字段
|
||
2. description 应该清晰说明此 Skill 的用途和使用场景
|
||
3. 行为指导部分应该简洁、具体，避免冗余的解释
|
||
4. 使用 markdown 格式
|
||
5. 返回完整的 SKILL.md 内容
|
||
|
||
请以 JSON 格式返回结果，包含以下字段：
|
||
- suggested_id: 建议 Skill ID（kebab-case，如 dialogue-writer-ancient）
|
||
- suggested_name: 建议 Skill 名称（简短中文）
|
||
- skill_content: 完整的 SKILL.md 内容
|
||
- category: 分类（如"编剧"、"审核"、"通用"等）
|
||
- suggested_tags: 建议标签数组
|
||
- explanation: 对生成的 Skill 的简要说明（中文）
|
||
"""
|
||
|
||
# 3. 调用 GLM-4.7 生成
|
||
response = await glm_client.chat(
|
||
messages=[
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": user_requirements}
|
||
],
|
||
temperature=request.temperature
|
||
)
|
||
|
||
# 4. 解析响应
|
||
import json
|
||
import re
|
||
|
||
response_text = response["choices"][0]["message"]["content"]
|
||
|
||
# 尝试提取 JSON
|
||
json_match = re.search(r'\{[\s\S]*\}', response_text)
|
||
if json_match:
|
||
result = json.loads(json_match.group())
|
||
else:
|
||
# 如果没有找到 JSON，创建一个默认响应
|
||
result = {
|
||
"suggested_id": "custom-skill",
|
||
"suggested_name": "自定义 Skill",
|
||
"skill_content": response_text,
|
||
"category": request.category or "通用",
|
||
"suggested_tags": request.tags or ["自定义"],
|
||
"explanation": "AI 生成的 Skill 内容"
|
||
}
|
||
|
||
return SkillGenerateResponse(**result)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"AI 生成 Skill 失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"AI 生成 Skill 失败: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.get("/{skill_id}/with-references", response_model=dict)
|
||
async def get_skill_with_references(
|
||
skill_id: str,
|
||
skill_manager: SkillManager = Depends(get_skill_manager),
|
||
include_references: bool = True
|
||
):
|
||
"""
|
||
获取 Skill 及其参考文件
|
||
|
||
这是实现参考文件融入 LLM 的关键端点：
|
||
- 返回 Skill 对象
|
||
- 返回所有参考文件的内容
|
||
|
||
Args:
|
||
skill_id: Skill ID
|
||
include_references: 是否包含参考文件内容
|
||
"""
|
||
try:
|
||
skill = await skill_manager.load_skill(skill_id)
|
||
if not skill:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail=f"Skill 不存在: {skill_id}"
|
||
)
|
||
|
||
result = {"skill": skill.model_dump()}
|
||
|
||
# 加载参考文件（如果需要）
|
||
if include_references:
|
||
references = await skill_manager.load_skill_references(skill.id)
|
||
result["references"] = references
|
||
|
||
return result
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"获取 Skill 失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"获取 Skill 失败: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.post("/analyze", response_model=SkillAnalysis)
|
||
async def analyze_skill_requirements(
|
||
request: SkillGenerationRequest,
|
||
skill_manager: SkillManager = Depends(get_skill_manager),
|
||
glm_client: GLMClient = Depends(get_glm_client)
|
||
):
|
||
"""
|
||
分析 Skill 创建需求（完整流程 Step 1: Understanding）
|
||
|
||
Args:
|
||
request: 包含用户意图、描述、用例的请求
|
||
|
||
Returns:
|
||
SkillAnalysis: 需求分析结果
|
||
"""
|
||
try:
|
||
# Step 1: 加载 skill-creator
|
||
skill_creator = await skill_manager.load_skill("skill-creator")
|
||
if not skill_creator:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail="skill-creator 未找到，请确保内置 Skills 正确安装"
|
||
)
|
||
|
||
# Step 2: 需求分析
|
||
user_intent = request.user_intent or "创建新的 Skill"
|
||
system_prompt = f"""你是一个专业的 Skill 创建分析师。
|
||
|
||
以下是 skill-creator 的行为指导（关于如何创建有效 Skill 的指南）：
|
||
{'━' * 60}
|
||
{skill_creator.behavior_guide}
|
||
{'━' * 60}
|
||
|
||
你的任务是分析用户的需求：{user_intent}
|
||
|
||
请从以下几个方面分析：
|
||
1. **核心功能**：这个 Skill 主要用于什么？
|
||
2. **目标用户**：谁会使用这个 Skill？
|
||
3. **技术要求**：前端、后端、数据分析等
|
||
4. **特殊要求**：多语言支持、高精度输出、实时处理
|
||
|
||
分析用户需求，并提出 Skill 创作建议。
|
||
请以 JSON 格式返回分析结果，包含以下字段：
|
||
- intent_analysis: 用户的意图分析
|
||
- suggested_category: 建议的分类（如 "dialogue", "analysis", "writing"）
|
||
- suggested_features: 建议的核心功能列表
|
||
- complexity_assessment: 复杂度评估（simple/medium/complex）
|
||
- recommended_approach: 推荐的实现方式
|
||
"""
|
||
|
||
# 构建分析请求
|
||
analysis_request = {
|
||
"user_intent": user_intent,
|
||
"user_description": request.user_intent,
|
||
"use_cases": request.use_cases or []
|
||
}
|
||
|
||
# 调用 LLM 进行需求分析
|
||
analysis_response = await glm_client.chat(
|
||
messages=[
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": f"分析以下需求：{json.dumps(analysis_request, ensure_ascii=False)}"}
|
||
],
|
||
temperature=0.5
|
||
)
|
||
|
||
# 解析分析结果
|
||
try:
|
||
import json
|
||
analysis_result = json.loads(analysis_response["choices"][0]["message"]["content"])
|
||
|
||
# 提取结构化数据
|
||
intent_analysis = analysis_result.get("intent_analysis", {})
|
||
suggested_category = analysis_result.get("suggested_category", "general")
|
||
suggested_features = analysis_result.get("suggested_features", [])
|
||
complexity_assessment = analysis_result.get("complexity_assessment", "medium")
|
||
recommended_approach = analysis_result.get("recommended_approach", "standard")
|
||
|
||
except json.JSONDecodeError:
|
||
# 如果无法解析，使用默认分析
|
||
intent_analysis = {"primary": "创建新的 Skill", "details": "用户想要创建自定义 Skill"}
|
||
suggested_category = "general"
|
||
suggested_features = ["standard_dialogue_creation", "basic_content_editing"]
|
||
complexity_assessment = "medium"
|
||
recommended_approach = "standard"
|
||
|
||
return SkillAnalysis(
|
||
intent_analysis=intent_analysis,
|
||
suggested_category=suggested_category,
|
||
suggested_features=suggested_features,
|
||
complexity_assessment=complexity_assessment,
|
||
recommended_approach=recommended_approach
|
||
)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"需求分析失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"需求分析失败: {str(e)}"
|
||
)
|
||
|
||
|
||
# ============================================================================
|
||
# Skill 选择和路由（LLM 自动选择合适的 Skills）
|
||
# ============================================================================
|
||
|
||
@router.post("/select-skills", response_model=SkillSelectionResult)
|
||
async def select_skills_for_context(
|
||
criteria: SkillSelectionCriteria,
|
||
skill_manager: SkillManager = Depends(get_skill_manager),
|
||
glm_client: GLMClient = Depends(get_glm_client)
|
||
):
|
||
"""
|
||
LLM 根据描述自动选择最合适的 Skills
|
||
|
||
流程：
|
||
1. 分析用户输入
|
||
2. 匹配所有 Skills 的 metadata
|
||
3. 评分和排序
|
||
4. 返回选择结果
|
||
|
||
Args:
|
||
criteria: 选择条件（用户意图、上下文、约束）
|
||
"""
|
||
try:
|
||
# 加载所有可用 Skills
|
||
all_skills = await skill_manager.list_skills(skill_type="user")
|
||
|
||
# 提取 Skills 的 metadata
|
||
skills_metadata = []
|
||
for skill in all_skills:
|
||
metadata = {
|
||
"id": skill.id,
|
||
"name": skill.name,
|
||
"description": skill.description,
|
||
"category": skill.category,
|
||
"tags": skill.tags,
|
||
"keywords": skill.description.split(),
|
||
"capabilities": skill.behavior_guide[:200] if skill.behavior_guide else ""
|
||
}
|
||
skills_metadata.append(metadata)
|
||
|
||
# 构建提示词
|
||
context_info = ""
|
||
if criteria.context:
|
||
context_info = f"\n上下文信息：{json.dumps(criteria.context, ensure_ascii=False)}\n"
|
||
if criteria.exclude_skills:
|
||
exclude_info = f"\n排除的 Skills：{', '.join(criteria.exclude_skills)}\n"
|
||
|
||
system_prompt = f"""你是一个智能 Skill 选择助手。
|
||
|
||
以下是可以使用的 Skills 及其元数据：
|
||
|
||
{'━' * 60}
|
||
"""
|
||
for i, skill_meta in enumerate(skills_metadata, 1):
|
||
system_prompt += f"\n{i+1}. {skill_meta['name']}: {skill_meta['description']}\n"
|
||
if skill_meta['capabilities']:
|
||
system_prompt += f"\n 能力：{skill_meta['capabilities']}\n"
|
||
system_prompt += f"\n"
|
||
|
||
system_prompt += f"""你的任务是根据用户的需求和偏好，从以下 Skills 中选择最合适的。
|
||
|
||
用户需求：
|
||
- {criteria.user_intent}
|
||
{criteria.exclude_info if criteria.exclude_skills else ""}
|
||
{criteria.context_info if criteria.context else ""}
|
||
{criteria.required_category if criteria.required_category else ""}
|
||
{criteria.required_tags if criteria.required_tags else ""}
|
||
|
||
选择标准：
|
||
1. 相关性（description/keywords 匹配度）
|
||
2. 功能覆盖度（capabilities 是否满足需求）
|
||
3. 难度适配（complexity_assessment 是否匹配）
|
||
|
||
请返回 JSON 数组，包含选中的 Skill ID 列表和选择理由。
|
||
每个元素应包含：
|
||
- skill_id: Skill ID
|
||
- score: 匹配分数（0-100）
|
||
- reason: 选择理由
|
||
"""
|
||
|
||
# 调用 LLM 进行选择
|
||
selection_response = await glm_client.chat(
|
||
messages=[
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": f"请分析以下 Skills，并返回最合适的 3-5 个：{json.dumps(skills_metadata, ensure_ascii=False)}"}
|
||
],
|
||
temperature=0.3
|
||
)
|
||
|
||
# 解析选择结果
|
||
try:
|
||
import json
|
||
selected_ids = json.loads(selection_response["choices"][0]["message"]["content"])
|
||
except:
|
||
selected_ids = []
|
||
|
||
# 如果是复杂场景，需要 LLM 进行智能选择
|
||
if len(skills_metadata) > 5 or criteria.user_intent == "复杂需求分析":
|
||
# 使用结构化输出确保可解析性
|
||
structured_prompt = """
|
||
请以以下 JSON 格式返回选择，必须是可以解析的 JSON 数组：
|
||
[
|
||
{{"skill_id": "...", "score": 85, "reason": "..."}},
|
||
...
|
||
]
|
||
确保输出是有效的 JSON 格式。
|
||
"""
|
||
selection_response = await glm_client.chat(
|
||
messages=[
|
||
{"role": "system", "content": structured_prompt},
|
||
{"role": "user", "content": f"用户需求：{criteria.user_intent or '标准 Skill 创建'}\n\n\nAvailable Skills: {json.dumps(skills_metadata, ensure_ascii=False)}"}
|
||
],
|
||
temperature=0.2
|
||
)
|
||
|
||
try:
|
||
selected_ids = json.loads(selection_response["choices"][0]["message"]["content"])
|
||
except:
|
||
selected_ids = []
|
||
|
||
return SkillSelectionResult(
|
||
selected_skills=[s for s in all_skills if s.id in selected_ids],
|
||
selection_reason=f"基于需求分析选择了最相关的 Skills",
|
||
confidence_scores={}
|
||
)
|
||
|
||
except HTTPException:
|
||
logger.error(f"Skill 选择失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"Skill 选择失败: {str(e)}"
|
||
)
|
||
|
||
|
||
# ============================================================================
|
||
# Agent 工作流配置 API
|
||
# ============================================================================
|
||
|
||
@router.post("/agent-workflow/configure", response_model=AgentWorkflowConfig)
|
||
async def configure_agent_workflow(
|
||
config: AgentWorkflowConfig,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
|
||
配置 Agent 工作流的 Skill 使用方式
|
||
|
||
这是实现 Agent 与 Skill 深度结合的关键配置：
|
||
- 定义哪些工作流步骤使用哪些 Skills
|
||
- 设置默认温度
|
||
- 启用/禁用 Skill 替代
|
||
|
||
Args:
|
||
config: 包含工作流步骤配置
|
||
"""
|
||
try:
|
||
# 加载 skill-creator 获取指导
|
||
skill_creator = await skill_manager.load_skill("skill-creator")
|
||
|
||
# 保存配置
|
||
await skill_manager.save_workflow_config(config)
|
||
|
||
return {
|
||
"success": True,
|
||
"message": "Agent 工作流配置已更新",
|
||
"config": config.model_dump()
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"配置失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"配置失败: {str(e)}"
|
||
)
|
||
|
||
|
||
# ============================================================================
|
||
# 基础 CRUD 端点继续
|
||
# ============================================================================
|
||
|
||
# ============================================================================
|
||
# Agent-Skill 生命周期端点 - 使用 skill-creator 脚本
|
||
# ============================================================================
|
||
|
||
class SkillInitRequest(BaseModel):
|
||
"""初始化 Skill 模板请求"""
|
||
skill_name: str # Skill 名称（hyphen-case）
|
||
output_path: Optional[str] = None # 输出路径
|
||
|
||
|
||
class SkillValidateScriptRequest(BaseModel):
|
||
"""脚本验证 Skill 请求"""
|
||
skill_id: str # 要验证的 Skill ID
|
||
|
||
|
||
class SkillPackageRequest(BaseModel):
|
||
"""打包 Skill 请求"""
|
||
skill_id: str # 要打包的 Skill ID
|
||
output_dir: Optional[str] = None # 输出目录
|
||
|
||
|
||
@router.post("/init", response_model=dict)
|
||
async def init_skill_template(
|
||
request: SkillInitRequest,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
|
||
初始化 Skill 模板（使用 skill-creator 的 init_skill.py 脚本）
|
||
|
||
这是 Agent-Skill 生命周期的正确实现：
|
||
- 调用 skill-creator 的 init_skill.py 脚本
|
||
- 创建标准的 Skill 目录结构（SKILL.md、scripts/、references/、assets/）
|
||
"""
|
||
try:
|
||
result = await skill_manager.init_skill_template(
|
||
skill_name=request.skill_name,
|
||
output_path=request.output_path
|
||
)
|
||
|
||
if result["success"]:
|
||
logger.info(f"成功初始化 Skill 模板: {request.skill_name}")
|
||
return {
|
||
"success": True,
|
||
"message": f"Skill 模板 '{request.skill_name}' 初始化成功",
|
||
"output": result["output"]
|
||
}
|
||
else:
|
||
logger.error(f"初始化 Skill 失败: {result.get('error')}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=result.get("error", "初始化失败")
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"初始化 Skill 异常: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"初始化 Skill 异常: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.post("/validate-script", response_model=dict)
|
||
async def validate_skill_script(
|
||
request: SkillValidateScriptRequest,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
|
||
验证 Skill 结构（使用 skill-creator 的 quick_validate.py 脚本）
|
||
|
||
这是 Agent-Skill 生命周期的正确实现：
|
||
- 调用 skill-creator 的 quick_validate.py 脚本
|
||
- 检查 YAML frontmatter、命名规范等
|
||
"""
|
||
try:
|
||
result = await skill_manager.validate_skill_structure(request.skill_id)
|
||
|
||
if result["success"]:
|
||
logger.info(f"Skill 验证通过: {request.skill_id}")
|
||
return {
|
||
"success": True,
|
||
"valid": True,
|
||
"message": result["output"]
|
||
}
|
||
else:
|
||
logger.warning(f"Skill 验证失败: {request.skill_id}")
|
||
return {
|
||
"success": True,
|
||
"valid": False,
|
||
"error": result.get("error")
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"验证 Skill 异常: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"验证 Skill 异常: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.post("/package", response_model=dict)
|
||
async def package_skill(
|
||
request: SkillPackageRequest,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
|
||
打包 Skill（使用 skill-creator 的 package_skill.py 脚本）
|
||
|
||
这是 Agent-Skill 生命周期的正确实现：
|
||
- 调用 skill-creator 的 package_skill.py 脚本
|
||
- 将 Skill 打包成 .skill 文件用于分发
|
||
"""
|
||
try:
|
||
result = await skill_manager.package_skill(
|
||
skill_id=request.skill_id,
|
||
output_dir=request.output_dir
|
||
)
|
||
|
||
if result["success"]:
|
||
logger.info(f"成功打包 Skill: {request.skill_id}")
|
||
return {
|
||
"success": True,
|
||
"message": f"Skill '{request.skill_id}' 打包成功",
|
||
"output": result["output"]
|
||
}
|
||
else:
|
||
logger.error(f"打包 Skill 失败: {request.skill_id}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=result.get("error", "打包失败")
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"打包 Skill 异常: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"打包 Skill 异常: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.get("/tools", response_model=List[dict])
|
||
async def list_available_tools(
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
|
||
列出所有已注册的工具（Agent-Skill 生命周期：注册阶段）
|
||
|
||
返回所有已注册工具的 JSON Schema，用于：
|
||
- 注入到 LLM System Prompt
|
||
- 或作为 API 的 tools 参数
|
||
"""
|
||
return skill_manager.get_available_tools_for_llm()
|
||
|
||
|
||
@router.post("/execute-tool", response_model=dict)
|
||
async def execute_tool(
|
||
tool_name: str,
|
||
parameters: dict,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
|
||
执行工具（Agent-Skill 生命周期：路由 → 执行 → 反馈）
|
||
|
||
实现：
|
||
- 路由：根据 tool_name 找到对应的 handler
|
||
- 执行：调用 handler 执行实际操作
|
||
- 反馈：返回结果给调用者（通常是 LLM）
|
||
|
||
这是 Agent-Skill 生命周期的核心实现
|
||
"""
|
||
result = await skill_manager.route_and_execute_tool(tool_name, parameters)
|
||
return result
|
||
|
||
|
||
# ============================================================================
|
||
# 增强的 Skill 生成工作流（文档驱动）
|
||
# ============================================================================
|
||
|
||
import uuid
|
||
from collections import defaultdict
|
||
from app.core.documentation_fetcher import get_documentation_fetcher, DocumentationFetcher
|
||
|
||
# 预览存储（生产环境应使用 Redis 或数据库）
|
||
_preview_storage: Dict[str, Dict[str, Any]] = defaultdict(dict)
|
||
_preview_references: Dict[str, Dict[str, str]] = defaultdict(dict)
|
||
|
||
|
||
@router.post("/fetch-doc", response_model=dict)
|
||
async def fetch_documentation(
|
||
request: DocFetchRequest,
|
||
doc_fetcher: DocumentationFetcher = Depends(get_documentation_fetcher)
|
||
):
|
||
"""
|
||
获取文档内容（用于预览或生成 Skill）
|
||
|
||
Args:
|
||
request: 包含文档 URL 和可选的 CSS 选择器
|
||
|
||
Returns:
|
||
获取的文档内容
|
||
"""
|
||
try:
|
||
result = await doc_fetcher.fetch_from_url(request.url, request.selector)
|
||
|
||
if result.get("success"):
|
||
return {
|
||
"success": True,
|
||
"title": result.get("title"),
|
||
"content": result.get("content"),
|
||
"word_count": result.get("word_count"),
|
||
"char_count": result.get("char_count"),
|
||
"url": result.get("url")
|
||
}
|
||
else:
|
||
return {
|
||
"success": False,
|
||
"error": result.get("error")
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取文档失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"获取文档失败: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.post("/fetch-github-doc", response_model=dict)
|
||
async def fetch_github_documentation(
|
||
request: GitHubDocFetchRequest,
|
||
doc_fetcher: DocumentationFetcher = Depends(get_documentation_fetcher)
|
||
):
|
||
"""
|
||
获取 GitHub 文档内容
|
||
|
||
Args:
|
||
request: GitHub 仓库和文档路径
|
||
|
||
Returns:
|
||
获取的文档内容
|
||
"""
|
||
try:
|
||
result = await doc_fetcher.fetch_from_github(
|
||
repo_url=request.repo_url,
|
||
docs_path=request.docs_path
|
||
)
|
||
|
||
if result.get("success"):
|
||
return {
|
||
"success": True,
|
||
"repo_url": result.get("repo_url"),
|
||
"docs_path": result.get("docs_path"),
|
||
"content": result.get("content"),
|
||
"word_count": result.get("word_count"),
|
||
"char_count": result.get("char_count")
|
||
}
|
||
else:
|
||
return {
|
||
"success": False,
|
||
"error": result.get("error")
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取 GitHub 文档失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"获取 GitHub 文档失败: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.post("/generate-from-docs", response_model=SkillGenerateFromDocsResponse)
|
||
async def generate_skill_from_documentation(
|
||
request: SkillGenerateFromDocsRequest,
|
||
skill_manager: SkillManager = Depends(get_skill_manager),
|
||
glm_client: GLMClient = Depends(get_glm_client),
|
||
doc_fetcher: DocumentationFetcher = Depends(get_documentation_fetcher)
|
||
):
|
||
"""
|
||
基于文档生成 Skill（新工作流）
|
||
|
||
完整流程：
|
||
1. 获取文档内容（URL/GitHub/上传）
|
||
2. 提取关键信息
|
||
3. 使用 LLM 生成 SKILL.md
|
||
4. 生成预览 ID
|
||
5. 返回可预览的内容
|
||
|
||
用户可以：
|
||
- 预览生成的内容
|
||
- 手动编辑
|
||
- 用 AI 调整
|
||
- 确认后保存到系统
|
||
"""
|
||
try:
|
||
logger.info(f"开始基于文档生成 Skill: {request.skill_name}")
|
||
|
||
# 1. 收集所有文档内容
|
||
all_docs = []
|
||
doc_summary_parts = []
|
||
|
||
# 从 URL 获取
|
||
if request.doc_urls:
|
||
for url in request.doc_urls:
|
||
result = await doc_fetcher.fetch_from_url(url)
|
||
if result.get("success"):
|
||
all_docs.append({
|
||
"source": f"URL: {url}",
|
||
"title": result.get("title"),
|
||
"content": result.get("content")
|
||
})
|
||
doc_summary_parts.append(f"- {result.get('title')} ({url})")
|
||
|
||
# 从 GitHub 获取
|
||
if request.github_repos:
|
||
for github_req in request.github_repos:
|
||
result = await doc_fetcher.fetch_from_github(
|
||
repo_url=github_req.repo_url,
|
||
docs_path=github_req.docs_path
|
||
)
|
||
if result.get("success"):
|
||
all_docs.append({
|
||
"source": f"GitHub: {github_req.repo_url}/{github_req.docs_path}",
|
||
"title": github_req.docs_path,
|
||
"content": result.get("content")
|
||
})
|
||
doc_summary_parts.append(f"- {github_req.repo_url}/{github_req.docs_path}")
|
||
|
||
# 用户上传的 references
|
||
if request.uploaded_references:
|
||
for filename, content in request.uploaded_references.items():
|
||
all_docs.append({
|
||
"source": f"上传: {filename}",
|
||
"title": filename,
|
||
"content": content
|
||
})
|
||
doc_summary_parts.append(f"- {filename} (用户上传)")
|
||
|
||
# 2. 构建 LLM 提示词
|
||
doc_context = ""
|
||
if all_docs:
|
||
# 取文档摘要（前 500 字）
|
||
doc_summary = "\n".join(doc_summary_parts)
|
||
|
||
# 构建文档上下文（避免超过上下文窗口）
|
||
doc_context_parts = []
|
||
for doc in all_docs[:3]: # 最多 3 个文档
|
||
content_preview = doc["content"][:500].replace("\n", " ")
|
||
doc_context_parts.append(f"#### {doc['title']}\n{content_preview}...")
|
||
|
||
doc_context = f"""
|
||
**文档来源**：
|
||
{doc_summary}
|
||
|
||
**文档内容摘要**：
|
||
{''.join(doc_context_parts)}
|
||
"""
|
||
|
||
# 3. 加载 skill-creator 标准
|
||
skill_creator = await skill_manager.load_skill("skill-creator")
|
||
creator_guide = skill_creator.behavior_guide if skill_creator else ""
|
||
|
||
# 4. 构建 LLM 提示词
|
||
system_prompt = f"""你是一个专业的 Skill 创建专家。
|
||
|
||
{creator_guide if creator_guide else ''}
|
||
|
||
你的任务是基于提供的文档内容，创建一个高质量的 Skill。
|
||
|
||
**重要要求**：
|
||
1. SKILL.md 必须以 YAML frontmatter 开始
|
||
2. YAML 必须包含：name, description, category, tags
|
||
3. description 应该清晰说明此 Skill 的用途和使用场景
|
||
4. 行为指导部分应该简洁、具体，从文档中提取关键信息
|
||
5. 如果文档包含代码示例、API、配置等，应该在行为指导中体现
|
||
6. 使用 markdown 格式
|
||
|
||
请以 JSON 格式返回结果，包含以下字段：
|
||
- skill_content: 完整的 SKILL.md 内容（以 --- 开头的 YAML frontmatter + markdown 内容）
|
||
- explanation: 对生成的 Skill 的简要说明（中文）
|
||
- suggested_category: 建议的分类
|
||
- suggested_tags: 建议的标签数组"""
|
||
|
||
user_prompt = f"""请基于以下文档创建一个 Skill：
|
||
|
||
**Skill 名称**：{request.skill_name}
|
||
**描述**：{request.description or f'基于文档生成的 Skill：{request.skill_name}'}
|
||
**指定分类**：{request.category or '自动推断'}
|
||
**指定标签**：{', '.join(request.tags) if request.tags else '自动推断'}
|
||
|
||
{doc_context if request.include_doc_summary else ''}
|
||
|
||
请生成完整的 SKILL.md："""
|
||
|
||
# 5. 调用 LLM 生成
|
||
response = await glm_client.chat(
|
||
messages=[
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": user_prompt}
|
||
],
|
||
temperature=request.temperature
|
||
)
|
||
|
||
response_text = response["choices"][0]["message"]["content"]
|
||
|
||
# 6. 解析 JSON 响应
|
||
import json
|
||
import re
|
||
|
||
json_match = re.search(r'\{[\s\S]*\}', response_text)
|
||
if json_match:
|
||
try:
|
||
result = json.loads(json_match.group())
|
||
skill_content = result.get("skill_content", response_text)
|
||
explanation = result.get("explanation", "AI 生成的 Skill")
|
||
suggested_category = result.get("suggested_category", request.category or "通用")
|
||
suggested_tags = result.get("suggested_tags", request.tags or ["自定义"])
|
||
except json.JSONDecodeError:
|
||
skill_content = response_text
|
||
explanation = "AI 生成的 Skill"
|
||
suggested_category = request.category or "通用"
|
||
suggested_tags = request.tags or ["自定义"]
|
||
else:
|
||
skill_content = response_text
|
||
explanation = "AI 生成的 Skill"
|
||
suggested_category = request.category or "通用"
|
||
suggested_tags = request.tags or ["自定义"]
|
||
|
||
# 7. 生成预览 ID
|
||
preview_id = str(uuid.uuid4())
|
||
|
||
# 8. 存储预览数据
|
||
_preview_storage[preview_id] = {
|
||
"skill_name": request.skill_name,
|
||
"skill_content": skill_content,
|
||
"category": suggested_category,
|
||
"tags": suggested_tags,
|
||
"doc_urls": request.doc_urls,
|
||
"github_repos": [r.model_dump() for r in request.github_repos],
|
||
"uploaded_references": request.uploaded_references,
|
||
"explanation": explanation,
|
||
"created_at": str(datetime.now())
|
||
}
|
||
|
||
# 9. 存储 references（后续保存时使用）
|
||
references = {}
|
||
for i, doc in enumerate(all_docs):
|
||
filename = f"{doc['title'].replace('/', '-').replace(' ', '_')}.md"
|
||
# 清理文件名
|
||
filename = ''.join(c for c in filename if c.isalnum() or c in '._-')
|
||
references[filename] = doc["content"]
|
||
|
||
_preview_references[preview_id] = references
|
||
|
||
# 10. 生成文档摘要
|
||
doc_summary = "\n".join(doc_summary_parts) if doc_summary_parts else "无文档来源"
|
||
|
||
return SkillGenerateFromDocsResponse(
|
||
success=True,
|
||
preview_id=preview_id,
|
||
skill_content=skill_content,
|
||
skill_name=request.skill_name,
|
||
suggested_id=request.skill_name.lower().replace(" ", "-").replace("_", "-"),
|
||
category=suggested_category,
|
||
tags=suggested_tags,
|
||
doc_summary=doc_summary,
|
||
references_count=len(references),
|
||
explanation=explanation
|
||
)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"基于文档生成 Skill 失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"生成失败: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.post("/preview", response_model=SkillPreviewResponse)
|
||
async def preview_skill(
|
||
request: SkillPreviewRequest
|
||
):
|
||
"""
|
||
预览 Skill（验证和解析）
|
||
|
||
返回：
|
||
- 解析后的元数据
|
||
- 验证警告
|
||
- 字数统计
|
||
- 预估 token 数
|
||
"""
|
||
try:
|
||
# 解析 YAML frontmatter
|
||
metadata = {}
|
||
content = request.skill_content
|
||
|
||
import yaml
|
||
yaml_match = re.match(r'^---\n(.*?)\n---', content, re.DOTALL)
|
||
if yaml_match:
|
||
try:
|
||
metadata = yaml.safe_load(yaml_match.group(1)) or {}
|
||
except:
|
||
pass
|
||
|
||
# 验证警告
|
||
warnings = []
|
||
|
||
if not metadata.get("name"):
|
||
warnings.append("缺少 name 字段")
|
||
|
||
if not metadata.get("description"):
|
||
warnings.append("缺少 description 字段")
|
||
|
||
if len(content) < 100:
|
||
warnings.append("内容过短，可能不够详细")
|
||
|
||
if len(content) > 50000:
|
||
warnings.append("内容过长，可能占用过多上下文")
|
||
|
||
# 统计
|
||
word_count = len(content.split())
|
||
estimated_tokens = int(len(content) * 1.3) # 粗略估计
|
||
|
||
# 生成预览 ID
|
||
preview_id = str(uuid.uuid4())
|
||
_preview_storage[preview_id] = {
|
||
"skill_name": request.skill_name,
|
||
"skill_content": content,
|
||
"category": request.category,
|
||
"tags": request.tags,
|
||
"metadata": metadata,
|
||
"warnings": warnings,
|
||
"created_at": str(datetime.now())
|
||
}
|
||
|
||
return SkillPreviewResponse(
|
||
preview_id=preview_id,
|
||
skill_content=content,
|
||
parsed_metadata=metadata,
|
||
validation_warnings=warnings,
|
||
word_count=word_count,
|
||
estimated_tokens=estimated_tokens
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"预览 Skill 失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"预览失败: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.post("/refine", response_model=SkillRefineResponse)
|
||
async def refine_skill_with_ai(
|
||
request: SkillRefineRequest,
|
||
glm_client: GLMClient = Depends(get_glm_client)
|
||
):
|
||
"""
|
||
使用 AI 调整/优化 Skill
|
||
|
||
用户可以输入自然语言提示词，让 AI 修改 Skill 内容。
|
||
|
||
示例提示词：
|
||
- "把行为指导改得更简洁"
|
||
- "增加一个快速开始部分"
|
||
- "添加更多代码示例"
|
||
- "优化描述，让它更清晰"
|
||
"""
|
||
try:
|
||
logger.info(f"AI 调整 Skill，提示词: {request.refinement_prompt[:50]}...")
|
||
|
||
prompt = f"""请根据以下要求调整 SKILL.md 内容：
|
||
|
||
**调整要求**：
|
||
{request.refinement_prompt}
|
||
|
||
**原始 SKILL.md**：
|
||
{request.skill_content}
|
||
|
||
请返回完整的调整后的 SKILL.md 内容（不要包含其他解释）："""
|
||
|
||
response = await glm_client.chat(
|
||
messages=[{"role": "user", "content": prompt}],
|
||
temperature=request.temperature
|
||
)
|
||
|
||
refined_content = response["choices"][0]["message"]["content"].strip()
|
||
|
||
# 清理可能的 markdown 代码块标记
|
||
if refined_content.startswith("```"):
|
||
lines = refined_content.split("\n")
|
||
refined_content = "\n".join(lines[1:])
|
||
if refined_content.endswith("```"):
|
||
refined_content = "\n".join(refined_content.split("\n")[:-1])
|
||
|
||
# 生成变更摘要
|
||
changes_summary = f"根据提示词「{request.refinement_prompt}」进行了调整"
|
||
|
||
return SkillRefineResponse(
|
||
success=True,
|
||
refined_content=refined_content.strip(),
|
||
changes_summary=changes_summary,
|
||
original_length=len(request.skill_content),
|
||
new_length=len(refined_content.strip())
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"AI 调整失败: {str(e)}")
|
||
return SkillRefineResponse(
|
||
success=False,
|
||
refined_content=request.skill_content,
|
||
changes_summary=f"调整失败: {str(e)}",
|
||
original_length=len(request.skill_content),
|
||
new_length=len(request.skill_content)
|
||
)
|
||
|
||
|
||
@router.post("/save-from-preview", response_model=Skill)
|
||
async def save_skill_from_preview(
|
||
request: SkillSaveFromPreviewRequest,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
|
||
从预览保存 Skill 到系统
|
||
|
||
保存流程：
|
||
1. 创建 Skill 目录结构
|
||
2. 保存 SKILL.md
|
||
3. 保存 references（如果有）
|
||
4. 注册到 SkillManager
|
||
"""
|
||
try:
|
||
logger.info(f"从预览保存 Skill: {request.skill_id}")
|
||
|
||
# 1. 创建 Skill
|
||
skill_data = SkillCreate(
|
||
id=request.skill_id,
|
||
name=request.skill_id.replace("-", " ").title(),
|
||
content=request.skill_content,
|
||
category="通用", # 从 YAML frontmatter 解析
|
||
tags=[] # 从 YAML frontmatter 解析
|
||
)
|
||
|
||
skill = await skill_manager.create_user_skill(skill_data)
|
||
|
||
# 2. 保存 references（如果有）
|
||
if request.references:
|
||
skill_path = skill_manager._find_skill_path(request.skill_id)
|
||
if skill_path:
|
||
refs_dir = skill_path.parent / "references"
|
||
refs_dir.mkdir(exist_ok=True)
|
||
|
||
for filename, content in request.references.items():
|
||
# 确保文件名安全
|
||
safe_filename = filename.replace("..", "").replace("/", "").replace("\\", "")
|
||
if not safe_filename.endswith(".md"):
|
||
safe_filename += ".md"
|
||
|
||
ref_file = refs_dir / safe_filename
|
||
ref_file.write_text(content, encoding="utf-8")
|
||
logger.info(f"保存 reference: {request.skill_id}/references/{safe_filename}")
|
||
|
||
# 3. 清理预览数据
|
||
if request.preview_id in _preview_storage:
|
||
del _preview_storage[request.preview_id]
|
||
if request.preview_id in _preview_references:
|
||
del _preview_references[request.preview_id]
|
||
|
||
return skill
|
||
|
||
except Exception as e:
|
||
logger.error(f"保存 Skill 失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"保存失败: {str(e)}"
|
||
)
|
||
|
||
|
||
@router.get("/preview/{preview_id}", response_model=dict)
|
||
async def get_preview(preview_id: str):
|
||
"""
|
||
获取预览数据
|
||
"""
|
||
if preview_id not in _preview_storage:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail=f"预览不存在: {preview_id}"
|
||
)
|
||
|
||
preview_data = _preview_storage[preview_id]
|
||
references = _preview_references.get(preview_id, {})
|
||
|
||
return {
|
||
"preview": preview_data,
|
||
"references": references,
|
||
"references_count": len(references)
|
||
}
|
||
|
||
|
||
@router.delete("/preview/{preview_id}", status_code=status.HTTP_204_NO_CONTENT)
|
||
async def delete_preview(preview_id: str):
|
||
"""
|
||
删除预览数据
|
||
"""
|
||
if preview_id in _preview_storage:
|
||
del _preview_storage[preview_id]
|
||
if preview_id in _preview_references:
|
||
del _preview_references[preview_id]
|
||
|
||
return None
|
||
|
||
|
||
@router.put("/{skill_id}/with-references", response_model=Skill)
|
||
async def update_skill_with_references(
|
||
skill_id: str,
|
||
request: SkillSaveFromPreviewRequest,
|
||
skill_manager: SkillManager = Depends(get_skill_manager)
|
||
):
|
||
"""
|
||
更新 Skill 及其 References
|
||
|
||
用于编辑模式，同时更新 SKILL.md 和 references/ 目录中的文件
|
||
"""
|
||
try:
|
||
logger.info(f"更新 Skill 及 references: {skill_id}")
|
||
|
||
# 1. 更新 SKILL.md
|
||
skill_update = SkillUpdate(content=request.skill_content)
|
||
skill = await skill_manager.update_user_skill(skill_id, skill_update)
|
||
|
||
if not skill:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail=f"Skill 不存在: {skill_id}"
|
||
)
|
||
|
||
# 2. 更新 references（如果有）
|
||
if request.references is not None: # 允许传入空字典清空references
|
||
skill_path = skill_manager._find_skill_path(skill_id)
|
||
if skill_path:
|
||
refs_dir = skill_path.parent / "references"
|
||
|
||
# 如果传入空字典，删除整个references目录
|
||
if len(request.references) == 0:
|
||
if refs_dir.exists():
|
||
import shutil
|
||
shutil.rmtree(refs_dir)
|
||
logger.info(f"清空 references: {skill_id}/references/")
|
||
else:
|
||
# 确保目录存在
|
||
refs_dir.mkdir(exist_ok=True)
|
||
|
||
# 清空现有文件
|
||
for existing_file in refs_dir.iterdir():
|
||
if existing_file.is_file():
|
||
existing_file.unlink()
|
||
|
||
# 写入新文件
|
||
for filename, content in request.references.items():
|
||
safe_filename = filename.replace("..", "").replace("/", "").replace("\\", "")
|
||
if not safe_filename.endswith(".md"):
|
||
safe_filename += ".md"
|
||
|
||
ref_file = refs_dir / safe_filename
|
||
ref_file.write_text(content, encoding="utf-8")
|
||
logger.info(f"更新 reference: {skill_id}/references/{safe_filename}")
|
||
|
||
# 清除缓存
|
||
if skill_id in skill_manager._cache:
|
||
del skill_manager._cache[skill_id]
|
||
|
||
# 重新加载并返回
|
||
return await skill_manager.load_skill(skill_id)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"更新 Skill 失败: {str(e)}")
|
||
raise HTTPException(
|
||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
detail=f"更新失败: {str(e)}"
|
||
)
|