feat: Lock LLM and embedding configurations to system environment variables, mask API keys, and refactor frontend logout.
This commit is contained in:
vinland100
parent
0176cb4d12
commit
2b0c7f5c2a
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@ -668,15 +668,11 @@ async def _get_user_config(db: AsyncSession, user_id: Optional[str]) -> Optional
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)
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config = result.scalar_one_or_none()
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if config and config.llm_config:
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user_llm_config = json.loads(config.llm_config) if config.llm_config else {}
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if config:
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user_other_config = json.loads(config.other_config) if config.other_config else {}
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user_llm_config = decrypt_config(user_llm_config, SENSITIVE_LLM_FIELDS)
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user_other_config = decrypt_config(user_other_config, SENSITIVE_OTHER_FIELDS)
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return {
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"llmConfig": user_llm_config,
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"otherConfig": user_other_config,
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}
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except Exception as e:
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@ -746,41 +742,19 @@ async def _initialize_tools(
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try:
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await emit(f"🔍 正在初始化 RAG 系统...")
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# 从用户配置中获取 embedding 配置
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user_llm_config = (user_config or {}).get('llmConfig', {})
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user_other_config = (user_config or {}).get('otherConfig', {})
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user_embedding_config = user_other_config.get('embedding_config', {})
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# 锁定模式：Embedding 配置始终来自 settings (.env)
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embedding_provider = settings.EMBEDDING_PROVIDER
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embedding_model = settings.EMBEDDING_MODEL
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# Embedding Provider 优先级：用户嵌入配置 > 环境变量
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embedding_provider = (
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user_embedding_config.get('provider') or
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getattr(settings, 'EMBEDDING_PROVIDER', 'openai')
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)
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# Embedding Model 优先级：用户嵌入配置 > 环境变量
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embedding_model = (
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user_embedding_config.get('model') or
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getattr(settings, 'EMBEDDING_MODEL', 'text-embedding-3-small')
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)
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# API Key 优先级：用户嵌入配置 > 环境变量 EMBEDDING_API_KEY > 用户 LLM 配置 > 环境变量 LLM_API_KEY
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# 注意：API Key 可以共享，因为很多用户使用同一个 OpenAI Key 做 LLM 和 Embedding
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# API Key 优先级：EMBEDDING_API_KEY > LLM_API_KEY
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embedding_api_key = (
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user_embedding_config.get('api_key') or
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getattr(settings, 'EMBEDDING_API_KEY', None) or
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user_llm_config.get('llmApiKey') or
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getattr(settings, 'LLM_API_KEY', '') or
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''
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getattr(settings, "EMBEDDING_API_KEY", None) or
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settings.LLM_API_KEY or
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""
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)
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# Base URL 优先级：用户嵌入配置 > 环境变量 EMBEDDING_BASE_URL > None（使用提供商默认地址）
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# 🔥 重要：Base URL 不应该回退到 LLM 的 base_url，因为 Embedding 和 LLM 可能使用完全不同的服务
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# 例如：LLM 使用 SiliconFlow，但 Embedding 使用 HuggingFace
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embedding_base_url = (
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user_embedding_config.get('base_url') or
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getattr(settings, 'EMBEDDING_BASE_URL', None) or
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None
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)
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# Base URL 优先级：EMBEDDING_BASE_URL > None
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embedding_base_url = getattr(settings, "EMBEDDING_BASE_URL", None)
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logger.info(f"RAG 配置: provider={embedding_provider}, model={embedding_model}, base_url={embedding_base_url or '(使用默认)'}")
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await emit(f"📊 Embedding 配置: {embedding_provider}/{embedding_model}")
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@ -18,7 +18,7 @@ from app.core.encryption import encrypt_sensitive_data, decrypt_sensitive_data
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router = APIRouter()
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# 需要加密的敏感字段列表
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# 需要加密的敏感字段列表 (LLM 已锁定至 .env，此处仅保留其他配置)
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SENSITIVE_LLM_FIELDS = [
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'llmApiKey', 'geminiApiKey', 'openaiApiKey', 'claudeApiKey',
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'qwenApiKey', 'deepseekApiKey', 'zhipuApiKey', 'moonshotApiKey',
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@ -27,6 +27,15 @@ SENSITIVE_LLM_FIELDS = [
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SENSITIVE_OTHER_FIELDS = ['githubToken', 'gitlabToken']
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def mask_api_key(key: Optional[str]) -> str:
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"""部分遮盖API Key，显示前3位和后4位"""
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if not key:
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return ""
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if len(key) <= 8:
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return "***"
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return f"{key[:3]}***{key[-4:]}"
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def encrypt_config(config: dict, sensitive_fields: list) -> dict:
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"""加密配置中的敏感字段"""
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encrypted = config.copy()
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@ -104,23 +113,23 @@ def get_default_config() -> dict:
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return {
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"llmConfig": {
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"llmProvider": settings.LLM_PROVIDER,
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"llmApiKey": "",
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"llmApiKey": mask_api_key(settings.LLM_API_KEY),
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"llmModel": settings.LLM_MODEL or "",
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"llmBaseUrl": settings.LLM_BASE_URL or "",
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"llmTimeout": settings.LLM_TIMEOUT * 1000, # 转换为毫秒
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"llmTemperature": settings.LLM_TEMPERATURE,
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"llmMaxTokens": settings.LLM_MAX_TOKENS,
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"llmCustomHeaders": "",
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"geminiApiKey": settings.GEMINI_API_KEY or "",
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"openaiApiKey": settings.OPENAI_API_KEY or "",
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"claudeApiKey": settings.CLAUDE_API_KEY or "",
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"qwenApiKey": settings.QWEN_API_KEY or "",
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"deepseekApiKey": settings.DEEPSEEK_API_KEY or "",
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"zhipuApiKey": settings.ZHIPU_API_KEY or "",
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"moonshotApiKey": settings.MOONSHOT_API_KEY or "",
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"baiduApiKey": settings.BAIDU_API_KEY or "",
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"minimaxApiKey": settings.MINIMAX_API_KEY or "",
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"doubaoApiKey": settings.DOUBAO_API_KEY or "",
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"geminiApiKey": mask_api_key(settings.GEMINI_API_KEY),
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"openaiApiKey": mask_api_key(settings.OPENAI_API_KEY),
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"claudeApiKey": mask_api_key(settings.CLAUDE_API_KEY),
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"qwenApiKey": mask_api_key(settings.QWEN_API_KEY),
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"deepseekApiKey": mask_api_key(settings.DEEPSEEK_API_KEY),
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"zhipuApiKey": mask_api_key(settings.ZHIPU_API_KEY),
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"moonshotApiKey": mask_api_key(settings.MOONSHOT_API_KEY),
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"baiduApiKey": mask_api_key(settings.BAIDU_API_KEY),
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"minimaxApiKey": mask_api_key(settings.MINIMAX_API_KEY),
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"doubaoApiKey": mask_api_key(settings.DOUBAO_API_KEY),
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"ollamaBaseUrl": settings.OLLAMA_BASE_URL or "http://localhost:11434/v1",
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},
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"otherConfig": {
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@ -178,7 +187,8 @@ async def get_my_config(
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print(f" - llmApiKey: {'***' + user_llm_config.get('llmApiKey', '')[-4:] if user_llm_config.get('llmApiKey') else '(空)'}")
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print(f" - llmModel: {user_llm_config.get('llmModel')}")
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merged_llm_config = {**default_config["llmConfig"], **user_llm_config}
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# LLM配置始终来自系统默认（.env），不再允许用户覆盖
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merged_llm_config = default_config["llmConfig"]
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merged_other_config = {**default_config["otherConfig"], **user_other_config}
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return UserConfigResponse(
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@ -247,7 +257,8 @@ async def update_my_config(
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user_llm_config = decrypt_config(user_llm_config, SENSITIVE_LLM_FIELDS)
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user_other_config = decrypt_config(user_other_config, SENSITIVE_OTHER_FIELDS)
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merged_llm_config = {**default_config["llmConfig"], **user_llm_config}
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# LLM配置始终来自系统默认（.env），不再允许用户覆盖
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merged_llm_config = default_config["llmConfig"]
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merged_other_config = {**default_config["otherConfig"], **user_other_config}
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return UserConfigResponse(
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@ -299,230 +310,52 @@ class LLMTestResponse(BaseModel):
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@router.post("/test-llm", response_model=LLMTestResponse)
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async def test_llm_connection(
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request: LLMTestRequest,
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db: AsyncSession = Depends(get_db),
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current_user: User = Depends(deps.get_current_user),
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) -> Any:
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"""测试LLM连接是否正常"""
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from app.services.llm.factory import LLMFactory, NATIVE_ONLY_PROVIDERS
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from app.services.llm.adapters import LiteLLMAdapter, BaiduAdapter, MinimaxAdapter, DoubaoAdapter
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from app.services.llm.types import LLMConfig, LLMProvider, LLMRequest, LLMMessage, DEFAULT_MODELS, DEFAULT_BASE_URLS
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import traceback
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"""测试当前系统 LLM 配置是否正常"""
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from app.services.llm.service import LLMService
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import time
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import traceback
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start_time = time.time()
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# 获取用户保存的配置
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result = await db.execute(
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select(UserConfig).where(UserConfig.user_id == current_user.id)
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)
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user_config_record = result.scalar_one_or_none()
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# 解析用户配置
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saved_llm_config = {}
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saved_other_config = {}
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if user_config_record:
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if user_config_record.llm_config:
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saved_llm_config = decrypt_config(
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json.loads(user_config_record.llm_config),
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SENSITIVE_LLM_FIELDS
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)
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if user_config_record.other_config:
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saved_other_config = decrypt_config(
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json.loads(user_config_record.other_config),
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SENSITIVE_OTHER_FIELDS
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)
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# 从保存的配置中获取参数（用于调试显示）
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saved_timeout_ms = saved_llm_config.get('llmTimeout', settings.LLM_TIMEOUT * 1000)
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saved_temperature = saved_llm_config.get('llmTemperature', settings.LLM_TEMPERATURE)
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saved_max_tokens = saved_llm_config.get('llmMaxTokens', settings.LLM_MAX_TOKENS)
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saved_concurrency = saved_other_config.get('llmConcurrency', settings.LLM_CONCURRENCY)
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saved_gap_ms = saved_other_config.get('llmGapMs', settings.LLM_GAP_MS)
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saved_max_files = saved_other_config.get('maxAnalyzeFiles', settings.MAX_ANALYZE_FILES)
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saved_output_lang = saved_other_config.get('outputLanguage', settings.OUTPUT_LANGUAGE)
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debug_info = {
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"provider": request.provider,
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"model_requested": request.model,
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"base_url_requested": request.baseUrl,
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"api_key_length": len(request.apiKey) if request.apiKey else 0,
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"api_key_prefix": request.apiKey[:8] + "..." if request.apiKey and len(request.apiKey) > 8 else "(empty)",
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# 用户保存的配置参数
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"saved_config": {
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"timeout_ms": saved_timeout_ms,
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"temperature": saved_temperature,
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"max_tokens": saved_max_tokens,
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"concurrency": saved_concurrency,
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"gap_ms": saved_gap_ms,
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"max_analyze_files": saved_max_files,
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"output_language": saved_output_lang,
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},
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}
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try:
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# 解析provider
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provider_map = {
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'gemini': LLMProvider.GEMINI,
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'openai': LLMProvider.OPENAI,
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'claude': LLMProvider.CLAUDE,
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'qwen': LLMProvider.QWEN,
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'deepseek': LLMProvider.DEEPSEEK,
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'zhipu': LLMProvider.ZHIPU,
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'moonshot': LLMProvider.MOONSHOT,
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'baidu': LLMProvider.BAIDU,
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'minimax': LLMProvider.MINIMAX,
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'doubao': LLMProvider.DOUBAO,
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'ollama': LLMProvider.OLLAMA,
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}
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# LLMService 已经重构为锁定读取 .env 配置
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llm_service = LLMService()
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provider = provider_map.get(request.provider.lower())
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if not provider:
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debug_info["error_type"] = "unsupported_provider"
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return LLMTestResponse(
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success=False,
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message=f"不支持的LLM提供商: {request.provider}",
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debug=debug_info
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)
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# 记录测试信息
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print(f"🔍 测试 LLM 连接: Provider={llm_service.config.provider}, Model={llm_service.config.model}")
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# 获取默认模型
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model = request.model or DEFAULT_MODELS.get(provider)
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base_url = request.baseUrl or DEFAULT_BASE_URLS.get(provider, "")
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# 简单测试：获取分析结果
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test_code = "print('hello world')"
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result = await llm_service.analyze_code(test_code, "python")
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# 测试时使用用户保存的所有配置参数
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test_timeout = int(saved_timeout_ms / 1000) if saved_timeout_ms else settings.LLM_TIMEOUT
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test_temperature = saved_temperature if saved_temperature is not None else settings.LLM_TEMPERATURE
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test_max_tokens = saved_max_tokens if saved_max_tokens else settings.LLM_MAX_TOKENS
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debug_info["model_used"] = model
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debug_info["base_url_used"] = base_url
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debug_info["is_native_adapter"] = provider in NATIVE_ONLY_PROVIDERS
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debug_info["test_params"] = {
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"timeout": test_timeout,
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"temperature": test_temperature,
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"max_tokens": test_max_tokens,
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}
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print(f"[LLM Test] 开始测试: provider={provider.value}, model={model}, base_url={base_url}, temperature={test_temperature}, timeout={test_timeout}s, max_tokens={test_max_tokens}")
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# 创建配置
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config = LLMConfig(
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provider=provider,
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api_key=request.apiKey,
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model=model,
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base_url=request.baseUrl,
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timeout=test_timeout,
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temperature=test_temperature,
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max_tokens=test_max_tokens,
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)
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# 直接创建新的适配器实例（不使用缓存），确保使用最新的配置
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if provider in NATIVE_ONLY_PROVIDERS:
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native_adapter_map = {
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LLMProvider.BAIDU: BaiduAdapter,
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LLMProvider.MINIMAX: MinimaxAdapter,
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LLMProvider.DOUBAO: DoubaoAdapter,
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}
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adapter = native_adapter_map[provider](config)
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debug_info["adapter_type"] = type(adapter).__name__
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else:
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adapter = LiteLLMAdapter(config)
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debug_info["adapter_type"] = "LiteLLMAdapter"
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# 获取 LiteLLM 实际使用的模型名
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debug_info["litellm_model"] = getattr(adapter, '_get_litellm_model', lambda: model)() if hasattr(adapter, '_get_litellm_model') else model
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test_request = LLMRequest(
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messages=[
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LLMMessage(role="user", content="Say 'Hello' in one word.")
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],
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temperature=test_temperature,
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max_tokens=test_max_tokens,
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)
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print(f"[LLM Test] 发送测试请求...")
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response = await adapter.complete(test_request)
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elapsed_time = time.time() - start_time
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debug_info["elapsed_time_ms"] = round(elapsed_time * 1000, 2)
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# 验证响应内容
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if not response or not response.content:
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debug_info["error_type"] = "empty_response"
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debug_info["raw_response"] = str(response) if response else None
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print(f"[LLM Test] 空响应: {response}")
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return LLMTestResponse(
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success=False,
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message="LLM 返回空响应，请检查 API Key 和配置",
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debug=debug_info
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)
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debug_info["response_length"] = len(response.content)
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debug_info["usage"] = {
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"prompt_tokens": getattr(response, 'prompt_tokens', None),
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"completion_tokens": getattr(response, 'completion_tokens', None),
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"total_tokens": getattr(response, 'total_tokens', None),
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}
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print(f"[LLM Test] 成功! 响应: {response.content[:50]}... 耗时: {elapsed_time:.2f}s")
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duration = round(time.time() - start_time, 2)
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return LLMTestResponse(
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success=True,
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message=f"连接成功 ({elapsed_time:.2f}s)",
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model=model,
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response=response.content[:100] if response.content else None,
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debug=debug_info
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message=f"连接成功！耗时: {duration}s",
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model=llm_service.config.model,
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response="分析测试完成",
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debug={
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"provider": llm_service.config.provider.value,
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"model": llm_service.config.model,
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"duration_s": duration,
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"issues_found": len(result.get("issues", []))
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}
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)
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except Exception as e:
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elapsed_time = time.time() - start_time
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duration = round(time.time() - start_time, 2)
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error_msg = str(e)
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error_type = type(e).__name__
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debug_info["elapsed_time_ms"] = round(elapsed_time * 1000, 2)
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debug_info["error_type"] = error_type
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debug_info["error_message"] = error_msg
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debug_info["traceback"] = traceback.format_exc()
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# 提取 LLMError 中的 api_response
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if hasattr(e, 'api_response') and e.api_response:
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debug_info["api_response"] = e.api_response
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if hasattr(e, 'status_code') and e.status_code:
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debug_info["status_code"] = e.status_code
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print(f"[LLM Test] 失败: {error_type}: {error_msg}")
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print(f"[LLM Test] Traceback:\n{traceback.format_exc()}")
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# 提供更友好的错误信息
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friendly_message = error_msg
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||||
|
||||
# 优先检查余额不足（因为某些 API 用 429 表示余额不足）
|
||||
if any(keyword in error_msg for keyword in ["余额不足", "资源包", "充值", "quota", "insufficient", "balance", "402"]):
|
||||
friendly_message = "账户余额不足或配额已用尽，请充值后重试"
|
||||
debug_info["error_category"] = "insufficient_balance"
|
||||
elif "401" in error_msg or "invalid_api_key" in error_msg.lower() or "incorrect api key" in error_msg.lower():
|
||||
friendly_message = "API Key 无效或已过期，请检查后重试"
|
||||
debug_info["error_category"] = "auth_invalid_key"
|
||||
elif "authentication" in error_msg.lower():
|
||||
friendly_message = "认证失败，请检查 API Key 是否正确"
|
||||
debug_info["error_category"] = "auth_failed"
|
||||
elif "timeout" in error_msg.lower():
|
||||
friendly_message = "连接超时，请检查网络或 API 地址是否正确"
|
||||
debug_info["error_category"] = "timeout"
|
||||
elif "connection" in error_msg.lower() or "connect" in error_msg.lower():
|
||||
friendly_message = "无法连接到 API 服务，请检查网络或 API 地址"
|
||||
debug_info["error_category"] = "connection"
|
||||
elif "rate" in error_msg.lower() and "limit" in error_msg.lower():
|
||||
friendly_message = "API 请求频率超限，请稍后重试"
|
||||
debug_info["error_category"] = "rate_limit"
|
||||
elif "model" in error_msg.lower() and ("not found" in error_msg.lower() or "does not exist" in error_msg.lower()):
|
||||
friendly_message = f"模型 '{debug_info.get('model_used', 'unknown')}' 不存在或无权访问"
|
||||
debug_info["error_category"] = "model_not_found"
|
||||
else:
|
||||
debug_info["error_category"] = "unknown"
|
||||
|
||||
print(f"❌ LLM 测试失败: {error_msg}")
|
||||
return LLMTestResponse(
|
||||
success=False,
|
||||
message=friendly_message,
|
||||
debug=debug_info
|
||||
message=f"连接失败: {error_msg}",
|
||||
debug={
|
||||
"error": error_msg,
|
||||
"traceback": traceback.format_exc(),
|
||||
"duration_s": duration
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -172,83 +172,29 @@ EMBEDDING_PROVIDERS: List[EmbeddingProvider] = [
|
|||
EMBEDDING_CONFIG_KEY = "embedding_config"
|
||||
|
||||
|
||||
def mask_api_key(key: Optional[str]) -> str:
|
||||
"""部分遮盖API Key，显示前3位和后4位"""
|
||||
if not key:
|
||||
return ""
|
||||
if len(key) <= 8:
|
||||
return "***"
|
||||
return f"{key[:3]}***{key[-4:]}"
|
||||
|
||||
|
||||
async def get_embedding_config_from_db(db: AsyncSession, user_id: str) -> EmbeddingConfig:
|
||||
"""从数据库获取嵌入配置（异步）"""
|
||||
result = await db.execute(
|
||||
select(UserConfig).where(UserConfig.user_id == user_id)
|
||||
)
|
||||
user_config = result.scalar_one_or_none()
|
||||
|
||||
if user_config and user_config.other_config:
|
||||
try:
|
||||
other_config = json.loads(user_config.other_config) if isinstance(user_config.other_config, str) else user_config.other_config
|
||||
embedding_data = other_config.get(EMBEDDING_CONFIG_KEY)
|
||||
|
||||
if embedding_data:
|
||||
config = EmbeddingConfig(
|
||||
provider=embedding_data.get("provider", settings.EMBEDDING_PROVIDER),
|
||||
model=embedding_data.get("model", settings.EMBEDDING_MODEL),
|
||||
api_key=embedding_data.get("api_key"),
|
||||
base_url=embedding_data.get("base_url"),
|
||||
dimensions=embedding_data.get("dimensions"),
|
||||
batch_size=embedding_data.get("batch_size", 100),
|
||||
)
|
||||
print(f"[EmbeddingConfig] 读取用户 {user_id} 的嵌入配置: provider={config.provider}, model={config.model}")
|
||||
return config
|
||||
except (json.JSONDecodeError, AttributeError) as e:
|
||||
print(f"[EmbeddingConfig] 解析用户 {user_id} 配置失败: {e}")
|
||||
|
||||
# 返回默认配置
|
||||
print(f"[EmbeddingConfig] 用户 {user_id} 无保存配置，返回默认值")
|
||||
# 嵌入配置始终来自系统默认（.env），不再允许用户覆盖
|
||||
print(f"[EmbeddingConfig] 返回系统默认嵌入配置（来自 .env）")
|
||||
return EmbeddingConfig(
|
||||
provider=settings.EMBEDDING_PROVIDER,
|
||||
model=settings.EMBEDDING_MODEL,
|
||||
api_key=settings.LLM_API_KEY,
|
||||
base_url=settings.LLM_BASE_URL,
|
||||
api_key=mask_api_key(settings.EMBEDDING_API_KEY or settings.LLM_API_KEY),
|
||||
base_url=settings.EMBEDDING_BASE_URL or settings.LLM_BASE_URL,
|
||||
dimensions=settings.EMBEDDING_DIMENSION if settings.EMBEDDING_DIMENSION > 0 else None,
|
||||
batch_size=100,
|
||||
)
|
||||
|
||||
|
||||
async def save_embedding_config_to_db(db: AsyncSession, user_id: str, config: EmbeddingConfig) -> None:
|
||||
"""保存嵌入配置到数据库（异步）"""
|
||||
result = await db.execute(
|
||||
select(UserConfig).where(UserConfig.user_id == user_id)
|
||||
)
|
||||
user_config = result.scalar_one_or_none()
|
||||
|
||||
# 准备嵌入配置数据
|
||||
embedding_data = {
|
||||
"provider": config.provider,
|
||||
"model": config.model,
|
||||
"api_key": config.api_key,
|
||||
"base_url": config.base_url,
|
||||
"dimensions": config.dimensions,
|
||||
"batch_size": config.batch_size,
|
||||
}
|
||||
|
||||
if user_config:
|
||||
# 更新现有配置
|
||||
try:
|
||||
other_config = json.loads(user_config.other_config) if user_config.other_config else {}
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
other_config = {}
|
||||
|
||||
other_config[EMBEDDING_CONFIG_KEY] = embedding_data
|
||||
user_config.other_config = json.dumps(other_config)
|
||||
# 🔥 显式标记 other_config 字段已修改，确保 SQLAlchemy 检测到变化
|
||||
flag_modified(user_config, "other_config")
|
||||
else:
|
||||
# 创建新配置
|
||||
user_config = UserConfig(
|
||||
id=str(uuid.uuid4()),
|
||||
user_id=user_id,
|
||||
llm_config="{}",
|
||||
other_config=json.dumps({EMBEDDING_CONFIG_KEY: embedding_data}),
|
||||
)
|
||||
db.add(user_config)
|
||||
|
||||
await db.commit()
|
||||
print(f"[EmbeddingConfig] 已保存用户 {user_id} 的嵌入配置: provider={config.provider}, model={config.model}")
|
||||
|
||||
|
||||
# ============ API Endpoints ============
|
||||
|
|
@ -274,6 +220,8 @@ async def get_current_config(
|
|||
config = await get_embedding_config_from_db(db, current_user.id)
|
||||
|
||||
# 获取维度
|
||||
dimensions = config.dimensions
|
||||
if not dimensions or dimensions <= 0:
|
||||
dimensions = _get_model_dimensions(config.provider, config.model)
|
||||
|
||||
return EmbeddingConfigResponse(
|
||||
|
|
@ -288,30 +236,12 @@ async def get_current_config(
|
|||
|
||||
@router.put("/config")
|
||||
async def update_config(
|
||||
config: EmbeddingConfig,
|
||||
db: AsyncSession = Depends(deps.get_db),
|
||||
current_user: User = Depends(deps.get_current_user),
|
||||
) -> Any:
|
||||
"""
|
||||
更新嵌入模型配置（持久化到数据库）
|
||||
更新嵌入模型配置（已禁用，固定从 .env 读取）
|
||||
"""
|
||||
# 验证提供商
|
||||
provider_ids = [p.id for p in EMBEDDING_PROVIDERS]
|
||||
if config.provider not in provider_ids:
|
||||
raise HTTPException(status_code=400, detail=f"不支持的提供商: {config.provider}")
|
||||
|
||||
# 获取提供商信息（用于检查 API Key 要求）
|
||||
provider = next((p for p in EMBEDDING_PROVIDERS if p.id == config.provider), None)
|
||||
# 注意：不再强制验证模型名称，允许用户输入自定义模型
|
||||
|
||||
# 检查 API Key
|
||||
if provider and provider.requires_api_key and not config.api_key:
|
||||
raise HTTPException(status_code=400, detail=f"{config.provider} 需要 API Key")
|
||||
|
||||
# 保存到数据库
|
||||
await save_embedding_config_to_db(db, current_user.id, config)
|
||||
|
||||
return {"message": "配置已保存", "provider": config.provider, "model": config.model}
|
||||
return {"message": "嵌入模型配置已锁定，请在 .env 文件中进行修改", "provider": settings.EMBEDDING_PROVIDER, "model": settings.EMBEDDING_MODEL}
|
||||
|
||||
|
||||
@router.post("/test", response_model=TestEmbeddingResponse)
|
||||
|
|
@ -319,22 +249,22 @@ async def test_embedding(
|
|||
request: TestEmbeddingRequest,
|
||||
current_user: User = Depends(deps.get_current_user),
|
||||
) -> Any:
|
||||
"""
|
||||
测试嵌入模型配置
|
||||
"""
|
||||
"""测试当前系统的嵌入模型配置"""
|
||||
import time
|
||||
from app.services.rag.embeddings import EmbeddingService
|
||||
|
||||
try:
|
||||
start_time = time.time()
|
||||
|
||||
# 创建临时嵌入服务
|
||||
from app.services.rag.embeddings import EmbeddingService
|
||||
# 始终使用系统的真实配置进行测试
|
||||
# API Key 优先级：EMBEDDING_API_KEY > LLM_API_KEY
|
||||
api_key = getattr(settings, "EMBEDDING_API_KEY", None) or settings.LLM_API_KEY
|
||||
|
||||
service = EmbeddingService(
|
||||
provider=request.provider,
|
||||
model=request.model,
|
||||
api_key=request.api_key,
|
||||
base_url=request.base_url,
|
||||
provider=settings.EMBEDDING_PROVIDER,
|
||||
model=settings.EMBEDDING_MODEL,
|
||||
api_key=api_key,
|
||||
base_url=settings.EMBEDDING_BASE_URL,
|
||||
cache_enabled=False,
|
||||
)
|
||||
|
||||
|
|
@ -345,13 +275,14 @@ async def test_embedding(
|
|||
|
||||
return TestEmbeddingResponse(
|
||||
success=True,
|
||||
message=f"嵌入成功! 维度: {len(embedding)}",
|
||||
message=f"嵌入成功! 提供商: {settings.EMBEDDING_PROVIDER}, 维度: {len(embedding)}",
|
||||
dimensions=len(embedding),
|
||||
sample_embedding=embedding[:5], # 返回前 5 维
|
||||
latency_ms=latency_ms,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 嵌入测试失败: {str(e)}")
|
||||
return TestEmbeddingResponse(
|
||||
success=False,
|
||||
message=f"嵌入失败: {str(e)}",
|
||||
|
|
|
|||
|
|
@ -393,12 +393,6 @@ async def get_user_config_dict(db: AsyncSession, user_id: str) -> dict:
|
|||
"""获取用户配置字典（包含解密敏感字段）"""
|
||||
from app.core.encryption import decrypt_sensitive_data
|
||||
|
||||
# 需要解密的敏感字段列表（与 config.py 保持一致）
|
||||
SENSITIVE_LLM_FIELDS = [
|
||||
'llmApiKey', 'geminiApiKey', 'openaiApiKey', 'claudeApiKey',
|
||||
'qwenApiKey', 'deepseekApiKey', 'zhipuApiKey', 'moonshotApiKey',
|
||||
'baiduApiKey', 'minimaxApiKey', 'doubaoApiKey'
|
||||
]
|
||||
SENSITIVE_OTHER_FIELDS = ['githubToken', 'gitlabToken']
|
||||
|
||||
def decrypt_config(config: dict, sensitive_fields: list) -> dict:
|
||||
|
|
@ -416,16 +410,13 @@ async def get_user_config_dict(db: AsyncSession, user_id: str) -> dict:
|
|||
if not config:
|
||||
return {}
|
||||
|
||||
# 解析配置
|
||||
llm_config = json.loads(config.llm_config) if config.llm_config else {}
|
||||
# 解析配置 (忽略 llm_config)
|
||||
other_config = json.loads(config.other_config) if config.other_config else {}
|
||||
|
||||
# 解密敏感字段
|
||||
llm_config = decrypt_config(llm_config, SENSITIVE_LLM_FIELDS)
|
||||
other_config = decrypt_config(other_config, SENSITIVE_OTHER_FIELDS)
|
||||
|
||||
return {
|
||||
'llmConfig': llm_config,
|
||||
'otherConfig': other_config,
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -39,55 +39,31 @@ class LLMService:
|
|||
"""
|
||||
获取LLM配置
|
||||
|
||||
🔥 优先级（从高到低）：
|
||||
1. 数据库用户配置（系统配置页面保存的配置）
|
||||
2. 环境变量配置（.env 文件中的配置）
|
||||
|
||||
如果用户配置中某个字段为空，则自动回退到环境变量。
|
||||
🔥 锁定模式：始终从环境变量（.env）读取
|
||||
不再合并数据库中的用户配置，确保系统一致性和安全性。
|
||||
"""
|
||||
if self._config is None:
|
||||
user_llm_config = self._user_config.get('llmConfig', {})
|
||||
|
||||
# 🔥 Provider 优先级：用户配置 > 环境变量
|
||||
provider_str = user_llm_config.get('llmProvider') or getattr(settings, 'LLM_PROVIDER', 'openai')
|
||||
# 锁定：全部来自 settings
|
||||
provider_str = settings.LLM_PROVIDER
|
||||
provider = self._parse_provider(provider_str)
|
||||
|
||||
# 🔥 API Key 优先级：用户配置 > 环境变量通用配置 > 环境变量平台专属配置
|
||||
api_key = (
|
||||
user_llm_config.get('llmApiKey') or
|
||||
getattr(settings, 'LLM_API_KEY', '') or
|
||||
self._get_provider_api_key_from_user_config(provider, user_llm_config) or
|
||||
self._get_provider_api_key(provider)
|
||||
)
|
||||
# API Key 优先级：平台专属配置 > 通用 LLM_API_KEY
|
||||
api_key = self._get_provider_api_key(provider) or settings.LLM_API_KEY
|
||||
|
||||
# 🔥 Base URL 优先级：用户配置 > 环境变量
|
||||
base_url = (
|
||||
user_llm_config.get('llmBaseUrl') or
|
||||
getattr(settings, 'LLM_BASE_URL', None) or
|
||||
self._get_provider_base_url(provider)
|
||||
)
|
||||
# Base URL 优先级：通用 LLM_BASE_URL > 平台默认
|
||||
base_url = settings.LLM_BASE_URL or self._get_provider_base_url(provider)
|
||||
|
||||
# 🔥 Model 优先级：用户配置 > 环境变量 > 默认模型
|
||||
model = (
|
||||
user_llm_config.get('llmModel') or
|
||||
getattr(settings, 'LLM_MODEL', '') or
|
||||
DEFAULT_MODELS.get(provider, 'gpt-4o-mini')
|
||||
)
|
||||
# Model
|
||||
model = settings.LLM_MODEL or DEFAULT_MODELS.get(provider, 'gpt-4o-mini')
|
||||
|
||||
# 🔥 Timeout 优先级：用户配置（毫秒） > 环境变量（秒）
|
||||
timeout_ms = user_llm_config.get('llmTimeout')
|
||||
if timeout_ms:
|
||||
# 用户配置是毫秒，转换为秒
|
||||
timeout = int(timeout_ms / 1000) if timeout_ms > 1000 else int(timeout_ms)
|
||||
else:
|
||||
# 环境变量是秒
|
||||
timeout = int(getattr(settings, 'LLM_TIMEOUT', 150))
|
||||
# Timeout (settings 中是秒)
|
||||
timeout = int(settings.LLM_TIMEOUT)
|
||||
|
||||
# 🔥 Temperature 优先级：用户配置 > 环境变量
|
||||
temperature = user_llm_config.get('llmTemperature') if user_llm_config.get('llmTemperature') is not None else float(getattr(settings, 'LLM_TEMPERATURE', 0.1))
|
||||
# Temperature
|
||||
temperature = float(settings.LLM_TEMPERATURE)
|
||||
|
||||
# 🔥 Max Tokens 优先级：用户配置 > 环境变量
|
||||
max_tokens = user_llm_config.get('llmMaxTokens') or int(getattr(settings, 'LLM_MAX_TOKENS', 4096))
|
||||
# Max Tokens
|
||||
max_tokens = int(settings.LLM_MAX_TOKENS)
|
||||
|
||||
self._config = LLMConfig(
|
||||
provider=provider,
|
||||
|
|
@ -100,27 +76,8 @@ class LLMService:
|
|||
)
|
||||
return self._config
|
||||
|
||||
def _get_provider_api_key_from_user_config(self, provider: LLMProvider, user_llm_config: Dict[str, Any]) -> Optional[str]:
|
||||
"""从用户配置中获取平台专属API Key"""
|
||||
provider_key_map = {
|
||||
LLMProvider.OPENAI: 'openaiApiKey',
|
||||
LLMProvider.GEMINI: 'geminiApiKey',
|
||||
LLMProvider.CLAUDE: 'claudeApiKey',
|
||||
LLMProvider.QWEN: 'qwenApiKey',
|
||||
LLMProvider.DEEPSEEK: 'deepseekApiKey',
|
||||
LLMProvider.ZHIPU: 'zhipuApiKey',
|
||||
LLMProvider.MOONSHOT: 'moonshotApiKey',
|
||||
LLMProvider.BAIDU: 'baiduApiKey',
|
||||
LLMProvider.MINIMAX: 'minimaxApiKey',
|
||||
LLMProvider.DOUBAO: 'doubaoApiKey',
|
||||
}
|
||||
key_name = provider_key_map.get(provider)
|
||||
if key_name:
|
||||
return user_llm_config.get(key_name)
|
||||
return None
|
||||
|
||||
def _get_provider_api_key(self, provider: LLMProvider) -> str:
|
||||
"""根据提供商获取API Key"""
|
||||
"""根据提供商从 settings 获取专属 API Key"""
|
||||
provider_key_map = {
|
||||
LLMProvider.OPENAI: 'OPENAI_API_KEY',
|
||||
LLMProvider.GEMINI: 'GEMINI_API_KEY',
|
||||
|
|
@ -132,12 +89,12 @@ class LLMService:
|
|||
LLMProvider.BAIDU: 'BAIDU_API_KEY',
|
||||
LLMProvider.MINIMAX: 'MINIMAX_API_KEY',
|
||||
LLMProvider.DOUBAO: 'DOUBAO_API_KEY',
|
||||
LLMProvider.OLLAMA: None, # Ollama 不需要 API Key
|
||||
}
|
||||
key_name = provider_key_map.get(provider)
|
||||
if key_name:
|
||||
return getattr(settings, key_name, '') or ''
|
||||
return 'ollama' # Ollama的默认值
|
||||
return ''
|
||||
|
||||
|
||||
def _get_provider_base_url(self, provider: LLMProvider) -> Optional[str]:
|
||||
"""根据提供商获取Base URL"""
|
||||
|
|
|
|||
|
|
@ -208,7 +208,7 @@ export default function EmbeddingConfigPanel() {
|
|||
<div className="bg-muted p-3 rounded-lg border border-border">
|
||||
<p className="text-xs text-muted-foreground uppercase mb-1">提供商</p>
|
||||
<Badge className="bg-primary/20 text-primary border-primary/50 font-mono">
|
||||
{currentConfig.provider}
|
||||
{providers.find(p => p.id === currentConfig.provider)?.name || currentConfig.provider}
|
||||
</Badge>
|
||||
</div>
|
||||
<div className="bg-muted p-3 rounded-lg border border-border">
|
||||
|
|
@ -231,8 +231,11 @@ export default function EmbeddingConfigPanel() {
|
|||
<div className="cyber-card p-6 space-y-6">
|
||||
{/* 提供商选择 */}
|
||||
<div className="space-y-2">
|
||||
<Label className="text-xs font-bold text-muted-foreground uppercase">嵌入模型提供商</Label>
|
||||
<Select value={selectedProvider} onValueChange={handleProviderChange}>
|
||||
<Label className="text-xs font-bold text-muted-foreground uppercase flex items-center justify-between">
|
||||
<span>嵌入模型提供商</span>
|
||||
<span className="text-[10px] text-amber-500/80 normal-case font-normal border border-amber-500/30 px-1 rounded">.env 固定配置 (只读)</span>
|
||||
</Label>
|
||||
<Select value={selectedProvider} onValueChange={handleProviderChange} disabled>
|
||||
<SelectTrigger className="h-12 cyber-input">
|
||||
<SelectValue placeholder="选择提供商" />
|
||||
</SelectTrigger>
|
||||
|
|
@ -270,6 +273,7 @@ export default function EmbeddingConfigPanel() {
|
|||
onChange={(e) => setSelectedModel(e.target.value)}
|
||||
placeholder="输入模型名称"
|
||||
className="h-10 cyber-input"
|
||||
disabled
|
||||
/>
|
||||
{selectedProviderInfo.models.length > 0 && (
|
||||
<div className="flex flex-wrap gap-2 mt-2">
|
||||
|
|
@ -279,8 +283,7 @@ export default function EmbeddingConfigPanel() {
|
|||
key={model}
|
||||
type="button"
|
||||
onClick={() => setSelectedModel(model)}
|
||||
className={`px-2 py-1 text-xs font-mono rounded border transition-colors ${
|
||||
selectedModel === model
|
||||
className={`px-2 py-1 text-xs font-mono rounded border transition-colors ${selectedModel === model
|
||||
? "bg-primary/20 border-primary/50 text-primary"
|
||||
: "bg-muted border-border text-muted-foreground hover:border-border hover:text-foreground"
|
||||
}`}
|
||||
|
|
@ -306,6 +309,7 @@ export default function EmbeddingConfigPanel() {
|
|||
onChange={(e) => setApiKey(e.target.value)}
|
||||
placeholder="输入 API Key"
|
||||
className="h-10 cyber-input"
|
||||
disabled
|
||||
/>
|
||||
<p className="text-xs text-muted-foreground">
|
||||
API Key 将安全存储，不会显示在页面上
|
||||
|
|
@ -334,6 +338,7 @@ export default function EmbeddingConfigPanel() {
|
|||
: "https://api.openai.com/v1"
|
||||
}
|
||||
className="h-10 cyber-input"
|
||||
disabled
|
||||
/>
|
||||
<p className="text-xs text-muted-foreground">
|
||||
用于 API 代理或自托管服务
|
||||
|
|
@ -350,6 +355,7 @@ export default function EmbeddingConfigPanel() {
|
|||
min={1}
|
||||
max={500}
|
||||
className="h-10 cyber-input w-32"
|
||||
disabled
|
||||
/>
|
||||
<p className="text-xs text-muted-foreground">
|
||||
每批嵌入的文本数量，建议 50-100
|
||||
|
|
@ -359,8 +365,7 @@ export default function EmbeddingConfigPanel() {
|
|||
{/* 测试结果 */}
|
||||
{testResult && (
|
||||
<div
|
||||
className={`p-4 rounded-lg ${
|
||||
testResult.success
|
||||
className={`p-4 rounded-lg ${testResult.success
|
||||
? "bg-emerald-500/10 border border-emerald-500/30"
|
||||
: "bg-rose-500/10 border border-rose-500/30"
|
||||
}`}
|
||||
|
|
@ -372,8 +377,7 @@ export default function EmbeddingConfigPanel() {
|
|||
<AlertCircle className="w-5 h-5 text-rose-400" />
|
||||
)}
|
||||
<span
|
||||
className={`font-bold ${
|
||||
testResult.success ? "text-emerald-400" : "text-rose-400"
|
||||
className={`font-bold ${testResult.success ? "text-emerald-400" : "text-rose-400"
|
||||
}`}
|
||||
>
|
||||
{testResult.success ? "测试成功" : "测试失败"}
|
||||
|
|
@ -398,37 +402,26 @@ export default function EmbeddingConfigPanel() {
|
|||
<div className="flex items-center gap-3 pt-4 border-t border-border border-dashed">
|
||||
<Button
|
||||
onClick={handleTest}
|
||||
disabled={testing || !selectedProvider || !selectedModel}
|
||||
disabled={testing}
|
||||
variant="outline"
|
||||
className="cyber-btn-outline h-10"
|
||||
className="h-10 border-orange-500/30 hover:bg-orange-500/10 text-orange-400"
|
||||
>
|
||||
{testing ? (
|
||||
<Loader2 className="w-4 h-4 mr-2 animate-spin" />
|
||||
) : (
|
||||
<PlayCircle className="w-4 h-4 mr-2" />
|
||||
<Zap className="w-4 h-4 mr-2" />
|
||||
)}
|
||||
测试连接
|
||||
</Button>
|
||||
|
||||
<Button
|
||||
onClick={handleSave}
|
||||
disabled={saving || !selectedProvider || !selectedModel}
|
||||
className="cyber-btn-primary h-10"
|
||||
>
|
||||
{saving ? (
|
||||
<Loader2 className="w-4 h-4 mr-2 animate-spin" />
|
||||
) : (
|
||||
<Check className="w-4 h-4 mr-2" />
|
||||
)}
|
||||
保存配置
|
||||
</Button>
|
||||
|
||||
<Button
|
||||
onClick={loadData}
|
||||
variant="ghost"
|
||||
className="cyber-btn-ghost ml-auto h-10"
|
||||
size="icon"
|
||||
className="h-10 w-10 text-muted-foreground hover:text-foreground ml-auto"
|
||||
title="刷新配置"
|
||||
>
|
||||
<RefreshCw className="w-4 h-4" />
|
||||
<RefreshCw className={`w-4 h-4 ${loading ? 'animate-spin' : ''}`} />
|
||||
</Button>
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -339,7 +339,7 @@ export function SystemConfig() {
|
|||
</span>
|
||||
) : (
|
||||
<span className="text-amber-400 flex items-center gap-2">
|
||||
<AlertCircle className="h-4 w-4" /> 请配置 LLM API Key
|
||||
<AlertCircle className="h-4 w-4" /> 请在 .env 文件中配置 LLM (只读模式)
|
||||
</span>
|
||||
)}
|
||||
</span>
|
||||
|
|
@ -378,8 +378,11 @@ export function SystemConfig() {
|
|||
<div className="cyber-card p-6 space-y-6">
|
||||
{/* Provider Selection */}
|
||||
<div className="space-y-2">
|
||||
<Label className="text-xs font-bold text-muted-foreground uppercase">选择 LLM 提供商</Label>
|
||||
<Select value={config.llmProvider} onValueChange={(v) => updateConfig('llmProvider', v)}>
|
||||
<Label className="text-xs font-bold text-muted-foreground uppercase flex items-center justify-between">
|
||||
<span>选择 LLM 提供商</span>
|
||||
<span className="text-[10px] text-amber-500/80 normal-case font-normal border border-amber-500/30 px-1 rounded">.env 固定配置 (只读)</span>
|
||||
</Label>
|
||||
<Select value={config.llmProvider} onValueChange={(v) => updateConfig('llmProvider', v)} disabled>
|
||||
<SelectTrigger className="h-12 cyber-input">
|
||||
<SelectValue />
|
||||
</SelectTrigger>
|
||||
|
|
@ -419,6 +422,7 @@ export function SystemConfig() {
|
|||
onChange={(e) => updateConfig('llmApiKey', e.target.value)}
|
||||
placeholder={config.llmProvider === 'baidu' ? 'API_KEY:SECRET_KEY 格式' : '输入你的 API Key'}
|
||||
className="h-12 cyber-input"
|
||||
disabled
|
||||
/>
|
||||
<Button
|
||||
variant="outline"
|
||||
|
|
@ -441,6 +445,7 @@ export function SystemConfig() {
|
|||
onChange={(e) => updateConfig('llmModel', e.target.value)}
|
||||
placeholder={`默认: ${DEFAULT_MODELS[config.llmProvider] || 'auto'}`}
|
||||
className="h-10 cyber-input"
|
||||
disabled
|
||||
/>
|
||||
</div>
|
||||
<div className="space-y-2">
|
||||
|
|
@ -450,6 +455,7 @@ export function SystemConfig() {
|
|||
onChange={(e) => updateConfig('llmBaseUrl', e.target.value)}
|
||||
placeholder="留空使用官方地址，或填入中转站地址"
|
||||
className="h-10 cyber-input"
|
||||
disabled
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
|
@ -462,7 +468,7 @@ export function SystemConfig() {
|
|||
</div>
|
||||
<Button
|
||||
onClick={testLLMConnection}
|
||||
disabled={testingLLM || (!isConfigured && config.llmProvider !== 'ollama')}
|
||||
disabled={testingLLM}
|
||||
className="cyber-btn-primary h-10"
|
||||
>
|
||||
{testingLLM ? (
|
||||
|
|
@ -503,71 +509,28 @@ export function SystemConfig() {
|
|||
{showDebugInfo && llmTestResult.debug && (
|
||||
<div className="mt-3 pt-3 border-t border-border/50">
|
||||
<div className="text-xs font-mono space-y-1 text-muted-foreground">
|
||||
<div className="font-bold text-foreground mb-2">连接信息:</div>
|
||||
<div>Provider: <span className="text-foreground">{String(llmTestResult.debug.provider)}</span></div>
|
||||
<div>Model: <span className="text-foreground">{String(llmTestResult.debug.model_used || llmTestResult.debug.model_requested || 'N/A')}</span></div>
|
||||
<div>Base URL: <span className="text-foreground">{String(llmTestResult.debug.base_url_used || llmTestResult.debug.base_url_requested || '(default)')}</span></div>
|
||||
<div>Adapter: <span className="text-foreground">{String(llmTestResult.debug.adapter_type || 'N/A')}</span></div>
|
||||
<div>API Key: <span className="text-foreground">{String(llmTestResult.debug.api_key_prefix)} (长度: {String(llmTestResult.debug.api_key_length)})</span></div>
|
||||
<div>耗时: <span className="text-foreground">{String(llmTestResult.debug.elapsed_time_ms || 'N/A')} ms</span></div>
|
||||
|
||||
{/* 用户保存的配置参数 */}
|
||||
{llmTestResult.debug.saved_config && (
|
||||
<div className="mt-3 pt-2 border-t border-border/30">
|
||||
<div className="font-bold text-cyan-400 mb-2">已保存的配置参数:</div>
|
||||
<div className="grid grid-cols-2 gap-x-4 gap-y-1">
|
||||
<div>温度: <span className="text-foreground">{String((llmTestResult.debug.saved_config as Record<string, unknown>).temperature ?? 'N/A')}</span></div>
|
||||
<div>最大Tokens: <span className="text-foreground">{String((llmTestResult.debug.saved_config as Record<string, unknown>).max_tokens ?? 'N/A')}</span></div>
|
||||
<div>超时: <span className="text-foreground">{String((llmTestResult.debug.saved_config as Record<string, unknown>).timeout_ms ?? 'N/A')} ms</span></div>
|
||||
<div>请求间隔: <span className="text-foreground">{String((llmTestResult.debug.saved_config as Record<string, unknown>).gap_ms ?? 'N/A')} ms</span></div>
|
||||
<div>并发数: <span className="text-foreground">{String((llmTestResult.debug.saved_config as Record<string, unknown>).concurrency ?? 'N/A')}</span></div>
|
||||
<div>最大文件数: <span className="text-foreground">{String((llmTestResult.debug.saved_config as Record<string, unknown>).max_analyze_files ?? 'N/A')}</span></div>
|
||||
<div>输出语言: <span className="text-foreground">{String((llmTestResult.debug.saved_config as Record<string, unknown>).output_language ?? 'N/A')}</span></div>
|
||||
</div>
|
||||
</div>
|
||||
<div className="font-bold text-foreground mb-2">测试详情:</div>
|
||||
{!!llmTestResult.debug.provider && (
|
||||
<div>提供商: <span className="text-foreground">{String(llmTestResult.debug.provider)}</span></div>
|
||||
)}
|
||||
{!!llmTestResult.debug.model && (
|
||||
<div>当前模型: <span className="text-foreground">{String(llmTestResult.debug.model)}</span></div>
|
||||
)}
|
||||
{!!llmTestResult.debug.duration_s && (
|
||||
<div>耗时: <span className="text-foreground">{String(llmTestResult.debug.duration_s)}s</span></div>
|
||||
)}
|
||||
{llmTestResult.debug.issues_found !== undefined && (
|
||||
<div>测试分析结果: <span className="text-emerald-400">发现 {String(llmTestResult.debug.issues_found)} 个问题 (测试代码)</span></div>
|
||||
)}
|
||||
|
||||
{/* 测试时实际使用的参数 */}
|
||||
{llmTestResult.debug.test_params && (
|
||||
<div className="mt-2 pt-2 border-t border-border/30">
|
||||
<div className="font-bold text-emerald-400 mb-2">测试时使用的参数:</div>
|
||||
<div className="grid grid-cols-3 gap-x-4">
|
||||
<div>温度: <span className="text-foreground">{String((llmTestResult.debug.test_params as Record<string, unknown>).temperature ?? 'N/A')}</span></div>
|
||||
<div>超时: <span className="text-foreground">{String((llmTestResult.debug.test_params as Record<string, unknown>).timeout ?? 'N/A')}s</span></div>
|
||||
<div>MaxTokens: <span className="text-foreground">{String((llmTestResult.debug.test_params as Record<string, unknown>).max_tokens ?? 'N/A')}</span></div>
|
||||
</div>
|
||||
</div>
|
||||
{!!llmTestResult.debug.error && (
|
||||
<div className="text-rose-400 mt-2 font-bold">错误详情: {String(llmTestResult.debug.error)}</div>
|
||||
)}
|
||||
|
||||
{llmTestResult.debug.error_category && (
|
||||
<div className="mt-2">错误类型: <span className="text-rose-400">{String(llmTestResult.debug.error_category)}</span></div>
|
||||
)}
|
||||
{llmTestResult.debug.error_type && (
|
||||
<div>异常类型: <span className="text-rose-400">{String(llmTestResult.debug.error_type)}</span></div>
|
||||
)}
|
||||
{llmTestResult.debug.status_code && (
|
||||
<div>HTTP 状态码: <span className="text-rose-400">{String(llmTestResult.debug.status_code)}</span></div>
|
||||
)}
|
||||
{llmTestResult.debug.api_response && (
|
||||
<div className="mt-2">
|
||||
<div className="font-bold text-amber-400">API 服务器返回:</div>
|
||||
<pre className="mt-1 p-2 bg-amber-500/10 border border-amber-500/30 rounded text-xs overflow-x-auto">
|
||||
{String(llmTestResult.debug.api_response)}
|
||||
</pre>
|
||||
</div>
|
||||
)}
|
||||
{llmTestResult.debug.error_message && (
|
||||
<div className="mt-2">
|
||||
<div className="font-bold text-foreground">完整错误信息:</div>
|
||||
<pre className="mt-1 p-2 bg-background/50 rounded text-xs overflow-x-auto max-h-32 overflow-y-auto">
|
||||
{String(llmTestResult.debug.error_message)}
|
||||
</pre>
|
||||
</div>
|
||||
)}
|
||||
{llmTestResult.debug.traceback && (
|
||||
<details className="mt-2">
|
||||
<summary className="cursor-pointer text-muted-foreground hover:text-foreground">完整堆栈跟踪</summary>
|
||||
<pre className="mt-1 p-2 bg-background/50 rounded text-xs overflow-x-auto max-h-48 overflow-y-auto whitespace-pre-wrap">
|
||||
{!!llmTestResult.debug.traceback && (
|
||||
<details className="mt-2 text-[10px]">
|
||||
<summary className="cursor-pointer text-muted-foreground hover:text-foreground">完整堆栈跟踪 (Traceback)</summary>
|
||||
<pre className="mt-1 p-2 bg-background/50 rounded overflow-x-auto max-h-48 overflow-y-auto whitespace-pre-wrap border border-border/20">
|
||||
{String(llmTestResult.debug.traceback)}
|
||||
</pre>
|
||||
</details>
|
||||
|
|
@ -589,6 +552,7 @@ export function SystemConfig() {
|
|||
value={config.llmTimeout}
|
||||
onChange={(e) => updateConfig('llmTimeout', Number(e.target.value))}
|
||||
className="h-10 cyber-input"
|
||||
disabled
|
||||
/>
|
||||
</div>
|
||||
<div className="space-y-2">
|
||||
|
|
@ -601,6 +565,7 @@ export function SystemConfig() {
|
|||
value={config.llmTemperature}
|
||||
onChange={(e) => updateConfig('llmTemperature', Number(e.target.value))}
|
||||
className="h-10 cyber-input"
|
||||
disabled
|
||||
/>
|
||||
</div>
|
||||
<div className="space-y-2">
|
||||
|
|
@ -610,6 +575,7 @@ export function SystemConfig() {
|
|||
value={config.llmMaxTokens}
|
||||
onChange={(e) => updateConfig('llmMaxTokens', Number(e.target.value))}
|
||||
className="h-10 cyber-input"
|
||||
disabled
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
|
@ -902,13 +868,15 @@ export function SystemConfig() {
|
|||
</Tabs>
|
||||
|
||||
{/* Floating Save Button */}
|
||||
{hasChanges && (
|
||||
{
|
||||
hasChanges && (
|
||||
<div className="fixed bottom-6 right-6 cyber-card p-4 z-50">
|
||||
<Button onClick={saveConfig} className="cyber-btn-primary h-12">
|
||||
<Save className="w-4 h-4 mr-2" /> 保存所有更改
|
||||
</Button>
|
||||
</div>
|
||||
)}
|
||||
)
|
||||
}
|
||||
|
||||
{/* Delete SSH Key Confirmation Dialog */}
|
||||
<AlertDialog open={showDeleteKeyDialog} onOpenChange={setShowDeleteKeyDialog}>
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@
|
|||
|
||||
import { useState, useEffect } from "react";
|
||||
import { useNavigate } from "react-router-dom";
|
||||
import { useAuth } from "@/shared/context/AuthContext";
|
||||
import { Button } from "@/components/ui/button";
|
||||
import { Input } from "@/components/ui/input";
|
||||
import { Label } from "@/components/ui/label";
|
||||
|
|
@ -29,6 +30,7 @@ import type { Profile } from "@/shared/types";
|
|||
|
||||
export default function Account() {
|
||||
const navigate = useNavigate();
|
||||
const { logout } = useAuth();
|
||||
const [profile, setProfile] = useState<Profile | null>(null);
|
||||
const [loading, setLoading] = useState(true);
|
||||
const [saving, setSaving] = useState(false);
|
||||
|
|
@ -128,13 +130,13 @@ export default function Account() {
|
|||
};
|
||||
|
||||
const handleLogout = () => {
|
||||
localStorage.removeItem('access_token');
|
||||
logout();
|
||||
toast.success("已退出登录");
|
||||
navigate('/login');
|
||||
};
|
||||
|
||||
const handleSwitchAccount = () => {
|
||||
localStorage.removeItem('access_token');
|
||||
logout();
|
||||
navigate('/login');
|
||||
};
|
||||
|
||||
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Reference in New Issue