{ "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" ] } } }