bishengWeb/build/locales/zh/bs2.json

{
    "title": "NPCs",
    "prompt": "提示词",
    "login": {
        "slogen": "灵活，易用，安全的企业级AI应用开发引擎和服务平台",
        "account": "账号",
        "password": "密码",
        "confirmPassword": "确认密码",
        "noAccountRegister": "没有账号，注册",
        "haveAccountLogin": "已有账号，登录",
        "loginButton": "登 录",
        "registerButton": "注 册",
        "document": "文档",
        "pleaseEnterAccount": "请填写账号",
        "pleaseEnterPassword": "请填写密码",
        "accountTooShort": "账号过短",
        "passwordTooShort": "请填写密码,至少六位",
        "passwordMismatch": "两次密码不一致",
        "registrationSuccess": "注册成功,请输入密码进行登录"
    },
    "menu": {
        "app": "应 用",
        "skills": "能 力 ",
        "knowledge": "知识库",
        "models": "模 型",
        "system": "系 统",
        "themeSwitch": "主题切换",
        "document": "文档",
        "logout": "退出",
        "logoutDescription": "退出登录",
        "forBestExperience": "为了您的良好体验，请在 PC 端访问该网站",
        "onlineDocumentation": "在线文档"
    },
    "system": {
        "userManagement": "用户管理",
        "roleManagement": "角色管理",
        "systemConfiguration": "系统配置",
        "username": "用户名",
        "confirmDisable": "确认禁用该用户？",
        "roleSelect": "角色选择",
        "roleList": "角色列表",
        "confirmText": "是否删除",
        "roleName": "角色名称",
        "skillAuthorization": "能力授权",
        "knowledgeAuthorization": "知识库授权",
        "skillName": "能力名称",
        "creator": "创建人",
        "usePermission": "使用权限",
        "managePermission": "管理权限",
        "roleNamePrompt": "角色名称不能超过50字符",
        "roleNameRequired": "角色名称不能为空",
        "roleNameExists": "角色名称已存在",
        "parameterConfig": "参数配置",
        "language": "语言"
    },
    "skills": {
        "manageTemplate": "管理能力模板",
        "createNew": "新建",
        "manageProjects": "这里管理您的个人项目，对能力上下线、编辑等等",
        "skillSearch": "能力搜索",
        "confirmDeleteSkill": "确认删除该能力？",
        "backToSkillList": "返回能力列表",
        "skillTemplateManagement": "能力模板管理，模板对所有用户可见，支持拖拽排序、删除操作",
        "templateName": "模板名称",
        "templateDescription": "模板描述",
        "confirmText": "是否确认删除该能力模板？",
        "skillSettings": "能力设置",
        "basicInfo": "基础信息",
        "skillName": "能力名称",
        "description": "描述",
        "parameterInfo": "参数信息",
        "advancedConfiguration": "高级配置",
        "nextStep": "下一步，高级配置",
        "skillNameRequired": "请填写能力名称",
        "skillNameTooLong": "能力名称过长，不要超过30字",
        "skillNameExists": "该名称已存在",
        "skillDescRequired": "请填写能力描述",
        "skillDescTooLong": "能力描述过长，不要超过200字",
        "errorTitle": "关键信息有误",
        "onlineFailure": "上线失败",
        "custom": "自定义",
        "skillTemplate": "能力模板",
        "skillTemplateChoose": "您可以从这里挑选一个模板开始，或者自定义高级模板",
        "createTemplate": "创建模板",
        "createSuccessTitle": "能力创建成功",
        "createFailureTitle": "创建失败",
        "createdBy": "创建用户",
        "offline": "下线",
        "online": "上线"
    },
    "chat": {
        "newChat": "新建会话",
        "selectChat": "选择一个对话开始文擎睿见",
        "inputPlaceholder": "请输入问题",
        "uploadFileTooltip": "上传文件",
        "sendTooltip": "发送",
        "skillTempsTitle": "能力选择",
        "skillTempsDesc": "选择一个您想使用的线上能力",
        "networkError": "网络连接出现错误,请尝试以下方法",
        "networkErrorList1": "操作不要过快",
        "networkErrorList2": "刷新页面",
        "networkErrorList3": "检查后台是否启动",
        "buildError": "您好像缺少了某些配置",
        "connectionbreakTip": "链接异常断开：",
        "connectionbreak": "网络断开！",
        "copyTip": "内容已复制",
        "noAccess": "因权限不足，该答案剔除了无权查看的内容",
        "source": "参考来源",
        "file": "文件",
        "filePrsing": "文件正在解析中",
        "sourceTooltip": "来源段落",
        "filterLabel": "筛选标签",
        "tooltipText": "系统自动根据答案生成关键信息标签,也可手动增删标签,系统根据标签计算各个文件及段落相关性。",
        "customLabel": "自定义",
        "addCustomLabel": "+自定义",
        "sourceDocumentsLabel": "来源文档",
        "downloadPDFTooltip": "下载双层PDF",
        "downloadOriginalTooltip": "下载原文件",
        "noMatchedFilesMessage": "无匹配的源文件",
        "fileStorageFailure": " 文件地址失效!",
        "confirmDeleteChat": "确认删除该会话？",
        "roundOver": "本轮结束",
        "chatDialogTip": "设置提示模板中定义的输入变量。与代理和链互动"
    },
    "model": {
        "modelConfiguration": "模型配置",
        "modelName": "模型名称",
        "modelConfigLabel": "模型配置",
        "modelConfigExplanationLink": "模型配置参数说明",
        "jsonFormatError": "JSON格式有误",
        "onlineStatus": "已上线",
        "offlineStatus": "未上线",
        "exceptionStatus": "异常",
        "warningTooltip": "处理异常",
        "inProgressOnlineStatus": "上线中",
        "inProgressOfflineStatus": "下线中",
        "confirmModelOffline": "是否确认下线该模型，下线后使用该模型服务的能力将无法正常工作",
        "confirmOfflineButtonText": "下线",
        "modelManagement": "模型管理",
        "modelFineTune": "模型Finetune",
        "refreshButton": "刷新",
        "gpuResourceUsage": "GPU资源使用情况",
        "rtServiceManagement": "RT服务管理",
        "modelCollectionCaption": "模型集合",
        "machine": "机器",
        "serviceAddress": "服务地址",
        "status": "状态",
        "online": "上线",
        "offline": "下线",
        "gpuResourceUsageTitle": "GPU资源使用情况",
        "gpuNumber": "GPU序号",
        "gpuID": "GPU-ID",
        "totalMemory": "总显存",
        "freeMemory": "空余显存",
        "gpuUtilization": "GPU利用率",
        "machineName": "机器名",
        "addOne": "加一条"
    },
    "flow": {
        "unsavedChangesConfirmation": "您有未保存的更改，确定要离开吗？",
        "leave": "离开",
        "leaveAndSave": "离开并保存",
        "simplifyConfig": "简化配置",
        "simplify": "简化",
        "notifications": "通知",
        "exit": "退出",
        "import": "导入",
        "export": "导出",
        "code": "代码",
        "searchComponent": "查找组件",
        "knowledgeBaseSelection": "知识库选择",
        "searchKnowledgeBase": "搜索知识库",
        "minimumParamSetDescription": "您可以在此设置能力所需的最小参数集",
        "paramList": "参数列表",
        "saveConfig": "保存配置",
        "componentLabel": "组件",
        "aliasLabel": "别名",
        "editAlias": "修改别名",
        "parameterLabel": "参数",
        "notification": "消息",
        "noNewNotifications": "没有新的通知",
        "skillName": "能力名",
        "nameTooLong": "名称过长",
        "skillDescription": "能力描述",
        "enterVarName": "请输入变量名",
        "varNameExists": "变量名重复",
        "text": "文本",
        "dropdown": "下拉框",
        "maxLength": "最大长度",
        "options": "选项",
        "variableName": "变量名",
        "varOptionRequired": "请输入选项内容",
        "optionRepeated": "选项重复"
    },
    "lib": {
        "enterLibraryName": "请输入知识库名称",
        "libraryNameLimit": "知识库名称字数不得超过30字",
        "selectModel": "请选择一个模型",
        "nameExists": "该名称已存在",
        "descriptionLimit": "知识库描述字数不得超过200字",
        "createLibrary": "创建知识库",
        "libraryName": "知识库名称",
        "description": "描述",
        "model": "模型",
        "fileData": "文件数据",
        "structuredData": "结构化数据",
        "libraryCollection": "知识库集合",
        "createUser": "创建用户",
        "details": "详情",
        "confirmDeleteLibrary": "确认删除该知识库?",
        "fileList": "文件列表",
        "systemIntegration": "系统对接",
        "upload": "上传",
        "fileName": "文件名称",
        "status": "状态",
        "uploadTime": "上传时间",
        "parseFailed": "解析失败",
        "parsing": "解析中",
        "completed": "完成",
        "confirmDeleteFile": "确认删除该文件？"
    },
    "code": {
        "editPythonCodeDescription": "编辑你的 Python 代码此代码片段接受模块导入和一个函数定义。确保您的函数返回一个字符串。",
        "editCode": "编辑代码",
        "codeReadyToRun": "代码准备运行",
        "functionError": "您的函数中存在一个错误",
        "importsError": "您的导入有误",
        "errorOccurred": "出错了，请重试",
        "codeError": "这段代码有问题，请检查以下",
        "checkAndSave": "检查 & 保存",
        "export": "导出",
        "exportToJSON": "导出能力到json文件中",
        "keyInformationMissing": "您有一些关键信息没有填: ",
        "skillNameMissing": "请填写能力名称",
        "useOwnAPIKeys": "使用自己的API keys",
        "exportSkill": "导出能力",
        "uploadFile": "上传文件",
        "clickOrDragHere": "点击或将文件拖拽到这里上传",
        "dropFileHere": "将文件拖拽到这里上传",
        "delimiter": "切分符(多个以;分隔)",
        "splitLength": "切分文本长度",
        "smartSplit": "智能语义切分",
        "manualSplit": "手动设置切分",
        "delimiterPlaceholder": "切分符号",
        "splitSizePlaceholder": "切分大小",
        "complete": "完成",
        "setSplitSize": "请设置文件切分大小",
        "selectFileToUpload": "请先选择文件上传",
        "fileSizeExceedsLimit": "文件不能超50M",
        "file": "文件",
        "sizeExceedsLimit": "超过50M",
        "editDictionary": "编辑词典",
        "exportCodeDialogTip": "生成代码，将流程集成到外部应用程序中 (打开此页面前请先build能力)。",
        "chunkOverlap": "切分文本重叠长度"
    },
    "report": {
        "reportTemplate": "报告模板",
        "reportDescription": "报告生成描述...",
        "newButton": "新建",
        "importButton": "导入",
        "start": "开始",
        "formSettings": "表单设置",
        "requiredLabel": "必填",
        "isRequired": "是必填项",
        "fileRequired": "当前文件为空",
        "selectComponent": "选择一个组件",
        "varLength": "长度不能超过"
    },
    "status": {
        "1004": "该能力已被删除",
        "1008": "当前能力未上线，无法直接对话",
        "1005": ""
    },
    "confirmButton": "确定",
    "add": "添加",
    "back": "返回",
    "create": "创建",
    "delete": "删除",
    "createTime": "创建时间",
    "updateTime": "更新时间",
    "success": "保存成功",
    "edit": "编辑",
    "enable": "启用",
    "disable": "禁用",
    "close": "关闭",
    "cancel": "取消",
    "save": "保存",
    "operations": "操作",
    "previousPage": "上一页",
    "nextPage": "下一页",
    "formatError": "格式错误",
    "agents": {
        "AgentInitializer": {
            "display_name": "AgentInitializer",
            "description": "以大语言模型LLM和工具来构建零样本Agent。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "tools": {
                    "display_name": "工具"
                },
                "agent": {
                    "display_name": "Agent",
                    "options": [
                        "zero-shot-react-description",
                        "react-docstore",
                        "self-ask-with-search",
                        "conversational-react-description",
                        "openai-functions",
                        "openai-multi-functions"
                    ]
                }
            }
        },
        "CSVAgent": {
            "display_name": "CSVAgent",
            "description": "用于创建解析处理CSV类型文件的Agent",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "path": {
                    "display_name": "路径"
                },
                "format_instructions": {
                    "display_name": "格式说明",
                    "value": "使用以下格式：\n\n问题：您必须回答的输入问题\n思考：您应该始终考虑要做什么\n操作：要执行的操作，应该是[{tool_names}]之一\n操作输入：操作的输入\n观察：操作的结果\n...（这个思考/操作/操作输入/观察可以重复N次）\n思考：我现在知道最终答案\n最终答案：原始输入问题的最终答案"
                },
                "input_variables": {
                    "display_name": "输入变量",
                    "value": [
                        "df_head",
                        "input",
                        "agent_scratchpad"
                    ]
                },
                "prefix": {
                    "display_name": "前缀提示词",
                    "value": "\n您正在使用Python中的pandas数据框。数据框的名称是 `df`。\n您应该使用下面的工具来回答您提出的问题："
                },
                "suffix": {
                    "display_name": "后缀",
                    "value": "\n这是 `print(df.head())` 的结果：\n{df_head}\n\n开始吧！\n问题：{input}\n{agent_scratchpad}"
                }
            }
        },
        "ChatglmFunctionsAgent": {
            "display_name": "ChatGLM3FunctionsAgent",
            "description": "利用ChatGLM3的FunctionsCall功能构建代理实现工具调用（目前支持开源的chatglm3-6b模型）。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "tools": {
                    "display_name": "工具"
                }
            }
        },
        "JsonAgent": {
            "display_name": "JsonAgent",
            "description": "从大语言模型LLM和工具构建处理JSON文件的Agent。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "toolkit": {
                    "display_name": "工具包"
                }
            }
        },
        "LLMFunctionsAgent": {
            "display_name": "LLMFunctionsAgent",
            "description": "适配了Qwen模型与OpenAI模型，目前我们测试下来Qwen1.8B可以实现较好的Function Call效果。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "tools": {
                    "display_name": "工具"
                }
            }
        },
        "SQLAgent": {
            "display_name": "SQL Agent",
            "description": "SQLAgent是专用于与SQL数据库交互的Agent，它能够将用户用自然语言描述的需求转换成SQL查询语句以及计算逻辑。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "database_uri": {
                    "display_name": "数据库URI"
                },
                "format_instructions": {
                    "display_name": "格式说明",
                    "value": "使用以下格式：\n\n问题：您必须回答的输入问题\n思考：您应该始终考虑要做什么\n操作：要执行的操作，应该是[{tool_names}]之一\n操作输入：操作的输入\n观察：操作的结果\n...（这个思考/操作/操作输入/观察可以重复N次）\n思考：我现在知道最终答案\n最终答案：原始输入问题的最终答案"
                },
                "input_variables": {
                    "display_name": "输入变量",
                    "value": [
                        "input",
                        "agent_scratchpad"
                    ]
                },
                "prefix": {
                    "display_name": "前缀提示词",
                    "value": "您是一个与SQL数据库交互的代理。\n给定一个输入问题，请创建一个语法正确的{dialect}查询，然后查看查询的结果并返回答案。\n除非用户指定要获取的特定示例数，否则始终限制查询至多返回{top_k}个结果。\n您可以按相关列对结果进行排序，以返回数据库中最有趣的示例。\n永远不要查询特定表的所有列，只有在给定问题的情况下才请求相关列。\n您可以使用与数据库交互的工具。\n只使用下面的工具。只使用下面工具返回的信息构建最终答案。\n在执行查询之前，务必仔细检查您的查询。如果在执行查询时出错，请重新编写查询并重试。\n\n不要对数据库进行任何DML语句（INSERT，UPDATE，DELETE，DROP等）。\n\n如果问题似乎与数据库无关，只需返回 '我不知道' 作为答案。"
                },
                "suffix": {
                    "display_name": "后缀",
                    "value": "开始吧！\n\n问题：{input}\n思考：我应该查看数据库中的表，看看我可以查询什么。然后，我应该查询最相关表的模式。\n{agent_scratchpad}"
                }
            }
        },
        "VectorStoreAgent": {
            "display_name": "VectorStoreAgent",
            "description": "从矢量存储构建代理。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "vectorstoreinfo": {
                    "display_name": "矢量数据库信息"
                }
            }
        },
        "VectorStoreRouterAgent": {
            "display_name": "VectorStoreRouterAgent",
            "description": "从矢量数据库路由器构建代理。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "vectorstoreroutertoolkit": {
                    "display_name": "矢量存储路由器工具包"
                }
            }
        },
        "ZeroShotAgent": {
            "display_name": "通用Agent",
            "description": "无需提供任何样本数据或案例，使用大语言模型LLM和工具直接构建通用Agent。使用ReAct理论构架Agent提示词",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "tools": {
                    "display_name": "工具"
                },
                "format_instructions": {
                    "display_name": "格式说明",
                    "value": "使用以下格式：\n\n问题：您必须回答的输入问题\n思考：您应该始终考虑要做什么\n操作：要执行的操作，应该是一种[{tool_names}]之一\n操作输入：操作的输入\n观察：操作的结果\n...（这个思考/操作/操作输入/观察可以重复N次）\n思考：我现在知道最终答案\n最终答案：原始输入问题的最终答案"
                },
                "input_variables": {
                    "display_name": "输入变量"
                },
                "prefix": {
                    "display_name": "前缀提示词",
                    "value": "尽力回答以下问题。您可以使用以下工具："
                },
                "suffix": {
                    "display_name": "后缀",
                    "value": "开始吧！\n\n问题：{input}\n思考：{agent_scratchpad}"
                }
            }
        }
    },
    "autogen_roles": {
        "AutoGenAssistant": {
            "display_name": "自动生成助手",
            "description": "助手代理，设计用于使用大语言模型LLM解决任务。",
            "template": {
                "model_name": {
                    "display_name": "模型名称"
                },
                "name": {
                    "display_name": "名称"
                },
                "openai_api_base": {
                    "display_name": "openai_api_base"
                },
                "openai_api_key": {
                    "display_name": "openai_api_key"
                },
                "openai_proxy": {
                    "display_name": "openai_proxy"
                },
                "system_message": {
                    "display_name": "系统消息"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            }
        },
        "AutoGenCoder": {
            "display_name": "自动生成编码器",
            "description": "Coder的代理，可以执行代码到其他代理。",
            "template": {
                "name": {
                    "display_name": "名称"
                },
                "system_message": {
                    "display_name": "系统消息"
                }
            }
        },
        "AutoGenCustomRole": {
            "display_name": "自动生成自定义角色",
            "description": "可以使用langchain代理和链的自定义代理。",
            "template": {
                "func": {
                    "display_name": "函数"
                },
                "name": {
                    "display_name": "名称"
                },
                "system_message": {
                    "display_name": "系统消息"
                }
            }
        },
        "AutoGenGroupChatManager": {
            "display_name": "自动生成群聊管理器",
            "description": "一个可以管理多个代理的群聊管理器代理。",
            "template": {
                "agents": {
                    "display_name": "代理"
                },
                "max_round": {
                    "display_name": "最大轮次"
                },
                "model_name": {
                    "display_name": "模型名称"
                },
                "name": {
                    "display_name": "名称",
                    "value": "chat_manage"
                },
                "openai_api_base": {
                    "display_name": "openai_api_base"
                },
                "openai_api_key": {
                    "display_name": "openai_api_key"
                },
                "openai_proxy": {
                    "display_name": "openai_proxy"
                },
                "system_message": {
                    "display_name": "系统消息"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            }
        },
        "AutoGenUser": {
            "display_name": "自动生成用户",
            "description": "用户的代理，可以向其他代理提供反馈。",
            "template": {
                "human_input_mode": {
                    "display_name": "人类输入模式"
                },
                "max_consecutive_auto_reply": {
                    "display_name": "最大连续自动回复"
                },
                "name": {
                    "display_name": "名称",
                    "value": "chat_manage"
                },
                "system_message": {
                    "display_name": "系统消息"
                }
            }
        }
    },
    "chains": {
        "APIChain": {
            "display_name": "API工作链",
            "description": "APIChain组件可以让使用者用自然语言与API进行交互，并得到最终响应结果。",
            "template": {
                "api_response_prompt": {
                    "display_name": "api响应提示"
                },
                "api_url_prompt": {
                    "display_name": "api URL提示"
                },
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "api_docs": {
                    "display_name": "api文档"
                },
                "headers": {
                    "display_name": "头部"
                }
            }
        },
        "AutoGenChain": {
            "display_name": "自动生成工作链",
            "description": "AutoGen是一个支持自定义的通用多智能体框架，它提供了一种新颖的问题解决思路，让多个智能体能够通过类似“群聊”的方式来解决较复杂的问题。人类在解决问题时，也通常会使用讨论的方式。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "recipient": {
                    "display_name": "AutogenRole"
                },
                "user_proxy_agent": {
                    "display_name": "用户代理"
                }
            }
        },
        "CombineDocsChain": {
            "display_name": "合并文档工作链",
            "description": "组件是合并文档和问题的链，可以连接RetrievalQA组件和大语言模型，从RetrievalQA组件处获取问题和检索到的文档，按照“Chain Type”参数，使用对应的策略与大语言模型进行交互问答。",
            "template": {
                "document_prompt": {
                    "display_name": "文档提示"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "prompt": {
                    "display_name": "提示词"
                },
                "chain_type": {
                    "display_name": "链类型",
                    "options": [
                        "stuff",
                        "map_reduce",
                        "map_rerank",
                        "refine"
                    ]
                },
                "token_max": {
                    "display_name": "最大Tokens"
                }
            }
        },
        "ConversationChain": {
            "display_name": "对话工作链",
            "description": "在LLM最简工作链的基础上增加从记忆中加载上下文的链。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "memory": {
                    "display_name": "记忆"
                }
            }
        },
        "ConversationalRetrievalChain": {
            "display_name": "对话检索工作链",
            "description": "在检索工作链组件的基础上，提供聊天历史组件，因此可以构建使用知识库与大语言模型进行问答的技能，且具有“记忆”。",
            "template": {
                "combine_docs_chain_kwargs": {
                    "display_name": "提示词"
                },
                "condense_question_prompt": {
                    "display_name": "压缩问题提示"
                },
                "document_prompt": {
                    "display_name": "文档提示"
                },
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "retriever": {
                    "display_name": "检索器"
                },
                "chain_type": {
                    "display_name": "链类型",
                    "options": [
                        "stuff",
                        "map_reduce",
                        "map_rerank",
                        "refine"
                    ]
                }
            }
        },
        "LLMChain": {
            "display_name": "LLM最简工作链",
            "description": "LLMChain是一个简单、基础的链，可以使用LLMChain构建与大语言模型进行对话的技能。用户可以预先将提示词中相同、固定的部分设置为提示词模板，各类输入的内容如文字、文档等作为模板中的变量，由LLMChain将各类输入的内容与提示词模板进行组装，统一传递给大语言模型。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "prompt": {
                    "display_name": "提示词"
                }
            }
        },
        "LLMMathChain": {
            "display_name": "LLM数学工作链",
            "description": "解释提示并执行Python代码进行数学运算的链。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "llm_chain": {
                    "display_name": "大语言模型LLM工作链"
                },
                "memory": {
                    "display_name": "记忆"
                }
            }
        },
        "LoaderOutputChain": {
            "display_name": "加载器输出工作链",
            "description": "打印加载器输出的链。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "input_node": {
                    "display_name": "预设问题"
                },
                "memory": {
                    "display_name": "记忆"
                }
            }
        },
        "MidJourneyPromptChain": {
            "display_name": "MidJourney提示词工作链",
            "description": "用户可以方便的生成优秀的MidJourney提示词，用于生成MJ图片。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "memory": {
                    "display_name": "记忆"
                }
            }
        },
        "MultiPromptChain": {
            "display_name": "多提示词工作链",
            "description": "使用大语言模型的以及合适的提示词从多个并存的工作链路中选择一个链路执行。",
            "template": {
                "LLMChains": {
                    "display_name": "大语言模型LLM工作链"
                },
                "default_chain": {
                    "display_name": "默认工作链"
                },
                "input_node": {
                    "display_name": "预设问题"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "router_chain": {
                    "display_name": "路由工作链"
                },
                "destination_chain_name": {
                    "display_name": "目标链名称"
                }
            }
        },
        "MultiRuleChain": {
            "display_name": "多规则工作链",
            "description": "按照规划或者编排好的多种规则从多个并存的工作链路中选择一个链路执行。",
            "template": {
                "LLMChains": {
                    "display_name": "大语言模型LLM工作链"
                },
                "default_chain": {
                    "display_name": "默认工作链"
                },
                "input_node": {
                    "display_name": "预设问题"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "router_chain": {
                    "display_name": "路由工作链"
                },
                "destination_chain_name": {
                    "display_name": "目标链名称"
                },
                "output_variables": {
                    "display_name": "输出变量"
                }
            }
        },
        "RetrievalChain": {
            "display_name": "检索工作链",
            "description": "对于企业内已有QA库的情况希望直接返回库中答案，不让大模型生成，提升这类答案的准确性。若QA库未命中的再走RAG方案让大模型生成。。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "retriever": {
                    "display_name": "检索器"
                }
            }
        },
        "RetrievalQA": {
            "display_name": "RetrievalQA",
            "description": "Chain for question-answering against an index.",
            "template": {
                "memory": {
                    "display_name": "memory"
                },
                "retriever": {
                    "display_name": "retriever"
                },
                "input_node": {
                    "display_name": "Preset Question"
                },
                "combine_documents_chain": {
                    "display_name": "combine_documents_chain"
                }
            }
        },
        "RuleBasedRouter": {
            "display_name": "按照规划或者编排好的多种规则从多个并存的工作链路中选择一个链路执行",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "rule_function": {
                    "display_name": "规则函数"
                },
                "input_variables": {
                    "display_name": "输入变量"
                }
            }
        },
        "SQLDatabaseChain": {
            "display_name": "SQL数据库工作链",
            "template": {
                "db": {
                    "display_name": "数据库"
                },
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "prompt": {
                    "display_name": "提示词"
                }
            }
        },
        "SequentialChain": {
            "display_name": "顺序工作链",
            "description": "当你想要获取一个调用的输出并将其用作另一个调用的输入时，使用该组件。Sequtential可以连接组合多个chains，并按顺序执行这些chains。",
            "template": {
                "chains": {
                    "display_name": "工作链"
                },
                "input_node": {
                    "display_name": "预设问题"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "chain_order": {
                    "display_name": "链顺序"
                },
                "input_variables": {
                    "display_name": "输入变量"
                },
                "output_variables": {
                    "display_name": "输出变量"
                }
            }
        },
        "SeriesCharacterChain": {
            "display_name": "系列角色工作链",
            "description": "SeriesCharacterChain是您可以使用的链，用于与系列中的角色进行对话。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "character": {
                    "display_name": "角色"
                },
                "series": {
                    "display_name": "系列"
                }
            }
        },
        "SimpleSequentialChain": {
            "display_name": "简单顺序工作链",
            "description": "简单的链，其中一个步骤的输出直接进入下一个步骤。",
            "template": {
                "chains": {
                    "display_name": "工作链"
                },
                "input_node": {
                    "display_name": "预设问题"
                },
                "memory": {
                    "display_name": "记忆"
                }
            }
        },
        "TimeTravelGuideChain": {
            "display_name": "时光导游工作链",
            "description": "时光导游链。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "memory": {
                    "display_name": "记忆"
                }
            }
        },
        "TransformChain": {
            "display_name": "转换工作链",
            "description": "链转换链输出。",
            "template": {
                "input_node": {
                    "display_name": "预设问题"
                },
                "memory": {
                    "display_name": "记忆"
                },
                "transform": {
                    "display_name": "转换"
                },
                "input_variables": {
                    "display_name": "输入变量"
                },
                "output_variables": {
                    "display_name": "输出变量"
                }
            }
        }
    },
    "documentloaders": {
        "AZLyricsLoader": {
            "display_name": "AZLyricsLoader",
            "description": "加载AZLyrics网页。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "web_path": {
                    "display_name": "Web路径"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "AirbyteJSONLoader": {
            "display_name": "AirbyteJSONLoader",
            "description": "加载本地airbyte json文件。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "BSHTMLLoader": {
            "display_name": "BSHTMLLoader",
            "description": "使用beautiful soup解析HTML文件的加载器。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "CSVLoader": {
            "display_name": "CSVLoader",
            "description": "将CSV文件加载到文档列表中。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "CoNLLULoader": {
            "display_name": "CoNLLULoader",
            "description": "加载CoNLL-U文件。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "CollegeConfidentialLoader": {
            "display_name": "CollegeConfidentialLoader",
            "description": "加载College Confidential网页。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "web_path": {
                    "display_name": "Web路径"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "CustomKVLoader": {
            "display_name": "CustomKVLoader",
            "description": "从pdf或图像中提取键值对的加载器。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "elem_server_id": {
                    "display_name": "元素服务器ID"
                },
                "elm_api_base_url": {
                    "display_name": "elm API基本URL"
                },
                "elm_api_key": {
                    "display_name": "elm API密钥"
                },
                "metadata": {
                    "display_name": "元数据"
                },
                "schemas": {
                    "display_name": "模式"
                },
                "task_type": {
                    "display_name": "任务类型"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "DirectoryLoader": {
            "display_name": "DirectoryLoader",
            "description": "从目录加载文档。",
            "template": {
                "glob": {
                    "display_name": "全局通配符"
                },
                "metadata": {
                    "display_name": "元数据"
                },
                "path": {
                    "display_name": "本地目录"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "ElemUnstructuredLoaderV0": {
            "display_name": "非结构化解析器",
            "description": "根据文件格式自动选择适当的解析器，并支持OCR的加载器。统一非结构化数据Loader，将任何文档转换成语义连续的token集合。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                },
                "unstructured_api_url": {
                    "display_name": "非结构化API URL"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "EverNoteLoader": {
            "display_name": "EverNoteLoader",
            "description": "EverNote加载器。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "FacebookChatLoader": {
            "display_name": "FacebookChatLoader",
            "description": "加载Facebook消息json目录转储。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "GitLoader": {
            "display_name": "GitLoader",
            "description": "从Git存储库加载文件到文档列表。",
            "template": {
                "branch": {
                    "display_name": "分支"
                },
                "clone_url": {
                    "display_name": "克隆URL"
                },
                "file_filter": {
                    "display_name": "文件扩展名（逗号分隔）"
                },
                "metadata": {
                    "display_name": "元数据"
                },
                "repo_path": {
                    "display_name": "存储库路径"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "GitbookLoader": {
            "display_name": "GitbookLoader",
            "description": "加载GitBook数据。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "web_page": {
                    "display_name": "Web页面"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "GutenbergLoader": {
            "display_name": "GutenbergLoader",
            "description": "使用urllib加载.txt网页文件的加载器。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "web_page": {
                    "display_name": "Web页面"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "HNLoader": {
            "display_name": "HNLoader",
            "description": "从Hacker News加载数据，可以是主页结果或评论页面。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "web_page": {
                    "display_name": "Web页面"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "IFixitLoader": {
            "display_name": "IFixitLoader",
            "description": "加载iFixit修复指南、设备维基和答案。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "web_page": {
                    "display_name": "Web页面"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "IMSDbLoader": {
            "display_name": "IMSDbLoader",
            "description": "加载IMSDb网页。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "web_page": {
                    "display_name": "Web页面"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "NotionDirectoryLoader": {
            "display_name": "NotionDirectoryLoader",
            "description": "加载Notion目录转储。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "path": {
                    "display_name": "本地目录"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "PDFWithSemanticLoader": {
            "display_name": "PDFWithSemanticLoader",
            "description": "使用pypdf加载PDF并以字符级别切块。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "layout_api_key": {
                    "display_name": "布局API密钥"
                },
                "layout_api_url": {
                    "display_name": "布局API URL"
                },
                "metadata": {
                    "display_name": "元数据"
                }
                
            },
            "base_classes": [
                "文档"
            ]
        },
        "PyPDFDirectoryLoader": {
            "display_name": "PyPDFDirectoryLoader",
            "description": "使用pypdf从目录加载具有字符级别切块的PDF文件。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "path": {
                    "display_name": "本地目录"
                }
                
            },
            "base_classes": [
                "文档"
            ]
        },
        "PyPDFLoader": {
            "display_name": "PyPDFLoader",
            "description": "使用pypdf加载具有字符级别切块的PDF文件。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }            
            },
            "base_classes": [
                "文档"
            ]
        },
        "ReadTheDocsLoader": {
            "display_name": "ReadTheDocsLoader",
            "description": "加载ReadTheDocs文档目录转储。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "path": {
                    "display_name": "本地目录"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "SRTLoader": {
            "display_name": "SRTLoader",
            "description": "用于.srt（字幕）文件的加载器。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "SlackDirectoryLoader": {
            "display_name": "SlackDirectoryLoader",
            "description": "从Slack目录转储加载文档。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "TextLoader": {
            "display_name": "TextLoader",
            "description": "加载文本文件。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "metadata": {
                    "display_name": "元数据"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "UniversalKVLoader": {
            "display_name": "UniversalKVLoader",
            "description": "从pdf或图像中提取键值对的加载器。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "ellm_model_url": {
                    "display_name": "ellm模型URL"
                },
                "max_pages": {
                    "display_name": "最大页数"
                },
                "metadata": {
                    "display_name": "元数据"
                },
                "schema": {
                    "display_name": "模式"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "WebBaseLoader": {
            "display_name": "WebBaseLoader",
            "description": "使用urllib和beautiful soup加载网页的加载器。",
            "template": {
                "metadata": {
                    "display_name": "元数据"
                },
                "web_path": {
                    "display_name": "Web页面"
                }
            },
            "base_classes": [
                "文档"
            ]
        }
    },
    "embeddings": {
        "CohereEmbeddings": {
            "display_name": "CohereEmbeddings",
            "description": "Cohere嵌入模型的封装。",
            "template": {
                "cohere_api_key": {
                    "display_name": "cohere_api_key"
                },
                "model": {
                    "display_name": "模型"
                }
            },
            "base_classes": [
                "CohereEmbeddings",
                "嵌入模型"
            ]
        },
        "HostEmbeddings": {
            "display_name": "本地Embeddings",
            "description": "加载使用私有化本地部署的Embedding是模型。",
            "template": {
                "host_base_url": {
                    "display_name": "主机基础URL"
                },
                "model": {
                    "display_name": "模型"
                }
            },
            "base_classes": [
                "嵌入模型",
                "HostEmbeddings"
            ]
        },
        "HuggingFaceEmbeddings": {
            "display_name": "HuggingFaceEmbeddings",
            "description": "sentence_transformers嵌入模型的封装。",
            "base_classes": [
                "嵌入模型",
                "HostEmbeddings"
            ]
        },
        "OpenAIEmbeddings": {
            "display_name": "OpenAIEmbeddings",
            "description": "OpenAI嵌入模型的封装。",
            "template": {
                "model": {
                    "display_name": "模型"
                },
                "openai_api_key": {
                    "display_name": "OpenAI API密钥"
                },
                "tiktoken_model_name": {
                    "display_name": "tiktoken模型名称"
                }
            },
            "base_classes": [
                "嵌入模型",
                "OpenAI嵌入模型"
            ]
        },
        "OpenAIProxyEmbedding": {
            "display_name": "OpenAIProxyEmbedding",
            "description": "使用自建的embedding服务使用OpenAI进行嵌入。",
            "base_classes": [
                "嵌入模型"
            ]
        },
        "WenxinEmbeddings": {
            "display_name": "WenxinEmbeddings",
            "description": "Wenxin嵌入模型。",
            "template": {
                "model": {
                    "display_name": "模型"
                },
                "wenxin_api_key": {
                    "display_name": "Wenxin API密钥"
                }
            },
            "base_classes": [
                "嵌入模型",
                "Wenxin嵌入模型"
            ]
        }
    },
    "input_output": {
        "InputFileNode": {
            "display_name": "输入文件节点",
            "description": "输入文件节点，当有上传文件需求时调用。",
            "template": {
                "file_path": {
                    "display_name": "文件路径"
                },
                "file_type": {
                    "display_name": "文件类型"
                }
            },
            "base_classes": [
                "文件节点"
            ]
        },
        "InputNode": {
            "display_name": "输入节点",
            "description": "用户可以自定义输入内容，并将其输入到下游组件中去。",
            "template": {
                "input": {
                    "display_name": "输入内容"
                }
            },
            "base_classes": [
                "输入节点"
            ]
        },
        "Report": {
            "display_name": "专业报告生成组件",
            "description": "把一个复杂的专业报告生成的困难任务，拆解成多个更简单的简单各具体章节内容生成任务。同时，通过模板框架的预先设置，实现了整个专业报告生成的可控（大框架可控+每块内容的生成可以独立控制优化）。",
            "template": {
                "chains": {
                    "display_name": "工作链"
                },
                "variables": {
                    "display_name": "变量"
                },
                "report_name": {
                    "display_name": "报告名称"
                }
            },
            "base_classes": [
                "报告",
                "功能链"
            ]
        },
        "VariableNode": {
            "display_name": "变量输入节点",
            "description": "用于设置所需的不同变量。",
            "template": {
                "variables": {
                    "display_name": "变量"
                }
            },
            "base_classes": [
                "变量节点"
            ]
        }
    },
    "llms": {
        "Anthropic": {
            "display_name": "Anthropic",
            "description": "Anthropic大型语言模型的封装。",
            "template": {
                "anthropic_api_key": {
                    "display_name": "anthropic_api_key"
                },
                "anthropic_api_url": {
                    "display_name": "anthropic_api_url"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "Anthropic",
                "BaseLLM",
                "_AnthropicCommon",
                "BaseLanguageModel",
                "LLM"
            ]
        },
        "AzureChatOpenAI": {
            "display_name": "AzureChatOpenAI",
            "description": "Azure OpenAI聊天完成API的封装。通过微软云使用OpenAI的大模型服务",
            "template": {
                "model_name": {
                    "display_name": "model_name"
                },
                "openai_proxy": {
                    "display_name": "OpenAI代理"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "BaseChatModel",
                "AzureChatOpenAI",
                "ChatOpenAI",
                "BaseLanguageModel",
                "BaseLLM"
            ]
        },
        "CTransformers": {
            "display_name": "CTransformers",
            "description": "C Transformers 大语言模型LLM接口的封装。",
            "template": {
                "model": {
                    "display_name": "model"
                },
                "model_file": {
                    "display_name": "model_file"
                },
                "model_type": {
                    "display_name": "model_type"
                }
            },
            "base_classes": [
                "CTransformers",
                "BaseLLM",
                "BaseLanguageModel",
                "LLM"
            ]
        },
        "ChatAnthropic": {
            "display_name": "ChatAnthropic",
            "description": "Anthropic大型语言模型的封装API调用。",
            "template": {
                "anthropic_api_key": {
                    "display_name": "anthropic_api_key"
                },
                "anthropic_api_url": {
                    "display_name": "anthropic_api_url"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "BaseChatModel",
                "_AnthropicCommon",
                "BaseLanguageModel",
                "ChatAnthropic",
                "BaseLLM"
            ]
        },
        "ChatMinimaxAI": {
            "display_name": "ChatMinimaxAI",
            "description": "代理Chat大型语言模型的封装API调用。",
            "template": {
                "minimaxai_api_key": {
                    "display_name": "minimaxai_api_key"
                },
                "minimaxai_group_id": {
                    "display_name": "minimaxai_group_id"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "ChatMinimaxAI",
                "BaseLanguageModel",
                "BaseChatModel",
                "BaseLLM"
            ]
        },
        "ChatOpenAI": {
            "display_name": "ChatOpenAI",
            "description": "OpenAI Chat大型语言模型的封装API调用。",
            "template": {
                "model_name": {
                    "display_name": "model_name",
                    "options": [
                        "gpt-3.5-turbo-0613",
                        "gpt-3.5-turbo",
                        "gpt-3.5-turbo-16k-0613",
                        "gpt-3.5-turbo-16k",
                        "gpt-4-0613",
                        "gpt-4-32k-0613",
                        "gpt-4",
                        "gpt-4-32k",
                        "gpt-4-1106-preview"
                    ]
                },
                "openai_api_base": {
                    "display_name": "OpenAI API Base"
                },
                "openai_api_key": {
                    "display_name": "OpenAI API Key"
                },
                "openai_proxy": {
                    "display_name": "OpenAI代理"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "ChatOpenAI",
                "BaseLanguageModel",
                "BaseChatModel",
                "BaseLLM"
            ]
        },
        "ChatQWen": {
            "display_name": "通意千问Chat",
            "description": "阿里千问代理Chat大型语言模型的封装API调用。",
            "template": {
                "api_key": {
                    "display_name": "api_key"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "ChatQWen",
                "BaseLanguageModel",
                "BaseChatModel",
                "BaseLLM"
            ]
        },
        "ChatWenxin": {
            "display_name": "文心一言Chat",
            "description": "文心一言代理Chat大型语言模型的封装API调用。",
            "template": {
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                },
                "wenxin_api_key": {
                    "display_name": "wenxin_api_key"
                },
                "wenxin_secret_key": {
                    "display_name": "wenxin_secret_key"
                }
            },
            "base_classes": [
                "BaseLanguageModel",
                "ChatWenxin",
                "BaseChatModel",
                "BaseLLM"
            ]
        },
        "ChatXunfeiAI": {
            "display_name": "科大讯飞Chat",
            "description": "科大讯飞代理Chat大型语言模型的封装API调用。",
            "template": {
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                },
                "xunfeiai_api_key": {
                    "display_name": "xunfeiai_api_key"
                },
                "xunfeiai_api_secret": {
                    "display_name": "xunfeiai_api_secret"
                },
                "xunfeiai_appid": {
                    "display_name": "xunfeiai_appid"
                }
            },
            "base_classes": [
                "ChatXunfeiAI",
                "BaseLanguageModel",
                "BaseChatModel",
                "BaseLLM"
            ]
        },
        "ChatZhipuAI": {
            "display_name": "智普ChatGLM",
            "description": "ChatGLM大型语言模型的API调用。",
            "template": {
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                },
                "zhipuai_api_key": {
                    "display_name": "zhipuai_api_key"
                }
            },
            "base_classes": [
                "ChatZhipuAI",
                "BaseLanguageModel",
                "BaseChatModel",
                "BaseLLM"
            ]
        },
        "Cohere": {
            "display_name": "Cohere",
            "description": "Cohere大型语言模型的封装API调用。",
            "template": {
                "cohere_api_key": {
                    "display_name": "cohere_api_key"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "BaseLLM",
                "Cohere",
                "BaseLanguageModel",
                "LLM"
            ]
        },
        "CustomLLMChat": {
            "display_name": "自定义LLMChat",
            "description": "自定义的其他LLMChat大模型封装API调用。",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "BaseChatModel",
                "BaseHostChatLLM",
                "CustomLLMChat",
                "BaseLanguageModel",
                "BaseLLM"
            ]
        },
        "HostBaichuanChat": {
            "display_name": "私有化百川Chat",
            "description": "百川大模型的私有化本地封装调用。",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "BaseChatModel",
                "BaseHostChatLLM",
                "HostBaichuanChat",
                "BaseLanguageModel",
                "BaseLLM"
            ]
        },
        "HostChatGLM": {
            "display_name": "私有化ChatGLM",
            "description": "ChatGLM系列大模型的私有化本地封装调用。",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "BaseChatModel",
                "BaseHostChatLLM",
                "BaseLanguageModel",
                "HostChatGLM",
                "BaseLLM"
            ]
        },
        "HostLlama2Chat": {
            "display_name": "私有化Llama2Chat",
            "description": "Llama2系列大模型的私有化本地封装调用。",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "HostLlama2Chat",
                "BaseChatModel",
                "BaseHostChatLLM",
                "BaseLanguageModel",
                "BaseLLM"
            ]
        },
        "HostQwenChat": {
            "display_name": "私有化通义千问Chat",
            "description": "通义千问系列大模型的私有化本地封装调用。",
            "template": {
                "host_base_url": {
                    "display_name": "host_base_url"
                },
                "model_name": {
                    "display_name": "model_name"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "BaseChatModel",
                "BaseHostChatLLM",
                "HostQwenChat",
                "BaseLanguageModel",
                "BaseLLM"
            ]
        },
        "HuggingFaceHub": {
            "display_name": "HuggingFaceHub",
            "description": "HuggingFaceHub模型的封装。",
            "template": {
                "huggingfacehub_api_token": {
                    "display_name": "HuggingFace Hub API Token"
                },
                "repo_id": {
                    "display_name": "repo_id"
                }
            },
            "base_classes": [
                "BaseLLM",
                "BaseLanguageModel",
                "HuggingFaceHub",
                "LLM"
            ]
        },
        "LlamaCpp": {
            "display_name": "HuggingFaceHub",
            "description": "llama.cpp模型的封装。",
            "template": {
                "model_path": {
                    "display_name": "model_path"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "BaseLLM",
                "LlamaCpp",
                "BaseLanguageModel",
                "LLM"
            ]
        },
        "OpenAI": {
            "display_name": "OpenAI API",
            "description": "包装了OpenAI大型语言模型。API调用",
            "template": {
                "model_name": {
                    "display_name": "模型名称",
                    "options": [
                        "text-davinci-003",
                        "text-davinci-002",
                        "text-curie-001",
                        "text-babbage-001",
                        "text-ada-001"
                    ]
                },
                "openai_api_base": {
                    "display_name": "OpenAI API基础地址"
                },
                "openai_api_key": {
                    "display_name": "OpenAI API密钥"
                },
                "openai_proxy": {
                    "display_name": "OpenAI代理"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "BaseLLM",
                "OpenAI",
                "BaseOpenAI",
                "BaseLanguageModel"
            ]
        },
        "ProxyChatLLM": {
            "display_name": "ProxyChatLLM",
            "description": "包装了代理Chat大型语言模型。",
            "template": {
                "elemai_api_key": {
                    "display_name": "elemai_api_key"
                },
                "elemai_base_url": {
                    "display_name": "elemai_base_url"
                },
                "model_name": {
                    "display_name": "模型名称"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "ProxyChatLLM",
                "BaseLanguageModel",
                "BaseChatModel",
                "BaseLLM"
            ]
        },
        "VertexAI": {
            "display_name": "VertexAI",
            "description": "包装了Google Vertex AI大型语言模型。",
            "template": {
                "credentials": {
                    "display_name": "凭据"
                },
                "location": {
                    "display_name": "位置"
                },
                "max_retries": {
                    "display_name": "最大重试次数"
                },
                "metadata": {
                    "display_name": "元数据"
                },
                "model_name": {
                    "display_name": "模型名称"
                },
                "project": {
                    "display_name": "项目"
                },
                "request_parallelism": {
                    "display_name": "请求并行度"
                },
                "temperature": {
                    "display_name": "生成自由度(越接近1自由度越高，越接近0自由度越低)"
                }
            },
            "base_classes": [
                "VertexAI",
                "BaseLLM",
                "_VertexAICommon",
                "BaseLanguageModel",
                "LLM"
            ]
        }
    },
    "memories": {
        "ConversationBufferMemory": {
            "display_name": "对话记忆缓存",
            "description": "用于存储对话记忆的缓冲区。",
            "template": {
                "chat_memory": {
                    "display_name": "聊天记忆"
                },
                "input_key": {
                    "display_name": "输入"
                },
                "memory_key": {
                    "display_name": "记忆"
                },
                "output_key": {
                    "display_name": "输出"
                },
                "return_messages": {
                    "display_name": "返回消息"
                }
            },
            "base_classes": [
                "BaseMemory",
                "BaseChatMemory",
                "ConversationBufferMemory"
            ]
        },
        "ConversationBufferWindowMemory": {
            "display_name": "ConversationBufferWindowMemory",
            "description": "用于存储对话记忆的缓冲区。",
            "template": {
                "chat_memory": {
                    "display_name": "聊天记忆"
                },
                "input_key": {
                    "display_name": "输入"
                },
                "k": {
                    "display_name": "记忆大小"
                },
                "memory_key": {
                    "display_name": "记忆"
                },
                "output_key": {
                    "display_name": "输出"
                },
                "return_messages": {
                    "display_name": "返回消息"
                }
            },
            "base_classes": [
                "ConversationBufferWindowMemory",
                "BaseMemory",
                "BaseChatMemory"
            ]
        },
        "ConversationEntityMemory": {
            "display_name": "ConversationEntityMemory",
            "description": "实体提取器和摘要生成器的记忆。",
            "template": {
                "chat_memory": {
                    "display_name": "聊天记忆"
                },
                "llm": {
                    "display_name": "大语言模型"
                },
                "chat_history_key": {
                    "display_name": "聊天历史"
                },
                "input_key": {
                    "display_name": "输入"
                },
                "k": {
                    "display_name": "记忆大小"
                },
                "output_key": {
                    "display_name": "输出"
                },
                "return_messages": {
                    "display_name": "返回消息"
                }
            },
            "base_classes": [
                "ConversationEntityMemory",
                "BaseMemory",
                "BaseChatMemory"
            ]
        },
        "ConversationKGMemory": {
            "display_name": "ConversationKGMemory",
            "description": "用于存储对话记忆的知识图记忆。",
            "template": {
                "chat_memory": {
                    "display_name": "聊天记忆"
                },
                "llm": {
                    "display_name": "大语言模型"
                },
                "input_key": {
                    "display_name": "输入"
                },
                "k": {
                    "display_name": "记忆大小"
                },
                "memory_key": {
                    "display_name": "记忆"
                },
                "output_key": {
                    "display_name": "输出"
                },
                "return_messages": {
                    "display_name": "返回消息"
                }
            },
            "base_classes": [
                "ConversationEntityMemory",
                "BaseMemory",
                "BaseChatMemory"
            ]
        },
        "ConversationSummaryMemory": {
            "display_name": "ConversationSummaryMemory",
            "description": "用于对话摘要的记忆。",
            "template": {
                "chat_memory": {
                    "display_name": "聊天记忆"
                },
                "llm": {
                    "display_name": "大语言模型"
                },
                "input_key": {
                    "display_name": "输入"
                },
                "memory_key": {
                    "display_name": "记忆"
                },
                "output_key": {
                    "display_name": "输出"
                },
                "return_messages": {
                    "display_name": "返回消息"
                }
            },
            "base_classes": [
                "BaseChatMemory",
                "SummarizerMixin",
                "ConversationSummaryMemory",
                "BaseMemory"
            ]
        },
        "MongoDBChatMessageHistory": {
            "display_name": "MongoDBChatMessageHistory",
            "description": "使用MongoDB存储AI大模型对话记忆。",
            "template": {
                "collection_name": {
                    "display_name": "集合名称"
                },
                "connection_string": {
                    "display_name": "连接字符串"
                },
                "database_name": {
                    "display_name": "数据库名称"
                },
                "session_id": {
                    "display_name": "会话ID"
                }
            },
            "base_classes": [
                "MongoDBChatMessageHistory",
                "BaseChatMessageHistory"
            ]
        },
        "PostgresChatMessageHistory": {
            "display_name": "PostgresChatMessageHistory",
            "description": "使用Postgres存储AI大模型对话记忆。",
            "template": {
                "connection_string": {
                    "display_name": "连接字符串"
                },
                "session_id": {
                    "display_name": "会话ID"
                },
                "table_name": {
                    "display_name": "表名"
                }
            },
            "base_classes": [
                "PostgresChatMessageHistory",
                "BaseChatMessageHistory"
            ]
        },
        "VectorStoreRetrieverMemory": {
            "display_name": "VectorStoreRetrieverMemory",
            "description": "用于基于VectorStore存储AI大模型对话记忆。",
            "template": {
                "retriever": {
                    "display_name": "检索器"
                },
                "input_key": {
                    "display_name": "输入"
                },
                "memory_key": {
                    "display_name": "记忆"
                },
                "return_messages": {
                    "display_name": "返回消息"
                }
            },
            "base_classes": [
                "VectorStoreRetrieverMemory",
                "BaseMemory"
            ]
        }
    },
    "output_parsers": {
        "ResponseSchema": {
            "display_name": "ResponseSchema",
            "description": "ResponseSchema只能与StructuredOutputParser 组件一起使用，ResponseSchema主要是定义结构化输出时，具体要输出哪些字段信息",
            "template": {
                "description": {
                    "display_name": "描述"
                },
                "name": {
                    "display_name": "名称"
                },
                "type": {
                    "display_name": "类型"
                }
            },
            "base_classes": [
                "ResponseSchema"
            ]
        },
        "StructuredOutputParser": {
            "display_name": "StructuredOutputParser",
            "description": "StructuredOutputParser是一个输出解析器组件，可以让语言模型的输出变为结构化形式的数据。",
            "template": {
                "response_schemas": {
                    "display_name": "响应模式"
                }
            },
            "base_classes": [
                "Generic",
                "StructuredOutputParser",
                "BaseOutputParser",
                "BaseLLMOutputParser"
            ]
        }
    },
    "prompts": {
        "ChatMessagePromptTemplate": {
            "display_name": "ChatMessagePromptTemplate",
            "description": "组件可以设置提示词模板，获得变量的输入后为大语言模型生成完整的提示词",
            "template": {
                "prompt": {
                    "display_name": "提示词"
                },
                "role": {
                    "display_name": "角色"
                }
            },
            "base_classes": [
                "BaseStringMessagePromptTemplate",
                "ChatMessagePromptTemplate",
                "BaseMessagePromptTemplate"
            ]
        },
        "ChatMessagePromptTemplate": {
            "display_name": "ChatMessagePromptTemplate",
            "description": "组件可以设置提示词模板，获得变量的输入后为大语言模型生成完整的提示词",
            "template": {
                "prompt": {
                    "display_name": "提示词"
                },
                "role": {
                    "display_name": "角色"
                }
            },
            "base_classes": [
                "BaseStringMessagePromptTemplate",
                "ChatMessagePromptTemplate",
                "BaseMessagePromptTemplate"
            ]
        },
        "ChatPromptTemplate": {
            "display_name": "ChatPromptTemplate",
            "description": "组件可以设置提示词模板，获得变量的输入后为大语言模型生成完整的提示词",
            "template": {
                "messages": {
                    "display_name": "消息"
                },
                "output_parser": {
                    "display_name": "输出解析器"
                }
            },
            "base_classes": [
                "BasePromptTemplate",
                "ChatPromptTemplate",
                "BaseChatPromptTemplate"
            ]
        },
        "HumanMessagePromptTemplate": {
            "display_name": "HumanMessagePromptTemplate",
            "description": "组件可以设置提示词模板，获得变量的输入后为大语言模型生成完整的提示词",
            "template": {
                "prompt": {
                    "display_name": "提示词"
                }
            },
            "base_classes": [
                "BaseStringMessagePromptTemplate",
                "HumanMessagePromptTemplate",
                "BaseMessagePromptTemplate"
            ]
        },
        "PromptTemplate": {
            "display_name": "提示词模版",
            "description": "PromptsTemplate组件可以设置提示词模板，获得变量的输入后为大语言模型生成完整的提示词。",
            "template": {
                "output_parser": {
                    "display_name": "输出解析器"
                },
                "template": {
                    "display_name": "模板"
                }
            },
            "base_classes": [
                "StringPromptTemplate",
                "BasePromptTemplate",
                "PromptTemplate"
            ]
        },
        "SystemMessagePromptTemplate": {
            "display_name": "SystemMessagePromptTemplate",
            "description": "组件可以设置提示词模板，获得变量的输入后为大语言模型生成完整的提示词",
            "template": {
                "prompt": {
                    "display_name": "提示词"
                }
            },
            "base_classes": [
                "BaseStringMessagePromptTemplate",
                "SystemMessagePromptTemplate",
                "BaseMessagePromptTemplate"
            ]
        }
    },
    "retrievers": {
        "MixEsVectorRetriever": {
            "display_name": "混合检索器",
            "description": "该类集成了ES检索器和向量检索器的结果。",
            "template": {
                "keyword_retriever": {
                    "display_name": "关键词检索器"
                },
                "vector_retriever": {
                    "display_name": "向量检索器"
                },
                "combine_strategy": {
                    "display_name": "合并策略",
                    "options": [
                        "keyword_front",
                        "vector_front",
                        "mix"
                    ]
                }
            },
            "base_classes": [
                "MixEsVectorRetriever",
                "BaseRetriever"
            ]
        },
        "MultiQueryRetriever": {
            "display_name": "多请求检索",
            "description": "多请求检索组件是一个检索召回组件，该组件使用大模型对用户输入的问题（请求）生成3-4个含义相同但表述有差别的问题，再对这几个问题分别进行检索。因为向量数据库是根据语义来进行向量搜索的，通过这种方法可以检索得到更丰富、全面的相关文档，提高回答的准确性。",
            "template": {
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "prompt": {
                    "display_name": "提示词"
                },
                "retriever": {
                    "display_name": "检索器"
                }
            },
            "base_classes": [
                "MultiQueryRetriever",
                "BaseRetriever"
            ]
        }
    },
    "textsplitters": {
        "CharacterTextSplitter": {
            "display_name": "CharacterTextSplitter",
            "description": "基于字符的文本拆分实现。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "chunk_overlap": {
                    "display_name": "块重叠"
                },
                "chunk_size": {
                    "display_name": "块大小"
                },
                "separator": {
                    "display_name": "分隔符"
                }
            },
            "base_classes": [
                "文档"
            ]
        },
        "RecursiveCharacterTextSplitter": {
            "display_name": "RecursiveCharacterTextSplitter",
            "description": "基于字符的文本拆分实现。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "chunk_overlap": {
                    "display_name": "块重叠"
                },
                "chunk_size": {
                    "display_name": "块大小"
                },
                "separator_type": {
                    "display_name": "分隔符类型"
                },
                "separators": {
                    "display_name": "分隔符"
                }
            },
            "base_classes": [
                "文档"
            ]
        }
    },
    "toolkits": {
        "JsonToolkit": {
            "display_name": "JsonToolkit",
            "description": "与JSON规范交互的工具包。",
            "template": {
                "spec": {
                    "display_name": "规范"
                }
            },
            "base_classes": [
                "BaseToolkit",
                "JsonToolkit",
                "工具"
            ]
        },
        "OpenAPIToolkit": {
            "display_name": "OpenAPIToolkit",
            "description": "与OpenAPI API交互的工具包。",
            "template": {
                "json_agent": {
                    "display_name": "JSON代理"
                },
                "requests_wrapper": {
                    "display_name": "请求封装"
                }
            },
            "base_classes": [
                "OpenAPIToolkit",
                "BaseToolkit",
                "工具"
            ]
        },
        "VectorStoreInfo": {
            "display_name": "VectorStoreInfo",
            "description": "关于矢量存储的信息。",
            "template": {
                "vectorstore": {
                    "display_name": "矢量存储"
                },
                "description": {
                    "display_name": "描述"
                },
                "name": {
                    "display_name": "名称"
                }
            },
            "base_classes": [
                "VectorStoreInfo"
            ]
        },
        "VectorStoreRouterToolkit": {
            "display_name": "VectorStoreRouterToolkit",
            "description": "用于在矢量存储之间进行路由的工具包。",
            "template": {
                "vectorstores": {
                    "display_name": "矢量存储"
                }
            },
            "base_classes": [
                "BaseToolkit",
                "VectorStoreRouterToolkit",
                "工具"
            ]
        },
        "VectorStoreToolkit": {
            "display_name": "VectorStoreToolkit",
            "description": "与矢量存储交互的工具包。",
            "template": {
                "vectorstore_info": {
                    "display_name": "矢量存储信息"
                }
            },
            "base_classes": [
                "BaseToolkit",
                "VectorStoreToolkit",
                "工具"
            ]
        }
    },
    "tools": {
        "BingSearchRun": {
            "display_name": "BingSearchRun",
            "description": "Bing搜索的封装。在需要回答有关当前事件的问题时很有用。输入应为搜索查询。",
            "template": {
                "api_wrapper": {
                    "display_name": "API封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "BingSearchRun",
                "BaseTool"
            ]
        },
        "Calculator": {
            "display_name": "Calculator",
            "description": "在需要回答数学问题时很有用。",
            "template": {
                "llm": {
                    "display_name": "大语言模型"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool"
            ]
        },
        "GoogleSearchResults": {
            "display_name": "GoogleSearchResults",
            "description": "Google搜索的封装。在需要回答有关当前事件的问题时很有用。输入应为搜索查询。输出是查询结果的JSON数组。",
            "template": {
                "api_wrapper": {
                    "display_name": "API封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "GoogleSearchResults",
                "BaseTool"
            ]
        },
        "GoogleSearchRun": {
            "display_name": "GoogleSearchRun",
            "description": "Google搜索的封装。在需要回答有关当前事件的问题时很有用。输入应为搜索查询。",
            "template": {
                "api_wrapper": {
                    "display_name": "API封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "GoogleSearchRun",
                "BaseTool"
            ]
        },
        "InfoSQLDatabaseTool": {
            "display_name": "InfoSQLDatabaseTool",
            "description": "此工具的输入是表的逗号分隔列表，输出是这些表的模式和示例行。\n\n    示例输入：'table1, table2, table3'",
            "template": {
                "db": {
                    "display_name": "数据库"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "InfoSQLDatabaseTool",
                "BaseTool",
                "BaseSQLDatabaseTool"
            ]
        },
        "JsonGetValueTool": {
            "display_name": "JsonGetValueTool",
            "description": "可用于查看给定路径上的字符串格式值。\n在调用之前，您应确保此路径存在。\n输入是Python语法中字典路径的文本表示形式（例如data[\"key1\"][0][\"key2\"]）。",
            "template": {
                "spec": {
                    "display_name": "规范"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "JsonGetValueTool",
                "BaseTool"
            ]
        },
        "JsonListKeysTool": {
            "display_name": "JsonListKeysTool",
            "description": "可用于查看给定路径上的字符串格式值。\n在调用之前，您应确保此路径存在。\n输入是Python语法中字典路径的文本表示形式（例如data[\"key1\"][0][\"key2\"]）。",
            "template": {
                "spec": {
                    "display_name": "规范"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "JsonListKeysTool",
                "BaseTool"
            ]
        },
        "JsonSpec": {
            "display_name": "JsonSpec",
            "template": {
                "path": {
                    "display_name": "路径"
                },
                "args_schema": {
                    "display_name": "参数模式"
                },
                "max_value_length": {
                    "display_name": "最大值长度"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "JsonSpec"
            ]
        },
        "ListSQLDatabaseTool": {
            "display_name": "ListSQLDatabaseTool",
            "description": "输入为空字符串，输出是数据库中表的逗号分隔列表。",
            "template": {
                "db": {
                    "display_name": "数据库"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "ListSQLDatabaseTool",
                "BaseTool",
                "BaseSQLDatabaseTool"
            ]
        },
        "News API": {
            "display_name": "News API",
            "description": "在您想要获取有关当前新闻头条的信息时使用。输入应为此API可以回答的自然语言中的问题。",
            "template": {
                "llm": {
                    "display_name": "大语言模型"
                },
                "args_schema": {
                    "display_name": "参数模式"
                },
                "news_api_key": {
                    "display_name": "新闻API密钥"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool"
            ]
        },
        "PAL-MATH": {
            "display_name": "PAL-MATH",
            "description": "一个非常擅长解决复杂数学问题的语言模型。输入应为完全用文字表达的复杂数学问题。",
            "template": {
                "llm": {
                    "display_name": "大语言模型"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool"
            ]
        },
        "Podcast API": {
            "display_name": "Podcast API",
            "description": "使用Listen Notes Podcast API搜索所有播客或剧集。输入应为此API可以回答的自然语言中的问题。",
            "template": {
                "llm": {
                    "display_name": "大语言模型"
                },
                "args_schema": {
                    "display_name": "参数模式"
                },
                "listen_api_key": {
                    "display_name": "Listen API密钥"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool"
            ]
        },
        "PythonAstREPLTool": {
            "display_name": "PythonAstREPLTool",
            "description": "一个Python shell。使用它来执行Python命令。输入应为有效的Python命令。在使用此工具时，有时输出会被缩写 - 在在答案中使用之前，请确保它看起来不是缩写的。",
            "template": {
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "PythonAstREPLTool",
                "BaseTool"
            ]
        },
        "PythonFunction": {
            "display_name": "PythonFunction",
            "description": "要执行的Python函数。",
            "template": {
                "code": {
                    "display_name": "代码"
                }
            },
            "base_classes": [
                "function"
            ]
        },
        "PythonFunctionTool": {
            "display_name": "PythonAstREPLTool",
            "description": "要执行的Python函数。",
            "template": {
                "code": {
                    "display_name": "代码"
                },
                "description": {
                    "display_name": "描述"
                },
                "name": {
                    "display_name": "名称"
                },
                "return_direct": {
                    "display_name": "直接返回"
                }
            },
            "base_classes": [
                "BaseTool",
                "Tool"
            ]
        },
        "PythonREPLTool": {
            "display_name": "PythonREPLTool",
            "description": "一个Python shell。使用它来执行Python命令。输入应为有效的Python命令。如果要查看值的输出，应使用`print(...)`将其打印出来。",
            "template": {
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "PythonREPLTool",
                "BaseTool"
            ]
        },
        "QuerySQLDataBaseTool": {
            "display_name": "PythonREPLTool",
            "description": "此工具的输入是详细而正确的SQL查询，输出是来自数据库的结果。\n如果查询不正确，将返回错误消息。\n如果返回错误，请重新编写查询，检查查询，然后重试。",
            "template": {
                "db": {
                    "display_name": "数据库"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "QuerySQLDataBaseTool",
                "BaseTool",
                "BaseSQLDatabaseTool"
            ]
        },
        "RequestsDeleteTool": {
            "display_name": "RequestsDeleteTool",
            "description": "互联网门户。在需要向URL发出DELETE请求时使用。输入应为特定的URL，输出将是DELETE请求的文本响应。",
            "template": {
                "requests_wrapper": {
                    "display_name": "请求封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "RequestsDeleteTool",
                "BaseRequestsTool",
                "BaseTool"
            ]
        },
        "RequestsGetTool": {
            "display_name": "RequestsGetTool",
            "description": "互联网门户。在需要从网站获取特定内容时使用。输入应为URL（即https://www.google.com）。输出将是GET请求的文本响应。",
            "template": {
                "requests_wrapper": {
                    "display_name": "请求封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "RequestsGetTool",
                "BaseRequestsTool",
                "BaseTool"
            ]
        },
        "RequestsPatchTool": {
            "display_name": "RequestsPatchTool",
            "description": "在想要对网站进行PATCH时使用。\n输入应为具有两个键的json字符串：\"url\"和\"data\"。\n\"url\"的值应为字符串，\"data\"的值应为要PATCH到url的键值对字典。\n务必始终在json字符串中使用双引号。\n输出将是PATCH请求的文本响应。\n",
            "template": {
                "requests_wrapper": {
                    "display_name": "请求封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "BaseRequestsTool",
                "BaseTool",
                "RequestsPatchTool"
            ]
        },
        "RequestsPostTool": {
            "display_name": "RequestsPostTool",
            "description": "在想要对网站进行POST时使用。\n输入应为具有两个键的json字符串：\"url\"和\"data\"。\n\"url\"的值应为字符串，\"data\"的值应为要POST到url的键值对字典。\n务必始终在json字符串中使用双引号。\n输出将是POST请求的文本响应。\n",
            "template": {
                "requests_wrapper": {
                    "display_name": "请求封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "RequestsPostTool",
                "BaseRequestsTool",
                "BaseTool"
            ]
        },
        "RequestsPutTool": {
            "display_name": "RequestsPutTool",
            "description": "在需要对网站进行PUT请求时使用此工具。\n输入应为包含两个键的JSON字符串：\"url\"和\"data\"。\n\"url\"的值应为字符串，\"data\"的值应为要PUT到URL的键值对字典。\n在JSON字符串中始终要小心使用双引号括起字符串。\n输出将是PUT请求的文本响应。",
            "template": {
                "requests_wrapper": {
                    "display_name": "请求封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "BaseRequestsTool",
                "BaseTool",
                "RequestsPutTool"
            ]
        },
        "Search": {
            "display_name": "搜索",
            "description": "一个搜索引擎。在需要回答有关当前事件的问题时非常有用。输入应为搜索查询。",
            "template": {
                "args_schema": {
                    "display_name": "参数模式"
                },
                "serpapi_api_key": {
                    "display_name": "serpapi_api_key"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool"
            ]
        },
        "TMDB API": {
            "display_name": "TMDB API",
            "description": "在您想要从电影数据库获取信息时非常有用。输入应为此API可以回答的自然语言问题。",
            "template": {
                "llm": {
                    "display_name": "大语言模型"
                },
                "args_schema": {
                    "display_name": "参数模式"
                },
                "tmdb_bearer_token": {
                    "display_name": "tmdb_bearer_token"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool"
            ]
        },
        "Tool": {
            "display_name": "工具",
            "description": "将链、代理或函数转换为工具。",
            "template": {
                "func": {
                    "display_name": "函数"
                },
                "args_schema": {
                    "display_name": "参数模式"
                },
                "description": {
                    "display_name": "描述"
                },
                "name": {
                    "display_name": "名称"
                },
                "return_direct": {
                    "display_name": "直接返回"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool"
            ]
        },
        "WikipediaQueryRun": {
            "display_name": "WikipediaQueryRun",
            "description": "围绕维基百科的包装器。在需要回答有关人物、地点、公司、事实、历史事件或其他主题的一般问题时非常有用。输入应为搜索查询。",
            "template": {
                "api_wrapper": {
                    "display_name": "API封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "WikipediaQueryRun",
                "BaseTool"
            ]
        },
        "WolframAlphaQueryRun": {
            "display_name": "WolframAlphaQueryRun",
            "description": "围绕沃尔夫拉姆阿尔法的包装器。在需要回答有关数学、科学、技术、文化、社会和日常生活的问题时非常有用。输入应为搜索查询。",
            "template": {
                "api_wrapper": {
                    "display_name": "API封装"
                },
                "args_schema": {
                    "display_name": "参数模式"
                }
            },
            "base_classes": [
                "Tool",
                "BaseTool",
                "WolframAlphaQueryRun",
                "BaseTool"
            ]
        }
    },
    "utilities": {
        "BingSearchAPIWrapper": {
            "display_name": "BingSearchAPIWrapper",
            "description": "Bing Search API的封装器。",
            "template": {
                "bing_search_url": {
                    "display_name": "必应搜索 URL"
                },
                "bing_subscription_key": {
                    "display_name": "必应订阅密钥"
                }
            },
            "base_classes": [
                "BingSearchAPIWrapper"
            ]
        },
        "GoogleSearchAPIWrapper": {
            "display_name": "GoogleSearchAPIWrapper",
            "description": "Google搜索API的封装器。",
            "template": {
                "google_api_key": {
                    "display_name": "Google API密钥"
                }
            },
            "base_classes": [
                "GoogleSearchAPIWrapper"
            ]
        },
        "GoogleSerperAPIWrapper": {
            "display_name": "GoogleSerperAPIWrapper",
            "description": "围绕 Serper.dev Google搜索API的封装器。",
            "template": {
                "result_key_for_type": {
                    "display_name": "类型的结果键"
                },
                "serper_api_key": {
                    "display_name": "Serper API密钥"
                }
            },
            "base_classes": [
                "GoogleSerperAPIWrapper"
            ]
        },
        "SearxSearchWrapper": {
            "display_name": "SearxSearchWrapper",
            "description": "Searx API的封装器。",
            "template": {
                "headers": {
                    "display_name": "标头"
                }
            },
            "base_classes": [
                "SearxSearchWrapper"
            ]
        },
        "SerpAPIWrapper": {
            "display_name": "SerpAPIWrapper",
            "description": "围绕 SerpAPI 的封装器。",
            "template": {
                "serpapi_api_key": {
                    "display_name": "SerpAPI API密钥"
                }
            },
            "base_classes": [
                "SerpAPIWrapper"
            ]
        },
        "WikipediaAPIWrapper": {
            "display_name": "WikipediaAPIWrapper",
            "description": "围绕 WikipediaAPI 的封装器。",
            "base_classes": [
                "WikipediaAPIWrapper"
            ]
        },
        "WolframAlphaAPIWrapper": {
            "display_name": "WolframAlphaAPIWrapper",
            "description": "Wolfram Alpha的封装器。",
            "base_classes": [
                "WolframAlphaAPIWrapper"
            ]
        }
    },
    "vectorstores": {
        "Chroma": {
            "display_name": "Chroma",
            "description": "从原始文档创建 Chroma 向量存储。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "embedding": {
                    "display_name": "嵌入"
                },
                "collection_name": {
                    "display_name": "集合名称"
                },
                "persist": {
                    "display_name": "持久化"
                },
                "persist_directory": {
                    "display_name": "持久化目录"
                }
            },
            "base_classes": [
                "Chroma",
                "VectorStore",
                "BaseRetriever",
                "VectorStoreRetriever"
            ]
        },
        "ElasticKeywordsSearch": {
            "display_name": "ElasticKeywordsSearch",
            "description": "从原始文档构建 ElasticKeywordsSearch 包装器。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "llm": {
                    "display_name": "大语言模型LLM"
                },
                "prompt": {
                    "display_name": "提示词"
                },
                "elasticsearch_url": {
                    "display_name": "ES 连接 URL"
                },
                "index_name": {
                    "display_name": "索引名称"
                },
                "ssl_verify": {
                    "display_name": "SSL 验证"
                }
            },
            "base_classes": [
                "ElasticKeywordsSearch",
                "VectorStore",
                "BaseRetriever",
                "VectorStoreRetriever"
            ]
        },
        "FAISS": {
            "display_name": "FAISS",
            "description": "从原始文档构建 FAISS 包装器。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "embedding": {
                    "display_name": "嵌入"
                },
                "folder_path": {
                    "display_name": "本地路径"
                },
                "index_name": {
                    "display_name": "索引名称"
                }
            },
            "base_classes": [
                "FAISS",
                "VectorStore",
                "BaseRetriever",
                "VectorStoreRetriever"
            ]
        },
        "Milvus": {
            "display_name": "Milvus向量数据库",
            "description": "创建 Milvus 集合，使用 HNSW 进行索引并插入数据。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "embedding": {
                    "display_name": "嵌入"
                },
                "collection_name": {
                    "display_name": "集合名称"
                },
                "connection_args": {
                    "display_name": "连接参数"
                }
            },
            "base_classes": [
                "Milvus",
                "VectorStore",
                "BaseRetriever",
                "VectorStoreRetriever"
            ]
        },
        "MongoDBAtlasVectorSearch": {
            "display_name": "MongoDB Atlas向量数据库",
            "description": "创建 Milvus 集合，使用 HNSW 进行索引并插入数据。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "embedding": {
                    "display_name": "嵌入"
                },
                "collection_name": {
                    "display_name": "集合名称"
                },
                "db_name": {
                    "display_name": "数据库名称"
                },
                "index_name": {
                    "display_name": "索引名称"
                },
                "mongodb_atlas_cluster_uri": {
                    "display_name": "MongoDB Atlas 集群 URI"
                }
            },
            "base_classes": [
                "MongoDBAtlasVectorSearch",
                "VectorStore",
                "BaseRetriever",
                "VectorStoreRetriever"
            ]
        },
        "Pinecone": {
            "display_name": "Pinecone向量数据库",
            "description": "从原始文档构建 Pinecone 包装器。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "embedding": {
                    "display_name": "嵌入"
                },
                "index_name": {
                    "display_name": "索引名称"
                },
                "namespace": {
                    "display_name": "命名空间"
                }
            },
            "base_classes": [
                "Pinecone",
                "VectorStore",
                "BaseRetriever",
                "VectorStoreRetriever"
            ]
        },
        "Qdrant": {
            "display_name": "Qdrant",
            "description": "从文本列表构建 Qdrant 包装器。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "embedding": {
                    "display_name": "嵌入"
                },
                "api_key": {
                    "display_name": "API 密钥"
                },
                "collection_name": {
                    "display_name": "集合名称"
                },
                "location": {
                    "display_name": "位置"
                }
            },
            "base_classes": [
                "Qdrant",
                "VectorStore",
                "BaseRetriever",
                "VectorStoreRetriever"
            ]
        },
        "SupabaseVectorStore": {
            "display_name": "Supabase",
            "description": "从文本和嵌入初始化 VectorStore 的 Supabase 包装器。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "embedding": {
                    "display_name": "嵌入"
                },
                "query_name": {
                    "display_name": "查询名称"
                },
                "supabase_service_key": {
                    "display_name": "Supabase 服务密钥"
                },
                "supabase_url": {
                    "display_name": "Supabase URL"
                },
                "table_name": {
                    "display_name": "表名称"
                }
            },
            "base_classes": [
                "SupabaseVectorStore",
                "VectorStore",
                "BaseRetriever",
                "VectorStoreRetriever"
            ]
        },
        "Weaviate": {
            "display_name": "Weaviate",
            "description": "从原始文档构建 Weaviate 包装器。",
            "template": {
                "documents": {
                    "display_name": "文档"
                },
                "embedding": {
                    "display_name": "嵌入"
                },
                "weaviate_url": {
                    "display_name": "Weaviate URL"
                }
            },
            "base_classes": [
                "Weaviate",
                "VectorStore",
                "BaseRetriever",
                "VectorStoreRetriever"
            ]
        }
    },
    "wrappers": {
        "SQLDatabase": {
            "display_name": "SQLDatabase",
            "description": "从 URI 构建一个 SQLAlchemy 引擎。",
            "template": {
                "database_uri": {
                    "display_name": "数据库 URI"
                }
            },
            "base_classes": [
                "SQLDatabase",
                "function"
            ]
        },
        "TextRequestsWrapper": {
            "display_name": "TextRequestsWrapper",
            "description": "对 requests 库的轻量级封装。",
            "template": {
                "headers": {
                    "display_name": "标头"
                }
            },
            "base_classes": [
                "TextRequestsWrapper"
            ]
        }
    }
}